INTEGRATED GENETIC AND
PHENOTYPIC ASSESSMENT OF
DILATED CARDIOMYOPATHY
This dissertation is submitted to Imperial College London for the degree of
Doctor of Philosophy
by
Upasana Tayal
MA(Oxon) BMBCh MRCP(UK)
Royal Brompton Hospital Cardiovascular Research Centre
Imperial College Faculty of Medicine
August 2017
ABSTRACT
Background: Dilated cardiomyopathy (DCM) affects up to 1 in 250 individuals and is the leading global indication for heart transplantation, though a subset of patients can recover myocardial function.
Aim: To integrate clinical, genetic, and advanced imaging data to generate new insights into DCM pathobiology.
Methods and Results 1) Evaluation of the genetic architecture of DCM in 647 unrelated Caucasian patients revealed that variants in only 5 of 57 putative DCM genes were significantly enriched compared to >30,000 reference samples. Truncating variants in the titin gene (TTNtv) accounted for the largest genetic contribution to DCM (excess burden 14.1%, p=6.4x10-82).
2) Cardiovascular magnetic resonance (CMR) phenotype study of 716 DCM patients demonstrated that TTNtv DCM was associated with a blunted hypertrophic response (mean 2 indexed left ventricular mass, g/m ; TTNtv-/+ 91.3 vs 83.5, padjusted=0.007). Moderate alcohol excess was an environmental modifier of the TTNtv phenotype (10.0% reduction in left ventricular ejection fraction, 95% CI -16.3 to -3.8%, p=0.002).
3) In 29 patients with recent onset DCM, myocardial contractile reserve during low-dose dobutamine stress CMR was an independent predictor of LV remodelling (p=0.007). Contractile reserve assessed by a novel cine-DENSE strain CMR sequence and relative RV contractile reserve were also predictive of LV remodeling.
4) Amongst 604 DCM patients, followed up for a median of 3.9 years for the primary composite endpoint of cardiovascular mortality, major arrhythmic events and major heart failure events, the presence of TTNtv did not influence the primary outcome (TTNtv unadjusted hazard ratio 0.81 [95% CI 0.41-1.63], p=0.56). Further analyses showed no evidence that gender or CMR mid-wall fibrosis status modified the effect of TTNtv.
Conclusion These data improve our understanding of the genetic basis of DCM, provide mechanistic insight and inform risk stratification in DCM.
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This thesis is dedicated to my parents and Mayank.
Your unconditional love and support has made me everything I am today.
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THESIS OVERVIEW DIAGRAM
Research aim: Evaluate whether integrated assessment of genotype and phenotype data in DCM can improve understanding of DCM pathogenesis, inform patient stratification, and identify predictors of remodeling and outcome.
INTRODUCTION Chapter 1: General Introduction Overview of background to research question
Chapter 2: Evaluating the genetic architecture of DCM
Burden of rare variants in DCM cohort compared to population genetic variation. Purpose of this chapter is to guide subsequent phenotype analysis.
RESULTS Chapter 3: Clinical manifestations and phenotypic drivers of titin cardiomyopathy
Evaluation of the phenotype and environmental modifiers of the commonest genetic contributor to Each chapter DCM using cardiovascular magnetic resonance contains: (CMR).
• Introduction Chapter 4: Imaging predictors of cardiac • Methods remodeling • Results • Discussion Imaging sub-study to evaluate imaging predictors of • Outline of remodeling in patients with recent onset DCM, with a further work focus on contractile reserve, assessed through low- dose dobutamine CMR.
Chapter 5: The role of clinical, imaging and genetic data in predicting clinical outcomes in dilated cardiomyopathy
Final chapter brings together chapters 2 and 3, Chapter 6: Summary discussion evaluating the prognosis of titin cardiomyopathy. What this PhD adds to the field
CONCLUSION Chapter 6: Summary overview What this PhD adds to the field and future work
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DECLARATIONS
Declaration of originality
This thesis is the result of my own work other than where duly acknowledged or
appropriately referenced. It has not been previously submitted, in part or whole, to any
university of institution for any degree, diploma, or other qualification.
Copyright declaration
The copyright of this thesis rests with the author and is made available under a Creative
Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy,
distribute or transmit the thesis on the condition that they attribute it, that they do not use it
for commercial purposes and that they do not alter, transform or build upon it. For any reuse
or redistribution, researchers must make clear to others the licence terms of this work.
Upasana Tayal , BMBCh, MA (Oxon), MRCP (UK)
Imperial College
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ACKNOWLEDGEMENTS
I am grateful to my supervisors for giving me the opportunity to undertake this PhD. I have learnt about science, innovation and resilience in equal measure. Sanjay, thank you for believing in me from the start and for your incredible support along the way. You gave me the academic freedom to explore and helped guide me through the more challenging moments. Stuart, thank you for taking a chance on me when I barely knew the difference between genes and jeans. Your academic guidance at every stage has been invaluable and I hope it is a resource I may draw upon for years to come. James, thank you for being so generous with your time, coming on board when I needed it the most. Thanks for your mentorship and scientific rigour. I am a better scientist and I now know how to use R (sort of).
I am grateful to the MRC for funding this PhD, providing much needed peer approval of the work and setting the foundation for my academic career ahead.
I am thankful to all the staff in the BRU, Genetics and Genomics Lab, and CMR department at the Royal Brompton Hospital. Particular thanks go to Gillian Rea and Rachel Buchan, my genetics guardian angels at the start of this PhD. The genetics group is the exemplar of team science and I have learnt an enormous amount from all who have been so generous with their knowledge, including (though by no means limited to) Nicky Whiffin, Liz Edwards, Roddy Walsh, Francesco Mazzarotto, and Paul Barton. Within the BRU, thanks to Prof Pennell for ongoing academic support, both personally and for the studies in this thesis. An enormous thank you to the wonderfully dedicated army of BRU research nurses, in particular Carmen Chan, Zohreh Farzad, Sally-Ann McRae, and Annashyl West for your help with my projects. Thanks also to Steve Collins and Yasin Karafil for keeping the BRU database alive and to Geraldine Sloane for your continuous project support. Thanks also to the research fellows (especially Brian, Amrit and Zohya) for your camaraderie and for making sure that coming to work was never a chore. Within CMR, an enormous debt of gratitude to Ric Wage for your support of the remodelling study and all round CMR wisdom. Thank you also to George Mathew and Karen Symmonds for teaching me how to scan. I am lucky to have had ready access to physics expertise and have found patient teachers in Peter Gatehouse, Jenny Keegan, Andy Scott, Iain Pierce, Pedro Ferreira, and Merlin Fair, thank you all. Last but not least, I thank Simon Newsome for his never-ending statistical mentorship and support. I hope the breadth and depth of what I have learnt from you is reflected in all my academic endeavours.
A brief but heartfelt thanks must also go to my family and friends for keeping me sane over the last few years. And for the cocktails, holidays, and cuddles when said sanity was absent.
Most importantly of all, I am of course indebted to the patients and volunteers who participated in this study. Clinical research is only possible thanks to the altruism and enthusiasm of these individuals.
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CONTENTS
Abstract ...... 2
Thesis Overview Diagram ...... 4
Declarations ...... 5
Acknowledgements ...... 6
Contents ...... 7
List of Tables ...... 13
List of Figures ...... 15
List of Abbreviations and Acronyms ...... 17
Selected awards and publications ...... 19
1 Introduction: Dilated Cardiomyopathy ...... 22
1.1 What is Dilated Cardiomyopathy? ...... 22
1.1.1 Definition of Dilated Cardiomyopathy ...... 22
1.1.2 Epidemiology of Dilated Cardiomyopathy ...... 22
1.1.3 Aetiology of Dilated Cardiomyopathy ...... 23
1.2 Unmet Needs in the Assessment of Patients with Dilated Cardiomyopathy ...... 24
1.2.1 Accurate Diagnosis of Dilated Cardiomyopathy ...... 24
1.2.2 Accurate risk stratification and development of novel therapeutic targets ...... 26
1.2.3 Harnessing genetic data to improve our understanding of DCM ...... 27
1.3 Current Understanding of the Genetics of Dilated Cardiomyopathy ...... 28
1.3.1 The genetic basis of DCM ...... 28
1.3.2 Disease-gene associations in DCM ...... 38
1.4 Truncating variants in titin in DCM ...... 47
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1.4.1 Function of the titin protein ...... 47
1.4.2 Prevalence of titin truncating mutations in DCM ...... 48
1.4.3 Established genotype phenotype correlations in TTNtv DCM ...... 48
1.4.4 Challenges in the interpretation of TTNtv ...... 54
1.4.5 Evidence for genetic and environmental modifiers of TTNtv cardiomyopathy ...... 54
1.5 Role of Cardiovascular Magnetic Resonance in Dilated Cardiomyopathy ...... 58
1.5.1 Overview of cardiovascular magnetic resonance ...... 58
1.5.2 The role of CMR in DCM ...... 59
1.5.3 Limitations of cardiac MRI in dilated cardiomyopathy ...... 70
1.6 Remodelling and Recovery in DCM ...... 71
1.6.1 Potential for LV remodelling in DCM ...... 71
1.6.2 Situations in which LV remodelling can occur in DCM ...... 72
1.6.3 Prognostic implications of LV remodelling ...... 72
1.6.4 Predictors of LV remodelling in DCM ...... 74
1.6.5 Predictors of sustained LV recovery in DCM ...... 78
1.7 Prognosis in DCM and Risk Stratification ...... 80
1.7.1 Prognosis in DCM ...... 80
1.7.2 Established and novel risk stratification biomarkers in adult onset DCM ...... 82
1.7.3 Current risk stratification in DCM and limitations ...... 104
1.7.4 Personalised medicine in DCM ...... 110
Aims, objectives and hypotheses ...... 111
2 Evaluating the genetic architecture of dilated cardiomyopathy ...... 113
2.1 Aims and hypotheses ...... 113
2.2 Background ...... 113
2.3 Methods ...... 115
2.3.1 Study population ...... 115
2.3.2 ExAC population reference dataset ...... 117
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2.3.3 Coverage analysis between cases or controls and ExAC ...... 117
2.3.4 Gene selection ...... 117
2.3.5 Targeted sequencing ...... 119
2.3.6 General and platform specific bioinformatic analysis ...... 121
2.3.7 Variant filtering ...... 122
2.3.8 Burden testing ...... 123
2.3.9 Sample size calculation ...... 124
2.4 Results ...... 125
2.4.1 Cohort overview ...... 125
2.4.2 Coverage comparison between DCM cases and ExAC ...... 127
2.4.3 Excluded variants ...... 130
2.4.4 Burden of rare genetic variation in DCM patients compared to the ExAC cohort ...... 131
2.4.5 Burden of rare genetic variation in healthy volunteers compared to the ExAC cohort
...... 139
2.4.6 Proportion of DCM cases with identifiable genetic variant ...... 142
2.5 Discussion ...... 143
2.5.1 Limitations of this study ...... 150
2.6 Summary ...... 153
2.7 Outline of further work ...... 154
2.8 Acknowledgements ...... 154
3 The clinical manifestations and phenotypic drivers of titin cardiomyopathy ...... 155
3.1 Aims and Hypotheses ...... 155
3.2 Background ...... 155
3.3 Methods ...... 158
3.3.1 Study Population ...... 158
3.3.2 Ethics ...... 158
3.3.3 Titin truncating variant curation for phenotype analysis ...... 158
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3.3.4 DCM phenotyping ...... 163
3.3.5 Clinical Data ...... 165
3.3.6 Statistical Analysis ...... 169
3.4 Results ...... 171
3.4.1 Cohort Recruitment ...... 171
3.4.2 Patient demographics ...... 172
3.4.3 Overall DCM phenotype ...... 174
3.4.4 Curation of titin truncating variants ...... 180
3.4.5 TTNtv cardiomyopathy phenotype ...... 193
3.4.6 Alcohol as a phenotypic modifier of TTNtv cardiomyopathy ...... 207
3.4.7 Other modifiers of TTNtv cardiomyopathy ...... 222
3.5 Discussion ...... 225
3.5.1 Strengths of this study ...... 230
3.5.2 Limitations of this study ...... 231
3.6 Summary ...... 232
3.7 Outline of further work ...... 233
3.8 Acknowledgements ...... 233
4 Imaging predictors of cardiac remodelling ...... 234
4.1 Aims and hypotheses ...... 234
4.2 Background ...... 235
4.3 Methods ...... 240
4.3.1 Study overview ...... 240
4.3.2 Study cohort, inclusion and exclusion criteria ...... 240
4.3.3 Baseline CMR protocol ...... 242
4.3.4 Follow up imaging ...... 254
4.3.5 Sample size calculation and statistical analysis ...... 254
4.4 Results ...... 257
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4.4.1 Cohort size and loss to follow up ...... 257
4.4.2 Baseline demographics and CMR findings ...... 258
4.4.3 Safety of dobutamine ...... 262
4.4.4 Heart rate and blood pressure response to dobutamine stress ...... 262
4.4.5 Baseline LV contractile reserve ...... 264
4.4.6 Baseline LV strain ...... 267
4.4.7 Baseline interstitial fibrosis ...... 273
4.4.8 Evaluating absolute contractile reserve as a predictor of LV remodelling ...... 277
4.4.9 Other predictors of LV remodelling ...... 284
4.4.10 Mechanistic basis of contractile reserve ...... 288
4.5 Discussion ...... 289
4.5.2 Strengths of this study ...... 296
4.5.3 Limitations of this study ...... 298
4.6 Summary ...... 302
4.7 Outline of further work ...... 303
4.8 Acknowledgements ...... 303
5 The role of clinical, imaging, and genetic data in predicting clinical outcomes in
dilated cardiomyopathy ...... 304
5.1 Aims and hypotheses ...... 305
5.2 Background ...... 305
5.3 Methods ...... 306
5.3.1 Study cohort ...... 306
5.3.2 Variable definition and analysis ...... 306
5.3.3 Endpoints and adjudication of events ...... 307
5.3.4 Statistical analysis ...... 310
5.4 Results ...... 313
5.4.1 Overview ...... 313
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5.4.2 Building the baseline model to predict the primary end point ...... 316
5.4.3 Analysis of the association between TTNtv and the primary end point ...... 325
5.4.4 Sensitivity analyses for the association between TTNtv and the primary endpoint . 331
5.4.5 Non-titin genetic variants and clinical outcomes: primary endpoint ...... 336
5.4.6 Analysis of the association between TTNtv and secondary outcome end points ...... 338
5.5 Discussion ...... 340
5.5.2 Strengths of this study ...... 345
5.5.3 Limitations of this study ...... 345
5.6 Summary ...... 349
5.7 Outline of further work ...... 349
5.8 Acknowledgements ...... 349
6 Summary: What this thesis adds to the field and future work ...... 351
7 References ...... 356
8 Appendices ...... 373
8.1 Gene panels ...... 373
8.2 Standard Operating Protocol for Date of Clinical Diagnosis of DCM ...... 387
8.3 CMR safety checklist ...... 388
8.4 Endpoint definitions ...... 389
8.5 Permissions ...... 392
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LIST OF TABLES
Table 1-1: Examples of identifiable causes of dilated cardiomyopathy ...... 23 Table 1-2: Genes implicated in monogenic dilated cardiomyopathy ...... 30 Table 1-3: Composition of the ExAC dataset ...... 36 Table 1-4: Genes linked to DCM, annotated by year of report in HGMD ...... 39 Table 1-5: Summary of key genotype-phenotype studies of TTNtv DCM ...... 53 Table 1-6: Biomarkers linked to heart failure classified by biological function ...... 98 Table 1-7: Current guidelines for primary prevention ICD implantation ...... 104 Table 2-1: Genes with variants reported to be associated with DCM in HGMD ...... 118 Table 2-2: Genes and transcript linked to DCM included on Tru Sight Cardio ...... 119 Table 2-3: Coverage comparison between DCM and ExAC datasets ...... 128 Table 2-4: Poor quality variants excluded from burden analysis ...... 130 Table 2-5: Burden of rare truncating variants in DCM genes in DCM cohort ...... 134 Table 2-6: Burden of rare non-truncating variants in DCM genes in DCM cohort ...... 137 Table 2-7: Burden of rare variants in RBM20 in DCM cohort compared to volunteers ...... 139 Table 2-8: Burden of truncating and non-truncating variants in TTN in DCM patients ...... 140 Table 3-1: Genes and variant classes selected for variant curation ...... 160 Table 3-2: Titin meta-transcript details ...... 163 Table 3-3: Data collected on DCM Events form as baseline phenotyping of cohort ...... 166 Table 3-4: Baseline demographics in DCM cohort ...... 173 Table 3-5: Baseline CMR phenotype in DCM cohort ...... 178 Table 3-6: Sequencing platform and assay used in DCM cohort ...... 182 Table 3-7: List of truncating variants in TTN gene ...... 186 Table 3-8: Baseline demographics in DCM patients stratified by TTNtv status ...... 194 Table 3-9: Baseline CMR variables in DCM cohort stratified by TTNtv status ...... 196 Table 3-10: Variables evaluated on univariable linear regression as predictors of left ventricular mass ...... 198 Table 3-11: Multivariable regression analysis evaluating predictors of left ventricular mass ...... 200 Table 3-12: Sarcomeric variant sensitivity analysis ...... 203 Table 3-13: Baseline demographics and cardiac phenotype, stratified by alcohol status ..... 209 Table 3-14: Results of univariable and multivariable linear regression evaluating predictors of LVEF ...... 212 Table 3-15: Results of nested ANOVA comparing baseline models to predict LVEF...... 214 Table 3-16: Results of univariable and multivariable linear regression analysis evaluating the effect of TTNtv and alcohol excess on LVEF ...... 216 Table 4-1 CMR baseline study protocol ...... 243 Table 4-2: Baseline demographics and CMR findings in cohort stratified by diagnosis ...... 261 Table 4-3: Linear regression models evaluating the effect of baseline variables on LV absolute contractile reserve...... 266 Table 4-4: Peak global long axis (HLA, VLA) and SAX strain in DCM patients and volunteers ...... 270 Table 4-5: Absolute difference in peak global long axis (HLA, VLA) and SAX strain in DCM patients and healthy volunteers ...... 273 Table 4-6: Extra-cellular volume fraction (ECV) in DCM patients and healthy volunteers . 274 Table 4-7: Univariable ANCOVA regression evaluating predictors of follow up LVEF. .... 279 Table 4-8: Multivariable ANCOVA regression analysis evaluating absolute contractile
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reserve ...... 281 Table 4-9: Contractile reserve sensitivity analysis ...... 283 Table 4-10: Contractile reserve assessed by myocardial strain as predictor of follow up LVEF...... 284 Table 4-11: Results of ANCOVA regression analysis evaluating baseline indices of strain as predictors of follow up LVEF...... 287 Table 5-1: Variables evaluated in survival analysis ...... 306 Table 5-2: Table showing the number of patients meeting each endpoint ...... 314 Table 5-3: Composition of events occurring during follow up leading to primary composite endpoint ...... 315 Table 5-4: Baseline demographics in the outcome cohort ...... 317 Table 5-5: Results of univariable Cox proportional hazard modeling ...... 319 Table 5-6: Adjusted baseline Cox proportional hazard model predicting the primary endpoint ...... 321 Table 5-7: Comparison of Cox models ...... 322 Table 5-8: Results of testing proportionality assumption of baseline model ...... 323 Table 5-9: Adjustment of Cox model for the time dependent covariate of cardiac device implantation ...... 333 Table 5-10: Sensitivity analysis: VT ...... 334 Table 5-11: Association between TTNtv and the primary endpoint, adjusted for the baseline Cox model in the subset of patients with complete serum creatinine data...... 335 Table 5-12: Numbers of patients with rare variants (MAF <0.0001) in non-titin DCM genes meeting the primary endpoint ...... 336 Table 5-13: Association between non-TTNtv genetic variants and the primary endpoint .... 337 Table 8-1: Definition of cardiovascular death ...... 389
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LIST OF FIGURES
Figure 1-1: Model of types of genetic susceptibility to disease ...... 29 Figure 1-2: Overview of NGS workflow ...... 34 Figure 1-3: Putative model of genetic and environmental modifiers of TTNtv ...... 55 Figure 2-1: Overview of methods for variant curation for burden testing ...... 123 Figure 2-2: Simulated power calculation for burden testing ...... 125 Figure 2-3: Overview of cohorts in burden testing ...... 126 Figure 2-4: Coverage plot ...... 127 Figure 2-5: Excess burden of protein altering variants ...... 132 Figure 2-6: Pie chart of excess burden (%) in significantly enriched genes (p<0.0009) in DCM cohort ...... 132 Figure 2-7: Burden of rare truncating variants in DCM compared to ExAC ...... 133 Figure 2-8: Burden of rare non-truncating variants in DCM compared to ExAC ...... 136 Figure 2-9: Burden of rare protein altering variants in volunteers compared to ExAC ...... 141 Figure 2-10: Bar chart showing percentage of DCM cases with identified genetic variant .. 142 Figure 3-1: Outline of DCM cohort recruitment for phenotype study ...... 171 Figure 3-2: Interval between date of diagnosis and baseline date ...... 174 Figure 3-3: Histograms showing the distribution of left and right ventricular function ...... 176 Figure 3-4: CMR phenotype of DCM cohort compared to controls ...... 177 Figure 3-5: CMR phenotype correlation plot ...... 179 Figure 3-6: Flowchart outlining filtering steps to curate TTNtv for phenotype analysis ...... 181 Figure 3-7: Percentage coverage of TTN at 30x ...... 183 Figure 3-8: IGV output of individual with 2 protein altering variants in TTN ...... 184 Figure 3-9: Position of TTNtv variants according to protein domains and PSI ...... 192 Figure 3-10: Cardiac phenotype in probands and affected relatives ...... 193 Figure 3-11: TTNtv hypertrophy phenotype ...... 197 Figure 3-12: LV mass and LV end diastolic volume ...... 200 Figure 3-13: Forest plot showing the predictors of left ventricular mass ...... 201 Figure 3-14: Model checking of final linear regression model predicting LV mass ...... 202 Figure 3-15: Plots show the relationship between TTNtv location and cardiac endophenotypes assessed by CMR ...... 205 Figure 3-16: Cronos: Plots show the relationship between TTNtv location and cardiac endophenotypes assessed by CMR ...... 206 Figure 3-17: Self reported weekly alcohol consumption (units) across study cohort ...... 207 Figure 3-18: LVEF in patients with TTNtv and alcohol excess ...... 210 Figure 3-19: Forest plot showing results of final linear regression model predicting LVEF and the effects of TTNtv and alcohol ...... 217 Figure 3-20: Model checking of final linear regression model predicting LVEF with the TTNtv and alcohol interaction ...... 218 Figure 3-21: Log2 transformation of alcohol consumption plotted against LVEF ...... 220 Figure 3-22: Weekly alcohol consumption grouped into categorical variables and plotted against LVEF ...... 221 Figure 3-23: Forest plot of estimate and 95% confidence intervals of TTNtv:Alcohol interaction term predicting LVEF ...... 221 Figure 3-24: LVEF stratified by TTNtv and hypertension status ...... 222 Figure 3-25: Forest plot showing results of final linear regression model predicting LVEF and the effects of TTNtv and hypertension ...... 223
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Figure 3-26: LVEF and age at study recruitment in patients with TTNtv ...... 224 Figure 4-1: DCM Remodelling study overview ...... 240 Figure 4-2: Example of threshold based measurement of cardiac volumes ...... 244 Figure 4-3: Schematic of dobutamine infusion stages ...... 245 Figure 4-4: Examples of defining single line contour regions of interest for the assessment of long axis strain ...... 250 Figure 4-5: Examples of defining regions of interest for the assessment of short axis strain 251 Figure 4-6: Figure demonstrating zonal excitation ...... 252 Figure 4-7: Diagram outlining the relationship between the exposure variable (contractile reserve) and the outcome variable (follow up LVEF) ...... 256 Figure 4-8: Overview of cohort recruitment and completion to follow up ...... 258 Figure 4-9: Baseline prognostic medication in DCM cohort ...... 259 Figure 4-10: Maximum change in systolic blood pressure (SBP) and heart rate during dobutamine infusion ...... 263 Figure 4-11: Maximal change in systolic blood pressure (SBP) and heart rate during dobutamine infusion ...... 263 Figure 4-12: Comparison of the absolute change in LVEF from baseline to peak dobutamine stress ...... 264 Figure 4-13: Absolute contractile reserve ...... 265 Figure 4-14: Plot of relative LV contractile reserve in DCM patients and controls ...... 267 Figure 4-15: Cine DENSE analysis ...... 268 Figure 4-16: Summary strain-time curves for DCM and control cohort ...... 269 Figure 4-17: Relationship between left ventricular ejection fraction and contour strains ..... 271 Figure 4-18: Correlation matrix ...... 272 Figure 4-19: Extracellular volume fraction (ECV) across cohort ...... 274 Figure 4-20: Relationship between ECV and LVEF ...... 275 Figure 4-21: LVEF change at 1 year ...... 276 Figure 4-22: Regression models evaluating absolute contractile reserve ...... 282 Figure 4-23: Absolute RV contractile reserve in DCM patients compared to controls ...... 285 Figure 4-24: Right and left ventricular contractile reserve ...... 285 Figure 4-25: Relative RV contractile reserve in DCM patients and healthy volunteers ...... 286 Figure 4-26: Contractile reserve and ECV ...... 288 Figure 5-1: Outline of variable selection for the baseline Cox proportional hazard model .. 312 Figure 5-2: Histogram of follow up time in original dataset ...... 314 Figure 5-3: Graphical diagnostic of the baseline Cox regression model ...... 324 Figure 5-4: Survival curves comparing freedom from the primary endpoint in patients with and without TTNtv ...... 325 Figure 5-5: Survival curves showing freedom from the primary endpoint comparing patients with and without TTNtv stratified by gender ...... 327 Figure 5-6: Survival curves showing freedom from primary endpoint in subset of patients with TTNtv, stratified by a history of alcohol excess ...... 328 Figure 5-7: Survival curves showing outcome in DCM cohort stratified by a baseline history of alcohol excess ...... 328 Figure 5-8: Freedom from primary endpoint in DCM stratified by TTNtv and baseline history of mid wall fibrosis LGE ...... 329 Figure 5-9: Forest plot of Cox proportional hazard modelling of TTNtv interactions ...... 331 Figure 5-10: Survival curve for patients meeting the primary endpoint stratified by LMNA variant status ...... 337 Figure 5-11: Freedom from secondary endpoints stratified by TTNtv status ...... 339 Figure 8-1: CMR safety checklist ...... 388
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LIST OF ABBREVIATIONS AND ACRONYMS
1000GP; 1000 Genomes Project ACEi; Angiotensin converting enzyme inhibitor ACMG; American College of Medical Genetics ARVD/ARVC: arrhythmogenic right ventricular dysplasia/cardiomyopathy BAG3; BCL2 Associated Athanogene 3 gene BNP; B-type natriuretic peptide BSA; body surface area CMR; cardiovascular magnetic resonance CRT-P/D; cardiac resynchronization therapy –pacemaker/defibrillator dbNSFP; database for non synonymous SNPs’ functional predictions DCM; dilated cardiomyopathy DENSE; displacement encoding with simulated echoes DSP; desmoplakin gene ECV; extracellular volume fraction EPS; electrophysiology studies ESC; European Society of Cardiology ESP; NHLBI GO Exome Sequencing Project ExAC; Exome Aggregation Consortium FLNC; filamin C gene FOV; field of view FWHM; full-width at half maximum gnomAD; Genome Aggregation Database GWAS; genome wide association study HCM; hypertrophic cardiomyopathy HLA; horizontal long axis HVOL; healthy volunteer ICD; implantable cardiac defibrillator LA; left atrial LGE; late gadolinium enhancement LMNA; lamin A/C gene LVEDd; left ventricular end-diastolic diameter LVEDVi; indexed left ventricular end diastolic volume LVEF; left ventricular ejection fraction LVESVi; indexed left ventricular end systolic volume LVMi; indexed left ventricular mass LVOT; left ventricular outflow tract LVRR: left ventricular reverse remodelling LVSVi; indexed left ventricular stroke volume MAF; minor allele frequency MMPs; Matrix metalloproteinases
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mTOR; mammalian target of rapamycin MYBPC3; myosin binding protein C gene MYH7; Beta myosin heavy chain gene NGS; next-generation DNA sequencing NSVT; non-sustained ventricular tachycardia NTproBNP; N-terminal pro BNP PKA; cyclic-AMP–dependent protein kinase PKG; cyclic-GMP–dependent protein kinase PLN; phospholamban gene PSI; percentage spliced in RBM20; RNA Binding protein 20 gene RVEDVi; indexed right ventricular end diastolic volume RVEF; right ventricular ejection fraction RVESVi; indexed right ventricular end systolic volume RVOT; right ventricular outflow tract RVSVi; indexed right ventricular stroke volume SAX; short axis SBP; systolic blood pressure SCD; sudden cardiac death SD; standard deviation SNP; single nucleotide polymorphism SSFP; Steady-state free precession MRI STIR; T2 weighted Short-Tau Inversion Recovery sequence TIMPs; tissue inhibitors of matrix metalloproteinases TNNC1; cardiac troponin C gene TNNI3; cardiac troponin I gene TNNT2; cardiac troponin T gene TTNtv; truncating variant in titin gene TWA; microvolt T wave alternans VCF; variant call format VF; ventricular fibrillation VLA; vertical long axis VT; ventricular tachycardia VUS; variants of uncertain significance WES; whole exome sequencing WGS; whole genome sequencing ZBTB17; zinc-finger and BTB domain-containing protein 17 gene
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SELECTED AWARDS AND PUBLICATIONS
Prizes related to work in this thesis
American College of Cardiology Mar 2017: Winner of the American College of Cardiology Young Investigator Award (Clinical) (ACC, Washington, USA).
London Cardiovascular Society Feb 2017: Winner of the London Cardiovascular Society JRSM Cardiovascular Disease Young Investigator of the Year Award (The Medical Society of London).
British Society of Cardiovascular Magnetic Resonance Mar 2017: Second place in British Society of Cardiac MRI (BSCMR) Young Investigator Award and second place in BSCMR e-poster competition (Manchester).
Imperial College London July 2017: Highly commended oral presentation at the National Heart Lung Institute Post Graduate Research Day (Imperial College London). April 2017: Awarded 2nd place in the Imperial Graduate School 3 minute thesis competition (Imperial College London).
British Cardiac Society June 2017: Evaluation of titin cardiomyopathy in patients with dilated cardiomyopathy reveals a blunted hypertrophic response, an early arrhythmic risk and a significant interaction with alcohol. Poster, British Cardiac Society Awarded BHF/BCS prize for the highest ranked abstract in the Valve Disease/Pericardial Disease/Cardiomyopathy category. June 2015: Comprehensive Assessment Of Rare Genetic Variation In Dilated Cardiomyopathy Genes In Patients And Controls. Poster, British Cardiac Society Awarded BHF/BCS prize for the highest ranked abstract in the Valve Disease/Pericardial Disease/Cardiomyopathy category.
Publications related to work in this thesis
Original articles 1. Tayal U, Newsome S, Buchan R, Whiffin N, Walsh R, Barton PJ, Ware JS, Cook SA, Prasad SK: Truncating Variants in Titin Independently Predict Early Arrhythmias in Patients With Dilated Cardiomyopathy. J Am Coll Cardiol 2017, 69:2466-2468.
2. Halliday BP, Gulati A, Ali A, Guha K, Newsome S, Arzanauskaite M, Vassiliou VS, Lota A, Izgi C, Tayal U, et al: Association Between Midwall Late Gadolinium Enhancement and Sudden Cardiac Death in Patients With Dilated Cardiomyopathy and Mild and Moderate Left Ventricular Systolic Dysfunction. Circulation 2017, 135:2106-2115.
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3. Schafer S, de Marvao A, Adami E, Fiedler LR, Ng B, Khin E, Rackham OJ, van Heesch S, Pua CJ, Kui M, Walsh R, Tayal U et al: Titin-truncating variants affect heart function in disease cohorts and the general population. Nat Genet 2017, 49:46- 53.
Reviews, editorials, or invited publications
1. Tayal U, Prasad S, Cook SA: Genetics and genomics of dilated cardiomyopathy and systolic heart failure. Genome Med 2017, 9:20.
2. Tayal U, Cook SA: Truncating Variants in Filamin C: The Challenges of Genotype- Phenotype Correlations in Cardiomyopathies. J Am Coll Cardiol 2016, 68:2452-2453.
Abstracts related to work in this thesis
1. Tayal, U., Buchan, R., Whiffin, N., Newsome, S., Walsh, R., Barton, P., . . . Prasad, S. (2017). Evaluation Of Titin Cardiomyopathy In Patients With Dilated Cardiomyopathy Reveals A Blunted Hypertrophic Response, An Early Arrhythmic Risk And A Significant Interaction With Alcohol. Heart 103 (Pp. A95). 2. Tayal, U., Newsome, S., Buchan, R., Whiffin, N., Walsh, R., Ware, J., . . . Cook, S. A. (2016). Defining titin cardiomyopathy: genotype- phenotype correlations in a large prospective cohort of dilated cardiomyopathy patients. European Heart Journal Vol. 37 (pp. 365). 3. Halliday, B. P., Ali, A., Gulati, A., Newsome, S., Tayal, U., Lota, A., . . . Prasad, S. K. (2016). Gender differences in the natural history and outcome of dilated cardiomyopathy. European Heart Journal Vol. 37 (pp. 325-326). 4. Tayal, U., Newsome, S., Buchan, R., Whiffin, N., Walsh, R., Ware, J., . . . Prasad, S. K. (2016). Genetic determinants of arrhythmia in dilated cardiomyopathy. European Heart Journal Vol. 37 (pp. 206). 5. Lota, A., Wassall, R., Tsao, A., Shakur, R., Tayal, U., Halliday, B., . . . Prasad, S. K. (2016). Prevalence and prognostic significance of right ventricular systolic function assessed by CMR in patients with suspected acute myocarditis. European Heart Journal Vol. 37 (pp. 1365-1366). 6. Halliday BP, Gulati A, Ali A, Guha K, Arzanauskaite M, Newsome S, Lota A, Tayal U,. . . Prasad, S. K. (2016). Risk stratification of mild-to-moderate phenotypes of dilated cardiomyopathy - the role of mid-wall fibrosis. European Heart Journal Vol. 37 (pp. 198-199). 7. Halliday, B. P., Ali, A., Gulati, A., Newsome, S., Tayal, U., Wage, R., . . . Prasad, S. K. (2016). The natural history of non-ischaemic dilated cardiomyopathy diagnosed after the age of 65 years of age. European Heart Journal Vol. 37 (pp. 1324). 8. Tayal, U., Buchan, R., Whiffin, N., et al. (2016). Clinical and genetic characteristics of familial dilated cardiomyopathy in a large UK prospective cohort. Heart 102(Suppl 6): A103 9. Tayal, U., Buchan, R., Whiffin, N., et al (2016). Effects of truncating variants in titin on cardiac phenotype and left ventricular remodelling in dilated cardiomyopathy. Heart 102(Suppl 6): A102 10. Tayal, U., Newsome, S., Buchan, R., et al (2016). The presence of a truncating mutation in titin independently associates with arrhythmic burden in patients with
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dilated cardiomyopathy. European Journal of Heart Failure 18 (Suppl 1):156 11. Tayal, U., Newsome S, Wage R, et al (2016). Evaluation of CMR predictors of right ventricular remodelling in dilated cardiomyopathy. Journal of Cardiovascular Magnetic Resonance 2016 18(Suppl 1):P281 12. Tayal, U., Newsome S, Wage R, et al (2016). Evaluating the significance of left ventricular midwall fibrosis detected by late gadolinium enhancement imaging on left ventricular functional remodelling in dilated cardiomyopathy. Journal of Cardiovascular Magnetic Resonance 2016 18(Suppl 1):P283 13. Scott AD, Tayal U, Nielles-Vallespin S, et al (2016). Accelerating cine DENSE using a zonal excitation. Journal of Cardiovascular Magnetic Resonance 2016 18(Suppl 1):O50 14. Tayal, U., Mazzarotto, F., Buchan, R., et al. (2015). Comprehensive Assessment of Rare Genetic Variation in Dilated Cardiomyopathy Genes in Patients and Controls. Heart 101(Suppl 4): A41-A42.
Invited talks
• CMR and Cardiomyopathy at the American College of Cardiology Conference March 2017, Washington, USA. • ‘Genetics of Dilated Cardiomyopathy: What’s new?’ Invited research presentation at the Institute of Cardiovascular Medicine and Science Symposium- The Future of Cardiovascular Medicine. National Heart and Lung Institute, London. September 2016 • ‘Big hearts and giant genes- why size does matter’. Invited lecture, Eton College Medical Society. Jan 2016. • ‘Titin cardiomyopathy’. Invited lecture at the 9th Cardiomyopathy Workshop, Imperial College. Dec 2014.
Book Chapters
• Chapter on Dilated Cardiomyopathy and Myocarditis for the Diagnosis and Management of Adult Congenital Heart Disease, Gatzoulis, Webb and Daubeney, 3rd edition [Churchill Livingstone]. • Chapter on Dilated Cardiomyopathy for Cardiovascular Magnetic Resonance- Manning and Pennell, 3rd edition [Elsevier]. (in press)
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1 INTRODUCTION: DILATED
CARDIOMYOPATHY
1.1 What is Dilated Cardiomyopathy?
1.1.1 Definition of Dilated Cardiomyopathy
Dilated cardiomyopathy (DCM) is a disease of the myocardium characterised by left
ventricular dilatation and systolic impairment in the absence of coronary artery disease or
abnormal loading conditions, such as hypertension or valvular heart disease, sufficient to
explain the observed myocardial abnormality (1-3).
1.1.2 Epidemiology of Dilated Cardiomyopathy
The landmark population study, the Olmsted County study, performed between 1975 and
1984, estimated the prevalence of DCM to be in the region of 1 in 2,700 individuals (4). This
however was prior to the widespread availability of echocardiography. The prevalence of
hypertrophic cardiomyopathy in the same study was estimated to be ~1 in 5000, which has
since been shown to be a gross under-estimate. Therefore, in the absence of large
contemporary population studies, estimated DCM prevalence has recently been revised to 1
in 250 individuals(5). DCM is a leading cause of heart failure and is the primary global
indication for heart transplantation (1,6,7).
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1.1.3 Aetiology of Dilated Cardiomyopathy
The major identifiable causes of DCM include genetic or syndromic forms, infectious
diseases, drugs and toxins, endocrine disorders, inflammatory conditions, and nutritional
deficiencies (examples in Table 1-1). DCM is classified as idiopathic in 50% of cases when
non-genetic identifiable causes have been excluded. The genetic basis of DCM is reviewed in
detail in Section 1.3.
Table 1-1: Examples of identifiable causes of dilated cardiomyopathy
Cause Examples
Over 60 genes reported to be associated with DCM including TTN (up to 25%), Genetic and LMNA, MYH7 and TTNT2 (<5%). syndromic Duchenne Muscular Dystrophy, Barth Syndrome
Viral: Coxsackie, HIV, Influenza, Adenovirus, Cytomegalovirus, Varicella, Hepatitis, Ebstein-Barr, Echovirus, Parvovirus Bacterial: Streptococci, Mycobacteria Infectious Spirochetal: Lyme disease, Syphilis Fungal: Histoplasmosis, Cryptococcocis, Parasitic: Toxoplasmosis, Trypanosomiasis, Schistosomiasis
Chemotherapeutic agents including anthracyclines and cyclophosphamide Drugs Antiretroviral drugs including Zidovudine, Other, e.g. Phenothiazines, Chloroquine, Clozapine
Toxins Alcohol, Cocaine, Amphetamines, Cobalt, Lead, Mercury Nutritional deficiencies and Thiamine, Selenium, Carnitine, Niacin electrolyte Hypocalcaemia, hypophosphatemia, Ureamia disturbances
Hypo- or hyper-thyroidism, Diabetes mellitus, Cushing’s syndrome, Endocrine Phaeochromocytoma, Growth hormone excess or deficiency
Inflammatory and Systemic Lupus Erythematosis, Scleroderma, Rheumatoid arthritis, Autoimmune Autoimmune myocarditis, Dermatomyositis
Other Tachycardia induced cardiomyopathy, Pregnancy
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1.2 Unmet Needs in the Assessment of Patients with Dilated
Cardiomyopathy
There are a number of areas of unmet need relating to the assessment and management of
patients with DCM, including accurate diagnosis, risk stratification, and development of
novel therapeutics to improve outcomes.
1.2.1 Accurate Diagnosis of Dilated Cardiomyopathy
DCM is an imaging and clinical diagnosis. According to the 2008 European Society of
Cardiology (ESC) guidelines, the diagnosis of DCM requires the presence of imaging
evidence of left ventricular dilation and systolic impairment, in the absence of clinical
evidence of abnormal loading conditions which would otherwise account for the imaging
phenotype and after the exclusion of coronary artery disease sufficient to cause global
systolic impairment(8).
The most commonly used imaging modality is echocardiography, in which the criteria for left
ventricular dilation and impairment are left ventricular end-diastolic diameter (LVEDd) >
117% of that predicted for age and body surface area (BSA) and left ventricular ejection
fraction (LVEF) < 45% and/or fractional shortening (FS) < 25% respectively(9).
However, these criteria are not definitive, and patients who do not meet these
echocardiographic criteria may still have DCM, as evidenced by clinically significant
myocardial disease on cardiovascular magnetic resonance (CMR) or endomyocardial
biopsy(10). As our understanding of the genetic basis of DCM increases, it is now
recognized that mutation carriers have intermediate phenotypes that do not meet standard
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disease definitions. Therefore in 2016, a revision to the ESC classification was proposed to
include a category of hypokinetic non-dilated cardiomyopathy, to recognise the presence of
left ventricular impairment (LVEF <45%) in the absence of dilation as being on the same
disease spectrum as dilated cardiomyopathy(2). In this revision, the authors also proposed a
broader definition for DCM: ‘Left ventricular or biventricular systolic dysfunction and
dilatation that are not explained by abnormal loading conditions or coronary artery
disease’(2). Crucially, the revised definition accepts abnormal LVEF measured using any
modality and defines LV dilation as LV end diastolic volumes or diameters greater than 2
standard deviations from age, gender and BSA adjusted nomograms(2).
These revisions acknowledge that historically there has been a marked heterogeneity in the
assessment of DCM. This has implications not only for the clinical assessment of patients,
but also in the interpretation of research studies with ‘DCM’ cohorts. Patients with DCM and
mild-moderate LV impairment may have been excluded from previous studies. Furthermore,
echocardiographic assessment is also limited by the need for a suitable anatomical window
and poor inter- and intra observer variability, and also does not provide tissue
characterisation (11). This could lead to misclassification of patients, particularly in
conditions with phenotypic overlap, for example arrhythmogenic ventricular
cardiomyopathy, or in the mid range of LVEF (40-50%) where the established magnitude of
measurement variability in echocardiography could affect whether a diagnosis of DCM is
made.
In these situations, CMR imaging offers great incremental value in the diagnosis of DCM,
enabling accurate assessment of cardiac volumes and function, as well as providing detailed
tissue characterisation. As yet, there are no large scale studies of patients with DCM
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confirmed by CMR.
1.2.2 Accurate risk stratification and development of novel therapeutic
targets
Despite improvements in optimal medical therapy, DCM remains associated with adverse
long term outcomes, with a 20% 5-year mortality rate(12,13). A key challenge in the
management of DCM patients is improved risk stratification, both the early identification of
the ‘at risk’ patient, as well as targeting of existing therapy to individuals likely to receive the
greatest benefit.
The current treatment paradigm in DCM is for all patients to broadly receive the same
therapy (ACE (Angiotensin converting enzyme) inhibitor, beta blocker, aldosterone
antagonist), irrespective of underlying aetiology or genetic basis. Novel heart failure
therapies such as the angiotensin-neprilysin inhibitor have recently emerged but it is too soon
to be able to evaluate long term prognostic benefit in DCM[11]. There is a pressing need to
identify novel therapeutic targets in DCM, which may be achieved through improved
understanding of the mechanistic basis of disease. Until then, tailoring current therapies more
precisely to an individual patient is an area of much promise, but has yet to be explored in
detail [12].
Risk stratification tools in DCM are limited and largely based on qualitative clinical data,
imaging features and biochemical markers, much of which reflect changes observed late in
the disease course. Faced with these difficulties the ideal risk assessment tool would be one
that identifies patients at risk of heart failure prior to overt disease, at a time when a
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preventative intervention could be used to avoid disease onset. Genetics offers one such
approach.
1.2.3 Harnessing genetic data to improve our understanding of DCM
There have been major advances in DNA sequencing technologies over recent years, which
have enabled the widespread application of DNA sequencing to cardiomyopathy cohorts(14).
This has led to a rapid increase in the number of genes reported to be associated with
DCM(5).
At an even more rapid pace, DNA sequencing at scale has been applied in very large
population cohorts, such as those included in the Exome Aggregation Consortium (ExAC)
data set (15) (now renamed the Genome Aggregation Database (gnomAD) to reflect the
inclusion of genome sequencing data). Against this background, understanding which genes
and variants are of importance for a patient with DCM is a challenge for the clinician.
The majority of the gene-DCM associations have no clear genotype-phenotype or genotype-
outcome correlations. How this increasing genetic knowledge informs our understanding of
the pathogenesis of DCM or changes clinical management is not yet fully established.
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1.3 Current Understanding of the Genetics of Dilated
Cardiomyopathy
1.3.1 The genetic basis of DCM
The proportion of DCM cases with a familial basis is between 20-30%, though up to 60% has
been suggested (16). In familial DCM, up to 40% of cases may have an identifiable genetic
basis (5). Inheritance of DCM is most commonly autosomal dominant, although autosomal
recessive, X-linked and mitochondrial inheritance have also been reported, particularly in
pediatric populations (17).
As a more critical evaluation of the genes linked to DCM continues and genes or variants are
discounted, the percentage of DCM with a monogenic basis may fall (14,18). Higher
estimates of sensitivity for genetic testing have been reported (from 46-73% in one study
(19)) but these estimates are likely confounded by insufficient control for population
variation in the genes studied. After excluding non-genetic causes, the low genetic detection
rate in the presence of familial DCM may be attributed to an incomplete understanding of the
genetic basis of DCM. One possibility is that DCM may have a more complex oligogenic
genetic inheritance, where genetic susceptibility may be determined by the cumulative effect
of multiple genetic variants, as opposed to a near Mendelian inheritance driven by a strong
monogenic component (Figure 1-1)(20).
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Figure 1-1: Model of types of genetic susceptibility to disease, ranging from monogenic (Mendelian)
whereby a single rare variant (frequency <0.1%) with a large effect size is sufficient to cause disease, to a
polygenic model whereby multiple common variants (frequency >5%) each with very small effect sizes
contribute to genetic susceptibility to disease. DCM is thought to exhibit monogenic and oligogenic
patterns of inheritance, with the realization that not all familial DCM has an identifiable genetic cause
and that even amongst patients with known variants, penetrance and expressivity are variable. Figure
adapted from(21).
1.3.1.1 Rare genetic variants in DCM
A rare genetic variant alters gene function and occurs at low frequency in a population. In the
25 years following the landmark study linking sarcomeric gene mutations with hypertrophic
cardiomyopathy (HCM) (22), there has been a rapid growth in our understanding of the
genetic basis of cardiomyopathies, including DCM. The genetic architecture of DCM has
emerged to be particularly complex, with almost 70 genetic loci and mutations affecting a
range of diverse cellular structures and functions, most notably with the sarcomere (Table
1-2). In addition to locus heterogeneity, there is marked allelic heterogeneity, whereby
different variants in the same gene can cause a similar phenotype. Of note, different variants
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in the same gene can also produce contrasting phenotypes (for example the TNNI3 gene can
cause HCM and DCM).
Rare genetic variants are typically defined as having a minor allele frequency (MAF) <1%,
though the frequency cut offs in the literature vary(23). For evaluation of potentially
pathogenic variants, a disease specific cut off informed by disease prevalence, penetrance,
and allelic contribution to disease is now recommended(24,25).
Table 1-2: Genes implicated in monogenic dilated cardiomyopathy and their cellular component.
Reproduced from Tayal et al (with permission)(26). Genes with the strongest evidence linking them to
DCM are marked with (*) and the most recently identified genes from 2011 onwards are marked with (#).
Causes of predominantly autosomal recessive DCM and older gene associations that have not been
replicated have not been included.
Gene Protein Function Estimated contribution in DCM patients and phenotypic comments Sarcomeric
MYH7* Myosin-7 (beta myosin heavy Muscle contraction Non truncating variants: 5% chain) TNNT2* Cardiac muscle troponin T Muscle contraction Non truncating variants: 3%
TTN*, # Titin Extensible scaffold/Molecular Truncating variants: 15-25% spring TPM1* Tropomysin Muscle contraction <2%
MYBPC3 Myosin-binding protein C, Muscle contraction Major hypertrophic cardiac type cardiomyopathy gene; purported association with DCM now less likely in light of population variation data(14) TNNC1 Cardiac muscle troponin C Muscle contraction Mutations also associated with hypertrophic cardiomyopathy TNNI3 Cardiac muscle troponin I Muscle contraction Mutations also associated with hypertrophic cardiomyopathy MYL2# Myosin light chain-2 Regulation of myosin ATPase Mutations also associated activity with hypertrophic cardiomyopathy FHOD3# Formin homology 2 domain Sarcomere organization containing 3 Cytoskeleton
DES* Desmin Contractile force transduction <1%
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Gene Protein Function Estimated contribution in DCM patients and phenotypic comments DMD* Dystrophin Contractile force transduction In patients with dystrophinopathies. X-linked VCL Vinculin Cell-matrix and cell-cell adhesion
Nuclear Envelope
LMNA* Lamin-A/C Nuclear membrane structure 4%
Mitochondrial
TAZ Tafazzin Associated with syndromic DCM (e.g. Barth syndrome). X-linked Spliceosomal
RBM20 RNA Binding protein 20 Regulates splicing of cardiac 2% genes Sarcoplasmic reticulum
PLN Phospholamban Sarcoplasmic reticulum calcium <1% regulator; inhibits SERCA2a Linked to an pump arrhythmogenic phenotype Desomosomal
DSP* Desmoplakin Desmosomal junction protein Truncating variants: 3% Linked to arrhythmogenic right and left ventricular cardiomyopathy DSC-2# Desmocollin-2 Desmosomal junction protein Linked to arrhythmogenic right and left ventricular cardiomyopathy DSG2# Desmoglein-2 Desmosomal junction protein Linked to arrhythmogenic right and left ventricular cardiomyopathy PKP2# Plakophilin-2 Desmosomal junction protein Linked to arrhythmogenic right and left ventricular cardiomyopathy; recent studies cast doubt on involvement in DCM JUP Junction plakoglobin Desmosomal junction protein Linked to arrhythmogenic right and left ventricular cardiomyopathy Ion channels
SCN5A sodium voltage-gated channel Sodium channel <2%. Associated with atrial alpha subunit 5 arrhythmias and conduction disease. Association with DCM in absence of segregation less strong in light of population variation data(14) Z disc
FLNC# Filamin C Structural integrity of cardiac - myocyte; actin cross linking protein NEBL Nebulette Z disc protein -
NEXN Nexilin Encodes a filamentous actin - binding protein CSRP3 Cysteine and glycine rich Mechanical stretch sensing - protein 3 TCAP Telethonin Mechanical stretch sensing -
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Gene Protein Function Estimated contribution in DCM patients and phenotypic comments LDB3 Lim domain-binding 3 Z disc structural integrity Associated with left ventricular non compaction phenotypes CRYAB Crystallin alpha-B Heat shock protein
Other
BAG3# BAG family molecular Inhibits apoptosis - chaperone regulator 3 ANKRD1 ankyrin repeat domain 1 Encodes CARP, a transcription <2% coinhibitor RAF1# RAF1 protein MAP3 kinase, part of the Ras- ~9% in childhood-onset MAPK signaling cascade DCM (one study)
Transcription factors
PRDM16# PR domain containing 16 Transcription factor Mutations cause cardiomyopathy in 1p36 deletion syndrome; also linked to isolated DCM and left ventricular non- compaction ZBTB17# Zinc-finger and BTB domain- Transcription factor containing protein 17 TBX5# Cardiac T-box transcription Transcription factor Associated with congenital factor 5 heart disease; also linked to adult onset DCM NKX2-5# NK2 homeobox 5 Transcription factor Associated with congenital heart disease; also linked to adult onset DCM GATA4# GATA Binding Protein 4 Transcription factor Linked to sporadic and familial DCM TBX20# Cardiac T-box transcription Transcription factor Associated with congenital factor 20 heart disease; also linked to adult onset DCM
1.3.1.2 Sequencing methodologies used to detect rare genetic variants
A number of methodologies have been used to understand the genetics of human disease.
Historically candidate gene studies and linkage studies underpinned Mendelian gene
discovery. Linkage studies establish the probability that a given genomic region is associated
with a phenotype, usually in an extended pedigree. These were time consuming, meticulous
studies, requiring large, well characterised families with clearly inherited disease. Traditional
Sanger capillary based sequencing or more contemporary next-generation DNA sequencing
(NGS) approaches such as targeted (panel based) sequencing, whole genome sequencing
(WGS), or whole exome sequencing (WES) are now used to identify rare variants.
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In 1977, Sanger and collaborators published the landmark DNA sequencing technique that
would become and remain the gold standard for DNA analysis(27). Automated Sanger
sequencing continues to be used as a complementary technology to NGS to sequence small
regions, regions difficult to target with NGS (for example those with high GC content) and to
validate NGS findings. The automated Sanger method underpinned the enormous 10 year
collaborative effort to sequence the entire human genome, published in 2001(28). Whilst
whole genome sequencing was then feasible, the cost and timescale prohibited routine use for
clinical or research purposes.
In the era of Sanger sequencing, the study of disease-gene associations commonly involved
sequencing the coding regions of a small number of genes. Pathogenic status was often
ascribed to any rare variant in these genes that was absent in a small control group, typically
~100 samples.
The subsequent advent of next generation DNA sequencing enabled a paradigm shifting
increase in high-throughput sequencing capability, making it possible to sequence large
numbers of genes simultaneously in many patients, thereby decreasing cost and sequencing
time compared to Sanger sequencing.
The term NGS, originally referred to second-generation systems after the ‘first generation’
automated Sanger methodology. Now however, whilst we are in ‘fourth’ generations and
beyond of technology, the term is used more broadly to refer to all high-throughput,
massively parallel sequencing systems.
The fundamental principles common to all NGS strategies is to sequence millions of short-
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DNA fragments in massively parallel arrays, before aligning and mapping the short reads to a
reference genome (overview shown in Figure 1-2). Key methodology consists firstly of
fragmentation of genomic DNA, followed by library preparation, enrichment of target
regions of interest (either by multiplexed PCR methods or in solution oligonucleotide
hybridization methods capturing baits with streptavidin beads), and then massively parallel
sequencing of all captured fragments. The sequencing data is then processed through
bioinformatics pipelines, which involve quality assessment of the raw sequence data, before
alignment of the reads against the reference genome, variant calling, and variant annotation.
Figure 1-2: Overview of NGS workflow
For targeted panel based sequencing, only a predefined set of genes with evidence linking
them to the disease of interest are sequenced. The advantage of this approach is that the depth
of coverage may be greater than for example WES or WGS, more samples can be processed
in a single run, and the bioinformatics approaches to variant calling and annotation, whilst
not trivial, are more straightforward than with WES or WGS. Of course, as all genes are pre-
selected, targeted panel sequencing is not suitable for unbiased gene discovery studies.
With WES, all coding regions of the genome are sequenced. The exome represents ∼1% of
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total genomic DNA and consists of ∼20 000 genes, 180,000 exons, and 30 million base pairs
of DNA. In DCM, WES has been used in patients who are likely to have a genetic basis of
disease but who do not have an identified mutation in known DCM genes. This approach has
led to the discovery of novel DCM variants, for example novel filamin C splicing variants in
3 large families with arrhythmogenic DCM without skeletal muscle defects(29). A key
weakness of WES is the dependence on a PCR amplification step and an exon capture method,
with the capture efficiency being potentially variable for different exons.
In WGS, all 3 billion base pairs of the human genome are sequenced, covering both coding
and non-coding regions. Whilst comprehensive, this is the most costly approach of the 3 NGS
strategies. Like WES, WGS can be used for gene discovery and compared to WES, offers the
advantage of being able to interrogate regulatory regions.
The limitations of WES, and to a greater extent WGS, relate to the difficulty obtaining
adequate sequencing quality, particularly coverage of known disease genes, and in the
informatics challenge of interpretation of the high number of variants discovered, many of
which are unlikely to be of clinical significance.
1.3.1.3 Challenges in the interpretation of rare genetic variants
As outlined, NGS identifies a large number genetic variants per individual. After
bioinformatics processing including variant calling and annotation, variants are then filtered
and interpreted for potential clinical significance. This can be a labour intensive process,
placing the effect of a single variant in the context of existing biological data.
The interpretation of potentially disease causing rare variants is made even more challenging
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due to the relatively high frequency of rare genetic variation in the population. This means
that that an individual variant may be rare (allele frequency <0.001), but collectively,
variation in a specific gene is common. There are a number of population databases such as
1000 Genomes Project (1000GP)(30), the NHLBI GO Exome Sequencing Project (ESP)(31),
the UK 10K Consortium project(32), and the Exome Aggregation Consortium (ExAC) (15).
The ExAC dataset is an aggregation of exome sequencing data from 60,706 individuals from
several large scale studies, including samples from 1000GP and ESP. All raw data from the
various disease-specific and population genetic studies were re-processed and jointly variant
called to enable consistency of bioinformatics analysis across datasets. The samples are not
necessarily from healthy individuals and include patients from the myocardial infarction
genetics consortium (Table 1-3), but are not expected to be enriched for patients with
cardiomyopathy. No individual phenotype information is available, but frequency counts for
any particular variant are broken down for 7 separate ethnic groups (Non-Finnish Europeans,
Finnish, African, Latinos, East Asians, South Asians, Others).
Table 1-3: Composition of the ExAC dataset
Consortium/Cohort Samples 1000 Genomes 1,851 Bulgarian Trios 461 GoT2D 2,502 Inflammatory Bowel Disease 1,675 Myocardial Infarction Genetics Consortium 14,622 NHLBI-GO Exome Sequencing Project (ESP) 3,936 National Institute of Mental Health (NIMH) Controls 364 SIGMA-T2D 3,845 Sequencing in Suomi (SISu) 948 Swedish Schizophrenia & Bipolar Studies 12,119 T2D-GENES 8,980 Schizophrenia Trios from Taiwan 1,505 The Cancer Genome Atlas (TCGA) 7,601 Tourette Syndrome Association International Consortium for Genomics 297 (TSAICG) Total 60,706
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Over 7.4 million variants have been identified in gene-coding regions in ExAC(15), with the
majority not entered in ClinVar, a database of disease variants(33). This highlights the need
for use of robust population-level control data to avoid spurious gene-disease associations.
We, and others, have shown how ExAC can be leveraged to aid the interpretation of rare
variants in cardiomyopathies (14,18). However, individuals appear to carry many unique
(private) variants that do not cause disease, with more than 60 unique coding variants found
on average in each participant in ExAC, further complicating approaches to rare variant
interpretation(15).
Therefore population data should also be placed in the context of other available resources to
aid clinicians and researchers in interpreting rare variants, such as disease variant databases
(for example, Human Gene Mutation Database (HGMD)(34), and ClinVar(33)),
computational data (such as in silico missense variant prediction tools, many of which are
amalgamated in the dbNSFP(35)), functional data and, critically, segregation data. Using
these data, variants can usually be classified into four main classes: known pathogenic, likely
(probably) pathogenic, variants of uncertain significance (VUS), and benign variants. The
recent American College of Medical Genetics and Genomics report provides comprehensive
guidelines on variant interpretation (36).
However, conflict may arise between these sources, leading to a greater proportion of
variants being categorised as of uncertain significance instead of likely-pathogenic or
pathogenic, and a degree of subjectivity remains, meaning that even experts can disagree with
respect to variant classification(37,38). Clinical Genome Resource (ClinGen) is a National
Institutes of Health programme to bring together expert panels to agree on approaches to
variant interpretation and curation, to establish clinical validity, pathogenicity and clinical
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usefulness of a given variant in a specific disease(39). Whilst such approaches are admirable,
they are resource and personnel intensive, and may not be able to keep pace with the rapid
discovery of novel disease-gene associations and potentially novel variants within established
genes.
1.3.2 Disease-gene associations in DCM
Whilst variants in almost 70 genes have been linked to DCM (Table 1-4), the evidence is
most robust for a “core disease set” encompassing the sarcomeric genes MYH7 (which
encodes beta myosin heavy chain), TNNT2 (which encodes troponin T2), TTN (encoding
titin) and the nuclear envelope protein encoding gene LMNA. Many of the existing predictive
tools used to distinguish benign variants from causative mutations are of limited utility in the
NGS and population database era, casting doubt on earlier findings from candidate gene
sequencing, where unclassified variants were deemed pathogenic based on their absence in
small control cohorts or limited in vitro data(40,41).
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Table 1-4: Genes linked to DCM, annotated by year of report in HGMD
Year Genes Year Genes Year Genes 1996 DMD 2006 CRYAB DOLK 1998 ACTC1 FKTN JUP 1999 DES DNAJC19 PKP2 LMNA FLT1 BAG3 2000 CTF1 PSEN2 TXNRD2 DSP SYNM 2013 ISL1 MYH7 PSEN1 EMD SGCD 2007 FOXD4 LAMP2 TNNT2 PDLIM3 PRDM16 2001 TPM1 ILK NPPA 2002 TTN LAMA4 SGCB CSRP3 2008 MYPN GATA4 MYBPC3 CHRM2 FHOD3 TCAP DSG2 ACTA1 VCL LAMA2 2014 GATA6 2003 ACTN2 2009 ANKRD1 CASQ2 LDB3 RBM20 RAF1 PLN NEXN GATA5 2004 ABCC9 2010 TAZ NKX2-5 SCN5A NEBL 2015 ZBTB17 TNNC1 SYNE1 TNNI3 TCF21 2005 MYH6 2011 GATAD1 TMPO MURC DSC2
A recent large scale analysis of rare genetic variation in cardiomyopathy cases from our
group compared to population variation has provided insights into the genetics of DCM. The
study tested for an excess of rare variants in 46 genes sequenced in up to 1315 DCM cases
compared to over 60,000 ExAC reference samples. Truncating variants in TTN were the most
common DCM rare variant (14.6%)(14). There was modest, statistically significant
enrichment in only 6 other genes (MYH7, LMNA, TNNT2, TPM1, DSP and TCAP) (Table
1-2). Based on available data, RBM20 is also likely to prove to be a significant disease gene
(reviewed below) but was not included in the published analysis due to poor coverage in the
ExAC data. Furthermore, sequencing methods were not uniform, and not all genes were
sequenced across the DCM cohorts included in the study. Even allowing for this however,
many genes that have previously been linked to DCM, including genes routinely sequenced
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in clinical practice such as MYBPC3 and MYH6 showed little or no excess burden in DCM
compared to the reference population. The accompanying Atlas of Cardiac Genetic Variation
web resource (14) summarizes this data and serves as a useful adjunct to facilitate the
interpretation of rare variants in DCM.
In the following section, I now briefly review the notable established DCM genes, before
reviewing genes recently reported to be associated with DCM. Titin will be discussed more
comprehensively in Section 1.4.
1.3.2.1 Established disease-gene associations in DCM
Sarcomeric proteins
Mutations in the sarcomeric proteins responsible for the generation and regulation of cardiac
contraction can lead to DCM, as well as HCM. For DCM, these mutations appear to lead to
reduced myofilament calcium sensitivity(42).
Mutations in proteins of both the thick and thin myofilaments cause DCM. The thin filament
protein cardiac actin (ACTC1) was the first sarcomeric gene linked to DCM but mutations are
very rare(43). Other sarcomeric mutations in DCM were originally identified after screening
of the genes MYH7, MYBPC3, TNNT2 and TPM1 in 46 patients with DCM(44).
Most commonly, β-Myosin heavy chain (MYH7) mutations are found in 5% DCM, though
with marked allelic heterogeneity(44). β-Myosin heavy chain plays a key role in force
generation by myosin. In the original study, two MYH7 mutations (Ala223Thr and
Ser642Leu) were found in two patients(44). Ser642Leu was part of the actin-myosin interface
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and Ala223Thr affected a buried residue near the ATP binding site.
Sarcomeric contraction is regulated by tropomysin and the troponin complex (troponins I, T,
and C). Mutations in the gene encoding Troponin T (TNNT2), which binds to tropomysin, are
found in <5% of DCM(42,45).
Other sarcomeric genes identified in familial DCM are α-tropomyosin (TPM1), troponin C
(TNNC1), and troponin I (TNNI3). Troponin I inhibits actin-myosin binding during diastole
and Troponin C (TNNC1) binds calcium during systole to promote cross bridge formation
between actin and myosin, playing important roles in the regulation of contraction(42,46).
