Statin medications and the risk of gynecomastia
Bibliographic record
Abstract
Summary Objective Case reports have suggested an increased risk of gynecomastia with HMG‐CoA reductase inhibitors (ie, statins). A recent meta‐analysis also found that statins decrease circulating testosterone levels in men. We investigated whether statin use was associated with an increased risk of gynecomastia. Design Case‐control study. Patients A cohort of patients from a random sample of 9 053 240 US subjects from the PharMetrics Plus™ health claims database from 2006 to 2016 was created. Measurements New cases of gynecomastia requiring at least two ICD‐9 codes were identified from the cohort and matched to 10 controls by follow‐up time and age using density‐based sampling. Rate ratios (RRs) for users of statins were computed using conditional logistic regression adjusting for alcoholic cirrhosis, hyperthyroidism, testicular cancer, Klinefelter syndrome, obesity, hypogonadism, hyperprolactinemia and use of spironolactone, ketoconazole, H2 receptor antagonists (H2 blockers), risperidone, testosterone and androgen deprivation therapy. Results Our cohort included 6147 cases of gynecomastia and 61 470 corresponding matched controls. The adjusted RR for current, recent and past statin use with respect to gynecomastia was 1.19 (1.04‐1.36), 1.38 (1.15‐1.65) and 1.20 (1.03‐1.40), respectively. Conclusions Statin use is associated with an increased risk of developing gynecomastia. Clinicians should be cognizant of this effect and educate patients accordingly.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".