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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Wanted to start a dedicated thread on this, as I am still learning, and have more questions than answers, but a bit of experience to bring to the table. Sorry in advance if this gets long.
I am two years in on the carnivore diet, and (to my knowledge) properly fat adapted. I can consume fats with relative abandon with no restroom related "side effects" shall we say.
As a type1 diabetic, I can definitely spend a good portion of anyone's time singing the praises of what I have been informed is something called gluconeogenesis, as it keeps me from having to carry sweet snacks always at the ready. I can work along, and not worry about low blood glucose most of the time, even as a type1. I have proven to myself that a zero carb diet is the way to health, especially with type1 in the mix, but I dont wish this thread to be isolated to those with a specific knowledge of diabetic physiology. As an additional thought, wearing a CGM (continuous glucose monitor) allows me to get a different perspective on what is happening as my body processes different foods and situations. Its my hope that some of this may be a help to others (as well as yours truly!)
Many folks here speak about being kicked out of ketosis if they consume carbs or sugars, and while I notice this myself, I ALSO notice that if I eat anything that contains sugar or carbohydrates, it will seemingly hamper my gluconeogenesis abilities, and I will drop terribly low in the hours following the compromised meal. This is as opposed to the normal days where I have eaten "clean" and my glucose level will drop to say, 70 and then self correct and I can go right on working (or sleeping) like its nothing.
I have been trying to get a straight story from all my internet searches (heh, get in line, right?!) ...so wanted to poll the community here for everyone's thoughts.
In another thread (that I didnt want to hijack) Bob spoke about "adaptive glucose sparing" and some of it's effects.
Here are my observations, please critique, or add to these with your thoughts!
- When I have been able to keep my diet SPOTLESSLY clean for many days in a row (no cheats, ever) my sugar levels seem to stay right on target and I feel simply amazing! I have been under the assumption that me being properly fat adapted, I am in these times reaping the benefits of adaptive glucose sparing.
Something that I am beginning to suspect, is that adaptive glucose sparing is akin to ketosis in that a dietary change can "kick us out" for a while. Is this at all correct?
I further notice that *sometimes * when I fast for any longer than one meal, my glucose levels can sometimes rise and stay elevated as if I just ate an oreo cookie! This happened most recently at a dr visit, and I was amazed at how high my glucose level was (145) even though I had not eaten anything for nearly 24 hours! ( @Geezy has made a comment in a recent post suggesting that I may have fasted too long before the appointment, which can throw your numbers off?) Anyway, I say *sometimes, because it doesnt seem to be a hard and fast rule, and on other occasions I can go for days and not have my sugar level rise at ALL, even with fasting!
There are also many regular days when no fasting is going on, and my diet has been a proper balance of meats and fats, but I throw some cheese in, I notice that after my midday meal, my sugar level goes up considerably as if I was back on the cookies again! Ill be sitting there later on thinking to myself "what the heck did I EAT??!!" as my blood sugar level is going up past 175! I have so far attributed this discrepancy to diet (perhaps as a result of eating things like cheese maybe?) and that in these times I am experiencing gluconeogenesis without the benefit of adaptive glucose sparing? Am I on the right track here?
Is the cheese (or even a small amount of dairy) enough to shut down adaptive glucose sparing to the point that my body begins creating a sizeable amount of glucose from the protein I just ate? Does it work like this? It certainly seems like it, but these are the questions I have in my head day to day.
Lastly, I hear just a bit about a process from time to time called glycogenolysis, that is similar to gluconeogenesis, but is instead the body's conversion of glycogen to glucose.
Is glycogenolysis actually what is saving me during the times I would drop "low"?
According to the internet at large, a high fat diet increases insulin resistance, but I have experienced my insulin resistance going DOWN as a result of carnivore, as well as my A1C.
Okay, Im done for a minute. Please let me know your thoughts!