Risk of Gynecomastia with Users of Proton Pump Inhibitors
Bibliographic record
Abstract
INTRODUCTION: Proton pump inhibitors (PPIs) are commonly prescribed for many gastrointestinal diseases. A number of case reports have linked PPIs to gynecomastia in men, but large epidemiologic studies are lacking. OBJECTIVE: To quantify the risk of gynecomastia with PPIs in male patients. METHODS: Using the PharMetrics Plus™ health claims database from the United States, a retrospective cohort study of new PPI users and new amoxicillin users from 2006 to 2016 was conducted. Diagnosis of gynecomastia was identified by the International Classification for Diseases, 9th edition (ICD-9) and 10th edition (ICD-10) codes. Cases were defined as patients with two codes for gynecomastia within 90 days, with the first code as the event code. Hazard ratios (HRs) were computed by adjusting for alcoholic cirrhosis, hyperthyroidism, testicular cancer, Klinefelter syndrome, and obesity, as well as the use of ketoconazole, risperidone, spironolactone, and androgen deprivation therapy. A sensitivity analysis defining exposure with two PPI prescriptions was also undertaken. RESULTS: There were 389 cases of gynecomastia diagnosed among 220,791 new PPI users, and 996 gynecomastia cases were diagnosed among 837,740 new amoxicillin users. The crude HR for PPI use compared to amoxicillin use was 1.70 (95% confidence interval [CI]: 1.461-1.976). The adjusted HR for the sensitivity analysis was 1.299 (95% CI: 1.146-1.473). The adjusted HR was 1.4795 (95% CI: 1.2431-1.7609) for patients over 50 years old and 1.324 (95% CI: 1.1133-1.5745) for patients 50 years old or younger. CONCLUSION: This large retrospective cohort study suggests that patients who used PPIs are at higher risk of developing gynecomastia. Clinicians may want to convey this information to male patients who require long-term PPI therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".