Abstract Background Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. Methods & findings Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04–1.06, per HbA1c-raising allele, p = 3 × 10−29); whereas GS-E was not (OR = 1.00, 95% CI 0.99–1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66–0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38–0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55–0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. Conclusions As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.

Author summary Why was this study done? Blood glucose binds in an irreversible manner to circulating hemoglobin in red blood cells (RBCs), generating “glycated hemoglobin,” called HbA1c. HbA1c is used to diagnose and monitor diabetes.

Previous large-scale human genetic studies have demonstrated that HbA1c is influenced by genetic variants. Some variants are thought to influence the function, structure, and lifespan of the red blood itself (“erythrocytic variants”), while others are thought to influence blood glucose control (“glycemic variants”). This study aimed to identify additional variants influencing HbA1c levels, and investigate the extent to which variants affecting this measurement independently of blood glucose concentration may lead to misdiagnosis, mistreatment, and human health disparities. What did the researchers do and find? We studied genetic variants and their association with HbA1c levels in almost 160,000 people from European, African, East Asian, and South Asian ancestry from 82 separate studies worldwide. We found 60 genetic variants influencing HbA1c, of which 42 variants were new. Of the 60 variants, we found 19 glycemic variants and 22 erythrocytic variants.

In approximately 33,000 people from 5 ancestry groups followed carefully over time, we found that the more glycemic variants a person had, the higher their risk to get diabetes (OR = 1.05 per HbA1c-raising allele, p = 3 × 10 −29 ). However, more erythrocytic variants did not lead to a higher risk of diabetes, meaning erythrocytic variants that lower HbA1c levels independently from glucose concentration could lead to missed diagnosis of diabetes.

). However, more erythrocytic variants did not lead to a higher risk of diabetes, meaning erythrocytic variants that lower HbA1c levels independently from glucose concentration could lead to missed diagnosis of diabetes. Next, we found that in everyone but those of African ancestry, those with more versus those with less of the 60 HbA1c genetic variants had a fairly small difference in HbA1c (about 0.2 units), while those of African ancestry had a larger difference (about 0.8 units, a fairly large number for this medical test).

This difference in African ancestry was explained by one erythrocytic variant on the X chromosome. This variant mutates the protein made by the gene “glucose-6-phosphate dehydrogenase” (G6PD), which can shorten RBC lifespan, and thus lower HbA1c levels, no matter the blood glucose level.

About 11% of people of African American ancestry carry at least one copy of this G6PD variant, while almost no one of any other ancestry does. We estimated that if we tested all Americans for diabetes using HbA1c, about 650,000 African Americans would be missed because of these genetically lowered HbA1c levels. What do these findings mean? We may want to investigate the benefits of screening for the G6PD genotype in specific communities or perform additional diagnostic tests to avoid health disparities between communities.

It will also be important to follow up with additional studies to check whether new standardized thresholds for diagnoses should be recommended for those that have this G6PD variant.

