Discordance between Circulating Atherogenic Cholesterol Mass and Lipoprotein Particle Concentration in Relation to Future Coronary Events in Women
Bibliographic record
Abstract
BACKGROUND: It is uncertain whether measurement of circulating total atherogenic lipoprotein particle cholesterol mass [non-HDL cholesterol (nonHDLc)] or particle concentration [apolipoprotein B (apo B) and LDL particle concentration (LDLp)] more accurately reflects risk of incident coronary heart disease (CHD). We evaluated CHD risk among women in whom these markers where discordant. METHODS: Among 27533 initially healthy women in the Women's Health Study (NCT00000479), using residuals from linear regression models, we compared risk among women with higher or lower observed particle concentration relative to nonHDLc (highest and lowest residual quartiles, respectively) to individuals with agreement between markers (middle quartiles) using Cox proportional hazards models. RESULTS: < 0.001). Over a median follow-up of 20.4 years, 1246 CHD events occurred (514725 person-years). Women with high particle concentration relative to nonHDLc had increased CHD risk: age-adjusted hazard ratio (95% CI) = 1.77 (1.56-2.00) for apo B and 1.70 (1.50-1.92) for LDLp. After adjustment for clinical risk factors including MetS, these risks attenuated to 1.22 (1.07-1.39) for apo B and 1.13 (0.99-1.29) for LDLp. Discordant low apo B or LDLp relative to nonHDLc was not associated with lower risk. CONCLUSIONS: Discordance between atherogenic particle cholesterol mass and particle concentration occurs in a sizeable proportion of apparently healthy women and should be suspected clinically among women with cardiometabolic traits. In such women, direct measurement of lipoprotein particle concentration might better inform CHD risk assessment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".