Effect of Bile Acid Sequestrants on the Risk of Cardiovascular Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it