Researchers at the University of Pennsylvania reported on Apr. 10 that artificial intelligence was used to analyze more than 400,000 Reddit posts, revealing patient-reported symptoms associated with GLP-1 drugs such as semaglutide and tirzepatide. The study, published in Nature Health, covered over half a decade of posts from nearly 70,000 Reddit users and identified two main groups of symptoms: reproductive issues like irregular menstrual cycles and temperature-related complaints including chills and hot flashes.
The findings are significant because they suggest that some side effects experienced by patients may not be fully captured in clinical trials or regulatory documents. "Clinical trials generally identify the most dangerous side effects of drugs," said Lyle Ungar, Professor in Computer and Information Science (CIS) and co-author on the study. "But they can fail to find what symptoms patients are most concerned about; even though social media is not necessarily representative, a large collection of posts may reflect additional concerns."
Neil Sehgal, the study's first author and a doctoral student in CIS advised by Guntuku and Ungar, cautioned that the research does not establish causality between GLP-1s and these symptoms. "We can't say that GLP-1s are actually causing these symptoms," said Sehgal. "But nearly 4% of the Reddit users in our sample reported menstrual irregularities, which would be even higher in a female-only sample. We think that's a signal worth investigating." Jena Shaw Tronieri from Penn's Center for Weight and Eating Disorders added context: "These drugs are thought to work by engaging part of the brain called the hypothalamus, which helps regulate a wide variety of hormones... it could suggest that reports of menstrual changes and body temperature fluctuations are worth studying more systematically."
Ungar noted his earlier involvement with mining online content for drug adverse effects dating back to 2011: "Online patient communities work a lot like a neighborhood grapevine... sharing experiences that rarely make it into a doctor's office visit or an official report." Since then, social media has become an increasingly valuable source for understanding medication side effects.
The use of large language models such as GPT or Gemini has enabled researchers to process social media data at much larger scales than before. According to Sehgal: "Large language models have made it possible to do this kind of analysis much faster with a level of standardization that could be difficult to achieve before." Despite limitations—such as Reddit’s user base being younger, predominantly male, and mostly U.S.-based—the patterns observed largely matched known drug side effects while also highlighting underreported ones like fatigue.
Looking ahead, researchers hope their findings will prompt clinicians to pay closer attention to what patients share online about their experiences with medications. They also plan future studies beyond English-language platforms or U.S.-centric communities. As Ungar said: "We don't really know yet whether what we're seeing on Reddit reflects the experience of GLP-1 users globally..." Ultimately, rapid AI-assisted analysis could help spot early warning signs for emerging drugs or wellness trends when speed is crucial.
