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Title: VARIATION IN SLC19A3 AND PROTECTION FROM MICROVASCULAR
DAMAGE IN TYPE 1 DIABETES
Running title: VARIATION IN SLC19A3 AND MICROVASCULAR DAMAGE
Authors:
Massimo Porta*1, MD., PhD., Iiro Toppila*2,3,4, BSc(Tech.), Niina Sandholm2,3,4, DSc(Tech.), S.
Mohsen Hosseini5, MD., PhD., Carol Forsblom2,3,4, DMSc, Kustaa Hietala2,6, MD., DMSc.,
Lorenzo Borio1, MD., Valma Harjutsalo2,3,4,7, PhD., Barbara E. Klein8, MD., MPH, Ronald Klein8,
MD., MPH, Andrew D. Paterson5 , MB., ChB., for the DCCT/EDIC Research Group†, and Per
Henrik Groop2,3,4,9, MD., DMSc, FRCPE, on behalf of the FinnDiane Study Group‡
*These authors contributed equally to this work
†A complete list of participants in the Diabetes Control and Complications Trial/Epidemiology of
Diabetes Interventions and Complications (DCCT/EDIC) Research Group is provided in the
Supplementary Appendix
‡A complete list of physicians and nurses at each center participating in the collection of
participants in the FinnDiane Study is provided in the Supplementary Appendix
Affiliations:
1 Department of Medical Sciences, University of Turin, Turin, Italy,
2 Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland,
3 Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki,
Finland,
4 Diabetes and Obesity Research Program, Research Programs Unit, University of Helsinki,
Helsinki, Finland,
1
Diabetes Publish Ahead of Print, published online December 30, 2015 Page 3 of 41 Diabetes
5 Genetics and Genome Biology Program, Hospital for Sick Children, Toronto, ON, Canada,
6 Department of Ophthalmology, Helsinki University Central Hospital, Helsinki, Finland,
7 National Institute for Health and Welfare, Helsinki, Finland,
8 Department Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA,
9 Baker IDI Heart and Diabetes Institute, Melbourne, Australia
Corresponding author:
Per Henrik Groop, Biomedicum Helsinki (C318b), Haartmaninkatu 8, FIN 00290 Helsinki,
FINLAND
Tel: +358 500 430 436, Email: per [email protected]
Word count: words 3341 (abstract 200 words) Tables: 4 Figures: 1
ESM tables: 4 ESM figures: 0
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ABSTRACT (word count 200)
The risk of long term diabetes complications is not fully explained by diabetes duration or long term glycemic exposure, suggesting the involvement of genetic factor(s). Since thiamine regulates intracellular glucose metabolism and corrects for multiple damaging effects of high glucose, we hypothesised that variants in specific thiamine transporters are associated with risk of severe retinopathy and/or severe nephropathy, as they might modify an individual’s ability to achieve sufficiently high intracellular thiamine levels. We tested 134 single nucleotide polymorphisms
(SNPs) in two thiamine transporters (SLC19A2/3) and their transcription factors (SP1/2) for association with severe retinopathy, nephropathy or their combination in the FinnDiane cohort.
Subsequently, the results were examined for replication in the DCCT/EDIC and WESDR cohorts.
We found two SNPs in strong linkage disequilibrium in the SLC19A3 locus associated with reduced rate of severe retinopathy and the combined phenotype of severe retinopathy and end stage renal disease. The association for the combined phenotype reached genome wide significance in a meta analysis including the WESDR cohort. These findings suggest that genetic variations in SLC19A3 may play an important role in the pathogenesis of severe diabetic retinopathy and nephropathy. This may help explain why some persons with type 1 diabetes are less prone than others to develop microvascular complications.
Key words: Genetic Association, Genetic Susceptibility, Retinopathy, End Stage Renal Failure,
Nephropathy, Vitamin B Group
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INTRODUCTION
Severe microvascular complications affect more than one third of people with diabetes (1). Diabetes
duration, poor glycemic control and high blood pressure are the strongest known risk factors for the
development of microangiopathy. Although glycemic control may be considered the most important
risk factor, a sub analysis of the Diabetes Control and Complications Trial (DCCT) revealed that a
3 step change in ETDRS level retinopathy occurs in people with sustained optimal levels of HbA1c,
whereas some individuals with poor metabolic control did not reach the 3 step change outcome
during the trial (2). This suggests that other factors, possibly genetically determined, may contribute
to the pathogenesis of microvascular damage. This phenomenon has also been detected in studies
showing familial clustering of microvascular complications (3, 4). Despite great efforts, only few
genetic loci have been robustly identified with conventional criteria, i.e. discovery p<5×10 8 and
independent replication p<0.05 in the same direction (5 7), for the risk of microvascular
complications.
Hyperglycemia may damage blood vessels through multiple biochemical mechanisms, such as
overproduction of advanced glycation end products (AGEs) and activation of the polyol,
hexosamine and diacylglycerol protein kinase C pathways. In particular, increased production of
reactive oxygen species (ROS) in the Krebs cycle may act as a common denominator, or “unifying
mechanism”, of the above pathways (8, 9). Thiamine (Vitamin B1) regulates intracellular glucose
management through multiple mechanisms (10) and was shown to correct all the above high
glucose induced abnormalities by reducing ROS production both in cellular studies (11, 12) and in
animal models (13). In addition, thiamine and its derivative benfotiamine were shown to reduce the
progression of retinopathy and nephropathy in animals with experimental diabetes (14). Hence,
impaired thiamine availability may facilitate metabolic damage, and evidence for reduced
circulating thiamine levels was described in people with diabetes, possibly secondary to renal loss
(15).
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Thiamine is carried into the cells by two high affinity thiamine transporters, hTHTR1 and hTHTR2, and by a low affinity transporter (16). Two transcription factors, Sp1 and Sp2, are known to affect the expression of SLC19A2/3 encoding hTHTR1/2 (17). It might be that individuals who are susceptible to developing diabetic retinopathy (DR) and/or nephropathy (DN) have impaired ability to achieve sufficiently high intracellular thiamine levels. This might be particularly relevant in insulin independent tissues, such as retinal capillary endothelium and pericytes, and the neuroretina, because they cannot regulate glucose movement into the cell, and thus are exposed to hyperglycemia. We also hypothesized that such a defect may be due to genetic variation in the genes encoding for thiamine transporter(s) and/or their transcription factor(s).
We examined single nucleotide polymorphisms (SNPs) in the genes encoding for hTHTR1/2 and
Sp1/2 in participants with type 1 diabetes in the Finnish Diabetic Nephropathy (FinnDiane) Study, for association with severe DR compared to no/minimal DR. We also compared participants with different stages of renal damage and finally the combined phenotype of severe DR and end stage renal disease (ESRD) vs. no/minimal lesions. The results were examined for replication in two independent type 1 diabetes cohorts: the Diabetes Control and Complications Trial and its long term follow up study Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) (18) and the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) (19).
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REASEARCH DESIGN AND METHODS
Participants and phenotype definitions
The FinnDiane Study is a nationwide multicenter study aimed at detecting genetic and
environmental risk factors for diabetic complications in type 1 diabetes. Details on participant
recruitment have been presented earlier (20). The FinnDiane Study includes 3,546 participants with
quality controlled genotype data from a genome wide association study (GWAS) (5). We included
in the analyses participants with type 1 diabetes and minimum duration of ten years, age at diabetes
onset ≤40 years and insulin treatment initiated within one year from diagnosis of diabetes. The
study protocol was approved by the local Ethnics Committees. All the participants gave informed
consent prior to participation. The study was performed in accordance with the Declaration of
Helsinki.
Analyses for DR included 1,566 cases with severe DR at the baseline visit, defined as ETDRS score
≥53 (severe non proliferative retinopathy or worse) or any retinal laser treatment, and 218 controls
with no/mild DR (ETDRS score <35, corresponding to isolated microaneurysms or blot
haemorrhages), no laser treatment, and diabetes duration >20 years (Table 1). Correspondingly, the
participants were divided by stage of DN at the baseline visit, including 415 with ESRD (defined as
ongoing dialysis treatment or transplanted kidney), 759 with macroalbuminuria (albumin excretion
rate (AER)≥200 µg/min or ≥300 mg/24h or albumin/creatinine ratio (ACR)≥25 mg/mmol for men
and ≥35 mg/mmol for women), 442 with microalbuminuria (AER≥20<200 µg/min or ≥30<300
mg/24h or an ACR of 2.5–25 mg/mmol for men and 3.5–35 mg/mmol for women in overnight, 24
hour or spot urine collections) and 1,623 with normal AER (Table 2). Association tests between
SNPs and DN (ESRD+macroalbuminuria) vs. participants with normal AER, and ESRD vs. normal
AER were performed. Finally the combined phenotype consisted of cases having both severe DR
and ESRD (n=369) and controls having no/minimal DR and no/minimal DN (normal AER or
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microalbuminuria, n=190) (Table S1). This combined phenotype compares the extreme cases that have the most severe irreversible stages of both DN and DR to those controls whose status in terms of both DN and DR shows only the very first signs of the complications, which could still regress back to normal states. Furthermore the long diabetes duration required for the controls makes it less likely that major progression will occur in these patients.