Citation: Wheeler E, Leong A, Liu C-T, Hivert M-F, Strawbridge RJ, Podmore C, et al. (2017) Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLoS Med 14(9): e1002383. https://doi.org/10.1371/journal.pmed.1002383 Academic Editor: Ed Gregg, Centers for Disease Control and Prevention, UNITED STATES Received: February 17, 2017; Accepted: August 3, 2017; Published: September 12, 2017 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability: The ancestry-specific and transethnic genome-wide meta-analysis summary statistics for association with HbA1c, and published data included in this study, are available to download from the MAGIC website, www.magicinvestigators.org/downloads. Uniform analysis plan(s) showing the QC and data analysis steps in detail are provided in the supporting information file S1 Analysis Plans. Funding: Please refer to the supporting information file S1 Financial Disclosure for full information with regard to funding and financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: We have read the journal's policy and the authors of this manuscript have the following competing interests: AYC is an employee of Merck, however all work for the manuscript was completed before the start of employment. CEE is a current employee of AstraZeneca. CLan receives a stipend as a specialty consulting editor for PLOS Medicine and serves on the journal's editorial board. EI is a scientific advisor for Precision Wellness, Cellink and Olink Proteomics for work unrelated to the present project. GKH declared institution support from Amgen, AstraZeneca, Cerenis, Ionis, Regeneron Pharmaceuticals, Inc. and Sanofi, Synageva. He has served as a consultant and received speaker fees from Aegerion, Amgen, Sanofi, Regeneron Pharmaceuticals, Inc., and Pfizer. IB and spouse own stock in GlaxoSmithKline and Incyte Corporation. JD declared grants from the National Heart, Lung, and Blood Institute (NHLBI) of the National Institute of Health (NIH) during the course of this study. JIR declared funding from NIH grants. MAN consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare among others. MBl receives speaker’s honoraria and/or compensation for participation in advisory boards from: Astra Zeneca, Bayer, Boehringer-Ingelheim, Lilly, Novo Nordisk, Novartis, MSD, Pfizer, Riemser and Sanofi. MIM was a member of the editorial board of PLOS Medicine at the time this manuscript was submitted. RAS is an employee and shareholder in GlaxoSmithKline. Abbreviations: ARIC, Atherosclerosis Risk in Communities Study; CVD, cardiovascular disease; EPIC-InterAct, European Prospective Investigation into Cancer and Nutrition InterAct project; FG, fasting glucose; FHS, Framingham Heart Study; GCTA, Genome-wide Complex Trait Analysis; GS-E, genetic scores of erythrocytic variants; GS-G, genetics scores of glycemic variants; G6PD, glucose-6-phosphate dehydrogenase; GWAS, genome-wide association studies; Hb, hemoglobin level; HbA1c, glycated hemoglobin; JHS, Jackson Heart Study; LD, linkage disequilibrium; LOLIPOP, London Life Sciences Prospective Population Study; MAF, minor allele frequency; MANTRA, Meta-Analysis of Transethnic Association; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume; MESA, Multiethnic Study of Atherosclerosis; NGSP, National Glycohemoglobin Standardization Program; NHANES, National Health and Nutrition Examination Survey; OR, odds ratio; QC, quality control; RBC, red blood cell; SCHS, Singapore Chinese Health Study; SiMES, Singapore Malay Eye Study; SP2, Singapore Prospective Study; TAICHI, Taiwan-Metabochip Study for Cardiovascular Disease; T2D, type 2 diabetes; WGHS, Women’s Genome Health Study; 2hrGlu, 2hr glucose

Introduction Type 2 diabetes (T2D) is a health scourge rising unabated worldwide, escaping all past and current control measures, in part because only half of prevalent T2D worldwide has been clinically diagnosed [1]. Glycated hemoglobin (HbA1c) is an accepted diagnostic test for T2D and a principal clinical measure of glycemic control in individuals with diabetes. T2D arises from the environment interacting with genetics. Studies investigating genetic contributions to HbA1c in individuals of European [2–4] and Asian ancestry [5–7] have identified 18 loci influencing HbA1c through glycemic and nonglycemic pathways, the latter primarily reflecting erythrocytic biology. Alterations in HbA1c that are due to genetic variation acting through nonglycemic pathways may not accurately reflect ambient glycemia or T2D risk and could affect the validity of HbA1c as a diagnostic test and measure of glycemic control in some individuals or populations. Some genetic variants (e.g., the sickle cell variant HbS) that vary in frequency across ancestries can interfere with the accuracy of certain assays [8]. Further, certain hematologic conditions associated with shortened erythrocyte lifespan (e.g., hemolytic anemias) lower HbA1c values irrespective of the assay performed. HbA1c values in such patients may no longer accurately reflect ambient glycemia [9]. Epidemiologic studies have reported ethnic differences in HbA1c, with African Americans having, on average, higher HbA1c than European ancestry Americans [10]. While these differences are largely due to demographic and metabolic factors [11,12], genetic factors associated with hematologic conditions that impact erythrocyte turnover may confound the relationship between HbA1c and glycemia, causing misclassification of T2D diagnosis [8,13]. This study had 3 aims, the first was to expand genetic discovery efforts to larger sample sizes, including populations of ancestries not previously studied, to uncover novel loci influencing HbA1c and that might capture a greater fraction of the variability in HbA1c. Second, as done in previous studies, we aimed to classify the variants as acting through glycemic or erythrocytic biology. Thirdly, as erythrocytic variants may influence HbA1c due to effects on the red blood cell (RBC), we wished to explore whether this might lead to HbA1c values that no longer reflected ambient glycemia. To do this, we specifically tested the hypothesis that HbA1c-associated genetic variants, particularly those that act through erythrocytic pathways, influence the performance of HbA1c for diabetes risk prediction and diabetes diagnoses (S1 Fig).

