Winchester et al. Genome Medicine (2018) 10:51 https://doi.org/10.1186/s13073-018-0556-z

RESEARCH Open Access indices and anaemia as causative factors for cognitive function deficits and for Alzheimer’s disease Laura M. Winchester1* , John Powell2, Simon Lovestone1 and Alejo J. Nevado-Holgado1

Abstract Background: Studies have shown that low haemoglobin and anaemia are associated with poor cognition, and anaemia is known to be associated with Alzheimer’s disease (AD), but the mechanism of this risk is unknown. Here, we first seek to confirm the association between cognition and anaemia and secondly, in order to further understand the mechanism of this association, to estimate the direction of causation using Mendelian randomisation. Methods: Two independent cohorts were used in this analysis: AddNeuroMed, a longitudinal study of 738 subjects including AD and age-matched controls with blood cell measures, cognitive assessments and gene expression data from blood; and UK Biobank, a study of 502,649 healthy participants, aged 40–69 years with cognitive test measures and blood cell indices at baseline. General linear models were calculated using cognitive function as the outcome with correction for age, sex and education. In UK Biobank, SNPs with known blood cell measure associations were analysed with Mendelian randomisation to estimate direction of causality. In AddNeuroMed, gene expression data was used in pathway enrichment analysis to identify associations reflecting biological function. Results: Both sample sets evidence a reproducible association between cognitive performance and mean corpuscular haemoglobin (MCH), a measure of average mass of haemoglobin per red blood cell. Furthermore, in the AddNeuroMed cohort, where longitudinal samples were available, we showed a greater decline in red blood cell indices for AD patients when compared to controls (p values between 0.05 and 10−6). In the UK Biobank cohort, we found lower haemoglobin in participants with reduced cognitive function. There was a significant association for MCH and red blood cell distribution width (RDW, a measure of cell volume variability) compared to four cognitive function tests including reaction time and reasoning (p < 0.0001). Using Mendelian randomisation, we then showed a significant effect of MCH on the verbal– numeric and numeric traits, implying that anaemia has causative effect on cognitive performance. Conclusions: Lower haemoglobin levels in blood are associated to poor cognitive function and AD. We have used UK Biobank SNP data to determine the relationship between cognitive testing and haemoglobin measures and suggest that haemoglobin level and therefore anaemia does have a primary causal impact on cognitive performance. Keywords: Anaemia, Alzheimer’s disease, Cognitive function, Mendelian randomisation

* Correspondence: [email protected] 1Department of Psychiatry, University of Oxford, Oxford, UK Full list of author information is available at the end of the article

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Winchester et al. Genome Medicine (2018) 10:51 Page 2 of 12

Background to explore the role of haemoglobin and anaemia as a Dementia, a syndrome increasingly common in our age- causal factor of cognitive phenotypes, including dementia, ing societies, is widely recognised as one of the world’s while integrating this growing understanding with modern largest unmet medical needs. Significant progress has MR methods able to combine multiple genetic loci. been made in identifying the determinative genes of fa- We use a range of analyses to draw inferences about milial diseases that cause dementia, such as early-onset the relationship of red blood cell indices, and therefore Alzheimer’s disease (AD) or fronto-temporal dementia anaemia, to both cognitive function and AD. Using both [1, 2]. For the commonest form of dementia, late-onset UK Biobank and AddNeuroMed data, we confirm the AD, genome-wide association studies have identified relationship between AD and anaemia. Then, MR genes that alter the risk of suffering from the condition methods suggest that altered red blood cell indices are [3]. The identification of these genetic factors has driven causally associated with reduced cognitive function and much of our understanding with respect to the mecha- finally, we provide transcriptomic evidence for molecular nisms of neurodegenerative disease. However, although pathways that might underpin this mechanism. modifiable environmental factors have also been identi- fied (reviewed in [4]), the role of environmental influ- Methods ences such as cardiovascular risk, depression and social Clinical measures and blood indices isolation in the disease process is less certain. Most sig- UK Biobank nificantly, factors associated with diseases, such as de- The UK Biobank study is comprised of 502,649 healthy pression and social isolation, could plausibly be participants, aged 40–69 years with comprehensive consequences, or even prodromal symptoms [5], rather phenotypic measures including cognitive testing and than causes of dementia. It has been suggested that meta- blood cell indices (Additional file 1: Table S1), with mea- bolic dysfunction plays a mechanistic role in disease [6] sures described in detail online [15]. Briefly, blood cell and could be a consequence of the genetically driven indices were calculated for participants using a haemo- molecular pathological process rather than its cause [7, tology analyser which generated complete count data, 8]. Clearly, this makes a difference when considering po- including red blood cell count (RBC) and haemoglobin tential interventions to identify or prevent AD. concentration (HGB). Other parameters were calculated Another potentially modifiable risk factor for poor from these same measures, e.g. mean corpuscular cognition in late life is anaemia. Systematic reviews sug- haemoglobin (MCH). All indices used in this analysis gest that anaemia is a risk factor for both dementia and were taken from the recruitment/baseline visit. Anaemia for cognitive impairment [9, 10]. In addition to these, classification was based on NICE guidelines, specifically Faux et al. [11] found lower haemoglobin and differences males with HGB below 13 g/100 mL and women with in blood measures for mean cell haemoglobin, packed HGB below 12 g/100 mL. cell volume and higher erythrocyte sedimentation rates Results from tests conducted at baseline were used to in people with AD, while Ferrer et al. [12] found that measure cognitive function. Full assessment methods are levels of neuronal haemoglobin are reduced in AD. In described by Lyall et al. [16] but a brief description of the Rush Memory and Aging Project, both high and low cognitive function test and value treatment follows: levels of haemoglobin were associated with AD and fas- ter cognitive decline [13]. In participants at post-mortem Verbal–numeric reasoning (fluid intelligence) 13 analyses, lower haemoglobin levels were associated with logic-based questions asked within a 2 minute time limit. macroscopic infarcts but not other pathologies of neuro- Total number of correct responses was used for analysis degeneration [14]. Although it is reasonably clear that (UKB Field Identifier (FID) 20016). there is a relationship between indices of red blood cell phenotypes and cognition, the directionality and there- Numeric memory Participants were asked to remember fore causality of the observation is unknown, just as it is a two-digit number after a brief pause. The number of for other environmental factors. digits was then increased and longest number of digits Determining whether potentially modifiable factors as- recalled was used for analysis (FID: 4282). sociated with dementia are drivers of disease process and hence targets for therapy is of critical importance. A Reaction time Time taken for participants to match powerful approach to determining such causality is the two identical symbols and press button. Mean reaction use of Mendelian randomisation (MR). One of the limi- time (ms) of eight trials was used for analysis after log tations of MR, however, is the availability of genetic loci transformation (FID: 20023). strongly associating with the phenotype under consider- ation. Here, we have utilised growing understanding of Visual memory Pair matching test based on memory of the genetic determinants of red blood cell characteristics card location. Number of pairs mismatched for the six Winchester et al. Genome Medicine (2018) 10:51 Page 3 of 12

