Polygenic modulation of lipoprotein(a)-associated cardiovascular risk
Bibliographic record
Abstract
ABSTRACT Aims Elevated levels of lipoprotein(a) are one of the strongest inherited risk factors for coronary artery disease (CAD). However, there is variability in cardiovascular risk among individuals with elevated lipoprotein(a). The sources of this variability are incompletely understood. We assessed the effects of a genomic risk score (GRS) for CAD on risk of myocardial infarction among individuals with elevated lipoprotein(a). Methods We calculated CAD GRSs for 408,896 individuals of British white ancestry from the UK Biobank using 6.27 million common genetic variants. Lipoprotein(a) levels were measured in 310,020 individuals. The prevalence and risk of myocardial infarction versus CAD GRS percentiles were compared for individuals with and without elevated lipoprotein(a) defined as ≥120 or 168 nmol/L (≈50 or 70 mg/dL, respectively). Results Individuals with elevated lipoprotein(a) displayed significantly greater CAD GRSs than individuals without elevated lipoprotein(a), which was largely dependent on the influence of genetic variants within or near the LPA gene. Continuous levels of CAD GRS percentile were significantly associated with risk of myocardial infarction for individuals with elevated lipoprotein(a). Notably, the risk of myocardial infarction for males with elevated lipoprotein(a) levels, but a CAD GRS percentile in the lower quintile (<20 th percentile), was less than the overall risk of myocardial infarction for males with non-elevated lipoprotein(a) levels (hazard ratio [95% CI]: 0.79 [0.64-0.97], p=0.02). Similar results were observed for females. Conclusion These data suggest that CAD genomic scores influence cardiovascular risk among individuals with elevated lipoprotein(a) and may aid in identifying candidates for preventive therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".