Discussion In a very large transancestry GWAS of HbA1c, we identified 42 novel and 18 known genetic variants associated with HbA1c, explaining 4%–14% of the trait variance. Genetic variants influencing HbA1c through erythrocytic pathways did not predict future T2D, and adjusting for their contribution to HbA1c led to a moderate misclassification of T2D by adjusted HbA1c. Notably, we detected strong ancestral differences in the contribution of genetic variants to HbA1c that substantially altered the performance of HbA1c as a diagnostic test for T2D in African Americans compared with Europeans and East Asians. Our findings elucidate the contribution of common genetic variants to the genetic architecture of HbA1c and identify an important interface of modern human genetics with clinical and public health. In people of European and Asian ancestry, we found multiple genetic loci with small-to-modest effects, whereas, in African American ancestry, the genetic architecture was dominated by a single variant at G6PD (G202A). This variant was responsible for 0.81%-units HbA1c difference in men and 0.68%-units in homozygous TT women, corresponding to adjusted T2D diagnosis thresholds of 5.7 (95% CI 5.5–5.8) and 5.8 (95% CI 5.5–6.1), respectively. To meet the NGSP certification criteria, laboratory-reported HbA1c ought to be within 6% of the standard reference laboratory mean values (e.g., 6.5%-units ± 0.4%-units) for the majority of patient samples [14]. The limits of acceptable analytic variability were exceeded by this G6PD variant. This may also have important implications for the management of diabetes, with carriers of the HbA1c-lowering G6PD allele requiring adjusted (lower) HbA1c treatment targets. Previous epidemiologic studies have shown that a 1%-unit increase in HbA1c in individuals without T2D was associated with a more than 2-fold increase in risk of future T2D and a 20%–50% increased risk of cardiovascular disease (CVD) [37]. HbA1c ≥ 6.5% compared to those with HbA1c < 5.7% had a higher risk of kidney disease and retinopathy [38]. Only one other African-specific variant, rs11954649, located in the intron of SOX30, reached genome-wide significance in African Americans. However, this variant had a relatively small effect size (β = 0.12 per G allele) on HbA1c and was not classified as glycemic or erythrocytic. The variant was thus not included in the genetic scores and, unlike G6PD, the causal transcript and biological mechanism through which it influences HbA1c remains unclear. Future studies on larger sample sizes of ethnic minorities can focus on dissecting the genomic and biological implications of novel HbA1c-related variants. When considering all ethnicities, both glycemic and erythrocytic variants influence measured HbA1c; yet, only glycemic variants were associated with increased T2D risk (5% per allele) over a decade-long follow-up period. For an equivalent HbA1c, individuals carrying more erythrocytic HbA1c-raising alleles, or fewer HbA1c-lowering alleles, had lower incident T2D risk (−5% per allele), implying that for the same HbA1c level those individuals with the greater number of erythrocytic HbA1c- raising alleles have artificially higher HbA1c values that do not reflect ambient glycemia. Thus, the influence of erythrocytic HbA1c variants may partly explain why some individuals with the same HbA1c may have different risks of future T2D. We note that the estimates of variance explained by genetic variants underlying HbA1c were comparable with those for FG in Europeans (4.8%) [17]. Our results on the reclassification of prevalent T2D were consistent with previous reports indicating that a diagnostic cut-point at 6.5% for HbA1c classified fewer cases than FG ≥ 7 mmol/L [39,40]. Adjusting for the contribution of erythrocytic variants correctly reclassified approximately 1 in 5 individuals with FG < 7 mmol/L who were incorrectly diagnosed as having T2D (HbA1c ≥ 6.5%) to having HbA1c < 6.5%, suggesting that a subset of these individuals may have artificially elevated HbA1c due to the contribution of the erythrocytic variants. Though the specific G6PD variant we identified is monomorphic in Asian and European ancestry, other diverse G6PD variant alleles have reached polymorphic frequencies in malarial endemic regions around the world [35]. G6PD deficiency is unlikely to be identified through routine screening for anemia in healthy individuals, and universal screening for G6PD deficiency is not currently recommended worldwide [32,41]. Testing for G6PD deficiency is only performed on individuals before being prescribed specific drugs, such an antimalarial medications, or in patients with clinical presentation consistent with the disease; for instance, prolonged neonatal jaundice or hemolytic crisis following exposure to specific drugs, infections, or foods [32]. Thus, asymptomatic individuals often remain unaware of their G6PD genotype status and screening for the G6PD genotype before using HbA1c to diagnose T2D may be warranted in populations or ethnic groups where G6PD deficiency is common. Similarly, a recent study identified a significant hemolytic risk in women heterozygous for the G6PD Mahidol variant when treated with primaquine who were not detected by current screening methods [42]. Rarer hematologic conditions that reduce erythrocyte lifespan, e.g., hereditary hemolytic anemias, hereditary spherocytosis, and hemoglobinopathies have also been shown to lower HbA1c [9,43], and should also be considered before using HbA1c in these patients. We recommend additional testing using direct glucose measurements (e.g., FG or oral glucose tolerance testing) or other erythrocyte-independent methods to diagnose T2D. This supports the use of a combination of HbA1c and FG to confirm T2D diagnosis in routine screening [44]. Future studies could also explore G6PD effect modification by HbA1c assay type. Further studies in large cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of genetic variants that have yet to be classified. Similarly, future analyses conditional on RBC distribution width or reticulocyte count will help to better understand the effects of erythrocytic HbA1c-associated variants, should such data become available. The relatively small sample size for Asian and African ancestry cohorts limited the discovery of ancestry-specific genetic variants, beyond the African-specific G6PD variant, and could explain why GS-G was associated with higher incident T2D in European, but not other, ancestries. This underscores the need to extend such studies to non-European populations, particularly those with a high prevalence of some hemoglobinopathies or iron deficiency anemias. Epidemiologic studies have reported higher mean HbA1c in African Americans compared to European ancestry individuals in the US [45,46]. While our genetic findings could not determine whether this difference was completely attributable to relative hyperglycemia, accounting for the effect of the G6PD variant that lowers HbA1c only in African Americans would further widen this disparity. In conclusion, HbA1c remains an appropriate diagnostic test for the majority of people of diverse genetic backgrounds, having lower intraindividual variability compared to FG with the ability to capture chronic hyperglycemia, and robust associations with T2D-related complications [37]. Nevertheless, nonglycemic lowering of measured HbA1c for 1 in 10 African American men who carry this G6PD variant, and 1 in a 100 African American women homozygous for this variant, could amount to 0.65 (95% CI 0.56–0.74) million African American adults in the US with a missed T2D diagnosis using HbA1c as a screening test. We therefore recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common, and screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency. This work supports a role for a precision medicine application to reduce race-ethnic health disparities using HbA1c genetics to improve T2D diagnosis and prediction and to inform screening strategies for T2D across the African continent where the prevalence of the G6PD variant can reach 20%.

