Association Between the Use of Thiazide Diuretics and the Risk of Skin Cancers: A Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Background: Thiazide diuretics are among the most widely prescribed antihypertensive agents. Given their photosensitizing effects, however, there are concerns that they may increase the risk of skin cancers. In this meta-analysis, we investigated an association between the use of thiazide diuretics and the risk of skin cancers. Methods: We identified studies by searching three electronic databases (PubMed, EMBASE, and the Cochrane Library) from their inception to October 30, 2017. Nine relevant observational studies (seven case-control and two cohort studies) were included in this study. Since included studies were unlikely to be functionally equal, pooled estimates were calculated using random-effects meta-analysis. Results: The use of thiazide diuretics was associated with an increased risk of squamous cell carcinoma (adjusted odds ratio (aOR), 1.86; 95% confidence interval (CI), 1.23 - 2.80) and marginally increased risk of basal cell carcinoma (aOR, 1.19; 95% CI, 1.02 - 1.38) and malignant melanoma (aOR, 1.14; 95% CI, 1.01 - 1.29). In the subgroup analysis, hydrochlorothiazide or hydrochlorothiazide combination medications were significantly associated with squamous cell carcinoma without significant heterogeneity among studies (aOR, 2.04; 95% CI, 1.79 - 2.33; Higgin’s I 2 value = 0.0 %; Q-statistics = 2.7, P value = 0.445). Conclusions: Our results suggested that the use of thiazide diuretics may be associated with an increased risk of skin cancers. This association was most prominent between the use of hydrochlorothiazide or hydrochlorothiazide combination medications and the risk of squamous cell carcinoma. Further studies are needed to confirm these findings. J Clin Med Res. 2019;11(4):247-255 doi: https://doi.org/10.14740/jocmr3744
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.060 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".