The thick filament core is formed by β-myosin heavy chain, with myosin binding protein C
(MYBPC3) highly concentrated in the M band region. Mutations in MYBPC3 have previously
been linked to DCM, with the original sarcomeric gene study identifying one missense
mutation in one individual out of 46 DCM patients, absent from 88 controls(44). However, it
has now been established that this gene has a high background level of variation and may not
be a causative DCM gene(14).
Nuclear proteins
The lamin A/C gene (LMNA) encodes the nuclear envelope proteins lamin A and lamin C. In
addition to DCM, mutations in LMNA cause a diverse range of phenotypes including Emery-
Dreifuss muscular dystrophy, limb-girdle muscular dystrophy, lipodystrophy, progeria and
restrictive dermopathy. LMNA missense variants were first linked to DCM after evaluation of
11 families with autosomal dominant DCM and conduction disease when 5 novel missense
variants were identified(47). The original and subsequent studies have demonstrated that
LMNA associated DCM is nearly always preceded by conduction disease and atrial and
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ventricular arrhythmias, and is often associated with skeletal involvement, early onset
cardiomyopathy and a higher risk of sudden cardiac death(47-49). LMNA associated DCM is
also highly penetrant, with manifestation of the phenotype in mutation carriers by 50-60
years of age. LMNA mutations have also been associated with an LV non compaction
phenotype(50).
1.3.2.2 Recent disease-gene associations in DCM
Since 2005, 47 new genes have been categorized as linked with DCM in the HGMD(34)
(Table 1-4). Many of these links have not been replicated outside of the original reports.
Examples of the most notable novel associations are discussed below, selected for critical
evaluation either due to robust evidence, novelty, or clinical importance.
BAG3
BAG3 encodes a heat shock chaperone protein and was first linked to DCM in 2011 through
the discovery of a large 8733bp deletion in exon 4 in 7 affected family members in a three
generation family, which was absent in 355 controls (51). Subsequently, coding exons in
BAG3 in 311 other unrelated DCM probands were sequenced, which identified 7 rare variants
(1 frameshift, 2 nonsense, and 4 missense variants) that were absent from 355 controls. The
authors were also able to recapitulate the DCM phenotype in a zebrafish bag3 knockdown
model. In separate studies, BAG3 was linked to DCM through a genome wide association
study (GWAS), with the discovery of a non-synonymous SNP in the coding sequence of
BAG3 in DCM cases compared to healthy controls (rs2234962, p=1.1x10-13) (52). The
authors then performed targeted sequencing in a cohort of 168 unrelated DCM probands and
identified 6 variants that were also detected in affected relatives, lending further support to
the role of BAG3 as a disease causing gene.
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RBM20
RBM20 encodes a spliceosome protein that regulates pre-mRNA splicing for many genes,
including TTN (53), which is why variants in this gene may hold particular relevance for
DCM, either in isolation or in digenic heterozygosity with TTN(54). RBM20 was initially
associated with DCM through linkage analysis in 2 large families with DCM (55). The
authors sequenced all 14 RBM20 exons in each family member and identified a heterozygous
missense mutation in exon 9 that co-segregated with disease in all affected individuals, and
that was absent in unaffected relations and 480 ethnically matched controls. The authors went
on to detect RBM20 missense mutations in exon 9 in 6 more families affected with DCM.
Since the original link with DCM (55), subsequent studies found mutations both within and
outside the original RBM20 hotspot in DCM probands, but the segregation data on these
variants is limited and the control population was modest in size, meaning that population
level missense variation was not accounted for in these regions (56,57). The association of
RBM20 and DCM appears most robust for variants in the original hotspot and further
curation is needed to understand the significance of variants in other regions.
PRDM16
The 1p36 deletion syndrome can be associated with cardiomyopathy, and the PRDM16 gene
(which encodes a transcription factor) was identified as a possible cardiomyopathy gene at
this locus, linked with a syndromic cardiomyopathy as well as with adult onset DCM (in 5
out of 131 individuals with 4 novel missense variants) (58). However, whilst there may be a
role for PRDM16 in cardiac development, its role as a cardiomyopathy gene has subsequently
been questioned (59).
ZBTB17
ZBTB17 is also encoded on chromosome 1, at the 1p36 locus. A study of cardiac myocytes
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and a mouse model of ZBTB17 deletion demonstrated that ZBTB17 is involved in cardiac
myocyte hypertrophy and is essential for cell survival (60). The authors also identified that
ZBTB17 encodes a transcription factor (zinc-finger and BTB domain-containing protein 17)
that binds the gene CSRP3, a Z disc protein, mutations of which are found in both HCM and
DCM. Given the association between CSRP3 and DCM (in a small cohort with limited
segregation data (61), with no subsequent replication), and this new found function of
ZBTB17 in binding CSRP3, the authors hypothesized that ZBTB17 could be a novel gene
implicated in DCM.
PLN
Phospholamban is a regulatory protein that controls calcium cycling in cardiomyocytes
through inhibition of the cardiac SERCA isoform leading to reduced calcium influx in the
sarcoplasmic reticulum. Mutations in the gene encoding phospholamban (PLN) were first
linked to DCM in 2003(62)and subsequently to heart failure through the study of a large
pedigree with hereditary heart failure(63). In the latter study, a heterozygous deletion of
arginine 14 in the coding region of the gene (PLN-R14Del) was found in all affected
individuals who had a phenotype of LV dilation, contractile impairment and ventricular
arrhythmia. The human phenotype was replicated in transgenic mice.
The PLN-R14Del has since been detected in larger cohorts of patients with DCM, although in
one study it was associated with a mild DCM phenotype(64), in contrast to the severe
phenotype in the original publication. The prevalence of the PLN mutations was higher in
Dutch studies (up to 15% of DCM)(65,66) compared to other cohorts (<1% of DCM)(67).
The PLN-R14Del has now been shown to be a founder mutation in the Netherlands(68).
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Transcription factors
Many additional transcription factors have also been linked to DCM in recent years, such as
GATA5(69), TBX20(70), TBX5(71), GATA6(72), GATA4(73) and NKX2-5(74). Some of these
genes are clearly linked to congenital heart disease phenotypes. However, many of the
variants claimed to be associated with DCM are missense variants that have been identified
within one relatively small group of DCM patients, with variable segregation data. Further
studies are required to confirm the link with DCM.
Desmosomal proteins
Desmosomal proteins, typically perturbed in arrhythmogenic right ventricular
dysplasia/cardiomyopathy (ARVD/ARVC) have also been linked to DCM. The association
has been most robust for DSP which encodes desmoplakin, a desmosomal protein (75), with
a strong excess of truncating variants in DSP in DCM(14). However, some of the more recent
associations of desmosomal protein gene variants have limited variant curation and
segregation data, such as PKP2 (76) (which encodes plakophilin 2), and these associations
are less clear. One such PKP2 variant (c.419C > T(p.(S140F)), previously linked to DCM has
been shown not to be associated with heart failure phenotypes (77). Therefore, of the
desmosomal proteins, DSP variants have the most robust association with DCM.
Filamin C
Filamin C (encoded by FLNC) is a Z disc protein that provides sarcomeric stability. In recent
work, two rare splicing variants in FLNC were detected though whole exome sequencing in
two Italian families and in one US family affected with DCM, with all variants co-
segregating with disease (29). Only one unaffected variant carrier was identified but this
individual declined further follow up. These variants were absent from 1000 Genomes,
NHLBI Go-ESP and ExAC. The FLNC cardiomyopathy phenotype was not associated with
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skeletal muscle involvement in this cohort, but was associated with arrhythmias and sudden
cardiac death. In the same study, a zebrafish knockdown model showed a phenotype of
cardiac dysfunction, with defects in the Z discs and sarcomere disorganization. Evaluation of
FLNC variants in a large (n=2,877) cohort of patients with inherited cardiac diseases,
including DCM, has shown that the phenotype of individuals with truncating variants in
FLNC is notable for left ventricular dilation, systolic impairment, ventricular arrhythmias,
cardiac fibrosis and sudden cardiac death (78). Further replication in DCM-specific cohorts is
needed to validate this potentially prognostically important phenotypic association.
Summary of recent disease-gene associations in DCM
In summary, there have been many novel gene and variant associations with DCM. Whilst
some appear robust and potentially clinically important (e.g. FLNC, BAG3, RBM20), others
require further study (e.g. variants in transcription factors). As yet, there is insufficient clarity
on the importance of variants outside of major disease genes and this remains a major unmet
need to be addressed. As the commonest genetic cause of DCM identified to date, truncating
variants in the titin gene (TTNtv) are now reviewed separately to the other genes.
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1.4 Truncating variants in titin in DCM
1.4.1 Function of the titin protein
The TTN gene (363 exons) encodes the largest-known human protein, titin (~35,000 amino
acids, ~2µm long). One molecule spans half the sarcomere from the Z disk to the M line. Its
main roles are in the contractile process, regulating sarcomeric contraction, stretch sensing
and signalling, interacting with numerous cellular proteins(5,79). Titin is the main source of
cardiomyocyte resting tension and stiffness(80).
The titin protein is composed of 4 modular domains, corresponding to their location in the
sarcomere(80,81). At the N-terminus, titin is embedded in the Z-disk of the sarcomere and at
the C-terminus, it is embedded in the M-band region. The core of the protein is composed of
the A and I bands. The A band is inextensible and is anchored to the thick filament. It plays a
role in active force generation and length dependent activation of the sarcomere. The highly
extensible I band is composed of repeat tandem immunoglobulin segments, a spring like
PEVK domain, and N2A and N2B elements. This section is responsible for the passive
elasticity of muscle. During active contraction, titin is extended in the opposite direction to
that which occurs in passive stretch, which results in a cardiomyocyte restoring force and
elastic recoil on relaxation. This property may contribute to ventricular diastolic suction(81).
The mature human heart has two main titin isoforms formed from alternative splicing of large
fetal type isoforms; the larger compliant N2BA isoform (containing the N2A and N2B
segments) and the shorter stiffer N2B form, differing in the length of the immunoglobulin
and PEVK repeat segments(81). The ratio of both isoforms is approximately 40:60
N2BA:N2B in adult myocardium(80,82), but the ratio switches in response to physiological
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perturbations and can occur in heart failure(81). As well as isoform switching, the stiffness of
titin is modulated by post-translational modifications, including phosphorylation by cyclic-
AMP–dependent protein kinase (PKA) and cyclic-GMP–dependent protein kinase (PKG)
(both decreasing stiffness) and protein kinase C alpha (increasing titin stiffness)(80,82).
1.4.2 Prevalence of titin truncating mutations in DCM
Variants in titin were first associated with DCM in 2002 through the study of two large
multigenerational families affected with DCM(83). In the first kindred, linkage analysis
identified a disease gene locus (maximum logarithm of odds (LOD) score 5.0, penetrance of
70%). In this study, TTN was chosen as a candidate gene due to high levels of cardiac
expression and its established role in muscle assembly and function. A 2bp insertion was
identified in exon 326, that resulted in a frameshift mutation generating a premature stop
codon, and this mutation segregated with disease in family members. In the second kindred, a
non-truncating TTN missense mutation in a highly conserved region was identified that also
segregated with disease (Trp930Arg).
More recently, next generation sequencing technologies have made the study of the giant titin
gene possible in large cohorts. This led to the discovery that truncating variants in TTN are
found in approximately 15% of unselected DCM cases and in up to 25% of end-stage DCM
cases (79,84).
1.4.3 Established genotype phenotype correlations in TTNtv DCM
There have been 4 key studies evaluating genotype-phenotype correlations to date in TTNtv
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DCM. They are summarized in Table 1-5. Establishing clear genotype-phenotype
correlations in TTNtv DCM is of interest because of the number of DCM patients affected
with TTNtv, as the commonest genetic contributor to DCM. There is therefore the potential
to improve patient stratification for a large proportion of DCM patients.
In the first notable cohort based phenotype study of TTNtv, there were 67 patients with
TTNtv amongst 312 patients with DCM from 3 distinct cohorts(79). There were no distinct
phenotypic differences at baseline between TTNtv positive and negative patients with respect
to LVEF or LV end diastolic diameter. The authors demonstrated earlier onset of adverse
events (cardiovascular death, left ventricular assist device implantation, and heart transplant)
in male patients with TTNtv compared to female patients. However, the 3 cohorts were fairly
distinct, including a relatively mild familial cohort and an end stage cohort, which may have
affected the ability to discriminate phenotypes or outcome differences. All patients in the
study underwent echocardiography based phenotyping and there was no reporting of
reproducibility in measurements between centres, particularly important as echocardiography
is known to be operator dependent, which may have confounded genotype-phenotype
correlation. Finally, the comparison of age at onset of adverse events between males and
females was only made within the TTNtv positive group and not in comparison to the overall
DCM cohort. The finding therefore could simply reflect the combination of a male
preponderance in DCM and an adverse prognosis with DCM overall.
In the next study, from our centre, 42 patients with TTNtv from a cohort of 319 patients with
DCM were evaluated(84). Whilst single centre, all patients underwent CMR phenotyping.
There was no difference in age of onset of disease between TTNtv positive and negative
groups. In this pilot analysis, the authors demonstrated a borderline reduction in LVEF and
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right ventricular ejection fraction (RVEF) in TTNtv positive patients compared to TTNtv
negative patients. They also showed evidence of a length dependent positional effect in
severity of disease, with more distal truncations demonstrating a more severe cardiac
phenotype. Although the sample size was relatively small, in outcome analysis, they
demonstrated a trend towards reduced survival in TTNtv positive patients compared to
TTNtv negative patients. Gender specific differences in outcome were not reviewed.
The next TTNtv phenotype study consisted of 118 patients with TTNtv, to be commended as
the largest study of TTNtv DCM patients to date. However, this cohort only included 45
probands. The remaining 73 individuals with TTNtv were detected through family
screening(85). The cohort was also subject to ascertainment bias, as the overall DCM cohort
was only 235 patients. It was not representative of an unselected DCM cohort. The remaining
non-TTNtv patients included 57 patients with LMNA variants and 60 patients who were
mutation negative. Therefore the phenotypic comparisons were largely driven by a
comparison of TTNtv DCM versus LMNA DCM. Allowing for this, there was no difference
in age, gender, or the presence of conduction disease between TTNtv positive and negative
patients, but there was a borderline reduction in LVEF in TTNtv positive patients. Patients
were followed up for a median of 2.5 years for the composite endpoint of all cause mortality,
sudden cardiac death, resuscitation, ICD implantation, heart transplantation or left ventricular
assist device implantation. It appeared that TTNtv were associated with better outcomes than
non-TTNtv DCM. However, this finding was largely driven by the high adverse event rate
known to be associated with LMNA cardiomyopathy. Therefore the authors were able to
demonstrate that TTNtv DCM did not have the adverse prognosis known to occur with
LMNA cardiomyopathy, but they did not provide sufficient evidence to demonstrate that
TTNtv DCM was associated with a more favourable prognosis compared to idiopathic
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mutation negative DCM.
Subsequently, the comparison between TTNtv and LMNA cardiomyopathy was contradicted
in a Finnish study of 145 patients with DCM, including 25 patients with TTNtv and 12
patients with LMNA variants. There was no difference in the rate of the composite end point
of cardiac transplant or death between TTNtv patients and patients with LMNA variants(75).
Whilst this was a much smaller study, follow up time was longer (mean 6 years). However,
the analysis was limited by studying age of onset of adverse event, as opposed to onset of
event as per duration of follow up. Such analysis strategy is limited because the majority of
patients in the study were aged between 40-60 years old, and it may be too early to detect
adverse events in adult onset DCM.
The most recent phenotype study of TTNtv consisted of 46 patients with TTNtv, of which 17
were TTNtv DCM probands, with 29 mutation carrier relatives, of whom only 9 were
definitely affected with DCM(86). The overall DCM cohort was 72 patients. There was no
difference in the age, gender, or cardiac phenotype between TTNtv positive and negative
individuals. On mean follow up of 63 months for the composite end point of heart failure
death, heart transplant, or left ventricular assist device implantation, there was no difference
in the rate of adverse events between TTNtv positive and negative individuals. Interestingly,
the authors reported worse outcomes in males with TTNtv (n=24) compared to females with
TTNtv (n=21), highlighting a possible female gender protective effect in the presence of
TTNtv. This had been alluded to in previous studies but this was the first study to explicitly
highlight this. However, this analysis was limited because the gender outcome analysis was
performed in all individuals who carried TTNtv variants, and was not limited to those who
were phenotype positive. Therefore the adverse events could have been driven simply by
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those individuals who had developed overt DCM, and not reflect gender specific effects of
TTNtv. The sample size in each group was small and these findings require confirmation in
larger prospective studies evaluating broader cardiovascular end points.
In summary, the phenotype studies of TTNtv DCM to date have differed in the presence and
extent of phenotypic differences in patients with and without TTNtv and the clinical course
of TTNtv DCM, particularly with regards to gender based differences in outcome(79,84-86).
These discrepancies may relate to variations in TTNtv curation between studies, for example
with respect to exon location, percentage spliced in (PSI) score, or variant frequency. They
may also be due to differing patient factors, such as baseline severity of disease at the time of
study recruitment, and differing environmental risk factors. The work in this PhD seeks to
bridge the gap between the previous studies by studying the effects of TTNtv including
clinical outcomes in patients with DCM who have been well phenotyped, with consistent
TTNtv variant curation, together with a comprehensive understanding of background genetic
variation and environmental risk factors.
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Table 1-5: Summary of key genotype-phenotype studies of TTNtv DCM. LVAD= left ventricular assist device, OCTx = orthotopic cardiac transplant, LVEDD =
left ventricular end diastolic diameter, LVEF= left ventricular ejection fraction
Study Cohort TTNtv Phenotype Follow Endpoint Outcome Limitation up Herman et al, 312 DCM 67 No difference in age, - Cardiac death, Younger age of onset of Variable phenotype between cohorts, including 2012(79) patients LVEDD or LVEF LVAD, OCTx events in men end stage DCM. Echo based phenotyping – from 3 limited reproducibility between centres. cohorts Outcome gender comparison made within TTN+ only, not compared to DCM overall. Roberts et al, 319 DCM 42 No difference in age. Censored Death, OCTx, A trend to reduced Well phenotyped cohort but relatively small 2015(84) with CMR Borderline reduction at 70 LVAD survival in TTNtv sample size and phenotypic differences (subset of in LVEF and reduced years old. positive patients main paper) RVEF in TTNtv + compared to TTNtv patients. Length negative (p=0.05). dependent positional phenotypic effect. Jansweijer et 235 DCM 118 TTNtv (45 No difference in age, 2.5 years Composite of ICD TTNtv appeared to be Ascertainment bias, cohort selected for TTNtv al, 2016(85) patients probands, 73 gender, conduction (median) implantation, associated with better and LMNA variants, small control cohort size (60 screened disease between TTN+ resuscitation, outcomes but this limiting ability to draw TTN+ vs idiopathic idiopathic relatives) and TTN- patients. sudden cardiac comparison was largely DCM conclusions DCM Borderline difference death, OCTx, driven by the adverse mutation in LVEF. LVAD, all cause prognosis of LMNA negative, mortality cardiomyopathy. No 57 LMNA gender differences in variants, outcome. 118 TTNtv) Franaszczyk 72 DCM 46 TTNtv (17 No difference in age, 63 Heart failure death, No difference in adverse Gender outcome analysis included all TTNtv et al, 2017(86) patients TTNtv gender, or phenotype. months OCTx, LVAD cardiac events between carriers, not limited to those phenotype positive. probands, 29 (mean) probands with and mutation without TTNtv. carrier relatives Reported worse of whom 9 outcomes in males with definitely TTNtv (n=24) compared affected). to females (n=21).
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1.4.4 Challenges in the interpretation of TTNtv
There are challenges in the interpretation of TTNtv. The finding of TTNtv in apparently
healthy control individuals (estimated at ~3% in early studies(79), reduced to ~0.5% in more
recent studies accounting for exon usage and variant location(84,87,88)), has led to doubt
over whether TTNtv by themselves are truly pathogenic. However, recent work has shown
that on deep phenotyping with 2d and 3d-CMR, TTNtv are penetrant in apparently healthy
individuals, with subtle abnormalities in cardiac structure and function(88).
Like many genetic variants in DCM, TTNtv exhibit variable penetrance and expressivity.
Variable penetrance means that not all individuals who carry TTNtv will develop a
phenotype. Variable expressivity refers to variation in the severity of the resulting phenotype.
This raises the possibility that additional modifiers, either genetic or environmental, are
needed to develop the phenotype of TTNtv DCM.
1.4.5 Evidence for genetic and environmental modifiers of TTNtv
cardiomyopathy
There is a growing body of evidence for the importance of genetic and environmental
modifiers of TTNtv cardiomyopathy. In this model, the presence of a TTNtv in isolation is
not sufficient to cause the DCM phenotype but requires the presence of an additional
environmental or genetic factor (Figure 1-3).
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Figure 1-3: Putative model of genetic and environmental modifiers of TTNtv
Genetic modifiers
In principle, genetic modifiers may act at local or distant sites. With local sites, DNA variants
near to the mutated allele or the wild type allele may influence regulation of the gene. In
distant-acting mechanisms, DNA variants away from the mutated allele exert effects, for
example mutations in separate loci.
The finding that digenic heterozygous missense variants in TTN and LMNA were associated
with severe DCM phenotypes(89) showed the potential for modifier genes or additive genetic
effects in DCM. This concept was alluded to in a multi-centre study of 639 patients with
sporadic or familial DCM, with the finding of a 38% rate of compound mutations, and up to
44% when considering patients with TTNtv (19). However, these findings must be
interpreted with great caution as the “yield” of DCM variants in this study was far higher than
in any previous study, background population variation was not well accounted for, and there
were no matched controls on the same sequencing platform. The interaction between TTN
and variants in RBM20 will be an area of interest for future studies given the known role of
RBM20 as a regulator of alternative splicing of TTN(53,90).
Epigenetics refers to a change in gene expression that is not explained by DNA sequence
variants, but occurs as a result of alterations to the packaging or translation of genetic
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information(91). These mechanisms can be inherited or acquired and involve 3 key processes
to either activate or silence a gene, namely methylation of CpG islands, modification of
histone proteins, or the role of microRNAs. MicroRNAs are small non-coding RNA
molecules that modify the expression of messenger RNAs by interfering with their
translation. Epigenetic modifiers of disease have not yet been shown in TTNtv
cardiomyopathy but may be a promising area of future research.
Environmental modifiers
With regards to environmental modifiers of TTNtv DCM, the discovery that peripartum
cardiomyopathy shares a genetic aetiology with DCM suggests that pregnancy may act as an
environmental modifier to unmask the phenotype of TTNtv cardiomyopathy (92). More
recently, a case report of 2 patients with chemotherapy induced DCM found to have TTNtv
was published(93), again supporting the ‘second hit’ concept of TTNtv cardiomyopathy.
Animal studies have consistently demonstrated that rodents with TTNtv only developed
impaired cardiac physiology under cardiac stress (88,94), providing further evidence of the
importance of gene-environment interactions in the development of the TTNtv
cardiomyopathy.
Gene-environmental interactions in DCM have been shown to have prognostic importance in
a study of 213 DCM patients(95). In isolation, the presence of genetic DCM did not confer an
adverse prognosis, but when combined with a history of an environmental risk factor such as
a significant viral load, immune factors, arrhythmia or toxic triggers, a worse outcome was
noted(95). Whilst this study did not specifically report on the interaction between TTNtv and
environmental factors, it is plausible that interactions with TTNtv (as the commonest single
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gene contributor to DCM) underpinned some of the findings.
There has been recent interest in the effects of alcohol on the cardiovascular system(96) and
it has long been established that excessive alcohol consumption can lead to
cardiomyopathy(97,98). It has also been recently shown that moderate alcohol consumption,
in excess of UK government guidelines, is associated with an increased risk of heart failure
(hazard ratio 1.22)(99). Not everyone with a history of excess consumption however develops
cardiac complications(98), therefore there may be additional risk factors, such as a genetic
susceptibility(100,101). Against this background, the next logical step would be to evaluate
the interaction between TTNtv and alcohol consumption. To date, it has not yet been
established whether alcohol is a phenotypic modifier of titin cardiomyopathy. The work in
this PhD seeks to address this gap.
This PhD is an integrated study of the genetic and phenotypic assessment in DCM. Having
reviewed the genetic basis of DCM, I now move on to review the role of cardiovascular
magnetic resonance in the phenotyping of DCM.
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1.5 Role of Cardiovascular Magnetic Resonance in Dilated
Cardiomyopathy
1.5.1 Overview of cardiovascular magnetic resonance
Cardiovascular magnetic resonance (CMR) is a non-invasive imaging technique that does not
use ionising radiation(102). Detailed tissue characterisation and longitudinal reliability and
reproducibility of key measurements make CMR a robust imaging biomarker.
CMR generates images using a combination of radiofrequency pulses in a magnetic field.
Hydrogen-1 nuclei are abundant in water and fat and possess an inherent property of spin.
When placed in an external magnetic field, these nuclei line up in the direction of the
magnetic field. Application of an external radiofrequency pulse causes the direction of the
nuclei to shift and creates local tissue magnetisation. As the magnetisation decays and the
nuclei return to their original state, that is relax, they emit their own radiofrequency signal.
This signal is captured and through complex Fourier transformation, an image of spatially
resolved radio signals is generated. This relaxation can occur in transverse (T2) and
longitudinal (T1) planes. In MR imaging, the signal from any tissue is determined by the
density of hydrogen atoms (proton density), and by the longitudinal relaxation time (T1) and
transverse relaxation time (T2). As the magnetic resonance characteristics of protons varies
between tissues, decoding of these separate signals permits tissue characterisation(103). In
addition, image contrast can be modified by altering the radiofrequency pulses. For example,
in T1 weighted images, myocardial tissue is dark whereas fat is bright, in contrast to T2
weighted images in which water is bright, for example myocardial oedema. A combination of
radiofrequency pulses, magnetic gradient field switches and timed data acquisitions is known
as an MR sequence.
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There are many types of MR sequences, but common categories include spin echo and
gradient echo. Dark blood spin echo sequences are mainly used for anatomic imaging and
tissue characterisation. White blood gradient echo sequences are used for cine images.
Steady-state free precession MRI (SSFP) is the most commonly used sequence to assess
cardiac function, providing excellent contrast between blood and myocardium. It is a type of
gradient echo MRI pulse sequence in which a steady, residual transverse magnetisation is
maintained between successive cycles.
Unlike static structure imaging (e.g. soft tissue or brain), CMR images a moving structure
which requires ECG gating, so that the final image is generated by capturing data from
certain parts of the cardiac cycle and sampling across multiple cardiac cycles. In addition,
respiratory motion artifact is avoided by acquiring CMR images in end expiratory breath
holds, though the success of this depends on patient compliance.
Overall, there are no known detrimental biological side effects of MR imaging if safety
guidelines are adhered to, particularly with regards to application of pulse sequences(102).
There have been recent reports demonstrating immediate post CMR double strand breaks in
DNA in T lymphocytes(104,105), but what the mechanism is, whether these persist, or are
associated with cancer or downstream biological effects is far from established(106).
1.5.2 The role of CMR in DCM
In DCM, imaging is used to establish the diagnosis and assess response to treatment in
probands, as well as screen family members. Imaging findings can also be used to predict
outcomes. CMR is an excellent imaging modality in DCM, particularly in patients with
suboptimal echocardiographic images. As the gold standard for assessment of cardiac
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volumes, combined with being non-invasive and not exposing subjects to ionising radiation,
it is the ideal imaging modality for accurate serial evaluation of patients with DCM. A
particular strength of CMR in DCM is to highlight the presence of additional comorbidity
such as coronary artery disease(107,108) or provide clues as to the underlying aetiology, for
example abnormally short T2* relaxation times in hemochromatosis(109).
This work in this study harnesses the power of CMR in a large cohort of DCM patients and
takes advantage of some of the applications highlighted in the following sections.
1.5.2.1 Imaging biomarkers of DCM identified through CMR
1.5.2.1.1 Morphology and function
DCM is defined by the phenotype of left ventricular chamber dilation and impaired systolic
function. In contrast to echocardiography, CMR can image in any plane with an unrestricted
field of view, allowing evaluation of cardiac structures, regardless of acoustic windows or
body habitus.
It is well established that LVEF is a robust prognostic marker in DCM(110) and forms the
basis for selection of patients for device therapy (111,112). Right ventricular dysfunction,
found in 30-60% of DCM cases, is also an independent predictor of transplant free survival,
adverse heart failure outcomes and arrhythmic events(113,114). Therefore precise
measurement of both is crucial.
CMR is the gold standard non invasive technique for accurate and reproducible assessment of
LV volumes, mass and ejection fraction, crucially without the need for geometrical
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assumptions that are required in 2d echocardiography for example (115,116). Measurements
are obtained through manual planimetry or the use of semi-automated software.
CMR also provides the gold standard non-invasive assessment of RV size and function due to
its 3-dimensional capabilities (117,118). Accurate assessment of RV size and function can be
challenging with echocardiography, due to its complex and variable shape. In addition, the
low inter- and intra-observer variability are great strengths of the use of CMR in DCM,
permitting the early detection of even subtle abnormalities in ventricular function(103,119-
121).
Situations in which CMR may not provide such accurate assessment of cardiac volumes and
function include the presence of motion or breathing artifacts, particularly in patients too
symptomatic to complete breath holding or in patients with irregular rhythms, where high
quality cine imaging may not be acquired.
CMR also allows accurate quantification of left atrial (LA) volume using the biplane area-
length method(122). This compares favourably against other non-invasive imaging methods
due to its excellent endocardial border definition and multiplanar imaging ability, even in the
presence of atrial fibrillation (123-126). LA size is often increased in cases of DCM
secondary to pressure overload from LV diastolic impairment, functional mitral regurgitation
and atrial fibrillation. It has been proposed that the degree of LA enlargement reflects the
degree of diastolic dysfunction and LA size has been used to predict heart failure outcomes
(125). It has also been demonstrated that LA volume indexed to body surface area calculated
using CMR independently predicts cardiac transplant-free survival in DCM, with a cut-off
value of >72mL/m2 predicting a three-fold increase in adverse outcomes (127).
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Functional mitral regurgitation is also a common consequence of DCM secondary to mitral
annular dilatation and leaflet tethering secondary to left ventricular impairment. A long-axis
cine stack and a short axis cine image across the mitral valve allows accurate assessment of
all parts of the valve apparatus including individual leaflet scallops, chordae and papillary
muscles (128). Mitral regurgitant volume can be calculated by estimating the aortic forward
flow volume, using phase contrast flow imaging, and subtracting this from the total left
ventricular stroke volume(128,129). This method has been validated against volumes
calculated from echocardiographic indices and catheterisation data with good inter-technique
agreement (129,130). Once again, the accurate assessment of the degree of functional mitral
regurgitation provides important prognostic information in DCM (131).
Given the accuracy in functional assessment, CMR has been proposed as the method of
choice for serial evaluation of patients with DCM after therapeutic intervention (132,133).
Given the favourable interobserver variability compared to other methods of assessment, the
use of CMR in clinical trials can reduce the sample size required, reducing the overall cost
and time needed to complete the research (134).
1.5.2.1.2 Tissue characterisation
The importance of tissue characterisation
In addition to its strengths in quantification of cardiac dimensions and function, CMR also
excels in non invasive tissue characterisation. Microscopically, cardiomyocytes in DCM are
characterized by myocyte hypertrophy, myocyte loss, and interstitial fibrosis(135). Tissue
characterisation in DCM holds the promise to characterise subphenotypes of disease which
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may offer insights into disease pathogenesis, identify novel therapeutic targets, monitor
disease progression or facilitate prognostication.
In particular, leveraging the detailed in vivo tissue phenotyping that advanced CMR offers
may enable precise phenotypic correlations in genetic cardiomyopathies(136). Current
imaging biomarkers do not permit the detection of specific imaging signatures of genetic
cardiomyopathies(137). At present, it is unknown whether this is because genetic variants are
not associated with a specific phenotype or whether subtle/latent changes are not properly
ascertained.
Non-contrast tissue characterisation
Myocardial tissue can be characterized through modified pulse sequences. For example, the
T2 weighted Short-Tau Inversion Recovery (STIR) sequence can image oedema, suppressing
signal from fat and flowing blood. Long T2 relaxation times of protons in water generate high
signal in areas of myocardial oedema(138). The sequence was initially validated in ischaemic
models, but is now used in cardiomyopathies, particularly to differentiate acute from chronic
myocarditis(139). The interpretation of STIR images however is subjective and novel direct
quantification of T2 by mapping techniques may address this limitation. Another technique,
myocardial T2* mapping, enables the direct identification and quantification of myocardial
iron in vivo, and has been validated against in vivo histology, making it particularly useful
for the detection of myocardial iron overload as a cause of DCM (109).
Replacement myocardial fibrosis
Replacement myocardial fibrosis occurs in response to cardiac injury (e.g. following
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myocardial infarction), following which apoptosis and myocyte loss occurs, with dead cells
being replaced by collagen-based scar formation. The conventional and gold standard method
to detect myocardial scar is through histological examination(140). However, replacement
myocardial fibrosis can now be detected non-invasively using CMR by exploiting the
alterations in tissue magnetic resonance properties following the administration of an
exogeneous contrast agent.
Gadolinium is paramagnetic, meaning it can become temporarily magnetised when placed in
an external magnetic field, a property conferred by its unpaired electrons. Gadolinium based
contrast agents rapidly diffuse from the vascular space to the extracellular space. In tissues
with uptake of gadolinium, T1 and T2 relaxation times are shortened. Therefore in areas of
replacement fibrosis, with a resulting loss of cells, the extracellular space is enlarged by a
dense, hydrated collagen matrix in which gadolinium accumulates. Upon T1 weighted
imaging these abnormal areas have bright signal.
This imaging is employed through an inversion recovery MRI sequence in which normal
myocardial signal is ‘nulled’ to appear black, leaving the area of fibrosis to appear extremely
bright. Signal detection depends upon contrast between normal and abnormal tissue. The
inversion time is set by the operator to null normal myocardium, so that abnormal
myocardium with focal scar appears bright white. This is known as late gadolinium
enhancement (LGE-CMR). The focal replacement myocardial fibrosis can be detected and
quantified using semi-automated software and validated methods.
CMR detected replacement myocardial fibrosis occurs in approximately one third of patients
with DCM, most frequently in a linear mid-wall distribution(141). CMR detected
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replacement fibrosis has robust histological validation in ischaemic and non-ischaemic
cardiomyopathies(13,142-145). Replacement myocardial fibrosis confers an adverse
prognosis in DCM, predicting mortality including sudden cardiac death irrespective of
LVEF(13,146,147). Therefore a great advantage of CMR over echocardiography in DCM is
that echocardiography does not detect replacement myocardial fibrosis therefore cannot
sufficiently stratify sudden cardiac death risk.
The most commonly used methods to quantify replacement myocardial fibrosis are the full-
width at half maximum (FWHM) and the >2 standard deviation approach (>2SD) (148). The
FWHM method quantifies regions of myocardium with a signal intensity >50% of the
maximally enhanced region while the >2SD approach includes regions with a signal intensity
>2SD above the signal intensity of a reference area of normal myocardium. As yet, there is
no universal agreement on standardized methods to enable comparison of fibrosis
quantification between studies.
The limitations of LGE-CMR are related however to the need to discriminate between normal
and abnormal signal. Diffuse myocardial fibrosis, leading to diffuse expansion of the
extracellular space, a feature of DCM, cannot be detected.
Interstitial fibrosis
Reactive, or interstitial, fibrosis is characterized by the expansion of the cardiac interstitial
space without significant myocyte loss and occurs in response to pressure overload, volume
overload, as well as cardiomyopathies. DCM is associated with major changes in the
extracellular matrix and myocardial collagen deposition in DCM can be diffuse(149). The
gold standard for the detection and quantification of diffuse myocardial fibrosis is through
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myocardial biopsy(140). However, this is limited by sampling error and is associated with
small but significant procedural complication risk (150). Diffuse myocardial fibrosis can now
be detected with CMR using T1 mapping.
Either a Modified Look-Locker Inversion Recovery (MOLLI)(151) sequence or shortened-
MOLLI (ShMOLLI)(152) with balanced steady state free precession (bSSFP) readout are
used. As collagen deposition increases the extracellular space, exogenous contrast
(gadolinium) accumulates, and myocardial tissue longitudinal relaxation time (T1) decreases.
The T1 time is inversely proportional to the concentration of gadolinium in the tissue.
The main biological determinant of an increase in native T1 are oedema and an increase in
the interstitial space (for example due to fibrosis). Native T1 times can be measured pre and
post contrast agents. Native T1 values are influenced by the field strength used (higher native
T1 values at 3 T than at 1.5 T), the specific pulse sequence used, the cardiac phase, and
region of measurement. Normal native T1 values are thus specific to the individual
institution, scanner and protocols and absolute T1 values can only be compared when they
were obtained with the same methods(153). Post contrast T1 times are also influenced by
contrast and patient specific factors such as gadolinium dose, time of measurement post
bolus, patient heart rate, body composition, haematocrit, and clearance rate. Many of these
factors cannot always be standardised leading to potentially unreliable post contrast T1
quantification.
To mitigate this, the extracellular volume fraction (ECV) is calculated. This is derived by
calculating the partition coefficient and correcting for the haematocrit. The partition
coefficient is calculated by measuring the change in T1 before and after equilibration of
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contrast distribution. The ECV provides a quantitative measure of myocardial interstitial
volume. ECV values are therefore more reproducible between different field strengths,
vendors and acquisition techniques than both native and post-contrast T1(153).
CMR T1 and ECV findings correlate with endomyocardial biopsy(154), are prognostically
important(155), and can detect reversible changes in patients with ‘early’ DCM(156).
However, with increasing adoption of these techniques in a variety of disease states, it has
become apparent that T1 mapping is most robust in detecting disease where the pathology is
characterised by high ECV values, such as amyloid disease(157) and to a lesser extent, aortic
stenosis(158). With regards to cardiomyopathies, whilst T1 seems to be able to differentiate
normal and abnormal myocardium, there is considerable overlap between values in control
and disease subjects, and discrimination between cardiomyopathies is poor(159). There has
been one large multicenter study showing the prognostic importance of T1 in DCM, reviewed
further in Section 1.7.2.3.2(160).
Myocardial perfusion
First pass gadolinium imaging (imaging at rest or stress immediately after administration) is
used for myocardial perfusion imaging. Acquisition protocols are usually based on gradient
echo or SSFP sequences combined with parallel imaging to enable ultra-fast acquisition. First
pass stress perfusion CMR can visualize inducible perfusion defects in DCM, often seen as
circumferential areas of subendocardial hypoperfusion. Patients with DCM have been shown
to have reduced myocardial perfusion reserve during stress, but not altered oxygenation(161).
This suggests that the perfusion deficit is not sufficient to cause a reduction in oxygenation
during stress. In addition, as abnormal resting cardiac energetics are not affected by oxygen
administration, the resting impairment in energy metabolism is not secondary to a myocardial
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oxygen deficit, meaning that the impaired perfusion in DCM is likely a reflection of disease
severity and not a pathophysiological driver(161). The degree of myocardial blood flow
impairment in DCM has also been strongly correlated with prognosis (162,163).
1.5.2.1.3 Myocardial strain
Cardiac strain is a unitless measure of myocardial deformation that can occur in 3 planes;
circumferential, radial and longitudinal. Shear strains are the angular strains that occur
between 2 of each of these planes.
Strain measurements are particularly useful in cardiomyopathies as abnormalities may be
detected prior to the development of overt ventricular dysfunction, with reduction in
longitudinal strain occurring prior to impairment in circumferential strain. Analysis of
longitudinal strain using myocardial feature tracking has been shown to predict adverse
outcomes, independently of other predictors such as LVEF and late gadolinium enhancement
(LGE), in patients with DCM (164). Abnormal strain can identify subclinical pathology. In a
study of sarcomeric gene positive/phenotype negative individuals, there were no overt
phenotypic differences compared to healthy controls, only echocardiography detected
abnormalities in myocardial strain discriminated pathology(165).
CMR assessment of strain has historically been challenging but there are novel techniques,
each with their own merits and limitations, such as CMR tissue tagging, feature tracking,
phase-contrast CMR, velocity encoded CMR, DENSE (displacement encoding with
simulated echoes) and SENC (strain encoding)(166). Tagged CMR is the method that has
been most widely studied. In this, selective saturation pre-pulses are used to superimpose a
grid across the field of view across the heart. The grid lines are deformed by myocardial
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contraction, strain, and torsion, allowing direct quantification of myocardial deformation and
strain(167). However it is heavily limited by tag fade in diastole, thereby reducing the amount
of information available in diastole. This is exacerbated at higher field strengths.
DENSE encodes tissue displacement into the phase of the magnetic resonance signal.
However, traditional cine DENSE is limited by fade of the signal throughout the cardiac
cycle. A novel spiral (as opposed to Cartesian) acquisition cine DENSE has been developed
and validated at 1.5T and also at 3T with the introduction of variable flip angles(168-170).
The latter is particularly important as 3T imaging is subject to field inhomogeneities and off
resonance artefacts(168). We have made iterative changes to this cine DENSE sequence to
reduce the breath hold time, making it suitable for patients with DCM, described further in
the methods section of Chapter 4.
1.5.2.1.4 Contractile reserve
Contractile reserve has been assessed as either changes in left ventricular ejection fraction or
changes in global strain measurements following pharmacological or physical stress(171).
The presence of left ventricular contractile reserve in DCM patients, historically assessed
with dobutamine stress echocardiography, is a strong prognostic variable, independent of
resting ventricular indices, even in patients with severely impaired LV function(172-179).
Recently right ventricular contractile reserve has also been shown to be prognostically
important(180). The precision assessment of biventricular ejection fraction with CMR makes
CMR assessed contractile reserve particularly accurate. Reduced myocardial response to
dobutamine indicates reduced adrenergic contractile reserve, thought to relate to the degree of
beta-adrenergic receptor down regulation and desensitization(181). Increasing beta-receptor
down regulation reflects progressive deterioration in left ventricular function(181). Therefore,
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improved contractility during dobutamine administration reflects preserved beta receptor
function, which reflects the potential to show improvement in LV function. The assessment,
prognostic significance, and role of myocardial contractile reserve in DCM is explored
further in Chapter 4.
1.5.3 Limitations of cardiac MRI in dilated cardiomyopathy
Historically, the use of CMR in DCM has been limited by cost and accessibility, though as
the field expands and the number of centres offering CMR increases, these limitations will
subside.
Until recently, patients with intracardiac devices could not undergo CMR, therefore
introducing significant bias into CMR based studies of DCM. Recently devices have been
produced that are MR conditional and patients with these devices can have CMR (182,183),
and in our institution there is increasing experience of safely scanning patients with non-MR
conditional devices.
The acquisition of high quality CMR images requires patient compliance, particularly with
breath holding, and ideally a regular heart rate for ECG gating. A CMR scan with gadolinium
contrast may take up to 60 minutes. Therefore CMR scanning may not be suitable for some
patients with DCM and NYHA class III/IV symptoms or patients with arrhythmia.
Having reviewed the role of CMR in DCM, I now move on to review remodelling and
outcomes in DCM.
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1.6 Remodelling and Recovery in DCM
1.6.1 Potential for LV remodelling in DCM
There has been longstanding recognition of the potential for improvement in DCM, with
spontaneous improvement in symptoms and potentially complete recovery of LV function
reported in approximately 25-70% of DCM patients (184-195).
However, quantification and predictors of this recovery are poorly defined. Higher estimates
of recovery are reported in more contemporary cohorts, but there is no universal definition of
improvement in LV function, with studies reporting either absolute change in
LVEF(185,187,189,193,194,196,197) (from 5-20%) or improvement above a threshold level,
often set at LVEF 50%(190,193,198), with some studies also reporting reduction in LV
dilation(189,190,193). However, subtle dysfunction in cardiac strain or energetics can
remain, even in the presence of apparently normalized LVEF(198,199).
The terms myocardial recovery and reverse remodelling are often used interchangeably but
they may represent distinct entities(200). In addition, this improvement often occurs in the
presence of medical or device therapy, therefore it is unknown whether LV recovery
represents disease remission(201) or disease cure. In one study of 85 patients with reported
LV recovery, the rate of recurrence of LV dysfunction, even in the presence of ongoing
medical therapy was as high as 38%(202). Baseline age, LV end diastolic diameter, and a
history of diabetes were the only independent predictors of recurrent dysfunction(202). There
are two ongoing randomized studies of withdrawal of medical therapy in recovered DCM
patients that may address the remission versus cure conundrum (Withdrawal of Medication in
Recovered DCM (WrecEF), ClinicalTrials.gov identifier NCT02770443; Therapy withdrawal
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in REcovered Dilated cardiomyopathy trial (TRED), EU Clinical Trials register identifier
2015-005351-27).
1.6.2 Situations in which LV remodelling can occur in DCM
Intuitively, it is plausible that recovery of LV function can occur after withdrawal of an
environmental trigger (e.g. alcohol, virus). It has also recently been shown that recovery is
possible in genetic DCM(191), specifically titin cardiomyopathy, both in response to medical
therapy(85,203) and advanced device therapy such as left ventricular assist device
implantation in apparently “end-stage” patients(204).
There are other specific situations in which LV recovery may occur. Tachycardia induced
cardiomyopathy, developing in response to atrial (atrial fibrillation or tachycardia most
frequently) or ventricular arrhythmia (slow ventricular tachycardia or ventricular ectopy), can
improve upon restoration of sinus rhythm(205). A small case series of 24 patients however
showed that recurrence of tachycardia could lead to a rapid decline in ventricular function,
long after apparent normalization(206), suggesting that LVEF recovery does not equate with
complete normalization of myocardial substrate. In other words, LV reverse remodelling does
not imply molecular myocardial recovery(200). Recovery has also been reported in cases of
acute myocarditis(192,207), peri-partum cardiomyopathy(208), and some forms of
chemotherapy induced cardiomyopathy (trastuzumab, as opposed to anthracycline)(209).
1.6.3 Prognostic implications of LV remodelling
Improved LVEF has also been associated with a survival benefit, though this has mainly been
demonstrated in heart failure cohorts, including ischaemic aetiologies. In the BEST trial
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(beta-Blocker Evaluation of Survival Trial), amongst 2484 patients (not all DCM) with at
least two serial evaluations of LVEF by radionuclide ventriculography, a change of LVEF by
at least 5% was associated with a reduced hazard ratio for all cause mortality (0.62, [0.52-
0.73])(210). Similarly, in the Veterans Affairs Cooperative Vasodilator-Heart Failure Trials
(V-HeFT), serial evaluation of 1446 patients with heart failure showed that a change in LVEF
>5% from baseline to 6 months was the strongest predictor of mortality(186). In the largest
study to date, incorporating data from almost 70,000 heart failure patients across 30 mortality
trials and almost 20,000 heart failure patients across 88 remodelling trials, the odds of
mortality decreased with increasing LVEF and decreasing LV end diastolic and end systolic
volumes(211). In line with the studies before it, a 5% increase in mean LVEF corresponded
to an improvement in survival (OR for all cause mortality with 5% improvement in LVEF
0.86, p=0.013)(211). In a study of over 700 patients with ICD therapy in the MADIT-CRT
trial, LVRR occurred in 25% of patients (defined as >15% reduction in LV end systolic
volume at 1 year follow up) and that this was associated with a reduced risk of heart failure or
death(212). Finally, in the subset of patients with repeat echocardiography imaging in the
Valsartan Heart Failure Trial (Val-HeFT), 321 of 3519 (9.1%) patients with initial LVEF
<35% (including ischaemic aetiologies) improved LVEF to >40% at 1 year follow up, and
recovery was associated with improved survival at 2 year short term follow up (log-rank p
value comparing survival in group with improved LVEF compared to group without
improved LVEF=0.005)(213).
In a small, retrospective DCM specific study of 59 patients, ~37% of 19 patients surviving
beyond 12 years showed LV reverse remodelling and amongst the 33 patients who died or
had a heart transplant, no LV reverse remodelling was noted prior to the event(188). A
cautious conclusion of the study would therefore be that any reverse remodelling, even
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limited, is associated with an improved prognosis. This of course requires replication and
confirmation in a larger prospective study. In a retrospective observational study of 408 DCM
patients, 63 patients with improved LV function (LVEF >50% and normal LV end diastolic
volume) had a greater freedom from death or heart transplant compared to patients without
evidence of recovery (p<0.001)(190).
In summary, recovery has been shown to be possible in the setting of DCM secondary to a
diverse range of aetiologies, albeit to a varying extent. This suggests that the potential for LV
recovery is intrinsically conserved in the setting of cardiac dysfunction (largely irrespective
of aetiology), and even in the presence of apparently severe myocardial dysfunction. The
complicating issue is that recovery is not universal, so identifying which hearts retain this
potential for recovery is a major unmet need.
1.6.4 Predictors of LV remodelling in DCM
Identification of DCM patients with a high probability of recovery has the potential to
improve outcomes, by permitting tailored therapy, stratifying early intensive and advanced
therapy to patients deemed less likely to recover.
There have been limited studies in the contemporary era of medical therapy evaluating
predictors of recovery for all-cause DCM beyond the removal of any initial environmental
trigger.
1.6.4.1 Left ventricular parameters and clinical predictors of LV remodelling
A key study in the field was from the IMAC investigators (Intervention in Myocarditis and
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Acute Cardiomyopathy)(192). In total, 373 subjects from 16 centers with recent onset (<6
months) idiopathic DCM or myocarditis with an initial LVEF <40% were followed up for a
mean of 2.2 years, with reassessment of LV function after 6 months. Crucially, 82% of
patients received beta blocker therapy and 91% of patients were on angiotensin converting
enzyme inhibitors (ACEi). Many previous studies in the field predated the widespread use of
these prognostic medications(187). All imaging was performed by echocardiography. Mean
baseline LVEF was 24%, increasing to 40% at 6 months. In multivariable analysis, baseline
left ventricular end diastolic diameter was the strongest predictor of follow up LVEF
(standardized coefficient -0.41, p<0.0001)(192). Other independent predictors of follow up
LVEF were systolic blood pressure (0.18, p=0.001), black race (-0.12, p=0.02) and NYHA
class (-0.11, p=0.04)(192). Interestingly, baseline LVEF did not predict follow up LVEF
(p=0.32)(192). The authors also noted differing recovery profiles stratified by gender and
race, with recovery (LVEF >50%) more likely in white women (38%) and least likely in
black men (15%)(192). Whilst 12% of subjects had an endomyocardial biopsy at baseline, a
limitation of this study was that imaging with CMR was not performed, which may have been
able to identify a reversible inflammatory myocarditis, as well as evaluate the role of
myocardial fibrosis in predicting recovery.
1.6.4.2 Role of CMR detected late gadolinium enhancement in predicting remodelling
A subsequent study of 44 patients with recent onset DCM evaluated CMR predictors of
remodelling, together with serum biomarkers, endomyocardial biopsy, cardiopulmonary
exercise testing and echocardiography(189). In this study, patients had CMR at baseline and
at 1 year follow up, and left ventricular reverse remodelling (LVRR) was defined as an
absolute increase in LVEF of 10% to a final value >35%, together with a reduction in LV end
diastolic dimension at the 1 year mark(189). In total, LVRR was observed in 45% of patients.
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Of all variables evaluated including serum biomarkers and myocarditis on biopsy, the
independent predictors of LVRR were the extent of myocardial fibrosis assessed by LGE
CMR (OR 0.67, p=0.008) and higher myocardial oedema ratio (T2 index) assessed on CMR
(OR 1.45, p=0.027)(189). The authors also measured BNP at 3, 6 and 12 months. At 3
months (though not at baseline), plasma BNP was the most powerful predictor of LVRR.
These data suggest that CMR predictors of remodelling were the earliest to identify the
potential for recovery.
Another CMR based study of remodelling in 68 DCM patients (disease onset <2 weeks) also
identified that improvement in LVEF (at 5 months) was inversely correlated with the extent
of LGE(214). Notably, this study excluded patients with suspected myocarditis (abnormal
troponin I or myocardial oedema on CMR), so may be a more accurate study of recovery in
true idiopathic DCM, with estimates of recovery not conflated by inclusion of patients with
myocarditis – a condition with a high degree of reversibility.
Not all studies agree with regards to CMR LGE predicting LVRR. In a Portugese study of
113 DCM patients followed for 7 years, LVRR (defined as absolute LVEF increase of 10%
and decrease in LV diastolic diameter) occurred in approximately one third of patients(194).
On multivariable analysis, only ACEi use was associated with LVRR. CMR-LGE was not a
predictor on univariable analysis. However, only 38 patients had CMR and of these, over
50% had LGE which is higher than conventional estimates, suggesting that their criteria for
identification of mid-wall fibrosis (as opposed to all forms of LGE) was inadequate(194).
Notably, and in line with other studies, baseline LVEF did not predict LVRR.
Similarly, in a separate study of 97 patients with DCM, LVRR (defined as an increase in
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LVEF of 5%) occurred in 71% (n=69) of patients after 1 year follow up(195). On
multivariable analysis, only LV end- diastolic volume and the duration of symptoms on
presentation were independent predictors of 1-year LVEF. Neither baseline LVEF nor the
presence of CMR-LGE (performed in 88 patients) predicted LVRR(195). In another sub-
study of 66 DCM patients who had CMR, LGE was detected both in the patients who did not
experience LVRR as well as in patients with late LVRR (LVEF increase of 10% after 1 year),
although the extent of LGE enhancement was lower in patients who responded compared to
the non-responders(196). This small study, subject to marked ascertainment bias (the original
cohort was >200 patients) suggests that the association between CMR-LGE and LV
remodelling may be more nuanced than previously imagined.
1.6.4.3 Role of left atrial and right ventricular assessment
Many of the studies described limit evaluation of imaging predictors to indices of LV
structure and function only. However, other cardiac structures such as the left atrium(215)
and right ventricle may be important predictors of remodelling, as they reflect broader
pathological involvement. A study of 44 patients with DCM without evidence of myocardial
LGE who had CMR assessment of LA volume showed that LA volume was the only
independent predictor of LVRR with a hazard ratio of 0.93 (0.88 to 0.99, p=0.024) (14
patients had LVRR defined as an increase in LVEF to >50% and a net increase of
>20%)(197). This was a small study, the upper confidence interval approaches 1, and the
results whilst interesting, are by no means definitive. Right ventricular function at baseline
has recently been shown to predict LV remodelling at follow up in peri-partum
cardiomyopathy patients(216). This remains unexplored in DCM.
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1.6.5 Predictors of sustained LV recovery in DCM
The Trieste Heart Muscle Disease Registry is a long running established database of
cardiomyopathy patients at a tertiary referral centre. Study of this cohort has permitted the
evaluation of both predictors of recovery overall(193), as well as predictors of sustained
recovery, implying potential myocardial ‘healing’(190).
In the first study, LVRR (defined as LVEF >50% or absolute LVEF increase by 10%) was
found in 89 of 242 (37%) patients with DCM. Only systolic blood pressure (OR 1.23 per
10mmHg systolic blood pressure, p=0.047) and the absence of left bundle branch block (OR
2.47, p=0.009) predicted LVRR(193). In the second study, published 4 years later with longer
term follow up, in total 15% (n=63) of 408 DCM patients demonstrated ‘apparent healing’,
defined as LVEF >50% and normal indexed LV end diastolic diameter, with 38 of the 63
patients (60%) demonstrating ‘persistent healing’ at long term follow up (mean 103 months).
Amongst those patients who did not demonstrate sustained recovery, LVEF appeared to
deteriorate after approximately 2 years of recovery, but with no clear discriminators of why
this subgroup should have a different clinical course. On univariate analysis, no clinical or
echocardiography imaging parameters predicted LV improvement either at mid-term (approx.
2 years follow up) or long-term follow up (over 8 years follow up)(190). Interestingly, even
amongst the group of patients with persistent apparent healing, 2 of the 38 (5%) patients died
or underwent heart transplant at very long term follow up, despite normalization of LVEF.
Whilst speculative, this suggests that LVRR represents myocardial remission, but not true
healing. In line with this, it has been suggested that in the presence of normalization of
molecular, cellular, myocardial and LV geometric changes permitting the heart to maintain
preserved LV structure or function, the term ‘myocardial recovery’ should only be used to
describe situations associated with freedom from future heart failure events(200) and the term
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‘myocardial remission’ should be used to describe preserved LV structure or function that is
not associated with freedom from adverse events(200). Further support for this notion comes
from a study evaluating outcomes in a cohort of 538 patients with heart failure with repeated
LVEF assessments after primary prevention ICD implantation, in which 40% of patients had
some improvement in LVEF over a mean follow up of 4.9 years (including 25% with LVEF
improvement to >35%) but they remained at risk for appropriate shock therapy(217),
suggesting that the myocardial substrate had not normalised.
In summary, it is clear that there is a distinct subgroup of DCM patients who can undergo
reverse remodelling, either spontaneously or after therapy, and whose clinical course is
associated with reduced adverse events. However, resting LV indices and clinical parameters
have been shown to be inadequate predictors of this LV remodelling and/or recovery. This
may be because resting indices do not reflect the reserve capacity of the myocardium to
remodel. In Chapter 4 therefore, I examine the potential of myocardial contractile reserve, a
dynamic index of LV function, to predict LV remodelling in DCM. In the next section, I
review predictors of prognosis in DCM and highlight how this information could inform risk
stratification in DCM.
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1.7 Prognosis in DCM and Risk Stratification
1.7.1 Prognosis in DCM
1.7.1.1 Estimates of survival in DCM
The natural history of DCM is difficult to precisely define as the disease may have a variable
latent period, prior to the onset of symptoms. Overall, adult onset DCM remains associated
with an adverse prognosis, though there have been marked improvements in survival with the
advent of medical and device therapy.
Historic estimates of long-term prognosis in DCM showed that the disease was associated
with a 5 year mortality rate of ~65% in the 1960s-70s(218), falling only slightly to 57% in the
1980s(219). In the earlier series, overall mortality approached 80%(218). In both studies, the
majority of deaths occurred within 2 years of diagnosis. The high mortality estimates may be
a reflection of clinical management prior to the introduction of prognostic neuro-hormonal
medication such as ACEi and beta blockers, as well as the referral bias of only severe cases
being referred to the tertiary centres which reported the studies. This theory is supported by a
population based study of outcome in 40 DCM patients in the Olmsted County study
(diagnosed between 1975-1984) in which 5 year mortality was estimated at 20%(220).
Survival estimates from studies in the 1990s showed a marked improvement in outcome, with
a 5 year survival rate of 77% in a study of 201 DCM patients(221). This has been attributed
to a combination of factors, including a tendency for patients in later studies to present
younger and with less advanced disease as well a higher proportion of patients treated with
ACEi and beta blockers(222).
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More recent studies, in the era of device therapy, screening for early disease detection, and
larger prospective cohort studies, show that approximate five year all cause mortality in DCM
is ~20%(12,13,223). Whilst this may reflect a substantial improvement compared to early
case series, DCM remains a condition associated with a poor prognosis. There is a pressing
need to identify improved predictors of outcome to be able to identify the ‘at risk’ patient at
an earlier stage in the natural history of disease, ideally at a stage when intervention can alter
prognosis.
1.7.1.2 The focus on sudden cardiac death
Mortality in DCM consists of both heart failure and sudden cardiac death (SCD), with SCD
accounting for 25-30% of deaths(224,225). Heart failure death occurs as a result of pump
failure. The focus of risk prediction in DCM has been in evaluation of predictors of sudden
cardiac death, in part because there is evidence of benefit of implantable cardiac defibrillator
(ICD) therapy in DCM.
Four randomised controlled trials evaluated ICD use in DCM, independently of cardiac
resynchronisation therapy. The DEFINITE trial randomized 458 patients with DCM (LVEF
<35%, NYHA class 1-3, with non-sustained ventricular tachycardia or ventricular ectopy) to
ICD placement or optimal medical therapy(226). The AMIOVIRT trial had similar
recruitment criteria but randomized 103 patients to ICD or amiodarone(227). In the CAT
(Cardiomyopathy Trial), 104 DCM patients with LVEF <30% and NYHA class 2 or 3 were
randomized to ICD or medical therapy(228). In SCD-HeFT (Sudden Cardiac Death in Heart
Failure Trial), 1210 patients with DCM, LVEF <35% and NYHA class 2 or 3, were
randomized to heart failure therapy plus amiodarone or heart failure therapy plus ICD(229).
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In a meta-analysis of their results, ICD use in DCM was associated with a 26% relative risk
reduction for all-cause mortality (95% CI: 0.59–0.93; p = 0.009)(230). DEFINITE(226) and
SCD-HeFT(229) showed a trend to reduction in all cause mortality with ICD therapy, though
none of the individual trials demonstrated a significant absolute reduction(227,228).
Importantly, separate subanalyses of the SCD-HeFT trial demonstrated a significant
reduction of arrhythmic mortality in DCM (relative risk 0.34; 95% CI: 0.17 to 0.70)(230) and
tachyarrhythmia mortality in NYHA class II patients only (adjusted hazard ratio, 0.40; 95% CI: 0.27 to 0.59)(231), with no reduction in heart failure mortality or death from non-cardiac causes(231). Both the CAT and AMIOVIRT trials were stopped early due to lack of statistical significance in reaching the primary endpoint.
The overall goal therefore is to identify high risk patients who would gain the greatest benefit
from ICD therapy whilst avoiding subjecting low risk patients to a complications from a
procedure with little prognostic benefit. This underlies the importance of risk stratification in
DCM.
In the following sections, I will first review established and novel risk biomarkers in DCM. I
will then review current guidelines for risk stratification in DCM and highlight their
limitations, before concluding with the justification for personalised medicine and multi-
modality risk stratification in DCM.
1.7.2 Established and novel risk stratification biomarkers in adult onset
DCM
DCM can be stratified based on clinical, imaging, biomarker and genetic data. Each domain
contains prognostic information, which I will briefly review, with a focus on novel and
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emerging markers. Paediatric cardiomyopathy has a different risk profile and will not be
reviewed(232).
1.7.2.1 Clinical phenotypes
Symptom status
Symptom status has long been one of the most robust predictors of outcome in DCM.
In a study of 201 DCM patients followed up for ~4.5 years(221), severity and duration of
symptoms, as well as indices of LV function, predicted outcome. Specifically, symptoms of
pulmonary or peripheral oedema and syncope were associated with a worse outcome. Five
year survival ranged from 90% in patients with no symptoms and better cardiac function, to
53% in patients with severe symptoms for a long duration and cardiac impairment. In a
separate study of 232 patients with cardiomyopathy referred for cardiac transplantation
evaluation, 40% with DCM, NYHA class was one of only 3 independent predictors of
survival (p<0.0001), the others being pulmonary capillary wedge pressure and plasma atrial
natriuretic factor(233). Syncope has also been shown to predict sudden cardiac death in
patients with ischaemic and non-ischaemic cardiomyopathy(234).
The importance of symptom status has since been borne out in an independent study,
showing that survival is better in asymptomatic compared to symptomatic DCM
patients(235). In this study, 220 asymptomatic patients within a cohort of 747 DCM patients
in the Heart Muscle Disease Registry of Trieste were followed up for ~ 9 years. Patients in a
higher NYHA class had a worse prognosis for the end points of sudden death, heart transplant
and cardiovascular hospitalization compared to asymptomatic patients (p < 0.001 among all
groups). There was no significant difference on sub-analysis of consistently asymptomatic
patients compared to those with a previous history of heart failure symptoms but now
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asymptomatic on medical therapy. This suggests that improved symptom status may be a
dynamic risk marker, such that the risk of adverse events over time decreases as symptoms
improve. Finally, NYHA class was also an independent predictor of all cause mortality, heart
failure death, heart failure hospitalization, and cardiac transplantation, but not sudden cardiac
death, in a large contemporary outcome study of 472 patients with DCM(13).
Age and gender
In some studies, age has been associated with outcome, but this has not been consistent
across historic case series(219,236). In the Olmstead county population study, older age was
associated with reduced survival with an adjusted hazard ratio for mortality of 1.59 for each
10 years increase in age(220). In the most recent study of outcomes in over 472 patients with
DCM, a one year increase in age was independently associated with all cause mortality with a
hazard ratio of 1.03 (p=0.001)(13).
Gender based differences in outcome have been noted in cardiovascular disease, particularly
ischemic heart disease, but somewhat surprisingly, gender has not been an important
independent predictor of survival in DCM after LVEF and NYHA class are accounted
for(13,113).
Co-morbidities
Systolic blood pressure has been associated with outcome in DCM to a varying
extent(237,238). It has not always been evaluated in historic studies of outcome in
DCM(233). Most recently a more favourable prognosis was observed in patients with a
higher blood pressure (hazard ratio for all cause mortality per 1mmHg increase in systolic
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blood pressure 0.98, p=0.01)(13). Conversely, in a study of 87 patients with DCM,
individuals with a lower mean arterial pressure were less likely to survive, though this was
not an independent predictor of survival(238). Other important co-morbidities in DCM
include diabetes(239) and renal dysfunction. Co-morbidities also present a competing risk of
death in patients at risk of SCD. For example, patients with an ICD can die due to co-morbid
conditions, which may become more advanced with increasing age(240).
