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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".