A prospective, population-based study of low density lipoprotein particle size as a risk factor for ischemic heart disease in men.
Bibliographic record
Abstract
BACKGROUND: The current interpretation of the increased risk of ischemic heart disease (IHD) associated with reduced low density lipoprotein (LDL) particle size is based entirely on data derived from relatively small case-control studies, with a lack of evidence from large, prospective, population-based cohort data. OBJECTIVES: To investigate the association between LDL particle size and incident IHD on the basis of data from the entire population-based, prospective cohort of men from the Quebec Cardiovascular Study. PATIENTS AND METHODS: Analyses were conducted in a cohort of 2057 men who were all initially free of IHD, and who were followed up over a five-year period, during which 108 first IHD events (myocardial infarction, angina or coronary death) were recorded. LDL particle size was measured by nondenaturing gradient gel electrophoresis. RESULTS: Cox proportional hazards analysis indicated that the relationship between LDL particle size and the risk of future IHD events was not linear. Men with an LDL particle size less than 256.0 A had a significant 2.2-fold increase in the five-year rate of IHD (P<0.001) compared with men having an LDL particle size greater than 256.0 A. Multivariate and subgroup analyses indicated that small, dense LDL particles predicted the rate of IHD independent of LDL cholesterol, triglycerides, high density lipoprotein (HDL) cholesterol, apolipoprotein B and the total cholesterol to HDL cholesterol ratio. Finally, the magnitude of the increase in IHD risk attributed to lipid risk factors was modulated to a significant extent by variations in LDL particle size. CONCLUSIONS: The present study provides the first large scale, population-based, prospective evidence supporting the hypothesis that small, dense LDL particles may be associated with an increased risk of IHD. The results also suggest that information on LDL diameter may improve the ability to predict IHD risk accurately over traditional lipid variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".