Other
Historically, haemodynamic abnormalities such as pulmonary capillary wedge pressure,
cardiac index, and central venous pressure have been associated with poor prognosis, but
their utility in long term outcome prediction is limited and will not be reviewed further(224).
1.7.2.2 ECG data
Ventricular arrhythmias, particularly ventricular ectopy and non-sustained ventricular
tachycardia (NSVT), are common in DCM, with NSVT identified in up to 50% of patients in
a prospective series of 74 DCM cases(241). In that study, patients with DCM and LVEF
<40% who had frequent episodes of NSVT were at a higher risk of SCD(241). Whether this
is a marker of an impaired left ventricle or an independent prognostic indicator remains to be
definitively evaluated(242), though there evidence to suggest that NSVT is of particular
prognostic importance amongst patients with mildly dilated cardiomyopathy in predicting
death or cardiac transplant (NSVT HR 2.21, p=0.047), where LVEF did not predict
outcome(243).
A major issue in evaluating this area is ascertainment bias, whereby even if a patient
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undergoes ambulatory monitoring for 24-48 hours as is common in clinical studies, it is
plausible that arrhythmia can go undetected. Longer periods of monitoring are carried out in
sicker patients, in whom ventricular arrhythmias are more likely to exist, therefore
reinforcing the notion that arrhythmia portends a worse outcome. Another complicating issue
is the variable definition of arrhythmia, for example a study of 218 DCM patients in whom
some form of ventricular arrhythmia (including ectopy) was found in 94% of patients, but
ventricular tachycardia found in only 27% of patients(244). By this definition, increasing
severity of ventricular arrhythmia was associated with reduced survival (p<0.001), but the
threshold of clinically important NVST was not clear. A study of a mixed cohort of ischaemic
and non-ischaemic cardiomyopathy patients suggested that ventricular tachycardia on
ambulatory monitoring was independently associated with total mortality (p=0.008) and
sudden death (p=0.003), with a tachycardia frequency of >0.088 events/hour being associated
with a 3 fold increase in mortality rate(245). However, the inclusion of patients with
ischaemic heart disease complicates the applicability of these findings to DCM patients.
Even recent studies evaluating the prognostic importance of ventricular arrhythmia do not
adequately address this issue. In a study of 285 DCM patients, an arrhythmic phenotype was
an independent predictor of SCD, sustained ventricular tachycardia or ventricular
fibrillation(246). However, the definition of arrhythmia was broad and included any one of
unexplained syncope, rapid NSVT (at least 5 beats over 150bpm), >1000 premature
ventricular contractions/24 hours, or >50 ventricular couplets/24 hours(246).
In summary, ventricular tachycardia is likely to be of prognostic importance in DCM but
there is insufficient evidence as yet to state which patients with ventricular arrhythmia are at
greatest risk of sudden death.
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Other ECG data may be of prognostic value in DCM, such as microvolt T wave alternans
(TWA), but despite evidence that it may have incremental value in predicting SCD risk in
patients with LVEF<35%(247-249), it is not a parameter widely used in clinical practice.
Abnormal T wave alternans provides an assessment of the temporal and spatial heterogeneity
of repolarization, which could trigger ventricular arrhythmias. In a meta-analysis of 45
studies of SCD risk stratification in DCM including 6088 patients, the highest odds ratio to
predict SCD was for T wave alternans (4.66, 95% CI 2.55 to 8.53) and fragmented QRS (OR
6.73, 95% CI 3.85 to 11.76)(250). The predictive value of the TWA test is higher when
performed on patients taking beta blockers. Other variables evaluated including parameters of
autonomic instability were not predictive of SCD. The incremental prognostic value of TWA
over and above LVEF has not been evaluated in a randomized controlled trial.
1.7.2.3 Imaging phenotypes
1.7.2.3.1 Established imaging markers
LVEF
Left ventricular ejection fraction (LVEF) is a robust predictor of outcomes in DCM, with
LVEF <35% associated with the highest risk of sudden cardiac death, as reflected in major
national and international guidelines recommending ICD therapy(251-253). Much of the
background data to support a cut off of LVEF <35% originates from studies in the setting of
ischaemic heart disease(229,254,255), but LVEF <35% is also of importance in SCD risk
stratification in DCM as shown in the SCD-HeFT trial as previously described(229).
In the two major early DCM mortality outcome studies of 104 patients followed up at the
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Mayo Clinic(218) and 169 patients followed up at the Hammersmith Hospital London(219),
the only independent predictor for outcome was the severity of LV impairment at
presentation, as assessed by increased cardiothoracic ratio on chest radiography and LV
dilation and LVEF respectively. In the Olmstead county population study, lower LVEF was
independently associated with reduced 1 year survival, with an adjusted hazard ratio of 1.90
per 10% decrease in LVEF(220). A number of other studies have reinforced the importance
of LVEF in predicting outcome in DCM(245,256). In more contemporary studies, LVEF
<30% remains a strong independent predictor of death, heart transplantation, and arrhythmia
in asymptomatic patients(235). In all-comer DCM patients, LVEF independently predicts all
cause mortality (HR 0.97 per 1% increase in LVEF, p=0.007), as well as composites of
cardiovascular mortality or cardiac transplant (HR per 1% increase in LVEF 0.96, p<0.001),
sudden cardiac death or ventricular arrhythmia (HR per 1% increase in LVEF 0.97, p=0.005),
and heart failure death, hospitalisations or heart transplant (HR per 1% increase in LVEF
0.95, p<0.001)(13).
The limitations of LVEF however are that it is a load dependent measure and can be affected
by heart rate variability as well as concomitant pathology e.g. mitral regurgitation. There is
also variability in LVEF measurement depending on imaging modality chosen. Furthermore,
as outlined in the review of remodelling in DCM, LVEF is known to vary with disease
course. The degree to which a single static measurement, taken at a variable timepoint in the
natural history of disease, can capture all future risk is unclear.
RVEF
Right ventricular dysfunction has also been shown to be an independent predictor of outcome
in DCM, with the presence of right ventricular dilation (assessed by echocardiography) being
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associated with reduced long term survival in a study of 67 patients with DCM(257). In a
larger prospective study of 250 patients with DCM who underwent CMR, the presence of
right ventricular systolic dysfunction (RVEF <45%, present in 34% of patients) was an
independent predictor of all cause mortality/cardiac transplantation (hazard ratio 4.20,
p<0.001), cardiovascular mortality, cardiac transplantation and heart failure death/heart
failure hospitalization(113). Interestingly given the limitations of LVEF, in a cohort of 314
patients (164 ischaemic and 150 non-ischaemic cardiomyopathy), RV dysfunction (RVEF
<45%) was an independent predictor of adverse events (sudden cardiac death and ICD
therapy) in patients with LVEF >35% (adjusted HR4.2, p=0.02)(258).
Left atrial size
Left atrial (LA) size has also been linked to prognosis in DCM. In a study of 144 patients
with DCM followed up for the composite end point of cardiovascular death or cardiac
transplantation, only left atrial dimension index as measured by echocardiography and
pulmonary wedge pressure were independent predictors of outcome(259). LVEF was not
associated with outcome after adjusting for LA dimension, thereby being one of the earliest
studies to highlight the role of the LA as a independent imaging biomarker in DCM(259). In
a CMR based study of 483 DCM patients, followed up for the primary endpoint of all-cause
mortality or cardiac transplantation, indexed LA volume was an independent predictor of the
endpoint (HR per 10mL/m2 increase in indexed LA volume 1.08, p=0.022)(127). LA volume
was also independently associated with the secondary endpoints of cardiovascular mortality
or cardiac transplant and heart failure death/heart failure hospitalizations/cardiac
transplant(127).
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1.7.2.3.2 Novel imaging markers
Replacement myocardial fibrosis
Myocardial fibrosis acts a substrate for ventricular arrhythmias, an important cause of sudden
cardiac death(260). There is now an increasing body of evidence demonstrating the
importance of mid-wall fibrosis, detected on late gadolinium enhancement CMR (LGE-
CMR), as a prognostic indicator in DCM.
Assomull et al reported the first study to demonstrate that LGE-CMR midwall fibrosis had
prognostic importance in DCM(141). In this study of 101 patients with DCM, mid wall
fibrosis (present in 35% of patients), was associated with a hazard ratio of 3.4 (p=0.01) for
the risk of all cause mortality and cardiovascular hospitalisation. On prespecified secondary
endpoint analysis, midwall fibrosis also predicted sudden cardiac death or ventricular
tachycardia, with a hazard ratio of 5.2 (p=0.03)(141).
Subsequently, Lehrke et al evaluated the significance of LGE-CMR in 184 patients with
DCM(261). The mean follow up time was relatively short at 1.9 years, and the composite end
point was cardiac death, heart failure hospitalisation, or appropriate ICD discharge. Overall,
39% of patients had LGE-CMR and both the presence and extent of fibrosis, predicted the
composite end point (adjusted hazard ratio for presence of LGE-CMR 3.4, 95% CI 1.26 -
9)(261).
Focusing specifically on arrhythmia, Marra et al evaluated whether LGE-CMR predicts
sustained ventricular tachycardia, appropriate ICD activation, ventricular fibrillation and
sudden cardiac death in 137 patients with DCM, followed up for a median 3 years for the
primary composite end point (262). In this study, the presence, but not the extent of LGE-
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CMR predicted malignant arrhythmia, with an adjusted hazard ratio of 3.8 (95% CI 1.3-10.4;
p = 0.01)(262).
In the largest single centre study to date, Gulati et al, evaluated the impact of LGE-CMR mid
wall fibrosis on all cause mortality and an arrhythmic composite endpoint in a prospective
study of 472 patients with DCM(13). Patients were followed for a median of 5.3 years. LGE-
CMR midwall fibrosis was independently associated with sudden cardiac death, appropriate
ICD shock and non fatal ventricular tachycardia or ventricular fibrillation (hazard ratio 4.61
[95% CI, 2.75-7.74], p < 0.001)(13). LGE-CMR midwall fibrosis was also independently associated with cardiovascular mortality or cardiac transplantation (hazard ratio, 3.22 [95%
CI, 1.95-5.31], p <0 .001)(13).
Aside from these clinical studies, there are a number of smaller studies evaluating LGE-CMR
in DCM, but they have variable definition of LGE-CMR fibrosis, variable end point
definition and variable follow up time(147). In this context, further evidence for the role of
LGE-CMR in predicting sudden cardiac death risk came from a meta-analysis of eleven
studies comprising 1105 patients with ischaemic and non-ischaemic cardiomyopathy. In a
median follow up of 41 months, 207 patients had ventricular arrhythmias, which was
associated with LGE-CMR fibrosis (relative risk 4.33, [95% confidence interval (CI) 2.98 -
6.29])(263). In the subgroup of 3 studies with DCM patients only (n=277), the relative risk
was 3.79 (95% confidence interval 1.20 -11.94)(263).
A recent DCM specific systematic review and meta-analysis of data from 29 studies,
encompassing 2948 patients with DCM, evaluated the association between LGE-myocardial
fibrosis and the risk for ventricular tachyarrhythmia in patients with DCM(264). Patients
were followed for an average of 3 years, and the composite arrhythmic endpoint consisted of
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sustained ventricular tachycardia, appropriate ICD activation or sudden cardiac death. Mid-
wall fibrosis was present in 44% of patients, and the arrhythmic composite was met by 21%
of patients in the LGE positive group, compared to 4.7% of patients in the LGE negative
group(264). Overall, the combined odds ratio for the arrhythmic composite endpoint was 4.3
(95% confidence interval 3.3 to 5.8), demonstrating that LGE was a strong independent
predictor of arrhythmic events in patients with DCM.
In summary, the evidence to date regarding the prognostic importance of mid-wall fibrosis in
DCM is compelling, with LGE-CMR an independent predictor of heart failure
hospitalisation, malignant arrhythmias, all cause mortality and sudden cardiac death.
However, there remain limitations and unanswered questions. For example, it is not yet
established what threshold of fibrosis confers arrhythmic risk. Most analyses to date have
evaluated the presence or absence of fibrosis as a binary variable. Quantitative reporting of
CMR detected fibrosis is variable and there is no standardised methodology(265). We also
know that peripheral biomarkers of fibrosis exist, but we do not yet know what the
significance of LGE-CMR is over and above peripheral fibrosis markers. With a few notable
exceptions such as CMR_GUIDE (ClinicalTrials.gov Identifier: NCT01918215), there are
limited randomised trials in this field to evaluate whether clinical management informed by
knowledge of CMR-LGE changes outcomes in DCM. Furthermore, the presence and extent
of mid-wall fibrosis varies between patients with the same degree of ventricular impairment.
The genetic susceptibility to the development and maintenance of a fibrotic substrate has also
not been fully elucidated.
Interstitial myocardial fibrosis
Areas of intermediate, or interstitial fibrosis, can be detected through CMR T1 mapping and
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the calculation of extracellular volume, as previously outlined. Its prognostic value in DCM
has been evaluated in a limited number of studies.
In a cohort of 139 patients with cardiomyopathy (59 with non-ischaemic aetiology), CMR T1
mapping was performed prior to ICD implantation(266). After follow up for a mean of 1.2
years, 23 patients (18%) met the primary end point of ICD therapy or sustained ventricular
arrhythmia. In multivariable analysis, native T1 was associated with the primary endpoint
(HR for every 10ms increase in T1 1.10, 95% CI 1.04 to 1.16). T1 mapping was a stronger
predictor of outcome than quantification of replacement fibrosis in the same patients(266).
Clearly this cohort was small, the follow up was relatively short and inclusion of ICD therapy
in the endpoint may over-estimate the SCD risk associated with interstitial fibrosis.
In the largest study of T1 mapping in DCM to date, 713 patients with DCM underwent T1
mapping (native, post contrast and ECV measurement) and were followed up for a median of
22 months for the primary endpoint of all cause mortality. In total, 28 patients (4.4%) met the
primary endpoint. On univariate analysis, all T1 indices were associated with outcome. On
multivariate analysis, adjusting for RVEF and LGE, native T1 was independently associated
with mortality, with an adjusted hazard ratio of 1.1 per 10ms change in T1(160). Whilst
potentially important for the field, highlighting the importance of interstitial fibrosis over and
above replacement fibrosis, this study was limited by the short follow up and low event rate,
meaning the authors could not evaluate all conventional predictors of outcome in DCM
alongside evaluation of T1 indices. In post-hoc analysis, the authors report that the
combination of LVEF <35% or LGE with native T1 did not improve predictive value, but
this analysis may have been insufficiently powered. Therefore interstitial fibrosis remains an
area of interest for future, likely multi-centre studies of outcome in DCM.
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Having reviewed clinical and imaging biomarkers in DCM, I now briefly review the potential
prognostic role of serum biomarkers in DCM.
1.7.2.4 Evaluation of blood biomarkers
1.7.2.4.1 Established blood biomarkers
Natriuretic peptides
A number of structurally similar natriuretic peptides have been identified: atrial natriuretic
peptide (ANP), urodilantin (an isoform of ANP), B-type natriuretic peptide (BNP), C-type
natriuretic peptide and Dendroaspis natriuretic peptide. Of these, B-type natriuretic peptide
and the biologically inert precursor cleavage product N-terminal proBNP (NTproBNP),
largely secreted by cardiomyocytes, regulate salt and water handling and cardiac
remodelling(267). Elevated levels reflect ventricular strain, predominantly stretch. They are
the most well studied biomarkers in heart failure and provide excellent biomarkers for heart
failure diagnosis, prognosis, and treatment, independently of left ventricular ejection
fraction(268-271).
Across the heart failure spectrum, from acute to chronic, and asymptomatic to symptomatic,
BNP and NTproBNP have been consistently shown to predict prognosis. A systematic review
of 19 studies evaluating the ability of BNP to predict death or cardiovascular events found
that each 100pg/mL increase in BNP was associated with a 35% increase in the relative risk
of death in heart failure patients (p=0.096)(270).
In the ADHERE study of 48,629 acutely decompensated heart failure patients, of whom
19,544 patients had reduced ejection fraction, there was a linear relationship between
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increasing admission BNP and in hospital mortality, which persisted after adjustment for
conventional risk factors(272).
In a direct comparison between NTproBNP and BNP in 3916 patients with chronic heart
failure in the Valsartan Heart Failure Trial, both were found to be independent predictors of
all cause mortality (area under ROC curve BNP 0.665, NTproBNP 0.679), mortality and
morbidity, and hospitalization for heart failure, with NTproBNP being marginally superior
for the latter two endpoints(273).
There has been limited evaluation of the role of natriuretic peptides in DCM specifically. The
use of NT-proBNP in guiding heart failure therapy in a randomised trial of 151 patients with
chronic left ventricular systolic dysfunction (LVEF <30%) showed that NT-proBNP guided
care was associated with a reduction in cardiovascular events, improved quality of life and a
favourable cardiac structural remodelling profile(274). In this cohort, 33% of patients in the
NT-proBNP and 24% of patients in the standard care arm had non ischaemic
cardiomyopathy, suggesting that NT-proBNP may have prognostic value in DCM as well as
all cause heart failure.
There are of course limitations in the use of these peptides. BNP has high levels of
physiological variability(275) and can also be affected by comorbid conditions of obesity,
sepsis and renal failure, the latter a particularly common comorbidity in heart failure patients.
Cardiac troponins
The cardiac troponin proteins are located in myocytes and regulate contraction. Cardiac
troponin, formed of three subunits (troponin-C, -I and –T), is released in response to myocyte
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necrosis and is the established biomarker for detection of acute myocardial infarction.
Patients with heart failure may have chronically elevated levels, which may be related to
elevated ventricular preload leading to increased myocardial strain(276). The degree of
troponin elevation indicates severity of myocardial damage, and may be higher in patients
with ischaemic as opposed to non-ischaemic cardiomyopathies(277), but does not
conclusively point to underlying aetiology. However, in the heart failure population, elevated
troponin is a poor prognostic indicator(278). In a large study of over 80,000 patients
hospitalised for acute decompensated heart failure, 6.2% of patients had an elevated troponin.
The adjusted odds ratio for death in patients with an elevated troponin was 2.55 (95%
confidence interval, 2.24 to 2.89; p<0.001)(278).
Within the studies limited to patients with DCM (as opposed to heart failure, all causes),
elevated troponin I in 310 patients with DCM predicted all cause mortality, together with
ECG QRS duration, NYHA class and systolic blood pressure(279). In another smaller study
of 95 patients with DCM, with a median follow up of 4.1 years, the composite endpoint of
cardiovascular mortality, heart transplantation, or ICD implantation was met by 37% of
patients with an elevated troponin compared to 8% of patients with a non-elevated troponin
(p<0.01)(280).
1.7.2.4.2 Novel blood biomarkers
Many other biomarkers have been associated with prognosis in heart failure and to a lesser
extent, in DCM. The end stage phenotype of dilated cardiomyopathy reflects insults across a
diverse range of pathways and cellular processes. Perturbations in many of these pathways
may precede overt phenotypic development. The breadth of novel biomarkers reflects a range
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of pathogenic processes that may occur in the development of DCM, covering inflammation,
oxidative stress, extracellular matrix turnover, neurohormonal activation, myocyte injury and
myocyte stress (Table 1-6).
Not all of these biomarkers have been linked to outcome in DCM and they will not be
reviewed further as they are not evaluated within this thesis. To conclude this section on risk
stratification biomarkers in DCM, I will next review genetic biomarkers of risk DCM.
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Table 1-6: Biomarkers linked to heart failure classified by biological function. Not all biomarkers listed
have been proven to predict outcome.
Myocyte injury Other
Cardiac-specific troponins Neutrophil gelatinase associated lipocalin (NGAL) I and T Cystatin C Creatine kinase MB fraction Growth differentiation factor 15 Myocyte stress Transforming growth factor-beta1 Brain natriuretic peptide Osteopontin N-terminal pro-brain natriuretic peptide Osteoglycin ST2/ interleukin-33 Syndecan-1 Inflammation Syndecan-4 C-reactive protein Extracellular-matrix remodelling/fibrosis Tumor necrosis factor Matrix metalloproteinases 1, 2, 3, 8, 9 Fas (APO-1) Tissue inhibitors of metalloproteinases 1, 4 Interleukins 1, 6,10, 18 Collagen propeptides: Oxidative stress • C-terminal propeptide of procollagen Myeloperoxidase type I Oxidized low-density lipoproteins • N-terminal propeptide of procollagen type I Neurohormones • N-terminal propeptide of procollagen Norepinephrine type III Renin • C-terminal telopeptide of collagen type I Angiotensin II Galectin-3 Aldosterone Osteoprotegerin Arginine vasopressin
Endothelin
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1.7.2.5 Evaluation of genetic determinants of risk in DCM
1.7.2.5.1 Evidence of genetic contribution to risk in DCM
As outlined, approximately 20-30% of DCM has a familial origin and a genetic basis has
been identified in up to 40% of these cases(5). In studies to date, DCM appears
morphologically similar irrespective of the underlying genetic cause. However, there is some
evidence that genetic variants modulate arrhythmic risk in DCM.
In a large study of predictors of arrhythmic risk in 285 DCM patients, it was found that a
family history of sudden cardiac death, sustained VT or ventricular fibrillation, was an
independent predictor of the arrhythmic endpoint (SCD, VT, VF) on long term follow up
(mean ~9 years)(246). This suggests that there may be a genetic contribution to the risk of
sudden cardiac death in DCM.
The 2013 report by the American Heart Association and leading American cardiac societies
(ACCF/HRS/AHA/ASE/HFSA/SCAI/SCCT/SCMR) on criteria for ICD implantation goes so
far as to recommend ICD implantation in patients with LVEF >35% (who therefore would
not meet conventional criteria) if they have familial DCM which has been associated with
SCD, regardless of the specific genetic mutation(112).
Regardless of a family history of SCD, it remains to be established whether having genetic or
familial DCM confers an adverse prognosis, irrespective of genetic variant. The evidence,
though limited, suggests that genetic DCM per se is not associated with poor outcomes
compared to non genetic DCM. In a study of 213 DCM patients, no differences in the rate of
adverse events was detected between the 70 (33%) patients with genetic or familial DCM and
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patients without such a history(95). A pathogenic mutation was identified in only 16 cases so
genotype-phenotype study was not performed.
Similarly, in a study of paediatric DCM patients (647 idiopathic DCM, 223 familial DCM),
there was no difference in outcomes (all cause mortality or cardiac transplant) on
multivariable analysis between children with familial or non familial DCM (281). In fact, on
univariable analysis, children with familial DCM had a trend to lower risk of death compared
to children with idiopathic DCM (HR 0.64, p=0.06). This of course may be confounded by
earlier disease detection and earlier institution of prognostic medication in familial cases and
may not reflect genetic contribution to risk at all.
Large prospective studies documenting the correlation between gene mutations and SCD risk
are lacking(282). Therefore to discriminate further, and for genetic risk stratification to be
useful, evaluation of well studied genotype-arrhythmic phenotype correlations is required. I
will focus the next section on rare genetic variants only.
1.7.2.5.2 Potential rare genetic variant biomarkers of arrhythmic risk
A meta-analysis of 48 genotype-phenotype studies including 8097 DCM patients with
mutations in LMNA, PLN, RBM20, MYBPC3, MYH7, TNNT2 and TNNI3 found that the
frequency of ventricular arrhythmia was highest for patients with variants in LMNA (50%)
and PLN (43%)(283).
DCM patients with variants in the LMNA gene are at high risk for malignant ventricular
arrhythmias(284). After studying 269 LMNA carriers for a median follow up time of 43
months, this risk was shown to be highest for patients with a history of NSVT, LVEF <45%,
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male gender and truncating variants (49). For patients with DCM, conduction disease and
identified LMNA variants, clinical guidance suggests that an implantable cardiac defibrillator
should be considered in preference to a conventional pacemaker due to the identified
genotype-phenotype correlation of an increased risk of malignant arrhythmias and sudden
cardiac death (49).
Therefore genetic risk stratification has a role in LMNA DCM but there are unanswered
questions. It is unclear whether all patients with DCM should be tested for LMNA variants, or
only those with a family history of disease. The degree to which the risk associated with
LMNA variants is modulated by other known SCD risk factors in DCM, for example CMR-
LGE enhancement, has not been studied. Finally, whether the presence of a LMNA variant
should be factored into decisions on ICD therapy for primary prevention in DCM also needs
to be evaluated in a large prospective study.
Variants in the PLN gene, common in the Netherlands due to the presence of the founder
mutation, are also notable for their association with an arrhythmic phenotype(63). In
comprehensive analysis of a family with the PLN-R14Del, it was found that all mutation
carriers who may or may not have developed overt ventricular impairment had evidence of
attenuated R wave amplitudes on ECG and that these areas corresponded to the regions of
LGE on CMR(285). As with previous studies, there was an increased association with sudden
cardiac death(285). In a large prospective Dutch cohort study of 418 patients with DCM, 43
patients had variants in PLN and 19 patients had variants in LMNA which were both
associated with a malignant ventricular arrhythmias and end stage heart failure (hazard ratio
for death, heart transplant, malignant arrhythmia in patient with a mutation 2.0, 95% CI 1.4-
3.0)(66). Therefore there is a strong evidence of the link between the PLN founder mutation
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and arrhythmic risk, but the low frequency of these variants outside of the founder population
may limit their utility in genetic risk stratification.
One of the early phenotype studies of TTNtv cardiomyopathy (n=42) suggested TTNtv DCM
was associated with sustained ventricular tachycardia at baseline (OR 6.7, p=0.001) and that
this association was robust to adjustment for LVEF(84). This has not yet been replicated in
other studies and the long term association with arrhythmic risk remains to be established
before TTNtv can be used as a genetic biomarker of arrhythmic risk in DCM.
As well as TTNtv, variants in other sarcomeric genes (MYH6, MYH7, MYBPC3 and TNNT2)
have, in combination, been shown to be associated with an increased risk of cardiovascular
death/heart transplant/VF in DCM in subjects after the age of 50 years(286). However, this
analysis was based on the presence of 24 variants in total therefore there were insufficient
events (3 sudden deaths) to adequately discriminate which genes were driving the findings
and whether they could discriminate SCD from heart failure death. This interesting
observation requires further study in a large prospective cohort of patients with DCM.
Recently, truncating variants in the gene encoding filamin C (FLNCtv) have been linked to
an arrhythmic cardiomyopathy phenotype(78). On evaluation of 2,877 patients with inherited
cardiac conditions, 23 truncating mutations in FLNC were identified. These patients had
previously been diagnosed with DCM or arrhythmic or restrictive cardiomyopathies. A
further 54 mutation carriers were identified amongst 121 screened relatives. Notably, the
presence of FLNCtv was associated with ventricular arrhythmias (found in 82% of cases) and
sudden cardiac death (40 cases in 21 out of 28 families)(78). Whilst these findings have not
yet been replicated in a distinct clinical cohort, the results are compelling and provide
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evidence to support screening for FLNCtv in patients with DCM to guide risk stratification.
Other variants in desmosomal proteins, classically associated with ARVC, have also been
identified in DCM which may confer an increased risk of arrhythmic adverse events(287).
However, because there is considerable phenotypic overlap between DCM and ARVC and
the recognition of a high level of background variation at a population level in some of these
genes, the importance of desmosomal gene mutations in predicting sudden cardiac death risk
in DCM requires further clarification. Similarly, variants in SCN5A (sodium channel) have
previously been linked to DCM and associated with arrhythmia(288). However, variation in
SCN5A is now known to be relatively common and the role of SCN5A as a DCM gene has
been questioned(14,67). Variants in SCN5A may still have a role in the modulation of
arrhythmic risk in patients with DCM and this requires further investigation.
In summary, there are a number of potential genetic biomarkers of arrhythmic risk in DCM,
but the most robust association with relevance to a global cohort of patients with DCM has
been defined for patients with variants in the LMNA gene. The unmet need is to evaluate the
additive effect of genetic risk prediction in the context of established and emerging non-
genetic prognostic markers in DCM.
Having reviewed potential clinical, imaging, biochemical and genetic prognostic biomarkers
in DCM, I conclude this Introduction with a review of our current approach to risk
stratification in DCM. I review the limitations of our current guidelines and show where the
work in this PhD seeks to address some of these limitations.
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1.7.3 Current risk stratification in DCM and limitations
1.7.3.1 Current guidelines for ICD implantation
Current guidelines for primary prevention ICD implantation from major international
societies are outlined in Table 1-7.
Table 1-7: Current guidelines for primary prevention ICD implantation from European and American
guidelines. OMT= optimal medical therapy; NSVT= non sustained ventricular tachycardia; LVEF= left
ventricular ejection fraction; NYHA = New York Heart Association functional class; SCD=sudden
cardiac death.
Society/Overall criteria DCM specific criteria Class of Level of Recommendation evidence ESC(289) Non ischaemic aetiology I B
DCM with LMNA IIa B • LVEF ≤35% mutation and risk factors • NYHA classes II or III (LVEF <45% at • At least 3 months of presentation, NSVT on OMT ambulatory ECG, non- • Expected survival missense mutation) for ≥1 year with good functional status
AHA/ACC/HRS(252) Non ischaemic aetiology I B
• LVEF ≤35% Familial cardiomyopathy IIb C • NYHA classes II or III with a history of SCD
In both European and American guidelines, LVEF is the major determinant of whether or not
to implant an ICD.
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1.7.3.2 Limitations of current guidelines in risk stratification of patients with DCM
LVEF below 35% is currently the major variable driving selection of patients with DCM for
implantable cardiac defibrillator therapy. The limitations of this approach have been
demonstrated in a recent landmark randomised control trial, the DANISH study(12).
In this study, 1,116 patients with symptomatic DCM (LVEF <35%) were randomized to
either an ICD or standard clinical care. Patients in either arm could have cardiac
resynchronization therapy (CRT); in the final analysis ~60% of patients in both groups had
CRT. The patients were followed for a median of 67.6 months and the primary result showed
that ICD therapy was not associated with a significant reduction in all cause mortality
compared to usual clinical care(12). It is worth noting that the ICD group showed reduction
in incidence of sudden cardiac death by half (4.3% vs 8.2%, HR 0.50, p=0.005), though this
was not the primary endpoint. In other words, although SCD was reduced, mortality risk was
converted from SCD to pump failure deaths. This leads us to one of three possible
conclusions.
One conclusion is that ICD therapy is not beneficial in DCM. There is insufficient evidence
to support this. In a recent meta-analysis of 6 randomised controlled trials enrolling 2970
patients with DCM, evaluating the use of ICDs for primary prevention, ICDs were associated
with a reduction in all-cause mortality (pooled HR 0.77, 95% CI 0.64-0.91)(290). After
excluding trials where patients had CRT therapy, ICDs were still associated with a reduction
in all cause mortality (pooled HR 0.76, 95% CI 0.62-0.94).
The second possible conclusion is that the benefit of ICD therapy in the modern era of
optimal medical therapy (including CRT) is reduced compared to the original trials. This has
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been supported by a recent meta-analysis of 5 trials including 2992 patients comparing ICD
therapy to medical therapy for primary prevention in DCM. Compared to medical therapy,
ICD therapy was associated with a reduction in overall mortality (pooled OR 0.77, p=0.006)
(291). Secondary endpoints of sudden cardiac death were also reduced in the ICD arms but
other cardiovascular and non-cardiovascular causes of death did not differ between groups.
The investigators noted that the DCM cohort in DANISH had the highest compliance with
optimal medical therapy of all trials included and it was the only study with a high rate of
CRT implantation, which may account for the discrepant data(291). In the previously referred
to meta-analysis of 6 RCTs, a direct comparison of the COMPANION and DANISH-CRT
subgroup trials, comparing ICD plus CRT plus medical therapy to CRT plus medical therapy,
there was a non-significant benefit of ICD therapy (pooled HR 0.70, 95% CI 0.39-1.26)(290).
The third possibility is that ICDs are beneficial in DCM, but current guidelines are not
adequately identifying patients most likely to benefit. There is a growing body of evidence to
support this. Even in subgroup analysis of the cohort of patients in the DANISH trial, there
was an interaction of survival benefit with ICD use in younger patients with DCM, indicative
of benefit of ICD in specific DCM populations.
The rate of appropriate ICD activation in those patients who do meet current criteria is under
20%, again suggesting that current ICD guidelines do not target those most at risk of
SCD(229,292). Observational data prior to the DANISH trial had already shown that adverse
cardiac events occur in patients who do not meet criteria for ICD implantation(293-296). In
the Oregon Sudden Unexpected Death Study, all cases of sudden cardiac death were
evaluated for left ventricular ejection fraction. Amongst a population of 660,486, there were
714 cases of sudden cardiac death. Of these, LV function was assessed in 121 cases and
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found to be normal in 58 cases, representing 48% of sudden cardiac death cases. LV function
was mild to moderately impaired in 27 cases, representing 22% of sudden cardiac death
cases. In this study, individuals with sudden cardiac death and normal cardiac function were
more likely to be female, younger, had a higher prevalence of seizure disorder and a lower
prevalence of coronary artery disease. Whilst this study is now over 10 years old, it
demonstrated the identification of sudden cardiac death risk factors beyond LVEF.
Whilst the absolute number of sudden cardiac death events in individuals with normal or
mildly impaired cardiac function is small(297), the number of patients in the subgroup of
mild-moderately impaired cardiac function is large compared to the number of patients with
severely impaired cardiac function(293). Therefore current risk stratification, based on
LVEF, does not manage the risk of death in a potentially large cohort of patients with DCM.
Subsequent studies have evaluated non LVEF predictors of sudden cardiac death, such as
QRS duration, T wave alternans(298), signal averaged ECG, and heart rate variability.
However, whilst many of these techniques showed promise in the early trials, they have not
been replicated or have limited negative predictive value, particularly in the setting of
patients with reduced LVEF(299). Few have been evaluated in the context of DCM as much
of the early work in this field has been in ischaemic cardiomyopathy.
Goldberger et al performed a meta-analysis evaluating these predictors in DCM specifically,
including data on 6,088 patients from 45 studies(250). Baroreflex sensitivity, heart rate
turbulence, heart rate variability, left ventricular end-diastolic dimension, LVEF,
electrophysiology study, NSVT, left bundle branch block, signal-averaged electrocardiogram,
fragmented QRS, QRS-T angle, and T-wave alternans were included. Each of the predictors
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had only modest predictive value for sudden cardiac death in DCM patients. After adjustment
for missing data and publication bias, LVEF had the highest odds ratio for prediction of
sudden cardiac death (LVEF OR: 2.73, 95% CI: 1.99 to 3.76, p < 0.001), but even then the
odds ratio was modest for clinical risk prediction to be based on LVEF alone(250). This
limits the utility of each predictor in isolation, and in such a situation, multimarker
approaches to risk stratification are warranted.
It has also been shown that sudden cardiac death risk is clinical context specific. For
example, in a retrospective multicentre study evaluating the outcomes of heart failure patients
with LVEF <45% who met and did not meet National Institute of Clinical Excellence (NICE)
criteria for ICD implantation, patients with diabetes who did not meet the criteria had a
similar absolute risk of sudden cardiac death or appropriate ICD shock as patients without
diabetes who met the criteria(300).
Together, these data therefore suggest that current ICD guidelines are not appropriately
targeting the population most likely to benefit from therapy. Given the limitations of
established guidelines, there is a need to integrate the multi-dimensional data we now have
with regards to the specific risk in DCM, particularly for patients who fall outside the
guideline criteria. As the absolute incidence of sudden cardiac death is still relatively low in
patients with mildly impaired cardiac function, any risk stratification tool will need to be
powerful to be able to identify patients at risk. It will also need to be discriminatory enough
to avoid creating an undue burden on the health care system.
This will involve evaluation of multiple phenotypes with the ability to discriminate risk
amongst homogenous groups, for example, discriminating risk between patients in the same
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LVEF strata. One potentially discriminatory phenotype is mid-wall fibrosis on cardiac MRI.
A clinical trial addressing the utility of LGE mid-wall fibrosis data in patients who do not
currently qualify for an ICD is currently underway (CMR_GUIDE, ClinicalTrials.gov
Identifier: NCT01918215). This is designed to evaluate whether, amongst heart failure
patients with mild-moderate left ventricular systolic dysfunction (LVEF 36-50%), the use of
a CMR guided management strategy is superior to a conservative strategy of standard care. In
this, patients with mild-moderate left ventricular systolic dysfunction and evidence of LGE
midwall fibrosis on CMR will be randomised to either ICD or implantable loop recorder
insertion and followed up for 3 years for the primary endpoint of sudden cardiac death or
ventricular tachycardia leading to syncope. The trial is due to report in 2020.
In summary, current risk stratification guidelines in DCM rely heavily on LVEF. LVEF is an
excellent predictor of sudden cardiac death in a subgroup of patients at risk of sudden cardiac
death, but in isolation, LVEF has low sensitivity and specificity for the prognostic
stratification of sudden cardiac death risk in DCM. Impaired LVEF reflects established
disease, at which point risk modulation may be limited. Reliance on only one or limited
phenotypic markers ignores the biological complexity of DCM. Our understanding of the
pathobiology of DCM now incorporates advanced imaging, genetic and biomarker data. As
outlined, we have an understanding of risk based on each of these domains. Critically, many
of these novel biomarkers, in particular genetic changes, can be identified early in the natural
history of DCM. Early identification of the ‘at risk’ patient maximises the effectiveness of
risk prevention, with the downstream potential to modify the disease course. Use of multiple
risk markers in combination could provide a more robust prediction of events(301).
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1.7.4 Personalised medicine in DCM
Personalised medicine ‘implies a tailored approach to patients that offers more effective
therapy for each individual, reduces risks and avoids unnecessary treatments or diagnostic
interventions’ (ESC Position Paper on Personalised Cardiovascular Medicine)(302).
The potential application of personalised medicine in DCM is vast, particularly with the
increasing understanding of the genetic basis of disease, as well as prognostically important
endophenotypes detected through advanced imaging. Yet this brings challenges, as by design,
the DCM populations in clinical studies cannot be entirely representative of all possible
subgroups of disease (either by genetic aetiology, clinical demographics or stage in disease
course for example). Although clinical, biological and imaging features are helpful in
determining risk in large populations, there is no seamless transition to the assessment of
prognosis for an individual patient. It is in this junction that personalized medicine fits,
offering the ability to stratify a phenotypically homogenous disease into distinct subgroups,
through clinical, biochemical, imaging, and genomic markers. The utility of these subgroups
would depend on their clinical robustness and the demonstration of benefit of earlier
detection of an at risk phenotype or introduction of tailored therapy leading to improved
outcomes.
At present there is no integrated patient stratification in DCM that incorporates clinical,
genetic and non-invasive imaging markers for the diagnosis, risk stratification, and
management of patients with DCM. Through an integrated understanding of the genetic basis
and phenotypic manifestation of DCM, the work in this thesis seeks to address this need.
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AIMS, OBJECTIVES AND HYPOTHESES
Research aim:
Evaluate whether integrated assessment of genotype and phenotype data in DCM can
improve understanding of DCM pathogenesis, inform patient stratification, and identify
predictors of remodelling and outcomes.
Objectives:
1. To define the genetic architecture of dilated cardiomyopathy in the context of
background population genetic variation (chapter 2).
2. To describe the clinical manifestations of titin cardiomyopathy and evaluate
phenotypic modifiers, with the purpose of informing patient stratification and
identifying novel insights into disease pathogenesis (chapter 3).
3. Evaluate imaging predictors of remodelling in dilated cardiomyopathy, including
myocardial contractile reserve assessed by low-dose dobutamine stress CMR,
myocardial strain, interstitial fibrosis and replacement myocardial fibrosis (chapter 4).
4. To define the clinical outcomes associated with titin cardiomyopathy to inform patient
stratification by genotype in DCM (chapter 5).
Hypotheses addressed in Chapter 2
- Truncating variants in the titin gene are the commonest genetic contributor to DCM.
- Evaluation of the genetic architecture of DCM, comparing the burden of rare variants in a
DCM cohort to population variation, will identify genes associated with DCM and will
better define putative gene-DCM associations.
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Hypotheses addressed in Chapter 3
- The cardiovascular phenotype of dilated cardiomyopathy in patients with titin truncating
variants is distinct to that observed in patients without titin truncating variants.
- Alcohol consumption is an environmental modifier of left ventricular ejection fraction in
patients with DCM and truncating variants in titin.
Hypotheses addressed in Chapter 4
- Baseline left ventricular contractile reserve in patients with recent onset DCM is
predictive of LV remodelling at 1 year.
- Baseline circumferential and longitudinal myocardial strain, replacement myocardial
fibrosis, interstitial myocardial fibrosis, and RV contractile reserve in patients with recent
onset DCM are each predictive of LV remodelling at 1 year.
- An adequate baseline left ventricular contractile reserve in patients with DCM reflects a
lower level of interstitial myocardial fibrosis when compared to patients with poor
baseline left ventricular contractile reserve.
Hypotheses addressed in Chapter 5
- Patients with DCM and titin truncating variants have an adverse event profile for the
composite endpoint of cardiovascular mortality, major heart failure and major arrhythmic
events, compared to patients with DCM without titin truncating variants.
- The effect on outcome of titin truncating variants in patients with DCM is modified in the
presence of additional environmental variables, including male gender, mid-wall
myocardial fibrosis, arrhythmia, and moderate excess alcohol consumption.
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2 EVA LU ATIN G TH E GENETIC
ARCHITECTURE OF DILATED
CARDIOMYOPATHY
2.1 Aims and hypotheses
The overall aim in this chapter is to evaluate the burden of rare genetic variation in suggested
DCM genes in a large cohort of DCM patients compared to reference population variation, in
order to evaluate the genetic architecture of DCM. The primary purpose of this chapter is for
the results to inform the phenotype analysis in Chapter 3. The secondary aim is to define
DCM disease gene associations that may in the future be informative for patient stratification.
Specific hypotheses are as follows:
- Truncating variants in the titin gene are the commonest genetic contributor to DCM.
- Evaluation of the genetic architecture of DCM, comparing the burden of rare variants in a
DCM cohort to population variation, will identify genes associated with DCM and will
better define putative gene-DCM associations.
2.2 Background
The genetic architecture of dilated cardiomyopathy (DCM) is complex, characterised by a
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large number of low frequency variants with marked locus and allelic heterogeneity.
The advent of next generation sequencing technologies has made DNA sequencing more time
and cost efficient. This has led to a rapid increase in the number of reported disease-gene
associations. To date, almost 70 genes have been implicated as causative for DCM, but many
of these associations have been made through studies with small case numbers, in the absence
of segregation or functional data, and without robust population specific controls.
The large scale exome sequencing project ExAC (Exome Aggregation Consortium) has
demonstrated a relatively high level of rare genetic variation at a population level(15). This
discovery provides an opportunity to evaluate putative DCM disease-gene associations
against population level background genetic variation. The ExAC dataset has recently been
used to refine rare variant thresholds, predominantly in cancer (BRCA genes), and to a lesser
degree respiratory disease (primary ciliary dyskinesia) and hypertrophic
cardiomyopathy(303). The authors found that of the previously published pathogenic variants
studied, most had an allele frequency less than 0.01% in ExAC, and the majority much lower
than that. Pathogenic variants with allele frequencies greater than 0.01% were often founder
mutations or reflected mutation hotspots. Similarly, a statistical framework for calculating
disease specific allele frequency (accounting for disease prevalence, genetic and allelic
heterogeneity, inheritance, penetrance and sampling variance in reference datasets), has
shown that allele frequency cut offs well below 0.1% are justified for a variety of human
diseases including for example hypertrophic cardiomyopathy, in which the maximum
credible population allele frequency for any causative variant is 4.0x10-5(24).
Over the last 20 years (1996-2015), there have been 68 DCM genes reported in Human Gene
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Mutation Database (HGMD), 44 of those in the last 10 years alone(34). Understanding which
associations are robust and have true clinical significance is a key challenge faced by
clinicians and geneticists. By leveraging the power of the ExAC dataset to inform us about
background population level rare genetic variation, our group has recently demonstrated the
potential this approach could have in understanding the genetic architecture of
cardiomyopathies, particularly hypertrophic cardiomyopathy(14,304). Whilst this study also
evaluated genetic variation in DCM genes in up to 1315 cases with DCM, this analysis was
limited to major DCM genes as many recently reported putative DCM genes were not
represented on older gene panels, for example ZBTB17(60) and BAG3(52). Furthermore, the
analysis was also limited by lower case numbers for the minor DCM genes or those more
recently reported, a limitation I set out to address in this study with sequencing of DCM
patients using a cardiac gene panel which included up to 57 of the 68 genes reported to be
associated with DCM(305).
2.3 Methods
2.3.1 Study population
2.3.1.1 DCM cohort
The primary DCM study population for genetic analysis was prospectively enrolled in the
National Institute for Health Research (NIHR) Royal Brompton Hospital Cardiovascular
Biobank project. This cohort consisted of consecutive referrals from a dedicated
cardiomyopathy service at the Royal Brompton Hospital, London, consecutive referrals to the
CMR unit, both from within the Royal Brompton Hospital and a network of thirty regional
hospitals, as well as patients with DCM who underwent echocardiography due to
contraindications to CMR. Patients were referred for diagnostic evaluation, screening and
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assessment of severity of DCM. All patients were prospectively enrolled for research
purposes.
All patients underwent cardiac phenotyping with either cardiovascular magnetic resonance
scanning (CMR) or transthoracic echocardiography. For patients with CMR, DCM was
diagnosed based on evidence of left ventricular dilation and systolic impairment with
reference to age, gender, and body surface area adjusted nomograms(116). For patients with
echocardiography, DCM was diagnosed in the presence of left ventricular end-diastolic
diameter (LVEDd) > 117% of that predicted for age and body surface and left ventricular
ejection fraction (LVEF) < 45% and/or fractional shortening (FS) < 25%(9).
Exclusion criteria for DCM included a history of uncontrolled systemic hypertension,
coronary artery disease (>50% stenosis in one or more major epicardial arteries or previous
percutaneous coronary intervention or coronary artery bypass grafting), chronic excess
alcohol consumption meeting criteria for alcoholic cardiomyopathy (>80g/day for more than
5 years(97)), systemic disease known to cause DCM, pericardial disease, congenital heart
disease, infiltrative disorders (e.g. sarcoidosis) or significant primary valvular
disease(8,306,307).
2.3.1.2 Healthy volunteer cohort
A cohort of healthy volunteers (HVOL) was recruited at the Hammersmith Hospital, London,
via advertisement for the UK Digital Heart Project at Imperial College London. These
individuals had no history of medical illness, were not taking regular medication, and did not
have evidence of cardiac structural or functional impairment on CMR scanning.
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All participants gave written informed consent and the study was approved by the relevant
regional research ethics committees.
2.3.2 ExAC population reference dataset
The ExAC dataset is a collation of multiple exome sequencing datasets, at the time of
analysis numbering 60,706 unrelated individuals. The raw data from the different common
disease and population genetics studies have been re-processed through the same pipeline and
jointly variant called. Whilst not a control dataset, the cohort is not expected to be
specifically enriched for individuals with DCM. The ExAC dataset was downloaded from the
ExAC website (http://exac.broadinstitute.org) [version 0.3, Jan 2015]. Only ExAC variants
with a PASS filter were used in this study.
2.3.3 Coverage analysis between cases or controls and ExAC
In order to control for technical differences between platforms that might introduce bias, the
coverage of each gene in ExAC was adjusted for when making the frequency comparison.
We adjust for the number of variants per individuals sequenced, and not simply the number
of variants as a function of all 60,706 individuals.
2.3.4 Gene selection
The Human Gene Mutation Database(34) (HGMD, professional version 2015.3) was
interrogated for all genes with variants associated with DCM reported between 1996-2015.
This generated a list of 68 genes (Table 2-1).
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Table 2-1: Genes with variants reported to be associated with DCM in HGMD between 1996-2015
Year Genes Year Genes 1996 DMD 2008 MYPN 1998 ACTC1 CHRM2 1999 DES DSG2 LMNA LAMA2 2000 CTF1 2009 ANKRD1 DSP RBM20 MYH7 NEXN SGCD 2010 TAZ TNNT2 NEBL 2001 TPM1 SYNE1 2002 TTN TCF21 CSRP3 2011 GATAD1 MYBPC3 MURC TCAP DSC2 VCL DOLK 2003 ACTN2 JUP LDB3 PKP2 PLN BAG3 2004 ABCC9 TXNRD2 SCN5A 2013 ISL1 TNNC1 EMD TNNI3 LAMP2 2005 MYH6 PRDM16 TMPO NPPA 2006 CRYAB SGCB FKTN GATA4 DNAJC19 FHOD3 FLT1 ACTA1 PSEN2 2014 GATA6 SYNM CASQ2 PSEN1 RAF1 2007 FOXD4 2015 GATA5 PDLIM3 NKX2-5 ILK ZBTB17 LAMA4
Of these, 57 genes with the strongest evidence of association with DCM are included on the
TruSight Cardio panel (Table 2-2 shows gene and transcript). As RBM20 has poor coverage
in ExAC (mean percentage of sample bases covered at x10=14.5%, mean percentage of
sample bases covered at x20=13.3%), it was excluded from the DCM versus ExAC
comparison, and instead the burden of variants in RBM20 in the DCM cohort was compared
to the control cohort sequenced with the same assay and platform.
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Table 2-2: Genes and transcript linked to DCM included on Tru Sight Cardio sequencing panel.
Gene Transcript Gene Transcript Gene Transcript
ABCC9 ENST00000261201 ILK ENST00000299421 PRDM16 ENST00000270722
ACTA1 ENST00000366684 JUP ENST00000393930 RAF1 ENST00000251849
ACTC1 ENST00000290378 LAMA2 ENST00000421865 RBM20 ENST00000369519
ACTN2 ENST00000366578 LAMA4 ENST00000424408 SCN5A ENST00000333535
ANKRD1 ENST00000371697 LAMP2 ENST00000200639 SGCB ENST00000381431
BAG3 ENST00000369085 LDB3 ENST00000361373 SGCD ENST00000337851
CASQ2 ENST00000261448 LMNA ENST00000368300 TAZ ENST00000299328
CRYAB ENST00000526180 MURC ENST00000307584 TBX20 ENST00000408931
CSRP3 ENST00000533783 MYBPC3 ENST00000545968 TBX5 ENST00000310346
DES ENST00000373960 MYH6 ENST00000356287 TCAP ENST00000309889
DMD ENST00000357033 MYH7 ENST00000355349 TMPO ENST00000266732
DNAJC19 ENST00000382564 MYL2 ENST00000228841 TNNC1 ENST00000232975
DOLK ENST00000372586 MYPN ENST00000358913 TNNI3 ENST00000344887
DSC2 ENST00000280904 NEXN ENST00000334785 TNNT2 ENST00000367318
DSG2 ENST00000261590 NKX2-5 ENST00000329198 TPM1 ENST00000403994
DSP ENST00000379802 NPPA ENST00000376480 TTN ENST00000589042
EMD ENST00000369842 PDLIM3 ENST00000284770 TXNRD2 ENST00000400521
FKTN ENST00000223528 PKP2 ENST00000070846 VCL ENST00000211998
GATAD1 ENST00000287957 PLN ENST00000357525 ZBTB17 ENST00000375743
2.3.5 Targeted sequencing
All DCM patients and HVOLs underwent next generation targeted sequencing of 174 genes
associated with inherited cardiac conditions using the custom developed Illumina TruSight
Cardio Sequencing kit for sequence capture and the Illumina MiSeq and NextSeq platforms
for sequencing. The genes sequenced are listed in Appendix Tables 1 and 2.
The TruSight Cardio gene panel was developed by our group to provide comprehensive, high
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coverage sequencing across a range of genes associated with inherited cardiac conditions.
The development, optimization and performance of this panel have been outlined
previously(305). The targeted panel has high levels of coverage of target regions (>99.8% at
≥ 20× read depth on MiSeq platform, >99.9% at ≥ 20× read depth on NextSeq platform)
compared to whole exome sequencing (WES), deep WES, or whole genome sequencing
(88.1, 99.2 and 99.3% coverage at ≥ 20× read depth respectively across the target regions).
Laboratory scientists performed the sequencing. A brief outline of the steps involved follows.
Firstly, targeted DNA libraries were prepared according to manufacturers’ protocols. A DNA
sequencing library consists of short fragments of DNA with appropriate sequencing platform
specific adaptors ligated. To create libraries, genomic DNA was extracted and purified using
standard automated approaches and quality and quantity were assessed by agarose gel
electrophoresis and/or fluorometry (Qubit, Life Technologies). DNA was then fragmented,
using an enzymatic fragmentation process whereby a transposase enzyme fragments the DNA
and inserts the adapter sequence into dsDNA (a process called tagmentation).
For targeted resequencing, custom hybridisation capture probes were designed to enrich the
sequencing libraries made from whole genome samples to target the selected genes
implicated in cardiac conditions. DNA capture probes were used for Illumina assays. Target
capture was performed according to the manufacturer’s protocols using a biotin/streptavidin
pull down method. Following elution from the magnetic beads, the targeted DNA sequencing
library was then amplified using primers that contained a unique 8 base pair barcode
sequence to enable sample multiplexing. The target DNA was then amplified. For the
Illumina platforms, solid phase amplification was used whereby the denatured DNA library
was annealed to adaptors on a slide to undergo bridge amplification to form clusters of
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amplified target DNA for sequencing. Next generation sequencing then occurred using
sequencing by synthesis.
Sequencing by synthesis for the Illumina platform occurs through cyclic reversible
termination. The primed DNA template is bound to the flow cell and a DNA polymerase adds
just one modified nucleotide (which is fluorescently labelled with a terminator). DNA
synthesis terminates after the addition of this one nucleotide. Fluorescence imaging is then
performed to identify the incorporated nucleotide. Then the terminating group and fluorescent
dye are cleaved and the process is repeated. This process occurs in parallel across the
numerous clusters so the recorded fluorescence is a consensus across all the identical
templates in one cluster at that time. This generates approximately 100-150bp reads.
2.3.6 General and platform specific bioinformatic analysis
Variants were annotated and filtered according to customised in house bioinformatics
pipelines as previously described(305).
A team of bioinfomaticians run the bioinformatics pipeline. In brief, the general steps
involved are outlined. The output of NGS reads are in a Fastq format. These undergo per
sample quality control, prior to mapping against a reference human genome. The subsequent
SAM and BAM files undergo pre-processing, in which duplicates are marked, quality scores
are recalibrated, read groups are added or replaced (which read belongs to which sample),
reads are sorted by start position and local realignment around indels occurs (because false
positive single nucleotide polymorphisms (SNPs) can occur near indels). Variant calling is
done with GATK Haplotype Caller or Unified Genotyper. The variant call format (VCF) file
is then uploaded to our in house database and variant filtering and annotation occurs.
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Variant calling can detect either the true SNP or can reflect errors resulting from the library
preparation, base calling error in sequencing, mapping error and misalignment (particularly
around indels and repetitive regions), or even an error in the reference genome. Variant
filtering is used to remove sequencing errors and keep the true variants. The filters can be
applied through a software specific calling algorithm, and include assessment of coverage,
allelic balance, strand bias, quality scores, mapping qualities, and read position (for example
proximity to an indel).
Specifically, as applied to the output of the MiSeq or NextSeq platforms: Reads were
demultiplexed (allowing zero mismatches) with Illumina's software and quality was checked
in FastQC v.0.10.1108. Low quality (<20) reads/bases were trimmed using PrinSeq
v0.20.4109 and the good quality reads were aligned to hg19 reference using BWA
v0.7.10110. Marking duplicates reads, local realignment around indels and base quality score
recalibration processes were done in Picard v1.115105 and GATK v3.2-2104. Alignment
summary metrics, callability and coverage reports were calculated using Picard, Samtools
v0.1.18106, Bedtools v2.11.2107 and in house perl scripts. A subset file was created
(ontarget), based on reads mapping quality > 8 and we used this “ontarget” file to make
consistent variant calls in GATK HaplotypeCaller and UnifiedGenotyper. Bases covered by
at least 10 reads with a mapping quality ≥10 and base quality ≥20 were denoted as “callable”,
i.e. adequately covered for variant calling with recommended GATK parameters.
2.3.7 Variant filtering
Poor quality variants that did not meet sequencing quality filters were excluded: any variant
with a quality of depth score less than 4, a read depth less than 10 and an allelic balance less
than 0.20. Following these filtering steps, a list of variants present was generated. To account
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for the differing bioinformatics pipelines used between our cohort and ExAC, any variants
found to be in excess in our cohorts but not present at all in ExAC would be further curated to
check whether these variants had been excluded in the ExAC quality filtering processes
(VQSR sensitivity filters and hard filters). Any such variants would be removed from the
analysis in the DCM and control cohorts to avoid false positive enrichment. The overall
variant filtering process including sequencing control steps is outlined in Figure 2-1.
Selection of Variant filtering Variant filtering Analysis target genes Step 1 Step 2
HGMD review Sequencing filters Burden tesVng All DCM genes Manual curaVon of Quality of depth >4 1996-2015 common variants versus Allelic balance >20% ExAC FAIL VSQR and Coverage >10x hard filters 68 genes Literature review Novel or rare (MAF <0.0001) protein altering Final variant list variants only 57 genes included in our customised sequencing Variant list generated panel (total 174 genes)
Figure 2-1: Overview of methods for variant curation for burden testing in DCM study cohort and
healthy volunteers
2.3.8 Burden testing
The frequency of rare variants (defined as a minor allele frequency (MAF) <0.0001 in the
ExAC dataset) was calculated for each gene in the DCM, HVOL and ExAC cohorts. This
MAF cut off is a disease specific frequency threshold, informed by disease prevalence,
penetrance, and allelic contribution to disease(24), outlined further in Section 3.3.3.2. Only
protein altering variants (missense, nonsense, frameshift, essential splice site and inframe
indels) were assessed, with separate calculations for truncating (nonsense, frameshift and
essential splice site) and non-truncating variants (missense and inframe indels).
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Burden testing was performed comparing the burden of rare variants in each gene between
Caucasian DCM patients and Non-Finnish Europeans (NFE) in ExAC, and between
Caucasian HVOLs and NFE in ExAC using Fisher’s exact test (one sided for cases vs ExAC,
two sided for HVOLs vs ExAC), with a significance level of p<0.05, adjusted with
Bonferroni correction for multiple testing (57 genes, p<0.0009).
2.3.9 Sample size calculation
To establish if the DCM cohort was adequately powered for testing the burden of disease
causing variants in DCM genes, the sample size calculation for case and control cohorts was
performed based on the frequency of truncating variants in TTN, the main gene of interest.
The power.prop.test() in R was used, with an estimated frequency of TTNtv 15% in
cases and 1% in controls(84), with an alpha level of 0.05 and power of 0.8, with one sided
testing. This resulted in a sample size of at least 54 in each group. For other genes, where the
excess burden in cases compared to controls was not known, I performed a simulated power
calculation based on an estimated 1% frequency of variation in controls, showing the change
in power for a given DCM gene burden, at differing sample sizes (Figure 2-2).
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Figure 2-2: Simulated power calculation for burden testing. X axis shows the estimated burden of the
DCM disease gene. A background population burden of 1% was assumed. The y axis shows the change in
power (for a significance level of 0.05) with differing sample sizes (colours as per legend). For a sample
size of 500 (black curve), there is 80% power to detect a DCM gene burden of 3.7% (dashed line),
compared to 1% burden in controls/population (2.7% excess in cases).
2.4 Results
2.4.1 Cohort overview
The final cohorts for burden testing consisted of 647 Caucasian DCM patients, compared to
33,370 Non-Finnish European participants in the ExAC dataset and 588 Caucasian healthy
volunteers (Figure 2-3).
Of the primary DCM study cohort, there were 443 men (68%), mean age at recruitment was
54.5 years (range 14-88 years) and the majority of the cohort were NYHA class 1 or 2
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(n=514, 79%). The majority of the cohort (n=587, 91%) underwent CMR at recruitment and
mean left ventricular ejection fraction was 39% (standard deviation, 12.3%).
Of the healthy volunteers, there were 260 men (44%), mean age at recruitment was 39.7 years
(range 18-76 years) and the mean left ventricular ejection fraction was 66% (s.d. 5.2%).
Figure 2-3: Overview of cohorts in burden testing
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2.4.2 Coverage comparison between DCM cases and ExAC
The mean percentage of sample bases covered at 30x in the DCM cohort and 10x in ExAC
per selected gene is shown in Figure 2-4. Four genes included in the burden analysis have
<70% of sample bases covered at 10x in ExAC: TPM1, TAZ, NKX2-5, and DNAJC19. All
except two genes (GATAD1 and LDB3) in the DCM cohort have >99% of sample bases
covered at ≥ 30x. Mean coverage at 10x and 20x in ExAC and mean and median coverage at
10x and 30x in the DCM cohort is shown in Table 2-3. Generally, coverage is better in the
selected genes in the DCM cohort compared to the ExAC samples.
Figure 2-4: Coverage plot showing the mean percentage of sample bases covered at 10x in the DCM
cohort (black dots) and 10x in the ExAC cohort (red dots) for the 57 selected genes. RBM20 is shown for
comparison, though it has not been included in the burden analysis.
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Table 2-3: Coverage comparison between DCM and ExAC datasets. Mean percentage of sample bases
per gene covered at 10x or 30x in the DCM cohort and at 10x or 20x in ExAC.
Gene DCM Mean DCM DCM Mean DCM ExAC mean ExAC mean 10x Median 10x 30x Median 30x 10x 20x ABCC9 100.0 100.0 100.0 100.0 95.7 92.0 ACTA1 100.0 100.0 99.8 100.0 97.4 91.3 ACTC1 100.0 100.0 99.9 100.0 99.2 96.4 ACTN2 100.0 100.0 99.9 100.0 91.8 86.8 ANKRD1 100.0 100.0 100.0 100.0 98.2 92.9 BAG3 100.0 100.0 99.9 100.0 92.5 87.2 CASQ2 100.0 100.0 100.0 100.0 98.1 90.0 CRYAB 100.0 100.0 100.0 100.0 75.6 63.7 CSRP3 100.0 100.0 100.0 100.0 99.8 97.7 DES 100.0 100.0 99.6 100.0 80.6 69.3 DMD 100.0 100.0 100.0 100.0 90.2 76.3 DNAJC19 100.0 100.0 100.0 100.0 48.8 41.5 DOLK 100.0 100.0 100.0 100.0 99.6 98.5 DSC2 100.0 100.0 100.0 100.0 92.4 87.1 DSG2 100.0 100.0 100.0 100.0 94.1 88.9 DSP 100.0 100.0 100.0 100.0 98.4 95.0 EMD 100.0 100.0 99.7 100.0 86.3 79.4 FKTN 100.0 100.0 100.0 100.0 78.1 74.1 GATAD1 99.9 100.0 97.8 100.0 74.6 72.3 ILK 100.0 100.0 100.0 100.0 94.5 90.9 JUP 100.0 100.0 99.5 100.0 94.0 84.4 LAMA2 100.0 100.0 100.0 100.0 97.3 92.6 LAMA4 100.0 100.0 100.0 100.0 95.6 90.9 LAMP2 100.0 100.0 99.9 100.0 94.9 87.5 LDB3 98.7 99.0 96.4 96.7 89.4 82.3 LMNA 100.0 100.0 99.7 100.0 74.3 57.8 MURC 100.0 100.0 100.0 100.0 98.7 95.7 MYBPC3 100.0 100.0 99.4 100.0 73.4 50.8 MYH6 100.0 100.0 99.6 100.0 98.9 95.2 MYH7 99.9 100.0 99.6 100.0 99.3 96.3 MYL2 100.0 100.0 99.9 100.0 99.1 95.0
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Gene DCM Mean DCM DCM Mean DCM ExAC mean ExAC mean 10x Median 10x 30x Median 30x 10x 20x MYPN 100.0 100.0 100.0 100.0 72.5 69.4 NEXN 100.0 100.0 100.0 100.0 90.0 81.4 NKX2-5 100.0 100.0 100.0 100.0 66.7 46.5 NPPA 100.0 100.0 100.0 100.0 98.8 97.1 PDLIM3 100.0 100.0 100.0 100.0 86.1 78.5 PKP2 100.0 100.0 99.9 100.0 94.6 89.2 PLN 100.0 100.0 100.0 100.0 99.7 97.2 PRDM16 100.0 100.0 99.3 100.0 82.0 74.4 RAF1 100.0 100.0 100.0 100.0 95.6 92.9 RBM20 100.0 100.0 99.9 100.0 14.6 13.4 SCN5A 100.0 100.0 99.7 100.0 92.2 78.4 SGCB 100.0 100.0 99.8 100.0 82.9 79.3 SGCD 100.0 100.0 100.0 100.0 72.2 57.0 TAZ 100.0 100.0 99.1 100.0 64.9 52.3 TBX20 100.0 100.0 100.0 100.0 93.1 80.6 TBX5 100.0 100.0 100.0 100.0 93.5 85.7 TCAP 100.0 100.0 99.6 100.0 95.7 79.1 TMPO 100.0 100.0 100.0 100.0 95.0 89.5 TNNC1 100.0 100.0 99.4 100.0 92.4 84.1 TNNI3 100.0 100.0 99.8 100.0 81.0 65.5 TNNT2 100.0 100.0 99.9 100.0 84.9 75.5 TPM1 100.0 100.0 100.0 100.0 65.4 57.9 TTN 100.0 100.0 99.9 100.0 91.4 85.4 TXNRD2 100.0 100.0 99.3 100.0 81.0 73.3 VCL 100.0 100.0 100.0 100.0 94.4 87.6 ZBTB17 100.0 100.0 99.9 100.0 79.6 67.7
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2.4.3 Excluded variants
In total, variants in 61 participants were excluded (Table 2-4). Seven patients and four
HVOLs carried a non-truncating variant in the gene MURC
(c.702_722delAAGGCAGTCAGGAGAGAGGCT) that had been excluded in the ExAC
quality filtering processes. The remaining variants were excluded due to poor quality
sequencing as outlined in Methods.
