Joint Distribution of Non-HDL and LDL Cholesterol and Coronary Heart Disease Risk Prediction Among Individuals With and Without Diabetes
Bibliographic record
Abstract
OBJECTIVE: To assess coronary heart disease (CHD) risk within levels of the joint distribution of non-HDL and LDL cholesterol among individuals with and without diabetes. RESEARCH DESIGN AND METHODS: We used four publicly available data sets for this pooled post hoc analysis and confined the eligible subjects to white individuals aged > or = 30 years and free of CHD at baseline (12,660 men and 6,721 women). Diabetes status was defined as either "reported by physician-diagnosed and on medication" or having a fasting glucose level > or = 126 mg/dl at the baseline examination. The primary end point was CHD death. Within diabetes categories, risk was assessed based on lipid levels (in mg/dl): non-HDL <130 and LDL <100 (group 1); non-HDL <130 and LDL > or = 100 (group 2); non-HDL > or = 130 and LDL <100 (group 3); and non-HDL > or = 130 and LDL > or = 100 (group 4). Group 1 within those without diabetes was the overall reference group. RESULTS: Of the subjects studied, approximately 6% of men and 4% of women were defined as having diabetes. A total of 773 CHD deaths occurred during the average 13 years of follow-up time. A Cox proportional hazard model was used to estimate the relative risk (RR) of CHD death. Those with diabetes had a 200% higher RR than those without diabetes. In a multivariate model, CHD risk in those with diabetes did not increase with increasing LDL, whereas it did increase with increasing non-HDL: RR (95% confidence interval) for group 1: 5.7 (2.0-16.8); group 2: 5.7 (1.6-20.7); group 3: 7.2 (2.6-19.8); and group 4: 7.1 (3.7-13.6). CONCLUSIONS: Non-HDL is a stronger predictor of CHD death among those with diabetes than LDL and should be given more consideration in the clinical approach to risk reduction among diabetic patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".