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Record W4404106641 · doi:10.1001/jamacardio.2024.3738

Distinct Genetic Risk Profile in Aortic Stenosis Compared With Coronary Artery Disease

2024· letter· en· W4404106641 on OpenAlexaff
Teresa Trenkwalder, Carlo Maj, Baravan Al‐Kassou, Radosław Dębiec, S. Doppler, Muntaser D. Musameh, Christopher P. Nelson, Pouria Dasmeh, Sandeep Grover, Katharina Knoll, Joonas Naamanka, Ify Mordi, Peter S. Braund, Martina Dreßen, Harald Lahm, Felix Wirth, Stephan Baldus, Malte Kelm, Moritz von Scheidt, Johannes Krefting, David Ellinghaus, Aeron M Small, Gina M. Peloso, Pradeep Natarajan, George Thanassoulis, James C. Engert, Line Dufresne, Andre Franke, Siegfried Görg, Matthias Laudes, Ulrike Nowak-Göttl, Mariliis Vaht, Andres Metspalu, Monika Stoll, Klaus Peter Berger, Costanza Pellegrini, Adnan Kastrati, Christian Hengstenberg, Chim C. Lang, Thorsten Kessler, Iiris Hovatta, Georg Nickenig, Markus M. Nöthen, Markus Krane, Heribert Schunkert, Johannes Schumacher, Mart Kals, Anu Reigo, Maris Teder‐Laving, Jan Gehlen, Tom R. Webb, Ann-Sophie Giel, Laura L. Koebbe, Nina Feirer, Maximilian Billmann, Sundar Srinivasan, Sebastian Zimmer, Ling Li, Chuhua Yang, Oleg Borisov, Matti Adam, Verena Veulemans, Michael Joner, Erion Xhepa

Bibliographic record

VenueJAMA Cardiology · 2024
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersMedical Research CouncilBritish Heart Foundation
KeywordsMedicineGenome-wide association studyCoronary artery diseaseBiobankInternal medicineCardiologyBioinformaticsGeneticsSingle-nucleotide polymorphismGenotypeBiology

Abstract

fetched live from OpenAlex

Importance: Aortic stenosis (AS) and coronary artery disease (CAD) frequently coexist. However, it is unknown which genetic and cardiovascular risk factors might be AS-specific and which could be shared between AS and CAD. Objective: To identify genetic risk loci and cardiovascular risk factors with AS-specific associations. Design, Setting, and Participants: This was a genomewide association study (GWAS) of AS adjusted for CAD with participants from the European Consortium for the Genetics of Aortic Stenosis (EGAS) (recruited 2000-2020), UK Biobank (recruited 2006-2010), Estonian Biobank (recruited 1997-2019), and FinnGen (recruited 1964-2019). EGAS participants were collected from 7 sites across Europe. All participants were of European ancestry, and information on comorbid CAD was available for all participants. Follow-up analyses with GWAS data on cardiovascular traits and tissue transcriptome data were also performed. Data were analyzed from October 2022 to July 2023. Exposures: Genetic variants. Main Outcomes and Measures: Cardiovascular traits associated with AS adjusted for CAD. Replication was performed in 2 independent AS GWAS cohorts. Results: A total of 18 792 participants with AS and 434 249 control participants were included in this GWAS adjusted for CAD. The analysis found 17 AS risk loci, including 5 loci with novel and independently replicated associations (RNF114A, AFAP1, PDGFRA, ADAMTS7, HAO1). Of all 17 associated loci, 11 were associated with risk specifically for AS and were not associated with CAD (ALPL, PALMD, PRRX1, RNF144A, MECOM, AFAP1, PDGFRA, IL6, TPCN2, NLRP6, HAO1). Concordantly, this study revealed only a moderate genetic correlation of 0.15 (SE, 0.05) between AS and CAD (P = 1.60 × 10-3). Mendelian randomization revealed that serum phosphate was an AS-specific risk factor that was absent in CAD (AS: odds ratio [OR], 1.20; 95% CI, 1.11-1.31; P = 1.27 × 10-5; CAD: OR, 0.97; 95% CI 0.94-1.00; P = .04). Mendelian randomization also found that blood pressure, body mass index, and cholesterol metabolism had substantially lesser associations with AS compared with CAD. Pathway and transcriptome enrichment analyses revealed biological processes and tissues relevant for AS development. Conclusions and Relevance: This GWAS adjusted for CAD found a distinct genetic risk profile for AS at the single-marker and polygenic level. These findings provide new targets for future AS research.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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