Cholesterol efflux capacity, HDL cholesterol, and risk of coronary heart disease: a nested case-control study in men
Bibliographic record
Abstract
The capacity of HDLs to accept cholesterol effluxing from macrophages has been proposed as a new biomarker of HDLs' anti-atherogenic function. Whether cholesterol efflux capacity (CEC) is independent of HDL cholesterol (HDL-C) as a biomarker for coronary heart disease (CHD) risk in a generally healthy primary-prevention population remains unanswered. Therefore, in this nested case-control study, we simultaneously assessed CEC (using J774 cells) and plasma HDL-C levels as predictors of CHD in healthy middle-aged and older men not receiving treatment affecting blood lipid concentrations. We used risk-set sampling of participants free of disease at baseline from the Health Professionals Follow-Up Study, and matched cases (n = 701) to controls 1:1 for age, smoking, and blood sampling date. We applied conditional logistic regression models to calculate the multivariable relative risk and 95% CIs of CHD over 16 years of follow-up. CEC and HDL-C were correlated ( r = 0.50, P < 0.0001). The risk (95% CI) of CHD per one SD higher CEC was 0.82 (0.71–0.96), but completely attenuated to 1.08 (0.85–1.37) with HDL-C in the model. The association per one SD between HDL-C and CHD (0.66; 0.58–0.76) was essentially unchanged (0.68; 0.53–0.88) after adjustment for CEC. These findings indicate that CEC's ability to predict CHD may not be independent of HDL-C in a cohort of generally healthy men.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".