Abstract 12141: ApoE-HDL Cholesterol Predicts Incident Cardiovascular Events: Aric Study
Bibliographic record
Abstract
Background: ApoE-HDL-C, a subfraction of HDL-C that contains ApoE, is thought to be cardioprotective via its inhibition of arterial stiffening and its role in reverse cholesterol transport. Objective: We examined the relationship between ApoE- HDL-C and incident cardiovascular events in the biracial Atherosclerosis Risk in Communities (ARIC) study. Methods: ApoE-HDL-C was measured in 9,258 ARIC participants using a novel automated homogeneous assay (Denka Seiken). The association between ApoE-HDL-C and HDL-C with incident cardiovascular events was assessed using Cox proportional hazard regression models. Covariates included age, race, gender, race, BMI, systolic blood pressure, use of anti-hypertensive medications, diabetes, smoking status, total cholesterol and triglycerides. Results: Mean ApoE-HDL-C and HDL-C levels were 5.06 ± 1.79 mg/dL and 51.10 ± 16.74 mg/dL, respectively. ApoE-HDL-C comprised roughly 10% of total HDL-C levels. ApoE-HDL-C levels were higher in non-diabetics, women and African Americans and showed strong positive correlations with HDL-C and total cholesterol levels. Both apoE-HDL-C and HDL-C were associated with incident CVD, driven mainly by CHD (Table 1). Each 10 mg/dL increase in HDL-C levels was associated with a decreased risk of incident CVD after multivariate adjustment (HR 0.89, 95% CI 0.85, 0.93, p<0.001). A 1 mg/dL increase in ApoE-HDL-C was associated with a decreased risk of incident CVD after similar multivariate adjustment (HR 0.91, 95% CI 0.88, 0.95, p<0.001). Conclusion: ApoE-HDL-C predicts incident CVD similarly to HDL-C in the biracial ARIC cohort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".