pair test was used for analysis after log transformation cognitive function. The same approach was used to test (FID: 399). for the association between AD status and blood traits, with a GLM per blood measure including the same co- Prospective memory An instruction was given at the be- variates as before. However, the population consisted of ginning of the assessment, which the participant needs to all participants older than 60 with a diagnosis of AD, remember in order to select the correct shape at the end plus a control participant (i.e. without AD) per case of the interview. A binary success or fail measure of the matched by age and gender. A representative residual first attempt was used for further analysis (FID: 20018). value for blood count was generated based on a linear model using device and acquisition route as covariants AddNeuroMed (FID: 30000-30284). This allowed correction for effects AddNeuroMed was a multi-national longitudinal study of blood collection method without impacting the cogni- of AD in Europe described elsewhere [17, 18]. It tive function model. included both AD and age-matched control subjects with blood cell measures, neuropsychological assess- AddNeuroMed ments and gene expression data [19]. NINCDS-ADRDA To test for differences in the case and control sample criteria and Diagnostic and Statistical Manual of Mental sets, different statistical tests were applied depending on Disorders (DSM-IV) were used to classify AD patients. the number of available samples. An unpaired t test was Blood cell count measurements were generated at King’s used to assess for significant differences between the College Hospital according to clinical standards for 285 mean rates of change, while Kolmogorov–Smirnov test of these subjects. For a subset of samples (n = 71), all was used to discern a difference between the distribu- these variables were available for two or more visits. tions of rates of change. p values were adjusted for false Blood measure rate of change was calculated as the discovery rate in both instances. These simpler methods slope of a linear model using individual age at visit were required to capture differences in the case of a (years) with blood measure as the dependent variable. small sample set while, where sample size was large Namely, blood measure = β0 + β1 age + ε (where β1 is the enough (for MMSE-tested patients), a GLM was applied slope used, β0 intercept and ε noise). instead with corrections for sex and age.

