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Record W3049150590 · doi:10.1161/circgen.119.002769

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus

2020· article· en· W3049150590 on OpenAlexfundno aff
Natalie R. van Zuydam, Claes Ladenvall, Benjamin F. Voight, Rona J. Strawbridge, Juan Fernández‐Tajes, Nigel W. Rayner, Neil R. Robertson, Anubha Mahajan, Efthymia Vlachopoulou, Anuj Goel, Marcus E. Kleber, Christopher P. Nelson, Lydia Coulter Kwee, Tõnu Esko, Evelin Mihailov, Reedik Mägi, Lili Milani, Krista Fischer, Stavroula Kanoni, Jitender Kumar, Ci Song, Jaana Hartiala, Nancy L. Pedersen, Markus Perola, Christian Gieger, Annette Peters, Liming Qu, Sara M. Willems, Alex S. F. Doney, Andrew D. Morris, Yan Zheng, Giorgio Sesti, Frank B. Hu, Lu Qi, Markku Laakso, Unnur Þorsteinsdóttir, Harald Grallert, Cornelia M. van Duijn, Muredach P. Reilly, Erik Ingelsson, Panos Deloukas, Sek Kathiresan, Andres Metspalu, Svati H. Shah, Juha Sinisalo, Veikko Salomaa, Anders Hamsten, Nilesh J. Samani, Winfried März, Stanley L. Hazen, Hugh Watkins, Danish Saleheen, Andrew P. Morris, Helen M. Colhoun, Leif Groop, Mark I. McCarthy, John Danesh, Jeanette Erdmann, Dongfeng Gu, Jaspal S. Kooner, Robert Roberts, Heribert Schunkert, Themistocles L. Assimes, Stefan Blankenberg, Bernhard O. Boehm, John C. Chambers, Robert Clarke, Rory Collins, George Dedoussis, Paul W. Franks, G. Kees Hovingh, Bong-Jo Kim, Terho Lehtimäki, Ruth McPherson, Markku S. Nieminen, Christopher O’Donnell, Samuli Ripatti, Manjinder S. Sandhu, Stefan Schreiber, Agneta Siegbahn, Cristen J. Willer, Pierre Zalloua, Mark Michael, Timo Kanninen, Barbara Thorand, Giuseppe Remuzzi, David B. Dunger, Angela C. Shore, Ulf Smith, Seppo Ylä‐Herttuala, Claudio Cobelli, Riccardo Bellazzi, Ele Ferrannini, Pirjo Nuutila, Paul McKeague, Birgit Steckel-Hamann, Li‐Ming Gan, Everson Nogoceke, Piero Tortoli, Bernd Jablonka, Mary-Julia Brosnan

Bibliographic record

VenueCirculation Genomic and Precision Medicine · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsnot available
FundersHelmholtz Zentrum MünchenAmity Institute of Biotechnology, Amity UniversitySchool of Medicine, Stanford UniversityTerveyden ja hyvinvoinnin laitosKarl-Franzens-Universität GrazMedical Research CouncilTartu ÜlikoolUppsala UniversitetSydäntutkimussäätiöStockholms Läns LandstingBritish Heart FoundationHáskóli ÍslandsKuopion Yliopistollinen SairaalaNational Institute of Diabetes and Digestive and Kidney DiseasesHelsingin ja Uudenmaan SairaanhoitopiiriKnut och Alice Wallenbergs StiftelseKing Abdulaziz UniversityKarolinska InstitutetYrjö Jahnssonin SäätiöFudan UniversityLunds UniversitetEesti TeadusagentuurPerelman School of Medicine, University of PennsylvaniaAmity UniversityJuho Vainion SäätiöAmerican Heart AssociationBroad InstituteDeutsches Zentrum für Herz-KreislaufforschungStanford Diabetes Research CenterOxford University Hospitals NHS Foundation TrustVetenskapsrådetTulane UniversityTorsten Söderbergs StiftelseGenomic HealthFoundation for Cardiovascular ResearchNational Heart, Lung, and Blood InstituteItä-Suomen YliopistoBrigham and Women's HospitalUniversity of LeicesterCleveland ClinicInstitute of GeneticsHjärt-LungfondenMedizinische Universität GrazUniversity of PennsylvaniaScience for Life LaboratoryQueen Mary University of LondonNational Institute for Health and Care ResearchMassachusetts General HospitalWellcome TrustUniversity of Southern CaliforniaStanford Cardiovascular Institute, School of Medicine, Stanford UniversityAstraZenecaEuropean CommissionHelsingin YliopistoErasmus Medisch CentrumStiftelsen för Strategisk ForskningUniversity of OxfordUniversity of Dundee
KeywordsCoronary artery diseaseCADType 2 diabetesInternal medicineDiabetes mellitusMedicineGenetic testingGenetic associationGenetic predispositionDiseaseGeneticsBiologySingle-nucleotide polymorphismEndocrinologyGenotypeGene

Abstract

fetched live from OpenAlex

BACKGROUND: Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D). METHODS: To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D). RESULTS: None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background. CONCLUSIONS: This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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
GenreEmpirical

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

Citations12
Published2020
Admission routes1
Has abstractyes

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