The significant associations were replicated in independent cohorts from the DCCT/EDIC (18) and the WESDR (19) studies. Participant recruitment and cohort summaries for both have been presented earlier (18, 19).
Both replication studies used the latest available participant data to achieve the long duration of diabetes required for this study. The WESDR cohort had 311 cases and 157 controls for the DR analyses. Of these, 82 cases and 112 controls were suitable for the analysis of the combined phenotype. Only participants with type 1 diabetes were included in the analyses. In the
DCCT/EDIC cohort, 209 cases and 228 controls were included for the analysis of DR. In the
DCCT/EDIC, we performed the analysis in three sub groups defined based on cohort and treatment group assignment: primary prevention cohort (53 cases vs. 58 controls, models adjusted for DCCT randomized treatment), secondary cohort – conventional treatment (114 cases vs. 51 controls) and secondary cohort – intensive treatment (42 cases vs. 119 controls). As the DCCT/EDIC cohort had only few participants with ESRD, it could not be used to replicate the results of the combined phenotype.
Genotyping
SNPs within the four candidate gene regions, including ten kilobases up and downstream, were extracted from the existing FinnDiane genome wide genotyping data imputed with HapMap II reference panel. The genome wide genotyping, quality control, and genotype imputation of the
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FinnDiane data has been previously described (5). The regions contained 134 SNPs in total: 40 in
SLC19A2, 46 in SLC19A3, 23 in SP1, and 25 in SP2. Details of genotyping, quality control and
genotype imputation measures in DCCT/EDIC and WESDR have been described elsewhere (7). Of
the two SNPs selected for replication rs6713116 was directly genotyped in both DCCT/EDIC and
WESDR while rs12694743 was imputed with good quality using HapMap II r22 as reference
(WESDR: INFO=0.96, DCCT/EDIC: INFO=0.97).
Statistical calculations
Case control association testing was performed with an additive logistic regression model using
plink v1.07. (21), with genotypes defined as continuous allelic dosages. The analyses were
primarily adjusted for age, diabetes duration, sex, and the first ten genetic principal components
(PCs) computed with Eigenstrat software (EIGENSOFT v.3.0) (22).
Significant associations were further re analysed using corresponding logistic regression models,
adjusted for risk factors such as HbA1c, BMI, plasma lipids and blood pressure, measured at the
baseline visit. Models were expanded step by step adding significant covariates from each class of
similar biomarkers, if they improved the model fit by Akaike information criterion. Covariates with
skewed distributions were log transformed.
In the DCCT/EDIC cohort, in addition to age, sex and duration of diabetes at the last follow up visit,
only the first three PCs were included as covariates in the models. Additional covariates in the
extended model included BMI (latest follow up visit) and mean HbA1c in both replication cohorts.
Both replication cohorts used mean HbA1c during the whole follow up period. The results were
merged with the FinnDiane data in a fixed effects meta analysis using the METAL software (23),
with the inverse variance based method using effect size estimates and their standard errors. The
heterogeneity of the cohorts was evaluated using the Cochran's Q test and the corresponding I2
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statistic implemented in the METAL software. We estimated the replication power using Genetic
Power Calculator (24), based on estimates of relative risk (RR) and disease prevalence evaluated from the FinnDiane and detected allele frequencies from DCCT/EDIC and WESDR. Odds ratios resulting from the basic models were equated to RR using the equation RR=OR/((1 P0)+(P0×OR)) where P0 is the incidence in the non exposed group (high risk genotype). To account for the
“winner’s curse”, we used 60% decreased effect size to achieve bias reduced estimate for power
(25, 26).
Adjustment for multiple testing
To account for multiple statistical tests, the threshold for statistical significance was adjusted with
Bonferroni correction. This correction included the initial tests performed for 134 SNPs using the basic model and four different phenotypes (4×134=536 tests). In addition, it included the tests from the four extended models used to analyse the two SNPs found associated with two of the phenotypes (4×2×2=16 additional tests). Thus the significance threshold was set at p<0.05/552≈9.06×10 5.
SNP association with covariates
We tested if the top SNPs were associated with other clinical covariates with univariate linear regression. Testing was performed both in the 462 FinnDiane participants included in the analysis of the combined phenotype (with complete data) and in all genotyped FinnDiane participants (N varying due to missing data, reported separately for each covariate) (Table S3).
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RESULTS
Two SNPs in the intronic region of SLC19A3 in strong linkage disequilibrium (LD) with each other
(FinnDiane: r2=0.93) showed significant association with DR: rs12694743: p=3.81×10 6, OR=0.51
(95% CI: 0.38–0.68); and rs6713116: p=3.15×10 6, OR=0.41 (95% CI: 0.28–0.60) (Table 3). The
minor alleles of both SNPs were associated with lower risk for severe DR.
No significant association (p<9.06×10 5) was detected with the DN or ESRD phenotypes in any of
the studied genes (data not shown). Nevertheless the same two SNPs showed nominal significance
when comparing people with normal AER and ESRD (rs12694743: p=8.9×10 4, OR=0.62, 95% CI:
0.47–0.82; rs6713116: p=1.3×10 3, OR=0.56, 95% CI: 0.38–0.80). Minor alleles were associated
with lower risk of complications.
The same two SNPs were also strongly associated with the combined phenotype of severe DR and
ESRD: rs12694743: p=7.51×10 8, OR=0.31 (95% CI: 0.20–0.47); rs6713116: p=7.49×10 7,
OR=0.26 (95% CI: 0.15–0.44) (Table 4).
The p values and ORs were virtually unchanged when adjusted for additional covariates in the DR
phenotype. However, adding covariates to the model of the combined phenotype gradually
increased the protective effect of the SNPs (i.e. the OR decreased) (Table S2). No significant
association was found between the two top SNPs and any clinical covariates in the FinnDiane
participants (Table S3), in the subsample described above (Table S4), or in the publicly available
studies of large consortia (HbA1c (27), BMI (28), Serum triglycerides (29), Systolic/diastolic blood
pressure (30)). The other genetic regions analysed (SLC19A2, SP1/2) showed no significant
associations with any of the phenotypes.
Replication
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The association of these two SNPs with DR phenotype was not replicated in the WESDR and
DCCT/EDIC cohorts (Table 3). Adjusting the models for mean HbA1c and BMI did not alter the results (data not shown). The results of the combined meta analysis of the replication cohorts and the FinnDiane were not significant (Table 3, Figure 1). Yet, there was significant evidence for heterogeneity of the effect across the cohorts (rs12694743: I2=64, p=0.02; rs6713116: I2=71, p=0.008, Cochran's Q test).
Due to the limited number of ESRD cases in the DCCT/EDIC, only the WESDR cohort was tested for replication of the association for the combined phenotype. Both SNPs were significantly associated with the combined phenotype (p<0.05, Table 4). Adjusting the models for BMI and mean HbA1c in WESDR made the association non significant (p>0.05) arguing for some level of mediation. When the replication results were combined with the FinnDiane data in meta analysis, rs12694743 reached genome wide significance (p<5×10 8) both before (p=7.14×10 9) and after
8 (p=2.30×10 ) adjustment for BMI and HbA1c (Table 4). The SNPs had similar effect size estimates in both WESDR and FinnDiane (Table 4, Figure 1) and there was no evidence for heterogeneity
(Cochran's Q test).
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DISCUSSION
We investigated the hypothesis that variations in the genes encoding the high affinity membrane
thiamine transporters hTHTR1/2 or their transcription factors Sp1/2 may affect susceptibility to
develop microvascular complications of diabetes. Our hypothesised mechanism is that this might be
due to enhanced capability of handling high intracellular glucose via thiamine modulated pathways
in insulin independent tissues.
We observed a strong association of rs12694743 and rs6713116 in SLC19A3 with severe DR
6 (plead=3.8×10 ), and an even stronger association with the merged phenotype of severe DR and
8 ESRD (plead=7.5×10 ) in the FinnDiane cohort. The severe DR results could not be replicated. Still
the combined phenotype of severe DR and ESRD showed associations in the WESDR cohort
(DCCT/EDIC not tested due to insufficient number of ESRD cases) and rs12694743 reached
genome wide significance in the meta analysis (p=7.1×10 9). Both strength and direction of
association were similar, further model adjustments behaved similarly in both cohorts, and there
was no evidence for heterogeneity of the SNP effects for the combined phenotype. Moreover, the
additional model adjustments in the FinnDiane cohort suggested that the protective effect is
independent of cross sectionally measured known risk factors of DN/DR. Even though the
adjustment resulted in exclusion of some individuals due to missing data, there should be no
selection bias affecting the results as the allele frequencies in the participant subsamples with non
missing data did not change (Table S2). No associations with other studied genetic regions were
found.
