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Large-scale analysis of more than 400,000 Reddit posts has identified patient-reported symptoms associated with glucagon-like peptide-1 receptor agonists that may not be fully captured in clinical trials, with reproductive and temperature-related effects emerging as signals for further investigation in real world evidence
A research team at the University of Pennsylvania, Philadelphia, USA, has used artificial intelligence to analyse more than 400,000 Reddit posts and identified patient-reported symptoms associated with glucagon-like peptide-1 (GLP-1) receptor agonists that may not have been explored in randomised clinical trials (RCTs) or regulatory documentation. The findings have drawn attention to patterns of experience that could warrant closer clinical scrutiny as use of these therapies expands.
The study focused on widely prescribed agents including semaglutide and tirzepatide, which have seen rapid uptake in both type 2 diabetes management and weight reduction. Drawing on posts spanning more than half a decade from nearly 70,000 Reddit users, the analysis identified two principal classes of symptoms that appear underrepresented in formal reporting systems:
The researchers emphasised that their findings do not establish any causality. However, the scale and consistency of patient-reported experiences have suggested that these signals merit further investigation. Nearly four per cent of users who reported adverse effects described menstrual irregularities. Alongside this, users frequently reported altered temperature perception, including sensations of cold, fever-like symptoms and episodic flushing.
“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 Dr Sharath Chandra Guntuku, research associate professor in computer and information science at the University of Pennsylvania and senior author of the study.
“The underreported symptoms are leads that came from patients themselves, unprompted and clinicians could potentially pay attention to them,” he said.
Established adverse effects associated with GLP-1 receptor agonists were also evident across the dataset. Approximately 44 per cent of users reported at least one side effect, with gastrointestinal symptoms such as nausea and diarrhoea predominating.
Fatigue emerged as the second most frequently reported complaint – despite limited representation in RCT datasets which may reflect differences between structured trial environments and real-world use.
“RCTs generally identify the most dangerous side effects of drugs,” said Dr. Lyle Ungar, professor in computer and information science and co-author of the study.
“But they can fail to find what symptoms patients are most concerned about. Even though social media is not necessarily representative, a large collection of posts may reflect additional concerns,” he added.
The analytical approach builds on earlier work in digital pharmacovigilance, an area in which Ungar had participated as early as 2011. At that time, attempts to extract safety signals from user-generated online content were constrained by the difficulty of mapping informal descriptions of symptoms to standardised clinical vocabularies such as the Medical Dictionary for Regulatory Activities.
That limitation has now begun to ease with the application of large language models, including systems such as GPT and Gemini, which enable automated classification and standardisation at scale.
“RCTs are the gold standard, but by design, they are slow,” said Guntuku.
“This is not a replacement for trials, but it can move much faster, and that speed matters when a drug goes from niche to mainstream almost overnight,” he said.
The concept, described by the authors as ‘computational social listening’, has relied on the ability of these models to interpret highly variable patient language and align it with clinically recognised terminology. This has allowed researchers to process datasets of a size that would previously have been impractical, offering a complementary perspective to conventional pharmacovigilance systems.
Despite these technical advances, the researchers have acknowledged important limitations. Reddit users are typically younger, more likely to be male and disproportionately based in the USA factors which all raise questions about generalisability of the results drawn from this data set.
Even so, the broad alignment between reported gastrointestinal effects and established safety profiles has lent credibility to the approach, while the emergence of less well-documented symptoms has highlighted potential gaps in current understanding.
“These drugs are thought to work by engaging part of the brain called the hypothalamus, which helps regulate a wide variety of hormones,” said Dr. Jena Shaw Tronieri, senior research investigator at the University of Pennsylvania’s Center for Weight and Eating Disorders and co-author of the study.
“That does not mean the medications are necessarily causing these symptoms but it could suggest that reports of menstrual changes and body temperature fluctuations are worth studying more systematically,” she added.
The researchers have argued that patient discussions on social media may offer a form of early signal detection, particularly for therapies that achieve rapid uptake or circulate in less regulated markets. In such contexts, informal reporting may capture experiences that would not reach clinicians, or formal reporting systems, until much later.
“Online patient communities work a lot like a neighbourhood grapevine. People who are living with these medications are swapping notes with each other in real time, sharing experiences that rarely make it into a doctor’s office visit or an official report,” said Ungar.
Future work will aim to extend the analysis beyond Reddit and beyond English-language datasets in order to assess whether similar patterns emerge across different populations and platforms. The researchers have suggested that such expansion will prove essential to determine whether the observed signals reflect global patient experience or platform-specific dynamics – with the inherent risk of confounding factors such as confirmation bias.
“We can’t say that GLP-1s are actually causing these symptoms,” said Neil Sehgal, a doctoral candidate and first author of the study.
“But nearly four per cent of the Reddit users in our sample reported menstrual irregularities which would [therefore] be even higher in a female-only sample. We think that’s a signal worth investigating,” he said.
In the near term, the team has expressed hope that clinicians and regulators will take greater account of patient-reported outcomes emerging from digital platforms. As use of GLP-1 receptor agonists continues to expand, the integration of conventional clinical evidence with large-scale observational data may offer a more complete picture of how these therapies affect patients in routine practice.
“The whole point of this kind of approach is that it can move quickly and that’s exactly when it’s most valuable,” said Guntuku.
For further reading please visit: 10.1038/s44360-026-00108-y
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