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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This supplement dramatically cuts heart attack risk, scientists say
Story by Vishwam Sankaran
A pharmacist removes boxes of "Uvesterol D" vitamin medication from a shelf© AFP via Getty Images
Scientists have found tailoring the intake of vitamin D in patients diagnosed with heart disease can dramatically cut the risk of heart attacks.
The finding suggests individualising vitamin D doses for patients based on their blood levels rather than administering uniform “one-size-fits-all” doses as has been done commonly until now.
Vitamin D is a hormone mainly synthesised in the skin via sunlight exposure, and is known for its positive effects on inflammation and heart health.
Widespread vitamin D insufficiency and the growing burden of heart disease globally emphasise the need for simple, cost-effective interventions to reduce death risk.
While low vitamin D concentration is linked to adverse heart disease outcomes, intervention studies until now have yielded inconsistent results, researchers say.
Now, scientists call for tailoring vitamin D intake for each patient to achieve optimal circulating levels as a key strategy to mitigate heart disease risk.
“Our results suggest that targeting vitamin D supplementation based on blood levels can significantly diminish the risk of subsequent heart attacks,” said epidemiologist Heidi T May from Intermountain Health.
“This approach demands a personalised, vigilant clinical protocol that diverges from the traditional fixed-dose model,” said Dr May, an author of the study presented at the American Heart Association’s Scientific Sessions 2025.
Researchers recommend a rigorous monitoring protocol which adjusts vitamin D supplementation every three months.
The latest clinical trial assessed 630 adult participants diagnosed with acute heart disease at Intermountain Medical Center in Utah for a six-year period, with an average follow-up of just over four years.
Nearly half had a history of prior heart attack.
Over 85 per cent of participants initially had suboptimal vitamin D levels below 40 nanograms per millilitre (ng/mL) of blood, underscoring widespread insufficiency of the hormone in this population.
Researchers grouped the participants randomly into two groups – one, which received standard care without vitamin D level management, and another treatment arm, which underwent serial blood testing and personalised dosing of the hormone to surpass the 40 ng/mL threshold safely.
Scientists found that the treatment group experienced a 52 per cent reduction in the risk of recurrent heart attacks compared to controls.
This finding marks one of the most significant effect sizes reported in recent vitamin D cardiovascular research, researchers noted.
However, researchers urge doctors to be cautious of the findings as it remains to be corroborated by larger, peer-reviewed trials.
Nonetheless, they say the data could pave the way for new heart attack prevention strategies centered on nutrient optimisation.
ARTICLE SOURCE: https://www.msn.com/en-us/health/other/this-supplement-dramatically-cuts-heart-attack-risk-scientists-say/ar-AA1QcrMa?
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