Although the association with severe DR could not be replicated, we estimated that after adjustment
for the winners curse there was only 31% power to detect corresponding lead associations (p<0.05)
in the combined set of replication cohorts, suggesting that lack of replication may be due to
insufficient statistical power. Thus it is possible that even when all the replication cohorts are
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combined the numbers are too small to detect the marginal effect of DR or DN alone, i.e. the association with just one of the phenotypes. As the analysis in the FinnDiane demonstrated, there are trends (in the same direction) in allele frequencies in both DN and DR complications alone.
Thus the combined phenotype (capturing both of these trends simultaneously) should show more extreme differences. This difference should be detectable also using smaller patient sample, suggesting one explanation on why the association was seen with the combined phenotype
(regardless the smaller patient sample) and not with the DR phenotype alone.
This study was based on the hypothesis that genetic variation in specific thiamine transporters would affect the capability of achieving sufficiently high intracellular thiamine levels. Thus only four genes of interest were studied. We have previously performed GWAS on DN in FinnDiane (5) and therefore setting the significance threshold for candidate gene study in the GWAS setting is not straight forward. Nevertheless, after replication, the detected associations reached genome wide significance. The resulting ORs were high compared with many other complex traits, but this might reflect the extreme phenotype setting used. Although in type 1 diabetes most of the individuals with
ESRD also have severe retinopathy, the more important aspect of the combined phenotype is that the controls seem to have something protecting them from the complications. People with this extreme phenotype are rare, as showing only no/minimal DR/DN after 20 years of diabetes is fairly uncommon.
The associated SNPs are located in intronic regions of SLC19A3 (rs6713116 in the first and rs12694743 in the fourth intron of ENST00000258403 transcript), but their functional mechanism remains unknown. One possibility is that these variants affect regulatory protein binding in the region and the expression of SLC19A3. However, we could not find any functional annotation or regulatory elements underlying the associated SNPs affecting hTHTR2 or its expression in the publicly available databases. It is therefore possible that these SNPs represent an association of
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another nearby variant in LD with them, possibly even in another gene, not genotyped or imputed in
our data. Nevertheless, public databases support the importance of SLC19A3 in DN. For instance, in
the NEPHROMINE database (http://www.nephromine.org) Schmid Diabetes (31) dataset shows
that SLC19A3 is under expressed in DN in the kidney (p=0.016, Fold Change (FC)= 1.14, rank:
1,720/12,624). Elucidating the true mechanism(s), however, requires additional functional studies.
SLC19A3 encodes hTHTR2, expressed mainly in the intestine, liver, kidney and placenta. It is
absent from marrow stem cells, pancreatic beta cells and cochlear hairy cells of the inner ear and is
probably involved above all in intestinal thiamine absorption (32 34). Mutations in SLC19A3 are
known to cause biotine responsive basal ganglial disease (OMIM 607483) (35) and thiamine
responsive encephalopathy (OMIM 606152) (36). However, those mutations produce truncated
proteins, whereas the SNP described in this paper may result in more subtle functional changes of
the thiamine transporter, hTHTR2, with biological importance in the development of both DR and
DN. Thiamine acts as a co enzyme for three key enzymes in glucose metabolism. Transketolase
shifts glyceraldehyde 3 phosphate (G3P), which is particularly active in glycation of proteins and
AGE formation, from glycolysis into the pentose phosphate shunt. Pyruvate dehydrogenase
converts pyruvate, the final product of glycolysis, into acetyl CoA, which then enters the Krebs’
cycle. α keto glutarate dehydrogenase catalyses the oxidation of α keto glutaric acid to succinyl
CoA in the Krebs’ cycle (10).
A limitation of this study is that it only focused on the known high affinity thiamine transporter
genes whereas additional variants in different regions involved in related processes may exist. In
addition, without performing functional analyses, we cannot comment on the mechanisms through
which the SNPs affect the gene. Strengths of this study include the large discovery cohort with
well defined phenotypes. On top of this, the finding for the combined phenotype was replicated in
one of the available independent cohorts with similar phenotypic data, and the lead SNP reached
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genome wide significant association with the combined phenotype of severe DR and ESRD in meta analysis. However, the number of subjects that fit our strict phenotype definitions was small in the WESDR cohort and the evidence was only modest if inspected independently, making the small replication a major limitation of this study. Furthermore, the strict phenotype inclusion criteria for the combined phenotype did not permit further replication in other type 1 diabetes cohorts, mainly due to lack of ETDRS based DR classifications.
Although the results indicate a plausible explanation for why hyperglycemia in some persons with type 1 diabetes may cause harm while others are protected from its deleterious effects, it is of note that the clinical trials with benfotiamine in microvascular complications have failed to show a protective effect. However, those trials have been short term and have included small numbers of individuals (37, 38), with some studies showing contradicting results (39). Another important aspect is that these trials have not targeted the same phenotype as shown to be associated with the genetic variants in this analysis. Clinical trials on thiamine have also shown successful results for treating nephropathy as reviewed previously (40). Therefore, we cannot yet draw any final conclusions regarding a potential clinical benefit of targeted treatment with vitamin B1 agents.
As the SNPs were not associated with other known risk factors affecting DR/DN, and the associations with DR/DN phenotypes remained even after adjustment for covariates, they appear to represent a novel independent risk factor for both complications. However, these results should be further studied in additional cohorts and/or by other methods to confirm them, and gain additional insight on the associated SNPs function. Finally, the results and observations fit well with the original working hypothesis, providing a novel interpretation for the pathogenesis of DR/DN. In particular, these results may help to explain why some individuals are less prone than others to develop the microvascular complications of diabetes and might lead to early identification of those at risk.
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ACKNOWLEDGMENTS
Contributors
M.P. and P H.G. designed the study. C.F., K.H. and V.H. collected data in FinnDiane. N.S.
prepared the genotype data in FinnDiane. M.H. analysed data from DCCT/EDIC and WESDR. I.T.
analysed data from FinnDiane, and combined results in meta analysis. M.P. and I.T. wrote the first
draft of the manuscript. M.P., I.T., N.S., S.M.H., C.F., K.H., L.B., V.H., B.E.K., R.K. A.D.P and P.
H.G. commented and participated in the writing of the manuscript. P H.G. is the guarantor of this
work and, as such, had full access to all the data in the study and takes responsibility for the
integrity of the data and the accuracy of the data analysis.
Duality of Interest
M.P. has received honorariums as advisory board member from Abbott, Novartis, Roche
Diagnostics and Sanofi.
K.H. has received lecture honorariums from Santen and Allergan. KH has also been advisory board
member of Allergan.
P H.G. has received lecture honorariums from AbbVie, Boehringer Ingelheim, Cebix, Eli Lilly,
Genzyme, Novartis, Novo Nordisk, MSD and Medscape, and research grants from Eli Lilly and
Roche. P HG is also an advisory board member of Boehringer Ingelheim, Eli Lilly and Novartis.
Other authors declare that there is no further duality of interests.
I.T., N.S., S.M.H., C.F., L.B., V.H., B.E.K., R.K. and A.D.P declare no conflict of interest.
Source of Funding
FinnDiane:
16 Diabetes Page 18 of 41
The study was supported by grants from the Folkhälsan Research Foundation, the Wilhelm and Else
Stockmann Foundation, the Liv och Hälsa Foundation, the Helsinki University Central Hospital
Research Funds (EVO), the Finnish Cultural Foundation, the Signe and Ane Gyllenberg
Foundation, Novo Nordisk Foundation, Academy of Finland, Tekes and the Finnish Medical
Society (Finska Läkaresällskapet).
DCCT/EDIC:
The DCCT/EDIC Research Group is sponsored through research contracts from the National
Institute of Diabetes, Endocrinology and Metabolic Diseases of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and the National Institutes of Health. Clinical data and DNA from the DCCT/EDIC study is available through the National Institute of Diabetes and
Digestive and Kidney Diseases repository at https://www.niddkrepository.org/niddk/home.do.
A.D.P. is the guarantor for DCCT/EDIC.
Industry contributors have had no role in the DCCT/EDIC study but have provided free or discounted supplies or equipment to support participants’ adherence to the study: Abbott Diabetes
Care (Alameda, CA), Animas (Westchester, PA), Bayer Diabetes Care (North America
Headquarters, Tarrytown, NY), Becton Dickinson (Franklin Lakes, NJ), CanAm (Atlanta, GA) ,
Eli Lilly (Indianapolis, IN), Lifescan (Milpitas, CA), Medtronic Diabetes (Minneapolis, MI), Nova
Diabetes Care (Billerica, MA), Omron (Shelton, CT), OmniPod® Insulin Management System
(Bedford, MA) , Roche Diabetes Care (Indianapolis, IN), and Sanofi Aventis (Bridgewater NJ).
The DCCT/EDIC has been supported by U01 Cooperative Agreement grants (1982 93, 2011 2016), and contracts (1982 2011) with the Division of Diabetes Endocrinology and Metabolic Diseases of the National Institute of Diabetes and Digestive and Kidney Disease (current grant numbers U01
DK094176 and U01 DK094157), and through support by the National Eye Institute, the National
Institute of Neurologic Disorders and Stroke, the General Clinical Research Centers Program
17 Page 19 of 41 Diabetes
(1993 2007), and Clinical Translational Science Center Program (2006 present), Bethesda,
Maryland, USA. Trial Registration: clinicaltrials.gov NCT00360815 and NCT00360893.