Acknowledgments Published data on glycemic traits were contributed by MAGIC investigators and have been downloaded from www.magicinvestigators.org. The ARIC authors thank the staff and participants of the ARIC study for their important contributions. The CAGE authors thank the participants who made this work possible and who gave it value. The CAGE authors would like to thank Drs. Toshio Ogihara, Yukio Yamori, Akihiro Fujioka, Chikanori Makibayashi, Sekiharu Katsuya, Ken Sugimoto, Kei Kamide, and Ryuichi Morishita and the many physicians of the participating hospitals and medical institutions in Amagasaki Medical Association for their assistance in collecting the DNA samples and accompanying clinical information. The CROATIA authors would like to acknowledge the invaluable contributions of the Institute for Antropological Research, Zagreb, Croatia, the administrative team in Split, and the people of Vis and Korcula. The D.E.S.I.R. Study Group would like to acknowledge its members: B. Balkau, P. Ducimetière, E. Eschwège (INSERM U1018); F. Alhenc-Gelas (INSERM U367); A. Girault (CHU D’Angers); F. Fumeron, M. Marre, R Roussel (Bichat Hospital); F. Bonnet (CHU de Rennes); S. Cauchi, P. Froguel (CNRS UMR8090, Lille); Alençon, Angers, Blois, Caen, Chateauroux, Chartres, Cholet, Le Mans, Orléans, Tours (Centres d’Examens de Santé); J. Cogneau (Institute de Recherche Médecine Générale); General practitioners of the region; C. Born, E. Caces, M. Cailleau, O. Lantieri, J. G. Moreau, F. Rakotozafy, J. Tichet, and S. Vol. (Institute inter-Regional pour la Santé). Analyses contributed by FHS/MGH/BU reflect intellectual input and resource development from the FHS investigators participating in the SNP Health Association Resource (SHARe) project. The DIAGEN authors are grateful to all of the patients who cooperated in this study and to their referring physicians and diabetologists in Saxony. The InterAct authors thank all EPIC participants and staff for their contribution to the study. The InterAct authors also thank staff from the Technical, Field Epidemiology and Data Functional Group Teams of the MRC Epidemiology Unit in Cambridge, UK, for carrying out sample preparation, DNA provision and QC, genotyping and data-handling work. The JHS authors thank the JHS participants and staff for their contributions to this work. The KORA authors are grateful to all members of the Helmholtz Zentrum München, the field staff in Augsburg, and the Augsburg registry team who were involved in the planning, organization, and conduct of the KORA studies. In addition, the authors express their appreciation to all study participants. The Leipzig-adult authors thank all those who participated in the study. The Leipzig-kid authors are grateful to all the patients and families for contributing to the study. They also highly appreciate the support of the Obesity Team and Auxo Team of the Leipzig University Children’s Hospital for management of the patients and to the Pediatric Research Center Lab Team for support with DNA banking. The Lifelines authors thank Behrooz Alizadeh, Annemieke Boesjes, Marcel Bruinenberg, Noortje Festen, Pim van der Harst, Ilja Nolte, Lude Franke, Mitra Valimohammadi for their help in creating the GWAS database, and Rob Bieringa, Joost Keers, René Oostergo, Rosalie Visser, Judith Vonk for their work related to data collection and validation. The Lifelines authors are also grateful to the study participants, the staff from the Lifelines Cohort Study and the contributing research centers delivering data to Lifelines and the participating general practitioners and pharmacists. The LOLIPOP authors thank the participants and research staff who made the study possible. The LURIC authors extend their appreciation to the participants of the LURIC study and thank the LURIC study team who were either temporarily