Interplay of Atherogenic Particle Number and Particle Size and the Risk of Coronary Heart Disease
Bibliographic record
Abstract
BACKGROUND: We examined the interplay of apolipoprotein B (apoB) and LDL particle size, approximated by the LDL-cholesterol (LDL-C)/apoB ratio, on the risk of new-onset coronary heart disease (CHD). METHODS: Participants without cardiovascular disease from the UK Biobank (UKB; n = 308 182), the Women's Health Study (WHS; n = 26 204), and the Framingham Heart Study (FHS; n = 2839) were included. Multivariable Cox models were used to assess the relationship between apoB and LDL-C/apoB ratio and incidence of CHD (14 994 events). Our analyses were adjusted for age, sex (except WHS), HDL-cholesterol (HDL-C), systolic blood pressure, antihypertensive treatment, diabetes, and smoking. RESULTS: In all 3 studies, there was a strong positive correlation between apoB and LDL-C (correlation coefficients r = 0.80 or higher) and a weak inverse correlation of apoB with LDL-C/apoB ratio (-0.28 ≤ r ≤ -0.14). For all 3 cohorts, CHD risk was higher for higher levels of apoB. Upon multivariable adjustment, the association between apoB and new-onset CHD remained robust and statistically significant in all 3 cohorts with hazard ratios per 1 SD (95% CI): 1.24 (1.22-1.27), 1.33 (1.20-1.47), and 1.24 (1.09-1.42) for UKB, WHS, and FHS, respectively. However, the association between LDL-C/apoB and CHD was statistically significant only in the FHS cohort: 0.78 (0.64-0.94). CONCLUSIONS: Our analysis confirms that apoB is a strong risk factor for CHD. However, given the null association in 2 of the 3 studies, we cannot confirm that cholesterol-depleted LDL particles are substantially more atherogenic than cholesterol-replete particles. These results lend further support to routine measurement of apoB in clinical care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".