Statistical analysis Mendelian randomisation UK Biobank The main genetic data analysis was based on the first To test for associations between each cognitive function released data batch of 152,736 participants from UK test and blood measure, we used a general linear model Biobank. Samples were filtered by ethnicity (FID: 22006, (GLM) per blood measure in which participants were fil- only keeping those with white genetic background); tered by age (> 60 years) to give a better comparison to genetic sex (FID: 22001, removing those where stated AD patients. Cognitive function test was used as the gender did not match with real X–Y chromosome); outcome variable, and blood measure as the main expos- related participants (FID: 22012, removing one from ure in each case. All p values were adjusted for multiple each common pair) and experimental checks (FID: testing using Benjamini and Hochberg correction. A rep- 22050 and 22010) to leave 116,478 samples. A secondary resentative residual value for blood count was generated replication analysis was performed on the interim set of based on a linear model using device and acquisition genetic data (UK Biobank Release 2) which contained route as covariants (FID: 30000-30284). This allowed 335,423 participants. The dataset was processed follow- correction for effects of blood collection method without ing the method outlined by Bycroft et al. [23]. impacting the cognitive function model. Demographic The SNPs for MR were selected based on two GWAS variables were also added as further covariates to correct studies of blood traits with secondary validations as a fil- for age, education, sex (FID: 31) and assessment centre ter [24, 25]. The SNP list was then filtered using the (FID: 54) as described by Nevado–Holgado et al. [20]. PhenomeScanner [26] tool to remove all SNPs with a Education level impacts on multiple outcome measures known AD relationship, including SNPs located in the [21, 22], here, we included education within our model APOE/TOMM40 locus, to reduce the potential of to adjust for socio-economic factors represented by pleiotropy errors. Remaining SNPs, with an info score > 0.9, schooling in different areas. However, we accept that were extracted from the imputed dataset. Subsets of SNPs education and cognition are correlated as people with specific to the blood measure were prepared to allow test- stronger cognitive ability tend to stay in education ing of instrument choice for pleiotropy. As blood measures longer and we have included education as a covariate are derived from common values, we selected three inde- assuming that as a generic adjustment of residual pendent traits to study based on their association with the confounders, it will lead to conservative estimate of outcome variables: MCH; red blood cell distribution width Winchester et al. Genome Medicine (2018) 10:51 Page 4 of 12

(RDW) and reticylocyte count (RET). Association analysis higher in participants with higher reasoning scores − was performed in SNPtest [27]forimputeddata. (beta = 0.04, 0.04, 0.05 and p value = 2.26 × 10 7, − − One-sample MR was implemented using the “Mendelian 1.92 × 10 28,7.33×1012 respectively). The same cor- Randomisation” package from R [28] which incorporates relation trend is seen in the numeric and prospective three methods with different assumptions. The median memory tests. Reaction time was inversely associated weighted method or two-stage least squares estimation uses with HGB, MCH and MCHC measures (beta = − 0.009, − − a median of the individual causal estimate per SNP, which is − 0.003, − 0.002 and p value = 6.67 × 10 46,7.94×108, − calculated from the ratio estimates of outcome’sregression 8.45 × 10 8 respectively); reflecting the same direction coefficient divided by exposure [29]. The inverse-variance of change as with other cognition measures as in- weighted (IVW) method uses the same ratio estimates but creased reaction time is reflective of relatively worse incorporates inverse-variance weights into the final sum- cognition. We found that RDW was inversely corre- mary estimate [30]. The Egger method is sensitive to SNP lated with four tests of cognitive function (beta between − pleiotropy and allows the estimation of underlying bias by − 0.053 and − 0.008, p value from 1.71 × 10 14 to 0.003). allowing a non-zero estimate for the intercept of the calcu- Interestingly, the (RET) measures, although lated ratio of beta values [31]. Comparing estimates from all highly variable, show the largest significant beta scores of the methods shows the robustness of the overall analysis. (beta between − 1.34 and − 1.310 with p values from 0.025 − Two-sample MR was performed with the “MRBase” R pack- to 9.4 × 10 5). As these sets of measures are used clinically age [32]usingthesameinstrumentset. to diagnose iron-deficient anaemia, we estimated the pro- portion of participants with anaemia according to NICE Gene expression analysis and pathway enrichment guidelines and repeated the analysis. Participants with an- RNA was extracted from blood samples and assayed on aemia, so defined, had a significant reduction in perform- Illumina Human HT-12 Expression Beadchips, full de- ance on cognitive tests for three measures (prospective, tails are described by Lunnon et al. [19]. While a subset numeric and reasoning) and increased reaction time score of these samples was used for this analysis based on data (p < 0.0005, Additional file 2:FigureS1). completion, the full raw dataset is available as GEO DataSets with accession numbers GSE63060 and Mean corpuscular haemoglobin and red blood cell GSE63061. Two approaches were used for array expres- distribution width have a causative relationship with sion analysis, LIMMA models were used for fold change verbal–numeric reasoning calculations and the SAMr correlation method was used Using UK Biobank to estimate a direction of effect, we to generate permutated statistics for the patient based applied a single-sample MR model where the cognitive approach. Finally, the Kolmogorov–Smirnov test was test was the outcome variable, the blood measure the used to evaluate KEGG pathways for significant enrich- mediating exposure variable, and SNPs known to be re- ment. This pathway approach is described by Nevado– lated to the blood measure were used as instruments Holgado et al. [33] which, similar to GSEA, takes signifi- (Fig. 2a). In all cases, we used three alternative MR cance values from each individual gene and compares methods to discount the possibility of pleiotropy among the overall distribution of expression rather than a sim- SNPs (Table 2) as well as plots to assess SNP beta scores ple binomial approach. (Fig. 2c–e). This approach identified a significant effect on the numeric and reasoning traits from the MCH Results measure (Fig. 2b). The effect between MCH and reason- Haemoglobin content has a significant association with ing traits was replicated in an analysis using in the in- cognitive function tests terim release of the full UK Biobank genetic data where Using the UK Biobank dataset, five cognitive function we were able to reproduce the same direction of effect tests were compared to the complete blood cell indices set (Additional file 1: Table S2). In addition, two-sample MR (Table 1). There was a significant association for red blood was used to analyse the association in an alternative cell distribution width (RDW) and mean corpuscular sample set (Additional file 2: Figure S2). The UK haemoglobin (MCH) with outcomes on four cognitive Biobank cognitive reasoning was used as the outcome, tests including reaction time and verbal–numeric reason- and MCH beta scores from the MRBase library were ing (Fig. 1a). Although reaction time was associated with introduced as the new exposure to duplicate the sig- white cell count and neutrophil number, associations with nificant results shown in our main one-sample results red cell indices were considerably more extensive. (p values < 0.05 for all three MR methods). Performance on the reasoning test was positively cor- RDW also showed significant effects in several of the related with red blood cell haemoglobin (Fig. 1b). MR tests for the reasoning and numeric traits (Table 2). Haemoglobin concentration (HGB), MCH and mean Beta scores were negative suggesting an inverse rela- corpuscular haemoglobin concentration (MCHC) were tionship whereby RDW decreases as the cognition Winchester et al. Genome Medicine (2018) 10:51 Page 5 of 12

