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Meta-Analysis
. 2017 Sep 12;14(9):e1002383.
doi: 10.1371/journal.pmed.1002383. eCollection 2017 Sep.

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

Eleanor Wheeler  1 Aaron Leong  2   3 Ching-Ti Liu  4 Marie-France Hivert  5   6 Rona J Strawbridge  7   8 Clara Podmore  9   10 Man Li  11   12   13 Jie Yao  14 Xueling Sim  15 Jaeyoung Hong  4 Audrey Y Chu  16   17 Weihua Zhang  18   19 Xu Wang  20 Peng Chen  15   21   22   23 Nisa M Maruthur  11   24   25 Bianca C Porneala  2 Stephen J Sharp  9 Yucheng Jia  14 Edmond K Kabagambe  26 Li-Ching Chang  27 Wei-Min Chen  28 Cathy E Elks  9   29 Daniel S Evans  30 Qiao Fan  31 Franco Giulianini  17 Min Jin Go  32 Jouke-Jan Hottenga  33 Yao Hu  34 Anne U Jackson  35 Stavroula Kanoni  36 Young Jin Kim  32 Marcus E Kleber  37 Claes Ladenvall  38   39 Cecile Lecoeur  40 Sing-Hui Lim  41 Yingchang Lu  42   43 Anubha Mahajan  44 Carola Marzi  45   46 Mike A Nalls  47   48 Pau Navarro  49 Ilja M Nolte  50 Lynda M Rose  17 Denis V Rybin  4   51 Serena Sanna  52 Yuan Shi  41 Daniel O Stram  53 Fumihiko Takeuchi  54 Shu Pei Tan  41 Peter J van der Most  50 Jana V Van Vliet-Ostaptchouk  50   55 Andrew Wong  56 Loic Yengo  40 Wanting Zhao  41 Anuj Goel  44   57 Maria Teresa Martinez Larrad  58 Dörte Radke  59 Perttu Salo  60   61 Toshiko Tanaka  62 Erik P A van Iperen  63   64 Goncalo Abecasis  35 Saima Afaq  18 Behrooz Z Alizadeh  50 Alain G Bertoni  65 Amelie Bonnefond  40 Yvonne Böttcher  66 Erwin P Bottinger  42 Harry Campbell  67 Olga D Carlson  68 Chien-Hsiun Chen  27   69 Yoon Shin Cho  32   70 W Timothy Garvey  71 Christian Gieger  45 Mark O Goodarzi  72 Harald Grallert  45   46 Anders Hamsten  7   8 Catharina A Hartman  73 Christian Herder  74   75 Chao Agnes Hsiung  76 Jie Huang  77 Michiya Igase  78 Masato Isono  54 Tomohiro Katsuya  79   80 Chiea-Chuen Khor  81 Wieland Kiess  82   83 Katsuhiko Kohara  84 Peter Kovacs  66 Juyoung Lee  32 Wen-Jane Lee  85 Benjamin Lehne  18 Huaixing Li  34 Jianjun Liu  15   81 Stephane Lobbens  40 Jian'an Luan  9 Valeriya Lyssenko  39 Thomas Meitinger  86   87   88 Tetsuro Miki  78 Iva Miljkovic  89 Sanghoon Moon  32 Antonella Mulas  52 Gabriele Müller  90 Martina Müller-Nurasyid  91   92   93 Ramaiah Nagaraja  94 Matthias Nauck  95 James S Pankow  96 Ozren Polasek  97   98 Inga Prokopenko  44   99   100 Paula S Ramos  101 Laura Rasmussen-Torvik  102 Wolfgang Rathmann  75 Stephen S Rich  103 Neil R Robertson  99   104 Michael Roden  74   75   105 Ronan Roussel  106   107   108 Igor Rudan  109 Robert A Scott  9 William R Scott  18   19 Bengt Sennblad  7   8   110 David S Siscovick  111 Konstantin Strauch  91   112 Liang Sun  34 Morris Swertz  113 Salman M Tajuddin  114 Kent D Taylor  14 Yik-Ying Teo  15   20   81   115   116 Yih Chung Tham  41 Anke Tönjes  117 Nicholas J Wareham  9 Gonneke Willemsen  33 Tom Wilsgaard  118 Aroon D Hingorani  119 EPIC-CVD ConsortiumEPIC-InterAct ConsortiumLifelines Cohort StudyJosephine Egan  68 Luigi Ferrucci  68 G Kees Hovingh  120 Antti Jula  60 Mika Kivimaki  121 Meena Kumari  121   122 Inger Njølstad  118 Colin N A Palmer  123 Manuel Serrano Ríos  58 Michael Stumvoll  117 Hugh Watkins  44   57 Tin Aung  41   124   125   126 Matthias Blüher  117 Michael Boehnke  35 Dorret I Boomsma  33 Stefan R Bornstein  127 John C Chambers  18   19   128 Daniel I Chasman  17   129   130 Yii-Der Ida Chen  14 Yduan-Tsong Chen  27 Ching-Yu Cheng  41   124   125   126 Francesco Cucca  52   131 Eco J C de Geus  33 Panos Deloukas  36   132 Michele K Evans  114 Myriam Fornage  133 Yechiel Friedlander  134 Philippe Froguel  100   135 Leif Groop  39   136 Myron D Gross  137 Tamara B Harris  138 Caroline Hayward  139 Chew-Kiat Heng  140   141 Erik Ingelsson  142   143 Norihiro Kato  54 Bong-Jo Kim  32 Woon-Puay Koh  15   144 Jaspal S Kooner  19   128   145 Antje Körner  82   83 Diana Kuh  56 Johanna Kuusisto  146 Markku Laakso  146 Xu Lin  34 Yongmei Liu  147 Ruth J F Loos  42   43   148 Patrik K E Magnusson  149 Winfried März  37   150   151 Mark I McCarthy  99   104   152 Albertine J Oldehinkel  73 Ken K Ong  9 Nancy L Pedersen  149 Mark A Pereira  96 Annette Peters  45 Paul M Ridker  17   153 Charumathi Sabanayagam  41   124 Michele Sale  103 Danish Saleheen  154   155 Juha Saltevo  156 Peter Eh Schwarz  127 Wayne H H Sheu  157   158   159 Harold Snieder  50 Timothy D Spector  160 Yasuharu Tabara  161 Jaakko Tuomilehto  162   163   164   165 Rob M van Dam  15 James G Wilson  166 James F Wilson  49   67 Bruce H R Wolffenbuttel  55 Tien Yin Wong  41   124   125   126 Jer-Yuarn Wu  27   69 Jian-Min Yuan  89   167 Alan B Zonderman  168 Nicole Soranzo  1   169   170 Xiuqing Guo  14 David J Roberts  171   172 Jose C Florez  3   173   174 Robert Sladek  175 Josée Dupuis  4   16 Andrew P Morris  104   176 E-Shyong Tai  15   144   177 Elizabeth Selvin  11   24   25 Jerome I Rotter  14 Claudia Langenberg  9 Inês Barroso  1   178 James B Meigs  2   3   174
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Free PMC article
Meta-Analysis

