Evaluating the Incremental Benefits of Raising High-Density Lipoprotein Cholesterol Levels During Lipid Therapy After Adjustment for the Reductions in Other Blood Lipid Levels
Bibliographic record
Abstract
BACKGROUND: The role of high-density lipoprotein cholesterol (HDL-C) as a therapeutic target to prevent cardiovascular (CV) events remains unclear. We examined data from the Framingham Offspring Study from 1975 through 2003 to determine whether increases in HDL-C levels after lipid therapy was started were independently associated with a reduction in CV events. METHODS: Using Cox proportional-hazards regression, we evaluated the risk of a CV event associated with changes in blood lipid levels among individuals who started lipid therapy. The independent effect of HDL-C levels on future CV risk (average follow-up, 8 years) was estimated after adjustment for changes in low-density lipoprotein cholesterol, plasma triglycerides, and pretreatment blood lipid levels. Potential confounders (eg, smoking status, weight, and the use of beta-blockers) were then added to the model. Interactions between blood lipid levels were also explored. RESULTS: The change in HDL-C level was a strong independent risk factor for CV events (hazard ratio, 0.79 per 5-mg/dL increase; 95% confidence interval, 0.67-0.93) after adjustment for the other lipid changes associated with treatment. This relationship remained stable across a wide range of patient subgroups and did not appear to be associated with a specific drug class. An important interaction was observed: the lower the pretreatment low-density lipoprotein cholesterol level, the greater the impact of raising the HDL-C. CONCLUSIONS: Raising HDL-C levels with commonly used lipid medications appears to be an important determinant of the benefits associated with lipid therapy. These results support the further evaluation of therapies to raise HDL-C levels to prevent CV events.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".