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Record W2755166087 · doi:10.1371/journal.pmed.1002383

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

2017· review· en· W2755166087 on OpenAlexaff
Eleanor Wheeler, Aaron Leong, Ching‐Ti Liu, Marie‐France Hivert, Rona J. Strawbridge, Clara Podmore, Man Li, Jie Yao, Xueling Sim, Jaeyoung Hong, Audrey Y. Chu, Weihua Zhang, Xu Wang, Peng Chen, Nisa M. Maruthur, Bianca Porneala, Stephen J. Sharp, Yucheng Jia, Edmond K. Kabagambe, Li-Ching Chang, Wei‐Min Chen, Cathy E. Elks, Daniel S. Evans, Qiao Fan, Franco Giulianini, Min Jin Go, Jouke‐Jan Hottenga, Yao Hu, Anne Jackson, Stavroula Kanoni, Young Jin Kim, Marcus E. Kleber, Claes Ladenvall, Cécile Lecœur, Sing-Hui Lim, Yingchang Lu, Anubha Mahajan, Carola Marzi, Mike A. Nalls, Pau Navarro, Ilja M. Nolte, Lynda M. Rose, Denis Rybin, Serena Sanna, Yuan Shi, Daniel O. Stram, Fumihiko Takeuchi, Peter J. van der Most, Jana V. van Vliet‐Ostaptchouk, Andrew Wong, Loïc Yengo, Wanting Zhao, Anuj Goel, Maria Teresa Martínez Larrad, Dörte Radke, Perttu Salo, Toshiko Tanaka, Erik P.A. van Iperen, Gonçalo R. Abecasis, Saima Afaq, Behrooz Z. Alizadeh, Alain G. Bertoni, Amélie Bonnefond, Yvonne Böttcher, Erwin P. Böttinger, Harry Campbell, Olga D. Carlson, Chien-Hsiun Chen, Yoon Shin Cho, W. Timothy Garvey, Christian Gieger, Mark O. Goodarzi, Harald Grallert, Anders Hamsten, Catharina A. Hartman, Christian Herder, Chao A. Hsiung, Jie Huang, Michiya Igase, Masato Isono, Tomohiro Katsuya, Chiea Chuen Khor, Wieland Kieß, Katsuhiko Kohara, Péter Kovács, Juyoung Lee, Wen‐Jane Lee, Benjamin Lehne, Huaixing Li, Jianjun Liu, Stéphane Lobbens, Jian’an Luan, Valeriya Lyssenko, Thomas Meitinger, Tetsuro Miki, Iva Miljkovic, Sanghoon Moon, Antonella Mulas, Gabriele Müller, Martina Müller‐Nurasyid, Ramaiah Nagaraja, Matthias Nauck, James S. Pankow, Ozren Polašek, Inga Prokopenko, Paula S. Ramos, Laura J. Rasmussen‐Torvik, Wolfgang Rathmann, Stephen S. Rich, Neil R. Robertson, Michael Roden, Ronan Roussel, Igor Rudan, Robert A. Scott, William R. Scott, Bengt Sennblad, David S. Siscovick, Konstantin Strauch, Liang Sun, Morris A. Swertz, Salman M. Tajuddin, Kent D. Taylor, Yik‐Ying Teo, Yih Chung Tham, Anke Tönjes, Nicholas J. Wareham, Gonneke Willemsen, Tom Wilsgaard, Aroon D. Hingorani, Josephine Egan, Luigi Ferrucci, G. Kees Hovingh, Antti Jula, Mika Kivimäki, Meena Kumari, Inger Njølstad, Manuel Serrano‐Ríos, Michael Stümvoll, Hugh Watkins, Tin Aung, Matthias Blüher, Michael Boehnke, Dorret I. Boomsma, Stefan R. Bornstein, John C. Chambers, Daniel I. Chasman, Yii‐Der Ida Chen, Yduan-Tsong Chen, Ching‐Yu Cheng, Francesco Cucca, Eco J. C. de Geus, Panos Deloukas, Michele K. Evans, Myriam Fornage, Yechiel Friedlander, Philippe Froguel, Leif Groop, Myron D. Gross, Tamara B. Harris, Caroline Hayward, Chew‐Kiat Heng, Erik Ingelsson, Norihiro Kato, Bong-Jo Kim, Woon-Puay Koh, Jaspal S. Kooner, Antje Körner, Diana Kuh, Johanna Kuusisto, Markku Laakso, Lin Xu, Ruth J. F. Loos, Patrik K. E. Magnusson, Winfried März, Mark I. McCarthy, Albertine J. Oldehinkel, Ken K. Ong, Nancy L. Pedersen, Mark A. Pereira, Annette Peters, Paul M. Ridker, Charumathi Sabanayagam, Michèle M. Sale, Danish Saleheen, Juha Saltevo, Peter E. H. Schwarz, Wayne H.-H. Sheu, Harold Snieder, Timothy D. Spector, Yasuharu Tabara, Jaakko Tuomilehto, Rob M. van Dam, James G. Wilson, James F. Wilson, Bruce H. R. Wolffenbuttel, Tien Yin Wong, Jer‐Yuarn Wu, Jian‐Min Yuan, Alan B. Zonderman, Nicole Soranzo, Xiuqing Guo, David J. Roberts, José C. Florez, Robert Sladek, Josée Dupuis, Andrew P. Morris, E Shyong Tai, Elizabeth Selvin, Jerome I. Rotter, Claudia Langenberg, Inês Barroso, James B. Meigs

Bibliographic record

VenuePLoS Medicine · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill UniversityCentre for Global Health Research
FundersMedical Research CouncilUniversität LeipzigCentre National de la Recherche ScientifiqueInstitut National de la Santé et de la Recherche MédicaleNational Institutes of HealthKidney Research UKNational Cancer InstituteBritish Heart FoundationIncyteNovo NordiskNational Institute for Health and Care ResearchRegeneron PharmaceuticalsSanofiGlaxoSmithKlineMichael J. Fox Foundation for Parkinson's ResearchAmgenNational Heart, Lung, and Blood InstitutePfizerAstraZenecaEli Lilly and Company
KeywordsGenome-wide association studyGlycemicType 2 diabetesBiologyGeneticsGenetic associationGlycated hemoglobinDiabetes mellitusMedicineInternal medicineGenotypeSingle-nucleotide polymorphismEndocrinologyGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.124
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.233
GPT teacher head0.428
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations441
Published2017
Admission routes1
Has abstractyes

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