Table 1 Associations between blood traits and cognitive function tests as revealed by linear modelling Numeric Numeric p Reaction Reaction p Prospective Prospective p Reasoning Reasoning p Visual Visual p beta beta beta beta beta Red RBC − 0.085 0.019 − 0.012 8.86 × 10−14 0.011 0.642 − 0.089 2.39 × 10−5 − 0.013 0.063 HGB 0.026 0.056 − 0.009 6.67 × 10−46 0.076 1.44 × 10−18 0.040 2.26 × 10−7 0.0004 0.884 HCT 0.002 0.701 − 0.002 8.78 × 10−31 0.020 6.97 × 10−12 0.005 0.045 0.0001 0.884 MCV 0.015 7.12 × 10−7 − 0.001 4.08 × 10−4 0.017 2.29 × 10−17 0.017 1.64 × 10−22 0.002 3.15 × 10−4 MCH 0.036 1.53 × 10−7 − 0.002 8.45 × 10−8 0.042 1.44 × 10−18 0.044 1.92 × 10−28 0.004 0.001 MCHC 0.033 0.004 − 0.003 7.94 × 10−8 0.041 1.15 × 10−5 0.048 7.33 × 10−12 0.001 0.787 RDW − 0.055 1.27 × 10−4 0.009 1.23 × 10−38 − 0.070 1.71 × 10−14 − 0.053 3.06 × 10− 10 − 0.008 0.003 Immature red RET% − 0.016 0.442 0.001 0.227 − 0.021 0.100 − 0.018 0.104 − 0.001 0.812 RET − 1.346 0.025 − 0.001 0.984 − 1.341 0.001 − 1.301 9.40 × 10−5 − 0.067 0.375 MRV − 0.002 0.432 0.001 6.34 × 10−21 − 0.003 0.009 − 0.005 7.18 × 10−6 0.001 0.022 MSCV 0.002 0.512 0.001 2.58 × 10−7 0.001 0.642 − 0.001 0.413 0.002 4.24 × 10−4 IRF − 0.857 1.41 × 10−4 0.066 3.70 × 10−10 − 0.784 4.05 × 10−7 − 0.544 5.97 × 10−5 − 0.077 0.083 NRBC − 0.186 0.799 0.072 2.67 × 10−4 − 0.404 0.227 − 0.385 0.241 0.026 0.879 NRBC% − 0.003 0.945 0.005 0.004 − 0.021 0.326 − 0.020 0.352 − 0.001 0.884 White WBC − 0.032 4.47 × 10−7 0.003 2.16 × 10−24 − 0.010 0.020 − 0.020 2.06 × 10−8 − 0.003 0.013 PLT − 0.0004 0.071 0.00004 1.53 × 10−4 0.000 0.020 0.000 0.825 0.000 0.273 PCT − 0.802 0.008 0.053 6.28 × 10−5 − 0.668 0.001 − 0.187 0.344 − 0.142 0.010 MPV − 0.016 0.214 − 0.00001 0.984 − 0.012 0.163 − 0.015 0.045 − 0.004 0.083 PDW 0.007 0.862 − 0.002 0.054 0.035 0.054 0.056 0.000 − 0.011 0.022 LYMPH − 0.001 0.945 0.002 8.65 × 10−5 − 0.011 0.109 − 0.018 0.002 − 0.003 0.155 MONO − 0.087 0.118 0.013 5.28 × 10−6 − 0.041 0.241 0.027 0.420 − 0.011 0.390 NEUT − 0.061 3.15 × 10−11 0.005 1.77 × 10−26 − 0.009 0.155 − 0.031 1.90 × 10−8 − 0.004 0.036 EO − 0.318 0.001 0.021 3.18 × 10−6 − 0.175 0.010 − 0.005 0.927 − 0.024 0.233 BASO − 0.010 0.973 0.046 3.61 × 10−4 − 0.498 0.014 − 0.025 0.910 − 0.007 0.884 LYMPH% 0.005 0.009 − 0.0002 0.004246 − 0.004 0.002 − 0.003 0.003 − 0.0004 0.232 MONO% 0.012 0.011 − 0.001 0.012 0.003 0.326 0.013 1.44 × 10−7 0.0001 0.884 NEUT% − 0.005 0.003 0.0002 0.003 0.003 0.005 0.0002 0.910 0.0003 0.294 EO% − 0.005 0.593 0.0003 0.316 − 0.009 0.085 0.011 0.013 − 0.0004 0.884 BASO% 0.037 0.186 0.002 0.126 − 0.030 0.056 0.022 0.140 0.002 0.787 Highly significant results are marked with italic fonts (p <10−5). See Abbreviations section for acronyms improves (Fig. 2e). Given the relationship between Changes in red blood indices are also associated with haemoglobin measures and cognitive tests, red blood Alzheimer’s disease cell indices were selected based on GLM results UK Biobank participants gave consent for linkage to (Table 1), and their unique derivation source, to fit medical records and using Hospital Episodes Statistics independent testing assumptions. MCH and RDW data a subset of participants with a recorded clinical were the best candidates based on results from diagnosis of AD or other dementia was identified using analyses with cognitive tests and imply that both ICD10 codes. This subset was then age and gender haemoglobin levels and red blood cells themselves matched to a control group (n = 1170). Using this have a potentially causative effect on cognition sub-cohort anaemia was found to be significantly increased − (Table 2). RET was included as it is an independent in people with AD (beta = 0.26, p value = 2.3 × 10 6) measure with strong beta scores but was not signifi- and the RBC and HGB indices were all decreased in cant (Additional file 1:TableS3). the AD participant set (beta = − 0.66 and − 0.18 Winchester et al. Genome Medicine (2018) 10:51 Page 6 of 12

