Reviewing the effects of thiazide and thiazide-like diuretics as photosensitizing drugs on the risk of skin cancer
Bibliographic record
Abstract
BACKGROUND: Thiazide diuretics and particularly hydrochlorothiazide were recently linked to an increased risk of skin cancer, which was attributed to the photosensitizing properties of these drugs. Given the widespread use of thiazide diuretics, a potential skin cancer promoting effect would impose an important public health concern. OBJECTIVE: To critically appraise in a narrative review, the association between use of thiazide and thiazide-like diuretics and risk of skin cancer. METHODS: We evaluated chemical structures and photosensitizing potential of selected thiazide and thiazide-like diuretics. Moreover, we searched PubMed up to December 2018 for observational studies assessing the association between use of thiazide or thiazide-like diuretics and risk of skin cancer. Study quality was assessed for major methodological biases. RESULTS: Commonly used thiazide and thiazide-like diuretics carry resonating structural components, such as sulfonamide groups that contribute to their photosensitizing activity. Overall, 13 observational (9 case-control, 4 cohort) studies assessed the association between use of different thiazide or thiazide-like diuretics and risk of several skin cancer types. Of those, nine studies showed positive associations ranging from 3% increased risk for bendroflumethiazide and basal cell carcinoma to 311% increased risk for thiazide diuretics and squamous cell carcinoma. All studies had important design-related methodological limitations including potential confounding by indication, detection bias, and time-window bias. CONCLUSION: Commonly used thiazide and thiazide-like diuretics have photosensitizing potential, and some observational studies with important methodological limitations have linked their use to an increased risk of skin cancer. Well designed observational studies are needed to provide more solid evidence on this possible association.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".