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PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study
Amand F. Schmidt, Daniel I. Swerdlow, Michael V. Holmes, Riyaz Patel, Zammy Fairhurst-Hunter, Donald M. Lyall, Fernando Pires Hartwig, Bernardo Lessa Horta, Elina Hyppönen, Christine Power, Max Moldovan, Erik Van Iperen, G. Kees Hovingh, Ilja Demuth, Kristina Norman, Elisabeth Steinhagen‐Thiessen, Juri Demuth, Lars Bertram, Tian Liu, Stefan Coassin, Johann Willeit, Stefan Kiechl, Karin Willeit, Dan Mason, John Wright, Richard Morris, Goya Wanamethee, Peter H. Whincup, Yoav Ben‐Shlomo, Stela McLachlan, Jackie F. Price, Mika Kivimäki, Catherine Welch, Adelaida Sánchez-Gálvez, Pedro Marques‐Vidal, Andrew Nicolaides, Andrie G. Panayiotou, N. Charlotte Onland‐Moret, Yvonne T. van der Schouw, Giuseppe Matullo, Giovanni Fiorito, Simonetta Guarrera, Carlotta Sacerdote, Nicholas J. Wareham, Claudia Langenberg, Robert A. Scott, Jian’an Luan, Martin Bobák, Sofia Malyutina, Andrzej Pająk, Růžena Kubínová, Abdonas Tamošiūnas, Hynek Pikhart, Lise Lotte N. Husemoen, Niels Grarup, Oluf Pedersen, Torben Hansen, Allan Linneberg, Kenneth Starup Simonsen, Jackie A. Cooper, Steve E. Humphries, Murray H. Brilliant, Terrie Kitchner, Håkon Håkonarson, David Carrell, Catherine A. McCarty, H. Lester Kirchner, Eric B. Larson, David R. Crosslin, Mariza de Andrade, Dan M. Roden, Joshua C. Denny, Cara L. Carty, Stephen Hancock, John Attia, Martin O’Donnell, Salim Yusuf, Michael Chong, Guillaume Paré, Pim van der Harst, M. Abdullah Said, Ruben N. Eppinga, Niek Verweij, Harold Snieder, Tim Christen, Dennis O. Mook‐Kanamori, Stefan Gustafsson, Lars Lind, Erik Ingelsson, Raha Pazoki, Oscar H. Franco, Albert Hofman, André G. Uitterlinden, Abbas Dehghan, Alexander Teumer, Sebastian E. Baumeister, Marcus Dörr, Markus M. Lerch, Uwe Völker, Henry Völzke, Joey Ward, Jill P. Pell, Daniel J. Smıth, Tom Meade, Anke H. Maitland‐van der Zee, E.V. Baranova, Robin Young, Ian Ford, Archie Campbell, Sandosh Padmanabhan, Michiel L. Bots, Diederick E. Grobbee, Philippe Froguel, Dorothée Thuillier, Beverley Balkau, Amélie Bonnefond, Bertrand Cariou, Melissa Smart, Yanchun Bao, Meena Kumari, Anubha Mahajan, Paul M. Ridker, Daniel I. Chasman, Alex P. Reiner, Leslie A. Lange, Marylyn D. Ritchie, Folkert W. Asselbergs, Juan-Pablo Casas, Brendan J. Keating, David Preiss, Aroon D. Hingorani, Naveed Sattar
Bibliographic record
VenueThe Lancet Diabetes & Endocrinology · 2016
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsPopulation Health Research Institute
FundersNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteSanofiUniversity College London Hospitals NHS Foundation TrustEconomic and Social Research CouncilLundbeckfondenNational Cancer InstituteBritish Heart FoundationUniversity College LondonAstraZenecaAmgenNational Institute for Health and Care ResearchMedical Research CouncilFoundation for the National Institutes of Health
KeywordsMedicineMendelian randomizationType 2 diabetesPCSK9Mendelian inheritanceDiabetes mellitusMEDLINEInternal medicineBioinformaticsGenetic variantsGeneticsEndocrinologyCholesterolGenotypeGene
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.071
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 head0.021
GPT teacher head0.266
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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
Citations391
Published2016
Admission routes1
Has abstractyes