Contribution of Lipoprotein(a) to polygenic risk of coronary artery disease: A prospective UK Biobank analysis
Bibliographic record
Abstract
Abstract Background Lipoprotein(a) (Lp[a]) is a highly atherogenic lipoprotein subfraction that may contribute to polygenic risk of coronary artery disease (CAD), but the extent of this contribution is unknown. Purpose Our objective was to estimate the contribution of Lp(a) to polygenic risk of CAD and to evaluate the respective contributions of Lp(a) and a CAD polygenic risk score (PRS) to CAD. Methods 372,385 UK Biobank participants of European ancestry free of CAD at baseline were included. Plasma Lp(a) levels were measured and a CAD-PRS was calculated using LDpred2. Over the median follow-up of 12.6 years, 13,538 participants had incident CAD (myocardial infarction, coronary artery bypass grafting or coronary angioplasty). Results The LPA region contribution to the CAD-PRS-mediated CAD risk was modest (6.5%). Lp(a) levels significantly increased the predictive performance of a CAD-PRS including age and sex in Cox regression (C-statistic 0.751 versus 0.746, difference = 0.005 (95% CI, 0.004-0.006). Compared to participants in the top CAD-PRS quintile with Lp(a) levels ≥125 nmol/L (CAD event rate 6.6%), the hazard ratio for incident CAD in participants in the bottom CAD-PRS quintile with Lp(a) levels <25 nmol/L was 0.18 (95% CI, 0.17-0.20, p=6.41e-241, CAD event rate 2.0%). Conclusions Compared to individuals with a high genetic risk of CAD (high CAD-PRS and high Lp(a) levels), those with a low genetic risk had an 82% lower CAD risk. These results highlight a substantial contribution of genetic risk factors to CAD and that accurate estimation of genetic risk of CAD should consider blood levels of Lp(a).
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".