Evaluation of the Pleiotropic Effects of Statins
Bibliographic record
Abstract
Objective— To reanalyze data from recent randomized trials of statins to assess whether the benefits and risks of statins are mediated primarily via their LDL-C (low-density lipoprotein cholesterol) lowering effects or via other mechanisms. Approach and Results— We adapted Egger regression, a technique frequently used in Mendelian randomization studies to detect genetic pleiotropy, to reanalyze the available randomized control trial data of statin therapy. For cardiovascular end points, each 1 mmol/L change in LDL-C with statin therapy was associated with a hazard ratio of 0.77 (95% confidence interval, 0.71–0.84) with an intercept that was indistinguishable from zero (intercept, −0.0032; [95% confidence interval, −0.090 to 0.084]; P =0.94), indicating no pleiotropy. For incident diabetes mellitus, a 1 mmol/L change in LDL-C with statin therapy was associated with a hazard ratio of 1.07 (95% confidence interval, 0.99–1.16) and an intercept nondistinguishable from zero (intercept, −0.015; [95% confidence interval, −0.30 to 0.27]; P =0.91), again indicating no pleiotropy. Conclusions— Our reanalysis of the randomized control trial data using Egger regression adds to the existing evidence that the cardiovascular benefits of statins and their association with incident diabetes mellitus are mediated primarily, if not entirely, via their LDL-C lowering properties rather than by any pleiotropic effects.
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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 | 0.044 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".