Improving the Prediction of Cardiovascular Risk: Interaction Between LDL and HDL Cholesterol
Bibliographic record
Abstract
BACKGROUND: The ratio of total cholesterol to high-density lipoprotein (HDL) cholesterol (or the ratio of low-density lipoprotein [LDL] to HDL) is currently advocated to estimate the coronary risk associated with LDL and HDL cholesterol levels. METHODS: We analyzed the relation between LDL and HDL cholesterol levels to predict the risk of future coronary events. Using data from the Lipid Research Clinics Follow-up Cohort, we developed multivariate equations to predict coronary deaths among 4684 men and women followed for approximately 12 years. We used these equations to compare the predictive power of the LDL/HDL ratio with the independent effects of LDL and HDL and an LDL-HDL interaction term. We then used each model to forecast the 10-year risk of coronary death based on various lipid levels after adjustment for conventional risk factors (eg, blood pressure, gender, cigarette smoking). RESULTS: Levels of LDL and HDL and the interaction between them are all independent risk factors for coronary death. The benefits of increasing HDL are strongest among persons with high LDL. Conversely, the benefits of decreasing LDL are greatest among those with low HDL. We confirmed these observations in a published dataset showing the effects of treatment of hyperlipidemia. Predictions of benefits of treatment that were based on interaction of LDL and HDL were more accurate than predictions without interaction. CONCLUSIONS: The LDL/HDL ratio alone may not fully capture the complex interaction between LDL and HDL and the relation of each to coronary risk.
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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 | 0.005 | 0.005 |
| 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".