Fig. 1 Cognitive tests have a significant effect on red blood cell measures. a There is a significant association between red blood cell measures and the reaction time, reasoning, numeric and prospective cognitive function tests. b Increased MCH and related indices have a positive effect on verbal–numeric reasoning, prospective and numeric memory (red squares). Reaction time is increased as haemoglobin decreases due to inverse nature of the reaction time test (blue squares). See Abbreviations for blood indices’ acronyms respectively, adjusted p values < 0.05; Additional file 1: therefore lack of power, a significant difference remained Table S4). (adjusted p value < 0.005) in three red blood cell rate of change measures between low and high MMSE (Fig. 3d). Replication of the red blood cell association in an Finally, using MMSE as a continuous measure in a linear independent cohort model, significant association was shown between MMSE We then turned to the AddNeuroMed cohort to repli- score and four red blood cell measures including MCH cate these findings using complementary analyses. We (Table 3). determined rate of change measures per participant to incorporate multiple visit data when the participant Pathway enrichment analysis indicates changes in MCH made at least three visits between age of patient at visit may have an impact on haematological gene expression (years) and each cell count measure (Additional file 2: As the AddNeuroMed cohort also contained whole Figure S3). These rate of change values were not corre- blood whole genome transcript data, we were able to use lated to the mean statistic (rho = − 0.031, Fig. 3a) this dataset to explore, using several approaches, the suggesting that they provide additional information over gene expression patterns and hence KEGG pathways, and above the mean. We found a significant difference be- linked to both blood traits and to AD. Initially, we used tween AD case and normal cognition control subjects in all subjects with both expression and rate of change in five red blood cell rate of change measures (p value < 0.05, MCH data in a fold change analysis to look for signifi- Table 3). A decline in rate of change was shown in the AD cantly associated genes (37 patients), finding an enrich- cases compared to control patients, with Fig. 3b, c ment for the glycosylphosphatidylinositol (GPI) anchor showing the difference in distributions between RBC biosynthesis pathway (p value = 0.0107) in those with − (p value = 2.21 × 10 4) and greatest rate of change in MCH. Defects in this pathway − (MCV, p value = 1.95 × 10 3). The test was repeated cause paroxysmal nocturnal haemoglobinuria, a genetic using the MMSE scores per patient as an assessment of disorder whereby the immune system destroys red blood cognition. Using the highest and lowest scores (± 20%) cells. We then focussed in on the AD group with and despite the low sample numbers (n = 53) and complete data as above (n = 22) to look for correlation Winchester et al. Genome Medicine (2018) 10:51 Page 7 of 12

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Fig. 2 MCH has a significant effect on the reasoning cognition in multiple MR analysis approaches. a Mendelian randomisation model used for analysis. b p values are significant (> 0.005) in multiple MR methods for the MCH measure (exposure) in the reasoning and numeric traits. Significance in more than one test method is important to rule out pleiotropy among instruments. c MCH instrument (SNP) causal estimates for the reasoning (outcome) show symmetry about 0 indicating a robust analysis (without pleiotropy). d MCH instrument causal estimates for the numeric trait. e Instrument causal estimates for the reasoning trait compared to RDW between rate of decline in blood indices and gene ex- Discussion pression. Using this filtered approach, we detected an Recently, increasing attention is being paid, with consid- enrichment for haematopoietic cell linage pathway cor- erable justification, to environmental factors that might relating with MCH rate of decline (p value = 0.0088, influence the development of dementia. As pharmaco- Additional file 1: Table S5). In both cases, we found logical strategies for prevention have not yet yielded suc- weaker p values at the initial analysis stage, which is to cess and as the number of people with dementia be expected given the sample size. continues to rise then modifying environmental factors

