Science1 publisher2 min readPublished
Penn team flags menstrual and temperature symptoms in 400,000 GLP-1 Reddit posts
A University of Pennsylvania group read five years of GLP-1 discussion from nearly 70,000 Reddit users and came out with two symptom families worth a real study, plus a nausea signal that shows the method finds what is already known.
The Scientist · Science desk
What happened
- A University of Pennsylvania team analysed more than 400,000 Reddit posts from nearly 70,000 users, covering over five years of discussion, and published the work in Nature Health.
- The posts came from people taking semaglutide, sold as Ozempic, Wegovy and Rybelsus, and tirzepatide, sold as Mounjaro and Zepbound.
- Two symptom groups stood out as deserving further investigation: reproductive symptoms including menstrual cycle changes, and body temperature problems such as chills and hot flashes.
- Nearly 4% of the users in the sample reported menstrual irregularities, a figure the study's first author called a signal worth investigating.
- The authors say they found associations in what people discussed online and cannot show that the drugs caused any of the symptoms.
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Why it matters
- capability A five-year archive of patient talk can put a menstrual-effects hypothesis in front of researchers years before a trial designed to test it could report.
- constraint Getting the posts is the hard part of repeating this work, and the release says platform access has tightened since these communities grew.
- decision A safety group now has to decide whether two forum-derived symptom families justify paying for a chart review or a targeted survey where dose and duration are recorded.
Nausea is the positive control. It is a well-known effect of these drugs, and it came out of the corpus alongside the unexpected findings [5][7]. "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, the study's senior author and a research associate professor in computer and information science at Penn Engineering [7][8]. Recovering a known effect demonstrates sensitivity. Specificity is a separate measurement, and it would need an estimate of how often the pipeline flags symptoms the drugs do not cause.
The corpus is smaller than 400,000 posts sounds. More than 400,000 posts from nearly 70,000 users across five years is fewer than six posts per user for the whole period [1][2][1]. Nearly 4% of those users mentioned menstrual irregularities, first author Neil Sehgal said, which is a little under 2,800 people at that sample size [10][2]. "We can't say that GLP-1s are actually causing these symptoms," Sehgal said [9].
Sehgal said the share would be higher in a female-only sample [10]. The release does not report the sex composition of the sample, so that 4% cannot be turned into a rate among people who menstruate [19]. The posters are not a random draw from patients either. Lyle Ungar, a professor in the same department and a co-author, said social media "is not necessarily representative" but that "a large collection of posts may reflect additional concerns" [12].
The approach predates the current wave of AI tooling. Ungar worked on one of the earliest efforts to use internet users' own material to identify possible adverse drug effects in 2011, fifteen years before this paper [13][3]. Online patient communities have grown enormously since, and the release says platform data has become harder to obtain [14].
Clinical trials are designed to find the most dangerous safety problems and cannot necessarily capture every symptom that matters once a drug is being used by a much larger population [16]. Menstrual change and body temperature complaints are candidates for that gap. The study puts them forward as leads and does not call them findings [4][6]. "The underreported symptoms are leads that came from patients themselves, unprompted, and clinicians could potentially pay attention to them," Guntuku said [17].
What to watch
- Whether the Nature Health paper reports a specificity check or comparison group beyond nausea as a positive control.
- Whether platform data terms permit a repeat pull, given the release's note that access has become harder.
- Whether a manufacturer or regulator follows the menstrual lead with a study where sex, dose and duration are recorded.