AI scans 400,000 Reddit posts to flag overlooked GLP-1 side effectsby University of Pennsylvania edited by Sadie Harley, reviewed by Robert Egan By using AI to analyze more than 400,000 Reddit posts, Penn researchers have identified patient-reported symptoms associated with GLP-1s, the popular weight-loss and diabetes drugs semaglutide and tirzepatide, that may not be fully captured in clinical trials or regulatory documents. The new study, published in Nature Health, covers more than half a decade of posts from nearly 70,000 Reddit users and highlights two main classes of symptoms that warrant further study: reproductive symptoms, including irregular menstrual cycles, and temperature-related complaints, such as chills and hot flashes. "Some of the side effects we found, like nausea, are well known, and that shows that the method is picking up a real signal," says Sharath Chandra Guntuku, Research Associate Professor in Computer and Information Science (CIS) at Penn Engineering and the study's senior author. "The underreported symptoms are leads that came from patients themselves, unprompted, and clinicians could potentially pay attention to them." "Clinical trials generally identify the most dangerous side effects of drugs," adds Lyle Ungar, Professor in CIS and a co-author on 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." The researchers caution that their findings are not causal. "We can't say that GLP-1s are actually causing these symptoms," notes Neil Sehgal, the study's first author and a doctoral student in CIS advised by Guntuku and Ungar. "But nearly 4% of the Reddit users in our sample reported menstrual irregularities, which would be even higher in a female-only sample. We think that's a signal worth investigating." Studying social media for healthIn 2011, Ungar participated in one of the earliest efforts to mine online, user-created content for information about drugs' adverse effects. "Online patient communities work a lot like a neighborhood grapevine," says Ungar. "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." In the years since, social media use has only grown, making data from these platforms increasingly promising as a source of information about the side effects of medications, even as the platforms themselves have made accessing the data more difficult. (Guntuku has also published research on strategies for adapting to changes in platform access.) "Clinical trials are the gold standard, but by design, they are slow," says 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." Leveraging AI to analyze social mediaUntil now, the most challenging part of this process, which Guntuku calls "computational social listening," has been scale. Because users vary in how they describe their symptoms, the effort required to map individual social media posts to language in the Medical Dictionary for Regulatory Activities (MedDRA), which clinicians use to describe symptoms, limited the amount of data this approach could handle. Now, large language models like GPT or Gemini have enabled the systematic analysis of social media posts at an unprecedented scale. "Large language models have made it possible to do this kind of analysis much faster with a level of standardization that could be difficult to achieve before," says Sehgal. Unreported symptomsWhile the population the researchers studied is admittedly not representative—Reddit users are younger, more likely to be male and disproportionately based in the United States—the symptoms described in their collective accounts largely match the known side effects of semaglutide and tirzepatide: about 44% of users in the study described at least one side effect, most commonly some form of gastrointestinal distress. What stood out was the nontrivial percentage of users who reported symptoms that may not be fully reflected in current drug labeling or routine adverse-event reporting. Nearly 4% of users who reported side effects described reproductive symptoms, including menstrual changes such as intermenstrual bleeding, heavy bleeding, and irregular cycles. Others reported temperature-related complaints, such as chills, feeling cold, hot flashes, and fever-like symptoms. In addition, fatigue ranked as the second most common complaint among Reddit users, despite reaching reporting thresholds in relatively few clinical trials. "These drugs are thought to work by engaging part of the brain called the hypothalamus, which helps regulate a wide variety of hormones," says Jena Shaw Tronieri, Senior Research Investigator at Penn's Center for Weight and Eating Disorders and a co-author of the study. "That doesn't 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." Future directionsIn the near term, the researchers hope their findings will encourage clinicians and researchers to take a closer look at the side effects patients are discussing online. "They're clearly on patients' minds, and that's worth paying attention to," says Sehgal. The team also hopes to expand the work beyond Reddit and beyond English-language communities to test whether the same patterns appear across different platforms and populations. "We don't really know yet whether what we're seeing on Reddit reflects the experience of GLP-1 users globally, or whether it's particular to the kind of person who posts on Reddit in the United States," Ungar says. Ultimately, the researchers believe this kind of rapid, AI-assisted social media analysis could become a useful way to spot early warning signs around emerging drugs and wellness trends. For substances that trend quickly online, especially those sold in loosely regulated or unregulated markets, like injectable peptides, patient discussions on platforms like Reddit and TikTok may offer one of the earliest clues to what users are actually experiencing. "The whole point of this kind of approach is that it can move quickly, and that's exactly when it's most valuable," says Guntuku. ARTICLE SOURCE: https://medicalxpress.com/news/2026-04-ai-scans-reddit-flag-overlooked.html
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Vegetarian Diet Linked To High Depression Scores, Suggests Large Meta-Analysis
by Jack Dunhill, Social Media Coordinator and Staff Writer
Vegetarian diets could be linked with higher depression scores, suggests a huge meta-analysis of almost 50,000 people by researchers in Bochum, Germany. The research backs up existing studies that have linked ditching meat to an increased likelihood of depression, but the reason why remains elusive.
Whether vegetarianism actually plays a role in depression is poorly understood. Some studies have pointed the finger at the diet, while others have refuted the findings. To identify a link – if there is one – Sebastian Ocklenburg and Jette Borawski performed a large-scale meta-analysis on published studies that compared the depression scores of non-vegetarians and vegetarians. Their results are published in the Journal of Affective Disorders.
After accounting for duplicates, there were a total of 8,057 vegetarians and 41,832 non-vegetarians included in the analysis. While the sample was large, many of the participants were from similar countries, and so the diversity within the study was relatively low.
The researchers then used a statistical program to scour the studies for mood disorder scores and sufficient data to be considered significant, and 13 studies fit the bill.
Once all the findings were analyzed, the researchers discovered a significant increase in the depression scores of vegetarians compared to non-vegetarians. However, while the data was significant, there was also significant heterogeneity in the studies (how conflicting the results were between each study), indicating there was certainly not a unanimous conclusion.
The authors are clear in their paper that they wish to make no conclusions based on the results – it is still unclear whether the link is causal from the diet, or whether those that experience depression are more likely to choose vegetarianism. In one study included in the analysis, for example, the results indicated that more often than not, people with depressive symptoms started their vegetarian diet after the onset of the disorder, suggesting it is not a causal link. It is suggested that depression may make the person more health-conscious, leading them to vegetarianism, or that depression enhances the feelings of empathy towards animals. This is purely speculation at the current time, however.
With a significant link established, the authors now call for further research to understand its true nature. The first step would be to include more countries into the studies, as there is a clear bias in many of the studies towards a small number of countries. Once these are included, identifying whether the diet underlies the symptoms, or is purely a resulting lifestyle, will be incredibly important.
ARTICLE SOURCE: https://www.iflscience.com/vegetarian-diet-linked-to-high-depression-scores-suggests-large-metaanalysis-60524
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