Artificial intelligence is giving researchers a new way to listen to what patients say about popular GLP-1 medications. After analyzing more than 400,000 Reddit posts, a team from the University of Pennsylvania identified several symptoms reported by people using semaglutide (Ozempic, Wegovy and Rybelsus) and tirzepatide (Mounjaro and Zepbound) that may not be fully represented in clinical trials or official regulatory information. The study, recently published in Nature Health, examined more than five years of posts from nearly 70,000 Reddit users. Two categories stood out as particularly worthy of further investigation: reproductive symptoms, including changes in menstrual cycles, and problems related to body temperature, such as chills and hot flashes. The findings do not establish that the medications caused these symptoms. Instead, researchers say mass collection of spontaneous patient reports may reveal signals worth closer examination. “Some of the side effects we found, such as nausea, are well known, and that shows that the method captures a real signal,” says Sharath Chandra Guntuku, associate research professor in Computer and Information Sciences (CIS) at Penn Engineering and senior author of the study. “Unreported symptoms are clues that come from the patients themselves, spontaneously, and doctors could potentially pay attention to them.” What patients report outside of clinical trials Clinical trials are designed to determine whether treatments work and identify important safety issues, but they can’t necessarily capture all the symptoms that matter to patients once a drug is being used by a much larger population. “Clinical trials generally identify the most dangerous side effects of medications,” adds Lyle Ungar, professor at CIS and co-author of the study. “But they may not find which symptoms patients are most concerned about; although social media is not necessarily representative, a large collection of posts may reflect additional concerns.” The distinction is important. The study found associations in what people discussed online, not evidence that GLP-1 medications were responsible for those experiences. “We can’t say that GLP-1 is actually causing these symptoms,” says Neil Sehgal, first author of the study and a doctoral student at the CIS advised by Guntuku and Ungar. “But almost 4% of Reddit users in our sample reported menstrual irregularities, which would be even higher in an all-female sample. We think it’s a sign worth investigating.” Using social media as a sign of early health The idea of extracting clues from online conversations about drug safety predates the current rise of AI. In 2011, Ungar participated in one of the first efforts to use material created by Internet users to identify possible adverse effects of medications. Social media can capture experiences that patients discuss with each other but may never formally report to a doctor, drug manufacturer, or regulator. “Online patient communities operate much like a neighborhood grapevine,” Ungar says. “People living on these medications exchange notes with each other in real time, sharing experiences that rarely appear in a doctor’s office visit or in an official report.” Since then, online patient communities have expanded greatly. That has made social media a potentially valuable source for studying how medications affect people in everyday life, although gaining access to data on the platform has become more difficult. Traditional clinical research is still essential, researchers emphasize, but online conversations can provide information much more quickly when millions of people start using a drug. “Clinical trials are the gold standard, but by design they are slow,” Guntuku says. “This doesn’t replace trials, but it can move much faster, and that speed matters when a drug goes from niche to mainstream almost overnight.” AI makes massive social media analysis possible One of the biggest obstacles has always been scale. Guntuku describes the approach as “computational social listening,” which uses computational methods to identify patterns in large collections of online health conversations. However, patients rarely describe symptoms using standardized medical terminology. One person might describe an unusual cold sensation, another might mention constant chills, while a doctor might classify those experiences using a specific medical term. Therefore, researchers need a way to translate everyday language into standardized categories. An important reference is the Medical Dictionary for Regulatory Activities (MedDRA), which provides terminology widely used to classify medical conditions, symptoms, and adverse events. Previously, connecting large numbers of informal social media posts to standardized medical terminology required enormous amounts of work, limiting the amount of data researchers could realistically analyze. Big language models like GPT and Gemini are changing that equation by allowing researchers to process and categorize large amounts of text more consistently and quickly. “Large language models have made it possible to do this type of analysis much faster with a level of standardization that might previously have been difficult to achieve,” says Sehgal. Unexpected symptoms emerge from 400,000 posts The researchers emphasize that Reddit users do not represent the general population of people taking GLP-1 medications. Reddit users tend to be younger, more likely to be male, and are disproportionately located in the United States. Even with that limitation, the analysis produced a reassuring signal that the approach was detecting genuine patterns. Many of the symptoms discussed by Reddit users closely matched the already known effects of semaglutide and tirzepatide. About 44% of users included in the study described at least one side effect. Gastrointestinal problems were the most common, consistent with nausea and other digestive problems already associated with these medications. More intriguing were symptoms that appeared frequently enough to attract researchers’ attention but may not be as well represented on current drug labels or in conventional adverse event reports. Almost 4% of users who reported side effects described reproductive symptoms. These included changes in menstruation, such as bleeding between periods, heavy bleeding, and irregular menstrual cycles. Users also described changes related to body temperature, including chills, feeling unusually cold, hot flashes, and fever-like symptoms. Fatigue was another notable finding. It was the second most common complaint in Reddit data, even though relatively few clinical trials reported fatigue frequently enough to meet established reporting thresholds. Why Menstrual and Temperature Changes Are Interesting One possible reason these reports caught the attention of researchers has to do with the hypothalamus, a small but extremely important region of the brain. Among its many functions, the hypothalamus helps regulate hunger, hormones, reproduction and body temperature. “These medications are thought to work by activating a part of the brain called the hypothalamus, which helps regulate a wide variety of hormones,” says Jena Shaw Tronieri, senior investigator at Penn’s Center for Weight and Eating Disorders and co-author of the study. “That doesn’t mean the medications are necessarily causing these symptoms, but it might suggest that reports of menstrual changes and body temperature fluctuations are worth studying more systematically.” The researchers do not propose that this biological connection proves that GLP-1 drugs are responsible. Instead, it offers another reason to more carefully test patient-reported patterns through controlled research. Turning online conversations into research leads For now, the team hopes the results will encourage scientists and doctors to pay more attention to the symptoms that patients repeatedly discuss online. “They are clearly on patients’ minds and are worth paying attention to,” Sehgal says. The researchers also want to expand their analysis beyond Reddit and beyond English-speaking communities. Doing so could help determine whether the same patterns emerge among different groups of people and on different social media platforms. “We don’t yet know if what we’re seeing on Reddit reflects the experience of GLP-1 users globally, or if it’s particular to the type of person posting on Reddit in the United States,” Ungar says. In the long term, rapid AI analysis of online patient conversations could become an early detection system for emerging health problems related to medications, supplements and wellness products. This could be particularly valuable for substances that become popular online faster than conventional research can keep up. Underregulated or unregulated products, including injectable peptides, can spread quickly through communities on Reddit, TikTok, and other platforms. Discussions between users may therefore provide some of the first indications of unexpected effects. “The whole point of this kind of approach is that you can move quickly, and that’s exactly when it’s most valuable,” Guntuku says. This study was conducted at the School of Engineering and Applied Sciences at the University of Pennsylvania. The authors do not report external 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.