Table 2 Associations from MCH and RDW to cognitive tests as revealed by MR Median weighted beta [CI] Median weighted p IVW beta [CI] IVW p Egger beta [CI] Egger p MCH trait Reaction 0.0199 [− 0.0005, 0.0404] 0.0562 0.0091 [− 0.0057, 0.0238] 0.2320 0.0363 [0.01, 0.0627] 0.0069 Reasoning 0.0568 [0.0195, 0.0941] 0.0028 0.0458 [0.0194, 0.0722] 0.0012 0.0871 [0.0392, 0.1351] 0.0004 Numeric 0.0977 [0.0364, 0.159] 0.0018 0.0443 [0.0021, 0.0864] 0.0392 0.1327 [0.0544, 0.2111] 0.0009 Visual − 0.0015 [− 0.0217, 0.0188] 0.8855 − 0.0097 [− 0.0235, 0.0041] 0.1521 − 0.0182 [− 0.0439, 0.0074] 0.1640 Prospective 0.0706 [− 0.0149, 0.1562] 0.1055 0.0311 [− 0.0289, 0.0912] 0.3075 0.0506 [− 0.0612, 0.1623] 0.3752 RDW trait Reaction − 0.0245 [− 0.0788, 0.0298] 0.3758 − 0.0135 [− 0.0541, 0.0271] 0.4528 − 0.0294 [− 0.1284, 0.0696] 0.5601 Reasoning − 0.1036 [− 0.2041, − 0.0031] 0.0432 − 0.1191 [− 0.1907, − 0.0474] 0.0025 − 0.0602 [− 0.235, 0.1146] 0.4996 Numeric − 0.2121 [− 0.3769, − 0.0472] 0.0117 − 0.111 [− 0.2345, 0.0124] 0.0714 − 0.3243 [− 0.6247, − 0.0238] 0.0344 Visual 0.0086 [− 0.0501, 0.0673] 0.7739 0.0428 [− 0.0092, 0.0947] 0.1307 0.0708 [− 0.0598, 0.2015] 0.2880 Prospective − 0.1024 [− 0.3368, 0.132] 0.3917 − 0.1108 [− 0.287, 0.0654] 0.1858 0.0278 [ − 0.4019, 0.4575] 0.8992 Significant results are marked with italic fonts (p < 0.05). Higher and lower confidence intervals for beta scores given in square brackets [CI] Winchester et al. Genome Medicine (2018) 10:51 Page 8 of 12

Fig. 3 Rate of change in red blood cells emphasises differences in AD case–control samples. a Rate of change per patient is not correlated with mean per patient. b The distribution of RBC is significantly decreased in AD compared to controls. c The distribution of MCV, a haemoglobin measure, is significantly decreased in AD patients. d RBC rate of change is significantly different for high and low MMSE scores

Table 3 Significant Differences for red blood cell measures in an independent sample set AD case–control AD case–control MMSE T test KS test T test KS test Beta p value Red Red blood cell count (RBC) 2.65 × 10−03 2.21 × 10−04 4.06 × 10−04 5.63 × 10−04 0.02 7.04 × 10−03 Haemoglobin (HGB) 0.036 1.92 × 10−03 4.06 × 10−04 2.25 × 10−04 0.04 0.036 Packed cell volume (PCV) 9.42 × 10−04 1.61 × 10−06 4.06 × 10−04 2.25 × 10−04 0.002 0.026 Mean corpuscular volume (MCV) 0.033 1.95 × 10−03 0.483 0.092 0.01 0.929 Mean corpuscular haemoglobin (MCH) 0.036 1.92 × 10−03 0.244 0.054 − 0.05 0.027 Red cell distribution width (RDW) 0.103 0.142 0.609 0.606 0.01 0.868 White (PLT) 0.036 1.55 × 10−03 0.244 0.092 − 1.18 0.369 Platelet distribution width (PDW) 0.036 0.142 0.244 0.606 − 0.36 0.369 White blood cell (WBC) 0.382 0.345 0.944 0.609 0.05 0.929 Neutrophils 0.729 0.105 0.992 0.487 − 0.02 0.914 Lymphocytes 0.867 0.379 0.944 0.154 0.00 0.929 Monocytes 0.867 0.379 0.944 0.606 0.00 0.914 Eosinophils 0.867 0.030 0.944 0.361 0.00 0.369 Basophils 0.382 0.105 0.449 0.065 0.00 0.127 The AddNeuroMed dataset was divided by two variables for testing. Cases and control groups were compared by t test and Kolmogorov–Smirnov (KS) tests. MMSE was used to split samples into two groups for t and Kolmogorov–Smirnov (KS) tests. Results using MMSE from all samples using a linear model are also shown (beta estimate and p value given). Significant results are marked with italic fonts (p < 0.05) Winchester et al. Genome Medicine (2018) 10:51 Page 9 of 12

