Impact of spironolactone exposure on prostate cancer incidence amongst men with heart failure: A Pharmacoepidemiological study
Bibliographic record
Abstract
AIMS: Aldosterone has been found to influence cancer cell growth, cell cycle regulation and cell migration, including in prostate cancer cells. Spironolactone is an aldosterone antagonist used for managing chronic heart failure (HF) with known antiandrogenic effects. We examined the effect of spironolactone exposure amongst men with HF on the incidence of prostate cancer. METHODS: This retrospective cohort study utilized provincial clinical and administrative databases from the Manitoba Centre for Health Policy. Incident cases of prostate cancer were identified from the provincial cancer registry, and spironolactone exposure was quantified from pharmacare databases. A multivariable proportional hazards model was used to assess the time-dependent impact of spironolactone exposure on prostate cancer incidence. RESULTS: A total of 18 562 men with newly diagnosed HF from 2007 to 2015 with a median age of 72 years (interquartile range: 61-81) and a median follow-up from HF diagnosis to prostate cancer incidence of 2.7 years (interquartile range: 1.1-4.9) were included. A time-dependent multivariable analysis of spironolactone exposure following HF diagnosis found a reduced the risk of prostate cancer hazard ratio 0.55 (95% confidence interval 0.31-0.98, P = .043). CONCLUSION: Spironolactone exposure significantly reduced the incidence of prostate cancer amongst men with HF. These findings support the plausibility of aldosterone as a promoter of prostate cancer growth and development. Prospective clinical trials are warranted to further assess the role of spironolactone or other mineralocorticoid receptor antagonists as a means to prevent prostate cancer development or as an adjunctive measure to prostate cancer treatments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".