or permanently involved in patient recruitment as well as sample and data handling, in addition to the laboratory staff at the Ludwigshafen General Hospital and the Universities of Freiburg and Ulm, Germany. The MESA authors thank the investigators and participants of the MESA study for their significant and ongoing contributions. The NHAPC authors are grateful to all participants of the NHAPC and also thank their colleagues at the laboratory and local CDC staffs of Beijing and Shanghai for their assistance with data collection. The NSHD authors are very grateful to the members of NSHD birth cohort for their continuing interest and participation in the study. The NSHD authors would also like to acknowledge the Swallow group, UCL, who performed the DNA extractions (Rousseau, et al 2006). DOI: 10.1111/j.1469-1809.2006.00250. The ORCADES authors would like to acknowledge the invaluable contributions of Lorraine Anderson and the research nurses in Orkney, the administrative team in Edinburgh, and the people of Orkney. The SardiNIA authors are grateful to all the volunteers who generously participated in the study, as well as the Lanusei team for their continuous work. The SCHS-CHD (cases and controls) authors thank Siew-Hong Low of the National University of Singapore for supervising the field work of the SCHS and the Ministry of Health in Singapore for assistance with the identification of AMI cases via database linkages. They also acknowledge the founding, longstanding principal investigator of the SCHS, Mimi C. Yu. The SHIP authors are grateful to the contribution of Ravi Kumar Chilukoti, Florian Ernst, Anja Hoffmann, and Astrid Petersmann in generating the SNP data. The contributions of the SHIP staff and participants are gratefully acknowledged. The Sorbs authors thank all those who participated in the study. The Sorbs authors would also like to thank Knut Krohn (Microarray Core Facility, University of Leipzig, Institute of Pharmacology) for the genotyping support and Joachim Thiery (Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig) for clinical chemistry services. The Sorbs authors thank Inga Prokopenko (Imperial College London, UK) and Anubha Mahajan (WTCHG, University of Oxford, UK) for statistical analyses. The TRAILS authors are grateful to all adolescents who participated in this research and to everyone who worked on this project and made it possible. The Twingene authors thank Tomas Axelsson, Ann-Christine Wiman, and Caisa Pöntinen at the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se) for their excellent assistance with genotyping. The TWT2D authors thank the Taiwan Diabetes Consortium for phenotypes assessment and the National Center for Genome Medicine of the National Core Facility Program for Biotechnology, Ministry of Science and Technology for the technical/bioinformatics support. Disclaimer: The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or NHSBT. Members of the EPIC-CVD Consortium Adam Butterworth, John Danesh. Members of the EPIC-InterAct Consortium Claudia Langenberg, Robert A. Scott, Stephen J. Sharp, Nita G. Forouhi, Nicola D. Kerrison, Matt Sims, Debora ME. Lucarelli, Inês Barroso, Panos Deloukas, Mark I. McCarthy, Antonio Agudo, Beverley Balkau, Aurelio Barricarte, Heiner Boeing, Miren Dorronsoro, Paul W. Franks, Sara Grioni, Rudolf Kaaks, Timothy J. Key, Carmen Navarro, Peter M. Nilsson, Kim Overvad, Domenico Palli, Salvatore Panico, J. Ramón Quirós, Olov Rolandsson, Carlotta Sacerdote, María, José Sánchez, Nadia Slimani, Annemieke MW. Spijkerman, Anne Tjonneland, Rosario Tumino, Yvonne T. van der Schouw, Elio Riboli, Nicholas J. Wareham. Members of the Lifelines Cohort Study Behrooz Z Alizadeh, H Marike Boezen, Lude Franke, Pim van der Harst, Gerjan Navis, Marianne Rots, Harold Snieder, Morris Swertz, Bruce HR Wolffenbuttel, Cisca Wijmenga.