Table 2-4: Poor quality variants excluded from burden analysis
Gene and variant DCM HVOL Total BAG3 1 1 c.636C>G 1 1 DES 1 1 c.1244G>A 1 1 DSC2 6 6 c.2048C>A 2 2 c.2056G>C 1 1 c.2059G>C 2 2 c.2071C>A 1 1 DSP 1 1 c.23A>C 1 1 FKTN 2 2 c.1261_1275delGCCAACTATGGTAAG 1 1 c.1280_1287delGGAAGATT 1 1 GATAD1 1 1 c.175_177delGGC 1 1 JUP 2 2 c.708-1G>C 1 1 c.708-2A>C 1 1 LDB3 3 4 7 c.1324G>C 2 2 c.1330G>C 2 1 3 c.1384A>G 1 1 2 MURC 7 4 11 c.702_722delAAGGCAGTCAGGAGAGAGGCT 7 4 11 MYBPC3 1 1 2 c.2858G>T 1 1 c.325G>T 1 1
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Gene and variant DCM HVOL Total PKP2 1 1 2 c.1460A>G 1 1 2 PRDM16 1 1 c.2780A>C 1 1 RBM20 3 1 4 c.166G>C 1 1 c.173_174delTA 3 3 SGCB 1 1 2 c.21_23delGGC 1 1 2 TTN 1 6 7 c.30683-1G>T 1 1 c.37461A>T 1 1 c.38066_38067insAGTGCCAGTGCCAGTGCCTCCTCCTAAAAAGCC 1 2 3 c.38067_38074delTGAAAAGA 1 1 c.38075_38076insGTGCC 1 1 VCL 11 11 c.152T>G 11 11
2.4.4 Burden of rare genetic variation in DCM patients compared to the
ExAC cohort
The overall excess burden of protein altering variants (truncating and non truncating) in
Caucasian DCM patients compared to Non-Finnish European ExAC cohorts is shown in
Figure 2-5.
Of the genes significantly enriched, variants in the TTN gene are the most common in this
DCM cohort, followed by non-truncating variants in MYH7 and TNNT2, and truncating
variants in DSP and LMNA (Figure 2-6). These are described further in the following
sections.
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Figure 2-5: Excess burden of protein altering variants (truncating and non-truncating) in DCM genes in
DCM cohort compared to ExAC. Titin variants are excluded in the right hand plot.
TTN non-tv, 7.0 MYH7 TTN tv, non-tv, 2.8 14.1 TNNT2 non-tv, 0.8 DSP tv, 0.9 Negative, Other, 17.6 56.4 LMNA tv, 0.4
Figure 2-6: Pie chart of excess burden (%) in significantly enriched genes (p<0.0009) in DCM cohort
compared to ExAC.
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2.4.4.1 Truncating variants
Truncating variants in the genes encoding titin (TTNtv), desmoplakin (DSPtv), and lamin
A/C (LMNAtv) were significantly enriched in DCM cases compared to ExAC (Figure 2-7,
Table 2-5). Of these, TTNtv were the commonest variant in DCM cases, with an excess
burden of 14.1% (p=6.34 x10-82), followed by DSPtv (0.85%, p=2.5 x10-5), then LMNAtv
(0.4%, p=0.0006).
Figure 2-7: Burden of rare truncating variants in DCM compared to ExAC. Truncating variants in the
labelled red genes (TTN, DSP, and LMNA) are significantly enriched in DCM cases compared to ExAC
(p<0.0009). The right hand plot excludes truncating variants in titin (TTN). Bars indicate 95% binomial
confidence intervals, for clarity shown only for significant genes.
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Table 2-5: Burden of rare truncating variants in DCM genes in DCM cohort (Caucasian) compared to
ExAC (NFE). Burden testing performed using Fisher’s exact test. Bonferonni corrected significance
threshold p<0.0009.
Gene No of DCM DCM No of ExAC ExAC % excess p-value variants total frequency variants total frequency in DCM in DCM in ExAC compared cohort cohort to ExAC TTN 96 647 0.1484 253 32435 7.80E-03 14.1 6.34E-82 DSP 6 647 0.0093 25 32752 7.63E-04 0.85 2.52E-05 LMNA 3 647 0.0046 5 28041 1.78E-04 0.44 0.00059 ANKRD1 2 647 0.0031 13 33129 3.92E-04 0.27 0.03 FKTN 2 647 0.0031 13 33017 3.94E-04 0.27 0.03 TBX5 1 647 0.0015 1 32833 3.05E-05 0.15 0.04 PRDM16 1 647 0.0015 1 31468 3.18E-05 0.15 0.04 MYH7 2 647 0.0031 18 33249 5.41E-04 0.26 0.06 RAF1 1 647 0.0015 2 33285 6.01E-05 0.14 0.06 ABCC9 2 647 0.0031 27 33246 8.12E-04 0.23 0.11 TNNT2 1 647 0.0015 9 31444 2.86E-04 0.12 0.18 TXNRD2 1 647 0.0015 9 31372 2.87E-04 0.12 0.18 MYPN 1 647 0.0015 10 33272 3.01E-04 0.12 0.19 PKP2 1 647 0.0015 22 32105 6.85E-04 0.08 0.37 LAMA2 2 647 0.0031 100 33006 3.03E-03 0.01 0.59 DSC2 0 647 0 9 32997 2.73E-04 -0.03 1 DOLK 0 647 0 6 33251 1.80E-04 -0.02 1 MURC 0 647 0 18 32939 5.46E-04 -0.05 1 VCL 0 647 0 10 32637 3.06E-04 -0.03 1 DSG2 0 647 0 22 32937 6.68E-04 -0.07 1 TNNC1 0 647 0 1 32414 3.09E-05 0.00 1 ACTC1 0 647 0 1 33073 3.02E-05 0.00 1 CASQ2 0 647 0 14 32995 4.24E-04 -0.04 1 ILK 0 647 0 13 32827 3.96E-04 -0.04 1 JUP 0 647 0 3 30975 9.69E-05 -0.01 1 SGCB 0 647 0 6 33302 1.80E-04 -0.02 1 ACTA1 0 647 0 7 32721 2.14E-04 -0.02 1 ACTN2 0 647 0 4 33199 1.20E-04 -0.01 1 BAG3 0 647 0 3 32671 9.18E-05 -0.01 1
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Gene No of DCM DCM No of ExAC ExAC % excess p-value variants total frequency variants total frequency in DCM in DCM in ExAC compared cohort cohort to ExAC CRYAB 0 647 0 3 27907 1.07E-04 -0.01 1 DES 0 647 0 12 27928 4.30E-04 -0.04 1 DMD 0 647 0 10 31320 3.19E-04 -0.03 1 GATAD1 0 647 0 6 32686 1.84E-04 -0.02 1 MYL2 0 647 0 9 33279 2.70E-04 -0.03 1 NKX2-5 0 647 0 1 27528 3.63E-05 0.00 1 NPPA 0 647 0 6 33039 1.82E-04 -0.02 1 PLN 0 647 0 1 33247 3.01E-05 0.00 1 SGCD 0 647 0 3 27960 1.07E-04 -0.01 1 TAZ 0 647 0 1 27865 3.59E-05 0.00 1 TBX20 0 647 0 4 32038 1.25E-04 -0.01 1 TCAP 0 647 0 3 30370 9.88E-05 -0.01 1 TNNI3 0 647 0 14 29802 4.70E-04 -0.05 1 TPM1 0 647 0 1 32332 3.09E-05 0.00 1 ZBTB17 0 647 0 0 29982 0.00E+00 0.00 1 CSRP3 0 647 0 16 33337 4.80E-04 -0.05 1 DNAJC19 0 647 0 8 30955 2.58E-04 -0.03 1 LDB3 0 647 0 16 31891 5.02E-04 -0.05 1 MYBPC3 0 647 0 26 26194 9.93E-04 -0.10 1 SCN5A 0 647 0 16 31392 5.10E-04 -0.05 1 TMPO 0 647 0 19 32447 5.86E-04 -0.06 1 LAMA4 0 647 0 28 32844 8.53E-04 -0.09 1 PDLIM3 0 647 0 14 32285 4.34E-04 -0.04 1 NEXN 0 647 0 28 32704 8.56E-04 -0.09 1 MYH6 0 647 0 31 33165 9.35E-04 -0.09 1 EMD 0 647 0 0 32132 0.00E+00 0.00 NA LAMP2 0 647 0 0 32758 0.00E+00 0.00 NA
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2.4.4.2 Non-truncating variants
Non-truncating variants in the genes MYH7 (2.8% excess, p=6.4 x10-7), TNNT2 (0.8%
excess, p=0.0003), and TTN (7.0% excess, p=8.3 x10-5) were significantly enriched in DCM
cases compared to ExAC (Figure 2-8, Table 2-6).
Figure 2-8: Burden of rare non-truncating variants in DCM compared to ExAC. Non-truncating variants
in the labelled red genes (MYH7, TNNT2, and TTN) are significantly enriched in DCM cases compared
to ExAC (p<0.0009). The right hand plot excludes variants in titin (TTN). Bars indicate 95% binomial
confidence intervals, for clarity shown only for significant genes.
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Table 2-6: Burden of rare non-truncating variants in DCM genes in DCM cohort (Caucasian) compared
to ExAC (NFE). Burden testing performed using Fisher’s exact test. Bonferonni corrected significance
threshold p<0.0009.
Gene No of DCM DCM No of ExAC ExAC % excess in p-value variants total frequency variants in total frequency DCM in DCM ExAC compared cohort cohort to ExAC MYH7 27 647 0.0417 449 33249 1.35E-02 2.82 6.38E-07 TTN 237 647 0.3663 9600 32435 2.96E-01 7.03 8.34E-05 TNNT2 6 647 0.0093 40 31444 1.27E-03 0.80 0.00031 PKP2 13 647 0.0201 274 32105 8.53E-03 1.16 0.005 DES 8 647 0.0124 110 27928 3.94E-03 0.85 0.0055 DSP 28 647 0.0433 832 32752 2.54E-02 1.79 0.0057 LAMA2 32 647 0.0495 998 33006 3.02E-02 1.93 0.0058 MYBPC3 19 647 0.0294 405 26194 1.55E-02 1.39 0.0076 NEXN 10 647 0.0155 218 32704 6.67E-03 0.88 0.01 ACTA1 3 647 0.0046 24 32721 7.33E-04 0.39 0.02 TNNC1 2 647 0.0031 12 32414 3.70E-04 0.27 0.03 LMNA 8 647 0.0124 153 28041 5.46E-03 0.69 0.03 PRDM16 16 647 0.0247 483 31468 1.53E-02 0.94 0.05 TXNRD2 7 647 0.0108 158 31372 5.04E-03 0.58 0.05 TPM1 2 647 0.0031 20 32332 6.19E-04 0.25 0.07 CRYAB 3 647 0.0046 39 27907 1.40E-03 0.32 0.07 SGCD 4 647 0.0062 65 27960 2.32E-03 0.39 0.07 TCAP 3 647 0.0046 56 30370 1.84E-03 0.28 0.13 ANKRD1 3 647 0.0046 72 33129 2.17E-03 0.24 0.17 NKX2-5 3 647 0.0046 62 27528 2.25E-03 0.23 0.19 TBX20 3 647 0.0046 76 32038 2.37E-03 0.22 0.21 SCN5A 14 647 0.0216 527 31392 1.68E-02 0.48 0.21 FKTN 4 647 0.0062 128 33017 3.88E-03 0.23 0.25 TNNI3 2 647 0.0031 47 29802 1.58E-03 0.15 0.28 DSC2 7 647 0.0108 278 32997 8.43E-03 0.24 0.31 DOLK 4 647 0.0062 147 33251 4.42E-03 0.18 0.33 MYPN 8 647 0.0124 350 33272 1.05E-02 0.19 0.38 DMD 14 647 0.0216 617 31320 1.97E-02 0.19 0.4 ABCC9 5 647 0.0077 215 33246 6.47E-03 0.12 0.41
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Gene No of DCM DCM No of ExAC ExAC % excess in p-value variants total frequency variants in total frequency DCM in DCM ExAC compared cohort cohort to ExAC LAMA4 12 647 0.0185 561 32844 1.71E-02 0.14 0.43 TAZ 1 647 0.0015 27 27865 9.69E-04 0.05 0.47 GATAD1 1 647 0.0015 33 32686 1.01E-03 0.05 0.49 ILK 3 647 0.0046 134 32827 4.08E-03 0.05 0.5 ACTN2 6 647 0.0093 297 33199 8.95E-03 0.04 0.52 LDB3 5 647 0.0077 240 31891 7.53E-03 0.02 0.54 SGCB 2 647 0.0031 94 33302 2.82E-03 0.03 0.55 JUP 6 647 0.0093 297 30975 9.59E-03 -0.03 0.59 EMD 1 647 0.0015 44 32132 1.37E-03 0.01 0.59 MYL2 1 647 0.0015 46 33279 1.38E-03 0.01 0.6 LAMP2 1 647 0.0015 50 32758 1.53E-03 0.00 0.63 PDLIM3 2 647 0.0031 122 32285 3.78E-03 -0.07 0.7 ZBTB17 2 647 0.0031 127 29982 4.24E-03 -0.11 0.76 VCL 4 647 0.0062 263 32637 8.06E-03 -0.19 0.76 BAG3 3 647 0.0046 214 32671 6.55E-03 -0.20 0.8 MYH6 10 647 0.0155 644 33165 1.94E-02 -0.39 0.8 DSG2 4 647 0.0062 288 32937 8.74E-03 -0.25 0.82 CSRP3 1 647 0.0015 91 33337 2.73E-03 -0.12 0.83 RAF1 1 647 0.0015 91 33285 2.73E-03 -0.12 0.83 TMPO 2 647 0.0031 195 32447 6.01E-03 -0.29 0.9 MURC 1 647 0.0015 135 32939 4.10E-03 -0.26 0.93 TBX5 0 647 0 120 32833 3.65E-03 -0.37 1 NPPA 0 647 0 68 33039 2.06E-03 -0.21 1 DNAJC19 0 647 0 30 30955 9.69E-04 -0.10 1 PLN 0 647 0 6 33247 1.80E-04 -0.02 1 ACTC1 0 647 0 21 33073 6.35E-04 -0.06 1 CASQ2 0 647 0 151 32995 4.58E-03 -0.46 1
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2.4.4.3 Variants in RBM20
Given the poor coverage of RBM20 in ExAC, I compared the burden of variants in RBM20 in
the DCM cohort versus the healthy volunteer cohort. There was no evidence of a significant
difference in the burden of truncating or non-truncating variants in RBM20 between 647
DCM cases and 588 Caucasian healthy volunteers (Table 2-7).
Table 2-7: Burden of rare variants in RBM20 in DCM cohort compared to volunteers. Burden testing
performed using Fisher’s exact test. HVOL= healthy volunteer.
RBM20 DCM DCM HVOL HVOL Burden P value
Variant type variants frequency variants frequency DCM vs
N=647 N=588 HVOL (%)
Non-truncating 14 0.022 10 0.017 0.46 0.35
Truncating 2 0.003 1 0.0017 0.14 0.54
2.4.5 Burden of rare genetic variation in healthy volunteers compared to
the ExAC cohort
The ExAC dataset is not a control cohort but it is not expected to be enriched for DCM cases.
However, there is potential for there to be differences in the sensitivity of targeted panel
sequencing compared to whole exome sequencing for the target genes. Therefore the
difference between protein altering variants in ExAC and a cohort of 588 Caucasian healthy
volunteers who underwent sequencing using the same assay and platform as the DCM
patients was assessed.
There was a significant excess in protein altering variants in the genes TTN and NKX2-5 in
controls compared to ExAC, suggesting that the sequencing panel is more sensitive than
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exome sequencing for these genes (Figure 2-9). Specifically with regards to TTN, there was
an excess of non-truncating variants in TTN, with an excess burden of 8.8% in volunteers
compared to ExAC (p=0.0004, Table 2-8). This suggests that the enrichment of TTN
missense variants in DCM compared to ExAC could have been driven by sensitivity of panel
sequencing, therefore I compared the burden of non-truncating TTN variants in DCM cases
and healthy controls, both subject to the same sequencing assay and platform. This showed
that there was no evidence of a significant difference in the burden of non-truncating variants
in TTN between DCM cases and healthy volunteers when assessed using these technically
matched cohorts (0.4% excess in DCM, p=0.004, p threshold for significance <0.0009, Table
2-8). Notably, the observed burden of TTNtv remained when controlling for coverage and
sensitivity differences using these cohorts. There was a significant excess of truncating
variants in TTN in DCM patients compared to healthy controls (14.3% excess in DCM, p=6.5
x10-25, Table 2-8).
Table 2-8: Burden of truncating and non-truncating variants in TTN in DCM patients compared to
ExAC and healthy volunteers. Burden testing performed using Fisher’s exact test.
Truncating Non-Truncating ExAC total 32435 32435 No of variants in ExAC 253 9600 HVOL total 588 588 No of variants in HVOLs 3 213 DCM total 647 647 No of variants in DCM 96 237 Excess burden DCM vs HVOL (%) 14.3 0.41 p-value DCM vs HVOL 6.54E-25 0.46 Excess burden DCM vs ExAC (%) 14.1 9.2 p-value DCM vs ExAC 6.34E-82 8.34E-05 Excess burden HVOL vs ExAC (%) -0.2 8.8 p-value HVOL vs ExAC 0.83 0.0004
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The apparent enrichment in NKXK2-5 may also be compounded by the relatively low
coverage of this gene in ExAC, with only 67% of sample bases covered ≥10x.
There was no significant difference in the frequency of variation in the other genes in
controls compared to ExAC, including the genes LMNA, DSP, MYH7 and TNNT2 found to be
enriched in DCM compared to ExAC (Figure 2-9).
Figure 2-9: Burden of rare protein altering variants in volunteers compared to ExAC. Variants in the
labelled red genes (TTN and NKX2-5) are significantly enriched in healthy volunteers compared to ExAC
(p<0.0009). Bars indicate 95% binomial confidence intervals, for clarity shown only for significant genes.
HVOL= healthy volunteers.
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2.4.6 Proportion of DCM cases with identifiable genetic variant
The proportion of individuals with a genetic variant in the five genes significantly enriched in
DCM compared to background population variation (TTNtv, DSPtv, LMNAtv, MYH7ms,
TNNT2ms) is shown in Figure 2-10. Amongst patients with a family history of DCM (n=92,
14%), a genetic variant is identified in 36% (n=33). Amongst patients without a family
history of DCM (n=555, 86%), a genetic variant is identified in 18% (n=101) (p=0.0003).
Of those patients with genetic variants, the majority had only one genetic variant in the target
genes. One patient with a TTNtv had a missense variant in MYH7. No other patients had two
variants in the target genes. Overall, in this cohort, 79% of cases with DCM remain
genetically unexplained.
Figure 2-10: Bar chart showing percentage of DCM cases with identified genetic variant in TTN, LMNA,
DSP, MYH7 and TNNT2 (variant classes in text). Cases are stratified by a family history of DCM.
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2.5 Discussion
This study, evaluating the genetic architecture of DCM, shows that truncating variants in titin
are the commonest genetic cause of DCM, with an excess burden of ~14% in Caucasian
DCM patients. Many previous studies that have reported genetic association with DCM have
not fully controlled for the high level of rare genetic variation in the population. This is the
first large scale study in DCM to do so. In this cohort, variation in only a further 4 genes
(LMNA, DSP, MYH7, TNNT2) are significantly enriched in DCM compared to background
population variation. This is much lower than previous studies have suggested and the
reasons for this are explored further. This study also demonstrates that a large proportion of
DCM cases (79%) remain genetically unexplained, even amongst those with a family history
of DCM (64%).
Frequency of TTNtv DCM – comparison to previously published cohorts
We identify that TTNtv in DCM represent the largest burden of genetic variation in DCM,
with a ~14% excess. The burden in the remaining 4 significantly enriched genes collectively
amounts to ~5%. Therefore, phenotype analysis in Chapter 3 will focus on TTNtv DCM.
Whilst the finding that TTNtv are the commonest genetic variant in DCM is in line with
previous reviews of studies in the field(5,67,308-310), the prevalence in this cohort differs
from previously published cohorts. In the landmark 2012 paper by Herman et al, confirming
the association between TTNtv and DCM across 3 clinical cohorts(79), the frequency of
TTNtv was reported as 25% in familial cases of idiopathic dilated cardiomyopathy and 18%
in sporadic cases. However, the frequency of TTNtv actually ranged from ~8% to 40% across
the cohorts, with the lowest frequency reported in a familial DCM cohort from Italy and
Colorado (13/149, 8.7%), sequenced using traditional sequencing methods. The higher
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frequencies were reported in subjects recruited during evaluation for cardiac transplantation
(17/71, 23.9%) and severely affected patients from a specialist centre, with a mean LVEF of
26% (37/92, 40.2%).
The patients with TTNtv DCM in these latter 2 groups had a much higher event rate for
cardiovascular mortality, transplant or ventricular assist device implantation (100% and 38%
respectively) compared to the first familial group (8%). Therefore it appears plausible that
TTNtv are enriched in severe/end stage cohorts, and a prevalence of 14% is a more accurate
reflection of an unselected DCM population. This hypothesis is supported by the largest
sequencing study of DCM patients referred for clinical genetic testing to date, in which 766
DCM patients underwent clinical genetic sequencing(67). Amongst those sequenced for TTN
(up to 25% of the cohort; inconsistencies reflected the duration of the study in which newer
genes were not sequenced in all patients), overall, TTNtv were found in 14% of cases(311).
Similarly, in a previous study of TTNtv in DCM patients from our institution by Roberts et
al(84), the prevalence of TTNtv was 13% in unselected DCM cases (49/374) and 22% in an
end-stage cohort (34/155), the latter overlapping with the transplant cohort in the original
Herman paper. Of note, 236 patients within the unselected cohort reported by Roberts et al
are included within the DCM cohort in this chapter. Finally, in a study of 72 DCM probands
with severely impaired ventricular function (mean LVEF 24%), TTNtv were identified in
24% of cases (30% of familial DCM, 18% of sporadic DCM)(86).
DCM genetic architecture – Fewer genes contribute to genetic architecture of DCM
than previously estimated
The second key finding of this study is that in this cohort, of the 57 genes reported to be
associated with DCM, only 5 were significantly enriched in cases compared to background
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population genetic variation. This suggests that previous estimates of the genetic architecture
of DCM may have been conflated by inadequate control for background population variation.
Indeed, the large scale exome sequencing project ExAC (Exome Aggregation Consortium)
has demonstrated a relatively high level of rare coding variants at a population level, with
approximately 54 variants per participant previously reported as disease causing(15).
With respect to DCM, the significantly enriched variants were TTNtv as outlined, as well as
truncating variants in DSP and LMNA, and non-truncating variants in MYH7 and TNNT2.
Non-truncating variants in TTN (TTNms) were not enriched in DCM compared to ExAC
when the sensitivity of sequencing panel was accounted for, as healthy volunteers in our
cohort also demonstrated an excess of TTNms variants compared to ExAC.
The findings of this Chapter are primarily used to inform phenotype analysis in subsequent
chapters. However, they also inform us about the genetic architecture of DCM and this data
may inform future patient stratification studies. This is the largest study to date of DCM
patients sequenced on a comprehensive targeted sequencing panel (including 57 DCM
genes). A demonstrated excess burden in a particular gene therefore provides good evidence
that the gene in question is a valid DCM gene. However, the significance of the absence of
excess for the remaining 52 genes requires a more nuanced approach. There are at least two
key possibilities.
Understanding the genes that were not enriched in DCM cases
The first possibility for a lack of enrichment is that the gene in question is not a valid DCM
gene, and the original reports either did not fully account for population variation, did not
provide adequate segregation data or did not provide adequate functional data. Indeed, many
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of the variants in genes linked to DCM were reported in relatively small case-control cohorts,
prior to our current knowledge of the extent of rare background genetic variation, and in the
absence of formal statistical association testing. Many report simply the presence of variants
in a case cohort and the absence of the specific variant in a similar sized control cohort. For
example the first reported link between MYBPC3 and DCM identified one missense variant
in 46 patients with DCM, absent in 88 healthy controls and 136 patients with hypertrophic
cardiomyopathy(44). Others report the absence of a particular variant in large datasets, but do
not consider any variation across the gene, for example the study linking PRDM16 to DCM,
in which 206 samples from DCM patients (including 131 cardiac biopsies) were sequenced.
In total, 2 truncating (0.97%) and 5 missense variants (2.4%) were identified which were
each absent in 6,400 controls. However, the authors did not report the total rare variation in
PRDM16 in this series. In the ExAC NFE cohort, 483 non-truncating variants (1.4%) and 1
truncating variant (0.004%) in PRDM16 are found in 31,468 samples(58). In the original
study, the cases with missense variants lacked segregation data and two of the missense
variants were annotated in dbSNP, with predicted benign PolyPhen scores(59). The two de-
novo truncating variants may be of interest and indeed, in our cohort, one DCM patient
carried a truncating variant in PRDM16 (burden testing compared to ExAC, p=0.002).
However, based on the current proportions, to evaluate this, a sample size of over 4000
patients with DCM would be required (calculation performed using power.prop.test in
R).
We purposely did not perform case-control burden analysis in this cohort, despite having 647
DCM cases and 588 controls, sequenced on the same platform using the same assay. Whilst
588 controls is a large sample size, it is possibly insufficient to be able to adequately capture
rare population genetic variation. For example, if we consider non-truncating variants in the
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gene ANKRD1. These were first reported to be associated with DCM in a study of 208 DCM
patients (312). The authors identified 3 missense mutations in ANKRD1 in 4 DCM patients,
absent in 180 control subjects in the dbSNP database. In our cohort, 3 DCM patients (0.5%)
have missense variants in ANKRD1 and there are no variants in the control subjects.
However, there are 72 missense variants in ANKRD1 in 33129 NFE subjects in ExAC (0.3%)
and no significant enrichment in cases compared to ExAC (p=0.17) or controls (p=0.14) on
burden testing.
The second possibility is that the gene in question is a valid DCM gene, but only for variation
in particular domains or hotspots, particularly when the original reports suggested that such
hotspots existed, for example variants in RBM20(55). As outlined, we were not able to fully
evaluate variations in RBM20 due to its low coverage in ExAC. This issue may be addressed
in future studies evaluating the genetic architecture of DCM compared to population variation
using whole genome sequencing data in gnomAD (Genome Aggregation Database)(313),
which appears to have a better coverage of RBM20 compared to ExAC. Finally, there may be
particular variants within a gene that are pathogenic, especially in the context of a Founder
mutation, for example PLN R14del(65), which will not be accounted for in the study design
of this project.
Other possible explanations for the genes that were not enriched could also be that whilst the
gene is a true disease gene, mutations are rare and account for a very small proportion of
cases, much lower than the threshold we are powered to detect. Furthermore, the assumption
tested in this study is that variation in each individual gene is sufficient to cause disease. If
their contribution however is more complex than monogenic, this will not be detected in this
study design.
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In this study, variants in the remaining 52 genes without strong evidence for pathogenicity,
according to the ACMG guidelines(36), may still be of importance for the individual patient,
but should only be classified as pathogenic with robust additional data, for example
segregation data. Moving forwards, large scale collaborative efforts (such as that proposed by
ClinGen) will be required to comprehensively review the available evidence linking the 57
genes to DCM to assess whether they should be incorporated in clinical genetic testing.
Previous studies evaluating the genetic architecture of DCM
I now place the result of this study in the context of earlier studies evaluating the genetic
architecture of DCM. The first, by Pugh et al, 2014, analysed variants in up to 46 cardiac
genes in 766 DCM patients referred for clinical genetic testing(67). Classifying variants as
pathogenic or likely pathogenic, using NHLI-ESP and 1000 Genomes to account for
population variation, they identified that TTNtv were the most common pathogenic variant
(12.2%), followed by variants in LMNA (4.1%), MYH7 (3.4%), TNNT2 (2.5%) and DSP
(2.4%), very much in line with our results. Importantly, the authors did not detect any
“pathogenic”, “likely pathogenic”, or “VUS (Variants of uncertain significance)-favour
pathogenic” variants in 24 of the 46 genes tested, including 11 which had previously been
reported to be associated with DCM (ANKRD1, CAV3, CRYAB, CTF1, DSC2, EMD, FHL2,
LAMA4, LAMP2, MYH6, and PKP2). Furthermore, the authors identified only 3 “VUS-
favour pathogenic” variants in the gene MYBPC3 across the cohort, 2 of which may have
been variants more associated with hypertrophy cardiomyopathy, reinforcing our finding that
MYBPC3 is not a DCM gene.
In the second study, published by Haas et al in 2015, 639 DCM patients from 8 countries
underwent targeted sequencing of 84 genes(314). No control populations were sequenced.
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The findings of the study by Haas et al are distinct to our study. Initially, the authors report
that 48% of patients have variants in known cardiomyopathy genes. However, when
restricted to variants that were absent in NHLI-ESP and included variants in DCM genes
only, variants were only detected in 16% of patients. The estimated frequency of genetic
variants was higher than in any previous study, with 73% of patients reported to have a
pathogenic variant and strikingly 38% of patients having at least two pathogenic variants.
However, this was likely related to the approach to variant classification that the authors
used. In contrast to ACMG guidelines(36), the authors adopted the following approach to
variant classification.
The primary inclusion category was variants in HGMD, including those linked to all
cardiomyopathies and channelopathies (not specifically DCM). The secondary categories
were ‘potential disease causing variants’, considered separately for truncating and non-
truncating variants, the latter annotated with a SNP annotation tool. Crucially, the frequency
threshold for exclusion was not clear, with the authors defining ‘not common’ variants.
Historically, rare variants are typically defined as having a minor allele frequency (MAF)
<1%, though the frequency cut offs in the literature vary(23). For SNPs, the authors excluded
variants with a frequency over 1%, but work from our group has shown that this threshold is
high and is likely to include many benign variants(14,24). The maximum credible population
allele frequency for any rare variant causative of DCM is <0.00006 (calculation outlined in
Section 3.3.3.2). Therefore, many of the variants in the study by Haas et al may have been
variants that are well tolerated at a population level. Re-evaluation of the original data with
reference to either the ExAC or gnomAD datasets would be of great interest.
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2.5.1 Limitations of this study
There are a number of potential limitations to this work. Firstly, I am comparing the burden
of variants detected through two distinct sequencing technologies, namely targeted
sequencing versus whole exome data. This means the two cohorts are not uniformly matched
and there is potential for bias, with differences detected being secondary to the sequencing
method used. The coverage in whole exome data, particularly for the targeted genes, is not as
high as in targeted sequencing data. I have taken steps to mitigate this, including evaluating
the frequency of the variant as a function of the actual number of individuals sequenced for
that gene in ExAC and not the total number of individuals in ExAC, though this remains a
potential limitation. However, whilst there are clear limitations, the strength of using the
ExAC dataset is for its size, enabling the detection of rare variants that would not be possible
in smaller matched case-control cohorts. We did also recruit and sequence an ethnicity
matched control cohort, sequenced on the same assay and platform, therefore were able to use
this data to evaluate the sensitivity of the panel compared to ExAC. This analysis showed that
the targeted panel was more sensitive to missense variation in TTN and NKXK2-5 compared
to exome sequencing in ExAC. For NKXK2-5, the sensitive panel did not lead to false
positive enrichment in cases. There was no evidence to support NKXK2-5 being a DCM gene
as there was no excess variation in DCM cases compared to controls or in DCM cases
compared to ExAC. With regards TTN missense variants, whilst these were enriched in DCM
cases compared to ExAC, this is likely driven by the sensitivity of the targeted panel. TTN
missense variants may still be of importance in DCM but further work is required to
determine which variants may act as disease modifiers and which are innocent bystanders.
As reviewed in the discussion, having both the control cohort and ExAC shows that reliance
on the control cohort alone for burden comparison may have been insufficient. Together,
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these limitations would have potentially led to an increase in false positive disease-gene
associations. On the contrary, the findings of this study do not support many of the reported
DCM disease-gene associations, but reinforce an emerging literature that the number of genes
causally implicated in monogenic DCM is smaller than previously estimated.
Due to the primary analysis being a burden comparison between targeted sequencing data and
whole-exome data, burden testing was performed using Fisher’s test, based on the collapsed
and combined count frequencies of variants in each cohort. This gene based burden testing
may be associated with reduced power compared to other statistical methods, particularly if
the rare variants may increase or decrease disease risk(315), though for DCM, we expect the
rare variants to increase disease risk. The factors affecting the power of this method of gene-
based burden testing using population data have recently been comprehensively
reviewed(316). Using model simulations on a set of ~2,600 whole exome sequencing
samples, the authors found that the primary determinants of power in such studies are the
background rates of rare variants in each gene, with genes such as TTN, with a high
background rate of variation, requiring large samples (approaching 5,000) to detect a
difference between cases and controls(316). The second key determinant of power was locus
heterogeneity, with larger sample sizes needed to achieve adequate power when individual
genes contribute to less than 5% of cases under a dominant model, as is the case with DCM.
However, for the lowest estimate (contribution of any individual gene= 1%), just over 300
case samples were required to achieve 80% power to detect a difference between case and
population cohorts, a sample size achieved in this study. Surprisingly, incomplete penetrance,
even as low as 10%, did not affect power. This was attributed to the overall low prevalence of
monogenic disorders(316). In our study, based on the simulated power calculations, we are
powered at 80% to detect a significant enrichment of genes with an excess burden of 2.7% in
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cases compared to population control. This means that it is unlikely that the significantly
enriched genes with an excess burden lower than this threshold are false positives (e.g.
TNNT2 non-truncating variants). It also however remains plausible that many of the lower
frequency genes that were not significantly enriched may be re-evaluated in future studies
with larger case sample size.
Another limitation of this study design and analysis is that non-coding variants in the genes
evaluated or the effects of epigenetic modifications, which may be important contributors to
DCM pathogenesis are not accounted for. Similarly, gene-gene interactions, structural
variants, or any common variants of small effect size are also not accounted for. Future
studies evaluating gene burden based on whole genome sequencing of DCM patients will be
important both for these reasons, as well as mitigating the bias associated with incomplete
coverage of the coding region in targeted sequencing and exome sequencing due to GC-rich
regions(317,318).
Steps have been taken to match the case and control cohorts for ethnicity to avoid false
positive gene-disease associations. However, this was based on self reported ethnicity and not
principal component analysis (PCA) due to the limited number of targeted SNPs in the panel
sequencing data. Work is now in development within our group to evaluate options to be able
to perform SNP based PCA on data from the TruSight Cardio panel. Future gene-based
burden studies will therefore be able to stratify by PCA-derived ethnicity in this cohort.
The results in this study could also be affected by selection bias within the primary study
cohort. The majority of the cohort underwent CMR at recruitment, which means that there is
potential bias against either individuals with severe disease (who were for example too
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unwell to undergo CMR), or individuals with non-MRI conditional or compatible cardiac
devices. The latter is particularly important as LMNA cardiomyopathy is known to be
arrhythmogenic and patients with this condition may have pacemakers or defibrillators
implanted(49). This may explain the relatively lower frequency of LMNA variants in this
cohort compared to previously published estimates up to 5%(5), as well as the absence of
significant enrichment of non-truncating variants in LMNA in DCM cases compared to
population controls.
The study cohort is also predominantly composed of individuals with adult onset DCM, with
a mean age at recruitment of 54 years. This means that we are not enriched for cases of
paediatric DCM, which is known to have a different genetic architecture to adult onset DCM,
both in terms of genes involved (e.g. TPM1 is associated with paediatric DCM) as well as
mode of inheritance (more evidence of recessive models)(319). This was illustrated nicely in
the series by Pugh et al, in which different gene burdens were identified when the primary
population was stratified by adult or paediatric status(67). To mitigate these ascertainment
biases, we have now identified a secondary replication cohort composed of patients with a
history of DCM referred for clinical genetic testing, both from the Partners Laboratory of
Molecular Medicine(67) and Oxford Medical Genetics Laboratory.
2.6 Summary
In this chapter, the genetic architecture of DCM was re-evaluated in the context of population
level genetic variation. This identified that truncating variants in TTN are the largest genetic
contributor to DCM with an excess of ~14% in Caucasian patients. Variants in only a further
4 genes (LMNA, DSP, MYH7, TNNT2) were significantly enriched in DCM compared to
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background population variation, leaving ~79% of DCM cases in this cohort genetically
unexplained. This data will direct the genotype-phenotype analysis in subsequent chapters.
2.7 Outline of further work
• We are extending the analysis to a secondary replication cohort composed of patients
with a history of DCM referred for clinical genetic testing from the Partners
Laboratory of Molecular Medicine(67) and the Oxford Medical Genetics Laboratory.
• We are extending the analysis to non-Caucasian patients, both from the UK and a
secondary cohort of DCM patients sequenced in Singapore.
• The next phase of this study will replicate the analysis but using gnomAD as the
reference database, which will address some of the limitations of coverage
encountered when using the ExAC dataset.
2.8 Acknowledgements
• Roddy Walsh, senior Bioinformatician in Prof Cook’s group, downloaded the ExAC
dataset, extracted the list of variants in the target genes from the ExAC dataset, the
DCM cohort, and the control cohort, and summarised the data in a format suitable for
burden analysis.
• Risha Govind, Bioinformatician in Prof Cook’s group, provided the coverage data for
the targeted sequencing panel samples, and together with Shibu John ran the
bioinformatics pipeline.
• Rachel Buchan, William Midwinter, and Alicja Wilk, laboratory scientists in Prof
Cook’s group, performed the sequencing of the DCM and HVOL samples.
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3 THE CLINICAL
MANIFESTATIONS AND
PHENOTYPIC DRIVERS OF TITIN
CARDIOMYOPATHY
3.1 Aims and Hypotheses
The primary aim of this chapter is to define the cardiovascular phenotype of titin
cardiomyopathy in patients with DCM, with the purpose of informing patient stratification
and identifying novel insights into disease pathogenesis. The hypotheses are as outlined:
- The cardiovascular phenotype of dilated cardiomyopathy in patients with titin truncating
variants is distinct to that observed in patients without titin truncating variants.
- Alcohol consumption is an environmental modifier of the cardiovascular endophenotype
of left ventricular ejection fraction in patients with DCM and truncating variants in titin.
3.2 Background
As previously outlined, despite improvements in pharmacological and device based therapy,
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clinical outcomes in DCM remain poor, with a 20% 5-year mortality rate (12,13). As such
there is a pressing need for improved risk stratification and the identification of novel
therapeutic targets. As presented in the previous chapter, truncating variants in the gene for
the sarcomeric protein titin (TTNtv) are the largest single genetic contributor to DCM, found
in up to 14% of cases in this cohort, and 10-20% of cases in previously reported cohorts
(79,84). Identifying genotype-phenotype correlations for TTNtv DCM may therefore
advance stratification approaches and treatment options for DCM patients.
In this chapter, the deep phenotyping attributes of cardiovascular magnetic resonance (CMR)
are used to gain insights into the pathobiology of TTNtv DCM. The identification of a
distinct cardiac endophenotype of TTNtv DCM may offer mechanistic insights into the
pathogenesis of DCM and highlight potential therapeutic targets for further translational
study.
There are key unanswered questions with respect to titin cardiomyopathy, which I set out to
explore in this chapter:
1. Genotype – phenotype correlations: As yet, there are few clear genotype-phenotype
correlations for TTNtv DCM. If they can be established, they may form the basis of
improved risk stratification in DCM.
2. Variable penetrance and expressivity: TTNtv are notable for variable penetrance and
expressivity. I explore the role of environmental modifiers, with a focus on alcohol
consumption.
3. Mechanism of disease: The mechanism of action of TTNtv leading to DCM is
incompletely understood. Studying the human phenotype may provide mechanistic
insight and identify novel therapeutic targets.
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Environmental modifiers
Informed genotype-phenotype risk stratification requires an understanding of potential
phenotypic modifiers. Genetic or environmental modifiers may underpin the variable
penetrance and expressivity seen in TTNtv DCM. Indeed, there is now good evidence for
environmental modifiers of the TTNtv phenotype. For example, peripartum cardiomyopathy
shares a genetic aetiology with DCM, suggesting that pregnancy may act as an environmental
modifier to unmask the phenotype of cardiomyopathy in TTNtv carriers (92). We have also
recently demonstrated that TTNtv are not phenotypically silent in otherwise apparently
healthy humans, with subtle differences in cardiac volumes and physiology compared to
control subjects without TTNtv (88). Furthermore, we found that rats with TTNtv developed
impaired cardiac physiology under cardiac stress (88), again providing evidence of the
potential role of gene-environmental interactions in the development of the TTNtv
cardiomyopathy phenotype.
Chronic excess alcohol consumption is an established contributor to cardiovascular
disease(96) and is known to cause cardiomyopathy (97). The effects of moderate alcohol
consumption however are less clearly established and the spectrum of alcoholic end organ
damage is not always related to quantitative consumption, suggestive of a putative genetic
modifier. Against this background, I therefore evaluate alcohol consumption as a phenotypic
modifier of TTNtv cardiomyopathy.
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3.3 Methods
3.3.1 Study Population
The primary DCM study population, as outlined in Chapter 2, was prospectively enrolled in
the National Institute for Health Research (NIHR) Royal Brompton Hospital Cardiovascular
Biobank project between 2009-2015. This cohort consisted of consecutive referrals from a
dedicated cardiomyopathy service at the Royal Brompton Hospital, London, and consecutive
referrals to the CMR unit, both from within the Royal Brompton Hospital and a network of
thirty regional hospitals. Patients were referred for diagnostic evaluation, screening and
assessment of severity of DCM. Within the cardiomyopathy service, our standard protocol is
that all patients without contraindication undergo a CMR scan at presentation.
The control participants included for illustrative purposes only in Figure 3-4 are taken from a
previously published cohort from our institution(116). They consist of 120 healthy
volunteers, with 10 men and 10 women in each of six age deciles from 20 to 80 years. All
subjects were asymptomatic, had no contraindication to CMR scan, no history of cardiac
disease or known cardiac disease risk factors, and normal physical examination and ECG.
3.3.2 Ethics
All patients provided written informed consent. The study was approved by the regional
ethics committee (REC reference 09/H0504/104).
3.3.3 Titin truncating variant curation for phenotype analysis
Based on the results of Chapter 2, truncating variants in titin were the most enriched variant
in DCM compared to population reference samples and are the focus of the genotype-
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phenotype analysis.
Genetic sequencing and bioinformatics analysis was performed as outlined in Chapter 2.
For the majority of patients, this was with the Illumina NextSeq platform and a customised
panel of 174 genes (TruSight Cardio, Illumina) as previously outlined. A small subset of
patients were sequenced on different platforms/assays. As part of the iterative process of
optimising sequencing metrics, sequencing was initially done on the SOLiD 5500 platform
(Life Technologies), and subsequently the MiSeq and HiSeq platforms (Illumina), with a
customised 201 gene panel assay, named Inherited Cardiac Conditions versions 4-6 (ICC
Sure Select, Agilent) from which the TruSight Cardio was developed. Gene panels are
summarised in Appendix Tables 1 and 2.
TTNtv were curated as outlined below. Truncating variants are defined as frameshift,
nonsense, and variants affecting the splice donor and acceptor regions (first and last two
bases of each intron).
3.3.3.1 Outline of variant filtering metrics
1. Using annotated VCF (variant call format) files, scripts were written to filter protein
altering variants in core DCM genes (Table 3-1) based on frequency and sequencing
metrics:
a. Minor allele frequency of variant in ExAC <0.0001* (threshold explained in
text)
b. Sequencing metrics:
i. Minimum coverage ≥ 10
1. Meaning at the variant position, each base has been sequenced
at least 10 times.
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ii. QD score > 4
1. GATK specific filter. QualbyDepth (QD) refers to the variant
call confidence normalized by depth of sample reads supporting
a variant.
iii. Allelic balance ≥ 20
1. Allelic balance in heterozygous genotypes refers to the
proportion of reads arising from the reference and alternate
alleles and is a useful indicator of quality.
2. For TTNtv only:
a. Variants in constitutively expressed exons (percentage spliced in, PSI > 90%.
PSI explained in text).
b. Confirmation of variants through Sanger sequencing or review mapped
sequencing reads on Integrative Genomics Viewer(320,321).
Table 3-1: Genes and variant classes selected for variant curation. Only truncating variants (frameshift,
nonsense, essential splice site) in TTN, DSP, and VCL were evaluated. Only non-truncating variants (non-
synonymous SNP) were evaluated in MYH7 and TNNT2. For the remaining genes, both variant classes
were evaluated. This gene list was based on existing literature reports and the results from Chapter 2.
Gene Truncating Non-truncating Titin (TTN) + - Lamin (LMNA) + + Myosin heavy chain (MYH7) - + Troponin T2 (TNNT2) - + Desmoplakin (DSP) + - BAG3 + + RBM20 + + Tropomysin (TPM1) + + Vinculin (VCL) + -
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3.3.3.2 DCM specific rare variant frequency threshold
Rare variants are typically defined as having a minor allele frequency (MAF) <1%, though
the frequency cut offs in the literature vary(23). However, for evaluation of potentially
pathogenic variants, a disease-specific frequency threshold for rare variants of <0.0001 was
used, informed by disease prevalence, penetrance, and allelic contribution to disease as
outlined below(24,25).
Calculating the maximum credible population allele frequency for any rare DCM
variant
!"#$%&% !"#$%&'# !"!#$%&'"( !""#"# !"#$%#&'( (!"#$%) =
!"#$%#$ !"#$%'(# × !"#$%&% !"#"$%& !" !""#"$% !"#$%&'($&"#
!"#$%#$ !"#"$%'"
Which can be rewritten as:
!"#$% = !"#$%'(# !"# !"#!$!#%&' × 0.5
!"v!"!#$ !"#$%'(# !"# !"#!$!#%&' !" !ℎ! !"#$%& !" !ℎ!"#"$"#%$ !"# !"#!$!#%&' ×
!"#$%#&'( !" !"##"$%&' !"#$"%& × ! !"#"$%'"
The threshold calculation as applied to DCM:
• Estimated disease prevalence 1/250(5)
• Commonest variant in a DCM cohort previously evaluated by our group:
TNNT2:c.629_631delAGA, 1.4%(14)
• Penetrance estimated to be 50%
=1/250 * 0.5 *0.014*(1/0.5) = 0.000056
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This would therefore be the estimated maximum credible population allele frequency for any
rare variant causative of DCM. I have rounded up this frequency, therefore any variants
found at a population frequency of >0.0001 are unlikely to be pathogenic disease causing
variants in DCM.
3.3.3.3 Constitutively expressed exons in the titin gene
The TTN gene undergoes extensive alternative splicing resulting in the production of different
protein isoforms, meaning that not all exons are included in the final processed mRNA
transcripts. Allowing for this process, which is quantified by assessing the percentage spliced
in (PSI), that is the percentage of final cardiac transcripts that include a particular exon,
appears to be important for distinguishing variants that are important for disease. The PSI
data were derived during previous work from our group using tissue from patients with
DCM. This work demonstrated that variants in exons that are included in the final cardiac
transcript more than 90% of the time are most significant for human cardiomyopathy and are
associated with DCM with an odds ratio over 50 (84,322). These are referred to as
constitutively expressed exons. While overall TTNtv are not uncommon in the population, a
much smaller proportion of variants meet these criteria, with a prevalence of 0.004 (0.001 in
the A band)(322).
All TTNtv variants were annotated to the titin meta-transcript, manually curated by the
Havana Group (Table 3-2).
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Table 3-2: Titin meta-transcript details
Meta transcript
Description Inferred complete meta-transcript
Translation length (amino acids) 35991
Number of exons 363
Ensembl transcript ENST00000589042
Ensembl protein ENSP00000467141
Havana transcript: OTTHUMT00000450680
3.3.4 DCM phenotyping
3.3.4.1 Confirmation of DCM diagnosis in the Cardiovascular Biobank
Upon recruitment to the Biobank, participants were assigned a preliminary clinical diagnosis
by the recruiting research nurse. This was then reviewed by myself (from 2014 onwards) or
the Senior Investigators (Prof Cook or Dr Prasad, pre 2014) and participants were assigned
the diagnosis of DCM if an imaging diagnosis of dilated cardiomyopathy was confirmed,
either by echocardiography or cardiovascular magnetic resonance (CMR).
For patients with CMR, DCM was diagnosed based on evidence of left ventricular dilation
and systolic impairment with reference to age, gender, and body surface area adjusted
nomograms(116). For patients with echocardiography, DCM was diagnosed in the presence
of left ventricular end-diastolic diameter (LVEDd) > 117% of that predicted for age and body
surface and left ventricular ejection fraction (LVEF) < 45% and/or fractional shortening (FS)
< 25%(323).
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Analysis was restricted to patients with MRI data and this cohort was curated further to
ensure criteria for both an imaging and clinical diagnosis of DCM were met. This involved
review of MRI images, clinical records, patient questionnaire, family pedigree data and
ECGs. The exclusion criteria were a history of uncontrolled systemic hypertension, coronary
artery disease (>50% stenosis in one or more major epicardial arteries or previous
percutaneous coronary intervention or coronary artery bypass grafting), chronic excess
alcohol consumption meeting criteria for alcoholic cardiomyopathy (>80g/day for more than
5 years(97)), systemic disease known to cause DCM, pericardial disease, congenital heart
disease, infiltrative disorders (e.g. sarcoidosis), or significant primary valvular
disease(8,306,307).
3.3.4.2 Cardiovascular magnetic resonance
Patients in the CMR cohort underwent CMR at 1.5T (Siemens Sonata or Avanto scanners).
Breath hold steady state free precession cine images were acquired in 3 long axis planes and
successive 8mm short axis slices (2mm gap) from the atrioventricular ring to the apex (13).
Late gadolinium enhanced (LGE) images were acquired using a breath hold inversion
recovery sequence following administration of 0.1mmol/kg of gadolinium contrast agent
(Magnevist or Gadovist, Bayer), with inversion times optimised to null normal myocardium.
Mid-wall myocardial fibrosis was recorded as present if detected in the primary and phase
swapped image and in 2 orthogonal views, with cross cuts taken as appropriate.
Left ventricular (LV) volumes, function and mass were measured using a semiautomated
threshold-based technique (CMRtools, Cardiovascular Imaging Solutions, London, UK).
Maximum left atrial (LA) volumes were assessed by a single experienced operator from the
2-chamber (2ch) and 4-chamber (4ch) views at end-systole. This operator was blinded to all
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other clinical and genetic data. The atrial endocardial border was traced to determine LA area
with exclusion of the pulmonary veins and LA appendage. In addition, the LA length was
measured from the midpoint of the mitral annulus plane to the superior aspect of the left
atrium on both views (4ch and 2ch). CMR features of left-ventricular non-compaction
(LVNC) were noted if criteria for LVNC were met (ratio of trabeculated to non trabeculated
myocardium 2.3:1 in diastole(324)).
Septal and lateral left ventricular wall thickness were measured at the level of the papillary
muscles, mid cavity in end-diastole. An average of at least 2 measurements was taken.
Maximum left ventricular wall thickness was measured between basal to apical short axis
images in end-diastole, with the exclusion of papillary muscles. All volume and mass
measurements were indexed to body surface area and referenced to age and gender based
tables(116).
3.3.5 Clinical Data
Detailed phenotyping was completed on all patients, with data entered into a customized
MySQL database via a ‘DCM Events’ form. The data collected is summarized in Table 3-3.
The baseline date for each patient was the date of the first diagnostic cardiac MRI study in
our institution. All participants were interviewed by a research nurse on recruitment whereby
NYHA class was determined, resting heart rate and blood pressure recorded, and self
reported alcohol consumption, ethnicity and medication history were documented.
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Table 3-3: Data collected on DCM Events form as baseline phenotyping of cohort
Data collected Notes Cardiac MRI LV =left ventricle, RV= right ventricle LV end diastolic volume Data imported from cardiac MRI report; missing data manually entered LV end systolic volume Data imported from cardiac MRI report; missing data manually entered LV stroke volume Data imported from cardiac MRI report; missing data manually entered LV ejection fraction Data imported from cardiac MRI report; missing data manually entered LV mass Data imported from cardiac MRI report; missing data manually entered RV end diastolic volume Data imported from cardiac MRI report; missing data manually entered RV end systolic volume Data imported from cardiac MRI report; missing data manually entered RV stroke volume Data imported from cardiac MRI report; missing data manually entered RV ejection fraction Data imported from cardiac MRI report; missing data manually entered Maximum LV wall Measured from DICOM files; methods outlined in text thickness Mean septal wall thickness Measured from DICOM files; methods outlined in text Mean lateral wall Measured from DICOM files; methods outlined in text thickness Mitral regurgitation Presence and categorical extent (none, mild, moderate, severe) based on MRI report and direct review of images if mild- moderate or moderate-severe. Mid wall gadolinium Presence (and most affected segment) or absence based on MRI report and direct review of images where necessary (e.g. to determine most affected area). Methods outlined in text. Left atrial volume Measured by operator blinded to all other data; methods outlined in text. Coincident myocardial Presence of subendocardial myocardial infarction. infarction (NB: Established coronary artery disease was an exclusion
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Data collected Notes criteria, these myocardial infarctions were detected in patients with ‘normal’ coronary arteries or in whom the degree of coronary artery disease was insufficient to account for the degree of LV dysfunction). Pattern of gadolinium Presence or absence of subepicardial LGE, particularly in the enhancement consistent inferolateral segments(325), based on MRI report and direct with chronic myocarditis review of images with senior imaging investigator in borderline cases. Features consistent with Using Petersen criteria(324) of non-compacted to compacted LV non compaction myocardium ratio >2.3 in diastole from long axis SSFP cines. Clinical factors History of myocarditis Review of clinical records prior to baseline date for Cardiologist documented history of myocarditis. History of alcohol excess Review of clinical records prior to baseline date and self reported patient questionnaire; criteria outlined in text History of chemotherapy Review of clinical records prior to baseline date and patient reported questionnaire for any history of chemotherapy use, irrespective of time interval. History of peri-partum Review of clinical records prior to baseline date for the cardiomyopathy development of heart failure with reduced ejection fraction in the last month of pregnancy or first 5 months post partum(326) History of iron overload Review of clinical records prior to baseline date for any Physician documented history of iron overload or clinical records alluding to iron overload (e.g. history of repeated blood transfusions). Inherited muscle disease Review of clinical records prior to baseline date for any Physician documented medical history of inherited muscle disease including review of family pedigree. Pedigree data Family history of DCM Review of family pedigree recorded by research nurse at recruitment as well as clinical records. Definition outlined in text. Family history of sudden Review of family pedigree recorded by research nurse at cardiac death recruitment as well as clinical records. Definition outlined in text. Family history of Review of family pedigree recorded by research nurse at hypertrophic recruitment as well as clinical records
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Data collected Notes cardiomyopathy Family history of Review of family pedigree recorded by research nurse at arrhythmogenic right recruitment as well as clinical records ventricular cardiomyopathy ECG/Arrhythmia data Conduction disease Presence and type of any conduction disease including left bundle branch block, 1st, 2nd, and 3rd degree AV block. Baseline arrhythmia Review of clinical records prior to baseline date for history of atrial fibrillation, sustained and non-sustained ventricular tachycardia. Coronary status Presence of normal Review of clinical records prior to baseline date; criteria outlined coronary arteries in DCM diagnosis text. Diagnosis Date of diagnosis Standard Operating Procedure (SOP) developed to determine this information (Appendix). This information was recorded by a team of research nurses.
Expanded definitions of the clinical data collected are provided below.
3.3.5.1 Family history
A family history of dilated cardiomyopathy (DCM in the proband plus at least one other
family member)(327) or sudden cardiac death (in at least 1 family member up to third degree
relative)(49) was recorded.
3.3.5.2 Quantification of alcohol consumption
Weekly alcohol consumption was recorded in units of alcohol. In the UK, 1 unit of alcohol
equals 10mL or 8g of pure alcohol, which is the amount of alcohol an average adult can
process within one hour(328). Consensus medical advice from 1987 to 2016 in the UK
recommended weekly ‘sensible limits’ for alcohol consumption to be no more than 21 units
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(168g) per week for men and 14 units (112g) per week for women(329).
All participants were interviewed by a research nurse and self-reported current weekly
alcohol consumption was recorded. In addition, hospital and primary care medical records
were reviewed for a history of alcohol excess prior to study recruitment. The binary outcome
of ‘alcohol excess’ was defined as any history of the presence or absence of alcohol
consumption greater than 21units (168g)/week for men and 14units (112g)/week for women.
3.3.5.3 Baseline Arrhythmia Phenotyping
The presence of confirmed sustained ventricular tachycardia (VT), non sustained ventricular
tachycardia (NSVT), or atrial fibrillation (AF) at baseline (prior to the date of the first
diagnostic CMR scan at our institution) was collated on all patients using data from Holter
recordings, electrophysiology studies, or clinical records as available from the referring
hospital or primary care provider. NSVT was defined as 3 or more consecutive ventricular
beats lasting up to 30 seconds in duration at a rate greater than 100 beats per minute(330).
Sustained VT was defined as ventricular tachycardia lasting over 30 seconds in duration at a
rate greater than 100 beats per minute. Any AF was included irrespective of duration.
3.3.6 Statistical Analysis
Continuous data are expressed as mean ± standard deviation or median ± interquartile range
and compared using t-tests or Mann-Whitney tests. Categorical data are expressed as number
and percentages, and compared using Fisher’s exact test. Binary baseline data was coded as
confirmed present or absent/missing.
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For the evaluation of predictors of left ventricular mass, univariable linear regression was
performed to identify variables that were predictive of indexed left ventricular mass (LVMi)
at study recruitment. Any variables with p<0.1 from the univariable analysis were considered
for inclusion in an optimised multivariable model, which was created by backward stepwise
selection until only significant variables were included the model. The pre-specified main
analysis was to assess the significance of the addition of TTNtv to this multivariable model
For the evaluation of alcohol as a phenotypic modifier, univariable linear regression was
performed to identify variables that were predictive of left ventricular ejection fraction
(LVEF) at study recruitment. Any variables with p<0.1 from the univariable analysis were
considered for inclusion in an optimised multivariable model, which was created by
backward stepwise selection until only significant variables were included the model. The
pre-specified main analysis for the evaluation of alcohol as a phenotypic modifier was to
assess the significance of the addition of an interaction term between TTNtv and ‘alcohol
excess’ to the optimised multivariable model predicting LVEF. This would test whether
having TTNtv and alcohol excess together has any additional effect other than the effects of
TTNtv and alcohol separately.
For all analyses, a p value ≤ 0.05 was considered statistically significant. All statistical
analysis was conducted in the R environment (version 3.3.1).
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3.4 Results
3.4.1 Cohort Recruitment
The recruitment of patients to the Biobank and curation of the cohort is outlined in Figure
3-1.
Figure 3-1: Outline of DCM cohort recruitment for phenotype study. CMR=cardiovascular magnetic
resonance, HCM=hypertrophic cardiomyopathy, IHD=ischaemic heart disease, LVH=left ventricular
hypertrophy, NAD=nothing abnormal detected, FS_NAD=normal family screen, ARVC=arrhythmogenic
right ventricular cardiomyopathy, Aorta=primary aortic pathology, LVNC=left ventricular non-
compaction.
As outlined, in the first step, 928 patients had an imaging (echo or CMR) diagnosis of DCM.
The phenotype analysis was restricted to patients with CMR only, to enable the quantitative
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in depth phenotyping that CMR affords. The final cohort for phenotype analysis comprised
732 patients with DCM confirmed by CMR, after exclusion of 39 patients with coronary
artery disease meeting exclusion criteria, 19 patients who had not had coronary artery
assessment but who were found to have myocardial infarction on CMR, and 8 patients in
whom bystander myocardial infarction status was unknown (for example, if gadolinium
contrast could not be administered).
3.4.2 Patient demographics
The final phenotype study population consisted of 732 patients with a clinical and imaging
diagnosis of DCM confirmed on CMR. There were 475 men (64.9%) and the majority of the
cohort was Caucasian (n=618, 84.4 %). The mean age of the cohort at recruitment was 53.4
years (±14.4; range 11.5 to 88.4 years). The majority of patients were in NYHA class I
(n=306, 41.8 %) or II (n=285, 38.9 %) at recruitment. A family history of DCM was found in
117 patients (16.0 %) and a family history of sudden cardiac death was found in 109 patients
(14.9 %). Of these, 41 patients (5.6 %) had both a family history of DCM and sudden cardiac
death. A summary of demographics, clinical factors and baseline medication are shown in
Table 3-4.
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Table 3-4: Baseline demographics in DCM cohort. Age is summarized as mean ± standard deviation. The
remaining data are categorical and summarised as count (percentages).
Variable Total cohort, n=732 Age at baseline scan (years) (mean (sd)) 53.4 (14.40) Race (self-reported) Afro-Caribbean 27 (3.7) African 20 (2.7) Asian 39 (5.3) Caucasian 618 (84.4) Chinese 3 (0.4) Mixed 4 (0.5) Other 21 (2.9) Male gender 475 (64.9) NYHA class 1 306 (43.7) 2 285 (40.7) 3 101 (14.4) 4 8 (1.1) Family history of DCM 117 (16.0) Family history of sudden cardiac death 109 (14.9) Clinical history of myocarditis 29 (4.0) Alcohol excess 114 (15.6) Chemotherapy 35 (4.8) Inherited muscle disease 4 (0.5) Peripartum cardiomyopathy 9 (1.2) Beta blocker use 516 (70.5) ACE inhibitor use 582 (79.5) Aldosterone Antagonist use 264 (36.1) Diuretic use 333 (45.5)
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3.4.2.1 Time interval since DCM diagnosis
For the majority of patients (n=381, 52.0%), the CMR scan at recruitment was also the first
confirmation of diagnosis. For the remaining patients, the interval between date of diagnosis
and the baseline study date is shown in Figure 3-2. Of these patients, the majority
(n=270/351, 76.9%) were diagnosed with DCM within the 2 years preceding their study
baseline CMR date.
Figure 3-2: Interval between date of diagnosis and baseline date for patients for whom the CMR study at
recruitment was not the diagnostic study. Right hand panel shows the interval limited to 2 years.
3.4.3 Overall DCM phenotype
Ventricular volumes and function for the cohort are illustrated in Figure 3-3 and the plots for
left ventricular ejection fraction and indexed volumes for patients with DCM compared to
healthy controls are shown in Figure 3-4. The baseline cardiac MRI phenotype is summarised
in Table 3-5.
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Overall, this was a cohort with moderately impaired left ventricular function, consistent with
the entry criteria of the diagnostic CMR scan. Mean left ventricular ejection fraction
(LVEF,%) was 39.0 ± 12.45 (median=40%, interquartile range 30 to 49%). There is an
approximately even distribution of patients with LVEF above and below 40%. One third of
the cohort had severely impaired left ventricular function with LVEF <35% (n=267, 36%).
Mean left ventricular end diastolic volume indexed to body surface area (LVEDVi) was
127.3± 35.9 mL/m2 (interquartile range 102.9 to 143.7mL/m2) and mean left ventricular end
systolic volume indexed to body surface area (LVESVi) was 80.2 ± 36.1 mL/m2 (interquartile
range 53.7 to 97.8 mL/m2). Left and right ventricular end diastolic and end systolic volumes
are positively skewed, reflecting a small proportion of individuals with very dilated
chambers. This is also reflected in the distribution of left ventricular mass, as elevated left
ventricular mass reflects increased ventricular dilation associated with eccentric hypertrophy.
Stroke volume is calculated as the difference between end diastolic and end systolic volume,
therefore this has a normal distribution in our cohort as both the end diastolic and end systolic
volumes are skewed in the same direction and are correlated within each patient.
Mean right ventricular ejection fraction (RVEF, %) was 51.2 ± 13.9 % (median= 53.0 %,
interquartile range 43.0 to 61.0%). Approximately one quarter of the cohort had evidence of
right ventricular systolic dysfunction, with RVEF <45% (n=194, 27%).
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Figure 3-3: Histograms showing the distribution of left and right ventricular function and volumes,
indexed to body surface area. L/RVEF=left/right ventricular ejection fraction, L/RVEDVi=indexed
left/right ventricular end diastolic volume, L/RVESVi= indexed left/right ventricular end systolic volume,
L/RVSVi=indexed left/right ventricular stroke volume, LVMi=indexed left ventricular mass.