Additional support for this DCCT/EDIC collaborative study was provided by grants from the
National Institute of Diabetes and Digestive and Kidney Diseases contract N01 DK 6 2204,
National Institute of Diabetes and Digestive and Kidney Diseases Grants R01 DK 077510, R01
DK077489, and P60 DK20595 and support from Genome Canada through the Ontario Genomics
Institute. A.D.P. holds a Canada Research Chair in the Genetics of Complex Diseases. K.M.E. is a
recipient of the Gibney Family Scholar Award of the Heart and Stroke Foundation. The content is
solely the responsibility of the authors and does not necessarily reflect the official views of
the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
We would like to acknowledge the DCCT/EDIC staff and study participants.
A complete list of participants in the DCCT/EDIC research group can be found in New England
Journal of Medicine, 2011;365:2366 2376 (PMC3270008).
WESDR:
The WESDR was supported by grant R01 EY016379 from the National Eye Institute, National
Institutes of Health.
Prior presentation
Parts of this study were presented in abstract form at the 50th European Association for the Study of
Diabetes (EASD) Annual Meeting, Vienna, Austria, 15 19 September 2014.
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19. Klein R, Klein BE, Moss SE: Epidemiology of proliferative diabetic retinopathy. Diabetes Care. 15:1875 1891, 1992 20. Thorn LM, Forsblom C, Fagerudd J, Thomas MC, Pettersson Fernholm K, Saraheimo M, Waden J, Ronnback M, Rosengard Barlund M, Bjorkesten CG, Taskinen MR, Groop PH, FinnDiane Study Group: Metabolic syndrome in type 1 diabetes: Association with diabetic nephropathy and glycemic control (the FinnDiane study). Diabetes Care. 28:2019 2024, 2005 21. Purcell S, Neale B, Todd Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ: PLINK: A tool set for whole genome association and population based linkage analyses. The American Journal of Human Genetics. 81:559 575, 2007 22. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D: Principal components analysis corrects for stratification in genome wide association studies. Nat Genet. 38:904 909, 2006 23. Willer CJ, Li Y, Abecasis GR: METAL: Fast and efficient meta analysis of genomewide association scans. Bioinformatics. 26:2190 2191, 2010 24. Purcell S, Cherny SS, Sham PC: Genetic power calculator: Design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 19:149 150, 2003 25. Ioannidis JP, Thomas G, Daly MJ: Validating, augmenting and refining genome wide association signals. Nature Reviews Genetics. 10:318 329, 2009 26. Sun L, Dimitromanolakis A, Faye LL, Paterson AD, Waggott D, Bull SB, DCCT/EDIC Research Group: BR squared: A practical solution to the winner’s curse in genome wide scans. Hum Genet. 129:545 552, 2011 27. Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL: Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways. Diabetes. 59:3229 3239, 2010 28. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Mägi R: Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 42:937 948, 2010 29. Global Lipids Genetics Consortium: Discovery and refinement of loci associated with lipid levels. Nat Genet. , 2013 30. International Consortium for Blood Pressure Genome Wide Association Studies: Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 478:103 109, 2011 31. Schmid H, Boucherot A, Yasuda Y, Henger A, Brunner B, Eichinger F, Nitsche A, Kiss E, Bleich M, Grone HJ, Nelson PJ, Schlondorff D, Cohen CD, Kretzler M, European Renal cDNA Bank (ERCB) Consortium: Modular activation of nuclear factor kappaB transcriptional programs in human diabetic nephropathy. Diabetes. 55:2993 3003, 2006 32. Eudy JD, Spiegelstein O, Barber RC, Wlodarczyk BJ, Talbot J, Finnell RH: Identification and characterization of the human and mouse SLC19A3 gene: A novel member of the reduced folate family of micronutrient transporter genes. Mol Genet Metab. 71:581 590, 2000 33. Rajgopal A, Edmondnson A, Goldman ID, Zhao R: SLC19A3 encodes a second thiamine transporter ThTr2. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease. 1537:175 178, 2001 34. Rindi G, Laforenza U: Thiamine intestinal transport and related issues: Recent aspects. Proceedings of the Society for Experimental Biology and Medicine. 224:246 255, 2000 35. Ozand PT, Gascon GG, Al Essa M, Joshi S, Al Jishi E, Bakheet S, Al Watban J, Al Kawi MZ, Dabbagh O: Biotin responsive basal ganglia disease: A novel entity. Brain. 121 ( Pt 7):1267 1279, 1998 36. Kono S, Miyajima H, Yoshida K, Togawa A, Shirakawa K, Suzuki H: Mutations in a thiamine transporter gene and wernicke's like encephalopathy. N Engl J Med. 360:1792 1794, 2009
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37. Alkhalaf A, Kleefstra N, Groenier KH, Bilo HJ, Gans RO, Heeringa P, Scheijen JL, Schalkwijk CG, Navis GJ, Bakker SJ: Effect of benfotiamine on advanced glycation endproducts and markers of endothelial dysfunction and inflammation in diabetic nephropathy. PloS one. 7:e40427, 2012 38. Alkhalaf A, Klooster A, van Oeveren W, Achenbach U, Kleefstra N, Slingerland RJ, Mijnhout GS, Bilo HJ, Gans RO, Navis GJ, Bakker SJ: A double blind, randomized, placebo controlled clinical trial on benfotiamine treatment in patients with diabetic nephropathy. Diabetes Care. 33:1598 1601, 2010 39. Du X, Edelstein D, Brownlee M: Oral benfotiamine plus α lipoic acid normalises complication causing pathways in type 1 diabetes. Diabetologia. 51:1930 1932, 2008 40. Rabbani N, Thornalley PJ: Emerging role of thiamine therapy for prevention and treatment of early‐stage diabetic nephropathy. Diabetes, Obesity and Metabolism. 13:577 583, 2011
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Table 1: Clinical characteristics of the participants included in the association analysis of the
diabetic retinopathy phenotype in the FinnDiane cohort.
Severe DR No/mild DR P Value*
Gender 1566 (886/680) 218 (91/127) <0.001 (Male/Female)
Mean ± SD Missing Mean ± SD Missing
Age [yr] 44.4 ± 10 41.0 ± 10 <0.001
Duration [yr] 31.6 ± 8.8 28.3 ± 6.9 <0.001
HbA1c [%] 8.7 ± 3.6 8.2 ± 3.3 42 <0.001 (mmol/mol) (71.1 ± 16) 65.6 ± 13
BMI 25.4 ± 3.9 181 25.3 ± 3.1 0.78
TG [mmol/l] 1.52 ± 1.1 45 1.1 ± 0.6 <0.001
SBP [mmHg] 144 ± 21 260 133 ± 19 2 <0.001
DBP [mmHg] 82 ± 11 263 79 ± 9 2 <0.001
*p values for difference between groups. p value for sex evaluated using Pearson’s Chi squared test
(1 df) and p values for continuous traits are evaluated using Welch Two Sample t test.
