Use of Thiazide Diuretics and Risk of All Types of Skin Cancers: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The use of thiazide diuretics is associated with skin cancer risk; however, whether this applies to all skin cancer types is unclear. Methods: In this meta-analysis, we searched multiple electronic databases and gray literature up to 10 April 2022, with no language restrictions, to identify relevant randomized controlled trials (RCTs) and non-randomized studies (cohort, case-control) that investigated the association between thiazide diuretics and skin cancer. The primary outcomes of interest were malignant melanoma and non-melanoma skin cancer (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]). Secondary outcomes included other skin cancers (lip cancer, Merkel cell carcinoma, malignant adnexal skin tumors, oral cavity cancer, and precursors of skin cancer). We used a random-effects meta-analysis to estimate pooled adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: Thirty non-randomized studies (17 case-control, 13 cohort, no RCTs) were included. Thiazide diuretic users had a higher risk of malignant melanoma (17 studies; n = 10,129,196; pooled adjusted OR, 1.10; 95% CI, 1.04−1.15; p < 0.001; strength of evidence, very low; very small harmful effect), BCC (14 studies; n = 19,780,476; pooled adjusted OR, 1.05; 95% CI, 1.02−1.09; p = 0.003; strength of evidence, very low; very small harmful effect), and SCC (16 studies; n = 16,387,862; pooled adjusted OR, 1.35; 95% CI, 1.22−1.48; p < 0.001; strength of evidence, very low; very small harmful effect) than non-users. Thiazide diuretic use was also associated with a higher risk of lip cancer (5 studies; n = 161,491; pooled adjusted OR, 1.92; 95% CI, 1.52−2.42; p < 0.001; strength of evidence, very low; small harmful effect), whereas other secondary outcomes were inconclusive. Conclusions: Thiazide diuretics are associated with the risk of all skin cancer types, including malignant melanoma; thus, they should be used with caution in clinical practice.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".