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Figure 3-4: CMR phenotype of DCM cohort compared to controls. Three plots show values for LVEF
(left ventricular ejection fraction), LVEDVi (indexed left ventricular end diastolic volume) and LVESVi
(indexed end systolic volume) for patients with DCM (red dots) compared to controls (blue dots). The
control participants, described in the methods, are included for illustrative purposes only and are taken
from a previously published cohort from our institution(116).
Mid-wall myocardial fibrosis, detected through LGE-CMR, was found in 256 patients (35%)
and the vast majority was located in the septum (n=188, 73%) (Table 3-5). This was a cohort
of patients with no known coronary artery disease yet a small proportion of patients had
bystander myocardial infarction (n=17, 2.3%). Overall, of the 336 patients with mitral
regurgitation, the majority of patients had mild regurgitation only (n=239, 33% of overall
cohort) (Table 3-5).
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Table 3-5: Baseline CMR phenotype in DCM cohort. Continuous data are summarized as mean ±
standard deviation. Categorical data are summarized as count and percentages.
CMR Phenotype DCM, n=732 Left ventricular ejection fraction (%) 39.0 (12.5) Indexed LV end diastolic volume (mL/m2) 127.3 (35.9) Indexed LV end systolic volume (mL/m2) 80.2 (36.1) Indexed LV stroke volume (mL/m2) 47.1 (13.3) Indexed LV mass (g/m2) 90.5 (26.0) Right ventricular ejection fraction (%) 51.7 (13.9) Indexed RV end diastolic volume (mL/m2) 87.9 (24.3) Indexed RV end systolic volume (mL/m2) 44.1 (21.9) Indexed RV stroke volume (mL/m2) 43.9 (12.7) CMR features of LV non-compaction 36 (4.9) Mid wall late gadolinium enhancement 256 (35.0) Mid wall late gadolinium enhancement most affected segment Anterior 8 (3.1) Inferior 18 (7.0) Lateral 44 (17.1) Septal 188 (72.9) Late gadolinium enhancement consistent with myocarditis No 671 (91.7) Unknown 12 (1.6) Yes 49 (6.7) Mitral regurgitation Mild 239 (32.7) Moderate 81 (11.1) None 396 (54.1) Severe 16 (2.2) Indexed left atrial volume (mL/m2) 60.3 (25.4) Maximum LV wall thickness (mm) 9.8 (2.2) Mean septal wall thickness (mm) 7.8 (1.9) Mean lateral wall thickness (mm) 5.5 (1.6)
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Mean indexed left atrial volume (LAVi) was 60.2 ± 25.4 mL/m2. The mean maximum left
ventricular wall thickness was 9.8mm and mean septal and lateral wall thickness at mid
cavity level were 7.8mm and 5.5mm respectively. The correlation between these markers and
other indices of cardiac structure and function are shown in Figure 3-5.
Figure 3-5: CMR phenotype correlation plot. Figure shows scatter plots, Pearson’s correlation
coefficients and significance thresholds for correlation between major indices of cardiac structure and
function. P-values: ***<0.001, **<0.01, *<0.05, <0.1, blank for p≥0.1.
This shows that LVEF is highly negatively correlated with LVESVi and LVEDVi, and
positively correlated with LVSVi, which is expected and demonstrates that left ventricular
dilation is associated with impaired left ventricular function in this cohort. As also expected,
LVEF is also highly positively correlated with RVEF and RVSVi, but not RVEDVi. It is
however interesting to note that the correlation between LVEF and RVEF is not perfectly
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linear (r=0.59) and that patients with impaired left ventricular function may still have
relatively preserved right ventricular function.
LAVi is weakly correlated to LVEF, RVEF, LVEDVi, LVESVi, RVEDVi, RVESVi, and
LVMi, suggesting that the biological basis for left atrial dilation may be distinct from
ventricular pathology, and highlights the utility of LAVi as an independent marker of cardiac
structure and function.
Finally, maximal left ventricular wall thickness is as expected positively correlated with left
ventricular mass. However, it is not correlated with LVEF or LVEDVi, suggesting that wall
thickness and the eccentric cardiac hypertrophy of DCM in this cohort (an increase in left
ventricular mass not accompanied by an increase in wall thickness) are not governed purely
by the extent of cardiac dilatation. I explore this further in the analysis of titin
cardiomyopathy.
3.4.4 Curation of titin truncating variants
Building upon the results from Chapter 2, I curated the truncating variants in titin (TTNtv) in
this cohort. The process is outlined in Figure 3-6.
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Figure 3-6: Flowchart outlining filtering steps to curate TTNtv for phenotype analysis. Details outlined in
text. TTNtv= truncating variants in the titin gene, ExAC=Exome Aggregation Consortium dataset,
QD=QualbyDepth score, PAV=Protein altering variants, tv=truncating variants, ms=missense variants,
PSI=percentage spliced in, IGV=Integrative Genomics Viewer, CMR=cardiovascular magnetic
resonance.
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Initially, all patients with an imaging diagnosis of DCM (n=928) underwent targeted
sequencing on a customised cardiac gene panel as outlined in Chapter 2. This identified
65469 protein altering variants in 888 patients. The DNA from 28 patients could not be
sequenced due to insufficient sample or poor sample quality. In these patients, we did not
have blood, but only saliva samples.
The majority of the remaining samples were sequenced on the TruSight Cardio Assay and
NextSeq platform (n=826, 89%) (Table 3-6).
Table 3-6: Sequencing platform and assay used in DCM cohort. ICC_169 was the precursor assay to
TruSight Cardio.
Assay and Platform Number of patients Number of patients Confirmed sequenced in overall sequenced in CMR TTNtv DCM cohort cohort N=928 N=732 TruSightCardio and NextSeq 826 660 102 TruSightCardio and MiSeq 47 34 6 ICCv6 and HiSeq 5 5 0 ICCv5 and 5500xl 4 3 0 ICCv4_TopUp and 5500xl 13 12 0 ICC_169 and MiSeq 5 2 0 Not sequenced 28 16 -
For patients with TTNtv, the percentage coverage at 30x read depth per case is shown in
Figure 3-7. All samples had >95% of reads with at least 30x read depth.
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Figure 3-7: Percentage coverage of TTN at 30x coloured by assay for patients with TTNtv. ICC_169 was
the precursor assay to TruSight Cardio (TS Cardio). All samples were sequenced on Illumina platforms.
From the 65,469 protein altering variants in 888 patients, filtering based on the target gene
list outlined in Table 3-1 resulted in 25,468 protein altering variants in 888 people. Filtering
for rare variants only, defined as a minor allele frequency <0.0001 in the ExAC dataset,
resulted in 787 protein altering variants in 503 people. Filtering on sequencing metrics
(minimum coverage of 10x, QD score over 4, and allelic balance over 20%), left 738 protein
altering rare variants in 491 people.
Then filtering by variant class (Table 3-1), left 265 disease specific variants in 240 people.
This included 117 TTNtv which were curated further, retaining only variants in constitutively
expressed exons (PSI >90%, n=112 TTNtv in 109 patients).
The TTNtv were then confirmed with Sanger sequencing or interactive review of mapped
reads using Integrative Genomics Viewer (IGV). During this process, 2 TTNtv variants were
excluded in 1 patient. These variants had each been annotated by both Unified Genotyper and
Haplotype Caller as frameshift truncating variants.
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c.74761_74767delTCCAGGG
c.74759_74760insA
However, these variants are a 7bp deletion and a 1 base pair insertion in an adjacent region
resulting in an inframe deletion and an amino acid substitution. The IGV output is shown in
Figure 3-8. Whilst these are clearly protein altering variants, they do not meet the criteria for
truncating variants and therefore they were excluded from the primary titin phenotype cohort.
Figure 3-8: IGV output of individual with 2 protein altering variants in TTN, one 7 base pair deletion and
1 base pair insertion.
There were 2 further patients (related) who carried only one TTNtv which had been
incorrectly called as two variants by the variant callers. These individuals were reclassified as
having one TTNtv each:
(1) c.100943_100944delGA Chr. 2: 179400398-179400399 (reverse strand). Called as
frameshift mutation.
(2) c.100942A>T Chr. 2: 179400400 (reverse strand). Called as a nonsense mutation.
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At this position (chr2: 179400398-179400400), the reference genome is TCT. The effect of
the 2 variants shown is TC-> and T>A. Therefore at this 3 base pair position, the overall
change is TCT->A, which is a 2bp deletion and a frameshift.
This left 108 confirmed TTNtv in 108 patients across the entire DCM cohort (n=928 with an
imaging diagnosis of DCM). Of these, 57 were confirmed through review on IGV and 51
were confirmed by Sanger sequencing.
In the subset of 716 patients with DCM confirmed by CMR who had undergone sequencing,
there were 83 confirmed rare TTNtv in constitutively expressed exons in 83 patients. The
variants are listed in Table 3-7. They are plotted according to protein domain in Figure 3-9.
Genotype-phenotype analysis was then performed in this cohort.
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Table 3-7: List of truncating variants in TTN gene (n=83). All variants annotated to titin meta-transcript ENST00000589042. Callers= U, Unified genotyper or
H=Haplotype caller. PSI= percentage spliced in, a measure of splicing derived from RNAseq, is an estimate of the percentage of TTN transcripts that incorporate a
given exon.
cDNA Variant Variant type Callers Coverage Allelic Confirmed ExAC ExAC TTN TTN band TTN PSI
Balance frequency count exon
c.45852_45853delCA frameshift U,H 984 0.5 IGV 0 0 248 I-band 100
c.79539T>A nonsense U,H 999 0.53 IGV 0 0 327 A-band 100
c.55037delG frameshift U,H 819 0.51 IGV 0 0 284 A-band 100
c.83635delA frameshift U,H 998 0.49 IGV 0 0 327 A-band 100
c.45812T>G nonsense U,H 999 0.43 Sanger 0 0 248 I-band 100
c.82513delA frameshift U,H 999 0.51 Sanger 0 0 327 A-band 100
c.78507delT frameshift U,H 1000 0.46 Sanger 0 0 327 A-band 100
c.6790+1G>T essential U,H 1000 0.55 Sanger 0 0 29 I-band 100
splice site
c.81262_81269delCAGATGCT frameshift U,H 927 0.46 Sanger 0 0 327 A-band 100
c.97062delA frameshift U,H 604 0.43 IGV 0 0 349 A-band 99
c.12757C>T nonsense U,H 1000 0.51 Sanger 0 0 49 I-band 99
c.55525_55531delGACAGGA frameshift U,H 999 0.46 Sanger 0 0 288 A-band 100
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cDNA Variant Variant type Callers Coverage Allelic Confirmed ExAC ExAC TTN TTN band TTN PSI
Balance frequency count exon
c.43792delG frameshift U,H 1000 0.5 Sanger 0 0 238 I-band 100
c.78184G>T nonsense U,H 1000 0.48 Sanger 0 0 327 A-band 100
c.10303+2T>C essential U,H 618 0.52 IGV 8.25E-06 1 44 I-band 100
splice site
c.59226T>G nonsense U,H 967 0.53 IGV 0 0 301 A-band 100
c.101996G>A nonsense U,H 644 0.52 Sanger 0 0 359 M-band 100
c.60931C>T nonsense U,H 999 0.52 Sanger 0 0 305 A-band 100
c.98506C>T nonsense U,H 1000 0.47 Sanger 0 0 353 A-band 100
c.48527G>A nonsense U,H 999 0.46 Sanger 0 0 260 A-band 100
c.45307C>T nonsense U,H 1000 0.47 IGV 0 0 246 I-band 100
c.100644dupT frameshift U,H 988 0.52 IGV 0 0 358 A-band 99
c.50170C>T nonsense U,H 502 0.54 Sanger 0 0 267 A-band 100
c.69630C>A nonsense U,H 999 0.51 Sanger 0 0 326 A-band 100
c.52035_52036insTT frameshift U,H 1000 0.57 Sanger 0 0 274 A-band 100
c.84120delT frameshift U,H 999 0.5 IGV 0 0 327 A-band 100
c.64449dupA frameshift U,H 1000 0.52 IGV 0 0 310 A-band 100
c.58732+2T>C essential U,H 1000 0.47 Sanger 0 0 299 A-band 100
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cDNA Variant Variant type Callers Coverage Allelic Confirmed ExAC ExAC TTN TTN band TTN PSI
Balance frequency count exon
splice site
c.55525_55531delGACAGGA frameshift U,H 999 0.48 Sanger 0 0 288 A-band 100
c.8307_8308delTG frameshift U,H 998 0.45 Sanger 0 0 35 I-band 100
c.63025C>T nonsense U,H 999 0.51 Sanger 0 0 305 A-band 100
c.81321C>G nonsense U,H 999 0.47 Sanger 0 0 327 A-band 100
c.50170C>T nonsense U,H 636 0.46 Sanger 0 0 267 A-band 100
c.47875+1G>A essential U,H 999 0.51 Sanger 0 0 256 A-band 100
splice site
c.47697C>A nonsense U,H 1000 0.51 Sanger 0 0 255 A-band 100
c.86967G>A nonsense U,H 999 0.49 Sanger 0 0 328 A-band 100
c.67567delG frameshift U,H 999 0.47 IGV 0 0 320 A-band 100
c.4724_4728delTGAAA frameshift U,H 998 0.48 Sanger 0 0 27 near Z-disk 100
c.12643_12644delCA frameshift U,H 839 0.52 Sanger 0 0 49 I-band 99
c.92683C>T nonsense U,H 528 0.45 Sanger 0 0 340 A-band 100
c.86641delC frameshift U,H 1000 0.46 Sanger 0 0 327 A-band 100
c.93291_93301delTGTTGGTGA frameshift U,H 1000 0.52 Sanger 0 0 340 A-band 100
GC
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cDNA Variant Variant type Callers Coverage Allelic Confirmed ExAC ExAC TTN TTN band TTN PSI
Balance frequency count exon
c.52223_52227dupAGAAA frameshift U,H 1000 0.53 Sanger 0 0 275 A-band 100
c.76666_76684dupATAATTGAT frameshift U,H 999 0.45 Sanger 0 0 327 A-band 100
GTCACTAGCA
c.100943_100944delGA frameshift U,H 996 0.47 IGV 0 0 359 M-band 100
c.51781C>T nonsense U,H 1000 0.52 IGV 0 0 274 A-band 100
c.61855delG frameshift U,H 1000 0.52 Sanger 0 0 305 A-band 100
c.6322G>T nonsense U,H 1000 0.46 IGV 0 0 28 near Z-disk / 100
I-band
c.51965dupT frameshift U,H 828 0.5 IGV 0 0 274 A-band 100
c.100267_100268delAA frameshift U,H 994 0.49 IGV 0 0 358 A-band 99
c.83928dupT frameshift U,H 1000 0.52 Sanger 0 0 327 A-band 100
c.3380+1G>C essential U,H 341 0.41 IGV 0 0 20 near Z-disk 100
splice site
c.47697C>A nonsense U,H 721 0.53 IGV 0 0 255 A-band 100
c.86821+2T>A essential U,H 717 0.49 IGV 8.91E-06 1 327 A-band 100
splice site
c.41473C>T nonsense U,H 955 0.49 Sanger 0 0 227 I-band 100
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cDNA Variant Variant type Callers Coverage Allelic Confirmed ExAC ExAC TTN TTN band TTN PSI
Balance frequency count exon
c.53881+1G>T essential U,H 985 0.47 Sanger 0 0 280 A-band 100
splice site
c.106629delA frameshift U,H 999 0.5 Sanger 0 0 361 M-band 100
c.87716delG frameshift U,H 366 0.56 Sanger 0 0 330 A-band 100
c.82240C>T nonsense U,H 999 0.51 IGV 1.66E-05 2 327 A-band 100
c.53206C>T nonsense U,H 1000 0.45 IGV 0 0 278 A-band 100
c.89216delC frameshift U,H 995 0.54 IGV 0 0 335 A-band 100
c.41447delG frameshift U,H 674 0.5 Sanger 0 0 227 I-band 100
c.89750dupG frameshift U,H 999 0.49 Sanger 0 0 336 A-band 100
c.85090C>T nonsense U,H 999 0.52 Sanger 0 0 327 A-band 100
c.102958delA frameshift U,H 1000 0.54 IGV 0 0 359 M-band 100
c.43602_43615delGCGCCTACA frameshift U,H 803 0.55 Sanger 0 0 237 I-band 100
CACCA
c.100943_100944delGA frameshift U,H 1000 0.41 Sanger 0 0 359 M-band 100
c.75250C>T nonsense U,H 1000 0.47 IGV 0 0 327 A-band 100
c.94721_94722delTC frameshift U,H 997 0.49 IGV 0 0 342 A-band 100
c.63025C>T nonsense U,H 997 0.49 IGV 0 0 305 A-band 100
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cDNA Variant Variant type Callers Coverage Allelic Confirmed ExAC ExAC TTN TTN band TTN PSI
Balance frequency count exon
c.94978dupG frameshift U,H 999 0.47 IGV 0 0 343 A-band 100
c.41473C>T nonsense U,H 473 0.54 IGV 0 0 227 I-band 100
c.40791dupG frameshift U,H 468 0.49 Sanger 0 0 224 I-band 100
c.63025C>T nonsense U,H 998 0.51 IGV 0 0 305 A-band 100
c.12010G>T nonsense U,H 584 0.48 IGV 0 0 49 I-band 99
c.83416C>T nonsense U,H 999 0.5 IGV 0 0 327 A-band 100
c.59201_59202delCT frameshift U,H 379 0.55 IGV 8.28E-06 1 301 A-band 100
c.73846C>T nonsense U,H 680 0.53 IGV 0 0 327 A-band 100
c.83416C>T nonsense U,H 658 0.48 IGV 0 0 327 A-band 100
c.58870C>T nonsense U,H 657 0.48 IGV 0 0 300 A-band 100
c.52254G>A nonsense U,H 377 0.47 IGV 0 0 275 A-band 100
c.44364delC frameshift U,H 250 0.52 IGV 0 0 241 I-band 100
c.55525_55531delGACAGGA frameshift S,U 250 0.23 Sanger 0 0 288 A-band 100
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| | || | | || || ||||| ||| | ||||| || || || | | | | | | | ||| |||||||| |||| || || || | | ||||| | n=83 DCM, domains TTN protein PSI 0
0 35991
Figure 3-9: Position of TTNtv variants according to protein domains and PSI (percentage spliced in). The colours of the protein domain cartoon correspond to: Z
disc =red, I band=blue, A band=green, M-line=purple. Only variants in constitutive exons are included in analysis. In line with previous data, the majority of
variants in DCM patients lie in the distal I band and A band.
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3.4.5 TTNtv cardiomyopathy phenotype
3.4.5.1 Demographics
Across the cohort of patients with DCM confirmed by CMR who underwent DNA
sequencing, 11.6% of patients had TTNtv (n=83/716). Patients with TTNtv were younger at
study recruitment compared to patients without TTNtv (49.0 vs 54.1 years, p =0.002). There
was no significant difference in gender or ethnicity between groups (Table 3-8).
As expected for a genetic condition, patients with TTNtv were more likely to have a family
history of DCM (31% vs 14%, p<0.001; Table 3-8). Amongst the TTNtv patients with a
family history of DCM (n=26), 22 in this cohort were probands and 4 were affected family
members. For the purposes of phenotype analysis, the 4 affected family members were
retained in the cohort. In two of the three families, the affected relatives had a milder
phenotype than the proband, though the sample size prohibited further statistical analysis
(Figure 3-10). The affected family members of the remaining 22 probands with a family
history of DCM had not been recruited to the study.
Figure 3-10: Cardiac phenotype in probands and affected relatives. Cardiac phenotype of proband (x axis
Proband Status =1) and affected relatives (x axis Proband Status =0) in 3 families with DCM. Colour of
points refers to one family. LV/RV=left/right ventricle; EDVi, indexed end-diastolic volume (mL/m2); EF,
ejection fraction (%).
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There was no difference in family history of sudden cardiac death, suggesting that TTNtv
may not be associated with adverse arrhythmic outcomes (Table 3-8). There was significantly
less left bundle branch block (LBBB) and less evidence of conduction disease in patients with
TTNtv compared to patients without TTNtv (Table 3-8).
Table 3-8: Baseline demographics in DCM patients stratified by TTNtv status. Age is summarised as
mean (standard deviation) and compared with t-test. The remaining categorical variables are
summarised as count and percentages and compared using Fisher’s exact test. * indicates significance
after correction for multiple testing (p<0.004).
TTNtv Absent TTNtv Present P value N=633 N=83 Age at baseline date (years) 54.1 (14.33) 49.0 (13.45) 0.002 Male gender 410 (64.8) 59 (71.1) 0.27 Race Caucasian 533 (84.2) 75 (90.4) 0.39 Family history of DCM 87 (13.7) 26 (31.3) <0.001* Family history of sudden cardiac death 95 (15.0) 12 (14.5) 1.0 Conduction disease 217 (34.3) 18 (21.7) 0.03 Left bundle branch block 179 (28.5) 8 (9.8) <0.001* History of myocarditis 27 (4.3) 1 (1.2) 0.24 History of peripartum cardiomyopathy 6 (0.9) 1 (1.2) 0.58 History of chemotherapy 32 (5.1) 2 (2.4) 0.41 History of alcohol excess 98 (15.5) 13 (15.7) 1.0 NYHA class 0.17 1 261 (43.1) 39 (49.4) 2 245 (40.4) 32 (40.5) 3 94 (15.5) 6 (7.6) 4 6 (1.0) 2 (2.5) Unknown 27 (4.3) 4 (4.8)
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3.4.5.2 CMR phenotype
Upon evaluation of cardiac structure and function, there was no significant difference in
biventricular ejection fraction in patients with and without TTNtv (LVEF (%; median [IQR])
TTN+/-; 39.0 [27.0, 49.0] vs 40.0 [30.0, 49.0], p=0.48, RVEF (%; median [IQR]) TTN+/-;
53.0 [44.0, 62.0] vs 54.0 [44.0, 61.0], p=0.65) (Table 3-9). After correction for multiple
testing, there were no significant differences in biventricular stroke volume or cardiac
chamber dimensions (left and right ventricular volumes or left atrial volume) between
patients with TTNtv and patients without TTNtv (Table 3-9).
There was no detectable difference in the frequency of CMR features of left ventricular non-
compaction between patients with TTNtv. Whilst the numbers of affected cases are too small
for definitive analysis, in this large cohort of patients with TTNtv, this suggests that the
LVNC phenotype in DCM is not driven by TTNtv.
There was no significant difference in the pattern of gadolinium enhancement seen in patients
with TTNtv compared to DCM patients without TTNtv (Table 3-9). Specifically, there was
no difference in the presence or location of left ventricular mid wall fibrosis, an important
prognostic marker detected in one third of cases with DCM. There was no difference in the
presence of bystander myocardial infarction or gadolinium enhancement consistent with
chronic myocarditis between TTNtv positive and negative groups, though there were limited
cases with these features limiting the scope of this analysis.
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Table 3-9: Baseline CMR variables in DCM cohort stratified by TTNtv status. Continuous data are summarised as mean (standard deviation) or median [interquartile range] and compared using Mann- Whitney tests. Categorical variables are summarised as count and percentages and between group differences tested with Fisher’s exact test. *significant after adjusting for multiple testing (p≤0.003)
TTNtv Absent TTNtv Present P value N=633 N=83 Left ventricular ejection fraction (%) 40.0 [30.0, 49.0] 39.0 [27.0, 49.0] 0.48
Indexed LV end diastolic volume (mL/m2) 118.5 [103.5, 143.8] 111.1 [100.6, 136.4] 0.14
Indexed LV end systolic volume (mL/m2) 68.9 [53.8, 96.7] 67.37 [51.0, 100.1] 0.52
Indexed LV stroke volume (mL/m2) 47.8 (13.3) 43.6 (12.8) 0.007 Indexed LV mass (g/m2) 87.4 [73.6, 106.5] 81.3 [69.4, 96.8] 0.02 Right ventricular ejection fraction (%) 54.00 [44.0, 61.0] 53.0 [44.0, 62.0] 0.65
Indexed RV end diastolic volume (mL/m2) 84.7 [70.5, 101.8] 84.9 [70.1, 99.4] 0.61
Indexed RV end systolic volume (mL/m2) 40.0 [28.8, 53.0] 41.0 [28.0, 51.5] 0.96
Indexed RV stroke volume (mL/m2) 44.2 (12.7) 41.7 (13.1) 0.10 Indexed left atrial volume (mL/m2) 54.1 [43.9, 68.4] 57.17 [42.4, 70.6] 0.79
Maximum LV wall thickness (mm) 9.9 (2.2) 8.8 (1.8) <0.001* Mean septal wall thickness (mm) 7.9 (1.9) 7.2 (1.5) 0.002* Mean lateral wall thickness (mm) 5.6 (1.6) 5.0 (1.2) 0.001* CMR features of LVNC 31 (4.9) 5 (6.0) 0.60 Mid wall late gadolinium enhancement 224 (35.4) 26 (31.3) 0.54 present Mid wall late gadolinium enhancement 0.32 most affected segment
Anterior 8 (3.6) 0 (0.0) Inferior 17 (7.6) 1 (3.7) Lateral 42 (18.7) 2 ( 7.4) Septal 158 (70.2) 24 (88.9) Late gadolinium enhancement consistent 46 (7.3) 2 (2.4) 0.08 with myocarditis
Mitral regurgitation 0.48 None 340 (53.7) 49 (59.0) Mild 210 (33.2) 23 (27.7) Moderate 66 (10.4) 11 (13.3) Severe 15 (2.4) 0 (0.0)
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Notably however, patients with TTNtv had thinner left ventricular walls and a trend towards
lower indexed left ventricular mass (LVMi) in the absence of statistically significant
differences in left ventricular dilation, compared to patients without TTNtv (Figure 3-11,
Table 3-9).
Figure 3-11: TTNtv hypertrophy phenotype. Beeswarm and boxplot (black bars showing median) of
indexed left ventricular (LV) mass, mean septal wall thickness and indexed left ventricular end diastolic
volume stratified by titin status, showing that patients with TTNtv have lower indexed LV mass and
thinner LV walls in the absence of evidence of differences in LV dilatation. Between group comparisons
are made using the Mann Whitney test.
This suggests that TTNtv cardiomyopathy is associated with a different hypertrophic
response compared to patients without TTNtv. To explore this further, linear regression
analysis was performed, evaluating predictors of left ventricular mass.
3.4.5.3 TTNtv and LV mass
3.4.5.3.1 Baseline predictors of left ventricular mass
On univariable linear regression analysis, potential predictors of LVMi were evaluated. To
build the baseline model, before evaluating the importance of TTNtv, biological and clinical
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variables that could plausibly predict LV mass were selected. These were age, gender, a
history of hypertension, race, systolic blood pressure (as measured on the day of recruitment),
and CMR features of LVNC (could contribute to increased LV mass). LVEDVi or LVESVi
were not included as they are strongly correlated with LVMi (LVEDVi, r=0.58, p<0.00001;
LVESVi, r=0.57, p<0.00001).
Initially, univariable linear regression was performed on each of these predictors against
LVMi and variables that were significant at a p value threshold of <0.10 were retained. Of
these variables, gender, race, and a history of hypertension were significant (Table 3-10).
Table 3-10: Variables evaluated on univariable linear regression as predictors of left ventricular mass
LVMi= indexed left ventricular mass.
Variable Estimate change P value Lower CI Upper CI in LVMi (g/m2) Age (per 1 year) 0.1 0.25 -0.1 0.2 Male Gender 17.7 <0.0001 13.9 21.5 Race (compared to Afro-Caribbean) African 6.0 0.46 -10.0 22.1 Asian -15.3 0.02 -28.2 -2.3 Caucasian -9.7 0.06 -19.8 0.5 Chinese -6.1 0.70 -36.9 24.7 Mixed -0.8 0.96 -27.9 26.4 Other -8.8 0.26 -24.1 6.4 History of hypertension 6.8 0.001 2.7 11.0 Systolic blood pressure (per 1mmHg) 0.0 0.76 -0.1 0.1 CMR features of LVNC -3.7 0.42 -12.6 5.3
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The full model was built including these 4 variables and then optimised using reverse
stepwise selection, with a p value threshold for exclusion of <0.05. At this stage race was
removed, leaving gender and hypertension. Each of the previously discarded variables was
then added in turn, both by themselves (systolic blood pressure, age, LVNC) and together
with race to assess if any variable either became significant in the context of other variables,
or whether their inclusion affected the coefficient or significance of the existing variables.
Race was also evaluated collapsed into Caucasian ethnicity or not. These discarded variables
remained non significant and did not have an effect on the retained variables of gender and
hypertension.
3.4.5.3.2 The effect of TTNtv on left ventricular mass
The initial plots (Figure 3-11) suggested that TTNtv might be associated with a blunted
hypertrophic response. This means that TTNtv are associated with a reduced LVMi and
thinner LV walls, in the absence of evidence of significant differences in LV dilation. To
explore the relationship between LVMi and LVEDVi further, the regression of
LVMi~LVEDVi, stratified by TTNtv status was plotted (Figure 3-12). This shows that for
any given increase in LVEDVi, patients with TTNtv had a reduced increased in LVMi
(Increase in LVMi per 1mL/m2 increase in LVEDVi: 0.26mg/m2 for patients with TTNtv,
0.42mg/m2 for patients without TTNtv).
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Figure 3-12: LV mass and LV end diastolic volume. Indexed LV mass (LVMi) plotted against indexed
end diastolic LV volume (LVEDVi). Red points are patients with TTNtv. The regression slope of LVMi
~LVEDVi (LVMi increase per 1mL/m2 increase in LVEDVi) is shown for patients with TTNtv (red line:
0.26g/m2) and without TTNtv (black line: 0.42g/m2). Typically, LVMi increases in line with increasing
indexed left ventricular end diastolic volume but this effect is reduced in TTNtv DCM (p=0.006).
On univariable linear regression analysis, the presence of TTNtv was associated with a
7.8g/m2 reduction in LVMi (95% CI -13.7 to -1.8g/m2, p=0.01). On multivariable analysis,
TTNtv remained predictive of a lower LVMi after adjusting for the covariates in the baseline
model, with an estimated 7.8g/m2 reduction in LVMi (adjusted p=0.007) (Table 3-11 and
Figure 3-13).
Table 3-11: Multivariable regression analysis evaluating predictors of left ventricular mass
Variable Estimate change in LVMi (g/m2) P value Lower CI Upper CI
Male gender 18.0 <0.0001 14.2 21.8
History of hypertension 6.2 0.002 2.2 10.2
Presence of TTNtv -7.8 0.007 -13.4 -2.1
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Figure 3-13: Forest plot showing the predictors of left ventricular mass, demonstrating that TTNtv are
associated with a lower LV mass after adjustment for gender and hypertension.
3.4.5.3.3 Model checking
The adjusted R2 of the final model was 0.12. This is low and suggests that the model explains
little of the overall variation in LVMi, which is not unexpected given that the model includes
only 3 binary predictors. However, all potential clinical variables were evaluated and
considered for inclusion in the model. In this cohort, there are as yet unidentified predictors
of LVMi.
Allowing for this, the model as it stands does demonstrate that TTNtv have an influence on
LVMi, which is a potentially important finding. Therefore I checked the model to evaluate
whether assumptions of linearity and homoscedasticity have been met and potential
influential outliers identified (Figure 3-14). These plots show that there is no clear pattern in
the distribution of the residuals and that assumptions of linearity and equal variance are valid.
However, whilst there are no points that are clear outliers, data row 392 is flagged for further
review.
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Figure 3-14: Model checking of final linear regression model predicting LV mass (LVMi~gender,
hypertension, + TTNtv). The Residuals vs Fitted plot shows that the residuals are equally spread above
and below the horizontal red line, without distinct patterns. This suggests that there are no underlying
non-linear relationships that have not been accounted for. The QQ plot shows whether the residuals are
normally distributed, and here the residuals appear to fit the expected distribution, with the exception of
data row 392. In the Scale-Location plot, the residuals appear to be spread equally across a range of
predictors, thereby meeting the assumption of equal variance (homoscedasticity). In the final Residuals vs
Leverage plot, checking for influential observations, there are no points outside of Cook’s distance
(dashed red line not seen on plot). However, data row 392 is again highlighted, suggesting it may be
influential to the regression results.
On review of the data, this individual is a 69 year old female, with no history of hypertension,
with a LVMi of 82.5g/m2, which is within a clinically appropriate range, and no TTNtv. No
inaccuracies in data collection and recording were identified. The model was re-run after
excluding this individual and there was little change to the regression coefficient for the
effect of TTNtv on LVMi (TTNtv present: -7.8g/m2 change in LVMi, 95% CI -13.4 to -2.1,
p=0.008). The original final model therefore appears reasonable.
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3.4.5.3.4 Sensitivity analysis: other sarcomeric variants
The data therefore suggest that TTNtv are associated with lower LVMi, after adjusting for
other predictors of LVMi in this cohort. However, the original analysis included patients with
rare variants in other genes encoding sarcomeric proteins, which could have a similar or
disparate effect on LV mass. The analysis was therefore repeated, including building of the
baseline model predicting LVMi and then adding TTNtv to the optimized model, after
excluding 41 patients with rare non-truncating variants in MYH7 (n=29) and TNNT2 (n=11)
(1 patient had a rare variant in both genes) (note, no formal ACMG interpretation of
pathogenicity was performed).
In this subset cohort (n=675), gender and hypertension remained the predictors of LVMi. On
addition of TTNtv to this model, TTNtv remained associated with lower LVMi with little
change to the original estimate or p value (Table 3-12).
Table 3-12: Sarcomeric variant sensitivity analysis. Results of multivariable regression analysis
evaluating predictors of left ventricular mass in a cohort excluding individuals with rare variants in
MYH7 or TNNT2.
Variable Estimate change in LVMi (g/m2) P value Lower CI Upper CI
Male gender 17.3 <0.0001 13.4 21.2
History of hypertension 5.8 0.005 1.8 9.9
Presence of TTNtv -7.5 0.01 -13.2 -1.8
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3.4.5.4 TTNtv positional effect
In previous pilot work from our group (n=42), it appeared that TTNtv that affected the
protein distally towards the C’ terminus were associated with more severe cardiac
endophenotypes. This was therefore evaluated further in this larger cohort. In Figure 3-15,
each CMR phenotype is plotted against the normalized cDNA position of the TTNtv
(individual cDNA position divided by 107976, the total cDNA length of the meta transcript).
When evaluating LVEF, RVEF, LVMi and biventricular volumes, there was no significant
linear relationship between TTNtv location and severity of the cardiac phenotype suggesting
that in this cohort there is no evidence to support a positional dependent phenotypic severity
effect with TTNtv (Figure 3-15).
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Figure 3-15: Plots show the relationship between TTNtv location and cardiac endophenotypes assessed by
CMR in the cohort (n=83). The normalized cDNA position of the TTNtv on the axis is represented as the
position relative to the amino (N) and carboxyl (C) termini of the titin protein. Only variants in
constitutive exons are plotted. A regression line with 95% confidence intervals is shown for each variable
and slope and p value of the regression shown above each plot. LV/RV=left/right ventricle; EDVi, indexed
end-diastolic volume (mL/m2); ESVi, indexed end-systolic volume (mL/m2); SVi, indexed stroke volume
(mL/m2); EF, ejection fraction (%).
Whilst there was no positional severity effect, it appeared that the majority of variants in the
DCM cohort (although crucially not exclusively) clustered towards the distal two thirds of the
protein. A recent zebrafish study identified an internal promoter in the distal I band (just
before exon 240) for a short titin isoform, named Cronos, active in mouse and human hearts,
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which when disrupted resulted in a severe phenotype(331). The hypothesis proposed was that
the Cronos isoform could partially rescue N-terminal truncations and that disruption of both
the full-length TTN and Cronos protein products results in more severe disease than
disruption of the full-length titin protein alone. Replotting the position-phenotype graphs,
annotated with the position of the Cronos promoter, shows a marked preponderance of
variants distal to this location, but no phenotypic distinction (Figure 3-16).
Figure 3-16: Cronos: Plots show the relationship between TTNtv location and cardiac endophenotypes
assessed by CMR in the cohort described in Figure 3-15, annotated by the expected position of the Cronos
promoter. Only variants in constitutive exons are plotted. The dashed line indicates the expected position
of the Cronos promoter (upstream of exon 240, numbered according to the LRG391_t1 meta-transcript).
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In summary, these data show that there is marked heterogeneity in the cardiac phenotype of
TTNtv and that this is not fully accounted for by either variation in position, alternative
splicing (as all variants studied are in constitutively expressed exons) or by potential Cronos
disruption. This suggests that additional factors may modify expressivity, which I now
explore further.
3.4.6 Alcohol as a phenotypic modifier of TTNtv cardiomyopathy
3.4.6.1 Alcohol consumption
As part of the entry criteria to the study, no patient in the study met accepted criteria for a
diagnosis of alcoholic cardiomyopathy (>80g/alcohol per day for at least 5 years(332)). There
were 111 (16%) patients with a history of moderate excess alcohol consumption as defined in
the study design. Across the cohort, the mean weekly current alcohol consumption was 8.1 ±
15.9 units (IQR 0-10 units; range 0-200 units). The majority of the cohort reported that no
alcohol was consumed on a weekly basis (n=366, 51%) (Figure 3-17). There were 2
individuals with self reported consumption of alcohol of 189 and 200 units per week.
Figure 3-17: Self reported weekly alcohol consumption (units) across study cohort, stratified by gender.
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Patients with a history of alcohol excess were more likely to be male and have a lower RVEF
compared to patients without a history of alcohol excess (Table 3-13). On unadjusted
analysis, there was evidence of increased biventricular dilation and reduced left ventricular
function in patients with alcohol excess, but this was not significant after correcting for
multiple testing. There was no difference between groups in age at recruitment, family
history of DCM, left atrial volume, or presence of mid wall fibrosis.
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Table 3-13: Baseline demographics and cardiac phenotype, stratified by alcohol status. Continuous data
are summarised as mean (standard deviation) or median [interquartile range] and compared using
Mann-Whitney tests. Categorical variables are summarised as count and percentages and between group
differences tested with Fisher’s exact test. *Indicates significance after adjusting for multiple testing
(p≤0.003).
Alcohol Excess FALSE Alcohol Excess TRUE P value
N=605 N=111
Age at recruitment (years) 53.4 (14.5) 53.9 (13.1) 0.77
Male gender 365 (60.3) 104 (93.7) <0.001*
Family history DCM 97 (16.0) 16 (14.4) 0.78
Family history SCD 88 (14.5) 19 (17.1) 0.47
LVEF (%) 41.0 [30.0, 49.0] 37.0 [27.5, 47.0] 0.02
LVEDVi (mL/m2) 116.8 [101.9, 142.8] 123.0 [110.4, 144.6] 0.02
LVESVi (mL/m2) 67.5 [52.4, 96.0] 79.6 [58.7, 102.7] 0.009
LVSVi (mL/m2) 47.5 (13.5) 45.7 (12.2) 0.19
LVMi (g/m2) 85.2 [72.5, 102.6] 89.7 [79.8, 111.1] 0.007
RVEF (%) 54.0 [44.5, 62.0] 48.0 [39.0, 56.0] <0.001*
RVEDVi (mL/m2) 83.8 [69.4, 100.6] 91.2 [75.7, 102.9] 0.007
RVESVi (mL/m2) 38.8 [27.3, 51.9] 45.1 [36.5, 58.3] <0.001*
RVSVi (mL/m2) 44.0 (12.8) 43.2 (12.6) 0.54
LAVi (mL/m2) 53.9 [43.0, 68.1] 57.6 [46.8, 72.7] 0.05
Mid-wall fibrosis LGE 203 (33.6) 47 (42.3) 0.08
TTNtv present 70 (11.6) 13 (11.7) 1.00
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3.4.6.2 Alcohol consumption and cardiac phenotype in patients with a truncating
variant in titin
Overall, amongst patients with a history of alcohol excess, 13 patients (16%) had a TTNtv
compared to 98 patients (15%) without TTNtv (p=1.00) (Table 3-13). Patients with TTNtv
and a history of alcohol excess had a mean LVEF of 27.7% compared to 39.8% in patients
with TTNtv without a history of alcohol excess and 37.8% in patients without TTNtv with a
history of alcohol excess (Figure 3-18). This suggested an additive effect of TTNtv and
alcohol on LVEF. To explore this further, a regression model was developed predicting
LVEF in this cohort and the significance of an interaction between TTNtv and alcohol was
evaluated.
Figure 3-18: LVEF in patients with TTNtv and alcohol excess. Beeswarm and overlaid boxplot showing
the distribution of LVEF (left ventricular ejection fraction) in cohort, stratified by TTNtv status and
alcohol history. Black bars are median values, blue squares indicate mean values. P value reported from
Kruskal-Wallis test compares LVEF across all 4 groups.
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3.4.6.3 Predictors of LVEF
On univariable linear regression analysis, potential predictors of LVEF were evaluated. To
build the baseline model, before evaluating the importance of TTNtv and alcohol, biological
and clinical variables that could plausibly affect LVEF were selected. These were age,
gender, family history of DCM, the presence of left bundle branch block, the presence of
mid-wall fibrosis LGE, a history of atrial fibrillation and heart failure medication use (beta
blocker, ACE inhibitor and aldosterone antagonist). Medication was included to control for
the potential confounding effect of prognostic heart failure medication. Diuretic use or
NYHA class were not evaluated as predictors of LVEF as whilst they are associated with
LVEF severity, they do not contribute to LVEF impairment. RVEF was not evaluated as it is
highly correlated with LVEF (r=0.56, p <0.0001). The presence of mitral regurgitation was
also not evaluated as a predictive variable of LVEF because primary valvular disease was an
exclusion criteria, meaning that any mitral regurgitation reflected LV dilation and impairment
and was not a primary contributor.
Firstly univariable linear regression of each of the selected predictors against LVEF was
performed. Variables that were significant at a p value threshold of <0.10 were retained. Of
these variables, age, gender, family history of DCM, mid wall fibrosis LGE, history of AF,
and medication use were significant (Table 3-14).
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Table 3-14: Results of univariable and multivariable linear regression evaluating predictors of LVEF.
The adjusted analysis is adjusted for gender, family history of DCM, a history of atrial fibrillation, beta
blocker and aldosterone antagonist use and mid wall fibrosis. The process of model building is outlined in
the text.
Unadjusted analysis Adjusted analysis Variable Estimate 95% CI P value Estimate 95% CI P value change in change in LVEF (%) LVEF (%) Age -0.9 -1.5 to -0.2 0.01 - - - (per 10 years) Male gender -3.4 -5.4 to -1.5 0.0007 -2.3 -4.1 to -0.5 0.013 Family 7.1 4.5 to 9.7 <0.0001 5.7 3.4 to 8.0 <0.0001 history DCM Mid wall -3.7 -5.6 to -1.7 0.0002 -1.4 -3.1 to 0.4 0.13 fibrosis LGE LBBB -1.8 -3.9 to 0.4 0.11 - - - Atrial -4.6 -6.8 to -2.4 <0.0001 -3.3 -5.4 to -1.3 0.001 fibrillation Beta blocker -6.1 -8.1 to -4.1 <0.0001 -3.3 -5.2 to -1.4 0.0008 ACE -3.7 -6.0 to -1.3 0.002 - - - inhibitor Aldosterone -7.9 -9.8 to -6.0 <0.0001 -6.7 -8.6 to -4.9 <0.0001 antagonist
The full model was built including these 8 variables and then optimized the model using
reverse stepwise selection, with a p value threshold for exclusion of <0.05. At this stage, age,
LGE and the use of ACE-inhibitors were excluded.
Each discarded variable was added in turn (age, ACEi, LBBB, LGE) to assess if any variable
either became significant in the context of other variables, or whether their inclusion affected
the coefficient or significance of the existing variables. Of these, the inclusion of LGE
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affected the gender variable significance value, though not the estimate. Therefore LGE was
retained, even though it was not a significant variable in the multivariable model (estimate
change in LVEF -1.4%, p= 0.13). On further analysis, 23% of female patients had LGE
compared to 41% of male patients (p<0.0001). The remaining discarded variables (age,
ACEi, LBBB) remained non significant and did not have an effect on the retained variables.
However, at this stage, given that age, a potentially important biological variable, was non
significant, and some of the included variables such as AF could be a reflection of LVEF (as
opposed to predictors of LVEF), subsets of the baseline model (termed Model 1a) were built
and compared using nested ANOVA testing. At this stage, I again tested the significance of
adding the previously discarded variables (Table 3-15). Models 1b-d were testing the variable
addition of age and ACEi compared to the baseline model, models 2a-2c were testing the
variable addition of LBBB, age, and LGE compared to the baseline model, and models 3a-3d
were testing the variable exclusion of medication compared to the baseline model. A simple
model of age and gender with the variable addition of family history of DCM and the
presence of LGE, whilst excluding medication use comprised models 4a-b. Finally, a model
excluding AF and medication use compared to the baseline model formed models 5a-5d.
The results, shown in Table 3-15, demonstrate that the baseline model was the best fit. The
addition of age, across all tested models, did not improve model fit. The exclusion of
medication and AF not did provide a better model for LVEF compared to the baseline model.
Therefore the final baseline model was Model 1a. The results of this adjusted model are
shown in Table 3-14.
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Table 3-15: Results of nested ANOVA comparing baseline models to predict LVEF.
Model Model variables Adjusted ANOVA ANOVA p number R2 testing value 1a: baseline Gender, family history DCM, AF, 0.1743 model Beta blocker, Aldosterone Antagonist, LGE 1b Gender, family history DCM, AF, 0.1709 Model 1a, 0.84 Beta blocker, Aldosterone Antagonist, Model 1b LGE, Age 1c Gender, family history DCM, AF, 0.1735 Model 1a, 0.62 Beta blocker, Aldosterone Antagonist, Model 1c LGE, ACEi 1d Gender, family history DCM, AF, 0.1702 Model 1a, 0.80 Beta blocker, Aldosterone Antagonist, Model 1d LGE, Age, ACEi 2a Gender, family history DCM, AF, 0.1738 Model 1a, 0.63 Beta blocker, Aldosterone Antagonist, Model 2a LGE, LBBB, Age 2b Gender, family history DCM, AF, 0.1732 Model 1a, 0.71 Beta blocker, Aldosterone Antagonist, Model 2b LGE, LBBB, Age, ACEi 2c Gender, family history DCM, AF, 0.1742 Model 1a, 0.54 Beta blocker, Aldosterone Antagonist, Model 2c LGE, LBBB, ACEi 3a Gender, family history DCM, AF, 0.1148 Model 1a, <0.0001 Beta blocker, LGE Model 3a 3b Gender, family history DCM, AF, 0.1637 Model 1a, 0.001 Aldosterone Antagonist, LGE Model 3b 3c Gender, family history DCM, AF, 0.1137 Model 1a, 1.0 Beta blocker, LGE, ACEi Model 3c 3d Gender, family history DCM, AF, 0.1629 Model 1a, 1.0 Aldosterone Antagonist, LGE, ACEi Model 3d 4a Age, gender 0.02688 Model 1a, <0.00001 Model 4a 4b Age, gender, family history DCM, 0.06588 Model 1a, <0.00001 LGE Model 4b 5a Gender, family history DCM, Beta 0.1623 Model 1a, 0.0006 blocker, Aldosterone Antagonist, LGE Model 5a 5b Gender, family history DCM, 0.1482 Model 1a, <0.00001 Aldosterone Antagonist, LGE Model 5b 5c Gender, family history DCM, Beta 0.1052 Model 1a, <0.00001 blocker, LGE Model 5c 5d Gender, family history DCM, LGE 0.06536 Model 1a, <0.00001 Model 5d
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3.4.6.4 Effect of TTNtv and alcohol interaction on LVEF: Univariable analysis
The TTN*Alcohol excess interaction term was estimated to be associated with an additional
10.3% reduction in baseline LVEF compared to an individual with either TTNtv alone or a
history of alcohol excess alone (95% confidence interval -18.1 to -2.6, p=0.009; Table 3-16).
In the absence of other predictors of baseline LVEF, the effects of TTNtv and alcohol in a
patient with DCM compared to a patient with neither TTNtv nor a history of alcohol excess
can be summarised as follows (Table 3-16):
1) A patient with TTNtv alone and no history of alcohol excess has no statistically
significant change in LVEF (0.2% increase in LVEF, 95% confidence interval -2.8 to
3.3, p=0.87).
2) A patient with a history of alcohol excess but no TTNtv has no statistically significant
change in LVEF (1.8% decrease in LVEF, 95% confidence interval -4.4 to 0.9,
p=0.19).
3) A patient with both a history of TTNtv and alcohol excess has a 11.9% reduction in
LVEF (95% confidence interval -18.6 to -5.1, p=0.0006).
3.4.6.5 Effect of TTNtv and alcohol interaction on LVEF: Multivariable analysis
When the TTNtv*Alcohol excess interaction was added to the optimised regression model of
predictors of baseline LVEF, the effects of TTNtv and alcohol together in patients with DCM
remained a significant predictor of reduced LVEF over and above the presence of either
TTNtv or alcohol excess alone (Table 3-16).
In this optimised multivariable model, the TTN*Alcohol excess interaction term was
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estimated to be associated with an additional 9.1% reduction in baseline LVEF compared to
an individual with either just TTNtv or just a history of alcohol excess (95% confidence
interval -16.2 to -2.0, p=0.01; Table 3-16). Therefore these data show that the presence of
TTNtv and a history of alcohol excess is estimated to be significantly different from having
either a TTNtv alone or a history of alcohol excess alone.
Table 3-16: Results of univariable and multivariable linear regression analysis evaluating the effect of
TTNtv and alcohol excess on LVEF. *indicates adjustment for variables in baseline Model 1a. § i.e. the
effect of TTNtv and alcohol excess compared to either TTNtv alone or alcohol excess alone.
Unadjusted analysis Adjusted analysis*
Variable Estimate 95% P value Estimate 95% P value (change in confidence (change in confidence LVEF, %) intervals LVEF, %) intervals Baseline: No 0 - - 0 - - TTNtv or alcohol excess TTNtv, no 0.2 -2.8 to 3.3 0.87 -0.6 -3.4 to 2.2 0.68 alcohol excess Alcohol excess, -1.8 -4.4 to 0.9 0.19 -0.3 -2.8 to 2.2 0.80 no TTNtv TTNtv and -11.9 -18.6 to -5.1 0.0006 -10.0 -16.3 to -3.8 0.002 alcohol excess
TTNtv*Alcohol -10.3 -18.1 to -2.6 0.009 -9.1 -16.2 to -2.0 0.01 excess interaction§
Adjusting for all variables predictive of baseline LVEF in this cohort, compared to a patient
without TTNtv or alcohol excess, the presence of both TTNtv and alcohol excess is
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associated with a 10.0% reduction in LVEF (95% confidence interval -16.3 to -3.8, p=0.002;
Table 3-16; Figure 3-19). The magnitude of effect is similar to the effect of the interaction
term due to the negligible individual effects of TTNtv and alcohol excess on LVEF.
Figure 3-19: Forest plot showing results of final linear regression model predicting LVEF and the effects
of TTNtv and alcohol. Alcohol XS=alcohol excess, LGE=late gadolinium enhancement mid wall fibrosis,
FHx =family history, AF=atrial fibrillation, A-Antag=aldosterone antagonist.
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3.4.6.6 Effect of TTNtv and alcohol interaction on LVEF: Model checking
Figure 3-20: Model checking of final linear regression model predicting LVEF with the TTNtv and
alcohol interaction. The Residuals vs Fitted plot shows that the residuals are equally spread above and
below the horizontal red line, without distinct patterns. This suggests that there are no underlying non-
linear relationships that have not been accounted for. The QQ plot shows whether the residuals are
normally distributed, and here the residuals appear to fit the expected distribution. In the Scale-Location
plot (square rooted standardized residual vs. predicted value, measure of homoscedasticity), the residuals
appear to be spread equally across a range of predictors until approximately 45%. After this point, the
residuals do not appear to have equal variance. However as the residuals vs fitted plot appears adequate,
the model appears acceptable. In the final Residuals vs Leverage plot, checking for influential
observations, there are no points outside of Cook’s distance (dashed red line not seen on plot). Points 164,
349 and 25 are discussed in the text.
The adjusted R2 of the final model (with TTNtv and alcohol) was 0.1824. The LVEF model
evaluating TTNtv and alcohol is significantly different to the LVEF model without TTNtv
and alcohol (nested ANOVA, p=0.02).
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The model demonstrates an important gene-environmental interaction between TTNtv and
alcohol. To assess the validity of the model, I evaluated whether assumptions of linearity and
homoscedasticity have been met and potential influential outliers identified (Figure 3-20).
These plots show that the model is acceptable. Data rows 164, 349 and 25 were investigated
to check for potential data entry errors and none were identified. They represented 3 males;
one individual with TTNtv with a history of alcohol excess with LVEF 56%, and two
individuals with TTNtv without a history of alcohol excess with LVEF 11% and 12%.
Therefore these individuals did not fit the model of TTNtv plus alcohol results in worse
LVEF. However, the overall model fit was acceptable and there were no errors in data
capture, therefore all these individuals were retained in the final model.
3.4.6.7 Evaluating alcohol as a quantitative trait
The model presented evaluated alcohol excess as a binary trait. Given the small number of
individuals with TTNtv and alcohol, there is a danger of model overfitting. To mitigate this,
evaluating alcohol as a continuous variable might be more statistically powerful, as alcohol
data would be available on all TTNtv individuals in the cohort, not simply collapsed into
n=13 positive cases.
However, the evaluation of alcohol consumption as a quantitative trait was not appropriate
for 2 reasons:
1. Current self reported alcohol consumption at the time of recruitment may not have
accurately reflected a previous history of alcohol excess, particularly relevant for
those individuals who may have reduced alcohol intake on medical advice following
the diagnosis of DCM. [NB: Therefore in the primary analysis, medical records were
also reviewed to identify a history of alcohol excess].
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2. The distribution of quantitative alcohol consumption was skewed and did not meet the
assumption of linearity required of linear regression modeling. This could however
have been partly addressed by transforming alcohol consumption as demonstrated
below:
log2(alcohol consumption +1)
Figure 3-21: Log2 transformation of alcohol consumption plotted against LVEF. Red dots represent
patients with TTNtv.
However, the large number of individuals with an appropriate value of 0 (i.e. not representing
missing data) limits the utility of this model. I then explored categorizing alcohol
consumption into 4 groups (no alcohol, 1-10 units, 11-20 units, >20 units per week).
However, this showed that there was no linear relationship between either these categories
and LVEF (Figure 3-22) or the TTN:alcohol interaction term of these categories and LVEF
(Figure 3-23). Together, these data confirm that it is not appropriate to evaluate alcohol as a
quantitative trait in this cohort.
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Figure 3-22: Weekly alcohol consumption grouped into categorical variables and plotted against LVEF.
Figure 3-23: Forest plot of estimate and 95% confidence intervals of TTNtv:Alcohol interaction term
predicting LVEF, with alcohol stratified by categorical variables outlined in Figure 3-22. Each level
compared to baseline of TTNtv: No alcohol. The results show that there is not a linear relationship
between increasing categories of alcohol consumption and LVEF.
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3.4.7 Other modifiers of TTNtv cardiomyopathy
3.4.7.1 Evaluation of hypertension as a phenotypic modifier of titin cardiomyopathy
The interaction between excess alcohol and hypertension is long established, showing
elevated blood pressure with excess consumption and a dose dependent reduction in blood
pressure with alcohol reduction(333,334). Therefore, it is possible that the alcohol-titin
interaction is either mediated via the effects of hypertension or actually reflects a
hypertension-titin interaction.
To address this, I evaluated hypertension as a possible phenotypic modifier of titin
cardiomyopathy. In this cohort, 213 patients had a history of hypertension, of whom 10 had
TTNtv and 33 had a history of alcohol excess. Amongst patients with TTNtv, there was no
difference in LVEF between patients with and without a history of hypertension (TTNtv
patients, LVEF (%): mean ± sd, HT+ 37.0 ± 14.8; HT- 37.9 ±13.8, p=1.0) (Figure 3-24).
Figure 3-24: LVEF stratified by TTNtv and hypertension status. Groups are compared using the
Kruskal-Wallis test.
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On multivariable linear regression analysis controlling for variables predictive of LVEF (as
built previously), the presence of both TTNtv and hypertension together was not associated
with a change in LVEF (estimate change in LVEF 0.9%, 95% CI -6.2 to 8.0, p=0.82) (Figure
3-25). Therefore, there is no evidence that a history of hypertension is a phenotypic modifier
of titin cardiomyopathy.
Figure 3-25: Forest plot showing results of final linear regression model predicting LVEF and the effects
of TTNtv and hypertension. LGE=late gadolinium enhancement mid wall fibrosis, FHx =family history,
AF=atrial fibrillation, A-Antag=aldosterone antagonist.
3.4.7.2 Additional genetic modifiers of the titin cardiomyopathy phenotype
A comprehensive review of additional genetic variants modulating the titin cardiomyopathy
phenotype in this cohort is beyond the scope of this chapter as this would require a study of
common genetic variants. In terms of additional rare genetic variants in DCM genes in this
cohort, amongst patients with TTNtv (n=83), one had an additional variant in MYH7, one had
an additional variant in BAG3 and 3 had an additional variant in RBM20. The phenotype and
age at study recruitment of these patients is shown in Figure 3-26, compared to TTNtv
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patients without additional variants. The numbers of patients with additional variants
precludes further analysis of the effect of genetic modifiers on the titin cardiomyopathy
phenotype.
Figure 3-26: LVEF and age at study recruitment in patients with TTNtv, coloured by the presence or
absence of additional rare genetic variants. In this cohort, 5 TTNtv patients had additional variants in
BAG3, MYH7 or RBM20. LVEF=left ventricular ejection fraction.
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3.5 Discussion
Truncating variants in titin are the commonest single genetic cause of DCM(5,79,84). DCM
affects up to 1 in 250 people worldwide and despite optimal therapy, 5 year mortality
approaches 20%(12,13). Therefore, improved understanding of the clinical manifestations
and phenotypic drivers of TTNtv cardiomyopathy offers the scope for genetic based risk
stratification of patients with DCM to improve clinical outcomes.
What this study shows: Overview
In this study of 716 patients with DCM, we used the power of informed variant curation and
detailed cardiac phenotyping with CMR to define novel genotype-phenotype correlations in
TTNtv cardiomyopathy. We demonstrate that DCM due to TTNtv is associated with a
blunted hypertrophic response, highlighting possible disease mechanisms. We show that
TTNtv DCM is not associated with a distinct functional cardiac phenotype compared to non
TTNtv DCM but identify a gene-environmental interaction between TTNtv and alcohol,
suggestive of alcohol as a phenotypic modifier of TTNtv cardiomyopathy.
Cardiac phenotype of TTNtv DCM: limited hypertrophic response
In this study we demonstrated that TTNtv DCM is associated with thinner LV walls, and
lower LV mass, in the absence of significant differences in LV dilatation, after controlling for
important clinical covariables. This suggests that TTNtv is associated with a limited
hypertrophic response. Whilst the magnitude of wall thickness and LV mass differences are
not clinically informative and cannot be used to predict genotype based on phenotype, they
may offer a potential insight into the pathogenesis of TTNtv DCM. Notably, these findings
from human studies correlate well with recently published preclinical models of TTNtv
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cardiomyopathy as outlined below(88).
At the sarcomeric level, titin plays a key role in the mechanotransductive response of the
cardiomyocyte and regulation of cardiac hypertrophy. Pathological hypertrophy, the presence
of cardiac dilation and cardiac dysfunction, develops in response to biological stressors, such
as neurohormonal activation or myocyte injury. It is thought to develop from an adaptive
compensatory response to a maladaptive state(335). The mammalian target of rapamycin
(mTOR) is an evolutionary conserved kinase that can modulate cardiac hypertrophy(336,337)
and increased mTORC1 signaling is an adaptive response to cardiac stress in rat models. In
patients with all cause DCM, elevated mTORC activation has been associated with a trend to
increased progression of heart failure(338).
It has recently been shown that rats with TTNtv had elevated mTORC1 signaling at baseline
(consistent with other cardiomyopathies) but critically were not able to increase adaptive
signaling when stressed(88). This may offer a biological explanation of the blunted
hypertrophic response observed in human TTNtv DCM, though the mechanistic link between
TTNtv and mTORC activation remains to be established and it is not yet known if this
aberrant mTORC activation is specific to titin cardiomyopathy(339).
Cardiac phenotype of TTNtv DCM: no evidence of positional dependent phenotypic
severity
In this study, we find that the functional cardiac phenotype in DCM patients with TTNtv is
not more severe than in patients without TTNtv. Left and right ventricular function was
similar in both groups. In this larger cohort compared to our previous pilot analysis on the
first tranche of this cohort, we did not replicate findings from our institution of a positional
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dependent effect of TTNtv on cardiac phenotype. We showed that a more severely impaired
cardiac phenotype was not associated with more distal variants. This is consistent with recent
data in rat models and induced pluripotent stem cells (iPSCs) suggesting the mechanism of
action of TTNtv is haploinsufficiency, as opposed to a dominant negative protein, although it
is clear that the mutated allele is translated but not detectable on agarose gels(88,340).
Our data does not support a role of the Cronos isoform in modulating the TTNtv
cardiomyopathy phenotype. Zou and colleagues studied homozygous titin truncations in
zebrafish embryos and identified less skeletal muscle dysfunction in fish with Z disc and I
band truncations. They identified a novel titin isoform, Cronos, which has an internal
promoter in the distal I band. The authors proposed that mutations proximal to the promoter
were rescued by upregulation of the distal Cronos isoform, which could not happen with
distal mutations. However, in this cohort, whilst we see a marked clustering of variants distal
to the position of the internal promoter, there are clearly patients with DCM and TTNtv with
variants proximal to the promoter. Therefore the Cronos isoform hypothesis does not fully
account for the TTNtv DCM phenotype. We also do not identify a pattern of phenotypic
severity related to the position of the promoter. The variants that cluster just before the
promoter are not splice variants, predicted to affect the promoter. It is worth noting that in the
zebrafish study, levels of Cronos were highest in adolescence and declined in adult fish, and
the Cronos protein has yet to be identified in human hearts.
Cardiac phenotype of TTNtv DCM: support for environmental modifiers
We observed that there was a similar ratio of males to females in TTNtv DCM compared to
non TTNtv DCM. The gender distribution is interesting because DCM is known to have a 3:1
male:female ratio, yet one might expect an autosomal dominant inheritance genetic form of
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DCM to have a 1:1 male to female ratio. This suggests that additional factors may be
important for the development of phenotype in individuals with TTNtv and the presence of
TTNtv alone may not be sufficient.
It is possible that female gender confers a protective effect in the presence of TTNtv, or that
males with TTNtv are susceptible to a greater extent to an additional genetic modifier and/or
have greater exposure/susceptibility to an environmental modifier. This latter hypothesis was
explored further when evaluating environmental phenotypic modifiers, with a particular focus
on alcohol.
Potentially pathogenic TTNtv are found in ~0.5% the general population (15,88), yet the
prevalence of TTNtv DCM in the population is ~1 in 2000 (DCM prevalence 1:250, with
~11% having TTNtv), indicating that not all individuals with TTNtv develop DCM. Pedigree
studies demonstrate variable expressivity in families with TTNtv. Preclinical models
demonstrate the development of a TTNtv phenotype only upon stress(94,341). These findings
are strongly suggestive of the 2nd hit hypothesis, requiring additional genetic or
environmental modifiers to develop a cardiac phenotype in the presence of TTNtv. To the
best of our knowledge, this is the first study to demonstrate an important gene-environmental
interaction between titin and alcohol.
We demonstrate that a history of at least moderate excess alcohol consumption, above UK
government weekly ‘sensible limits’ but well below the threshold defined for alcoholic
cardiomyopathy(97), is a modifier of the TTNtv phenotype in DCM patients. In the presence
of a history of TTNtv and alcohol excess, a patient with DCM has a 10.0% reduction in
LVEF compared to a patient without TTNtv or alcohol excess. Whilst the number of patients
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with TTNtv and alcohol excess were small, the effect size is large and statistically significant,
suggesting that these observations have not occurred by chance.
The biological basis of the titin-alcohol interaction is unclear. Alcoholic cardiomyopathy has
long been linked to chronic excess alcohol abuse(342). It is thought to develop as a result of
oxidative stress, apoptotic cell death, impaired mitochondrial stress, perturbations in fatty
acid metabolism and accelerated protein catabolism(101). Crucially however, the mechanism
of the primary injury which initiates these changes is unknown. Two leading theories
implicate the alcohol metabolite, acetaldehyde, as directly toxic to cardiomyocytes, and
propose that alcohol directly leads to apoptosis(342-344). These theories however cannot
explain the mechanism by which alcohol cessation enables reversal of cardiomyopathy in a
subset of patients.
