Popular weight loss and diabetes medications such as semaglutide and tirzepatide have transformed treatment for obesity and blood sugar control. Now, researchers at the University of Pennsylvania say artificial intelligence may also help uncover side effects that patients are discussing online but that are not always fully reflected in clinical trials or official drug documentation.
In a new study published in Nature Health, researchers analyzed more than 400,000 Reddit posts written by nearly 70,000 users over a span of more than five years. Their findings highlighted several commonly discussed symptoms, including some that may deserve closer scientific attention, such as menstrual irregularities and temperature-related complaints like chills and hot flashes.
“Some of the side effects we found, like nausea, are well known, and that shows that the method is picking up a real signal,” says Sharath Chandra Guntuku, Research Associate Professor in Computer and Information Science (CIS) at Penn Engineering and the study’s senior author. “The underreported symptoms are leads that came from patients themselves, unprompted, and clinicians could potentially pay attention to them.”
Lyle Ungar, Professor in CIS and a co-author of the study, says social media can offer insight into concerns patients may not always bring up during medical visits.
“Clinical trials generally identify the most dangerous side effects of drugs,” says Ungar. “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.”
AI and Reddit Reveal Emerging GLP-1 Concerns
The researchers emphasize that the study does not prove the medications caused the symptoms discussed online. Instead, the findings point to patterns that may warrant further investigation.
“We can’t say that GLP-1s are actually causing these symptoms,” says Neil Sehgal, the study’s first author and a doctoral student in CIS advised by Guntuku and Ungar. “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.”
The study builds on years of work examining online conversations for clues about drug side effects. Ungar participated in one of the earliest projects to mine user-generated internet content for reports of adverse drug reactions back in 2011.
“Online patient communities work a lot like a neighborhood grapevine,” says Ungar. “People who are living with these medications are swapping notes with each other in real time, sharing experiences that rarely make it into a doctor’s office visit or an official report.”
As social media platforms have expanded, researchers say these discussions have become an increasingly valuable source of health-related information, even though collecting and analyzing the data has become more difficult over time.
“Clinical trials are the gold standard, but by design, they are slow,” says Guntuku. “This is not a replacement for trials, but it can move much faster, and that speed matters when a drug goes from niche to mainstream almost overnight.”
Large Language Models Speed Up Side Effect Detection
One major challenge in studying online health discussions has been scale. People describe symptoms in many different ways, making it difficult to systematically compare social media posts with standardized medical terminology from the Medical Dictionary for Regulatory Activities (MedDRA), which clinicians use to classify symptoms.
The rise of large language models such as GPT and Gemini has changed that. According to the researchers, these AI systems now make it possible to process enormous amounts of online discussion much faster and with more consistency.
“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,” says Sehgal.
Although Reddit users do not perfectly represent the general population because they tend to be younger, more likely to be male, and disproportionately based in the United States, many of the reported symptoms matched already known side effects of semaglutide and tirzepatide. About 44% of users in the study mentioned at least one side effect, most commonly gastrointestinal problems.
Unexpected Symptoms Reported by GLP-1 Users
What stood out to researchers were symptoms that may not be fully represented in current drug labeling or standard adverse event reporting systems.
Nearly 4% of users who reported side effects also described reproductive symptoms, including irregular menstrual cycles, intermenstrual bleeding, and heavy bleeding.
Other users reported temperature-related symptoms such as chills, feeling cold, hot flashes, and fever-like sensations.
Fatigue also emerged as one of the most frequently discussed complaints. In fact, it ranked as the second most common symptom reported by Reddit users, despite appearing less prominently in many clinical trials.
“These drugs are thought to work by engaging part of the brain called the hypothalamus, which helps regulate a wide variety of hormones,” says Jena Shaw Tronieri, Senior Research Investigator at Penn’s Center for Weight and Eating Disorders and a co-author of the study. “That doesn’t mean the medications are necessarily causing these symptoms, but it could suggest that reports of menstrual changes and body temperature fluctuations are worth studying more systematically.”
Researchers Hope to Expand Beyond Reddit
The team hopes the findings encourage scientists and healthcare providers to pay closer attention to the kinds of side effects patients are discussing online.
“They’re clearly on patients’ minds, and that’s worth paying attention to,” says Sehgal.
Researchers also plan to expand the analysis beyond Reddit and beyond English-speaking communities to determine whether similar patterns appear across other social media platforms and populations worldwide.
“We don’t really know yet whether what we’re seeing on Reddit reflects the experience of GLP-1 users globally, or whether it’s particular to the kind of person who posts on Reddit in the United States,” says Ungar.
Ultimately, the researchers believe AI-assisted analysis of social media conversations could become an important tool for identifying emerging concerns around medications and wellness trends much earlier than traditional systems allow.
For rapidly spreading health products, especially substances sold in loosely regulated or unregulated markets such as injectable peptides, online conversations on platforms like Reddit and TikTok may provide some of the earliest clues about what users are experiencing.
“The whole point of this kind of approach is that it can move quickly, and that’s exactly when it’s most valuable,” says Guntuku.
This study was conducted at the University of Pennsylvania School of Engineering and Applied Science. The authors report no outside funding. Tronieri reports receiving an investigator-initiated grant, on behalf of the University of Pennsylvania, from Novo Nordisk and receiving consulting fees from Currax Pharmaceuticals, LLC. The other authors report no conflicts of interest.