Penn Study Finds Unreported GLP-1 Side Effects in Reddit Posts

PHILADELPHIA — Researchers at the University of Pennsylvania used artificial intelligence to sift through more than 410,000 Reddit posts and uncovered side effects of popular GLP-1 weight-loss drugs that may not be fully captured in clinical trials or drug labeling. The study, published Thursday in Nature Health, analyzed posts from roughly 67,000 users over more than five years and found that reproductive symptoms and temperature-related complaints emerged as two classes of underreported effects of semaglutide and tirzepatide.

The findings add to growing scrutiny of the world's most prescribed weight-loss medications. Semaglutide, sold by Novo Nordisk under the brand names Ozempic and Wegovy, and tirzepatide, manufactured by Eli Lilly under Mounjaro and Zepbound, collectively generated more than $40 billion in global sales in 2025. The Penn study suggests that the real-world side-effect profile of these drugs may be broader than what formal clinical trials and FDA adverse-event databases have captured.

GLP-1 Side Effects by the Numbers | What Reddit Users Reported

About 43.5% of users in the study described at least one side effect, with gastrointestinal symptoms dominating the reports. But what caught the researchers' attention were symptom categories largely absent from formal adverse-event filings.

Nearly 4% of users who reported side effects described reproductive issues, including irregular menstrual cycles, heavy bleeding, and intermenstrual spotting. Others reported temperature-related complaints such as chills, hot flashes, and fever-like symptoms. Fatigue ranked as the second most common complaint despite reaching reporting thresholds in relatively few clinical trials, suggesting a gap between real-world experience and the trial data that informs drug labeling.

"Some of the side effects we found, like nausea, are well known, and that shows that the method is picking up a real signal," said Sharath Chandra Guntuku, a research associate professor 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."

Reproductive Symptoms | A Signal Worth Investigating

The reproductive symptom findings are particularly significant because GLP-1 drugs are disproportionately prescribed to women of reproductive age, a population that was underrepresented in early clinical trials for semaglutide. The 4% figure from the Reddit sample may understate the true prevalence because the platform skews male, meaning a female-only subsample would likely show a higher rate.

"We can't say that GLP-1s are actually causing these symptoms," said Neil Sehgal, the study's first author and a doctoral student at Penn. "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."

Co-author Jena Shaw Tronieri, a senior research investigator at Penn's Center for Weight and Eating Disorders, offered a potential biological mechanism. GLP-1 drugs are thought to work in part by engaging the hypothalamus, which regulates a wide variety of hormones beyond appetite, including reproductive hormones, thyroid function, and thermoregulation. "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," she said.

The AI Method | Large Language Models Meet Drug Safety

The study used large language models to map informal patient descriptions, the unstructured, colloquial language people use on Reddit, to standardized medical terminology at a scale that was previously impractical. Traditional pharmacovigilance relies on the FDA Adverse Event Reporting System (FAERS), which captures reports submitted by healthcare providers, manufacturers, and patients. But FAERS suffers from well-documented underreporting. Patients who experience side effects they consider minor or embarrassing often do not file formal reports, but they do post about them anonymously on social media.

The Penn team's approach flips that dynamic. Instead of waiting for patients to navigate a formal reporting process, the AI system meets them where they already are, discussing their experiences in plain language on a public forum. The model identifies medical concepts embedded in casual descriptions, "I've been freezing since I started the shot" maps to "chills/hypothermia-like symptoms," and aggregates them across hundreds of thousands of posts to identify statistically significant clusters.

The research team plans to expand the methodology beyond Reddit and beyond English-language communities, potentially capturing side-effect signals from populations that are even less represented in formal pharmacovigilance systems. If the approach scales, it could function as a real-time complement to FAERS, identifying safety signals months or years before they surface through traditional reporting channels.

Limitations | Reddit Is Not a Clinical Trial

The researchers were careful to note several important caveats. Reddit users skew younger, more male, and disproportionately American, meaning the findings may not reflect the broader population of GLP-1 users. The study cannot establish causation, only correlation between drug use and reported symptoms. Users may attribute symptoms to their medication that are actually caused by other factors, including the weight loss itself, which is known to affect menstrual cycles independently of any drug effect.

There is also a self-selection bias inherent in the data. People who experience side effects may be more likely to post about them than people who tolerate the drugs well, potentially inflating the apparent prevalence of adverse reactions. Conversely, people who stop taking the drugs due to intolerable side effects may leave the communities entirely, removing their experiences from the dataset.

Despite these limitations, the study's authors argue that the sheer volume of data, 410,000 posts from 67,000 users, provides statistical power that individual case reports cannot match. The consistency of the reproductive and temperature-related signals across multiple subreddits, time periods, and both drug classes strengthens the case that these are real patterns rather than noise.

Novo Nordisk and Eli Lilly | Growing Safety Scrutiny

The study arrives at a sensitive moment for both Novo Nordisk and Eli Lilly. Prescriptions for GLP-1 agonists continue to climb globally, with an estimated 30 million people worldwide now taking some form of semaglutide or tirzepatide. The drugs have become the most commercially successful pharmaceutical class since the statin era, generating combined annual revenue that exceeds the GDP of many small nations.

Novo Nordisk, the maker of Ozempic and Wegovy, has faced multiple lawsuits alleging that the company failed to adequately warn patients about gastrointestinal side effects, including gastroparesis (stomach paralysis) and intestinal blockages. The Penn study's identification of previously unreported symptom classes could broaden the scope of regulatory and legal scrutiny. Eli Lilly faces similar exposure with Mounjaro and Zepbound, though tirzepatide's dual-agonist mechanism (targeting both GLP-1 and GIP receptors) may produce a somewhat different side-effect profile.

Neither company has publicly responded to the Nature Health study. Both have previously stated that their drugs' safety profiles are well-characterized through extensive clinical trial programs and post-marketing surveillance. The FDA has not indicated any plans to update labeling based on the Penn findings, though the agency has increasingly acknowledged the value of real-world evidence, including social media data, in pharmacovigilance.

For the millions of patients taking these drugs, the study's message is not that GLP-1 agonists are unsafe. It is that the side-effect conversation may be incomplete. Symptoms that patients experience but do not report to their doctors, because they seem unrelated, embarrassing, or minor, can aggregate into population-level signals when captured at scale. The Penn team's AI-powered approach demonstrates that the next generation of drug safety monitoring may not come from hospitals or regulatory agencies, but from the patients themselves, talking openly in the places where they already gather.