DR: Diabetic retinopathy
TG: Serum triglycerides
SBP: Systolic blood pressure
DBP: Diastolic blood pressure
22 Diabetes Page 24 of 41
Table 2: Clinical characteristics of participants included in the association analysis of the diabetic nephropathy phenotype in the FinnDiane cohort, divided by diabetic nephropathy status
Normal AER Microalbuminuria Macroalbuminuria ESRD P Value*
Gender 1623 (679/944) 442 (253/189) 759 (453/306) 415 (248/167) <0.001 (Male/Female)
Mean ± SD Missing Mean ± SD Missing Mean ± SD Missing Mean ± SD Missing
Age [yr] 40.9 ± 12 40.4 ± 12 43.1 ± 10 45.7 ± 8.8 <0.001
Duration [yr] 25.7 ± 10 27.9 ± 10 29.9 ± 8.7 33.0 ± 8.5 <0.001
HbA1c [%] 8.2 ± 3.4 8.2 ± 3.6 9.0 ± 3.7 8.6 ± 3.8 62 2 40 26 <0.001 (mmol/mol) (65.9 ± 14) (65.9 ± 16) (75.0 ± 17) (70.9 ± 18)
BMI 25.0 ± 3.4 128 25.9 ± 3.6 30 26.0 ± 4.0 145 24.1 ± 3.8 90 0.89
TG [mmol/l] 1.06 ± 0.7 35 1.33 ± 0.9 7 1.71 ± 1.1 55 1.69 ± 1.0 35 <0.001
SBP [mmHg] 131 ± 17 193 138 ± 17 42 145 ± 20 206 152 ± 24 106 <0.001
DBP [mmHg] 78 ± 9 194 81 ± 10 42 83 ± 10 207 84 ± 12 108 <0.001
23 Page 25 of 41 Diabetes
*p values for difference between groups. p value for sex evaluated using Pearson’s Chi squared test (3 df) and p values for continuous traits are
evaluated using one way ANOVA
AER: albumin excretion rate
ESRD: End stage renal disease
TG: Serum triglycerides
SBP: Systolic blood pressure
DBP: Diastolic blood pressure
24 Diabetes Page 26 of 41
Table 3: Significant results of association analyses with the diabetic retinopathy phenotype in the FinnDiane cohort, results of replication cohorts and results of meta-analyses
Minor Major Imputation N MAF chr SNP bp Allele Allele Cohort Quality Cases Controls Total Cases Controls All OR* [CI 95%] P Value
FinnDiane 0.93 1566 218 1784 0.14 0.22 0.15 0.51 [0.38 0.68] 3.81×10 6
WESDR 0.96 311 157 468 0.12 0.12 0.12 1.14 [0.73 1.80] 0.57
DCCT: primary cohort 0.97 53 58 111 0.18 0.17 0.18 1.29 [0.53 3.14] 0.58
DCCT: 2ndary cohort
conventional Rx 0.97 114 51 165 0.11 0.15 0.12 0.81 [0.42 1.56] 0.52
2 G A DCCT: 2ndary cohort
intensive Rx 0.97 42 119 161 0.09 0.13 0.12 0.64 [0.26 1.57] 0.33 rs12694743 rs12694743 228,266,664 228,266,664
Meta analysis: replication
cohorts 520 385 905 0.12 0.13 0.13 0.99 [0.72 1.37] 0.96
Meta analysis: All cohorts
combined 2086 603 2689 0.14 0.16 0.14 0.68 [0.55 0.85] 4.76×10 4
25 Page 27 of 41 Diabetes
FinnDiane 0.81 1566 218 1784 0.15 0.21 0.16 0.41 [0.28 0.60] 3.15×10 6
WESDR 1 311 157 468 0.13 0.14 0.14 1.01 [0.67 1.54] 0.95
DCCT: primary cohort 1 53 58 111 0.22 0.21 0.21 1.33 [0.57 3.10] 0.51
DCCT: 2ndary cohort
conventional Rx 1 114 51 165 0.14 0.16 0.15 0.93 [0.5 1.74] 0.82
2 T C DCCT: 2ndary cohort
rs6713116 rs6713116 intensive Rx 1 42 118 160 0.12 0.16 0.15 0.73 [0.34 1.58] 0.42 228,275,342 228,275,342
Meta analysis: replication
cohorts 520 384 904 0.14 0.16 0.15 0.98 [0.73 1.32] 0.89
Meta analysis: All cohorts
combined 2086 602 2688 0.15 0.18 0.16 0.70 [0.56 0.88] 2.74×10 3
*Odds ratio of logistic regression for each copy of minor allele
chr: chromosome
bp: location in chromosome in base pairs
MAF: Minor allele frequency
26 Diabetes Page 28 of 41
Table 4: Significant results of association analyses with the combined phenotype of diabetic retinopathy and end-stage renal disease in the
FinnDiane cohort, replication results in the WESDR cohort and results of meta-analyses
Minor Major Imputation N MAF chr SNP bp Allele Allele Cohort Quality Cases Controls Total Cases Controls All OR* [CI 95%] P Value
FinnDiane: Basic † 0.93 369 190 559 0.12 0.23 0.16 0.31 [0.20 0.47] 7.51×10 8
FinnDiane: Adjusted ‡ 0.93 298 190 488 0.12 0.23 0.16 0.28 [0.17 0.45] 1.29×10 7
WESDR: Basic § 0.96 82 112 194 0.06 0.11 0.09 0.34 [0.13 0.91] 0.033 2 G A WESDR: Adjusted || 0.96 82 112 194 0.06 0.11 0.09 0.29 [0.08 1.10] 0.068 rs12694743 rs12694743 228,266,664 228,266,664 Meta analysis: Basic 451 302 753 0.11 0.18 0.14 0.32 [0.22 0.47] 7.14×10 9
Meta analysis: Adjusted 380 302 682 0.11 0.18 0.14 0.28 [0.18 0.44] 2.30×10 8
FinnDiane: Basic † 0.81 369 190 559 0.14 0.22 0.17 0.26 [0.15 0.44] 7.49×10 7
FinnDiane: Adjusted ‡ 0.81 298 190 488 0.14 0.22 0.17 0.23 [0.13 0.42] 1.90×10 6
WESDR: Basic § 1 82 112 194 0.08 0.15 0.12 0.39 [0.16 0.92] 0.032 2 T C WESDR: Adjusted || 1 82 112 194 0.08 0.15 0.12 0.51 [0.17 1.50] 0.219 rs6713116 rs6713116 228,275,342 228,275,342 Meta analysis: Basic 451 302 753 0.13 0.19 0.15 0.29 [0.18 0.46] 9.62×10 8
Meta analysis: Adjusted 380 302 682 0.13 0.19 0.16 0.28 [0.17 0.47] 1.90×10 6
27 Page 29 of 41 Diabetes
* Odds ratio of logistic regression for each copy of minor allele
† Model adjusted for age, sex, diabetes duration, and first ten genetic principle components
‡ Model adjusted for age, sex, diabetes duration, first ten genetic principle components, log(BMI), and HbA1c
§ Model adjusted for age, sex, duration of diabetes, and first ten PCs
|| Model adjusted for age, sex, duration of diabetes, first ten PCs, mean HbA1c during WESDR, and BMI
chr: chromosome
bp: location in chromosome in base pairs
MAF: Minor allele frequency
28 Diabetes Page 30 of 41
Figure 1. Forest-plots of replication results and meta-analyses for lead SNP in the analysis of diabetic retinopathy and the combined phenotype. Point size proportional to the precision of the estimates of OR. Results for rs6713116 behave similarly.
DCCT1: DCCT primary cohort
DCCT2: DCCT secondary cohort – conventional treatment
DCCT3: DCCT secondary cohort – intensive treatment
29 Page 31 of 41 Severe DR: Diabetes Severe DR + ESRD: rs12694743 rs12694743
Cohort OR [CI 95%] Cohort OR [CI 95%]
FinnDiane 0.51 [ 0.38 , 0.68 ] FinnDiane: Basic 0.31 [ 0.20 , 0.47 ]
WESDR 1.14 [ 0.73 , 1.80 ] FinnDiane: Adjusted 0.28 [ 0.17 , 0.45 ]
DCCT1 1.29 [ 0.53 , 3.14 ]
DCCT2 0.81 [ 0.42 , 1.56 ] WESDR: Basic 0.34 [ 0.13 , 0.91 ]
DCCT3 0.64 [ 0.26 , 1.57 ] WESDR: Adjusted 0.29 [ 0.08 , 1.10 ]
Meta: Replication 0.99 [ 0.72 , 1.37 ] Meta: Basic 0.32 [ 0.22 , 0.47 ]
Meta: All 0.68 [ 0.55 , 0.85 ] Meta: Adjusted 0.28 [ 0.18 , 0.44 ]
0.25 0.50 1.00 2.00 4.00 0.125 0.25 0.5 1.0 2.0 Odds Ratio Odds Ratio Diabetes Page 32 of 41
Title: VARIATION IN SLC19A3 AND PROTECTION FROM MICROVASCULAR
DAMAGE IN TYPE 1 DIABETES
Running title: VARIATION IN SLC19A3 AND MICROVASCULAR DAMAGE
Authors:
Massimo Porta, Iiro Toppila, Niina Sandholm, S. Mohsen Hosseini, Carol Forsblom, Kustaa
Hietala, Lorenzo Borio, Valma Harjutsalo, Barbara E. Klein, Ronald Klein, Andrew D. Paterson
and Per-Henrik Groop
Supplementary Appendix
Table of Contents
1. The DCCT-EDIC Research Group
2. The FinnDiane Study Group
3. Supplemental Tables
1 Page 33 of 41 Diabetes
1. The DCCT-EDIC Research Group The members of the DCCT-EDIC Research Group at the time of this publication follow. Study Chairpersons – S. Genuth, D.M. Nathan, B. Zinman (vice-chair), O. Crofford (past)
