Relations of Change in Plasma Levels of LDL‐C, Non‐HDL‐C and apoB With Risk Reduction From Statin Therapy: A Meta‐Analysis of Randomized Trials
Bibliographic record
Abstract
BACKGROUND: Identifying the best markers to judge the adequacy of lipid-lowering treatment is increasingly important for coronary heart disease (CHD) prevention given that several novel, potent lipid-lowering therapies are in development. Reductions in LDL-C, non-HDL-C, or apoB can all be used but which most closely relates to benefit, as defined by the reduction in events on statin treatment, is not established. METHODS AND RESULTS: We performed a random-effects frequentist and Bayesian meta-analysis of 7 placebo-controlled statin trials in which LDL-C, non-HDL-C, and apoB values were available at baseline and at 1-year follow-up. Summary level data for change in LDL-C, non-HDL-C, and apoB were related to the relative risk reduction from statin therapy in each trial. In frequentist meta-analyses, the mean CHD risk reduction (95% CI) per standard deviation decrease in each marker across these 7 trials were 20.1% (15.6%, 24.3%) for LDL-C; 20.0% (15.2%, 24.7%) for non-HDL-C; and 24.4% (19.2%, 29.2%) for apoB. Compared within each trial, risk reduction per change in apoB averaged 21.6% (12.0%, 31.2%) greater than changes in LDL-C (P<0.001) and 24.3% (22.4%, 26.2%) greater than changes in non-HDL-C (P<0.001). Similarly, in Bayesian meta-analyses using various prior distributions, Bayes factors (BFs) favored reduction in apoB as more closely related to risk reduction from statins compared with LDL-C or non-HDL-C (BFs ranging from 484 to 2380). CONCLUSIONS: Using both a frequentist and Bayesian approach, relative risk reduction across 7 major placebo-controlled statin trials was more closely related to reductions in apoB than to reductions in either non-HDL-C or LDL-C.
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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 | 0.040 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.067 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".