A recent study offers insight into the titin-alcohol synergy seen in our data. The authors
hypothesized that chronic alcohol exposure (8 weeks of ethanol vapour) would exacerbate
cardiac remodelling due to volume overload, modeled in rats through aortocaval fistula
surgery(345). At the end of the exposure period, rats with volume overload and chronic
alcohol exhibited increased cardiac wall stress and reduced collagen I/III expression
compared to rats with volume overload alone. Rats with volume overload alone showed
increased collagen III synthesis in the acute remodelling phase. This points to an impaired
compensatory hypertrophic response in rats with volume overload and alcohol. The authors
proposed that alcohol abuse accelerated the decompensation of the stressed heart. In isolation
these findings are interesting, but together with our data that TTNtv are associated with a
blunted hypertrophic response, we can piece together a ‘double hit’ mechanism by which
TTNtv and alcohol excess together lead to an impaired compensatory hypertrophic response
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to volume overload, resulting in a more impaired phenotype than would be expected from
either TTNtv or alcohol alone. Further studies in animal models with TTNtv and alcohol
excess are required to investigate this hypothesis further.
3.5.1 Strengths of this study
As noted in recent ACCF/AHA guidelines(346) and many expert review papers(311,347) a
major limitation of studies documenting risk in patients with DCM and heart failure is
suboptimal phenotyping. By leveraging the precision phenotyping with CMR we were able to
identify and robustly quantify the subtle phenotypic finding of a blunted hypertrophic
response and the interaction between TTNtv and alcohol. These findings may not have been
detected on other imaging modalities such as echocardiography due to the marked inter and
intra observer variability and reduced sensitivity compared to CMR. CMR is the recognized
gold standard for accurate quantification of cardiac chamber dimensions and function.
In addition to the detailed phenotyping, a major strength of this work is careful informed
variant curation. Our group has developed expertise in the understanding of TTNtv and were
the first to report the association of TTNtv with DCM in a large scale study(79) and the first
to report the importance of exon usage (PSI) in TTNtv phenotype(84). All TTNtv in this
cohort were rare variants in constitutively expressed exons and were confirmed through
Sanger sequencing or review of mapped reads.
Finally, a key strength of this work is the sample size. To the best of our knowledge, this is
the largest prospective cohort of patients with DCM confirmed by CMR to undergo genetic
analysis. The findings reported as a result of this analysis are novel and have improved our
understanding of TTNtv DCM and of DCM.
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3.5.2 Limitations of this study
There are several potential limitations to this work. The study is single centre, though as a
major regional centre for CMR referrals, patients were recruited from over 30 regional
district general hospitals, alongside consecutive referrals from a dedicated Inherited
Cardiomyopathy Service at our tertiary centre. It is routine practice in our institution for all
patients with DCM to undergo CMR (including prior to device implantation). This broad
referral base should mitigate bias and it is to be noted our study population reflects a range of
phenotypic severities. However, the CMR requirement may bias against those with the most
severe phenotypes, for example those who presented with severe life threatening arrhythmias
for whom device implantation was indicated prior to CMR.
The passive ascertainment bias of alcohol consumption data needs to be acknowledged. This
data was accrued via patient interview and clinical records. A history of alcohol excess was
not reflected in current alcohol consumption levels, and data on time between alcohol
cessation and recruitment to the study and the duration of alcohol excess was not consistently
available. Proxy measures of alcohol consumption such as serum gamma glutamyltransferase
(GGT) were also unavailable across the cohort. However, these limitations reflect a real
world setting where such variation is inevitable.
The cohort of individuals with TTNtv comprised probands and a smaller number of affected
relatives. The genotype-phenotype correlation has the potential to be biased by this, as a
milder phenotype was demonstrated in affected relatives compared to the proband.
Furthermore, a family history of DCM was associated with a milder LVEF on multivariable
regression analysis. This could have been skewed by individuals with a milder phenotype due
to earlier disease detection on family screening. However, in reality the number of affected
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relatives with TTNtv was 4, therefore this is unlikely to represent a major bias. The
seemingly protective effect of family history on LVEF was driven by probands.
Variable penetrance has the potential to limit genotype-phenotype correlations in studies that
begin with genotype information and then screen for phenotype. In this study, DCM was
diagnosed prior to genetic testing, therefore it was not limited by penetrance. However, this
did limit our ability to evaluate the penetrance of TTNtv which would have been valuable
information in the field of titin cardiomyopathy.
This study builds upon a previously published cohort of patients with TTNtv DCM(84). The
overlap between the two studies is 275 patients, of whom 32 have TTNtv. This study
therefore reports findings on a much enriched, novel cohort of DCM TTNtv patients,
addressing novel hypotheses.
Finally, whilst we have presented novel results, these findings have not yet been replicated in
an additional cohort of patients with DCM and TTNtv.
3.6 Summary
These data show the potential of genotype-phenotype studies in DCM to inform patient
stratification and provide mechanistic insight into disease pathogenesis. We have
demonstrated that truncating variants in the gene for the sarcomeric protein titin are
associated with a limited hypertrophic response that may inform our understanding of TTNtv
DCM pathogenesis. We also demonstrate that the phenotypic expression of TTNtv
cardiomyopathy is modified by even moderate excess alcohol consumption. These data may
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provide guidance for the clinical management of both individuals with TTNtv DCM and
those found to have TTNtv prior to the onset of overt cardiomyopathy.
3.7 Outline of further work
• Genetic study in the context of high alcohol intake is currently underway; in
collaboration with a Spanish group, we are currently undertaking sequencing of
patients with alcoholic cardiomyopathy to evaluate the burden of TTNtv. Our
hypothesis is that TTNtv contribute to alcoholic cardiomyopathy.
• In Chapter 5, the phenotype data forms the foundation to prospectively evaluate
clinical outcome stratified by genetic characteristics.
3.8 Acknowledgements
All work in this chapter is my own except where credited below:
• Sequencing and Sanger confirmation of variants was performed by– Rachel Buchan,
Sam Wilkinson, Alicja Wilk, Will Midwinter.
• The customized in house bioinformatics pipeline was developed and run by – Nicky
Whiffin, Risha Govind, Roddy Walsh, Shibu John, Liz Edwards.
• Nicky Whiffin extracted the list of all protein altering variants in 928 DCM samples
from the in house cardiodb database.
• Cardiac MRI measurements of chamber volumes and dimensions was performed by
the CMR reporting fellow at the time of clinical study.
• Inga Voges performed all left atrial measurements.
• The BRU research nurses recruited all patients to this study.
• Statistical mentorship and advice was provided by Simon Newsome.
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4 IMAGING PREDICTORS OF
CARDIAC REMODELLING
4.1 Aims and hypotheses
The overall aim is to evaluate imaging predictors of remodelling in DCM. Specific
hypotheses are as follows:
Primary Hypothesis
- Baseline left ventricular contractile reserve in patients with recent onset DCM is
predictive of LV remodelling at 1 year.
Secondary Hypotheses
- Baseline circumferential and longitudinal myocardial strain, replacement myocardial
fibrosis, interstitial myocardial fibrosis, and RV contractile reserve in patients with recent
onset DCM are each predictive of LV remodelling at 1 year.
- An adequate baseline left ventricular contractile reserve in patients with DCM reflects a
lower level of interstitial myocardial fibrosis when compared to patients with poor
baseline left ventricular contractile reserve.
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4.2 Background
In this chapter I evaluate imaging predictors of cardiac remodelling, with a specific focus on
the predictive effect of left ventricular (LV) contractile reserve as assessed by low dose
dobutamine stress on left ventricular ejection fraction after 1 year of follow up in patients
with DCM.
As previously outlined, the prognosis of patients with DCM is variable, with a 5 year
mortality rate of ~20%(12,13), yet a potential recovery rate of over 20%(348-351). Resting
LV indices have not however been consistently shown to accurately predict myocardial
remodelling(172,179,194,195,350,351).
Myocardial contractile reserve as a prognostic marker
Myocardial contractile reserve is the difference in LV function at rest and under stress, either
pharmacological or exercise. Several studies have shown that contractile reserve, determined
through dobutamine stress, is a strong prognostic variable, independent of resting ventricular
indices, even in patients with severely impaired LV function(172-179). None of these studies
used CMR as the imaging modality. In one study, 62 patients with LVEF <30% (mean age 48
years) underwent dobutamine infusion up to 10 µg/kg/minute, with assessment of LVEF by
radionuclide ventriculography. Mean change in LVEF was 9%, and on multivariate analysis,
only the change in LVEF at stress was predictive of 1 year transplant free survival. In
another prognostic study, 71 patients with DCM underwent exercise radionuclide
angiography and echocardiography. The change in LVEF between rest and peak exercise was
an independent predictor for survival (p=0.0002)(177). The largest study of the prognostic
significance of contractile reserve was performed in 186 DCM patients who were assessed
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for echocardiography wall motion score index changes following high dose dobutamine
infusion(352). The investigators showed that changes in the wall motion score index were a
stronger prognostic indicator for cardiovascular death during follow up compared to baseline
LVEF(352).
A limited number of historic studies have also evaluated the potential prognostic significance
of right ventricular (RV) contractile reserve(353). For example in a study of 67 patients with
advanced heart failure who underwent exercise stress and radionuclide ventriculography, an
increase in RVEF over 35% at exercise was an independent predictor of event free survival
(p=0.01)(354). A subsequent study, however evaluating both RV and LV contractile reserve
together, has shown that LV contractile reserve may be a stronger prognostic predictor than
RV contractile reserve(355).
Myocardial contractile reserve and reverse remodelling
Aside from prognosis, echocardiography based studies have evaluated the ability of LVEF
change during dobutamine infusion at baseline to predict subsequent improvement in LVEF
in patients on medical therapy. In one study of 18 patients with DCM (mean age 53 years,
mean LVEF 28%), changes in LVEF during low-dose dobutamine stress correlated with an
improvement in LV function during medium term follow up (r = 0.74, p < 0.001), though
analysis was limited by the relatively small number of cases and lack of adjustment for
confounders(173). In another study of 22 patients with new onset DCM, the LVEF change in
response to high dose dobutamine infusion (30 µg/kg/min) was also associated with 6-month
follow up LVEF (r=0.84, p=0.0001)(174). Similarly, a SPECT (Single-photon emission
computed tomography) imaging study of 26 patients with DCM also showed that the change
in LVEF following low dose dobutamine infusion (10 µg/kg/min) was associated with
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improved LVEF, with a cut off stress induced LVEF change of 7% associated with 84.7%
sensitivity and specificity for improvement in LVEF(175). Another study of 51 patients with
heart failure showed that myocardial contractile reserve assessed by exercise
echocardiography is also a predictor of LV reverse remodelling (LVRR) after cardiac
resynchronization therapy (CRT) (6.5% exercise-induced increase in LVEF was associated
with a 90% sensitivity and 86% specificity to predict response after 6 months of CRT) (356).
Whilst these studies all indicate that contractile reserve is predictive of remodelling, these
studies were performed in relatively small cohorts, with variable definitions of LVRR,
variable medical therapy and inclusion criteria, and limited statistical analyses.
Limitations of LVEF based myocardial contractile reserve assessment
The assessment of LV contractile reserve through assessment of LVEF change following
dobutamine stress is potentially limited as it is a load dependent measurement(357). To
mitigate this potential limitation, in this current study, we sought to assess myocardial strain
as an additional measurement of myocardial contractile reserve. Strain is a unitless measure
of tissue deformation. As the left ventricle contracts, the myocardium shortens in a
longitudinal and circumferential dimension (negative strain) and thickens or lengthens in the
radial direction (positive strain). Displacement encoding with stimulated echoes
(DENSE)(358,359) is an accurate and reproducible technique to measure myocardial strain,
but its clinical application is limited by long breath hold times or navigator gating which can
be impractical and time consuming. We therefore developed an accelerated cine DENSE
sequence to enable the evaluation of strain in a breath hold time suitable for patients with
DCM.
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Biological basis of contractile reserve
The biological basis of the dobutamine induced contractile reserve predicting myocardial
recovery is thought to relate to the degree of beta-adrenergic receptor downregulation and
desensitization(181). Increasing beta-receptor downregulation reflects progressive
deterioration in left ventricular function(181). Therefore, improved contractility during
dobutamine administration reflects preserved beta receptor function, which reflects the
potential to show improvement in LVEF. Crucially, the original study linking contractile
reserve to beta-receptors showed that beta receptor down regulation can occur in patients
with only mild-moderate ventricular dysfunction, demonstrating how contractile reserve may
have additional prognostic value over and above LVEF alone(181). This data is in line with
clinical studies showing a poor correlation between resting LV function and contractile
reserve, suggesting that the presence or absence of contractile reserve reflects an independent
pathophysiology which could determine the likelihood of myocardial remodelling(172).
Further support for the link between cardiac sympathetic nervous system function and
myocardial contractile reserve was provided by a study of 24 DCM patients who underwent
iodine-123- metaiodobenzylguanidine (123-I-MIBG) and invasive assessment of contractile
reserve through LV catheterization(360). 123-I-MIBG is an imaging tracer that shares similar
myocardial uptake, storage, and release mechanisms as norepinephrine in sympathetic nerve
terminals, so is used to evaluate sympathetic nervous function. Myocardial contractile reserve
measured as the percentage change in myocardial contractile function (rate of pressure LV
pressure rise in systole; dp/dtmax(361)) between rest and peak atrial pacing, was reduced in
patients with reduced sympathetic function(360). The authors proposed that abnormal
myocardial contractile reserve was secondary to depleted intramyocardial norepinephrine.
They also demonstrated that reduced 123I-MIBG accumulation was associated with down
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regulation of SERCA2 mRNA expression, indicative of altered calcium handling. These
findings were supported by a further study of 46 DCM patients which showed reduced
myocardial contractile reserve in response to low dose dobutamine was related to reduced
myocardial expression of regulatory protein mRNAs, including beta-adrengeric receptor,
SERCA, and phospholamban, involved in calcium signaling(362).
However, sympathetic dysfunction and the degree of beta-receptor down-regulation may not
fully account for the improved LV function reported in patients with preserved contractile
reserve. Abnormal myocardial perfusion has been linked to reduced contractile reserve in
small studies(363,364). The extent of myocardial contractile reserve has also been inversely
linked to myocardial interstitial fibrosis measured on endomyocardial biopsy(365). This has
not yet been demonstrated in vivo, but techniques such as CMR T1 mapping and extracellular
volume fraction quantification may provide a valuable insight(153). It is plausible that
contractile reserve reflects an absence of myocardial interstitial fibrosis, thereby providing an
LV substrate with the potential to remodel. Therefore the direct in vivo measurement of
myocardial fibrosis may be a novel prognostic marker of remodelling.
Summary
In summary, baseline myocardial contractile reserve has been associated with both prognosis
and myocardial remodelling in response to therapy patients with DCM. However, historic
studies have used LVEF echocardiographic assessment of contractile reserve with only
limited exploration of the biological basis of contractile reserve. CMR offers gold-standard
evaluation of cardiac structure and function, as well as in depth tissue characterisation. This
study therefore aims to bridge the gap between historic studies of contractile reserve and the
increasing utility of CMR in DCM, with in vivo tissue characterisation permitting assessment
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of replacement and interstitial myocardial fibrosis and precision quantification of cardiac
function, to identify imaging predictors of LV remodelling.
4.3 Methods
4.3.1 Study overview
The study was a prospective observational study, evaluating whether the imaging parameters
of contractile reserve, myocardial strain and myocardial fibrosis, measured at baseline in
patients with recent onset DCM, predict follow up LVEF at 1 year (Figure 4-1). Contractile
reserve was the primary imaging parameter evaluated.
Figure 4-1: DCM Remodelling study overview.
4.3.2 Study cohort, inclusion and exclusion criteria
4.3.2.1 Dilated cardiomyopathy cohort
The primary study cohort consists of prospectively recruited patients with DCM diagnosed
within the preceding 1 year. Inclusion criteria were a diagnosis of DCM, over the age of 18
years old, the presence of sinus rhythm, and the absence of a contraindication to CMR
scanning or dobutamine administration.
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The diagnosis of DCM was made based on CMR evidence of left ventricular dilation and
systolic impairment with reference to age, gender, and body surface area adjusted
nomograms(116) as previously outlined (Section 2.3.1.1, p115). Exclusion criteria for DCM
included a history of uncontrolled systemic hypertension, coronary artery disease (>50%
stenosis in one or more major epicardial arteries or previous percutaneous coronary
intervention or coronary artery bypass grafting), chronic excess alcohol consumption meeting
criteria for alcoholic cardiomyopathy (>80g/day for more than 5 years(97)), systemic disease
known to cause DCM, pericardial disease, congenital heart disease, infiltrative disorders (e.g.
sarcoidosis), or significant primary valvular disease(8,306,307).
As the primary analysis was the evaluation of LVEF at baseline and upon incremental
administration of dobutamine, sinus rhythm was a strict inclusion criteria, to ensure accurate
assessment of cardiac volumes using CMR.
A contraindication to CMR at 3T included the presence of a pacemaker, defibrillator, or
pacing wires, metal implants (including cochlear or spinal implants, hydrocephalus shunts),
vascular clips, or foreign bodies or metal in the eye (checklist shown in Appendix Figure
8-1).
A contraindication to dobutamine included any known hypersensitivity to dobutamine, recent
acute myocardial infarction, a history of unstable angina, recent life threatening arrhythmia,
severe dynamic or fixed left ventricular outflow tract obstruction, active endocarditis,
myocarditis or pericarditis, serious systemic illness, NYHA symptoms class IV, known
moderate left main stem or proximal left anterior descending coronary artery stenosis, or
severe systemic hypertension (systolic blood pressure >180mmHg and/or diastolic blood
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pressure >120mmHg).
Patients were recruited via referral from a network of regional Cardiologists, via recruitment
at the time of referral for CMR to our institution, or via self-referral from the
Cardiomyopathy UK website.
4.3.2.2 Healthy volunteer cohort
The primary analysis was evaluation of contractile reserve as a predictor of change in LVEF
within the DCM patient cohort. A cohort of healthy volunteers (HVOL) was recruited to
permit comparison of the baseline contractile reserve response in DCM patients and normal
subjects. These individuals had no history of medical illness, were not taking regular
medication, and did not have evidence of cardiac structural or functional impairment on CMR
scanning. Initially, they were recruited via advertisement within the hospital Trust. In order to
recruit more older males (to age match the controls), a subsequent major ethics amendment
was submitted to permit recruitment beyond the hospital, in local community sports centres,
libraries, community centres and churches.
All participants gave written informed consent and the study was approved by the relevant
regional research ethics committees.
4.3.3 Baseline CMR protocol
All patients underwent CMR at 3T (Siemens Skyra scanner). The scan protocol is shown
Table 4-1.
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Table 4-1 CMR baseline study protocol
DCM Remodelling CMR scan protocol
1. HASTE (Half Acquisition Single Shot Turbo-spin Echo) x 3 (transaxial, saggital, coronal)
2. Vertical long-axis (VLA), horizontal long axis (HLA), Left ventricular outflow tract (LVOT),
right ventricular outflow tract (RVOT)
3. Short axis (SAX) Cines
4. DENSE: 1 SAX, 2 long-axis (LAX) (HLA and VLA)
5. T1 mapping pre dobutamine (5,3,3)- basal and mid ventricular SAX slice (repeat x2)
6. Low-dose Dobutamine stress as an inotropic agent
Dose: 50mg made up to 50mL normal saline [concentration: 1mg/mL]
i. 5minutes at 5µg/kg/min
ii. Acquire SAX cines
iii. Further 5 minutes at 10 µg/kg/min
iv. Acquire DENSE: 1 SAX, 2 LAX
v. Acquire SAX cines (after 10 minutes of dobutamine infusion)
7. Wait until HR normalises
8. T1 mapping pre contrast– basal and mid ventricular SAX slice (Repeat x2)
9. Late Enhancement (Gadolinium 0.1mmol/kg)
10. T1 post contrast -15mins after Gd – basal and mid ventricular SAX slice (Repeat x2)
4.3.3.1 Assessment of ventricular volumes and function
Cardiac volumes were assessed 3 times: at baseline; following the administration of
dobutamine at 5µg/kg/minute; and at 10µg/kg/minute. Each time, end-expiratory breath hold
steady state free precession (SSFP) cine images were acquired in the 3 long axis planes
(horizontal long axis, HLA; right ventricular outflow tract, RVOT; left ventricular outflow
tract, LVOT) and 8mm short axis slices (2mm gap) from the atrioventricular ring to the apex
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(13). At 3T (Skyra, Siemens), imaging parameters were slice thickness 8mm, time per
acquired cine frame / echo time (TR/TE) 2.8/1.2ms, acquired resolution typically 1.65
x1.65mm, flip angle typically 44 degrees, field of view 340x240mm2, acquired temporal
resolution 54ms and 25 reconstructed frames.
Left ventricular (LV) volumes, function and mass were measured using a semiautomated
threshold-based technique (CMRtools, Cardiovascular Imaging Solutions, London, UK)
(Figure 4-2).
Figure 4-2: Example of threshold based measurement of cardiac volumes using CMR tools. Orange= left
ventricle blood pool excluding papillary muscles; yellow= left ventricular myocardium; purple= right
ventricle blood pool.
Left and right atrial area (LAA, RAA) was measured by tracing the atrial endocardial borders
with exclusion of the pulmonary veins and LA appendage in the horizontal long axis view.
The mean of two LAA and RAA measurements was recorded. Septal and lateral left
ventricular wall thickness was measured at the level of the papillary muscles, mid cavity in
end-diastole. An average of at least 2 measurements was taken. Maximum left ventricular
wall thickness was measured in each short axis slice at end-diastole, with the exclusion of
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papillary muscles. All volume and mass measurements were indexed to body surface area and
referenced to age and gender based tables(116).
4.3.3.2 Dobutamine assessment of contractile reserve
All patients were asked to stop beta blocker therapy for 48 hours prior to the scan. An ECG
was performed at baseline to exclude high degree atrio-ventricular block (second or third
degree heart block).
Figure 4-3: Schematic of dobutamine infusion stages. Baseline (Stage 0) is prior to the commencement of
dobutamine. Heart rate (HR) and blood pressure (BP) are recorded at baseline and every two minutes. At
Stage 1, infusion of low dose 5µg/kg/minute dobutamine is commenced and continued for at least 5
minutes, during which short and long-axis cines are acquired to assess cardiac volumes (Stage 1a). The
dobutamine infusion is then increased to 10µg/kg/minute (Stage 2), during which myocardial strain data
is acquired. After infusion of dobutamine 10µg/kg/minute for at least 5 minutes, short and long-axis cines
are acquired to assess cardiac volumes (Stage 2a).
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Following the first myocardial T1 map (see below), baseline heart rate and blood pressure
were recorded, then dobutamine infusion was commenced. An outline of the dobutamine
infusion protocol is shown in Figure 4-3. The dobutamine was administered via a peripheral
intravenous cannula with long line extension to the infusion pump located in the control
room. The concentration was 1mg/mL (50mg dobutamine in 50mL normal saline). Infusion
was commenced at 5 µg/kg/minute for 5 minutes (Stage 1). This very low dose ramped
protocol was administered to increase patient tolerability of the infusion. During this period,
cardiac volumes were assessed as outlined above (Stage 1a). The infusion was then increased
to the target dose of 10 µg/kg/minute for at least 5 minutes (Stage 2). At 2.5 minutes after
increasing the infusion, myocardial strain was assessed with the cine DENSE sequence. After
the 5 minute mark (at least 10 minutes of dobutamine infusion, Stage 2a), cardiac volumes
were again assessed. After the final SAX image, the dobutamine infusion was stopped.
Heart rate and non-invasive blood pressure were recorded at baseline, every 2 minutes during
dobutamine infusion, and after the infusion was stopped until heart rate normalized, blood
pressure normalized, and symptoms resolved. Patients were asked to report any symptoms at
regular intervals. In addition, all patients had an emergency alarm to press if they experienced
any untoward symptoms. Continuous ECG monitoring was performed throughout the scan,
including during the infusion of dobutamine. In addition to a cardiologist, a registered nurse
assigned to the study was present in the control room to assist in the event of any
complication. Intravenous beta blocker (metoprolol), and atropine was kept available in the
control room if needed, and access to full resuscitation equipment was always available. A
protocol for recognising and treating dobutamine complications was kept in the control room
for reference. Indications to terminate the dobutamine infusion were the development of
severe angina, dyspnea or other intolerable symptoms, a fall of systolic blood pressure more
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than 20mmHg below baseline or more than 20mmHg below the previous stage, the
development of hypertension >240/120mmHg, serious arrhythmia including ventricular
fibrillation, ventricular tachycardia or supraventricular tachycardia, second or third degree
heart block, symptomatic bradycardia, ST segment elevation, physical signs of hypoperfusion
such as cyanosis or pallor, or cerebrovascular symptoms such as ataxia or presyncope.
4.3.3.3 Assessment of interstitial fibrosis
All patients underwent T1-mapping at basal and mid-ventricular short axis levels pre and 15
minutes post administration of a bolus dose of gadolinium-based contrast agent (Magnevist or
Gadovist, Bayer) (0.1mmol/kg). A shortened Modified Lock-Locker Imaging (MOLLI)
sequence(151) was acquired in 11 cardiac-cycle breath-holds. The acquired inversion-
recovery times and recovery periods within each breath-hold were designed to optimise
accuracy and precision for the typical T1 ranges expected pre and post gadolinium(153)
known as 5(3)3 and 4(1)3(1)2 respectively (these are the numbers of images acquired after
each inversion pulse, with recovery periods given in brackets). The T1-mapping package
provided by the scanner manufacturer automatically performs per-pixel (“pixelwise”) curve-
fitting with Look-Locker correction (366) to generate T1 “maps” (Siemens Medical Systems,
Erlangen, Germany; “Myomaps” product).
T1 maps were acquired both pre and post infusion of dobutamine (after heart rate returned to
baseline), to control for any potential effect that dobutamine could have on the accuracy of
measurement or on T1 itself.
The trigger time for the T1 mapping sequence was adjusted to image in the diastolic rest
period, determined based on short axis cine images.
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Imaging parameters for T1 mapping were: non-selective inversion pulses, steady state free
precession single shot imaging with 20 degree flip angle, field of view 360x308mm, at
acquired resolution (frequency encoding x phase encoding) 1.9x2.4mm, slice thickness 8mm,
repetition time/echo time (TR/TE) 2.4/1.0ms. To achieve a minimal single-shot image
duration of 159ms, two further acceleration methods were applied: parallel imaging with 24
fully sampled central phase-encoding lines, and partial phase-encode sampling (7/8ths) with
zero-filling before reconstruction.
Images were analysed using CMR tools (CMRtools, Cardiovascular Imaging Solutions,
London, UK). T1 values were measured in a narrow region of interest in the septum, taking
care to avoid blood pool contamination, and a circular region in the blood pool, taking care to
avoid papillary muscles. Each patient’s packed cell volume (haemtocrit: the cellular fraction
of blood) was measured on the same day as the scan and extracellular volume fraction (ECV
= interstitium and extracellular matrix) was calculated according to the formula(153):
1 1 − (!"#$ !"#$%&'$ !1 !"#) (!"#$%& !1 !"#) !"# = 1 − ℎ!"#!$%&'($ 1 1 !"#$ !"#$%&'$ !1 !"##$ − !"#$%& !1 !"##$
All myocardial T1mapping and ECV quantification was performed in line with the Society of
Cardiovascular Magnetic Resonance and European Society of Cardiology consensus
statement(367).
4.3.3.4 Assessment of myocardial strain using cine DENSE imaging
All patients underwent baseline assessment of myocardial strain using a modified cine spiral
DENSE sequence. Images were acquired at the mid-ventricular SAX level and 2 long axis
planes (horizontal and vertical) at rest and during administration of the 10 µg/kg/min
dobutamine dose.
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Image parameters for DENSE acquisition were: 3.2x3.2x8.0mm3 spatial resolution, 30ms
temporal resolution and 2 direction encoding at 0.06cycles/mm, slice thickness 8mm,
repetition time/echo time (TR/TE) 15/1.0ms. The field of view (FOV) was 224mm2 with a
breath hold duration of 14 RR intervals.
Images were analysed and strain extracted from the DENSE data using semi-automated
Matlab post-processing software from the University of Virginia (170,368,369). The first
stage of analysis was anatomical delineation, using either a contour or a region of interest
covering the LV in the imaged slice. For long axis images, this was done with a line contour
(Figure 4-4). For SAX images, both contour analysis and region of interest were defined, with
the region of interest manually defined between endo- and epicardial borders (Figure 4-5).
These contours were defined in either the peak systolic frame or a diastolic frame before
automated propagation to the other frames (motion guided segmentation), with subsequent
manual adjustment as required. Frames with significant artefact (at the start or end) were
discarded. Strain was then calculated in the segmented areas, generating regional polar-
strain/time curves for radial and circumferential strain, contour strain/time curves for
longitudinal strain in 2 planes and contour strain/time curves for short axis strain.
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Figure 4-4: Examples of defining single line contour regions of interest for the assessment of long axis
strain. Panel A= horizontal long axis, panel B= vertical long axis. The contour lines are shown in orange.
The top images in each panel are magnitude images and the lower images are the phase images. The
displacement of each pixel is encoded in the image phase (x and y directions). The software uses this
phase data to track the displacement of the LV tissue throughout the cardiac cycle, relative to a reference
time, defined before the onset of systolic contraction. The software then uses the pixelwise displacements
to calculate myocardial strain.
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Figure 4-5: Examples of defining regions of interest for the assessment of short axis strain. Panel A=
endo-epicardial contour for assessment of radial and circumferential strain, panel B= single line contour
for global short axis contour strain. The top images in each panel are magnitude images and the lower
images are the phase images. The displacement of each pixel is encoded in the image phase (x and y
directions). The software uses this phase data to track the displacement of the LV tissue throughout the
cardiac cycle, relative to a reference time, defined before the onset of systolic contraction. The software
then uses the pixelwise displacements to calculate myocardial strain.
4.3.3.4.1 Development of the modified cine DENSE sequence
A physicist made iterative modifications to a spiral cine DENSE sequence that had
previously been validated at 3T(168) in order to reduce the breath hold time from ~21
seconds to a suitable range for patients with DCM(370). The primary modification was to
decrease the field of view (FOV) through a novel technique called zonal excitation, which
uses selective radiofrequency pulses to image just the heart whilst avoiding wrap (Figure 4-6).
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Limit the field
of view in y
Limit the field
of view in x
Figure 4-6: Figure demonstrating zonal excitation. Modified pulse sequence showing use of in plane slice
select gradients to reduce field of view. The first radiofrequency (RF) pulse (purple arrow) limits the field
of view in the x direction to the small region between the purple lines shown on the SAX cine figure and
the second RF pulse (orange arrow) limits the field of view in the perpendicular direction, leaving a small
region of interest around the heart to be imaged. The schematic sequence diagram also shows the R wave
of the ECG used to trigger the acquisition, fat suppression RF pulse (fat sat.), the DENSE encoding and
spiral imaging gradients (blue) and the slice selective RF pulses (purple; RF(z)) with increasing flip angle
used for imaging. Figure courtesy of Andrew Scott (physicist)(370).
The biggest limitation encountered in this process was retaining enough signal to noise ratio
(SNR) for DENSE analysis as a FOV reduction occurs at the cost of a reduction in SNR.
Scanning at 3T partially helps to compensate for this loss of SNR. We also reduced the
bandwidth, akin to reducing the frequency of sampling across the spiral. Lower bandwidths
give higher SNR, though at the cost of longer spiral readouts which can result in image
blurring. This sequence also uses a variable flip angle to mitigate tag fade through the cardiac
cycle, increasing the diastolic SNR. A compromise was therefore achieved between SNR,
FOV size and breath hold duration. During the initial acquisitions used in optimising protocol
parameters we used navigator (non breath hold) acquisitions as reference data. At a ‘medium’
breath hold of 14 cardiac cycles, we achieve a spatial resolution of 3.2mm and a temporal
resolution of ~30ms.
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Initial data analysis of a pilot healthy volunteer cohort revealed problems with the phase
wrapping of the images, meaning that the data was not suitable for analysis. This was a
particular problem for the long axis images because the longitudinal motion of the heart at the
base, shown in the long axis planes, is greater than the radial or circumferential
displacements. To address this, following discussion with the developers of the DENSE
sequence we modified (Epstein Lab, University of Virginia), we switched from balanced
encoding to simple encoding. Balanced encoding is more time efficient at the expense of
more phase wrap as it encodes in diagonal directions. Simple encoding encodes
displacements in x and y directions only. High displacement encoding frequencies are
associated with phase wrapping. As a result, we also reduced the encoding frequency from
0.1cycles/mm to 0.06cycles/mm, again a compromise between being able to detect phase
changes corresponding to small displacements and avoiding phase wrap. We also found that
it was important to shim carefully to avoid off-resonance artefacts (which are particularly
problematic in spiral MRI techniques) and select appropriate receive coils in order to avoid
residual image wrap.
4.3.3.5 Assessment of replacement myocardial fibrosis
Renal function was checked in all patients prior to gadolinium contrast. In patients with
eGFR >30mL/min/1.73m2 late gadolinium enhanced (LGE) images were acquired using a
breath hold inversion recovery sequence following administration of 0.1mmol/kg of
gadolinium contrast agent (Gadovist, Bayer) and saline flush to ensure complete delivery,
with inversion times optimised to null normal myocardium. Images were acquired in 3 long
axis planes and all short axis levels corresponding to the cine images. Typical image
parameters were: slice thickness 8mm, flip angle 20°, repetition time/echo time (TR/TE)
6ms/1.54ms, acquired resolution (frequency x phase-encoding) 1.45x1.93mm, field of view
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370mm x 308mm adjusted to suit body habitus or to minimise breathhold duration. All LGE
images were acquired a second time with a 90 degree rotation of the phase encode direction.
This assists in identifying true enhancement in the presence of artefact.
Magnitude reconstructions of the inversion recovery images were used, on the assumption
that only focal enhancement was relevant, which enabled the radiographer to adjust the
normal myocardium to near zero intensity, so that focal fibrosis was clearly identified by its
relatively bright signal (371). Mid-wall myocardial fibrosis was recorded as present if
detected in both phase-encoding directions and in 2 orthogonal views, with cross cuts taken
as appropriate.
4.3.4 Follow up imaging
After one year, patients underwent follow up imaging with CMR for the primary purpose of
evaluation of LVEF. For patients with a contraindication to CMR scanning, focused 3d
echocardiography for assessment of LV volumes was performed. Evaluation of follow up
LVEF was performed blinded to baseline scan results.
4.3.5 Sample size calculation and statistical analysis
Following statistical advice, the sample size calculation was performed using the
pwr.f2.test in R for linear regression models. For a power of 80% and a significance
level of 0.05, for univariable regression with an effect size of 0.33, a sample size of 24 is
required (pwr.f2.test(f2=0.33, sig.level = 0.05, power=0.8, u=1).
The effect size (f2) was calculated based on a R2 of 0.25 (f2=R2/(1-R2) for the relationship
between absolute contractile reserve (the change in LVEF between 0-10 µg/kg/min
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dobutamine infusion) and follow up LVEF. This is a relatively conservative estimate, as the
previous echo studies showed a strong correlation between stress LVEF and follow up LVEF,
with r =0.7-0.8(173,372), which would give an f2 of >1, which would be considered a very
large effect size. Therefore, a more conservative estimate was used, to ensure that an
appropriate sample size was recruited and that the study did not remain underpowered should
such a large effect size not be observed. Allowing for a 10% drop out rate, target recruitment
was 26 patients with DCM.
The primary hypothesis would be evaluated through linear regression analysis, evaluating the
predictive capacity of absolute LV contractile reserve (difference between baseline LVEF
and maximum LV change after peak dobutamine stress) as a continuous independent variable
against the dependent variable of follow up LVEF. A specific cut off for contractile reserve
will not be applied (to indicate the presence/absence of contractile reserve). Relative LV
contractile reserve will also be evaluated (absolute contractile reserve/baseline LVEF). To
control for regression to the mean which can occur with repeated measurements on the same
individual, the regression model will be adjusted for the baseline LVEF (difference between
baseline LVEF and the sample mean of baseline LVEF), known as analysis of covariance
(ANCOVA)(373). Absolute contractile reserve will first be evaluated as a univariable
predictor and then in a multivariable analysis, adjusting for confounders and variables of
interest (Figure 4-7). Confounders affect both the exposure and outcome variable, whereas
variables of interest may cause extra variation in either variable (but do not affect both)
therefore adjusting for these variables may reduce the standard error of the exposure or
outcome variables. In pre-specified secondary analyses, RV contractile reserve, baseline long
and short axis myocardial strain, and contractile reserve assessed by peak global
circumferential and longitudinal myocardial strain will also be evaluated as predictors of LV
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remodelling.
A control cohort was recruited to establish a descriptive comparator of baseline contractile
reserve between DCM patients and healthy individuals. Following statistical advice, target
recruitment was set at 20 patients. This was not the primary analysis. With 20 volunteers, we
would have 80% power, at an alpha level of 0.05, to detect a 9% difference in baseline
contractile reserve between patients and volunteers (using pwr.t.test in R).
A p value of <0.05 was considered significant and all analyses were conducted in the R
statistical environment (version 3.3.1).
Figure 4-7: Diagram outlining the relationship between the exposure variable (contractile reserve) and
the outcome variable (follow up LVEF), together with potential confounders (white circles - variables that
affect both the exposure and outcome variables) and variables of interest (blue and green circles- that
may affect either the exposure or outcome variables). Graph produced using DAGitty(374).
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4.4 Results
4.4.1 Cohort size and loss to follow up
In total, 38 patients with an initial clinical and imaging diagnosis of DCM were recruited. All
diagnoses had been made by a Consultant Cardiologist in our institution or a regional
referring hospital. Of these, 31 patients had CMR evidence of DCM at the time of study
recruitment, completed the baseline study protocol, and were included in the primary
analysis. One patient had CMR evidence of DCM but we did not start the dobutamine stress
protocol due to the presence of large pleural effusions and the potential risk of
decompensation with stress. Three patients had initial echocardiographic evidence of LV
impairment (in the referring centre), but normalised LVEF on CMR by the time of
recruitment, so were not included in the primary analysis. A further 3 patients had an initial
echocardiographic diagnosis of DCM (made in the referring centre) but evidence of an
alternative diagnosis on CMR study (1 patient with ischaemic heart disease, extensive
territory full thickness myocardial infarction; 2 patients with marked left ventricular
hypertrophy not consistent with DCM).
Of the 31 patients in the primary analysis, 2 were lost to follow up. One patient developed
bladder cancer and withdrew from the study, the other patient suffered a family bereavement
and withdrew from the study. Therefore, there were 29 patients with DCM who completed
the study protocol (Figure 4-8). In addition, 21 healthy volunteers were enrolled as control
participants.
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Figure 4-8: Overview of cohort recruitment and completion to follow up
4.4.2 Baseline demographics and CMR findings
Baseline demographics in the cohort are shown in Table 4-2. There was no significant
difference in age at enrollment (mean age (years) DCM 52.8 vs controls 46.1, p=0.06),
gender (DCM 72% male vs controls 67% male, p=0.76), or body surface area between
patients with DCM and healthy volunteers.
All healthy volunteers were asymptomatic. The majority of patients with DCM were NYHA
class I/II (n=28, 97%). Patients with DCM had a higher systolic blood pressure compared to
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volunteers (123 vs 110mmHg, p=0.004), but no difference in resting heart rate (66 vs 61,
p=0.13). All patients were in sinus rhythm and six DCM patients (20.7%) had evidence of
left bundle branch block (LBBB) on surface ECG.
Amongst the DCM patients, 24 patients (83%) were on beta blocker therapy, 25 patients
(86%) were taking an ACE inhibitor or an Angiotensin 2 receptor blocker, and 17 patients
were taking an aldosterone antagonist (59%). Two patients had contraindications to beta
blocker therapy (asthma) and one patient could not tolerate beta blocker therapy due to
bradycardia. Two patients were taking ivabradine, one due to a contraindication to beta
blocker therapy. Nineteen patients (66%) were taking diuretics at the time of study
enrollment. Only one patient was not on any medication due to personal choice. The overlap
in medication is shown in Figure 4-9.
Figure 4-9: Baseline prognostic medication in DCM cohort. BB=beta blocker, ACEARB= ACE inhibitor
or angiotensin 2 receptor blocker, AA= aldosterone antagonist. One patient was not on any medication.
Two patients were also taking ivabradine (not shown).
As expected, DCM patients had higher indexed LV end diastolic and end systolic volumes,
higher indexed LV mass, and lower LV stroke volume and LVEF compared to healthy
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volunteers (Table 4-2). There was no evidence of significant difference in RV end diastolic
volume between groups, though DCM patients had higher RV end systolic volume with
lower RV stroke volume and RVEF compared to controls (Table 4-2). There was no
difference in mean left or right atrial area between DCM patients and controls. DCM patients
had a higher maximal wall thickness and higher mean septal wall thickness compared to
controls (Table 4-2).
Amongst DCM patients, 1 patient was not given gadolinium contrast due to renal
impairment. Of the remaining, 14 patients (50%) had evidence of mid-wall fibrosis LGE
enhancement. No control participants had evidence of mid-wall LGE. In total, 9 DCM
patients (31%) had mild mitral regurgitation and 2 patients (7%) had moderate mitral
regurgitation on CMR study. There was no evidence of other valvular disease. One control
participant had minimal mitral regurgitation, otherwise no control subjects had evidence of
valvular disease.
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Table 4-2: Baseline demographics and CMR findings in cohort stratified by diagnosis. Continuous data
are shown as mean (± standard deviation) and compared using the Mann-Whitney test, categorical data
are shown as count (percentages) and compared using Fisher’s exact test. LV/RV=left/right ventricular;
EF=ejection fraction; EDVi/ESVi=indexed end diastolic/end systolic volume; SVi=indexed stroke volume;
LVMi=indexed LV mass; LAA/RAA=left/right atrial area; ECV=extracellular volume fraction.
DCM HVOL P value N=29 N=21 Age at enrollment (years) 52.80 (12.1) 46.05 (11.9) 0.06 Gender = M (%) 21 (72.4) 14 (66.7) 0.76 Body surface area (m2) 2.00 (0.26) 1.91 (0.22) 0.20 LBBB 6 (20.7) 0 (0.0) 0.03 Systolic BP (mmHg) 123.2 (16.4) 110.1 (12.4) 0.004 Resting HR (bpm) 66.7 (15.2) 61.1 (8.1) 0.13 NYHA <0.001 1 14 (48.3) 21 (100.0) 2 14 (48.3) 0 (0.0) 3 1 (3.4) 0 (0.0) LVEDVi (mL/m2) 122.7 (24.9) 84.4 (10.4) <0.001 LVESVi (mL/m2) 74.3 (23.8) 28.5 (6.2) <0.001 LVSVi (mL/m2) 48.5 (12.6) 55.8 (7.9) 0.02 LVMi (g/m2) 79.9 (23.5) 58.8 (20.7) 0.002 LVEF (%) 40.2 (10.4) 66.3 (5.6) <0.001 RVEDVi (mL/m2) 94.6 (21.9) 90.5 (12.2) 0.44 RVESVi (mL/m2) 48.2 (15.3) 36.9 (8.6) 0.004 RVSVi (mL/m2) 46.4 (12.3) 53.7 (9.5) 0.03 RVEF (%) 49.4 (9.1) 59.4 (6.9) <0.001 Midwall LGE 14/28 (50) 0 (0) <0.001 Mean LAA (cm2) 25.7 (5.3) 23.7 (4.3) 0.16 Mean RAA (cm2) 23.8 (4.4) 24.6 (5.4) 0.56 Maximum LV wall thickness (mm) 11.4 (2.2) 9.4 (1.6) 0.001 Mean lateral wall thickness (mm) 6.5 (1.4) 5.9 (1.1) 0.11 Mean septal wall thickness (mm) 9.1 (1.6) 7.1 (1.1) <0.001 Basal septal ECV 0.29 (0.06) 0.25 (0.02) 0.003 Mid septal ECV 0.29 (0.05) 0.25 (0.02) 0.014
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4.4.3 Safety of dobutamine
One patient did not complete the infusion protocol to 10 µg/kg/min due to hypertension
(systolic blood pressure >180mmHg), and only had a maximal dobutamine dose of 5
µg/kg/min. No other adverse incidents occurred during or after the administration of
dobutamine in the remaining patients.
4.4.4 Heart rate and blood pressure response to dobutamine stress
During low-dose dobutamine infusion, there was no evidence of a significant difference in
chronotropic response between DCM patients and healthy volunteers (Figure 4-10). DCM
patients appeared to have a reduced range of heart rate response compared to volunteers but
this difference was likely driven by one volunteer with a high heart rate response (Figure
4-10). During stress, DCM patients had a different systolic blood pressure response compared
to volunteers. All participants showed either a rise or fall in systolic blood pressure (SBP)
greater than 5mmHg. Five DCM patients had a fall in SBP during stress compared to only
one volunteer, though this was not related to the degree of LVEF impairment (Figure 4-11).
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Figure 4-10: Maximum change in systolic blood pressure (SBP) and heart rate during dobutamine
infusion in DCM patients and healthy volunteers (HVOL). Black lines indicate median values. Groups are
compared using the Mann-Whitney test.
Figure 4-11: Maximal change in systolic blood pressure (SBP) and heart rate during dobutamine infusion
plotted against baseline LVEF in patients with DCM and healthy volunteers.
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4.4.5 Baseline LV contractile reserve
Amongst DCM patients, the range of absolute LV myocardial contractile reserve (difference
in LVEF at peak dobutamine 10 µg/kg/min compared to baseline LVEF) was -9 units to +23
units change in LVEF (%), with mean change of 9.7 units and median of 11.0 units. Amongst
healthy volunteers, no individuals had a fall in LVEF during stress. The range of absolute
contractile reserve in volunteers was +1 to +20 units change in LVEF (%), with mean 10.1
units and median 10.0 units. There was no evidence of a statistically significant difference in
the absolute contractile reserve response between DCM patients and healthy volunteers
(p=0.98) (Figure 4-12).
Figure 4-12: Comparison of the absolute change in LVEF from baseline to peak dobutamine stress (10
µg/kg/min) in patients and volunteers (HVOL). Black lines indicate median values. Groups were
compared using the Mann-Whitney test. There was no evidence of a significant difference in absolute
contractile reserve between groups.
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Absolute contractile reserve was not correlated with the SBP response (r=0.10, p=0.49) or
baseline LVEF (r=0.03, p=0.81) (Figure 4-13). This SBP relation is important because it
suggests that the reduced contractile reserve was not secondary to insufficient stress, so there
were situations where both patients or controls demonstrated a physiological response to
stress (change in blood pressure) but without a change in LVEF.
Figure 4-13: Absolute contractile reserve. Plots showing no clear relationship between absolute
contractile reserve and the maximal change in (left hand panel) systolic blood pressure (SBP) during peak
dobutamine stress or (right hand panel) baseline LVEF.
Although there was no statistically significant difference in age between DCM patients and
controls, DCM patients were almost 7 years older than controls on average. Therefore further
analysis was performed to check for any possible effect of age on the contractile reserve
response. On univariable and multivariable linear regression analysis, there was no evidence
that age, gender, case/control status, baseline LVEF, or SBP response were predictors of the
absolute contractile reserve response (Table 4-3).
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Table 4-3: Linear regression models evaluating the effect of baseline variables on LV absolute contractile
reserve.
Unadjusted Adjusted Variable Change in Lower Upper P Change in Lower Upper P absolute CI CI value absolute CI CI value LV LV contractile contractile reserve reserve Age -0.3 -0.2 0.1 0.67 -0.01 -0.2 0.2 0.91 (per year) Male gender -3.2 -7.6 1.2 0.15 -3.8 -8.7 1.00 0.12 Control 0.3 -3.9 4.5 0.87 -0.4 -8.4 7.6 0.92 status Baseline 0.02 -0.1 0.1 0.81 -0.02 -0.3 0.2 0.9 LVEF (per 1%) Change in 0.04 -0.08 0.2 0.48 0.07 -0.08 0.2 0.35 SBP (per 1mmHg)
As shown in the regression analysis and illustrated in Figure 4-13, there was no significant
association baseline LVEF and absolute contractile reserve response in either patients or
volunteers. This is important for the ANCOVA analysis in which all regressions evaluating
the importance of contractile reserve are adjusted for baseline LVEF.
The distribution of relative contractile reserve (absolute contractile reserve/baseline LVEF) is
shown in Figure 4-14. The distribution is skewed by a DCM patient with a baseline LVEF of
11% who showed an LVEF increase to 24% on dobutamine stress.
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Figure 4-14: Plot of relative LV contractile reserve in DCM patients and controls. Black bars indicate
median values. Groups are compared using the Mann-Whitney test.
4.4.6 Baseline LV strain
4.4.6.1 Feasibility of acquisition and analysis of strain data
Cine DENSE data was acquired in all participants at baseline and during peak stress. The data
was however only analyzable in a subset of participants. Baseline data from two volunteers
and peak stress data from two volunteers were not analysed. Baseline data from 3 DCM
patients and peak stress data from 5 DCM patients were not analysed. This was not
necessarily all strain analysis, but at least one of SAX contour, radial, circumferential, HLA
contour, VLA contour, at baseline or stress. Images were excluded from analysis because of
off resonance artefact or insufficient signal to noise ratio (SNR) to permit analysis. An
example of insufficient SNR is given in Figure 4-15. This was exacerbated in, but not limited
to, images acquired during peak stress and occurred in similar frequency in patients (1.4%)
and controls (0.9%). The loss of SNR was most likely a result of the compromise between
SNR, FOV size and breath hold time as outlined in the Methods.
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Figure 4-15: Cine DENSE analysis. Example of a study in which cine DENSE data analysis was not
possible due to insufficient signal to noise ratio. In the upper panel (the magnitude images) of this
horizontal long axis analysis, the signal intensity is low and almost fades completely in the lateral wall
(orange arrow). In the lower panel (the phase images), the corresponding region in the phase data is very
noisy (white arrow), and the delineation between this and the blood pool (speckled area corresponding to
phase noise– black arrow) is poor.
4.4.6.2 Baseline strain results
Summary strain curves for the DCM and control cohorts are shown in Figure 4-16. The
following results were derived for each participant at baseline and stress: peak global HLA
contour strain, peak global VLA contour strain, peak global SAX contour strain, peak global
radial strain and peak global circumferential strain.
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Figure 4-16: Summary strain-time curves for DCM and control cohort. The plots show the median
longitudinal (horizontal long axis, HLA; vertical long axis, VLA) strain, short axis (SAX) contour strain,
and radial and circumferential strain in healthy volunteers (blue curves) and the DCM cohort (red
curves). Curves are shown with the inter-quartile range (dashed lines). Peak strain is reduced in all
dimensions in DCM patients compared to controls.
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Compared to healthy volunteers, DCM patients had reduced peak global long and short axis
strain (Table 4-4).
Table 4-4: Peak global long axis (HLA, VLA) and SAX strain in DCM patients and volunteers . HVOL=
healthy volunteer. Data are shown as median [interquartile range]. Groups are compared using the
Mann-Whitney test.
DCM HVOL P value
VLA contour peak global strain -0.10 [-0.11, -0.06] -0.15 [-0.16, -0.14] <0.001
HLA contour peak global strain -0.09 [-0.11, -0.07] -0.16 [-0.16, -0.14] <0.001
SAX contour peak global strain -0.10 [-0.12, -0.07] -0.16 [-0.18, -0.14] <0.001
Radial peak global strain 0.18 [0.08, 0.28] 0.42 [0.37, 0.51] <0.001
Circumferential peak global strain -0.11 [-0.13, -0.08] -0.17 [-0.19, -0.17] <0.001
Across the cohort, there was a strong linear relationship between LVEF and contour strain
and LVEF and circumferential strain in DCM patients and healthy volunteers (Figure 4-17).
There was also a linear relationship between LVEF and radial strain in healthy volunteers,
though in DCM patients this could not be clearly established, particularly for patients with
severely impaired LVEF (Figure 4-17). This may reflect LV wall thinning in patients with
DCM, leading to inaccurate estimates of radial strain likely because there are fewer pixels
from which to derive the strains, therefore the effect of an erroneous blood pool pixel is
heightened. There was no linear relationship between mean lateral wall thickness and radial
strain (r=-0.30, p=0.15).
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Figure 4-17: Relationship between left ventricular ejection fraction and contour strains (top 3 panels) and
circumferential and radial strains (bottom 2 panels). Univariable linear regression lines are plotted and
the slope and p value of the regression shown above the plot. Points are colour coded by diagnosis;
red=DCM, blue=healthy volunteer. LVEF=left ventricular ejection fraction.
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The strength of correlation between measurements of strain and LVEF and mean septal and
lateral wall thickness is shown in Figure 4-18.
Figure 4-18: Correlation matrix showing the size of the correlation (from -1 to +1) between LVEF and
mean wall thickness (WT) and strain, in DCM patients only. Only correlations with a p value <0.05 are
displayed. The larger the circle and the more intense colour indicates a stronger correlation. LVEF is
strongly negatively correlated with circumferential, VLA, HLA and SAX contour strains and weak-
moderately positively correlated with radial strain. Lateral wall thickness is moderately correlated with
radial, circumferential and HLA contour strain estimates, whereas septal wall thickness is not correlated
with strain in DCM patients. Both horizontal and vertical long axis strain estimates are strongly
positively correlated. The SAX contour strain is strongly positively correlated with circumferential strain
in DCM patients. Together, this suggests that radial strain in this cohort is a less robust marker for the
assessment of LVEF function.
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4.4.6.3 Contractile reserve as evaluated by assessment of strain
The absolute difference in baseline and stress peak global circumferential strain, SAX
contour strain, horizontal and long axis strains in DCM patients and healthy volunteers is
shown in Table 4-5. With the exception of absolute difference in horizontal long axis strain,
there was no difference in contractile reserve assessed by myocardial strain between DCM
patients and healthy volunteers. This suggests that long axis strain may be a subtle or earlier
marker of LV dysfunction in DCM patients compared to other estimates of myocardial strain.
Table 4-5: Absolute difference in peak global long axis (HLA, VLA) and SAX strain in DCM patients and
healthy volunteers (HVOL). Data are shown as median [inter-quartile range]. Groups are compared
using the Mann-Whitney test. Negative contour strains and negative circumferential strain indicate better
LV function, whereas positive radial strain indicates better LV function.
DCM HVOL P value
SAX contour -0.01 [-0.02, 0.01] -0.01 [-0.04, -0.00] 0.15
HLA contour 0.01 [-0.01, 0.01] -0.02 [-0.03, -0.01] 0.002
VLA contour -0.01 [-0.02, 0.01] -0.01 [-0.02, -0.00] 0.34
Radial 0.03 [-0.01, 0.13] 0.10 [-0.01, 0.21] 0.39
Circumferential -0.01 [-0.03, -0.00] -0.02 [-0.05, -0.01] 0.48
4.4.7 Baseline interstitial fibrosis
T1 measurements were taken pre and post dobutamine infusion, after heart rate had returned
to baseline levels. There was a strong correlation between native T1 measurements pre and
post dobutamine, both at basal (r=0.96, p<0.00001) and mid-ventricular (r=0.93, p<0.00001)
levels, suggesting that dobutamine did not affect T1 measurement at 3T.
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Extra-cellular volume fraction (ECV) was calculated from pre and post contrast T1
measurements at LV basal ventricular and mid-ventricular levels. The mean of two
measurements was taken. Whilst DCM patients had a higher overall ECV at LV basal and
mid-ventricular levels (Table 4-6), there was considerable overlap in ECV between DCM
patients and control participants (Figure 4-19).
Table 4-6: Extra-cellular volume fraction (ECV) in DCM patients and healthy volunteers (HVOL) at LV
basal and mid-ventricular levels. Data are expressed as mean (standard deviation) and compared using
the Mann-Whitney test.
DCM HVOL P value
N=29 N=21
LV Basal ECV 0.29 (0.06) 0.25 (0.02) 0.002
LV Mid ECV 0.29 (0.05) 0.25 (0.02) 0.006
Figure 4-19: Extracellular volume fraction (ECV) across cohort. Whilst healthy volunteers (HVOL) had a
lower ECV compared to DCM patients, the beeswarm and boxplots show overlap in LV basal (left) and
mid (right) ECV measurements. Black lines indicate median values The ECV values are a mean of two
measurements.
There was a strong correlation between basal and mid LV ECV estimates (r=0.93, p<0.0001),
suggesting that there is no regional distribution of interstitial fibrosis in the septum in DCM
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and healthy individuals(Figure 4-20). There may be other regional differences in interstitial
fibrosis distribution that were not assessed in this study such as in the lateral wall or apex.
One DCM patient had a much higher ECV compared to the rest of the cohort (ECV >0.40).
This was a 56 year old male, NYHA class I, with severe LV impairment (LVEF 36%) and
septal mid-wall fibrosis. For all analyses, care was taken so that all regions of interest for T1
calculations were drawn away from any clear areas of replacement fibrosis or the blood pool.
Amongst patients and volunteers, there was a weak correlation between ECV and LVEF
(Figure 4-20).
Figure 4-20: Relationship between ECV and LVEF. Scatterplot showing [TOP PLOT] the strong
correlation between ECV measurements at the basal septal and mid septal level in DCM patients (blue
dots) and healthy volunteers (red dots) and the weak correlation between ECV and LVEF [LOWER
TWO PLOTS].
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4.4.7.1 LVEF remodelling
After 1 year follow up, all patients underwent repeat evaluation of LV function. Seven
patients had contra-indications to CMR so had 3d echocardiography assessment of LV
function. Of these, 3 patients had CRT devices, 4 patients had ICD devices. The baseline and
follow up LVEF for each patient is shown in Figure 4-21. The median absolute change in
LVEF between baseline and follow up studies was 13.0% (range -1% to 47%). Seventeen
patients (58.6%) showed an absolute improvement of LVEF greater than 10 units. One
patient showed a marked improvement from baseline LVEF 11% to follow up LVEF 58%.
Three patients showed either no change or a deterioration in LVEF on follow up.
Figure 4-21: LVEF change at 1 year. Figure shows baseline LVEF (blue dots) and follow up LVEF (red
dots) after 1 year for each of the 29 DCM patients. The majority of patients showed improvement in
LVEF, with only 3 patients showing a deterioration in LVEF on follow up imaging.
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4.4.8 Evaluating absolute contractile reserve as a predictor of LV
remodelling
Univariable analysis
Absolute LV contractile reserve was a significant predictor of the follow up LVEF, after
adjusting for baseline LVEF in ANCOVA regression analysis (each 1% increase in absolute
LVEF during dobutamine stress was associated with a 0.4% increase in follow up LVEF,
p=0.007, Table 4-7).
The correlation between absolute contractile reserve and follow up LVEF (without adjusting
for baseline LVEF) was r=0.52 (R2=0.28), p=0.004.
Next, potential confounder variables (age and gender) and variables of interest (baseline
prognostic medication, follow up imaging modality, CRT implantation) were evaluated.
Univariable ANCOVA regression was performed on these and additional variables of interest
that could potentially affect remodelling and the results are summarized in Table 4-7.
Of these variables, only gender was a significant predictor of follow up LVEF on univariable
analysis, with male gender being associated with a 7.5% reduction in follow up LVEF
(p=0.01, Table 4-7). NYHA class was associated with follow up LVEF but this was driven by
one patient in NYHA class 3 who showed an LVEF improvement of 22 units. Surprisingly,
age and prognostic medication (either as a binary variable or dose as a continuous variable)
did not predict LVEF remodelling.
On univariable analysis, the follow up imaging modality (3d echocardiography or CMR) did
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not predict the follow up LVEF, however this will still be adjusted for in multivariable
analysis to control for any variation in follow up LVEF due to imaging modality and not
contractile reserve. Only 3 patients had CRT devices and this did not predict follow up
LVEF.
The presence of baseline mid-wall LGE enhancement, left bundle branch block (LBBB) and
mitral regurgitation did not predict follow up LVEF (Table 4-7).
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Table 4-7: Univariable ANCOVA regression evaluating predictors of follow up LVEF.
Variable Unit Estimate change to P value follow up LVEF (95% confidence interval) Absolute LV Per 1% change in LVEF 0.4 (0.12 to 0.73) 0.007 contractile reserve after 10 µg/kg/min dobutamine stress Age at enrollment Per 1 year 0.02 (-0.23 to 0.27) 0.89 Gender Male -7.5 (-13.3 to -1.8) 0.01 Number of prognostic Per each unit increase in the 0.5 (-2.5 to 3.6) 0.73 medications (BB, count of number of ACEi/A2RB, AA, medications Ivrabadine) Beta blocker use Present 2.1 (-5.4 to 9.6) 0.57 ACE inhibitor/A2RB Present -0.4 (-7.2 to 6.4) 0.91 use Aldosterone Antagonist Present 2.1 (-4.1 to 8.2) 0.49 use Beta blocker use Per 1mg increase in dose -0.06 (-1.7 to 1.6) 0.94 ACE inhibitor use Per 1mg increase in dose 0.17 (-0.54 to 0.88) 0.63 Aldosterone Antagonist Per 1mg increase in dose 0.08 (-0.47 to 0.62) 0.77 use Follow up imaging 3d Echocardiography -1.4 (-9.1 to 6.3) 0.71 modality CRT Implanted during follow up -0.8 (-10.8 to 9.2) 0.87 period LBBB Present 0.89 (-6.5 to 8.3) 0.81 NYHA class Class 2 compared to class I 1.9 (-3.6 to 7.5) 0.48 NYHA class Class 3 compared to class I 15.7 (1.0 to 30.4) 0.03 Hypertension History of hypertension -0.2 (-6.1 to 5.7) 0.94 Mid wall LGE Present -3.5 (-9.2 to 2.2) 0.22 Mitral regurgitation Mild -1.4 (-14.0 to 11.3) 0.82 Mitral regurgitation Moderate -2.4 (-8.7 to 3.9) 0.43
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Multivariable analysis
In multivariable analysis, adjusting for potential confounders and variables of interest,
absolute contractile reserve remained an independent predictor of follow up LVEF.
Multivariable models were built, first including only variables significant on univariable
analysis (Model 1: absolute contractile reserve, gender, NYHA class) and then also including
pre-specified variables of interest (Model 2: absolute contractile reserve, gender, NYHA
class, age, follow up imaging modality, prognostic medication use, and CRT use).
In the adjusted analyses, the effect size of absolute contractile reserve on follow up LVEF
was slightly reduced. Each 1 percentage unit increase in absolute contractile reserve was
associated with a 0.34 percentage unit increase in follow up LVEF in Model 1 (p=0.03), and
a 0.36 percentage unit increase in follow up LVEF in Model 2 (p=0.04) (Table 4-8). These
models are shown in Figure 4-22.
Of note, male gender, whilst having a large negative effect size on follow up LVEF, was not
significant in the multivariable models.
In summary therefore, in this cohort, absolute LV contractile reserve was the only identified
predictor of LVEF remodelling at 1 year follow up in ambulant patients with DCM.
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Table 4-8: Multivariable ANCOVA regression analysis evaluating absolute contractile reserve. Model 1
includes only variables significant on univariable analysis, Model 2 also includes pre-specified variables of
interest.
Model Estimate unit 95% confidence P value change in follow up interval LVEF (%) Model 1: Absolute CR, gender, NYHA class Absolute CR – per 1% absolute 0.34 0.03 to 0.65 0.03 increase in LVEF after dobutamine infusion Male gender -5.3 -11.0 to 0.46 0.07 NYHA class III 7.0 -7.2 to 21.2 0.32
Model 2: Absolute CR, gender, NYHA class, age, follow up imaging modality, CRT, prognostic medication use Absolute CR – per 1% increase in 0.36 0.01 to 0.72 0.04 LVEF after dobutamine infusion Male gender -6.3 -12.8 to 0.11 0.05 NYHA class III 5.6 -10.4 to 21.6 0.47 Age per 1 year increase -0.12 -0.38 to 0.15 0.36 3d echo follow up imaging modality -0.16 -9.0 to 8.7 0.97 CRT implantation 0.58 -10.8 to 12.0 0.92 Prognostic medication –Per each unit 1.43 -1.68 to 4.54 0.35 increase in the count of number of medications
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Figure 4-22: Regression models evaluating absolute contractile reserve as a predictor of follow up LVEF,
adjusted for baseline LVEF. The left panel is the univariable model, the middle panel is Model 1 (absolute
contractile reserve, gender, NYHA class) and the right panel is Model 2 (absolute contractile reserve,
gender, age, NYHA class, prognostic medication, CRT use, follow up imaging modality). The slope of the
regression line and p value of the estimate is shown above each plot.
Sensitivity analysis
Contractile reserve estimates from the change in LVEF could be affected by factors which
affect LVEF, including heart rate variability and mitral regurgitation.
Adjusting for the maximal change in heart rate during dobutamine infusion, and the degree of
baseline mitral regurgitation, there was little change in the estimate of absolute contractile
reserve on follow up LVEF (Table 4-9). Heart rate variability and mitral regurgitation
themselves were not predictive of follow up LVEF. Nested ANOVA showed that there was
no significant difference between Model 1 (above) and Model 1 with the addition of these
variables (p=0.76).