Clinical Centers Albert Einstein College of Medicine – J. Crandall (past), M. Reid (past), J. Brown-Friday (past), S. Engel (past), J. Sheindlin (past), M. Phillips (past), H. Martinez (past), H. Shamoon (past), H. Engel (past) Case Western Reserve University – R. Gubitosi-Klug, L. Mayer, S. Pendegast, H. Zegarra, D. Miller, L. Singerman, S. Smith-Brewer, M. Novak, J. Quin (past), Saul Genuth (past), M. Palmert (past), E. Brown (past), J. McConnell (past), P. Pugsley (past), P. Crawford (past), W. Dahms (deceased) Weill Cornell Medical College – D. Brillon, M.E. Lackaye, S. Kiss, R. Chan, A. Orlin, M. Rubin, V. Reppucci (past), T. Lee (past), M. Heinemann (past), S. Chang (past), B. Levy (past), L. Jovanovic (past), M. Richardson (past), B. Bosco (past), A. Dwoskin (past), R. Hanna (past), S. Barron (past), R. Campbell (deceased) Henry Ford Health System – F. Whitehouse, D. Kruger, J.K. Jones, P.A. Edwards, A. Bhan, J.D. Carey, E. Angus, A. Thomas, M. McLellan (past), A. Galprin (past) International Diabetes Center – R. Bergenstal, M. Johnson, K. Gunyou, K. Morgan, D. Kendall (past), M. Spencer (past), D. Etzwiler (deceased) Joslin Diabetes Center – L.P. Aiello, E. Golden, P. Arrigg, V. Asuquo, R. Beaser, L. Bestourous, J. Cavallerano, R. Cavicchi, O. Ganda, O. Hamdy, R. Kirby, T. Murtha, D. Schlossman, S. Shah, G. Sharuk, P. Silva, P. Silver, M. Stockman, E. Weimann, H. Wolpert, L.M. Aiello (past), A. Jacobson (past), L. Rand (past), J. Rosenzwieg (past) Massachusetts General Hospital – D.M. Nathan, M.E. Larkin, M. Christofi, K. Folino, J. Godine, P. Lou, C. Stevens, E. Anderson (past), H. Bode (past), S. Brink (past), C. Cornish (past), D. Cros (past), L. Delahanty (past), A. deManbey (past), C. Haggan (past), J. Lynch (past), C. McKitrick (past), D. Norman (past), D. Moore (past), M. Ong (past), C. Taylor (past), D. Zimbler (past), S. Crowell (past), S. Fritz (past), K. Hansen (past), C. Gauthier- Kelly (past) Mayo Foundation – F.J. Service, G. Ziegler, R. Colligan, L. Schmidt (past), B. French (past), R. Woodwick (past), R. Rizza (past), W.F. Schwenk (past), M. Haymond (past), J. Pach (past), J. Mortenson (past), B. Zimmerman (deceased), A. Lucas (deceased) Medical University of South Carolina – L. Luttrell, M. Lopes-Virella, S. Caulder, C. Pittman, N. Patel, K. Lee, M. Nutaitis, J. Fernandes, K. Hermayer, S. Kwon, A. Blevins, J. Parker, J. Colwell (past), D. Lee (past), J. Soule (past), P. Lindsey (past), M. Bracey (past), A. Farr (past), S. Elsing (past), T. Thompson (past), J. Selby (past), T. Lyons (past), S. Yacoub- Wasef (past), M. Szpiech (past), D. Wood (past), R. Mayfield (past) Northwestern University – M. Molitch, B. Schaefer, L. Jampol, A. Lyon, M. Gill, Z. Strugula, L. Kaminski, R. Mirza, E. Simjanoski, D. Ryan, C. Johnson, A. Wallia, S. Ajroud-Driss, P. Astelford, N. Leloudes, A. Degillio University of California, San Diego – O. Kolterman, G. Lorenzi, M. Goldbaum, K. Jones (past), M. Prince (past) , M. Swenson (past), I. Grant (past) , R. Reed (past), R. Lyon (past), M. Giotta (past), T. Clark (past), G. Friedenberg (deceased) University of Iowa – W.I. Sivitz, B. Vittetoe, J. Kramer, M. Bayless (past), R. Zeitler (past), H. Schrott (past), N. Olson (past), L. Snetselaar (past), R. Hoffman (past), J. MacIndoe (past), T. Weingeist (past), C. Fountain (past) University of Maryland School of Medicine – D. Counts, S. Johnsonbaugh, M. Patronas, M. Carney, P. Salemi (past), R. Liss (past), M. Hebdon (past), T. Donner (past), J. Gordon (past), R. Hemady (past), A. Kowarski (past), D. Ostrowski (past), S. Steidl (past), B. Jones (past) University of Michigan – W.H. Herman, C.L. Martin, R. Pop-Busui, D.A. Greene (past), M.J. Stevens (past), N. Burkhart (past), T. Sandford (past), J. Floyd (deceased)
2 Diabetes Page 34 of 41
University of Minnesota – J. Bantle, N. Wimmergren, J. Terry, D. Koozekanani, S. Montezuma, B. Rogness (past), M. Mech (past), T. Strand (past), J. Olson (past), L. McKenzie (past), C. Kwong (past), F. Goetz (past), R. Warhol (past) University of Missouri – D. Hainsworth, D. Goldstein, S. Hitt, J. Giangiacomo (deceased) University of New Mexico – D.S. Schade, J.L. Canady, M.R. Burge, A. Das, R.B. Avery, L.H. Ketai, J.E. Chapin, M.L Schluter (past) J. Rich (past), C. Johannes (past), D. Hornbeck (past) University of Pennsylvania – M. Schutta, P.A. Bourne, A. Brucker, S. Braunstein (past), S. Schwartz (past), B.J. Maschak-Carey (past), L. Baker (deceased) University of Pittsburgh – T. Orchard, L. Cimino, T. Songer, B. Doft, S. Olson, D. Becker, D. Rubinstein, R.L. Bergren, J. Fruit, R. Hyre, C. Palmer, N. Silvers (past), L. Lobes (past), P. Paczan Rath (past), P.W. Conrad (past), S. Yalamanchi (past), J.Wesche (past), M. Bratkowksi (past), S. Arslanian (past), J. Rinkoff (past), J. Warnicki (past), D. Curtin (past), D. Steinberg (past), G. Vagstad (past), R. Harris (past), L. Steranchak (past), J. Arch (past), K. Kelly (past), P. Ostrosaka (past), M. Guiliani (past), M. Good (past), T. Williams (past), K. Olsen (past), A. Campbell (past), C. Shipe (past), R. Conwit (past), D. Finegold (past), and M. Zaucha (past), A. Drash (deceased) University of South Florida – A. Morrison, J.I. Malone, M.L. Bernal, P.R. Pavan, N. Grove, E.A. Tanaka (past), D. McMillan (past), J. Vaccaro-Kish (past), L. Babbione (past), H. Solc (past), T.J. DeClue (past) University of Tennessee – S. Dagogo-Jack, C. Wigley, H. Ricks, A. Kitabchi, E. Chaum, M.B. Murphy (past), S. Moser (past), D. Meyer (past), A. Iannacone (past), S. Yoser (past), M. Bryer- Ash (past), S. Schussler (past), H. Lambeth (past) University of Texas Southwestern Medical Center at Dallas – P. Raskin, S. Strowig, M. Basco (past), S. Cercone (deceased) University of Toronto – B. Zinman, A. Barnie, R. Devenyi, M. Mandelcorn, M. Brent, S. Rogers, A. Gordon, N. Bakshi, B. Perkins, L. Tuason, F. Perdikaris, R. Ehrlich (past), D. Daneman (past), K. Perlman (past), S. Ferguson (past) University of Washington – J. Palmer, R. Fahlstrom, I.H.de Boer, J. Kinyoun, L. Van Ottingham, S. Catton (past), J. Ginsberg (past) University of Western Ontario – J. Dupre, C. McDonald, J. Harth, M. Driscoll, T. Sheidow, J. Mahon (past), C. Canny (past), D. Nicolle (past), P. Colby (past), I. Hramiak (past), N.W. Rodger (past), M. Jenner (past), T. Smith (past), W. Brown (past) Vanderbilt University – M. May, J. Lipps Hagan, A. Agarwal, T. Adkins, R. Lorenz (past), S. Feman (past), L. Survant (deceased) Washington University, St. Louis – N.H. White, L. Levandoski, G. Grand, M. Thomas, D. Joseph, K. Blinder, G. Shah, D. Burgess (past), I. Boniuk (deceased), J. Santiago (deceased) Yale University School of Medicine – W. Tamborlane, P. Gatcomb, K. Stoessel, P. Ramos, K. Fong, P. Ossorio, J. Ahern (past)
Clinical Coordinating Center Case Western Reserve University – R. Gubitosi-Klug, C. Beck, S. Genuth, J. Quin (past), P. Gaston (past), M. Palmert (past), R. Trail (past), W. Dahms (deceased)
Data Coordinating Center George Washington University, The Biostatistics Center – J. Lachin, P. Cleary, J. Backlund, I. Bebu, B. Braffett, L. Diminick, X. Gao, W. Hsu, K. Klumpp, P. McGee (past), W. Sun (past), S. Villavicencio (past), K. Anderson (past), L. Dews (past), Naji Younes (past), B. Rutledge (past), K. Chan (past), D. Rosenberg (past), B. Petty (past), A. Determan (past), D. Kenny (past), C. Williams (deceased)
National Institute of Diabetes and Digestive and Kidney Disease
3 Page 35 of 41 Diabetes
National Institute of Diabetes and Digestive and Kidney Disease Program Office – C. Cowie, C. Siebert (past)
Central Units Central Biochemistry Laboratory (University of Minnesota) – M. Steffes, V. Arends, J. Bucksa (past), M. Nowicki (past), B. Chavers (past) Central Carotid Ultrasound Unit (New England Medical Center) – D. O’Leary, J. Polak, A.Harrington, L. Funk (past) Central ECG Reading Unit (University of Minnesota) – R. Crow (past), B. Gloeb (past), S. Thomas (past), C. O’Donnell (past) Central ECG Reading Unit (Wake Forest University) – E. Soliman, Z.M. Zhang, C. Campbell, L. Keasler, S. Hensley, R. Prineas (past) Central Neurologic Reading Center (University of Michigan, Mayo Clinic, Southern Illinois University) – E.L. Feldman (past), J.W. Albers (past), P. Low (past), C. Sommer (past), K. Nickander (past), T. Speigelberg (past), M. Pfiefer (past), M. Schumer (past), M. Moran (past), J. Farquhar (past) Central Neuropsychological Coding Unit (University of Pittsburgh) – C. Ryan (past), D. Sandstrom (past), T. Williams (past), M. Geckle (past), E. Cupelli (past), F. Thoma (past), B. Burzuk (past), T. Woodfill (past) Central Ophthalmologic Reading Center (University of Wisconsin) – R. Danis, B. Blodi, D. Lawrence, H. Wabers, S. Gangaputra (past), S. Neill (past), M. Burger (past), J. Dingledine (past), V. Gama (past), R. Sussman (past), M. Davis (past), L. Hubbard (past) Computed Tomography Reading Center (Harbor UCLA Research and Education Institute) – M. Budoff, S. Darabian, P. Rezaeian, N. Wong (past), M. Fox (past), R. Oudiz (past), L. Kim (past), R. Detrano (past) Audiometry Reading Center (EpiSense, University of Wisconsin) – K. Cruickshanks, D. Dalton, K. Bainbridge (National Institute on Deafness and Other Communication Disorders) Cardiac MR Reading Center (Johns Hopkins University, National Heart Lung and Blood Institute) – J. Lima, D. Bluemke, E. Turkbey, R. J. van der Geest (past), C. Liu (past), A. Malayeri (past), A. Jain (past), C. Miao (past), H. Chahal (past), R. Jarboe (past) Editor, EDIC Publications – D.M. Nathan
