AI Analyzes 150K Reddit Posts: Unapproved Weight-Loss Drug Shows Surprising Appetite Increase

Researchers from the University of Pennsylvania published a study in JAMA Network Open that applied large language models to analyze nearly 150,000 Reddit posts and comments from about 40,000 users discussing retatrutide, a triple-hormone-receptor agonist (GLP-1, GIP, glucagon) still in Phase III trials and not yet FDA-approved. Despite its unapproved status, versions of the drug circulate on gray markets, and users share experiences online.

Unexpected Symptom Pattern: Increased Appetite Tops the List

After excluding expected effects like weight loss and appetite suppression, the most frequently reported classifiable symptom was increased appetite —a striking contrast to Phase II clinical trial results where the most common adverse events were nausea and other gastrointestinal issues. The study identified 7,823 users who reported at least one classifiable potential symptom out of an estimated 14,000 users who described personal use.

Comparison with Approved GLP-1 Drugs

Retatrutide activates three hormone receptors, whereas semaglutide activates one and tirzepatide activates two. Compared with the team's prior analyses of Reddit discussions on semaglutide and tirzepatide (over 400,000 posts), retatrutide discussions showed higher proportions of reports for appetite-related symptoms, sleep disturbances, increased heart rate, and muscle-related changes, while nausea and gastrointestinal complaints were relatively less prominent. The authors caution that these are proportions of online reports, not incidence rates, and the user population—people willing to obtain unapproved drugs from gray markets, often using multiple experimental peptides—differs from clinical trial cohorts.

AI Methodology: From Forum Chatter to Structured Data

The pipeline first filtered posts to retain only those describing personal use (≈14,000 users). Then an LLM extracted self-attributed symptoms and mapped colloquial descriptions to standard medical terminology (MedDRA). This automated approach made it feasible to process 150,000 posts—a task impractical for manual review—and revealed recurring patterns across thousands of individual narratives.

From Signals to Testable Hypotheses

The senior author, Sharath Chandra Guntuku, director of the Computational Social Listening Lab, emphasized that the goal is to generate testable hypotheses from online discourse, not to establish causality. A prior application of this method flagged menstrual irregularities in semaglutide/tirzepatide discussions; a subsequent independent analysis of the FDA Adverse Event Reporting System (FAERS) published in Obstetrics & Gynecology also found disproportionately high reporting of certain menstrual changes, though causation remains unproven.

Limitations and Editorial Perspective

Co-author Lyle Ungar stressed that the team cannot confirm the symptoms were caused by retatrutide, nor even verify that users received genuine retatrutide, given the unregulated supply. Jena Shaw Tronieri noted that the self-selected gray-market user base and poly-pharmacy use confound interpretation. The editorial observation underscores that AI's value lies in surfacing signals worthy of rigorous follow-up; independent validation determines their weight.