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Record W4386516282 · doi:10.1038/s41588-023-01462-3

GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification

2023· review· en· W4386516282 on OpenAlexaff
Vasiliki Lagou, Longda Jiang, Anna Ulrich, Liudmila Zudina, Karla Sofia Gutiérrez González, Zhanna Balkhiyarova, Alessia Faggian, Jared G. Maina, Shiqian Chen, Petar V. Todorov, Sodbo Sharapov, Alessia David, Letizia Marullo, Reedik Mägi, Roxana‐Maria Rujan, Emma Ahlqvist, Guðmar Þorleifsson, Ηe Gao, Εvangelos Εvangelou, Beben Benyamin, Robert A. Scott, Aaron Isaacs, Wei Zhao, Sara M. Willems, Toby Johnson, Christian Gieger, Harald Grallert, Christa Meisinger, Martina Müller‐Nurasyid, Rona J. Strawbridge, Anuj Goel, Denis Rybin, Eva Albrecht, Anne Jackson, Heather M. Stringham, Ivan R. Corrêa, Eric Farber‐Eger, Valgerður Steinthórsdóttir, André G. Uitterlinden, Patricia B. Munroe, Morris J. Brown, Julian Schmidberger, Oddgeir L. Holmen, Barbara Thorand, Kristian Hveem, Tom Wilsgaard, Karen L. Mohlke, Zhe Wang, Marcel den Hoed, Aleksey Shmeliov, Ruth J. F. Loos, Wolfgang Kratzer, Mark Martin Haenle, Wolfgang Köenig, Bernhard O. Boehm, Tricia Tan, Alejandra Tomás, Victoria Salem, Inês Barroso, Jaakko Tuomilehto, Michael Boehnke, Jose C. Florez, Anders Hamsten, Hugh Watkins, Inger Njølstad, H.‐Erich Wichmann, Mark J. Caulfield, Kay‐Tee Khaw, Cornelia M. van Duijn, Albert Hofman, Nicholas J. Wareham, Claudia Langenberg, John B. Whitfield, Nicholas G. Martin, Grant W. Montgomery, Chiara Scapoli, Ioanna Tzoulaki, Paul Elliott, Unnur Þorsteinsdóttir, Kāri Stefánsson, Evan L. Brittain, Mark I. McCarthy, Philippe Froguel, Patrick M. Sexton, Denise Wootten, Leif Groop, Josée Dupuis, James B. Meigs, Giuseppe Deganutti, Ayşe Demirkan, Tune H. Pers, Christopher A. Reynolds, Yurii S. Aulchenko, Marika Kaakinen, Ben Jones, Inga Prokopenko

Bibliographic record

VenueNature Genetics · 2023
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsMcGill University
FundersHelmholtz Zentrum MünchenLee Kong Chian School of Medicine, Nanyang Technological UniversityCARIM School for Cardiovascular Diseases, Universiteit MaastrichtBarts and The London School of Medicine and DentistryNational Health and Medical Research CouncilNational Institute of Diabetes and Digestive and Kidney DiseasesWorld Cancer Research FundUniversity of IoanninaUK Dementia Research InstituteH. Lundbeck A/SNovo Nordisk Foundation Center for Basic Metabolic ResearchMedical Research CouncilTartu ÜlikoolUppsala UniversitetRussian Science FoundationLomonosov Moscow State UniversityLudwig-Maximilians-Universität MünchenCentre National de la Recherche ScientifiqueUniversité de LilleKing Abdulaziz UniversityUniversity of QueenslandKarolinska InstitutetUniversiteit MaastrichtKU LeuvenHorizon 2020 Framework ProgrammeVetenskapsrådetDeutsches Zentrum für Herz-KreislaufforschungUniversità degli Studi di TrentoNovo NordiskAgence Nationale de la RechercheHjärt-LungfondenLundbeckfondenUniversity of South AustraliaEuropean Foundation for the Study of DiabetesUniversity of GlasgowWorld Cancer Research Fund InternationalErasmus Medisch CentrumSchool of Public Health, Imperial College LondonUniversity of OxfordBiotechnology and Biological Sciences Research CouncilNanyang Technological UniversityUniversität UlmSociety for EndocrinologyBritish Heart FoundationBarts CharityMassachusetts General HospitalUK Research and InnovationLunds UniversitetVanderbilt Institute for Clinical and Translational ResearchEuropean Genomic Institute for DiabetesUniversitetet i TromsøImperial College LondonNovo Nordisk FondenUniversità degli Studi di FerraraSouth Australian Health and Medical Research InstituteNorges Teknisk-Naturvitenskapelige UniversitetAlzheimer's SocietyDiabetes UKResearch EnglandEngineering and Physical Sciences Research CouncilEuropean CommissionHelsingin YliopistoTechnische Universität MünchenUniversity of ExeterUniversity of SurreyAmgenRoyal SocietyUniversity of North Carolina at Chapel HillScience for Life LaboratoryQueen Mary University of LondonVlaamse regeringNational Institute for Health and Care ResearchMindich Child Health and Development Institute, Icahn School of Medicine at Mount SinaiCoventry UniversityWellcome TrustVanderbilt University
KeywordsBiologyGenome-wide association studyPathophysiologyDiabetes mellitusRisk stratificationComputational biologyStratification (seeds)BioinformaticsInternal medicineEndocrinologyGeneticsMedicineSingle-nucleotide polymorphismGeneGenotype

Abstract

fetched live from OpenAlex

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.048
GPT teacher head0.371
Teacher spread0.323 · 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 designOther design
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".

Quick stats

Citations102
Published2023
Admission routes1
Has abstractyes

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