Antihypertensive drugs use and the risk of prostate cancer: a meta-analysis of 21 observational studies
Bibliographic record
Abstract
BACKGROUND: Due to the lack of strong evidence to identify the relationship between antihypertensive drugs use and the risk of prostate cancer, it was needed to do a systematic review to go into the subject. METHODS: We systematically searched PubMed, Web of Science and Embase to identify studies published, through May 2015. Two evaluators independently reviewed and selected articles involving the subject. We used the Newcastle-Ottawa Scale (NOS) to assess the quality of the studies. All extracted results to evaluate the relationship between antihypertensive drugs usage and prostate cancer risk were pool-analysed using Stata 12.0 software. RESULTS: A total of 12 cohort and 9 case-control studies were ultimately included in our review. Most of the studies were evaluated to be of high quality. There was no significant relationship between angiotensin converting enzyme inhibitors (ACEI) usage and the risk of prostate cancer (RR 1.07, 95% CI 0.96-1.20), according to the total pool-analysed. Use of angiotensin receptor blocker (ARB) was not associated with the risk of prostate cancer (RR 1.09, 95% CI 0.97-1.21), while use of CCB may well increase prostate cancer risk based on the total pool-analysed (RR 1.08, 95% CI 1-1.16). Moreover, subgroup analysis suggested that use of CCB clearly increased prostate cancer risk (RR 1.10, 95% CI 1.04-1.16) in terms of case-control studies. There was also no significant relationship between use of diuretic (RR 1.09, 95% CI 0.95-1.25) or antiadrenergic agents (RR 1.22, 95% CI 0.76-1.96) and prostate cancer risk. CONCLUSIONS: There is no significant relationship between the use of antihypertensive drugs (ACEI, ARB, beta-blockers and diuretics) and prostate cancer risk, but CCB may well increase prostate cancer risk, according to existing observational studies.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.051 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".