to reduce incidence of dementia is an increasingly loci strongly associated with these blood traits, we find as- attractive prospect. Supportive evidence for such an sociations with poorer cognitive function strongly suggest- approach has come from multiple lines of evidence that ing a causative relationship with cognitive performance despite increasing prevalence, the incidence of dementia and by implication with dementia. Finally, pathway ana- might be falling; an observation that might be due to lysis of gene expression in blood in the AddNeuroMed co- improved modification of cardiovascular risk factors. hort finds genes known to be linked with anaemia and the However, other non-genetic risk factors derived from pathway of haematopoietic cell linage to be associated observational study cannot be assumed to be causative with changes in red cell indices adding further to the and because of this, modification may not prove to be weight of evidence suggesting that these observations are successful in reducing further the incidence of dementia. indicative of true biological association. It becomes therefore, of paramount importance to deter- The RBC indices we observe to be most strongly asso- mine causality, including through the use of MR tech- ciated with cognitive outcomes are MCH and RDW, niques. However, previously, this approach has offered measures commonly associated with iron deficiency an- relatively little support to the hypothesis that modifica- aemia [36] indicating a possible deficit in haem synthesis tion of environmental risk factors such as LDL choles- or iron metabolism as an underlying trait. A possible terol, glycemic traits, diabetes, body mass index or relationship between neurodegeneration and iron has education would reduce incidence of dementia [34]. In been investigated in other MR studies. Pichler et al. [37] fact, counter intuitively, Ostergaard et al. [35] find higher used MR with three SNP instruments to find that in- systolic blood pressure to be associated with decreased creased iron reduces the risk of Parkinson’s disease and risk of dementia, suggesting either that blood pressure implying that there may well be a causal association in has opposite effects on risk of dementia and of cardio- other similar diseases. However, Lupton et al. [38] used vascular disease or that another factor associated with genetic determinants of the serum iron measures trans- hypertension, most obviously anti-hypertensive medica- ferrin and ferritin in a reanalysis of large-scale GWAS tion, has a protective effect. There is therefore an evi- data but found no association with AD. One possible dence gap at present between observational studies explanation for this apparent discrepancy is the use of proposing risk factors for modification and robust proof MCH in the present study, reportedly a more reliable of concept for such modification including causality. measure of haemoglobin not influenced by sample Without this evidence, the only approach is to perform storage conditions or cell counter methods [36]. Another an interventional study of environmental modification, a potential explanation is the difference in instrument challenge given the difficulties and costs of such public choice available from comprehensive GWAS studies of health measures. Evidence from approaches such as MR the blood indices [39]. By approaching the problem from for causality would add considerably to the justification the opposite direction using known genetic blood traits, for such interventional studies. we were able to detect a significant link not seen using We present evidence here for a primary causative AD genetics. The complexities of relationship between association between indices indicative of relatively poor iron and AD have been shown using other experimental red cell function and cognitive function and, using MR methods. For example, iron metabolism is disrupted in with genetic loci previously found to have robust rela- cortical neurons and the beta-amyloid protein precursor tionship with red cell phenotypes, findings that strongly has ferroxidase activity in mouse models [40]. Telling et suggest that lower haemoglobin has a causal impact on al. [41] have described a correlation between iron bio- cognitive performance. Moreover, secondary analyses are chemistry and amyloid beta. These results show the rela- in line with previous findings showing an association be- tionship at the molecular level and may indicate a tween anaemia and meeting operationalised criteria is a potential mechanism for iron within AD. The relevance risk factor for dementia as well as lower cognition. Specif- of blood indices to the iron deposition has been shown ically, in UK Biobank data, we find lower MCH and RDW in other UK Biobank based studies. Miller et al. [42] to be associated with relatively lower verbal–numeric rea- showed a correlation between the blood indices and T2* soning and numeric memory and that measures indicative image derived phenotypes from the brain scans (which of anaemia, or a clinical diagnosis of anaemia, are associ- reflects iron deposition). In addition, a recent GWAS ated with decreased cognitive function. This result repli- study showed significant associations between the T2* cates findings in a larger healthy population (n > 37,000) subcortical regions and genes related to iron transport compared to previous studies [11, 14]. In complementary such as TF, HFE and SLC25A37 [43]. analyses in AddNeuroMed, a cohort study of dementia, We recognise that there are limitations to this study. we similarly find that red blood cell indices including red The five cognitive tests were generally in agreement; cell count, PCV and HGB are associated with AD and however, there was some discrepancy in the visual mem- with decline in cognitive function measures. Using genetic ory task. The task itself involved matching of pairs and Winchester et al. Genome Medicine (2018) 10:51 Page 10 of 12