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Table 4-9: Contractile reserve sensitivity analysis, showing the effect of controlling for heart rate
variability and mitral regurgitation on absolute contractile reserve as a predictor of follow up LVEF.
Variables Estimate unit 95% confidence P value change in follow interval up LVEF (%) Absolute CR – per 1% absolute 0.39 0.05 to 0.73 0.03 increase in LVEF after dobutamine infusion Δ HR during dobutamine 0.07 -0.22 to 0.36 0.63 Mitral regurgitation: Mild -0.9 -12.5 to 10.6 0.87 Mitral regurgitation: Severe -1.7 -7.5 to 4.1 0.55
4.4.8.1 Contractile reserve as assessed by longitudinal, circumferential and radial strain
The assessment of LV contractile reserve through assessment of LVEF change following
dobutamine stress is potentially limited as it is a load dependent measurement. To mitigate
this potential limitation, contractile reserve assessed by the difference in peak strain at
maximal dobutamine dose and baseline was also evaluated as a predictor of follow up LVEF.
As shown in Table 4-10, peak global circumferential and radial strain contractile reserve,
short axis contour strain contractile reserve and horizontal long axis longitudinal strain
contractile reserve were not associated with follow up LVEF. In contrast, longitudinal strain
contractile reserve, measured in the vertical long axis plane, was significantly associated with
follow up LVEF (-6.8 absolute % units change in follow up LVEF for each 0.1 unit increase
in vertical longitudinal axis strain reserve, p=0.03).
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Table 4-10: Contractile reserve assessed by myocardial strain as predictor of follow up LVEF.
Strain contractile Estimate change in 95% Confidence P value reserve follow up LVEF interval (absolute % units) for each 0.1 unit increase in strain Peak global 3.5 -7.1 to 14.2 0.49 circumferential Peak global radial -0.5 -2.8 to 1.7 0.63 Peak short axis -2.1 -5.9 to 1.8 0.28 contour Peak horizontal long -3.4 -8.4 to 1.6 0.17 axis Peak vertical long axis -6.8 -12.7 to -0.8 0.03
4.4.9 Other predictors of LV remodelling
Because of the skewed distribution (Figure 4-14), relative LV contractile reserve was not
evaluated as a predictor of LV remodelling. As pre-specified secondary analyses, RV
contractile reserve, myocardial strain, interstitial fibrosis and replacement fibrosis were also
evaluated as predictors of LV remodelling.
RV contractile reserve
Amongst DCM patients, the range of absolute RV contractile reserve (difference in RVEF at
peak dobutamine 10 µg/kg/min compared to baseline RVEF) was -17 units to +24 units
change in RVEF (%), with mean change of 4.2 units and median of 4.5 units. Amongst
healthy volunteers, the range of absolute contractile reserve in volunteers was -4 to +17 units
change in RVEF (%), with mean 8.0 units and median 10.0 units.
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Figure 4-23: Absolute RV contractile reserve in DCM patients compared to controls. Groups were
compared using the Mann-Whitney test. Black bars indicate median values. HVOL= healthy volunteers.
There was no evidence of a statistically significant difference in the absolute RV contractile
reserve response between DCM patients and healthy volunteers (p=0.47) (Figure 4-23). RV
contractile reserve was not correlated with baseline LVEF (r=0.13, p=0.35). RV contractile
reserve was highly correlated with LV contractile reserve (r=0.81, p<0.00001, Figure 4-24).
Figure 4-24: Right and left ventricular contractile reserve. Scatterplot with regression line and 95%
confidence intervals showing the strong correlation between absolute right and left ventricular contractile
reserve. The slope of the univariable regression line and p value are shown above the plot.
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However, despite the strong correlation between RV and LV contractile reserve, on
univariable ANCOVA analysis absolute RV contractile reserve was not predictive of follow
up LVEF (estimate change in follow up LVEF for each 1 unit absolute increase in RVEF
after dobutamine infusion: 0.2 units, 95% CI -0.04 to 0.47, p=0.10).
In contrast to relative LV contractile reserve, the distribution of relative RV contractile
reserve (absolute RV contractile reserve/baseline RVEF) was not skewed (Figure 4-25). On
univariable ANCOVA analysis, relative RV contractile reserve was predictive of follow up
LVEF, although the borderline p value suggests that this predictor is not as robust as absolute
LV contractile reserve (estimate change in follow up LVEF for each 0.1 unit increase in
relative RV contractile reserve: 1.2 units, 95% CI 0.02 to 2.33, p=0.047).
Figure 4-25: Relative RV contractile reserve in DCM patients and healthy volunteers (HVOL). Relative
RV contractile reserve is the absolute RV contractile reserve after dobutamine infusion divided by the
baseline RVEF. Black bars indicate median values. Groups are compared using the Mann-Whitney test.
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Baseline long and short axis strain
On univariable ANCOVA analysis no baseline measure of strain, either in the longitudinal,
circumferential or radial dimensions, was predictive of follow up LVEF (Table 4-11).
Table 4-11: Results of ANCOVA regression analysis evaluating baseline indices of strain as predictors of
follow up LVEF.
Variable Estimate change in follow 95% Confidence interval P value up LVEF (absolute % units) for each 0.1 unit increase in strain VLA contour peak 6.2 -3.9 to 16.3 0.22 global strain
HLA contour peak -1.4 -6.4 to 3.6 0.58 global strain SAX contour peak -3.2 -11.5 to 5.1 0.43 global strain
Radial peak global 0.74 -1.1 to 2.6 0.42 strain Circumferential peak -5.4 -21.4 to 10.7 0.50 global strain
Replacement myocardial fibrosis
Amongst the 28 DCM patients who were given gadolinium contrast, 50% had evidence of
mid-wall myocardial enhancement. The majority was located in the septum (n=10, 71%). The
presence of mid-wall fibrosis was not a significant predictor of follow up LVEF in this cohort
(estimate 3.5% reduction in follow up LVEF, 95% CI -9.2 to 2.2%, p=0.22).
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Interstitial fibrosis
No ECV measurements were predictive of follow up LVEF (ECV basal: -4.3% change in
follow up LVEF per 0.1 unit change in ECV, 95% CI -9.3 to 0.7, p=0.09; ECV mid: -4.3%
change in follow up LVEF per 0.1 unit change in ECV, 95% CI -9.9 to 1.2, p=0.11).
4.4.10 Mechanistic basis of contractile reserve
Absolute contractile reserve was not correlated with interstitial fibrosis, measured either via
basal (r= -0.19, p=0.35) or mid LV ECV (r=-0.20, p=0.31). Whilst the individual with a very
high ECV also had a fall in absolute contractile reserve, for the remaining cohort, there was
no strong linear relationship between higher ECV and lower contractile reserve, as might be
expected if reduced contractile reserve was underpinned by interstitial fibrosis (Figure 4-26).
Most notably, individuals with a fall in contractile reserve have similar ECV values to
individuals with higher absolute contractile reserve values.
Figure 4-26: Contractile reserve and ECV. The graph shows that there is no clear relationship between
ECV (left y axis for basal and right y axis for mid LV levels) and absolute contractile reserve (% unit
change in LVEF after peak dobutamine infusion, x axis).
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4.5 Discussion
This main finding of this prospective study is that LV contractile reserve, assessed by low-
dose dobutamine stress CMR, is the only independent predictor of LV remodelling in patients
with recent onset DCM, over and above age, gender, symptom status, prognostic medication
use and mid-wall replacement fibrosis.
The study also demonstrates that the degree of myocardial contractile reserve is not related to
the burden of myocardial interstitial fibrosis, providing insight into the mechanistic basis of
contractile reserve. In addition, the study demonstrates that contractile reserve assessed by
longitudinal strain and relative RV contractile reserve are predictive of LV remodelling, but
baseline myocardial strain, replacement myocardial fibrosis and interstitial fibrosis are not
predictive of LV remodelling.
These results prompt 3 notable points for discussion: 1) the capacity for remodelling in DCM,
2) the role of contractile reserve in predicting remodelling in DCM, and 3) the biological
basis of contractile reserve.
4.5.1.1.1 The capacity for remodelling in DCM
Whilst there is no universally accepted threshold to define left ventricular reverse
remodelling (LVRR), almost 60% of patients in this study showed an improvement of LVEF
greater than 10 percentage units. This is towards the upper limit of previous findings (25-
70%) (184-195) and confirms the capacity for apparent recovery in DCM. That the majority
of the cohort demonstrated improved LV function may reflect that this is a contemporary
cohort of DCM patients treated with optimal medical therapy.
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This study also confirmed that the capacity for remodelling was not intuitive and cannot be
predicted based on resting LVEF alone. Improvement in LVEF was seen across the spectrum
of baseline LVEFs including those with initial severely reduced LVEFs. This was the basis to
evaluate contractile reserve as a predictor of LV remodelling in DCM.
4.5.1.1.2 The role of LV contractile reserve in predicting LV remodelling in DCM
In this study of 29 patients with DCM, a 1% absolute increase in LVEF during dobutamine
stress was associated with a 0.4% increase in 1 year follow up LVEF. Whilst contractile
reserve has previously been evaluated as a predictor of remodelling in echocardiography
studies, this is the first study to evaluate contractile reserve assessed by CMR in combination
with LGE CMR for the prediction of LV remodelling in DCM. This provided the opportunity
to evaluate contractile reserve in the context of novel DCM imaging biomarkers such as
replacement fibrosis.
Absolute LV contractile reserve was the only independent predictor of LV remodelling in this
cohort, over and above age, gender, symptom status, and mid-wall replacement fibrosis.
Importantly, in this study, the majority of patients were on appropriate medical therapy and
prognostic heart failure medication was not predictive of follow up LVEF.
This current study therefore demonstrates that predictors of outcome in DCM (e.g. mid-wall
replacement fibrosis) and predictors of remodelling in DCM are not necessarily
interchangeable, and at least for the 12 month follow up period of the study, contractile
reserve was the strongest imaging predictor of functional myocardial recovery.
There are plausible biological explanations as to why LGE was not a significant predictor of
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remodelling in DCM. A previous study evaluating CMR predictors of remodelling in
ischaemic heart disease showed that, compared to LGE, low dose dobutamine stress was both
an incremental predictor of remodelling and could stand alone with the same predictive
capacity as LGE(375). This suggests that contractile reserve may reflect the global capacity
of the myocardium to remodel, for example preserved beta-adrenergic receptor sensitivity,
whereas LGE mid-wall fibrosis is a focal insult that does not affect remote myocardial
remodelling.
Of course it must be stressed that the current study was not formally powered to evaluate the
role of age, gender and mid-wall fibrosis as predictors of LV remodelling. Male gender in
particular was a significant predictor of adverse LV remodelling on univariable analysis but
did not remain significant on multivariable analysis. This may reflect sample size as the
magnitude of effect of male gender was similar in multivariable analysis compared to
univariable analysis. Further interrogation of this could form the basis of future studies.
These findings may be of significance to patients and clinicians to understand which patients
may remodel with appropriate medical therapy. At present, there are no integrated robust
predictors of recovery in DCM. One potential utility of these findings could be in guiding
decisions about the timing of device therapy, particularly in younger patients who may meet
criteria for ICD insertion. Whilst potentially life saving, there are procedural risks of ICD
implantation as well as long-term risks of multiple generator replacements in younger
patients. The presence of demonstrable contractile reserve could provide support for a
deferred implantation strategy to allow an adequate interval for possible recovery (current
guidelines recommend at least 3 months of medical therapy). Conversely, identification of a
patient with little potential for reverse remodelling could identify a patient in need of early
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intensive therapy including prompt referral for advanced therapy. This of course requires
further evaluation in a prospective study. The data could also inform clinical trial design,
stratifying therapies by groups with different remodelling profiles.
Aside from the potential clinical utility of LV contractile reserve as a predictor of LV
remodelling, this finding could provide insight into mechanisms and potential treatment of
LV dysfunction. This however requires an understanding of the biological basis of contractile
reserve.
4.5.1.1.3 The biological basis of contractile reserve.
A recognized limitation of previous studies of LV remodelling is a failure to correlate
functional outcomes with molecular, cellular or histological findings(376). There is therefore
great interest in the exploring the biological basis of myocardial recovery. As contractile
reserve is a predictor of myocardial recovery, the biological basis of contractile reserve could
be informative of the mechanism of myocardial recovery.
There are several possible mechanistic explanations for contractile reserve. Primarily,
improved contractility during dobutamine stress is thought to reflect preserved beta-
adrenergic receptor function. Down-regulation of beta-receptors occurs with LV dysfunction,
irrespective of severity(181). In line with this, resting LVEF is not predictive of contractile
reserve(172). Reduced contractile reserve in vivo is linked to beta-receptor down-regulation,
even amongst patients with mildly impaired LV function(362). This is supported by our
study, in which the magnitude of the LV contractile reserve response was similar in patients
and controls and could not therefore be predicted based on LVEF alone.
Contractile reserve may therefore simply reflect beta-receptor upregulation or sensitisation,
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leading to a myocardial substrate that is more likely to respond to prognostic therapy,
including beta-blockade(372,377). However, these studies also showed that contractile
reserve may also predict myocardial recovery in patients not receiving beta blocker therapy.
In addition, the basis for contractile reserve has also been linked to altered calcium handling
(reduced expression of SERCA2a and phosholamban mRNA)(362), suggesting that the
biological basis for contractile reserve is likely to reside in multiple molecular pathways.
Therefore, this study set out to evaluate one of the mechanisms of myocardial remodelling
that has also been implicated in the contractile reserve response, notably changes in the extra-
cellular matrix. Whilst molecular pathways defining reverse remodelling have not been
clearly defined, the transition from compensated to decompensated hypertrophy is better
understood and involves multiple molecular pathways, including re-expression of fetal genes,
impaired excitation-contraction coupling, vascular and cardiomyocyte growth mismatch,
myocyte necrosis, and changes in the extracellular matrix(378).
Interstitial fibrosis on endomyocardial biopsy has been inversely linked to the extent of LV
myocardial contractile reserve (365). Myocardial recovery is thought to be possible if there is
both a sufficient mass of viable myocytes and an absence of extensive fibrosis(379,380).
We therefore hypothesized that patients with a high ECV (an in vivo estimate of interstitial
fibrosis assessed through CMR T1 mapping) would have diminished contractile reserve.
However, this study showed that the magnitude of the contractile reserve response was not
related to the degree of interstitial fibrosis. Patients with both a rise and fall in LVEF during
dobutamine stress had a range of ECV values, showing that absolute contractile reserve is
unaffected by the burden of interstitial fibrosis. In addition, the magnitude of interstitial
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fibrosis at baseline was not predictive of LV remodelling. This suggests that the interstitial
fibrosis previously noted to be correlated with contractile reserve may have simply been a
reflection of a failing myocardium and unrelated to contractile reserve or reverse cardiac
remodelling. Whilst interstitial fibrosis reflects dysfunctional myocardium, in this study, the
degree of interstitial fibrosis does not reflect the capacity of the myocardium to remodel.
4.5.1.1.4 Additional novel findings: the importance of longitudinal myocardial strain
In this study myocardial strain was assessed through the use of a novel modified cine DENSE
sequence. Previous cine DENSE sequences have been limited by long breath hold times but
in this study all patients were able to complete the breath hold required, demonstrating the
feasibility of evaluation of myocardial strain using cine DENSE CMR.
This enabled the assessment of circumferential, radial and longitudinal strain in DCM
patients. At baseline, DCM patients had reduced peak strain compared to controls, as
expected. However, the study also highlighted the importance of longitudinal function in
DCM patients.
At baseline, contractile reserve assessed by LVEF did not differ between DCM patients and
controls. Similarly, contractile reserve assessed by circumferential, radial and SAX contour
strain also did not differ between the DCM cohort and controls. However, contractile reserve
assessed by longitudinal strain (HLA) was reduced in DCM patients compared to controls,
suggesting that it is a more sensitive measure of LV dysfunction than LVEF. In line with
this, longitudinal strain contractile reserve (VLA) was predictive of follow up LVEF, which
has not previously been demonstrated by CMR assessment.
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This replicates the finding that contractile reserve predicts LV remodelling in DCM and also
suggests that longitudinal strain is a good discriminator of LV function in DCM patients,
particularly amongst patients with a similar LVEF. Longitudinal strain measures different
aspects of myocardial deformation compared to LVEF, which largely reflects radial
contraction. Longitudinal strain is a measure of the active shortening of the LV in the
longitudinal direction and represents the function of subendocardial longitudinal myocardial
fibres. These fibres are more sensitive to reduced coronary perfusion and increased wall
stress, therefore longitudinal dysfunction may be an early marker of LV dysfunction(381).
The discrepancy between HLA and VLA measurements cannot be explained biologically, but
the direction of effect is that longitudinal strain assessment may be of value in patients with
DCM.
It is plausible that because global circumferential and radial strain were only measured at the
mid-papillary level, overall LV dysfunction may not have been adequately captured and this
is why these variables were not predictive of LV remodelling.
This finding of the importance of longitudinal strain is in line with a CMR dobutamine stress
study evaluating subclinical cardiac dysfunction in patients with liver cirrhosis and normal
LVEF compared to controls. The study found that only longitudinal strain differences on
intermediate dose dobutamine stress (and not circumferential or radial strain) discriminated
patients and controls(382). Echocardiography assessed longitudinal strain has been shown to
be of prognostic importance in patients with dilated cardiomyopathy(383), ischaemic heart
disease(384) and all-cause heart failure(385), attributed to it being a more sensitive measure
of LV dysfunction than LVEF. A recent retrospective echocardiography study of 96 patients
with LVEF <50% has also shown that baseline global longitudinal strain was moderately
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correlated with follow up LVEF (r=0.33, p<0.001)(386).
In summary, this study demonstrates that CMR assessed longitudinal myocardial strain
predicts myocardial recovery. In addition, the feasibility of CMR assessment of longitudinal
strain established in this study may form the basis of further studies exploring the prognostic
role of longitudinal strain in DCM. Echocardiography assessed longitudinal strain, combined
with CMR late gadolinium enhancement (LGE), has been shown to predict outcome in
patients with dilated cardiomyopathy(383). LV longitudinal strain assessed by CMR feature
tracking has also been shown to be an independent predictor of survival in DCM(164).
However, DENSE has been shown to be more reproducible than feature tracking(387) and
further exploration of this is a promising area for future research.
4.5.2 Strengths of this study
One of the main strengths of this CMR study is the assessment of cardiac function, interstitial
fibrosis, replacement myocardial fibrosis, and myocardial strain in one study, enabling a
comprehensive, state of the art imaging evaluation of potential predictors of LV remodelling.
Another key strength is its prospective study design, with the inclusion of a DCM ‘inception
cohort’(388). This means that there is no survival bias that can occur in retrospective cohort
studies of LV remodelling, whereby only patients who survived to remodel have repeated
estimates of LV function. This therefore ensures that the estimate of the proportion of
patients who exhibited LV reverse remodelling is also not biased.
The number of patients who experienced improvement in LV function in this study was at the
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higher end of estimates from previous studies, although still consistent with previous studies
(~60% of patients showed an absolute improvement of LVEF greater than 10 units). This
may reflect the composition of this cohort, with most patients in NYHA class I/II. This may
be viewed as a limitation (e.g. results not applicable to advanced DCM), but also a strength,
as highlighted in an editorial by Wilcox and Yancy, stating that ‘the future of successful
cardiac recovery programs includes all stages of HF, especially stage B or asymptomatic LV
dysfunction, when certain pathophysiological mechanisms may still be reversible’(388).
Another strength of the current study is the standardized baseline phenotyping using CMR
assessed LVEF contractile reserve, thereby reducing variability in LVEF assessment
compared to previous echocardiography based studies. CMR is the established gold standard
for assessment of cardiac volumes and function.
Further strength lies in the careful inclusion criteria whereby patients with a potentially
reversible cause of DCM were excluded. Previous studies of remodelling in DCM have
included cases where there is uncertainty regarding reversible disease causality, such as toxin
induced LV dysfunction, which may limit the generalizability of findings to all-cause DCM.
Many studies evaluating predictors of LV remodelling have done so in the context of
response to advanced heart failure therapy such as CRT or LVAD(389-393). Whilst such
studies make important contributions to the field as a whole, identifying that a favourable
response often occurs in females, without a history of ischaemic heart disease, with left
bundle branch block and lower LA volumes, they do not have direct applicability to patients
with ambulatory DCM.
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In contrast to these prior studies, the focus of the current study has been to evaluate predictors
of LV reverse remodelling in DCM patients earlier in their disease course. This may have
identified predictors that the studies of advanced heart failure cannot. For example, it is as yet
unclear at what stage in the DCM disease course is recovery still possible. In addition,
patients in these earlier studies could often not undergo CMR due to device contraindications,
therefore the variables such as fibrosis in the current study would not have been evaluated.
Another important strength in this study is the statistical design and analysis, in which no
threshold for LVRR was defined. Previous studies have had variable definitions of LVRR,
making between study comparisons difficult. By not making such an assumption, this study
has evaluated predictors of overall LV functional improvement, thereby ensuring that the
results from this study can be readily compared to other studies. Whilst a threshold for LVRR
was not defined, the improvement in LV function remained clinically meaningful. All 9
patients with an initial LVEF <35% improved LV function to move out of current guideline
criteria for ICD implantation.
4.5.3 Limitations of this study
There are a number of potential limitations to this study which I will review in detail.
The first potential limitation relates to ascertainment bias. This could have arisen from
academic centre referral bias (recruitment via referrals to the CMR unit at a tertiary hospital
or from the Cardiomyopathy Service at a tertiary hospital). To address this, recruitment was
also performed via referral from Cardiology Consultants in a network of regional general
hospitals. In addition, a significant minority of patients self-referred via the Cardiomyopathy
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UK website. The final DCM cohort consisted of patients across the mild-moderate-severe
disease severity spectrum, suggesting that there was not significant referral bias.
Another source of bias results from the inclusion criteria. As the primary hypothesis was to
evaluate LVEF contractile reserve, it was imperative that the most accurate assessment of
cardiac volumes and function was made with CMR. Therefore patients with atrial fibrillation
were excluded, due to variable RR intervals and the possibility of inaccurate quantification of
cardiac volumes. However, atrial fibrillation can be found in ~10% of patients with
DCM(394), therefore the generalisability of these findings to DCM patients with AF is not
known.
The second limitation arises from the method chosen to evaluate contractile reserve
(pharmacological dobutamine stress with evaluation of change in LVEF). Exercise stress has
been shown to be a more physiologically appropriate and demanding stress agent in the
setting of ischaemic heart disease(395) as it may invoke a more complete contractile
response. Whilst this would have been an interesting study design, as yet there are no
facilities for exercise-stress CMR in our CMR unit. Furthermore, exercise stress may be
submaximal, hampered by poor patient effort, limiting the utility of this technique.
In recognition of the limitations of load dependent LVEF contractile reserve
measurements(396), a novel method was developed to estimate strain contractile reserve,
which yielded insight into the importance of longitudinal strain in DCM. Reproducibility of
the strain measures will, to some extent, determine the diagnostic utility of DENSE derived
strain within this context, particularly given the relatively small changes in strain between
DCM and healthy cohorts.
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With regards to dobutamine stress, it could also be argued that variability in contractile
reserve response could reflect insufficient stress, as opposed to true physiological impairment
of contractile reserve. However, all patients had a minimum 10 minutes of dobutamine
infusion and markers for response (heart rate or blood pressure change) were seen in all
participants. In this context, invasive haemodynamic cardiac catheter measurements of
contractile reserve would ideally have been included. This could form the basis of a future
study, though haemodynamic cardiac catheter based methods are unlikely to be of routine
clinical utility due to their inherent risks in a patient population that has no other indication
for them.
Another potential limitation is that the follow up assessment of LVEF with either 3d
echocardiography or CMR could introduce variability in the follow up LVEF measure.
However, this was assessed and the follow up imaging modality (CMR or echo) was not
associated with follow up LVEF on regression analysis. Whilst a limitation, this was also
unavoidable as even though all patients had MRI conditional devices, the safety of scanning
these devices at 3T had not been established.
The strengths of this study are also its limitations. The study was not designed to evaluate the
role of CRT in LV reverse remodelling in DCM or the utility of CMR contractile reserve to
predict response to CRT. Only 3 patients had CRT during follow up. CRT was therefore only
included in the analysis as a variable of interest. Similarly, patients with cardiac devices at
baseline were not included in the study, therefore introducing selection bias into the study
cohort. To address this, a separate study arm consisting of DCM patients with cardiac devices
could have been introduced, with 3d echocardiography assessment of contractile reserve. At
the time of study design, this was explored but not deemed feasible.
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Given the largely asymptomatic/minimally symptomatic patient population with moderate
LV dysfunction, the findings of this study describe contractile reserve as a predictor of
remodelling in ambulant DCM patients. The findings are not necessarily applicable to an end-
stage DCM cohort.
Established and novel cardiac biomarkers were not evaluated as predictors of LV remodelling
in this study. This will be addressed in future work. However, it will be important to study a
broad range of biomarkers and interpret such results with caution, with careful adjustment for
potential confounders as it is unclear how much these biomarker signatures would reflect
current disease status as opposed to potential for recovery. For example, contractile reserve in
this study was not correlated with baseline LVEF, making it a marker that reflected
pathophysiology beyond LVEF alone. Lower levels of many cardiac biomarkers may simply
reflect less LV dysfunction, lower symptom class or shorter disease duration and not
independent pathology.
Due to the time course of the study, the study was also not designed to evaluate either the
prognostic role of contractile reserve in DCM, or whether the remodelling observed is
sustained recovery (cure) and associated with improved clinical outcomes or temporary
recovery on medical therapy (remission). There is emerging evidence to support the notion
that LV remodelling is associated with improved clinical outcomes, with reduced events
observed in a heart failure cohort with recovered ejection fraction(397) and DCM specific
cohorts with LVRR(398,399). However, this is not definitive, as CRT ‘super-responders’ do
also experience appropriate ICD shocks on follow up, suggesting that reverse remodelling
does not remove all clinical risk(400). In these scenarios, other non-LVEF factors such as
replacement myocardial fibrosis could underpin the persistent adverse outcome (for example
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in the promotion of re-entrant arrhythmia).
This remains an important unanswered question which should form the basis of further
prospective study, though in an ambulatory DCM cohort such as this, the event rate is likely
to require multi-centre study design. A crucial unanswered question is whether the risk of
DCM ‘resets’ upon recovery of LV function, or whether impaired LVEF, at any stage,
portends ongoing increased risk of adverse events. Another remaining question is whether
myocardial recovery simply represents reversal of dysfunction or an additional process
(beyond repair/response to an initial insult). To address such a question, a pathophysiological
model of LV dysfunction that adequately captures the complexity of idiopathic DCM would
be required.
Finally, the results of this study are single centre, and although the study was appropriately
powered, the findings require replication in an independent cohort.
4.6 Summary
LV contractile reserve can be safely assessed by low-dose dobutamine stress CMR and is an
independent predictor of LV remodelling in patients with recent onset DCM. LV contractile
reserve assessed by longitudinal strain and relative RV contractile reserve are also predictive
of LV remodelling, but baseline myocardial strain, replacement myocardial fibrosis and
interstitial fibrosis are not predictive of follow up LVEF in this cohort. In this cohort, the LV
contractile reserve response is not related to baseline LVEF or the degree of interstitial
fibrosis measured on CMR.
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4.7 Outline of further work
• Evaluate contractile reserve as a predictor of LV remodelling in the context of
established and novel biomarkers markers of cardiac disease.
• Validate the strain data from CMR cine DENSE against echocardiography strain
measures.
4.8 Acknowledgements
• Rick Wage, Chief CMR Radiographer, oversaw acquisition of all baseline and follow
up CMR studies.
• Consultant Cardiologists Dr Carl Shakespeare, Dr Amal Muthumala, Dr Jason Dungu,
and Dr Ravi Assomull actively recruited patients into the study.
• Research nurses Zohreh Farzad, Carmen Chan, Sally-Ann McRae, Iulia Muntenu and
Kevin Kirby assisted with participant preparation and were present during the
dobutamine scan as part of the safety protocol.
• Manivarmane Ramasamy performed all 3-d echocardiography studies.
• Peter Gatehouse, CMR Physicist, provided technical advice about the CMR
sequences, protocol and optimisations required in the context of dobutamine stress.
• Andrew Scott, CMR Physicist, developed the modified cine DENSE sequence as
outlined in the Methods.
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5 THE ROLE OF CLINICAL,
IMAGING, AND GENETIC DATA
IN PREDICTING CLINICAL
OUTCOMES IN DILATED
CARDIOMYOPATHY
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5.1 Aims and hypotheses
The primary aim of this chapter is to describe the prognosis of titin cardiomyopathy in order
to inform patient stratification by genotype. The hypotheses are as outlined:
- Patients with DCM and titin truncating variants have an adverse event profile for the
composite endpoint of cardiovascular mortality, major heart failure and major arrhythmic
events, compared to patients with DCM without titin truncating variants.
- The effect on outcome of titin truncating variants in patients with DCM is modified in the
presence of additional environmental variables, including male gender, mid-wall
myocardial fibrosis, arrhythmia, and alcohol consumption.
5.2 Background
The preceding chapters have highlighted the importance of truncating variants in the titin
gene in dilated cardiomyopathy. It is the commonest genetic contributor to DCM. Therefore
in this chapter, I evaluate the prognostic implications of titin truncating variants in the context
of established clinical and imaging risk factors in DCM.
There is precedence for the use of genetic data in risk stratification in DCM, for example the
arrhythmic phenotypes associated with LMNA cardiomyopathy (49). As TTNtv are found in
up to 4 times as many patients with DCM compared to LMNA variants (14), risk stratification
using TTNtv would have broad implications in tailoring the clinical management of patients
with DCM.
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5.3 Methods
5.3.1 Study cohort
For outcome analysis, the primary study cohort was a subset of the main study cohort (Figure
3-1), consisting only of patients recruited prior to 31st December 2014, to allow at least 1 year
of follow up time for all recruited subjects. Data on survival and events prior to 31st
December 2015 were recorded. Follow-up was censored at 31st December 2015 to allow for
data collection and adjudication of all events.
5.3.2 Variable definition and analysis
Variables from clinical, imaging, and genetic categories, defined in Chapters 2 and 3, were
evaluated in survival analysis (Table 5-1).
Table 5-1: Variables evaluated in survival analysis
Variables evaluated Methods Demographic/ Age, Gender, Ethnicity, NYHA class, a family history of As described in Clinical DCM, a family history of sudden cardiac death, a history of Chapter 3. sustained ventricular tachycardia, a history of atrial fibrillation, diabetes, hypertension, left bundle branch block, resting heart rate, beta blocker use, aldosterone antagonist use, diuretic use, ACE inhibitor use. CMR LVEF, RVEF, indexed left atrial volume, indexed LV end As described in diastolic volume, indexed LV end systolic volume, indexed Chapter 3. left ventricular mass, indexed left ventricular stroke volume, indexed RV end diastolic volume, indexed RV end systolic volume, indexed right ventricular stroke volume, mid-wall fibrosis late gadolinium enhancement, CMR features of left ventricular non-compaction
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Variables evaluated Methods Genetic Truncating variants in titin, lamin variants As described in Chapter 2. Blood Baseline serum creatinine Described in text.
Serum creatinine was measured using electrochemiluminescence immunoassay on a Cobas
8000 c702 modular analyser (Roche Diagnostics, Mannheim, Germany) following the
manufacturer’s protocol or determined from the point-of-care testing on the day of CMR
study.
5.3.3 Endpoints and adjudication of events
5.3.3.1 Endpoints
Endpoints were defined according to the 2014 American College of Cardiology/American
Heart Association definitions for cardiovascular endpoints in clinical trials(401) and the 2006
ACC/AHA/ESC guidelines for management of patients with ventricular arrhythmias (330).
They are defined in detail in the Appendices (p373). The primary endpoint was a composite
of cardiovascular mortality, major arrhythmic events and major heart failure events.
Major arrhythmic events were defined as:
o Haemodynamically stable and haemodynamically unstable ventricular
tachycardia
o Ventricular fibrillation
o Appropriate implantable cardiac defibrillator (ICD) discharge
o Aborted sudden cardiac death
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Major heart failure events were defined as:
o Heart transplantation
o Left ventricular assist device implantation
o Unplanned heart failure hospitalisation
Cardiovascular mortality and the arrhythmic and heart failure composites were predefined
secondary endpoints.
5.3.3.2 Follow up data collection and adjudication
Follow up data was collected and curated in 4 stages. Initially, medical data was collected
from primary care records, hospital care records, and patient questionnaire. The main sources
of data were primary and hospital care records. All primary care providers were contacted at
least once to request all patient medical records since the date of the patient’s recruitment to
the study. In the case of non-responding primary care practices, up to 2 further requests for
information were made. Secondary and tertiary care providers were contacted directly if the
primary care records received did not contain complete information about secondary care
admissions or consultations.
In addition, all patients were sent an annual questionnaire either by post or email (as per
patient preference) in which they were able to report any medical updates including hospital
admissions. The patient questionnaire served as a prompt to search for evidence of any events
not yet identified from the available medical records.
Survival status was also identified using the UK Health and Social Care Information Service
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to ensure no deaths were missed. Death certificates and post mortem reports were obtained
where applicable.
This information was collated into a summary ‘Follow-up’ event form on the research
database by a team of up to 7 research nurses.
In the second stage, myself and 2 other research fellows reviewed all the Follow-Up event
data for each patient to confirm all major and minor events had been correctly recorded.
Events were reclassified in the event of inaccurate data entry. In addition, I reviewed a subset
(10%) of patient records with no events recorded to confirm that no major or minor events
were missed. In the third stage, I curated all primary end-point events to ensure each event
was linked to primary evidence of the event, as a final quality control step.
In the fourth stage, I presented the data to the independent data adjudication committee. A
committee of 3 cardiologists, each with expertise in electrophysiology, heart failure
management or clinical trial adjudication, blinded to imaging and genetic data, adjudicated all
primary end-point events. The adjudication committee established whether each arrhythmic
and heart failure event fulfilled pre-specified criteria using medical records including device
data where applicable, in line with the ACC/AHA guidance as outlined above(401) (330).
Cause of death was established from death certification, post mortem results, and medical
records, using the ACC/AHA guidance as outlined above(401) (330).
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5.3.4 Statistical analysis
5.3.4.1 Survival analysis
Outcome analysis was conducted on the subset of patients who were recruited to the study
prior to 31st December 2014, to allow at least 1 year of follow up time for all recruited
subjects. For the survival analysis, event free survival was calculated from the date of study
entry to the date of the first event in the composite endpoint.
Data for all patients who were last known to be alive, or who had died after December 31st
2015, were censored on December 31st 2015.
The Kaplan–Meier method was used estimate the cumulative freedom from each endpoint
and the log-rank statistic was used to test the null hypothesis that there was no difference
between groups in the probability of an event at any time point. Kaplan Meier survival curves
were plotted and log rank tests performed using the survminer package in R.
The study design was a prospective observational study and the sample size obtained was the
maximal possible within constraints of resources and study timeframe to enable the
recruitment and assessment of patients with and without TTNtv.
5.3.4.2 Cox proportional hazard modelling
For the primary analysis, a Cox model was built to estimate the hazard ratios for each
endpoint associated with the presence of TTNtv.
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To do this, an optimised baseline model was built using Cox proportional hazard modelling
evaluating clinical and demographic variables predicting the primary end point. This model
was built without inclusion of genetic data.
An outline of the steps to build the optimised baseline model predicting the primary endpoint
is shown in Figure 5-1. The same principle was applied to the secondary endpoints. All
modelling was performed using the survival package in R. The tmerge function was
used to create time dependent covariates.
There are various approaches to model selection. A purely automated variable selection
process was considered but rejected due to the limitations of this approach such as retention
of variables with inflated coefficients, model over-fitting, falsely narrow confidence intervals,
and not being able to incorporate scientific knowledge for important variables, data quality,
or collinearity between variables (402-405).
Model selection based on the purposeful selection of covariates, proposed by Hosmer and
Lemeshow, was the method chosen (Figure 5-1)(406,407). Whilst still based on stepwise
selection with the inherent limitations, the exact methods and criteria for variable inclusion
and exclusion have been defined so that the analysis is reproducible and objective. Genetic
data was then added to this baseline model.
The primary analysis and goal of this chapter was to evaluate the association of TTNtv with
the primary endpoint, on univariable and multivariable analysis. The secondary analysis was
to evaluate the association of TTNtv with the pre-specified secondary endpoints.
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Selec/on of variables of interest based on prior knowledge/literature
Univariable cox propor/onal hazard modelling of all variables of interest
All variables significant at p≤0.1 retained
Exclusion of correlated variables
Full model built with all significant variables
Purposeful backward stepwise selec/on with stepwise removal of least significant variable un/l only significant variables retained (p<0.05)
Model with only significant variables retained (p<0.05)
Addi/on of each previously discarded variable (p>0.1) to evaluate any effect on the coefficients of retained variables or the significance of the added variable in the presence of retained variables
Model tes/ng including tes/ng of propor/onal hazard assump/on (individual variables and overall model)
Transforma/on of any variable viola/ng propor/onal hazard assump/on
Tes/ng model with transformed variable(s)
Final op/mised model
Addi/on of variable of interest
Figure 5-1: Outline of variable selection for the baseline Cox proportional hazard model for analysis of
outcome endpoints.
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5.4 Results
5.4.1 Overview
5.4.1.1 Cohort description
In total, 633 patients with a confirmed diagnosis of DCM recruited prior to 31st December
2014 were included in the follow up analysis cohort. Of these, 8 patients were excluded from
analysis due to loss from follow up. In these patients, there was no follow up data despite at
least 2 requests for information from the primary care provider. This included patients who
had moved overseas. A further 21 patients were excluded from analysis because there was
missing data in any one of the endpoints. For example, we may have had information about a
heart failure hospitalisation, but we did not have complete medical records, therefore could
not be certain that the individual did not have another class of event such as an arrhythmic
episode. Prior to excluding these 29 patients from analysis, further efforts were made to
collect the data, including contacting the primary care providers and secondary care
cardiologists where such contact data was available. This resulted in 604 patients in the final
analysis.
5.4.1.2 Follow up time
In total, 604 patients were followed up for a median of 3.9 years (IQR 1.94 to 5.76 years).
Follow up time was truncated at 10 years given the reduced number of individuals with
follow up beyond 10 years (Figure 5-2). The decision to truncate at 10 years was made to
ensure that model assumptions were not violated due to sparse data but acknowledging the
compromise of losing events after the truncation date (n=2 patients who met the primary end
point).
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Figure 5-2: Histogram of follow up time in original dataset. Follow up time truncated at 10 years (dashed
red line).
5.4.1.3 Endpoints
Table 5-2: Table showing the number of patients meeting each endpoint (truncated dataset). Patients
were censored at the first event. *Cardiovascular death included 18 patients with heart failure death, 3
patients with sudden cardiac death, and 1 patient each with acute myocardial infarction, cerebrovascular
accident and cardiovascular –other.
Primary composite Cardiovascular death Arrhythmic Heart Failure
secondary secondary
Yes 78 24* 24 50
No 526 580 580 554
Overall, 78 patients met the primary composite endpoint (Table 5-2). Patients were censored
at first event, but may have had more than one event as outlined in Table 5-3.
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Table 5-3: Composition of events occurring during follow up leading to primary composite endpoint. Patients were censored at the first event.
CV death Heart Failure Cardiac LVAD Stable Unstable Aborted Appropriate Ventricular Count hospitalisation transplant sustained sustained sudden ICD activation fibrillation (n=78) VT VT cardiac death No Yes No No No No No No No 24 Yes No No No No No No No No 12 Yes Yes No No No No No No No 8 No No No No Yes No No No No 7 No No No No Yes No Yes Yes No 3 No No No No Yes Yes Yes Yes No 3 No Yes Yes No No No No No No 3 No No Yes No No No No No No 2 No Yes No Yes No No No No No 2 No No No No No No Yes Yes Yes 1 No No No No Yes Yes No No No 1 No No No No Yes Yes Yes Yes Yes 1 No No No Yes No No No No No 1 No No Yes Yes No No No No No 1 No Yes No No No Yes Yes Yes Yes 1 No Yes No No Yes No No No No 1 No Yes No Yes No Yes Yes Yes No 1 No Yes Yes No Yes Yes Yes Yes No 1 No Yes Yes Yes No No No No No 1 Yes No No Yes Yes Yes Yes Yes No 1 Yes Yes No No Yes No No No Yes 1 Yes Yes No No Yes No Yes Yes No 1 Yes Yes No Yes Yes No Yes No Yes 1
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5.4.2 Building the baseline model to predict the primary end point
As outlined in Figure 5-1, the baseline model was built following evaluation of the
demographic, clinical and imaging variables in Table 5-1. The summary statistics of these
variables grouped by the primary end point are shown in Table 5-4.
The results of univariable regression Cox proportional hazard modelling of these variables for
the primary endpoint are shown in Table 5-5.
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Table 5-4: Baseline demographics in the outcome cohort, stratified by the primary endpoint. Data are
presented as mean (standard deviation) for continuous variables and number (percentage) for categorical
variables. For continuous variables, between group comparisons are made with the t-test or Mann
Whitney test. Categorical variables are compared with the Chi square test or the Fisher’s exact test if
there are fewer than 10 observations per cell. *Effect of serum creatinine evaluated separately in
sensitivity analyses due to high degree of missing data. SCD= sudden cardiac death, ACEi=ACE
inhibitor, LV/RV=left/right ventricle, EF= ejection fraction, EDVi=indexed end diastolic volume,
ESVi=indexed end systolic volume, SVi=indexed stroke volume, LVMi= indexed left ventricular mass,
LAVi= indexed left atrial volume, LVNC= left ventricular non compaction.
Variable All patients Primary Endpoint n=604 Yes (n=78) No (n=526) Mean (sd)/ Missing (%) Mean (sd)/ Mean (sd)/ P value n(%) n(%) n(%) Demographic Age (years) 53.45 (14.35) 0 54.9 (15.9) 53.2 (14.1) 0.29 Male gender 398 (65.9) 0 52 (66.7) 346 (65.8) 0.98
Caucasian ethnicity 524 (86.8) 0 66 (84.6) 458 (87.1) 0.68 Clinical Family history SCD 88 (14.6) 0 8 (10.3) 80 (15.2) 0.30 Family history 100 (16.6) 0 DCM 8 (10.3) 92 (17.5) 0.14 VT 11 (1.8) 0 6 (7.7) 5 (1.0) 0.001 NSVT 71 (11.8) 0 19 (24.4) 52 (9.9) <0.001 Atrial fibrillation 150 (24.8) 0 24 (30.8) 126 (24.0) 0.25 LBBB 164 (27.2) 0 21 (26.9) 143 (27.2) 1.00 Hypertension 180 (29.8) 0 30 (38.5) 150 (28.5) 0.097 Diabetes mellitus 71 (11.8) 0 12 (15.4) 59 (11.2) 0.38 Resting heart rate 74.55 (15.71) 33 (5.5) 72.6 (15.2) 74.8 (15.8) 0.30
NYHA class I 252 (41.7) 30 (5) 25 (32.1) 227 (43.2) II 230 (38.1) 28 (35.9) 202 (38.4) III 87 (14.4) 20 (25.6) 67 (12.7) 0.012
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Variable All patients Primary Endpoint n=604 Yes (n=78) No (n=526) Mean (sd)/ Missing (%) Mean (sd)/ Mean (sd)/ P value n(%) n(%) n(%) IV 5 (0.8) 2 (2.6) 3 (0.6) Medication Diuretic 270 (44.7) 0 47 (60.3) 223 (42.4) 0.005 Beta blocker 425 (70.4) 0 61 (78.2) 364 (69.2) 0.14 ACEi 478 (79.1) 0 68 (87.2) 410 (77.9) 0.09 Aldosterone 218 (36.1) 0 Antagonist 33 (42.3) 185 (35.2) 0.27 Imaging LVEF (%) 39.4 (12.4) 0 34.1 (13.1) 40.1 (12.1) 0.0001 LVEDVi (mL/m2) 127.3 (36.4) 0 143.3 (48.3) 124.9 (33.7) 0.003 LVESVi (mL /m2) 79.7 (36.5) 0 98.1 (47.3) 77.0 (33.8) 0.0002 LVSVi (mL /m2) 47.4 (13.0) 0 45.3 (14.4) 47.8 (12.8) 0.12 LVMi (g/m2) 90.8 (25.5) 14 (2.3) 99.1 (29.9) 89.6 (24.6) 0.007 RVEF (%) 52.0 (13.7) 13 (2.2) 47.0 (15.6) 52.7 (13.2) 0.002 RVEDVi (mL /m2) 87.5 (24.0) 13 (2.2) 90.4 (31.0) 87.0 (22.8) 0.64 RVESVi (mL /m2) 43.5 (21.6) 13 (2.2) 51.0 (29.7) 42.4 (19.9) 0.05 RVSVi (mL /m2) 44.0 (12.6) 13 (2.2) 39.6 (11.6) 44.7 (12.6) 0.001 Mid-wall fibrosis 213 (35.3) 0 LGE 42 (53.8) 171 (32.5) <0.001 LAVi (mL /m2) 60.9 (26.2) 24 (4.0) 76.9 (45.4) 58.6 (21.2) <0.00001 LVNC 28 (4.6) 0 3 (3.8) 25 (4.8) 0.95 Blood Creatinine 93.8 (29.6) 82 (13.6) 99.3 (30.5) 93.1 (29.4) 0.11 (µmol/L)*
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Table 5-5: Results of univariable Cox proportional hazard modeling of demographic, clinical, and
imaging variables predicting primary endpoint. HR= hazard ratio, CI= confidence interval
Variable Unit HR 95% CI P value
ACE inhibitor use Yes 1.74 0.9-3.38 0.10
Age per 10 years 1.09 0.93-1.28 0.3
Aldosterone antagonist use Yes 1.44 0.92-2.23 0.11
Atrial fibrillation Present 1.31 0.82-2.11 0.26
Beta blocker use Yes 1.39 0.82-2.34 0.22
Diabetes Mellitus Present 1.26 0.69-2.31 0.45
Diuretic Yes 2.1 1.34-3.29 0.001
Ethnicity Caucasian 0.79 0.43-1.45 0.44
Family history of DCM Present 0.47 0.23-0.99 0.05
Family history of sudden cardiac death Present 0.64 0.31-1.32 0.23
CMR features of left ventricular non compaction Present 0.89 0.28-2.81 0.84
Gender Male 0.98 0.62-1.56 0.93
Heart rate Per 1 bpm 0.99 0.98-1.01 0.40
History of hypertension Present 1.66 1.06-2.61 0.03
History of sustained ventricular tachycardia Present 4.7 2.04-10.85 <0.001
Indexed left ventricular end systolic volume per 10mL /m2 1.13 1.08-1.19 <0.0001
Indexed left atrial volume per 10mL /m2 1.11 1.06-1.15 <0.0001
Indexed left ventricular end diastolic volume per 10 mL /m2 1.11 1.06-1.16 <0.0001
Indexed left ventricular mass per 10g/m2 1.13 1.05-1.21 0.002
Indexed left ventricular stroke volume per 10mL /m2 0.86 0.73-1.01 0.07
Indexed right ventricular end diastolic volume per 10mL /m2 1.03 0.94-1.14 0.47
Indexed right ventricular end systolic volume per 10mL /m2 1.16 1.06-1.27 0.001
Indexed right ventricular stroke volume per 10mL /m2 0.73 0.61-0.86 <0.001
Left bundle branch block Present 1.08 0.66-1.76 0.77
Left ventricular ejection fraction per 10% 0.69 0.58-0.82 <0.0001
Mid wall fibrosis LGE Present 2.28 1.47-3.54 <0.0001
NYHA Class IV 7.93 1.32-47.66 0.02
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Variable Unit HR 95% CI P value
NYHA Class III 3.66 1.09-12.3 0.04
NYHA Class II 1.55 0.47-5.08 0.47
NYHA Class I 1.26 0.38-4.16 0.71
Right ventricular ejection fraction per 10% 0.76 0.65-0.89 <0.001
The variables that were significant at a threshold of p<0.10 were considered for inclusion in
full model building. At this stage, any variables with potential for inclusion in the next stage
of model building were reviewed for co-linearity. In addition, their appropriateness for
inclusion based on literature review was re-evaluated. Following this review, I decided to
exclude left and right stroke volume as they are highly correlated with left and right
ventricular ejection fraction, as well as being subject to heart rate variability and a greater
susceptibility to loading conditions compared to ejection fraction. Left ventricular end
diastolic and end systolic volumes are also correlated therefore only one of these was
retained. Either variable may have been appropriate and both represent left ventricular
dilatation. Left ventricular end systolic volume was chosen as previous studies have
demonstrated its prognostic importance(408,409). Left ventricular mass was excluded as it is
correlated with ventricular dilation. For right ventricular indices, right ventricular ejection
fraction was retained based on previous work from our group demonstrating its prognostic
importance over and above right ventricular volume indices(113).
Therefore the final variables included in the full model, prior to reverse stepwise selection
were left ventricular ejection fraction, right ventricular ejection fraction, mid wall fibrosis late
gadolinium enhancement, indexed left ventricular end systolic volume, indexed left atrial
volume, a family history of dilated cardiomyopathy, a history of sustained ventricular
tachycardia, NYHA class, a history of hypertension, diuretic use, and ACE inhibitor use.
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From this, stepwise selection was performed, removing the least significant variable in turn
until only significant variables were remaining (p<0.05). This occurred in the order of
hypertension, ACE inhibitors use, diuretic use, family history of dilated cardiomyopathy,
indexed left ventricular end systolic volume, NYHA status, and right ventricular ejection
fraction.
This left the baseline model of left ventricular ejection fraction, indexed left atrial volume,
mid wall fibrosis late gadolinium enhancement and a history of sustained ventricular
tachycardia (Table 5-6).
Table 5-6: Adjusted baseline Cox proportional hazard model predicting the primary endpoint
Variable Unit HR 95% CI P
Left ventricular ejection fraction Per 10% 0.7 0.58-0.84 0.0002
Indexed left atrial volume Per 10mL/m2 1.11 1.06-1.17 0.00001
Mid wall fibrosis late-gadolinium enhancement Present 2.26 1.41-3.64 0.0008
Sustained ventricular tachycardia Present 3.24 1.28-8.24 0.01
Each previously discarded variable was added to the model in turn (hypertension, ACE
inhibitor use, diuretic use, family history of dilated cardiomyopathy, indexed left ventricular
end systolic volume, NYHA class, and right ventricular ejection fraction), as well as variables
that were not significant on univariable analysis. None of these variables became significant
on addition to the baseline model, nor did their inclusion affect the hazard ratios or p value
for the remaining variables (which may have suggested a possible interaction or confounder
effect). As left and right ventricular ejection fraction are correlated, the forced inclusion of
both variables as well as the inclusion of either variable was tested (Table 5-7). The model
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with just left ventricular ejection fraction had a marginally lower AIC (Akaike information
criterion) compared to the model with just right ventricular ejection fraction. In the model
with both right and left ventricular ejection fraction, neither variable remained significant.
Therefore left ventricular ejection fraction only was retained, based partly on these results as
well as the overwhelming existing evidence for its utility as a prognostic marker in dilated
cardiomyopathy and heart failure.
Table 5-7: Comparison of Cox models with forced inclusion of left and right ventricular ejection fraction
(LV/RVEF). AIC= Akaike information criterion.
Baseline Model plus: Number of variables AIC
LVEF only 4 727.9822
RVEF only 4 728.5374
LVEF and RVEF 5 727.8523
LVESVi, LVEDVi and LVMi were not included in the multivariate model to avoid
colinearity, but separate models substituting these variables individually for LVEF showed
that LVESVi and LVEDVi were independently associated with the primary outcome, though
with smaller effect sizes and higher p values than LVEF (HR and 95% CI for primary
endpoint: LVESVi per 10mL/m2= 1.09, 1.03 to 1.16, p=0.001; LVEDVi per 10mL/m2= 1.07,
1.01 to 1.13, p=0.02). LVMi was not predictive of the primary outcome (HR and 95% CI;
LVMi per 10g/m2 = 1.04, 0.94-1.15, p=0.42).
At this stage, the baseline model was tested using the cox.zph() function in R to see if the
proportional hazard assumption was met. This function correlates the corresponding set of
scaled Schoenfeld residuals with time, to test for independence between residuals and time,
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for each covariate and the global model. The output from this test was non-significant (Table
5-8), indicating the absence of evidence to contradict the proportionality assumption, either
for individual variables or the global model. This is illustrated graphically in Figure 5-3.
Table 5-8: Results of testing proportionality assumption of baseline model
rho chisq P value Indexed left atrial volume 0.0269 0.0423 0.84 Mid wall fibrosis late- gadolinium enhancement -0.1346 1.2337 0.27 Left ventricular ejection fraction 0.1033 0.8024 0.37 Sustained ventricular tachycardia 0.0563 0.2237 0.64 GLOBAL NA 2.2894 0.68
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Global Schoenfeld Test p: 0.6827
Schoenfeld Individual Test p: 0.837 Schoenfeld Individual Test p: 0.2667 4
0.8 2
0.4
Beta(t) for lavi10 Beta(t) for 0 Beta(t) for lgeYes Beta(t) for
0.0
−2 0.12 0.42 0.94 1.9 2.4 3.1 4.6 8.1 0.12 0.42 0.94 1.9 2.4 3.1 4.6 8.1 Time Time Schoenfeld Individual Test p: 0.3704 Schoenfeld Individual Test p: 0.6363
1 15
10 0
5 Beta(t) for vtYes Beta(t) for Beta(t) for lvef10 Beta(t) for −1
0
0.12 0.42 0.94 1.9 2.4 3.1 4.6 8.1 0.12 0.42 0.94 1.9 2.4 3.1 4.6 8.1 Time Time
Figure 5-3: Graphical diagnostic of the baseline Cox regression model predicting the primary endpoint.
The y axis represents the scaled Schoenfeld residuals, plotted against time on the x axis. The solid line is a
smoothing spline fit and the dashed lines are +/- 2 standard error bands. There are no systematic
departures from the fit line over time. ‘Lavi10’ and ‘lvef10’ are continuous variables, ‘lgeYes’ and
‘vtYes’ are categorical variables and their displays reflect this (2 strata).
In summary therefore the final baseline model predicting the primary endpoint consisted of
LVEF, mid wall fibrosis LGE, LAVi and sustained ventricular tachycardia. This model did
not violate proportional hazard assumptions. The variables in this baseline model suggest that
this is a representative DCM cohort as these predictors are in line with previously reported
predictors of outcome in DCM.
In the next section, I evaluate the prognostic importance of the presence of TTNtv, in
univariable analysis and when added to the optimized baseline model.
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5.4.3 Analysis of the association between TTNtv and the primary end point
5.4.3.1 Titin and clinical outcomes: primary endpoint
Of the 604 patients in the outcome cohort, 71 patients had TTNtv (11.8%). Of these, 9
patients (12.7%) with TTNtv met the primary endpoint compared to 69 patients (12.9%)
without TTNtv. Survival curves comparing the freedom from the primary endpoint for
patients with and without TTNtv are shown in Figure 5-4.
Figure 5-4: Survival curves comparing freedom from the primary endpoint in patients with and without
TTNtv. Curves are compared using the log rank test. Confidence intervals are shown as dashed lines.
There is no significant difference between the two groups for the primary endpoint on unadjusted
analysis.
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The 5 year event rate (95% confidence interval) for patients with TTNtv was 10.4% (2.0-
18.1%) compared to 13.6% for patients without TTNtv (10.1-16.9%).
On univariable cox proportional hazard modeling, the presence of a TTNtv was associated
with a hazard ratio of 0.81 (0.41 to 1.63, p=0.56) for the primary endpoint. On multivariable
analysis, adjusting for variables that predict the primary endpoint in this cohort (the baseline
Cox model), the presence of a TTNtv was not associated with the primary endpoint (HR 0.92,
0.45 to 1.87, p=0.82).
Therefore, in this cohort, the presence of a TTNtv alone was not a prognostic indicator. Given
the results of Chapter 3, outlining an interaction between titin and alcohol, and previous data
suggesting gender specific outcome effects in TTNtv DCM(79), exploratory analyses were
then performed to test the hypothesis that TTNtv confer an adverse prognosis in the presence
of an additional risk variable.
5.4.3.1.1 Titin, gender and the primary endpoint
It has previously been suggested that TTNtv are associated with adverse outcomes in males,
particularly for heart failure endpoints (79). Therefore the prognostic effect of TTNtv,
stratified by gender, was evaluated.
Of the 71 patients with TTNtv, 19 were female. Of these, only 1 female patient (5.3%) met
the primary endpoint, compared to 8/52 males (15.4%) with TTNtv. Cox proportional hazard
analysis showed no differences in primary outcome between these groups (p value for
TTN:gender interaction=0.36, Figure 5-9, Figure 5-5) though the comparison is limited by
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the low event rate and number of females in the TTNtv cohort. On inspection of the survival
curves, males with TTNtv seem to have reduced freedom from the primary endpoint after 8
years of follow up but this is likely secondary to the small numbers of individuals with long
term follow up.
Figure 5-5: Survival curves showing freedom from the primary endpoint comparing patients with and
without TTNtv stratified by gender. Curves are compared using the log-rank test. The confidence
intervals of the curves have not been plotted for clarity but they overlap between all groups.
5.4.3.1.2 Titin, alcohol and the primary endpoint
Amongst patients with TTNtv, there was no evidence of a significant difference in outcome
when stratified by a history of alcohol excess (Figure 5-6). However, there were only 12
patients with TTNtv, a history of alcohol excess, and outcome data, and amongst these
patients, only 2 met the primary endpoint (17%), compared to 7 patients with TTNtv without
a history of alcohol excess (12%). There is therefore insufficient data to evaluate this
interaction further within this cohort.
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Figure 5-6: Survival curves showing freedom from primary endpoint in subset of patients with TTNtv,
stratified by a history of alcohol excess (EtOH) as defined in Chapter 3. Curves are compared using the
log rank test.
Of note, a history of alcohol excess in isolation was not associated with an adverse outcome
compared to patients without alcohol excess (Figure 5-7).
Figure 5-7: Survival curves showing outcome in DCM cohort stratified by a baseline history of alcohol
excess (EtOH). There is no significant difference between groups for the primary endpoint. Curves are
compared using the log rank test.
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5.4.3.1.3 Titin, fibrosis and the primary endpoint
Mid-wall fibrosis LGE has been shown to be an important predictor of outcome in
DCM(147,264). In this cohort, this was replicated, with the presence of mid-wall fibrosis
LGE an independent predictor of adverse outcome (adjusted HR 2.26, 95% CI 1.41 to 3.64,
p=0.0008). There was no evidence however that the effect of TTNtv differed based on LGE
status (Figure 5-9, Figure 5-8).
Figure 5-8: Freedom from primary endpoint in DCM stratified by TTNtv and baseline history of mid
wall fibrosis LGE (LGE). The presence of LGE is clearly associated with adverse outcome, irrespective of
TTNtv status. There is no evidence of an interaction between TTNtv and LGE on event-free survival.
Curves are compared using the log-rank test.
5.4.3.1.4 Titin, arrhythmia and the primary endpoint
Of the 71 patients with TTNtv, 37 (52%) did not have a history of baseline arrhythmia (atrial
fibrillation, sustained ventricular tachycardia, non-sustained ventricular tachycardia prior to
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study enrollment) compared to 34 patients (48%) with TTNtv with a history of arrhythmia.
On follow up, 7 TTNtv patients with a history of arrhythmia (9.9%) met the primary end-
point (endpoints: 5 heart failure, 1 arrhythmia, 1 cardiovascular death) compared to 2 TTNtv
patients without a history of arrhythmia (2.8%) (arrhythmia endpoint). In outcome analysis,
the presence of baseline arrhythmia was associated with adverse outcome independent of
TTNtv status (HR 1.91, p=0.007). There was no evidence that the effect of TTNtv differed in
the presence of a history of arrhythmia (TTNtv HR without history of arrhythmia 0.49, vs.
TTNtv HR with history of arrhythmia 0.85, p=0.51).
On analysis of arrhythmia subtype, 14 patients with TTNtv had baseline non-sustained
ventricular tachycardia prior to enrollment (NSVT). Of these, 5 (36%) met the primary
endpoint compared to 4 (7%) of TTNtv patients without NSVT. Across the whole cohort, the
presence of baseline NSVT alone was associated with an adverse outcome (unadjusted HR
1.84, p=0.04). In patients with TTNtv, the estimated effect of baseline NSVT was stronger
(HR 4.40; HR of NSVT plus NSVT:TTN interaction) but this difference did not attain
statistical significance in this small sample size (p=0.24) (Figure 5-9). There was no outcome
difference when stratified by a history of AF.
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Figure 5-9: Forest plot of Cox proportional hazard modelling of TTNtv interactions. Cox proportional
hazard modelling evaluating the interaction between TTNtv and gender, non-sustained ventricular
tachycardia (NSVT) or mid-wall late gadolinium enhancement (LGE) on the primary composite endpoint
of cardiovascular death, major heart failure events and major arrhythmic events. The hazard ratios of
the paired variables (not the interaction terms) represent the hazard ratios of each variable in isolation
(e.g. HR of TTNtv in patients without LGE, HR of LGE in patients without TTNtv).
5.4.4 Sensitivity analyses for the association between TTNtv and the
primary endpoint
5.4.4.1 Exclusion of individuals with LMNA variants
Currently, LMNA variants are the only clinically actionable DCM genetic variants therefore
sensitivity analysis was performed after exclusion of 8 patients with rare variants in LMNA,
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of whom 4 met the primary end-point. The results from the unadjusted and adjusted HR for
TTNtv on the primary end-point were very similar (unadjusted HR 0.85, p=0.64; adjusted HR
0.92, p= 0.83).
5.4.4.2 Adjusting for device therapy
During the study follow up period, 160 patients underwent implantation of a cardiac device
(n=85 CRT-D, n=24 CRT-P, n=51 ICD). Of these, 131 were implanted for primary
prevention.
In total, 144 patients had devices implanted prior to censoring. Of these, 30 patients (20.8%)
met the primary endpoint compared to 114 patients who did not meet the primary endpoint
(79.2%).
To evaluate whether this could have affected the results for the association between TTNtv
and the primary endpoint, the Cox regression analysis was repeated with inclusion of cardiac
device therapy as a time dependent covariate.
On univariable analysis, the implantation of a cardiac device during study follow up was
associated with a hazard ratio of 3.4 (95% CI 2.14 to 5.40, p<0.0001) for the primary
endpoint. On multivariable analysis, adjustment of the baseline model inclusive of TTNtv for
the time dependent covariate of device implantation, did not affect the association between
TTNtv and the primary endpoint (TTNtv adjusted HR for primary endpoint 0.99, p=0.97;
Table 5-9).
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Table 5-9: Adjustment of Cox model for the time dependent covariate of cardiac device implantation does
not affect the association between TTNtv and the primary endpoint.
Variable Unit HR (95% CI) P value
TTNtv Present 0.99 (0.49-2.01) 0.97
Indexed left atrial Per 10mL/m2 1.13 (1.07-1.18) <0.00001 volume Mid wall fibrosis Present 2.26 (1.4-3.65) 0.0009 late-gadolinium enhancement Left ventricular Per 10% 0.74 (0.61-0.9) 0.0028 ejection fraction Sustained Present 2.51 (0.97-6.49) 0.057 ventricular tachycardia Cardiac device Present (implanted 2.25 (1.34-3.78) 0.002 during follow up)
The analysis was then restricted to cardiac-resynchronisation therapy only. In total, 101
patients had CRT-P or CRT-D implanted prior to censoring. Of these, 21 patients (20.8%)
met the primary endpoint compared to 80 patients who did not meet the primary endpoint
(79.2%).
On univariable analysis, the implantation of a cardiac-resynchronisation device during study
follow up was associated with a hazard ratio of 2.9 (95% CI 1.76 to 4.83, p<0.0001) for the
primary endpoint. On multivariable analysis, adjustment of the baseline model inclusive of
TTNtv for the time dependent covariate of cardiac-resynchronisation device implantation, did
not affect the association between TTNtv and the primary endpoint (TTNtv adjusted HR for
primary endpoint 1.05, 95% CI 0.91 to 2.14, p=0.90).
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5.4.4.3 Exclusion of individuals with baseline VT
It may be argued that the presence of sustained VT at baseline already identifies a ‘high-risk’
patient and that further risk stratification is redundant. This strategy does not however
account for patients who may have presented acutely and not yet received optimal medical
therapy. Nevertheless, to address this potential limitation, the primary analysis was repeated
after exclusion of 11 patients with sustained VT at enrollment. In this cohort, 72 patients met
the primary endpoint (12.1%). There were 70 patients with TTNtv (11.8%), of whom 9
patients met the primary endpoint (12.9%).
