Value of HDL Cholesterol, Apolipoprotein A-I, Lipoprotein A-I, and Lipoprotein A-I/A-II in Prediction of Coronary Heart Disease
Bibliographic record
Abstract
OBJECTIVE: We have examined the association between the incidence of coronary heart disease (CHD) and plasma high density lipoprotein (HDL) cholesterol, apolipoprotein A-I (apoA-I), and 2 HDL fractions, lipoprotein A-I and lipoprotein A-I:A-II. METHODS AND RESULTS: These parameters were measured in subjects recruited in France and in Northern Ireland in the Prospective Epidemiological Study of Myocardial Infarction (PRIME) Study, a prospective cohort study. Among the subjects free of CHD on entry, 176 in France and 113 in Northern Ireland suffered an ischemic attack (CHD patients) during the 5-year follow-up, whereas 6612 French and 2172 Northern Irish men showed no CHD symptoms (CHD-free subjects). All 4 HDL parameter levels were lower in CHD patients than in CHD-free subjects. After the cohort was divided into quintiles based on the distribution of HDL parameter levels, a significant (P<0.0001) linear increase in relative risk was observed for each HDL parameter level. However, regression logistic analyses showed that apoA-I was the strongest predictor (more powerful than HDL cholesterol) and that lipoprotein A-I and lipoprotein A-I:A-II did not supplement apoA-I in predicting CHD. CONCLUSIONS: Among the parameters related to HDL, apoA-I appears to be the strongest independent risk factor.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".