Polygenic Contribution in Individuals With Early-Onset Coronary Artery Disease
Bibliographic record
Abstract
Background Despite evidence of high heritability, monogenic disorders are identified in a minor fraction of individuals with early-onset coronary artery disease (EOCAD). We hypothesized that some individuals with EOCAD carry a high number of common genetic risk variants, with a combined effect similar to Mendelian forms of coronary artery disease, such as familial hypercholesterolemia. Methods and Results To confirm the polygenic contribution to EOCAD (age of ≤40 years for men and ≤45 years for women), we calculated in 111 418 British participants from the UK Biobank cohort a genetic risk score (GRS) based on the presence of 182 independent variants associated with coronary artery disease (GRS182). Participants with a diagnosis of EOCAD who underwent a revascularization procedure (n=96) had a significantly higher GRS182 ( P =3.21×10 −9 ) than those without EOCAD. An increase of 1 SD in GRS182 corresponded to an odds ratio of 1.84 (1.52–2.24) for EOCAD. The prevalence of a polygenic contribution that increased EOCAD risk similar to what is observed in heterozygous familial hypercholesterolemia was estimated at 1 in 53. In a local cohort of individuals with EOCAD (n=30), GRS182 was significantly increased compared with UK Biobank controls ( P =0.001). Seven participants (23%) had a GRS182 corresponding to an estimated 2-fold increase in EOCAD risk; none had a rare mutation involved in monogenic dyslipidemia or EOCAD. Conclusions These results suggest a significant polygenic contribution in individuals presenting with EOCAD, which could be more prevalent than familial hypercholesterolemia. Determination of the polygenic risk component could be included in the diagnostic workup of patients with EOCAD.
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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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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.
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".