Use of angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers and cancer risk: a meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: Epidemiologic studies have reported inconsistent findings regarding the association between the use of angiotensin-converting-enzyme (ACE) inhibitors or angiotensin-receptor blockers and the risk of cancer. We performed a meta-analysis of observational studies to assess the association. METHODS: We searched MEDLINE, EMBASE and the Cochrane Library to identify studies through January 2011. Two evaluators independently reviewed and selected articles of cohort and case-control studies on the basis of predetermined selection criteria. RESULTS: Of 3970 screened articles, 12 cohort studies and 16 case-control studies were selected for analysis. We found no significant association between the use of ACE inhibitors or angiotensin-receptor blockers and the overall risk of cancer (relative risk [RR] 0.96, 95% confidence interval [CI] 0.90-1.03). We found a decreased risk of cancer associated with use of either medication when we restricted the analyses to cohort and nested case-control studies (RR 0.90, 95% CI 0.83-0.97) or to studies with long-term follow-up of more than five years (RR 0.89, 95% CI 0.83-0.96). In the subgroup meta-analyses by cancer site, a decreased risk was identified for esophageal cancer, whereas an increased risk was found for melanoma and kidney cancer. INTERPRETATION: No significant association was found between the use of ACE inhibitors or angiotensin-receptor blockers and overall risk of cancer. A possible beneficial effect associated with use of either medication was suggested in sensitivity analyses, including those of studies with long-term follow-up. Large randomized controlled trials with long-term follow-up are needed to specifically test the effect of each of these medications on the risk of cancer.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.056 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".