Collaborators Advanced Glycation End Products (Case Western Reserve University) – V. Monnier, D. Sell, C. Strauch Biomarkers (Cleveland Clinic) – S. Hazen, A. Pratt, W. Tang Central Obesity Study (University of Washington) – J. Brunzell, J. Purnell Epigenetics (Beckman Research Institute of City of Hope Medical Center) – R. Natarajan, F. Miao, L. Zhang, Z. Chen Genetic Studies (Hospital for Sick Children) – A. Paterson, A. Boright, S. Bull, L. Sun, S. Scherer (past) Molecular Risk Factors Program Project (Medical University of South Carolina) – M. Lopes- Virella, T.J. Lyons, A. Jenkins, R. Klein, G. Virella, A. Jaffa, R. Carter, J. Stoner, W.T. Garvey (past), D. Lackland (past), M. Brabham (past), D. McGee (past), D. Zheng (past), R. K. Mayfield (past) SCOUT (Veralight) – J. Maynard (past) UroEDIC (University of Washington, University of Michigan) – H. Wessells, A. Sarma, A. Jacobson, R. Dunn, S. Holt, J. Hotaling, C. Kim, Q. Clemens, J. Brown (past), K. McVary (past)
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2. The FinnDiane Study Group
Physicians and nurses participating in the collection of FinnDiane participants Anjalankoski Health Center – S. Koivula and T. Uggeldahl Central Finland Central Hospital – T. Forslund, A. Halonen, A. Koistinen, P. Koskiaho, M. Laukkanen, J. Saltevo and M. Tiihonen Central Hospital of Åland Islands – M. Forsen, H. Granlund, A.-C. Jonsson and B. Nyroos Central Hospital of Kanta-Häme – P. Kinnunen, A. Orvola, T. Salonen and A. Vähänen Central Hospital of Kymenlaakso – R. Paldanius, M. Riihelä and L. Ryysy Central Hospital of Länsi-Pohja – H. Laukkanen, P. Nyländen and A. Sademies Central Ostrobothnian Hospital District – S. Anderson, B. Asplund, U. Byskata, P. Liedes, M. Kuusela and T. Virkkala City of Espoo Health Center – (Espoonlahti): A. Nikkola and E. Ritola (Tapiola): M. Niska and H. Saarinen (Viherlaakso): A. Lyytinen City of Helsinki Health Center – (Puistola): H. Kari and T. Simonen (Suutarila): A. Kaprio, J. Kärkkäinen and B. Rantaeskola (Töölö): P. Kääriäinen, J. Haaga and A-L. Pietiläinen City of Hyvinkää Health Center – S. Klemetti, T. Nyandoto, E. Rontu and S. Satuli-Autere City of Vantaa Health Center – (Korso): R. Toivonen and H. Virtanen (Länsimäki): R. Ahonen, M. Ivaska-Suomela and A. Jauhiainen (Martinlaakso): M. Laine, T. Pellonpää and R. Puranen (Myyrmäki): A. Airas, J. Laakso and K. Rautavaara (Rekola): M. Erola and E. Jatkola (Tikkurila): R. Lönnblad, A. Malm, J. Mäkelä and E. Rautamo Heinola Health Center – P. Hentunen and J. Lagerstam Helsinki University Central Hospital (Department of Medicine, Division of Nephrology) – A. Ahola, M. Feodoroff, D. Gordin, O. Heikkilä, K Hietala, S. Hägg, J. Kytö, R. Lithovius, S. Lindh, M. Parkkonen, K. Pettersson-Fernholm, M. Rahkonen, M. Rosengård-Bärlund, A. Sandelin, A-R Salonen, L. Salovaara, M. Saraheimo, T. Soppela, A. Soro-Paavonen, L. Thorn, H. Tikkanen, N. Tolonen, J. Tuomikangas, J. Wadén Herttoniemi Hospital – V. Sipilä Hospital of Lounais-Häme – T. Kalliomäki, J. Koskelainen, R. Nikkanen, N. Savolainen, H. Sulonen and E. Valtonen Iisalmi Hospital – E. Toivanen Jokilaakso Hospital – A. Parta and I. Pirttiniemi Jorvi Hospital – S. Aranko, S. Ervasti, R. Kauppinen-Mäkelin, A. Kuusisto, T. Leppälä, K. Nikkilä and L. Pekkonen Jyväskylä Health Center – K. Nuorva and M. Tiihonen Kainuu Central Hospital – S. Jokelainen, P. Kemppainen, A-M. Mankinen and M. Sankari Kerava Health Center – H. Stuckey and P. Suominen Kirkkonummi Health Center – A. Lappalainen, M. Liimatainen and J. Santaholma Kivelä Hospital – A. Aimolahti and E. Huovinen Koskela Hospital – V. Ilkka and M. Lehtimäki Kotka Health Center – E. Pälikkö-Kontinen and A. Vanhanen Kouvola Health Center – E. Koskinen and T. Siitonen Kuopio University Hospital – E. Huttunen, R. Ikäheimo, P. Karhapää, P. Kekäläinen, M. Laakso, T. Lakka, E. Lampainen, L. Moilanen, L. Niskanen, U. Tuovinen, I. Vauhkonen and E. Voutilainen Kuusamo Health Center – T. Kääriäinen and E. Isopoussu Kuusankoski Hospital – E. Kilkki, I. Koskinen and L. Riihelä Laakso Hospital, Helsinki – T. Meriläinen, P. Poukka, R. Savolainen and N. Uhlenius Lahti City Hospital – A. Mäkelä and M. Tanner Lapland Central Hospital – L. Hyvärinen, S. Severinkangas and T. Tulokas Lappeenranta Health Center – P. Linkola and I. Pulli Lohja Hospital – T. Granlund, M. Saari and T. Salonen
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Länsi-Uusimaa Hospital – I.-M. Jousmaa and J. Rinne Loimaa Health Center – A. Mäkelä and P. Eloranta Malmi Hospital – H. Lanki, S. Moilanen and M. Tilly-Kiesi Mikkeli Central Hospital – A. Gynther, R. Manninen, P. Nironen, M. Salminen and T. Vänttinen Mänttä Regional Hospital – I. Pirttiniemi and A-M. Hänninen North Karelian Hospital – U-M. Henttula, P. Kekäläinen, M. Pietarinen, A. Rissanen and M. Voutilainen Nurmijärvi Health Center – A. Burgos and K. Urtamo Oulaskangas Hospital – E. Jokelainen, P.-L. Jylkkä, E. Kaarlela and J. Vuolaspuro Oulu Health Center – L. Hiltunen, R. Häkkinen and S. Keinänen-Kiukaanniemi Oulu University Hospital – R. Ikäheimo Päijät-Häme Central Hospital – H. Haapamäki, A. Helanterä, S. Hämäläinen, V. Ilvesmäki and H. Miettinen Palokka Health Center – P. Sopanen and L. Welling Pieksämäki Hospital – V. Javtsenko and M. Tamminen Pietarsaari Hospital – M-L. Holmbäck, B. Isomaa and L. Sarelin Pori City Hospital – P. Ahonen, P. Merensalo and K. Sävelä Porvoo Hospital – M. Kallio, B. Rask and S. Rämö Raahe Hospital – A. Holma, M. Honkala, A. Tuomivaara and R. Vainionpää Rauma Hospital – K. Laine, K. Saarinen and T. Salminen Riihimäki Hospital – P. Aalto, E. Immonen and L. Juurinen Salo Hospital – A. Alanko, J. Lapinleimu, P. Rautio and M. Virtanen Satakunta Central Hospital – M. Asola, M. Juhola, P. Kunelius, M.-L. Lahdenmäki, P. Pääkkönen and M. Rautavirta Savonlinna Central Hospital – T. Pulli, P. Sallinen, M. Taskinen, E. Tolvanen, H. Valtonen and A. Vartia Seinäjoki Central Hospital – E. Korpi-Hyövälti, T. Latvala and E. Leijala South Karelia Central Hospital – T. Ensala, E. Hussi, R. Härkönen, U. Nyholm and J. Toivanen Tampere Health Center – A. Vaden, P. Alarotu, E. Kujansuu, H. Kirkkopelto-Jokinen, M. Helin, S. Gummerus, L. Calonius, T. Niskanen, T. Kaitala and T. Vatanen Tampere University Hospital – I. Ala-Houhala, T. Kuningas, P. Lampinen, M. Määttä, H. Oksala, T. Oksanen, K. Salonen, H. Tauriainen and S. Tulokas Tiirismaa Health Center – T. Kivelä, L. Petlin and L. Savolainen Turku Health Center – I. Hämäläinen, H. Virtamo and M. Vähätalo Turku University Central Hospital – K. Breitholz, R. Eskola, K. Metsärinne, U. Pietilä, P. Saarinen, R. Tuominen and S. Äyräpää Vaajakoski Health Center – K. Mäkinen and P. Sopanen Valkeakoski Regional Hospital – S. Ojanen, E. Valtonen, H. Ylönen, M. Rautiainen and T. Immonen Vammala Regional Hospital – I. Isomäki, R. Kroneld and M. Tapiolinna-Mäkelä Vaasa Central Hospital – S. Bergkulla, U. Hautamäki, V.-A. Myllyniemi and I. Rusk.