This resulting baseline Cox model consisted of the same remaining variables as the original
model. On multivariable analysis in this subset, the presence of a TTNtv was not associated
with the primary endpoint (TTNtv adjusted HR for primary endpoint 0.94, 95% CI 0.46 to
1.92, p=0.87) (Table 5-10).
Table 5-10: Sensitivity analysis: VT. Unadjusted and adjusted Cox proportional hazard model predicting
the primary endpoint in subset of cohort after exclusion of patients with baseline sustained VT at
enrollment. HR=hazard ratio; LVEF=left ventricular ejection fraction; LAVi=indexed left atrial volume;
LGE=mid wall fibrosis late gadolinium enhancement; TTNtv=truncating variant in titin gene.
Unadjusted Adjusted
Variable Unit HR 95% CI P HR 95% CI P
LVEF Per 10% 0.67 0.55 to 0.80 <0.0001 0.69 0.57 to 0.84 0.0002
LAVi Per 10mL/m2 1.11 1.07 to 1.16 <0.0001 1.12 1.06 to 1.17 <0.0001
LGE Present 2.26 1.42 to 3.59 0.0006 2.25 1.39 to 3.67 0.001
TTNtv Present 0.94 0.47 to 1.89 0.86 0.94 0.46 to 1.92 0.87
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5.4.4.4 Adjusting for renal function
In 416 patients, serum creatinine was measured using electrochemiluminescence
immunoassay and in 106 patients, serum creatinine was determined from the point-of-care
testing on the day of CMR study. In 82 patients, serum creatinine data was unavailable. Due
to the high degree of missingness, serum creatinine was therefore not evaluated in the
primary models. Survival analysis was then repeated on the subset of data with complete
creatinine data.
In this cohort of 522 patients, 65 patients (12.5%) met the primary endpoint. There were 57
patients with TTNtv (10.9%), of whom 5 met the primary endpoint (8.8%). On univariable
analysis, serum creatinine was associated with the primary endpoint (HR per 10µmol/L
increase in serum creatinine: 1.09, 95% CI 1.00 to 1.18, p=0.04). On multivariable analysis,
adjusting for serum creatinine in the baseline Cox model, there remained no association
between TTNtv and the primary endpoint (Table 5-11). On multivariable analysis, serum
creatinine was not an independent predictor of the primary endpoint (Table 5-11).
Table 5-11: Association between TTNtv and the primary endpoint, adjusted for the baseline Cox model in
the subset of patients with complete serum creatinine data.
Variable Unit HR 95% CI P Left ventricular ejection fraction Per 10% 0.69 0.56 to 0.86 0.0007 Indexed left atrial volume Per 10mL/m2 1.11 1.07 to 1.17 <0.0001 Mid wall fibrosis late-gadolinium enhancement Present 2.45 1.43 to 4.20 0.001 Sustained ventricular tachycardia Present 3.41 1.21 to 9.62 0.02 Creatinine per 10µmol/L 1.02 0.93 to 1.12 0.62 TTNtv Present 0.67 0.27 to 1.69 0.39
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5.4.5 Non-titin genetic variants and clinical outcomes: primary endpoint
The primary focus of the analyses in this Chapter was to evaluate the prognostic importance
of TTNtv. Other DCM genetic variants were identified (as outlined in Chapters 2 and 3) and
their association with the primary endpoint is shown for completion. However, further
interpretation based on this analysis is limited by the relatively smaller proportion of patients
with non TTNtv genetic variants.
Table 5-12: Numbers of patients with rare variants (MAF <0.0001) in non-titin DCM genes meeting the
primary endpoint. Data shown as count (percentages). Variant class: ms=missense variants, tv=
truncating variants, if no qualifier then variants reported include both variant classes.
Gene Primary endpoint = No Primary endpoint = Yes N=527 N=78 LMNA 4 (0.8) 4 (5.1) MYH7ms 21 (4.0) 1 (1.3) TNNT2ms 7 (1.3) 1 (1.3) DSPtv 5 (0.9) 0 (0.0) BAG3 4 (0.8) 1 (1.3) RBM20 17 (3.2) 4 (5.1)
On univariable and multivariable analysis, adjusting for the baseline Cox model predicting
the primary endpoint, only rare variants in LMNA were associated with the primary endpoint
(Table 5-13). The survival curve for individuals with and without LMNA variants is shown in
Figure 5-10. Although the confidence intervals for patients with LMNA variants are wide,
reflecting the small sample size, there is evidence of early progression to adverse events in
this subgroup. This reflects the large effect size with LMNA variants (adjusted HR for
primary endpoint 3.80). This is consistent with published reports on LMNA cardiomyopathy.
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Table 5-13: Association between non-TTNtv genetic variants and the primary endpoint. The adjusted
analysis is adjusted for the variables in the baseline Cox model (LVEF, LAVi, VT, LGE).
Unadjusted Adjusted
Gene HR 95% CI P value HR 95% CI P value
LMNA 3.80 1.39 to 10.41 0.009 3.80 1.04 to 13.92 0.04
MYH7 0.59 0.14 to 2.40 0.46 1.04 0.25 to 4.32 0.96
TNNT2 0.66 0.09 to 4.77 0.68 0.51 0.07 to 3.86 0.52
BAG3 2.22 0.31 to 15.98 0.43 1.09 0.15 to 8.07 0.93
RBM20 2.17 0.79 to 5.95 0.13 2.21 0.69 to 7.08 0.18
Figure 5-10: Survival curve for patients meeting the primary endpoint stratified by LMNA variant status.
Curves are compared using the log rank test.
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5.4.6 Analysis of the association between TTNtv and secondary outcome
end points
In total, 24 patients met the secondary cardiovascular mortality endpoint, including 2 patients
with TTNtv (2.8%) and 22 patients without TTNtv (4.1%). Fifty patients met the major heart
failure secondary endpoint, including 6 patients with TTNtv (8.5%) and 44 patients without
TTNtv (8.3%). In total, 24 patients met the secondary major arrhythmic endpoint, comprising
3 patients with TTNtv (4.2%) and 21 patients without TTNtv (3.9%). There was no
significant difference between TTNtv positive and negative DCM patients for the secondary
endpoints of cardiovascular mortality, major heart failure events or major arrhythmic events
(Figure 5-11).
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page 1 of 1 Outcome in DCM stratified by titin status: HF composite
Strata TTNtv negative TTNtv positive
1.00
0.75
0.50
0.25 p = 0.82 Freedom from HF endpoint
0.00 0 2 4 6 8 10 Years Outcome in DCM stratified by titin status: Arrhythmia composite
Strata TTNtv negative TTNtv positive
1.00
0.75
0.50
0.25 p = 0.82 Freedom from Arrhythmia endpoint Freedom from Arrhythmia 0.00 0 2 4 6 8 10 Years Outcome in DCM stratified by titin status: CVS mortality
Strata TTNtv negative TTNtv positive
1.00
0.75
0.50
0.25 p = 0.39 Freedom from CVS death
0.00 0 2 4 6 8 10 Years
Figure 5-11: Freedom from secondary endpoints stratified by TTNtv status. There is no difference
between TTNtv positive and negative groups for cardiovascular mortality or the pre-specified arrhythmic
and heart failure endpoints. Curves are compared using the log rank test.
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5.5 Discussion
In this outcome study of 604 patients with DCM, we leveraged the power of informed variant
curation, detailed cardiac phenotyping with CMR, and careful adjudication of clinical
outcomes to understand the prognosis of TTNtv cardiomyopathy. As the commonest genetic
cause of DCM(5,79,84), improved understanding of the clinical manifestations of TTNtv
cardiomyopathy offers scope for refined patient stratification by genotype. We demonstrate
that overall the presence of TTNtv DCM has a similar event profile to non TTNtv DCM, in a
cohort of ambulant DCM patients with medium term (~4 year) follow up.
In addition, this study also highlights a marked improvement in DCM outcomes overall when
compared to previous registry and cohort data. This data may inform clinical management
and guide future studies of risk stratification in DCM to focus on reducing the burden of heart
failure complications.
5.5.1.1.1 Clinical outcomes in titin cardiomyopathy
The primary analysis in this chapter was to evaluate the incidence of major adverse cardiac
events in titin cardiomyopathy. Of the 604 patients in the outcome cohort, 71 patients had
TTNtv. There was no difference in the number of patients with TTNtv who met the primary
composite endpoint (cardiovascular mortality, major arrhythmic events, major heart failure
events) compared to patients without TTNtv.
This suggests therefore that patients with TTNtv DCM have a similar prognosis to patients
without TTNtv DCM. In this cohort, the presence of a family history of DCM was associated
with the primary endpoint on univariable analysis, suggesting that there may be a genetic
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influence on prognosis in DCM, though the association was not robust to multivariable
analysis.
With the exception of lamin cardiomyopathy, no other genetic variants in DCM have been
reported to be associated with an adverse prognosis. In this cohort, non TTNtv genetic
variants were not the primary focus of analysis, though we were also able to replicate the
adverse prognosis of LMNA cardiomyopathy, despite the very small numbers, a reflection of
the large effect size. We did not demonstrate an adverse prognosis for any other DCM genetic
variant.
The absence of association between TTNtv and an adverse prognosis is in line with the
phenotype data described in Chapter 3, in which the phenotype of TTNtv DCM was not more
severe than non-TTNtv DCM, except in the presence of an important environmental modifier.
Therefore exploratory analysis was performed to test the hypothesis that the prognosis of
TTNtv DCM was altered in the presence of an environmental modifier. It must however be
stressed that conclusive analysis of the role of environmental modifiers was not possible with
the sample and event size in this study.
Titin and gender
Our cohort of TTNtv DCM patients were 71% male. The gender distribution is interesting
because DCM is known to have a 3:1 male:female ratio, yet one might expect an autosomal
dominant inheritance genetic form of DCM to have a 1:1 male to female ratio. This suggests
that additional factors may be important for the development of phenotype in individuals with
TTNtv and the presence of TTNtv alone may not be sufficient. It is possible that female
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gender confers a protective effect in the presence of TTNtv, or that males with TTNtv are
susceptible to a greater extent to an additional genetic modifier and/or have greater
exposure/susceptibility to an environmental modifier. Previous studies have reported
conflicting outcomes for TTNtv DCM stratified by gender(79,84-86). There may be an
important difference in outcome profile between males and females with TTNtv suggested by
the 10% difference in event rate observed in this cohort. However, in this largest single centre
study of TTNtv DCM, with long follow up and carefully adjudicated endpoints, the event rate
was not sufficient to show a gender protective effect on outcomes in TTNtv DCM patients.
Titin and mid-wall fibrosis
There was no evidence of difference in the presence of left ventricular mid wall fibrosis, a
marker of adverse outcome in DCM(13), between TTNtv positive and negative groups. In
addition, there was no significant interaction between adverse event rates in TTNtv patients
with and without mid-wall fibrosis LGE. Importantly, this suggests that LGE remains an
independent predictor of outcome in DCM, regardless of TTNtv status. This data is critical as
we begin to understand the multi-modality nature of risk stratification in DCM(347).
Titin and alcohol
Whilst there is evidence of a phenotypic interaction between TTNtv and alcohol (Chapter 3),
the sample size of TTNtv patients with a history of alcohol excess did not permit definitive
analysis of the prognostic implications of a TTNtv:alcohol interaction. This remains an area
of interest and may be addressed in larger, multicentre studies of outcome in TTNtv DCM.
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Titin and arrhythmia
An important aspect of the clinical management of DCM is the early identification of the at
risk patient. In this outcome study, we find that TTNtv DCM is not associated with an
increased risk of long term arrhythmic events (secondary endpoint) compared to patients
without TTNtv DCM.
In this cohort, exploring the interaction between TTNtv and arrhythmia further, amongst
TTNtv patients with early NSVT, there was an increased rate of adverse events (primary
endpoint) compared to patients without TTNtv with early NSVT, though this did not attain
statistical significance. This suggests there may be a subtle interaction between TTNtv and
arrhythmia generation requiring further exploration in larger studies with longer follow up.
The absence of a significant association between TTNtv and arrhythmic endpoints may
reflect that either TTNtv are not associated with long term arrhythmic risk, or this finding
may be driven by the overall low arrhythmic risk in this cohort, explored further in the
limitations section. Future studies focusing on subpopulations of DCM with a higher risk of
arrhythmia are therefore required to elucidate a potential interaction more fully. For example,
TTNtv may have a role to play in risk stratification within a higher risk subset of DCM
patients, such as those with low LVEF and mid-wall fibrosis.
5.5.1.1.2 Overall adverse event rate in DCM cohort and predictors of outcome
The overall baseline Cox model to predict the composite endpoint in this cohort consisted of
left ventricular ejection fraction, mid-wall fibrosis late gadolinium enhancement, left atrial
volume and a history of sustained ventricular tachycardia. These predictors are in line with
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previously reported predictors of outcome in DCM, not just from our institution, and
demonstrate that this is a representative DCM cohort(13,127,147,410). In particular, left atrial
volume was the variable in the baseline model that had the lowest p value. This finding is
concordant with recent studies highlighting the importance of the left atrium as an emerging
cardiovascular biomarker(411,412).
Somewhat surprisingly, we find that the rate of adverse events in this cohort (13% of patients
met the primary endpoint, median follow up 3.9 years), particularly with regards to sudden
cardiac death (3 patients experienced sudden cardiac death), is much lower than previous
registry data(235), but also historic experience from our own institution(13,146), suggesting
that our findings are not simply a reflection of CMR bias.
To some extent, this is likely to reflect the milder, less symptomatic cohort of patients
recruited (the majority in NYHA I/II), but is also a reflection of optimal medical therapy,
with over 70% of patients on beta blocker therapy and almost 80% on ACE inhibitor
treatment. This improvement in outcome benefits patient care and highlights that future
studies of risk in this field should focus on reducing the increasing burden of heart failure
hospitalisations and heart failure deaths. This reduction in sudden cardiac death is also
mirrored in a recent study of over 40,000 heart failure with reduced ejection fraction patients
across 12 clinical trials between 1995-2014, demonstrating a reduction in the cumulative
incidence of sudden cardiac death from 2.4% in the earliest trial to 1.0% in the recent
trial(413).
The low arrhythmic event rate does have implications for the findings of our study, meaning
that we could not conclusively evaluate genetic and non genetic predictors of arrhythmia,
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particularly the association between TTNtv and arrhythmia. This is despite having a
relatively broad inclusion criteria for the arrhythmic endpoint, including major arrhythmias
and aborted sudden cardiac death and not just sudden cardiac death. This is explored further
in the limitations section.
5.5.2 Strengths of this study
This study, commenced in 2009, was an enormous endeavour in terms of cost, resources,
physician and nursing staffing and of course patient contribution. To date, this is the largest
integrated study of clinical, imaging, and genetic predictors of outcome in a precisely
phenotyped cohort of patients with DCM with independently adjudicated event data.
5.5.3 Limitations of this study
There are a number of potential limitations to this work. The low arrhythmic event rate in this
cohort has been established. Whilst this may reflect a CMR bias, with exclusion of patients
from other institutions who may not have had a CMR prior to ICD implantation (in our
institution all patients have a CMR prior to ICD implantation where clinically possible), it
could be argued that optimal design of an arrhythmic risk analysis study should probably
exclude individuals who have already declared as arrhythmic risk. It is also possible that
previous estimates of risk in DCM were conflated by inadvertent inclusion of patients with
ischaemic heart disease into echocardiography based studies, which has been avoided through
careful CMR based phenotyping in this cohort. We have previously demonstrated that CMR
phenotyping identifies ~15% of patients with DCM diagnosed on another imaging modality
initially who have evidence of ischaemic heart disease despite a history of apparently
unobstructed coronary arteries(108).
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The low event rate justifies our selection of a composite endpoint. The composite end point
was originally chosen based on previous studies from our institution, with an expected 20%
event rate at 5 years for the composite endpoint. However, the use of a composite end point
means that the findings of our study are not particularly informative for the prediction of
arrhythmic events, a major target for risk stratification in DCM.
The low event rate may also be a reflection of cardiac device therapy implanted during the
follow up period of the study, though on analysis with inclusion of device therapy as a time
dependent covariate, TTNtv were still not associated with the primary endpoint. Whilst
device therapy was adjusted for, there are other changes in therapy that may have occurred
during the course of the study that were not captured and therefore not adjusted for. For
example, temporal changes not only in medication dose during the course of a patient’s
treatment, but changes in medication class, for example the introduction of novel heart failure
therapy such as Ivrabadine or Entresto. The data collected could have spanned a 6 year time
frame (2009-2015) yet minimum follow up was 1 year. Therefore there is a possible 5 year
difference between the first and last patient, during which time changes in treatment could
have affected our findings.
Also, as with any model built using data from one time point, the model does not account for
time varying changes in event risk. Although genetic data is the exception to this, most other
variables evaluated are likely to vary during the time course of the study, which may affect
the risk of events, particularly sudden cardiac death. To avoid this would require resampling
of data at pre-specified intervals during the study, but this was not feasible during this study
and this method would still not fully account for all time varying changes in risk.
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Importantly, a key limitation of this work is that this is a single centre study. All findings
require replication in an independent cohort. However, at present, this is the largest published
cohort of patients with DCM who have undergone CMR, genetic sequencing, and adjudicated
long term follow up and as such, a comparable replication cohort has not yet been identified.
The study design is a prospective observational cohort study. The sample size obtained was
in line with the target mega cohort sample size of n=1000 patients with DCM (n=928 patients
with confirmed imaging diagnosis of DCM recruited), which was the estimated number of
patients that could be recruited within the timeframe and resources of the study. Such a study
design enables us to draw inferences about the differences between patients with and without
TTNtv, as well as identify other associations from careful analysis of the rich dataset. Based
on our current sample size of patients with TTNtv, we are powered at 80% and an alpha level
of 0.05 to detect a hazard ratio for the association between TTNtv and the primary composite
endpoint of ≥2.15 (using the powerSurvEpi package in R). This hazard ratio would be
considered relatively low for risk stratification in DCM patients in a clinical context(250).
Therefore we can state that we are adequately powered within this study to detect a clinically
meaningful difference in outcomes should one exist between DCM patients with and without
TTNtv. A perceived limitation may be that the study had not been formally powered to detect
a difference in outcome between patients with and without TTNtv. However, at the time of
study design, there was no pilot data to support such a calculation. Going forwards, the
results of this study could help plan larger multi-centre studies evaluating TTNtv
cardiomyopathy. A sample size of at least 1826 patients with TTNtv would be required to
detect a difference based on the event rate observed in this cohort (using the
powerSurvEpi package in R), which could only be achieved through meta-analysis of data
from several projects.
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These are challenges in observational studies, but they do not diminish the contribution that
the findings of this study make to the advancement of knowledge regarding titin
cardiomyopathy and risk stratification in DCM.
Other statistical challenges when analysing data from observational studies include missing
data, heterogeneity in samples and selection bias. These limitations with respect to
ascertainment and selection bias have been discussed in Chapter 3. Whilst the findings of
observational studies like this one may not be as accurate as those from clinical trials, they do
have greater external validity compared to those studies, meaning that the findings are
potentially more generalizable to clinical DCM cohorts.
Every effort was made during this study to evaluate key variables known to be implicated in
risk in DCM. However, a major exception is the absence of prognostic biomarker data
including natriuretic peptides and troponin. Subsequent to this analysis, this data has been
collected on a subset of the cohort (n=426 patients) and will form the basis of future work on
multi-modality risk stratification in DCM, incorporating biomarker, imaging, genetic and
clinical data. The analyses outlined in this Chapter were not performed in this biomarker
subset cohort to avoid model-overfitting. The number of primary endpoint events in the
cohort of n=426 was 44, meaning that only 4 variables could be evaluated to keep the
‘number of events per variable’ above the recommended threshold of 10(414). In the full
study cohort however, evaluating the prognostic importance of TTNtv in DCM, the number
of events per variable was 19, which mitigates against model overfitting.
Another major exception was ECG markers such as signal averaged electrograms,
fragmented QRS, QRS-T angle and T wave alternans data which were not recorded in the
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cohort. Whilst they have been previously linked to adverse events in DCM, the individual
hazard ratios for these variables have been low(250), and certainly much lower than CMR
parameters, they have not been consistently replicated and are not widely available in clinical
practice. Therefore their absence from this dataset is not a major limitation in the wider
context of DCM risk research.
5.6 Summary
In this outcome study of 604 patients with DCM, we leveraged the power of informed variant
curation, detailed cardiac phenotyping with CMR, and careful adjudication of clinical
outcomes to understand the prognosis of TTNtv cardiomyopathy. While TTNtv are common
in DCM and may be used to confirm a genetic cause for DCM and for cascade screening they
do not appear to predict clinical outcomes in ambulant DCM cases with medium term (~4
year) follow up.
5.7 Outline of further work
• To harness the prognostic potential of the rich dataset, more complex machine
learning methods will be used to develop fuller models to predict risk in this cohort.
This will be performed in collaboration with researchers at the University of
California, San Francsico and the University of Oxford.
• Develop an integrated multi-modality risk score in DCM incorporating biomarker
data.
5.8 Acknowledgements
• The NIHR Royal Brompton Cardiovascular Biomedical Research Unit (BRU)
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research nurses recruited all patients to this study and together with BRU admin
support staff coordinated the retrieval and first stage data entry of follow up event
data from primary care, hospital records, coroners’ offices and national registry data.
• Clinical research fellows Dr Brian Halliday and Dr Amrit Lota contributed to the
initial quality control of the follow up data collected by the research nurses.
• Dr Julian Jarman, Dr Resham Baruah and Prof Michael Frenneaux sat on the
independent data adjudication committee, reviewing all endpoints.
• Steve Collins and Yasin Karafil developed and maintained the BRU database for
collation, storage and processing of all event data.
• Statistical mentorship and advice was provided by Simon Newsome.
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6 SUMMARY: WHAT THIS THESIS
ADDS TO THE FIELD AND
FUTURE WORK
The overall aim of this thesis was to evaluate whether the integration of clinical, genetic and
advanced imaging data could improve our understanding of DCM pathobiology, inform
patient stratification, and identify predictors of myocardial remodelling or clinical outcomes
in DCM.
Genetic architecture of DCM
To address these aims, the first part of this thesis defined the genetic architecture of DCM, by
evaluating the burden of rare genetic variation in putative DCM genes over and above
background population genetic variation. To date, this is the largest cohort study of DCM
patients to undergo standardised sequencing for almost 60 putative DCM genes. The study
confirmed that titin was the largest single gene contributor to DCM, a finding which
underpinned the subsequent phenotype study in this thesis. The study also provided an
updated frequency estimate of truncating variants in titin in an ambulant DCM cohort, as
opposed to the end-stage cohorts of the original reports.
In addition, the study confirmed rare variation in a further 4 genes with robust association to
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DCM and suggested that the remaining putative DCM genes require further evidence to
support their association with DCM. This is not to say that the remaining genes are not valid
DCM genes, but that in light of the degree of tolerated background population genetic
variation we now know to exist, a more critical evaluation of their contribution to DCM is
required. As extensively discussed, there were of course limitations to the analysis strategy
used. Going forwards, some of these will be addressed through planned studies using whole
genome sequencing (gnomAD) data as a comparator instead of whole exome sequencing
(ExAC) data, and the identification of replication cohorts from collaborating national and
international centres.
Together, these findings will be informative for clinicians trying to make sense of genetic
variation in their patients, for diagnostic sequencing laboratories deciding which genes to
prioritise for analysis, and for researchers designing studies in DCM.
Phenotype of titin cardiomyopathy
The thesis then moved on to detailed phenotype study of titin cardiomyopathy in over 700
DCM patients. As the commonest genetic contributor to DCM, the findings of this study will
be of great interest to clinicians who manage DCM patients. The novelty of the study lay in
the use of cardiovascular magnetic resonance for phenotype analysis, providing in vivo tissue
characterization and accurate quantification of cardiac chamber dimensions and function.
This enabled the discovery of a subtle but potentially important gene-environmental
interaction between titin and moderate excess alcohol consumption, whereby DCM patients
with titin truncating variants had more severely impaired left ventricular function in the
presence of alcohol excess, compared to patients with titin truncations alone or alcohol excess
alone. This was the first study to demonstrate a titin-alcohol interaction and these data add to
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the growing body of clinical and pre-clinical literature supporting a ‘two-hit’ hypothesis for
some cases of titin cardiomyopathy, whereby an additional environmental modifier is needed
for the development of the phenotype in the presence of a titin truncating variant(88,92).
Going forwards, the findings require replication in an independent DCM cohort, although our
research group have recently completed sequencing of an independent cohort of patients with
alcoholic cardiomyopathy and found a similar prevalence of titin truncating variants as in
DCM. It could be argued that this provides biological replication at least for a titin-alcohol
interaction. These findings provide clinically informative data to guide lifestyle advice for
affected patients and has guided further translational study, with a study planned to assess the
effect of alcohol in a rodent model of titin cardiomyopathy (collaboration with a Spanish
group).
The study also found that titin cardiomyopathy was associated with a limited hypertrophic
response meaning that affected patients had lower left ventricular mass and thinner left
ventricular walls, after adjustment for potential confounders. This finding supports recent
pre-clinical data(88) and advances our understanding of the pathobiology of titin
cardiomyopathy.
Imaging predictors of remodelling in DCM
The thesis then moved to evaluate remodelling in DCM, specifically imaging predictors of
left ventricular functional improvement at 1 year. This study demonstrated that low-dose
dobutamine stress was a safe and feasible method to evaluate contractile reserve in DCM
patients and that contractile reserve assessed this way was an independent predictor of
myocardial remodelling. This was the first CMR study to evaluate dobutamine stress-
contractile reserve. Unlike previous studies, it was therefore possible to evaluate interstitial
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and replacement fibrosis as remodelling predictors as well as perform in vivo tissue
characterisation to explore the biological basis of contractile reserve. In contrast to previous
research, in this cohort, myocardial fibrosis did not underpin contractile reserve, nor did it
predict remodelling. This study therefore added to the field by highlighting that predictors of
remodelling are distinct compared to predictors of outcomes in DCM, and added to our
understanding of the pathobiology of contractile reserve. The study also demonstrated the
feasibility of a novel cine DENSE CMR sequence to assess myocardial strain and showed the
importance of longitudinal strain in defining subtle abnormalities in DCM patients compared
to healthy volunteers, as well as as a useful predictor of response to therapy in DCM patients.
This sequence is now being used by researchers studying other cardiomyopathies. These
results advance our understanding of the pathobiology of DCM and may have utility in
detection of subclinical disease, particularly in genotype positive, phenotype negative
individuals.
Predictors of clinical outcomes in DCM
The thesis concluded with a study of the prognosis of titin cardiomyopathy in DCM. The
study demonstrated that there was no difference in the rate of adverse cardiovascular events
between DCM patients with and without titin cardiomyopathy. As the largest and most
comprehensive genotype-phenotype study of titin cardiomyopathy to date, these data provide
useful information to guide patient stratification, though it must be stressed that the overall
cohort was representative of a largely ambulant DCM cohort with medium term follow up
only. The number of events within this cohort was not sufficient to show an effect of TTNtv
on outcomes in DCM patients. Larger, likely multi-centre studies with longer term follow up
are needed to explore the prognostic role of potential titin-environmental interactions such as
alcohol consumption and to determine the long term prognosis of titin cardiomyopathy,
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particularly with regards to arrhythmia.
Finally, these analyses were performed with conventional statistical methodology and yielded
novel and informative findings. Going forwards, working with UK (University of Oxford)
and international collaborators (University of California, San Francsico), I am now exploring
using unsupervised machine learning on this dataset to detect clinically or biologically
important relationships within the data that may offer novel prognostic phenotypes(415).
Limitations
Whilst much has been learnt from this genotype and phenotype study in DCM, the study is
limited by its cross sectional nature, with phenotyping at only one timepoint in the course of a
complex disease. The information gleaned, whilst extensive, may still be insufficient to
identify risk for multi-level disease outcomes in DCM. In addition, whilst risk prediction has
its role, the field is in particular need of therapies which modulate risk by targeting the
upstream drivers of adverse outcomes. Much remains to be explored regarding factors
influencing disease onset and progression of genetic DCM, epigenetics and environmental
factors not evaluated in this thesis.
Conclusions
The work in this thesis refines our current understanding of the genetics of DCM and
demonstrates the utility of genetic, advanced imaging, and phenotypic analysis for
stratification of patients with DCM.
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396. Konstam MA, Abboud FM. Ejection Fraction: Misunderstood and Overrated (Changing the Paradigm in Categorizing Heart Failure). Circulation 2017;135:717-719. 397. Kalogeropoulos AP, Fonarow GC, Georgiopoulou V et al. Characteristics and Outcomes of Adult Outpatients With Heart Failure and Improved or Recovered Ejection Fraction. JAMA Cardiol 2016;1:510-518. 398. Ruiz-Zamora I, Rodriguez-Capitan J, Guerrero-Molina A et al. Incidence and prognosis implications of long term left ventricular reverse remodeling in patients with dilated cardiomyopathy. Int J Cardiol 2015;203:1114-1121. 399. Cho JY, Kim KH, Song JE et al. Predictors of Left Ventricular Functional Recovery and Their Impact on Clinical Outcomes in Patients With Newly Diagnosed Dilated Cardiomyopathy and Heart Failure. Heart Lung Circ 2017;10.1016/j.hlc.2017.02.013. 400. van der Heijden AC, Hoke U, Thijssen J et al. Super-responders to cardiac resynchronization therapy remain at risk for ventricular arrhythmias and benefit from defibrillator treatment. Eur J Heart Fail 2014;16:1104-1111. 401. Hicks KA, Tcheng JE, Bozkurt B et al. 2014 ACC/AHA Key Data Elements and Definitions for Cardiovascular Endpoint Events in Clinical Trials: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards). Circulation 2015;132:302-361. 402. Altman DG, Andersen PK. Bootstrap investigation of the stability of a Cox regression model. Stat Med 1989;8:771-783. 403. Walter S, Tiemeier H. Variable selection: current practice in epidemiological studies. Eur J Epidemiol 2009;24:733-736. 404. Hernan MA, Hernandez-Diaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol 2002;155:176- 184. 405. Greenland S. Invited commentary: variable selection versus shrinkage in the control of multiple confounders. Am J Epidemiol 2008;167:523-529. 406. Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression: John Wiley & Sons, 2013. 407. Hosmer DJ. S.. Lemeshow;(1999).‘Applied Survival Analysis. Regression Modeling of Time to Event Data.’. Wiley: New York. 408. Gold MR, Daubert C, Abraham WT et al. The effect of reverse remodeling on long-term survival in mildly symptomatic patients with heart failure receiving cardiac resynchronization therapy: results of the REVERSE study. Heart Rhythm 2015;12:524-530. 409. McManus DD, Shah SJ, Fabi MR et al. Prognostic value of left ventricular end-systolic volume index as a predictor of heart failure hospitalization in stable coronary artery disease: data from the Heart and Soul Study. J Am Soc Echocardiogr 2009;22:190-197. 410. Pontone G, Guaricci AI, Andreini D et al. Prognostic Benefit of Cardiac Magnetic Resonance Over Transthoracic Echocardiography for the Assessment of Ischemic and Nonischemic Dilated Cardiomyopathy Patients Referred for the Evaluation of Primary Prevention Implantable Cardioverter- Defibrillator Therapy. Circ Cardiovasc Imaging 2016;9: 004956. 411. Wijesurendra RS, Rider OJ, Neubauer S. Left Atrial Volumes in Health and Disease Measured Using Cardiac Magnetic Resonance. Circ Cardiovasc Imaging 2017;10. 412. Zemrak F, Ambale-Venkatesh B, Captur G et al. Left Atrial Structure in Relationship to Age, Sex, Ethnicity, and Cardiovascular Risk Factors: MESA (Multi-Ethnic Study of Atherosclerosis). Circ Cardiovasc Imaging 2017;10. 413. Shen L, Jhund PS, Petrie MC et al. Declining Risk of Sudden Death in Heart Failure. N Engl J Med 2017;377:41-51. 414. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995;48:1503-1510. 415. Shah SJ, Katz DH, Selvaraj S et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 2015;131:269-279. 416. Zipes DP, Camm AJ, Borggrefe M et al. ACC/AHA/ESC 2006 Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (writing committee to develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death): developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society. Circulation 2006;114:e385-484.
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8 APPENDICES
8.1 Gene panels Appendix Table 1: Genes sequenced on Tru Sight Cardio Gene Panel- 174 genes
Gene Gene Name ICC Diseases
LQTS, JLNS, KCNQ1 potassium voltage-gated channel, KQT-like subfamily, member 1 SQTS, HCM, AF
LQTS, SQTS, KCNH2 potassium voltage-gated channel, subfamily H (eag-related), member 2 BrS
LQTS, BrS, SCN5A sodium channel, voltage-gated, type V, alpha subunit DCM, ARVD/C, AF
ANK2 ankyrin 2, neuronal LQTS
LQTS, JLNS, KCNE1 potassium voltage-gated channel, Isk-related family, member 1 CPVT, AF
KCNE2 potassium voltage-gated channel, Isk-related family, member 2 LQTS, AF
LQTS, SQTS, KCNJ2 potassium inwardly-rectifying channel, subfamily J, member 2 CPVT, AF
CACNA1C calcium channel, voltage-dependent, L type, alpha 1C subunit LQTS, BrS, HCM
CAV3 caveolin 3 LQTS, HCM
SCN4B sodium channel, voltage-gated, type IV, beta LQTS, AF
AKAP9 A kinase (PRKA) anchor protein (yotiao) 9 LQTS
SNTA1 syntrophin, alpha 1 (dystrophin-associated protein A1, 59kDa, acidic component) LQTS
KCNJ5 potassium inwardly-rectifying channel, subfamily J, member 5 LQTS
ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 BrS, DCM, AF
TNNC1 troponin C type 1 (slow) HCM, DCM
ANKRD1 ankyrin repeat domain 1 (cardiac muscle) HCM, DCM
HCM, DCM, LMNA lamin A/C ARVD/C, AF
HCM, DCM, DES desmin ARVD/C, RCM
RBM20 RNA binding motif protein 20 DCM
VCL vinculin HCM, DCM
HCM, DCM, ACTN2 actinin, alpha 2 ncCM
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Gene Gene Name ICC Diseases
HCM, DCM, MYH7 myosin, heavy chain 7, cardiac muscle, beta RCM, ncCM
HCM, DCM, TNNT2 troponin T type 2 (cardiac) RCM, ncCM
LDB3 LIM domain binding 3 HCM, DCM
HCM, DCM, CSRP3 cysteine and glycine-rich protein 3 (cardiac LIM protein) ncCM
HCM, DCM, TNNI3 troponin I type 3 (cardiac) RCM
HCM, DCM, MYBPC3 myosin binding protein C, cardiac ncCM
HCM, DCM, TPM1 tropomyosin 1 (alpha) RCM, ncCM
HCM, DCM, ACTC1 actin, alpha, cardiac muscle 1 RCM, ncCM
TCAP titin-cap (telethonin) HCM, DCM
MYH6 myosin, heavy chain 6, cardiac muscle, alpha HCM, DCM, AF
HCM, DCM, PLN phospholamban ARVD/C
HCM, DCM, TTN titin ARVD/C
MYLK2 myosin light chain kinase 2 HCM, FAA
MYL2 myosin, light chain 2, regulatory, cardiac, slow HCM, RCM
CALR3 calreticulin 3 HCM
JPH2 junctophilin 2 HCM, AF
MYL3 myosin, light chain 3, alkali; ventricular, skeletal, slow HCM, RCM
MYOZ2 myozenin 2 HCM
DSG2 desmoglein 2 DCM, ARVD/C
BrS, DCM, PKP2 plakophilin 2 ARVD/C
DSP desmoplakin DCM, ARVD/C
TGFB3 transforming growth factor, beta 3 ARVD/C, LDS
DCM, ARVD/C, DSC2 desmocollin 2 AF
TMEM43 transmembrane protein 43 ARVD/C
JUP junction plakoglobin DCM, ARVD/C
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Gene Gene Name ICC Diseases
LQTS, CPVT, RYR2 ryanodine receptor 2 (cardiac) ARVD/C
ACTA1 actin, alpha 1, skeletal muscle HCM, DCM
CPVT, HCM, CASQ2 calsequestrin 2 (cardiac muscle) DCM, ncCM
COX15 COX15 homolog, cytochrome c oxidase assembly protein (yeast) HCM
CRYAB crystallin, alpha B HCM, DCM
CTF1 cardiotrophin 1 DCM
FKTN fukutin DCM
FXN frataxin HCM
ILK integrin-linked kinase DCM
LAMA2 laminin, alpha 2 DCM
LAMA4 laminin, alpha 4 DCM
MYO6 myosin VI HCM
HCM, DCM, MYPN myopalladin RCM
NEXN nexilin (F actin binding protein) HCM, DCM
RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 HCM, DCM, NS
solute carrier family 25 (mitochondrial carrier; adenine nucleotide translocator), SLC25A4 HCM member 4
TTR transthyretin
TXNRD2 thioredoxin reductase 2 DCM
DMD dystrophin DCM
PRKAG2 protein kinase, AMP-activated, gamma 2 non-catalytic subunit HCM
SGCD sarcoglycan, delta (35kDa dystrophin-associated glycoprotein) DCM
TAZ tafazzin DCM, ncCM
TMPO thymopoietin DCM
EYA4 eyes absent homolog 4 (Drosophila) DCM
CACNA2D1 calcium channel, voltage-dependent, alpha 2/delta subunit 1 SQTS, BrS
CACNB2 calcium channel, voltage-dependent, beta 2 subunit BrS
GPD1L glycerol-3-phosphate dehydrogenase 1-like BrS
KCND3 potassium voltage-gated channel, Shal-related subfamily, member 3 BrS, AF
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Gene Gene Name ICC Diseases
KCNE3 potassium voltage-gated channel, Isk-related family, member 3 LQTS, BrS, AF
SCN1B sodium channel, voltage-gated, type I, beta BrS
SCN3B sodium channel, voltage-gated, type III, beta BrS, AF
HCN4 hyperpolarization activated cyclic nucleotide-gated potassium channel 4 BrS, ncCM, AF
GLA galactosidase, alpha HCM
SOS1 son of sevenless homolog 1 (Drosophila) HCM, NS
PTPN11 protein tyrosine phosphatase, non-receptor type 11 HCM, NS
KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog NS
SDHA succinate dehydrogenase complex, subunit A, flavoprotein (Fp) DCM
BAG3 BCL2-associated athanogene 3 DCM
GATAD1 GATA zinc finger domain containing 1 DCM
ALMS1 Alstrom syndrome 1 DCM
DNAJC19 DnaJ (Hsp40) homolog, subfamily C, member 19 DCM
DTNA dystrobrevin, alpha DCM, ncCM
EMD emerin DCM, AF
FHL2 four and a half LIM domains 2 DCM
FKRP fukutin related protein DCM
hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase HADHA DCM (trifunctional protein), alpha subunit
HFE hemochromatosis DCM
LAMP2 lysosomal-associated membrane protein 2 HCM, DCM
PDLIM3 PDZ and LIM domain 3 HCM, DCM
SGCB sarcoglycan, beta (43kDa dystrophin-associated glycoprotein) DCM
SGCG sarcoglycan, gamma (35kDa dystrophin-associated glycoprotein) DCM
TBX20 T-box 20 DCM
NPPA natriuretic peptide A DCM, AF
KCNA5 potassium voltage-gated channel, shaker-related subfamily, member 5 AF
GJA5 gap junction protein, alpha 5, 40kDa AF
ACTA2 actin, alpha 2, smooth muscle, aorta FAA
COL3A1 collagen, type III, alpha 1 FAA
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Gene Gene Name ICC Diseases
KCNJ8 potassium inwardly-rectifying channel, subfamily J, member 8 BrS, AF
HSPB8 heat shock 22kDa protein 8
MYH11 myosin, heavy chain 11, smooth muscle FAA
SMAD3 SMAD family member 3 LDS, FAA
LMF1 lipase maturation factor 1 FHT
TGFBR2 transforming growth factor, beta receptor II (70/80kDa) MFS, LDS, FAA
TRIM63 tripartite motif containing 63, E3 ubiquitin protein ligase HCM
DPP6 dipeptidyl-peptidase 6
TGFBR1 transforming growth factor, beta receptor 1 MFS, LDS, FAA
APOA5 apolipoprotein A-V FHT, FHL
MYLK myosin light chain kinase FAA
ZBTB17 zinc finger and BTB domain containing 17
LDLR low density lipoprotein receptor FH
LPL lipoprotein lipase FHT, FHL
RANGRF RAN guanine nucleotide release factor BrS
MFS, LDS, FAA, FBN1 fibrillin 1 AVD
CALM1 calmodulin 1 (phosphorylase kinase, delta) LQTS, CPVT
GAA glucosidase, alpha; acid HCM
RYR1 ryanodine receptor 1 (skeletal)
BRAF v-raf murine sarcoma viral oncogene homolog B1 HCM, NS
DOLK dolichol kinase DCM
FHL1 four and a half LIM domains 1 HCM
KLF10 Kruppel-like factor 10 HCM
MURC muscle-related coiled-coil protein DCM
NRAS neuroblastoma RAS viral (v-ras) oncogene homolog NS
TRDN triadin CPVT
APOB apolipoprotein B (including Ag(x) antigen) FH, FHT
PCSK9 proprotein convertase subtilisin/kexin type 9 FH
GCKR glucokinase (hexokinase 4) regulator FH, FHT
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Gene Gene Name ICC Diseases
ELN elastin FAA, AVD
APOE apolipoprotein E FH, FHT, FHL
GPIHBP1 glycosylphosphatidylinositol anchored high density lipoprotein binding protein 1 FHT, FHL
NOTCH1 notch 1 FAA, AVD
APOC2 apolipoprotein C-II FHT, FHL
ZHX3 zinc fingers and homeoboxes 3 FHT
CETP cholesteryl ester transfer protein, plasma FH, FHL
TGFB2 transforming growth factor, beta 2 LDS, FAA
CREB3L3 cAMP responsive element binding protein 3-like 3 FHT
LDLRAP1 low density lipoprotein receptor adaptor protein 1 FH
APOA4 apolipoprotein A-IV FHL
SREBF2 sterol regulatory element binding transcription factor 2 FH
LTBP2 latent transforming growth factor beta binding protein 2 MFS
ABCG5 ATP-binding cassette, sub-family G (WHITE), member 5
ABCG8 ATP-binding cassette, sub-family G (WHITE), member 8 FH
PRDM16 PR domain containing 16 DCM, ncCM
NKX2-5 NK2 homeobox 5 AF
TBX5 T-box 5
TBX3 T-box 3
SALL4 sal-like 4 (Drosophila)
SEPN1 selenoprotein N, 1
SLC2A10 solute carrier family 2 (facilitated glucose transporter), member 10 FAA
MIB1 mindbomb E3 ubiquitin protein ligase 1 ncCM
MAP2K1 mitogen-activated protein kinase kinase 1 HCM, NS
MAP2K2 mitogen-activated protein kinase kinase 2 HCM, NS
SHOC2 soc-2 suppressor of clear homolog (C. elegans) NS
JAG1 jagged 1
ZIC3 Zic family member 3
PRKAR1A protein kinase, cAMP-dependent, regulatory, type I, alpha
CRELD1 cysteine-rich with EGF-like domains 1
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Gene Gene Name ICC Diseases
NODAL nodal homolog (mouse)
EFEMP2 EGF containing fibulin-like extracellular matrix protein 2 FAA
CBS cystathionine-beta-synthase
SMAD4 SMAD family member 4 FAA
FBN2 fibrillin 2 FAA
HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog NS
COL5A1 collagen, type V, alpha 1
COL5A2 collagen, type V, alpha 2
CBL Cbl proto-oncogene, E3 ubiquitin protein ligase NS
SCN2B sodium channel, voltage-gated, type II, beta subunit BrS, AF
TRPM4 transient receptor potential cation channel, subfamily M, member 4 BrS
SCO2 SCO2 cytochrome c oxidase assembly protein
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Appendix Table 2: Genes sequenced on Inherited Cardiac Conditions (ICC) Gene Panels Versions 4-6– 201 genes
Gene Gene Name ICC Diseases
LQTS, JLNS, SQTS, KCNQ1 potassium voltage-gated channel, KQT-like subfamily, member 1 HCM, AF
KCNH2 potassium voltage-gated channel, subfamily H (eag-related), member 2 LQTS, SQTS, BrS
LQTS, BrS, DCM, SCN5A sodium channel, voltage-gated, type V, alpha subunit ARVD/C, AF
ANK2 ankyrin 2, neuronal LQTS
KCNE1 potassium voltage-gated channel, Isk-related family, member 1 LQTS, JLNS, CPVT, AF
KCNE2 potassium voltage-gated channel, Isk-related family, member 2 LQTS, AF
LQTS, SQTS, CPVT, KCNJ2 potassium inwardly-rectifying channel, subfamily J, member 2 AF
CACNA1C calcium channel, voltage-dependent, L type, alpha 1C subunit LQTS, BrS, HCM
CAV3 caveolin 3 LQTS, HCM
SCN4B sodium channel, voltage-gated, type IV, beta LQTS, AF
AKAP9 A kinase (PRKA) anchor protein (yotiao) 9 LQTS
SNTA1 syntrophin, alpha 1 (dystrophin-associated protein A1, 59kDa, acidic component) LQTS
KCNJ5 potassium inwardly-rectifying channel, subfamily J, member 5 LQTS
ABCC9 ATP-binding cassette, sub-family C (CFTR/MRP), member 9 BrS, DCM, AF
PSEN1 presenilin 1 DCM
TNNC1 troponin C type 1 (slow) HCM, DCM
PSEN2 presenilin 2 (Alzheimer disease 4) DCM
ANKRD1 ankyrin repeat domain 1 (cardiac muscle) HCM, DCM
HCM, DCM, ARVD/C, LMNA lamin A/C AF
HCM, DCM, ARVD/C, DES desmin RCM
RBM20 RNA binding motif protein 20 DCM
VCL vinculin HCM, DCM
ACTN2 actinin, alpha 2 HCM, DCM, ncCM
HCM, DCM, RCM, MYH7 myosin, heavy chain 7, cardiac muscle, beta ncCM
HCM, DCM, RCM, TNNT2 troponin T type 2 (cardiac) ncCM
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Gene Gene Name ICC Diseases
LDB3 LIM domain binding 3 HCM, DCM
CSRP3 cysteine and glycine-rich protein 3 (cardiac LIM protein) HCM, DCM, ncCM
TNNI3 troponin I type 3 (cardiac) HCM, DCM, RCM
MYBPC3 myosin binding protein C, cardiac HCM, DCM, ncCM
HCM, DCM, RCM, TPM1 tropomyosin 1 (alpha) ncCM
HCM, DCM, RCM, ACTC1 actin, alpha, cardiac muscle 1 ncCM
TCAP titin-cap (telethonin) HCM, DCM
MYH6 myosin, heavy chain 6, cardiac muscle, alpha HCM, DCM, AF
PLN phospholamban HCM, DCM, ARVD/C
TTN titin HCM, DCM, ARVD/C
MYLK2 myosin light chain kinase 2 HCM, FAA
MYL2 myosin, light chain 2, regulatory, cardiac, slow HCM, RCM
CALR3 calreticulin 3 HCM
JPH2 junctophilin 2 HCM, AF
MYL3 myosin, light chain 3, alkali; ventricular, skeletal, slow HCM, RCM
MYOZ2 myozenin 2 HCM
DSG2 desmoglein 2 DCM, ARVD/C
PKP2 plakophilin 2 BrS, DCM, ARVD/C
DSP desmoplakin DCM, ARVD/C
TGFB3 transforming growth factor, beta 3 ARVD/C, LDS
DSC2 desmocollin 2 DCM, ARVD/C, AF
TMEM43 transmembrane protein 43 ARVD/C
JUP junction plakoglobin DCM, ARVD/C
RYR2 ryanodine receptor 2 (cardiac) LQTS, CPVT, ARVD/C
CPVT, HCM, DCM, CASQ2 calsequestrin 2 (cardiac muscle) ncCM
CRYAB crystallin, alpha B HCM, DCM
CTF1 cardiotrophin 1 DCM
FKTN fukutin DCM
FLT1 fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular DCM
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Gene Gene Name ICC Diseases
permeability factor receptor)
FOXD4 forkhead box D4 DCM
FXN frataxin HCM
ILK integrin-linked kinase DCM
LAMA2 laminin, alpha 2 DCM
LAMA4 laminin, alpha 4 DCM
MYPN myopalladin HCM, DCM, RCM
NEXN nexilin (F actin binding protein) HCM, DCM
RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 HCM, DCM, NS
SGCA sarcoglycan, alpha (50kDa dystrophin-associated glycoprotein)
SYNE1 spectrin repeat containing, nuclear envelope 1 DCM
SYNM synemin, intermediate filament protein DCM
TTR transthyretin
DMD dystrophin DCM
PRKAG2 protein kinase, AMP-activated, gamma 2 non-catalytic subunit HCM
SGCD sarcoglycan, delta (35kDa dystrophin-associated glycoprotein) DCM
TAZ tafazzin DCM, ncCM
TMPO thymopoietin DCM
TP63 tumor protein p63 ARVD/C
EYA4 eyes absent homolog 4 (Drosophila) DCM
CACNA2D1 calcium channel, voltage-dependent, alpha 2/delta subunit 1 SQTS, BrS
CACNB2 calcium channel, voltage-dependent, beta 2 subunit BrS
GPD1L glycerol-3-phosphate dehydrogenase 1-like BrS
KCNE3 potassium voltage-gated channel, Isk-related family, member 3 LQTS, BrS, AF
SCN1B sodium channel, voltage-gated, type I, beta BrS
SCN3B sodium channel, voltage-gated, type III, beta BrS, AF
HCN4 hyperpolarization activated cyclic nucleotide-gated potassium channel 4 BrS, ncCM, AF
GLA galactosidase, alpha HCM
SOS1 son of sevenless homolog 1 (Drosophila) HCM, NS
PTPN11 protein tyrosine phosphatase, non-receptor type 11 HCM, NS
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Gene Gene Name ICC Diseases
KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog NS
SDHA succinate dehydrogenase complex, subunit A, flavoprotein (Fp) DCM
BAG3 BCL2-associated athanogene 3 DCM
ALMS1 Alstrom syndrome 1 DCM
APOA1 apolipoprotein A-I DCM, FHL
DNAJC19 DnaJ (Hsp40) homolog, subfamily C, member 19 DCM
DTNA dystrobrevin, alpha DCM, ncCM
EMD emerin DCM, AF
FHL2 four and a half LIM domains 2 DCM
FKRP fukutin related protein DCM
hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase HADHA DCM (trifunctional protein), alpha subunit
HFE hemochromatosis DCM
HOPX HOP homeobox DCM
LAMP2 lysosomal-associated membrane protein 2 HCM, DCM
NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 DCM
PDLIM3 PDZ and LIM domain 3 HCM, DCM
PLEC plectin DCM, FH
SGCB sarcoglycan, beta (43kDa dystrophin-associated glycoprotein) DCM
SGCG sarcoglycan, gamma (35kDa dystrophin-associated glycoprotein) DCM
SOD2 superoxide dismutase 2, mitochondrial DCM
TBX20 T-box 20 DCM
CALM3 calmodulin 3 (phosphorylase kinase, delta) CPVT
HSP90AB1 heat shock protein 90kDa alpha (cytosolic), class B member 1
NDRG4 NDRG family member 4
ATG5 autophagy related 5
UNC45B unc-45 homolog B (C. elegans)
CCT3 chaperonin containing TCP1, subunit 3 (gamma)
CNOT3 CCR4-NOT transcription complex, subunit 3
LSM14A LSM14A, SCD6 homolog A (S. cerevisiae)
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Gene Gene Name ICC Diseases
CALM2 calmodulin 2 (phosphorylase kinase, delta) LQTS
RBM7 RNA binding motif protein 7
RBM4 RNA binding motif protein 4
RXRA retinoid X receptor, alpha
NR1H2 nuclear receptor subfamily 1, group H, member 2
HSPB1 heat shock 27kDa protein 1
CCT4 chaperonin containing TCP1, subunit 4 (delta)
serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), SERPINE1 member 1
GPR183 G protein-coupled receptor 183
STUB1 STIP1 homology and U-box containing protein 1, E3 ubiquitin protein ligase
NR1H3 nuclear receptor subfamily 1, group H, member 3
UBE4B ubiquitination factor E4B
HSPB6 heat shock protein, alpha-crystallin-related, B6
CCT6B chaperonin containing TCP1, subunit 6B (zeta 2)
RXRG retinoid X receptor, gamma
AGL amylo-alpha-1, 6-glucosidase, 4-alpha-glucanotransferase
KCNJ8 potassium inwardly-rectifying channel, subfamily J, member 8 BrS, AF
CACNA1D calcium channel, voltage-dependent, L type, alpha 1D subunit
KCNQ2 potassium voltage-gated channel, KQT-like subfamily, member 2
CCT6A chaperonin containing TCP1, subunit 6A (zeta 1)
PFDN1 prefoldin subunit 1
SF3B3 splicing factor 3b, subunit 3, 130kDa
HSPB8 heat shock 22kDa protein 8
SLC8A1 solute carrier family 8 (sodium/calcium exchanger), member 1
SNRNP70 small nuclear ribonucleoprotein 70kDa (U1)
SCN10A sodium channel, voltage-gated, type X, alpha subunit BrS, AF
MBNL2 muscleblind-like splicing regulator 2
PRPF8 PRP8 pre-mRNA processing factor 8 homolog (S. cerevisiae)
CCT2 chaperonin containing TCP1, subunit 2 (beta)
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Gene Gene Name ICC Diseases
RBM4B RNA binding motif protein 4B
RNF207 ring finger protein 207 LQTS
FOXO3 forkhead box O3
C21orf7 chromosome 21 open reading frame 7
CCT5 chaperonin containing TCP1, subunit 5 (epsilon)
VBP1 von Hippel-Lindau binding protein 1
HNRNPM heterogeneous nuclear ribonucleoprotein M
ADRB2 adrenoceptor beta 2, surface
HSPB7 heat shock 27kDa protein family, member 7 (cardiovascular)
ADRA2B adrenoceptor alpha 2B
RBM45 RNA binding motif protein 45
RBM39 RNA binding motif protein 39
TRIM55 tripartite motif containing 55 HCM
UBE2D3 ubiquitin-conjugating enzyme E2D 3
PTBP1 polypyrimidine tract binding protein 1
TRIM63 tripartite motif containing 63, E3 ubiquitin protein ligase HCM
MDM2 Mdm2, p53 E3 ubiquitin protein ligase homolog (mouse)
SNRPB small nuclear ribonucleoprotein polypeptides B and B1
GATA4 GATA binding protein 4 DCM, AF
DPP6 dipeptidyl-peptidase 6
F7 coagulation factor VII (serum prothrombin conversion accelerator)
CNOT1 CCR4-NOT transcription complex, subunit 1
KCNJ11 potassium inwardly-rectifying channel, subfamily J, member 11
LITAF lipopolysaccharide-induced TNF factor
ABCC8 ATP-binding cassette, sub-family C (CFTR/MRP), member 8 FH
CCT8 chaperonin containing TCP1, subunit 8 (theta)
HNRNPK heterogeneous nuclear ribonucleoprotein K
TCP1 t-complex 1
CNOT4 CCR4-NOT transcription complex, subunit 4
UBE4A ubiquitination factor E4A
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Gene Gene Name ICC Diseases
SRL sarcalumenin
TRIM54 tripartite motif containing 54 HCM
ZBTB17 zinc finger and BTB domain containing 17
UBE2D1 ubiquitin-conjugating enzyme E2D 1
IL18 interleukin 18 (interferon-gamma-inducing factor)
PFDN5 prefoldin subunit 5
PFDN2 prefoldin subunit 2
CAPN1 calpain 1, (mu/I) large subunit
HSP90AA1 heat shock protein 90kDa alpha (cytosolic), class A member 1
ATP2A2 ATPase, Ca++ transporting, cardiac muscle, slow twitch 2
RANGRF RAN guanine nucleotide release factor BrS
NOS1AP nitric oxide synthase 1 (neuronal) adaptor protein
SNRPA small nuclear ribonucleoprotein polypeptide A
FBXO32 F-box protein 32
LIPC lipase, hepatic FH, FHL
SELP selectin P (granule membrane protein 140kDa, antigen CD62)
POLR2F polymerase (RNA) II (DNA directed) polypeptide F
GINS3 GINS complex subunit 3 (Psf3 homolog)
UBE2D2 ubiquitin-conjugating enzyme E2D 2
ATP1B1 ATPase, Na+/K+ transporting, beta 1 polypeptide
CALM1 calmodulin 1 (phosphorylase kinase, delta) LQTS, CPVT
CCT7 chaperonin containing TCP1, subunit 7 (eta)
PFDN4 prefoldin subunit 4
GAA glucosidase, alpha; acid HCM
DNAJC1 DnaJ (Hsp40) homolog, subfamily C, member 1
UBE2D4 ubiquitin-conjugating enzyme E2D 4 (putative)
RBM46 RNA binding motif protein 46
ENDOG endonuclease G
HNRNPU heterogeneous nuclear ribonucleoprotein U (scaffold attachment factor A)
RXRB retinoid X receptor, beta
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8.2 Standard Operating Protocol for Date of Clinical Diagnosis of DCM
Date of clinical diagnosis of DCM
In chronological order, the first date the following findings are noted is the date of diagnosis. In order of preference: 1. Earliest date of hospital-based specialist (cardiologist) diagnosis regardless of assessment modality a. Examples: Cardiology clinic letter, discharge summary (any team), MDT report b. Does not include: GP referrals, non-specialist letters c. Diagnosis terms in order of preference: i. Dilated cardiomyopathy ii. Heart failure/Congestive cardiac failure – only if accompanied by imaging evidence of dilated and impaired LV
2. Earliest date of isolated investigation confirming diagnosis (in order of preference) a. Cardiac MRI specifically referring to diagnosis of dilated cardiomyopathy b. Coronary angiogram specifically referring to diagnosis of dilated cardiomyopathy c. Cardiac MRI with gadolinium contrast showing dilated and impaired LV in absence of infarct pattern d. Coronary angiogram demonstrating normal coronary arteries with recent (<3 months) echocardiogram showing dilated and impaired LV e. Echocardiogram showing dilated and impaired LV
In the absence of any of the above: 3. GP summary referring to date of diagnosis of (in order of preference) a. Dilated cardiomyopathy b. Heart failure/Congestive cardiac failure c. Cardiomyopathy (e.g. Peri-partum cardiomyopathy) d. Excludes: Hypertrophic cardiomyopathy, ischaemic cardiomyopathy
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8.3 CMR safety checklist
Figure 8-1: CMR safety checklist
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8.4 Endpoint definitions
8.4.1.1 Endpoint definitions
Endpoints were defined according to the 2014 American College of Cardiology/American Heart Association definitions for cardiovascular endpoints in clinical trials(401).
8.4.1.1.1 Cardiovascular death
Cardiovascular death was defined as death due to sudden cardiac death, heart failure, acute myocardial infarction, cerebrovascular accident, cardiovascular haemorrhage, cardiovascular procedures, or other cardiovascular causes, that is death not included in the previous categories but with a specific, known cause such as pulmonary embolus (Table 8-1).
Table 8-1: Definition of cardiovascular death. Taken from the 2014 American College of Cardiology and American Heart Association definition of cardiovascular endpoints in clinical trials document(401). CV=cardiovascular, MI= myocardial infarction, HF= heart failure, ICD=implantable cardiac defibrillator.
Acute Death by any cardiovascular mechanism (arrhythmia, sudden death, heart myocardial failure, stroke, pulmonary embolus, peripheral arterial disease) within 30 d after infarction an acute MI, related to the immediate consequences of the MI, such as (MI) progressive HF or recalcitrant arrhythmia. There may be assessable (attributable) mechanisms of cardiovascular death during this time period, but for simplicity, if the cardiovascular death occurs within 30 d of an acute MI, it will be considered a death due to MI. Note: Acute MI should be verified to the extent possible by the diagnostic criteria outlined for acute MI or by autopsy findings showing recent MI or recent coronary thrombosis. Death resulting from a procedure to treat an MI (PCI or CABG), or to treat a complication resulting from MI, should also be considered death due to acute MI. Death resulting from an elective coronary procedure to treat myocardial ischemia (ie, chronic stable angina) or death due to an MI that occurs as a direct consequence of a cardiovascular investigation/procedure/operation should be considered as a death due to a cardiovascular procedure. Sudden Death that occurs unexpectedly and not within 30 d of an acute MI. cardiac Note: Sudden cardiac death includes the following scenarios: death • Death witnessed and occurring without new or worsening symptoms • Death witnessed within 60 min of the onset of new or worsening cardiac symptoms unless the symptoms suggest acute MI • Death witnessed and attributed to an identified arrhythmia (eg, captured on an electrocardiographic recording, witnessed on a monitor, or unwitnessed but found on ICD review) • Death after unsuccessful resuscitation from cardiac arrest (eg, ICD unresponsive sudden cardiac death, pulseless electrical activity arrest) • Death after successful resuscitation from cardiac arrest and without identification of a specific cardiac or noncardiac etiology • Unwitnessed death in a subject seen alive and clinically stable ≤24 h before being found dead without any evidence supporting a specific noncardiovascular cause of death (information about the patient’s clinical status preceding death
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should be provided if available) Unless additional information suggests an alternate specific cause of death (eg, Death due to Other Cardiovascular Causes), if a patient is seen alive ≤24 h before being found dead, sudden cardiac death should be recorded. For patients who were not observed alive within 24 h of death, undetermined cause of death should be recorded (eg, a subject found dead in bed but who had not been seen by family members for >24 h). Heart failure Death associated with clinically worsening symptoms and/or signs of HF, regardless of HF etiology Note: Deaths due to HF can have various etiologies, including single or recurrent MIs, ischemic or nonischemic cardiomyopathy, hypertension, or valvular disease.
Stroke Death after a stroke that is either a direct consequence of the stroke or a complication of the stroke. Note: Acute stroke should be verified to the extent possible by the diagnostic criteria outlined for stroke.
CV Death caused by the immediate complication(s) of a Cardiovascular procedure procedure
CV Death related to hemorrhage such as a nonstroke intracranial hemorrhage, (eg, hemorrhage subdural hematoma) nonprocedural or nontraumatic vascular rupture (eg, aortic aneurysm), or hemorrhage causing cardiac tamponade
CV other Cardiovascular death not included in the above categories but with specific, known cause (eg, pulmonary embolus)
8.4.1.1.2 Heart Failure composite
The heart failure composite consisted of either a heart transplant, left ventricular assist-device implantation or an unplanned heart failure hospitalisation.
An unplanned heart failure hospitalisation was defined as ‘an event in which the patient is admitted to the hospital with a primary diagnosis of heart failure, the length of stay is at least 24 h (or extends over a calendar date), the patient exhibits new or worsening symptoms of heart failure on presentation, has objective evidence of new or worsening heart failure, and receives initiation or intensification of treatment specifically for heart failure’(401). Notably, changes to oral diuretic therapy did not qualify as initiation or intensification of treatment(401). New or worsening symptoms due to heart failure consisted of at least one of dyspnea, decreased exercise tolerance, fatigue, worsened end organ perfusion or volume overload(401).
Objective evidence of heart failure consisted of at least 2 physical examination findings or at least 1 physical examination finding and at least 1 laboratory criterion of new or worsening heart failure on presentation(401).
Physical examination findings included new or worsening peripheral oedema, ascites, pulmonary crepitations, increased jugular venous pressure, S3 gallop, or clinically significant weight gain judged to be secondary to fluid retention(401). Laboratory criteria included new
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or worsening BNP/NT-proBNP, radiological evidence of pulmonary congestion, or other invasive or non invasive diagnostic evidence of heart failure(401).
8.4.1.1.3 Arrhythmia composite
All ventricular arrhythmias were defined in line with the American College of Cardiology, American Heart Association and European Society of Cardiology guidelines(416). The arrhythmia endpoint was a composite of sustained ventricular tachycardia, ventricular fibrillation, aborted sudden cardiac death and appropriate implantable cardioverter- defibrillator (ICD) discharge.
Sustained ventricular tachycardia was defined as repetitive ventricular beats in a row lasting over 30 seconds in duration at a rate greater than 100 beats per minute (cycle length less than 600 ms), and/or requiring termination due to hemodynamic compromise in less than 30 seconds(416).
Ventricular fibrillation was defined as a rapid, usually more than 300 bpm (cycle length 180 ms or less), grossly irregular ventricular rhythm with marked variability in QRS cycle length, morphology, and amplitude(416).
Aborted sudden cardiac death was diagnosed if patients received an appropriate ICD shock for ventricular arrhythmia, or had a nonfatal episode of ventricular fibrillation or spontaneous sustained ventricular tachycardia (>30 seconds in duration) causing hemodynamic compromise and requiring cardioversion(13,416). Appropriate ICD discharge was limited to shock therapy for ventricular arrhythmias (anti-tachycardia pacing and inappropriate shocks for atrial or other arrhythmias were excluded) and all discharges were adjudicated by an Electrophysiology Cardiology Consultant.
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8.5 Permissions
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