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 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) | 0.001 | 0.001 |
| 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".