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

Eleanor Wheeler et al. PLoS Med. .
Free PMC article

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.

Conflict of interest statement

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.

Figures

Fig 1
Fig 1. Manhattan plot of HbA1c associated variants.
Manhattan plot of the transethnic meta-analysis results in MANTRA. The dashed grey line indicates log10BF = 6. Grey and green points denote known/novel loci, respectively. The lead HbA1c-associated variants identified through the ancestry-specific/transethnic analyses are circled in purple (the G6PD variant was not included in the MANTRA analysis, but the locus on the X-chromosome is indicated in the figure). Lines joining the plot & SNP number denote known loci (black), novel loci (green), and loci with a secondary distinct signal (red). MANTRA, Meta-Analysis of Transethnic Association.
Fig 2
Fig 2. T2D prediction, glycemic genetic score.
Forest plot of association between glycemic genetic score with incident T2D over a decade-long follow-up period, by ancestry. MESA (European and Asian ancestry) and the G6PD variant (rs1050828) in ARIC (European and African American) were not included in the discovery GWAS analysis. Effect estimates were combined in a fixed effects meta-analysis. Overall effect estimate: 1.05, 95% CI 1.04–1.06, p = 2.5 × 10−29. ARIC, Atherosclerosis Risk in Communities Study; ES, Effect Size; FHS, Framingham Heart Study; GWAS, genome-wide association study; G6PD, glucose-6-phosphate dehydrogenase; I-Squared, Higgin's I-squared statistic, a measure of heterogeneity; MESA, Multiethnic Study of Atherosclerosis; SCHS, Singapore Chinese Health Study; T2D, type 2 diabetes.
Fig 3
Fig 3. T2D prediction, erythrocytic genetic score.
Forest plot of association between erythrocytic genetic score with incident T2D over a decade-long follow-up period, by ancestry. MESA (European and Asian ancestry) and the G6PD variant (rs1050828) in ARIC (European and African American) were not included in the discovery GWAS analysis. Effect estimates were combined in a fixed effects meta-analysis. Overall effect estimate: 1.00, 95% CI 0.99–1.01, p = 0.60. ARIC, Atherosclerosis Risk in Communities Study; ES, Effect Size, FHS, Framingham Heart Study; GWAS, genome-wide association study; G6PD, glucose-6-phosphate dehydrogenase; I-Squared, Higgin's I-squared statistic, a measure of heterogeneity; MESA, Multiethnic Study of Atherosclerosis; SCHS, Singapore Chinese Health Study; T2D, type 2 diabetes.
Fig 4
Fig 4. T2D prediction, erythrocytic genetic score adjusted for HbA1c as a binary variable.
Forest plot of association between erythrocytic genetic score with incident T2D over a decade-long follow-up period adjusted for HbA1c as a binary variable (≥5.7% versus <5.7%), by ancestry. HbA1c at baseline was not available in SCHS and was excluded from the meta-analysis. MESA (European and Asian ancestry) and the G6PD variant (rs1050828) in ARIC (European and African American) were not included in the discovery GWAS analysis. Effect estimates were combined in a fixed effects meta-analysis. Overall effect estimate: 0.95, 95% CI 0.94–0.96, p = 3.3 × 10−16. ARIC, Atherosclerosis Risk in Communities Study; ES, Effect Size; GWAS, genome-wide association study; FHS, Framingham Heart Study; G6PD, glucose-6-phosphate dehydrogenase; HbA1c, glycated hemoglobin; I-Squared, Higgin's I-squared statistic, a measure of heterogeneity; MESA, multiethnic study of atherosclerosis; SCHS, Singapore Chinese Health Study; T2D, type 2 diabetes.
Fig 5
Fig 5. Mean HbA1c of individuals at the bottom 5% and top 5% of the distribution of ancestry-specific genetic scores and rs1050828 by genotype.
The difference in measured HbA1c of individuals at the bottom 5% and top 5% of the distribution of an ancestry-specific additive GS composed of all 60 variants (GS-Total), and the equivalent calculation for an ancestry-specific GS composed of up to 20 erythrocytic variants (GS-E). Far right of the figure shows the mean HbA1c by genotype for chromosome X rs1050828. AA men, African American men; AA women, African American women; HbA1c, glycated hemoglobin; GS, genetic scores.

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