Low-Density Lipoprotein Subfractions and the Long-Term Risk of Ischemic Heart Disease in Men
Bibliographic record
Abstract
OBJECTIVE: The objective of the present study was to investigate the association between large and small low-density lipoprotein (LDL) and long-term ischemic heart disease (IHD) risk in men of the Quebec Cardiovascular Study. METHODS AND RESULTS: Cholesterol levels in the large and small LDL subfractions (termed LDL-C> or =260A and LDL-C<255A, respectively) were estimated from polyacrylamide gradient gel electrophoresis of whole plasma in the cohort of 2072 men of the population-based Quebec Cardiovascular Study. All men were free of IHD at the baseline examination and followed-up for a period of 13 years, during which 262 first IHD events (coronary death, nonfatal myocardial infarction, and unstable angina pectoris) were recorded. Our study confirmed the strong and independent association between LDL-C<255A levels as a proxy of the small dense LDL phenotype and the risk of IHD in men, particularly over the first 7 years of follow-up. However, elevated LDL-C> or =260A levels (third versus first tertile) were not associated with an increased risk of IHD over the 13-year follow-up (RR=0.76; P=0.07). CONCLUSIONS: These results indicated that estimated cholesterol levels in the large LDL subfraction were not associated with an increased risk of IHD in men and that the cardiovascular risk attributable to variations in the LDL size phenotype was largely related to markers of a preferential accumulation of small dense LDL particles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".