A Penn team used natural language processing on 410,198 posts from self identified semaglutide and tirzepatide users and surfaced reproductive and temperature related symptoms that the drugs' labels don't list.
Chills, hot flushes, and menstrual irregularities: these are the side effects that GLP-1 drug labels do not currently list, and they show up clearly in a new analysis of 410,198 Reddit posts written by people taking semaglutide or tirzepatide in everyday life.
The work, published in Nature Health by a team based at the University of Pennsylvania's School of Engineering and Applied Science, is one of the largest social-media pharmacovigilance studies of the GLP-1 drug class to date. It treats Reddit not as a source of medical truth, but as a megaphone for the patient experience that randomized trials structurally undersample: thousands of people describing, in their own words, what a drug did to their body.
The dataset covers posts from May 2019 through June 2025 that mention either semaglutide (sold as Ozempic for type 2 diabetes and Wegovy for weight management) or tirzepatide (sold as Mounjaro and Zepbound). 67,008 unique Reddit users self-identified as taking one of the drugs. 43.5% of those users described at least one side effect, according to the team's natural-language processing pipeline.
The recognized cluster dominated. Nausea at 36.9%, fatigue at 16.7%, vomiting at 16.3%, constipation at 15.3%, and diarrhea at 12.6% were the most common complaints. These mirror the gastrointestinal profile that has shown up in the clinical trial literature and on the drugs' FDA labels. Anyone who has read the prescribing information for Ozempic or Wegovy will recognize the list.
The genuinely new material sits elsewhere. The researchers flag two clusters of symptoms that patients describe repeatedly but that are not well represented in current labeling or in the trial record: reproductive effects, including menstrual irregularities, and temperature-related effects, including chills and hot flushes. The paper's authors describe these as "potentially unrecognized" adverse events, a careful phrase, and stop short of claiming causality.
That distinction matters. The Penn team is not alleging that the FDA or the manufacturers concealed known side effects. They are documenting a coverage gap between the patient experience and the trial-derived label, and proposing a method to surface it at scale. "This study provides early real-world evidence of adverse events not adequately captured in current GLP-1 RA labeling," the authors write, in language that researchers, not the press, chose. The contribution is methodological. It is a way to listen to 410,000 patient voices at once.
The mechanics are worth understanding. The team built a text-analysis pipeline that ingests Reddit posts mentioning the drugs, identifies users who describe personal use, and clusters the side effects they report. The methodology and code are public on GitHub, and the lead researcher's academic page lists Penn Engineering as the institutional home. The intent is complement, not replacement. The authors frame social-media pharmacovigilance as an early-warning layer that runs alongside, and feeds questions into, traditional safety surveillance systems like the FDA's FAERS database. A patient reporting chills on Reddit is not a confirmed adverse drug reaction. It is, however, a signal at a population scale that no pre-approval trial can match.
That scale is the second thing the press has consistently underplayed. The dominant "AI finds hidden side effects" framing treats the model as the protagonist. The model is a tool. The data is anonymous, voluntary patient speech, written by people describing what happened to their bodies in the months after starting a drug. The AI is just the part that makes the data legible to a regulator.
It is also worth saying what the study does not do. It does not establish that chills, hot flushes, or menstrual irregularities are caused by GLP-1 drugs. The data is self-reported, the population is selected (Reddit users are not a representative sample of all patients), and the symptom clusters are associations, not causal findings. Medical News Today's coverage of the paper notes the same boundary, and the authors themselves emphasize it. The ScienceDaily summary of the release repeats the institutional framing, so the responsible version of this story is straightforward: the paper is a methodological contribution, the new signal is real but preliminary, and the symptom categories the trial pipeline missed are the part the field should now investigate properly.
The next move belongs to the people who run those follow-up investigations. If reproductive and temperature-related effects are real, structured post-market surveillance and reproductive-health sub-studies are how the field finds out. The Penn team has handed regulators and academic pharmacovigilance groups a candidate list, drawn from 410,000 people who took the time to write about it. Whether the labels eventually update will depend on what those investigators do next, not on what an AI found in a corpus.