although the mismatched score was used to improve re- might be both preventative and therapeutic at least in liability of the testing measure there are still weaknesses part. If this was proven in interventional studies then in this data set. Other studies have shown the measure such screening measures, already in widespread use in has a low reliability score of 0.15 [16] and potential the population, might be used to identify people for weaknesses of test method may be impacting on our these and indeed for other secondary prevention inter- own analysis results. The main inference for the MR ventions as they become available. analysis is use of cognitive performance as a proxy rep- resentative for AD. An alternative would have been to Additional files use the AD phenotype as the mediating exposure, but the low number of AD patients recorded in UK Biobank Additional file 1: Table S1. Sample set description. Table S2. MR seriously limits the statistical sensitivity of this approach. replication in release 2 of the UK Biobank data. Table S3. MR results for RET trait. Table S4. General linear model results show an association Additionally, this only had borderline significance in between AD patients and red blood indices in UK Biobank. Table S5. other studies [44]. Significantly enriched KEGG pathways for MCH decline correlated genes. Pleiotropy of instruments is a common limitation of (XLSX 18 kb) MR approaches. We used a number of tests to check for Additional file 2: Figure S1. Anaemia has a significant effect on four cognitive test measures. Figure S2. Two sample MR results replicating pleiotropy effects on the results including Egger the direction of effect for MCH on the verbal–numeric reasoning methods and confirmational plots. outcome. Figure S3. Rate of change as a measure of red blood cell Using the rate of change statistic from the blood mea- count. (PDF 371 kb) sures, we were able to determine a difference between AD patients and controls. This is not a standard ap- Abbreviations AD: Alzheimer’s disease; BASO: Basophill count; BASO%: Basophill percentage; proach, possibly due to limited data available for mul- EO: Eosinophill count; EO%: Eosinophill percentage; HCT: Haematocrit tiple visits; however, it was very informative. We found percentage; HGB: Haemoglobin concentration; IRF: Immature reticulocyte differences that were reproduced in a larger set that fraction; LYMPH: Lymphocyte count; LYMPH%: Lymphocyte percentage; MCH: Mean corpuscular haemoglobin; MCHC: Mean corpuscular were not detected otherwise. Using the same dataset but haemoglobin concentration; MCV: Mean corpuscular volume; taking a mean statistic per patient, rather than time MONO: Monocyte count; MONO%: Monocyte percentage; MPV: Mean decline, we detected a difference in white blood cell platelet volume; MR: Mendelian randomisation; MRV: Mean reticulocyte volume; MSCV: Mean sphered cell volume; NEUT: Neutrophill count; measure for basophils [45]. Given the known effects of NEUT%: Neutrophill percentage; NRBC: count; AD on blood measures, it seems likely that both blood NRBC%: Nucleated red blood cell percentage; PCT: Platelet crit; PCV: Packed types are affected. Nonetheless, both methods warrant cell volume; PDW: Platelet distribution width; PLT: Platelet count; RBC: Red blood cell count; RDW: Red blood cell distribution width; RET: Reticulocyte replication in a larger, independent dataset. We have also count; RET%: Reticulocyte percentage; WBC: White blood cell count presented some interesting pathway enrichment results yielding pathways which warrant replication in an Acknowledgements The authors would like to thank Chi-Hun Kim for his assistance with data independent sample set with the goal of identifying acquisition and discussions and Danielle Newby for helpful discussion and related genes. comment on the manuscript. This study was conducted using the UK Biobank resource, and the authors would like to thank the UK Biobank participants. Conclusions Funding We have presented here further evidence for the associ- We received funding for this analysis through the Medical Research Council ation between red blood cell measures normally indica- Dementias Platform UK (MR/L015382/1) and the European Union’s Horizon tive of anaemia and measures of both poor cognitive 2020 research and innovation programme 2014–2020 under Grant Agreement No. 634143 to the MedBioInformatics programme. performance and of dementia. Using a robust MR approach, we are able to determine that this relationship Availability of data and materials is one of causality and not consequence suggesting that The data used in this study are available via a direct application to UK Biobank (http://www.ukbiobank.ac.uk/register-apply/). Data is open access, reversing these changes might slow or prevent the onset after a registration process and approval from the access committee to of dementia. These findings require replication in other confirm reasonable request. AddNeuroMed data is available for download datasets but already derive from one very large and one from GSE63063. very detailed cohort study. If they are replicated then the Authors’ contributions implications are considerable. As our findings apply to AJN-H, LW and SL contributed to study design. LW conducted data people with decreased cognitive function within the nor- processing and statistical analyses. LW, SL, JP and AJN-H contributed to the mal range as well as to people with established dementia writing of the paper. All authors read and approved the final manuscript. then the implication is that the causal relationship Ethics approval and consent to participate between decreased red cell function and anaemia are an This research has been conducted using the UK Biobank Resource under early, preclinical influence on disease that continues Application Number 15181. UK Biobank has received ethics approval from the Research Ethics Committee (ref. 11/NW/0382). Full details for the through to the dementia syndrome. It follows that AddNeuroMed study are available in the primary publications [17, 18]. The measures to reduce or reverse poor red cell function study conformed to the Declaration of Helsinki. Winchester et al. Genome Medicine (2018) 10:51 Page 11 of 12

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