We analyzed 14,252 Reddit posts about GLP-1 medications

About the Researcher: This project was built by Tyler Brown, who lost 90 pounds on retatrutide over 8 months and documented every dose, side effect, and weigh-in publicly. The research grew out of wanting to know whether his experience was normal. After months in the GLP-1 Reddit communities, he built this dataset to answer the questions those communities were asking. Read the full personal journey or the detailed side effects breakdown.

Data Source: 14,252 posts from 7 subreddits. The core dataset covers r/Retatrutide, r/Zepbound, r/Ozempic, r/GLP1microdosing, r/Semaglutide, and r/tirzepatidecompound (10,129 posts). A supplementary women’s health scan added 4,123 new posts from r/RetatrutideWomen, r/PCOS, and r/loseit. Posts were collected over the 12-month period ending March 2026. 4,651 posts (32.6%) are tagged as confirmed female based on self-identification, subreddit context, or women’s health indicators.

How Posts Were Selected: This is not a full subreddit scrape. For the core dataset, we used Reddit’s search API with approximately 22 targeted keyword queries per subreddit — terms like “side effects,” “nausea,” “hair loss,” “switching from,” “food noise,” “cured my,” and “dosing protocol,” among others. Each query was run twice per subreddit (sorted by relevance and by top posts). After deduplication by post ID, we had ~10,400 unique posts. The only filter applied before analysis was a minimum of 100 characters of body text, which removed title-only posts, image-only posts, and link posts with no text. The women’s health supplementary scan used the same methodology across 3 additional subreddits, adding 4,123 new unique posts after deduplication against the core dataset.

Analysis Method: Every post that passed the length filter was sent to Claude Sonnet 4.6 in batches for structured extraction. No posts were skipped based on content — even posts with no identifiable side effects were analyzed and received a summary. Claude extracted: drug name, dose, side effects (with severity and remedies tried), unexpected benefits, drug switching events, and a one-line summary.

What this dataset does NOT include: Comments and replies (top-level posts only); posts older than one year; posts from subreddits not in our list; posts that matched none of our 22 search queries; and posts with fewer than 100 characters of body text.

What to trust: The direction of between-drug differences for side effects with large sample sizes. Drug-specific signals that appear at 3–7× the rate of comparators. Remedy effectiveness for remedies with 10+ trials.

What to treat with caution: Specific percentages (inflated for problem-driven posts, deflated for benefits). Low-sample dose tiers. The keyword-based collection method means posts discussing side effects in unusual language may be underrepresented. The women’s filter (4,651 posts) is based on self-identification and contextual signals — some women in the dataset may not be tagged, and the untagged population is not exclusively male.

Known Biases: Selection bias (people with problems post more than people doing well); self-reported doses and diagnoses; no true denominator for incidence rates; severity ratings based on AI interpretation of language; Reddit users skew younger and more tech-literate than the general patient population.

Last Updated: March 10, 2026

Dataset & DOI: The full structured dataset (14,252 posts) and this report are publicly archived on Zenodo under CC BY 4.0. Free to download, cite, and reference. https://doi.org/10.5281/zenodo.18943922