Importance of Clinical, Laboratory, and Genetic Risk Factors for Incident CAD
Bibliographic record
Abstract
BACKGROUND: Prior work suggests modifiable cardiovascular risk factors (CRFs) account for 80% to 90% of the risk for incident myocardial infarction. The contributions of genetic and other novel CRFs have not been simultaneously assessed in contemporary data sets. METHODS: In the United Kingdom Biobank, CRFs were identified and Cox proportional hazards models with traditional CRFs (hypertension, diabetes, dyslipidemia, waist-to-hip ratio, diet, exercise, alcohol, and socioeconomic deprivation) and contemporary/genetic CRFs (Lp(a) [lipoprotein(a)], hsCRP [high-sensitivity C-reactive protein], familial hypercholesterolemia variants, and polygenic risk score for coronary artery disease) were constructed for coronary artery disease. Coronary artery disease was defined as a first-time myocardial infarction diagnosis or coronary revascularization. R 2 was calculated for each model, and the percent contribution of each individual CRF was calculated by the R 2 percent decrease after its removal. RESULTS: Among 299 707 individuals, the mean (SD) age was 56.2 (8.1) years, and 166 533 (55.6%) were women. Over a median (interquartile range) follow-up of 11.0 (9.6–12.5) years, 17 409 (5.8%) of participants developed myocardial infarction. R 2 increased from the base model (R 2 , 0.021 [0.020–0.022]), to the clinical model (R 2 , 0.045 [0.043–0.046]), to the contemporary/genetic model (R 2 , 0.053 [0.052–0.055]). The most powerful individual CRFs were hypertension (R 2 loss, 15.2% [14.5–17.1]) and polygenic risk score for coronary artery disease (R 2 loss, 12.4% [10.8–13.3]), followed by dyslipidemia (R 2 loss, 3.4% [2.6–3.5]), diabetes (R 2 loss, 2.2% [1.5–2.0]), hsCRP (R 2 loss, 1.8% [1.5–2.0]), and Lp(a) (R 2 loss, 1.5% [1.2–1.8]). CONCLUSIONS: Novel CRFs like polygenic risk score for coronary artery disease, hsCRP, and Lp(a) have similar importance, comparable to traditional CRFs such as hypertension, dyslipidemia, and diabetes, for incident myocardial infarction, highlighting important identifiable residual risk factors.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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.
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".