Risk of Hair Loss with Semaglutide for Weight Loss
Bibliographic record
Abstract
ABSTRACT GLP-1 (glucagon-like peptide-1) agonists including semaglutide are some of the most prescribed medications in the world. Many recent studies have quantified their adverse events with a focus on gastrointestinal adverse events. However, no large epidemiologic study has investigated the risk of hair loss with these medications. We conducted a retrospective cohort study using a large health claims database to investigate this risk. Using a random sample of 16 million patients, we identified new users of semaglutide and an active comparator bupropion-naltrexone. We excluded those with a diagnosis of diabetes or use of antihyperglycemics prior to cohort entry. Cohort members were followed from the first date of a study drug prescription to the diagnosis of hair loss. We adjusted for age, sex, geographic location, depression, steroid use, hypothyroidism, polycystic ovary syndrome, and anemia. Our cohort included 1,926 semaglutide users and 1,348 users of bupropion-naltrexone. The incidence of hair loss was higher among the semaglutide group than the active comparator, bupropion-naltrexone. The adjusted hazard ratio for hair loss for all patients using semaglutide, for men, and for women compared to bupropion-naltrexone were 1.52 (95% confidence interval (CI):0.86-2.69), 0.86 (95% CI: 0.05-14.49), and 2.08 (95% CI: 1.17-3.72) respectively. Our results demonstrate an increased risk of hair loss with semaglutide in women. Future studies are required to ascertain the association between semaglutide and hair loss.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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) | 0.004 | 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".