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3. Supplemental Tables
Table S1 presents the clinical characteristics (mean, standard deviation and number of participants with missing data) of participants included in analysis of the combined phenotype of both diabetic retinopathy and end-stage renal disease.
Table S2 presents the association analysis results of both diabetic retinopathy and combined phenotype of diabetic retinopathy and end-stage renal disease using the expanded models with additional covariates included in them.
Table S3 presents results of univariate linear regression with top SNPs and significant covariates used in adjusted models in all genotyped FinnDiane participants with non-missing data, and the number of participants included in each regression.
Table S4 presents results of univariate linear regression with top SNPs and significant covariates used in adjusted models in subsample of participants used in analysis of combined phenotype of diabetic retinopathy and end-stage renal disease with complete data (N=462).
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Table S1: Clinical characteristics of participants included in analysis of the combined phenotype of both diabetic retinopathy and end-stage renal disease No/mild DR + Normal Severe DR + ESRD P Value* AER / Microalbuminuria Gender 369 (217/152) 190 (76/114) <0.001 (Male/Female) Mean ± SD Missing Mean ± SD Missing Age [yr] 45.4 ± 8.5 - 40.8 ± 10 - <0.001 Duration [yr] 33.1 ± 8.4 - 28.0 ± 6.7 - <0.001 HbA [%] 8.7 ± 3.7 8.1 ± 3.3 1c 9 - <0.001 (mmol/mol) (71.8 ± 17) (65.1 ± 13) BMI 24.1 ± 3.9 63 25.3 ± 3.2 - <0.001 TG [mmol/l] 1.71 ± 1.0 16 1.07 ± 0.6 - <0.001 SBP [mmHg] 153 ± 24 76 132 ± 18 1 <0.001 DBP [mmHg] 85 ± 12 78 78 ± 9 1 <0.001
*p-values for distribution differences between groups. p-value for sex evaluated using Pearson’s Chi-squared test (1 df) and p-values for continuous traits are evaluated using Welch Two Sample t- test. DR: Diabetic retinopathy ESRD: End-stage renal disease AER: albumin excretion rate TG: Serum triglycerides SBP: Systolic blood pressure DBP: Diastolic blood pressure
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Table S2: Results of expanded models in analysis of both diabetic retinopathy and combined phenotype of diabetic retinopathy and end-stage renal disease N MAF Phenotype SNP Model Cases Controls Cases Controls OR* [CI 95%] P Value 1a 1566 218 0.142 0.218 0.51 [0.38 - 0.68] 3.81×10-6 1b 1255 216 0.139 0.218 0.50 [0.37 - 0.67] 2.85×10-6 2 1356 218 0.139 0.218 0.49 [0.36 - 0.66] 2.30×10-6 3 1346 218 0.139 0.218 0.49 [0.36 - 0.66] 3.72×10-6 rs12694743 4 1255 216 0.139 0.218 0.49 [0.36 - 0.66] 4.82×10-6 DR 1a 1566 218 0.154 0.213 0.41 [0.28 - 0.60] 3.15×10-6 1b 1255 216 0.153 0.213 0.40 [0.27 - 0.58] 2.49×10-6 2 1356 218 0.152 0.213 0.40 [0.27 - 0.58] 2.56×10-6 -6
rs6713116 rs6713116 3 1346 218 0.152 0.213 0.39 [0.26 - 0.58] 2.80×10 4 1255 216 0.153 0.213 0.38 [0.25 - 0.57] 2.60×10-6 1a 369 190 0.122 0.228 0.31 [0.21 - 0.48] 7.51×10-8 1b 273 189 0.120 0.229 0.30 [0.19 - 0.47] 3.05×10-7 2 298 190 0.118 0.228 0.28 [0.17 - 0.45] 1.29×10-7 3 294 190 0.116 0.228 0.19 [0.11 - 0.34] 1.41×10-8 rs12694743 4 273 189 0.120 0.229 0.15 [0.08 - 0.29] 9.56×10-9 DR+ESRD 1a 369 190 0.140 0.218 0.26 [0.15 - 0.44] 7.49×10-7 1b 273 189 0.139 0.218 0.25 [0.14 - 0.45] 3.35×10-6 2 298 190 0.138 0.218 0.23 [0.13 - 0.42] 1.90×10-6 3 294 190 0.136 0.218 0.15 [0.07 - 0.30] 1.51×10-7 rs6713116 rs6713116 4 273 189 0.139 0.218 0.10 [0.04 - 0.23] 3.56×10-8
Models: 1a: Adjusted for age, sex, duration of diabetes, first 10 PCs 1b: model 1a using only participants with non-missing data for all covariates used in model 4 2: model 1a + log(BMI) and HbA1c 3: model 2 + log(TG) 4: model 3 + SBP and DBP
* Odds ratio of logistic regression for each copy of minor allele
MAF: Minor Allele Frequency DR: Diabetic retinopathy ESRD: End-stage renal disease
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Table S3: Association of top SNPs and significant covariates used in adjusted models in all genotyped FinnDiane participants with non-missing data rs12694743 rs6713116 Covariate beta* SE P Value beta* SE P Value N HbA 1c 0.26 0.96 0.78 0.97 1.25 0.44 3347 [mmol/mol] BMI -0.25 0.15 0.10 -0.22 0.19 0.25 3044 TG [mmol/l] 0.05 0.04 0.20 0.06 0.05 0.17 3257 SBP [mmHg] 0.93 0.78 0.23 0.82 1.01 0.42 3272 DBP [mmHg] 0.22 0.40 0.59 0.29 0.52 0.58 3268
*Regression coefficient from linear regression giving increase in absolute values for every copy of minor allele
SE: Standard error for regression coefficient TG: Serum triglycerides SBP: Systolic blood pressure DBP: Diastolic blood pressure
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Table S4: Association of top SNPs and significant covariates used in adjusted models in subsample of participants used in analysis of combined phenotype of diabetic retinopathy and end-stage renal disease with complete data (N=462) rs12694743 rs6713116 Covariate beta* SE P Value beta* SE P Value HbA 1c 2.21 1.48 0.14 2.35 1.88 0.21 [mmol/mol] BMI 0.28 0.36 0.45 0.29 0.46 0.53 TG [mmol/l] 0.05 0.08 0.56 0.00 0.10 0.99 SBP [mmHg] 2.37 2.37 0.32 0.39 3.02 0.90 DBP [mmHg] 0.12 1.13 0.91 -0.33 1.44 0.82
*Regression coefficient from linear regression giving increase in absolute values for every copy of minor allele
SE: Standard error for regression coefficient TG: Serum triglycerides SBP: Systolic blood pressure DBP: Diastolic blood pressure
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