Researchers used large language models to analyze more than 400,000 Reddit posts from nearly 70,000 users spanning five years to catalog self-reported effects of GLP-1 drugs semaglutide and tirzepatide (marketed as Ozempic, Wegovy, Rybelsus, Mounjaro, Zepbound). By mapping everyday language to MedDRA medical terms, the team found that 44% of posters described at least one side effect, with gastrointestinal complaints most common. Notably, several symptom clusters appeared more often than expected in public reports: reproductive issues (about 4% of users reporting side effects described menstrual changes such as intermenstrual bleeding, heavy or irregular cycles), body-temperature disturbances (chills, hot flashes, fever-like sensations), and fatigue, which was the second-most frequent complaint. The analysis identifies associations in spontaneous patient conversations rather than proving the drugs caused those experiences.
The study argues that AI-powered “computational social listening” can rapidly surface patient-reported signals that clinical trials and formal reporting systems may miss, especially as drugs scale from niche to mainstream. Authors note biological plausibility - GLP-1 action in the hypothalamus could affect hormones and temperature - but emphasize the Reddit sample is not population-representative and that controlled research is required to test causality. The team calls for broader, multi-platform and multilingual analyses to validate these leads and suggests rapid AI screening could become an early-warning tool for emerging drug effects.
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