Statins and Calcium Channel Blockers as Probes into the Biological Mechanisms of Hypercholesterolemia and Colonic Hypomotility in Clinical Gallstone Disease
Bibliographic record
Abstract
Purpose In addition to their primary effects, statins and non-dihydropyridine (DHP) calcium channel blockers (CCBs) may influence the development of clinical gallstone disease (cGD) due to their unintended side effects. We assessed whether statins and non-DHP CCBs decrease and increase cGD incidence, respectively. Methods We performed a retrospective active-comparator new-user (ACNU) cohort study using the Merative MarketScan Database (2015–2019) to assess associations between statin or CCB use and incident cGD. Adults initiating statins or non-DHP CCBs were compared with active comparators after a 183-day washout. cGD was identified by diagnostic or procedural codes within 12 months of drug initiation. Relative risks (RRs) and 95% confidence intervals were estimated using multivariable Poisson regression, adjusted for age, sex, and comorbidities, with sensitivity analyses restricted to diagnostic-only or procedural-only outcomes. Results Among over 2.9 million new users, statin initiation was associated with a lower risk of cGD compared with non-statin lipid-lowering agents (adjusted RRs 0.55–0.67) and ACEI/ARB users (adjusted RRs 0.78–0.81) across all follow-up periods. Non-DHP CCB use was associated with a higher cGD risk compared with dihydropyridine CCBs and ACEI/ARBs at 6 and 12 months (adjusted RRs 1.09–1.16). Sensitivity analyses using diagnostic-only or procedure-only outcome definitions yielded consistent results. Conclusions This study finds new statin use and non-DHP CCB use are associated with decreased and increased cGD risk, respectively. These effects may be mediated by statin-induced cholesterol reduction and non-DHP CCB’s prolongation of large bowel transit time.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".