AI scans 400,000 Reddit posts and finds hidden Ozempic side effects
Penn researchers built a machine-learning pipeline that translates what patients say in Reddit posts into the standardized medical codes regulators use worldwide — and released the full thing on GitHub last week. That is the story. The side effects are the demo.
The pipeline, publicly released May 26 by a team at the University of Pennsylvania, uses off-the-shelf GPT models to scan Reddit discussions of GLP-1 weight-loss drugs — posts from people describing brain fog, nausea, menstrual changes, temperature sensations — and converts that informal language into MedDRA codes, the taxonomy the FDA and other agencies rely on to categorize adverse events. It processed more than 400,000 Reddit posts across nine forums spanning May 2019 through June 2025. The researchers say the method is reproducible for roughly the cost of API calls.
The side effects the pipeline surfaced match what clinical trials already found: nausea most common at 36.9%, fatigue at 16.7%, vomiting at 16.3%, constipation at 15.3%, and diarrhea at 12.6%, according to the study published in Nature Health. Of 67,008 users who self-reported GLP-1 medication use in the sample, 43.5% described at least one side effect. These are confirmations, not surprises.
The more interesting signals were less visible before. Nearly 4% of Reddit users in the sample reported menstrual irregularities — a category the researchers flagged as not well-documented in existing surveillance systems. Temperature-related complaints, including chills, hot flashes, and feeling cold, also emerged as a cluster the researchers called unrecognized in current systems. Both reproductive and temperature symptoms are biologically plausible: GLP-1 drugs work via the hypothalamus, which regulates hormones and body temperature. But the findings are correlation in self-reported social media data, not clinical evidence — the researchers cannot say GLP-1s are actually causing these symptoms.
The architectural significance is what the pipeline found and how. Social media surveillance has historically struggled to capture signals that don't map cleanly to structured adverse-event reports. The gap between what patients write — "I have not had a period in six weeks" — and what MedDRA codes can count — "menstrual irregularities" — has been a data problem. The Penn team automated the translation using GPT-4o-mini to classify self-reported GLP-1 use and GPT-4.1-mini with retrieval-augmented generation against MedDRA terminology files to extract and map side effects. They used the OpenAI Batch API to process the full workload at lower cost.
The key detail is what the pipeline is not. The researchers used off-the-shelf GPT models — no proprietary training, no custom fine-tuning. Any pharmaceutical company, academic researcher, or regulator with an API key and a question could run the same pipeline against Reddit or any other patient forum. The code is publicly released on GitHub, and the study is reported by ScienceDaily.
One coauthor, Jena Shaw Tronieri, reports an investigator-initiated grant from Novo Nordisk and consulting fees from Currax Pharmaceuticals, according to ScienceDaily. The authors report no other outside funding. Reddit users tend to be younger, more male, and US-based compared to the broader GLP-1 patient population — a known limitation of social media data.
FDA's existing side effect tracking system, FAERS, relies on voluntary reports submitted by healthcare providers and patients. It is real data but reactive — someone has to notice and report a symptom before it enters the system. If the Penn pipeline holds up in replication, it suggests a faster, cheaper, more continuous way to listen to what patients are actually saying. Whether pharma companies will build those tools, regulators will require them, or the findings will simply live in published papers while real-world patients keep reporting in forums — that has not been decided.