Abstract 4161: Cholesterol Levels in Different LDL Subclasses and CHD Incidence in Apparently Healthy Men and Women: The EPIC-Norfolk Prospective Population Study
Bibliographic record
Abstract
Background: Measurement of cholesterol levels in small LDL particles represents a novel metabolic marker that might add to the coronary risk prediction attributed to LDL cholesterol and LDL particle size taken separately. We examined, in a nested case-control study performed in the prospective EPIC-Norfolk cohort, the association of LDL cholesterol in small LDL particles to future CHD in apparently healthy men and women aged 45 to 79 years who developed fatal or nonfatal CHD over 10-year follow-up. Methods and results: Cases (n=1035) were matched to 1920 controls who remained free of CHD, by age, gender and enrollment time. Electrophoretic characteristics of LDL particles were measured using 2–16% nondenaturing polyacrylamide gradient gel electrophoresis. Cholesterol levels in small LDL particles (<255 Å) were higher in cases than in controls (1.26 ± 0.87 vs. 1.07 ± 0.78 mmol/l, P<0.001) Participants in the top tertile of cholesterol concentrations in small LDL particles were characterized by the highest waist circumference, by the highest triglyceride, C-reactive protein and apolipoprotein B levels and by the lowest HDL cholesterol concentrations. These participants had an unadjusted odds ratio (OR) for future CHD of 1.72 (95% CI, 1.42 to 2.09; P<0.001) which remained significant after adjustment for classical risk factors such as age, smoking, diabetes, body mass index, systolic blood pressure and LDL cholesterol concentrations (OR, 1.43; 95% CI, 1.16 to 1.76; P=0.001). However, after further adjustments for triglyceride and HDL cholesterol levels, the association between cholesterol in small LDL particles and CHD was no longer significant (OR, 1.18; 95% CI, 0.95 to 1.48; NS). Conclusions: Cholesterol concentrations in different LDL subclasses show different relationships with CHD incidence in a European cohort representative of a contemporary Western population.
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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 | 0.000 |
| 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) | 0.000 | 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".