Effect of Bile Acid Sequestrants on the Risk of Cardiovascular Events
Bibliographic record
Abstract
BACKGROUND: Statins lower low-density lipoprotein cholesterol (LDL-C) and risk of coronary artery disease (CAD), but they may be ineffective or not tolerated. Bile acid sequestrants (BAS) reduce LDL-C, yet their clinical efficacy on CAD remains controversial. METHODS AND RESULTS: We conducted a systematic review and meta-analysis of randomized controlled trials to assess the effect of cholestyramine and colesevelam. We then used Mendelian randomization to estimate the effect of BAS on reducing the risk of CAD. First, we quantified the effect of rs4299376 (ABCG5/ABCG8), which affects the intestinal cholesterol absorption pathway targeted by BAS and then we used these estimates to predict the effect of BAS on CAD. Nineteen randomized controlled trials with a total of 7021 study participants were included. Cholestyramine 24 g/d was associated with a reduction in LDL-C of 23.5 mg/dL (95% confidence interval [CI] -26.8,-20.2; N=3806) and a trend toward reduced risk of CAD (odds ratio 0.81, 95% CI 0.70-1.02; P=0.07; N=3806), whereas colesevelam 3.75 g/d was associated with a reduction in LDL-C of 22.7 mg/dL (95% CI -28.3, -17.2; N=759). Based on the findings that rs4299376 was associated with a 2.75 mg/dL decrease in LDL-C and a 5% decrease in risk of CAD outcomes, we estimated that cholestyramine was associated with an odds ratio for CAD of 0.63 (95% CI 0.52-0.77; P=6.3×10(-6)) and colesevelam with an odds ratio of 0.64 (95% CI 0.52-0.79, P=4.3×10(-5)), which were not statistically different from BAS clinical trials (P>0.05). CONCLUSIONS: The cholesterol lowering effect of BAS may translate into a clinically relevant reduction in CAD.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".