Хиймэл оюун ухаан ашиглан GLP-1 бүлгийн эмүүдийн гаж нөлөөг Reddit-ийн 400 мянган пост дээр шинжилжээ

The study, published recently in Nature Health, examined more than five years of posts from nearly 70,000 Reddit users. Two categories stood out as particularly deserving of further investigation: reproductive symptoms, including changes in menstrual cycles, and problems involving body temperature, such as chills and hot flashes.

The findings do not establish that the medications caused these symptoms. Instead, researchers say the massive collection of spontaneous patient reports may reveal signals worth examining more closely.

“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.”

What Patients Report Outside Clinical Trials

Clinical trials are designed to determine whether treatments work and to identify important safety problems, but they cannot necessarily capture every symptom that matters to patients once a medication is being used by a much larger population.

“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 distinction is important. The study found associations in what people discussed online, not proof that GLP-1 drugs were responsible for those experiences.

“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.”

Using Social Media as an Early Health Signal

The idea of mining online conversations for clues about drug safety predates today’s AI boom. In 2011, Ungar participated in one of the earliest efforts to use material created by internet users to identify possible adverse effects from medications.

Social media can capture experiences that patients discuss with one another but may never formally report to a doctor, drug manufacturer, or regulator.

“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.”

Since then, online patient communities have expanded enormously. That has made social media a potentially valuable source for studying how medications affect people in everyday life, although gaining access to platform data has become more difficult.

Traditional clinical research remains essential, the researchers emphasize, but online conversations can provide information far more quickly when millions of people begin using a drug.

“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.”

AI Makes Massive Social Media Analysis Possible

One of the biggest obstacles has always been scale.

Guntuku describes the approach as “computational social listening,” which uses computational methods to identify patterns in large collections of online conversations about health.

Patients, however, rarely describe symptoms using standardized medical terminology. One person might describe feeling unusually cold, another might mention constant chills, while a clinician could categorize those experiences using a specific medical term.

Researchers therefore need a way to translate everyday language into standardized categories. One important reference is the Medical Dictionary for Regulatory Activities (MedDRA), which provides terminology widely used to classify medical conditions, symptoms, and adverse events.

Previously, connecting huge numbers of informal social media posts with standardized medical terminology required enormous amounts of work, limiting how much data researchers could realistically analyze.

Large language models such as GPT and Gemini are changing that equation by allowing researchers to process and categorize vast amounts of text more consistently and quickly.

“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.

Unexpected Symptoms Emerge From 400,000 Posts

The researchers stress that Reddit users do not represent the overall population of people taking GLP-1 drugs. Reddit users tend to be younger, are more likely to be male, and are disproportionately located in the United States.

Even with that limitation, the analysis produced a reassuring sign that the approach was detecting genuine patterns. Many of the symptoms discussed by Reddit users closely matched already known effects of semaglutide and tirzepatide.

About 44% of users included in the study described at least one side effect. Gastrointestinal problems were the most common, consistent with the nausea and other digestive issues already associated with these medications.

More intriguing were symptoms that appeared frequently enough to attract the researchers’ attention but may not be as well represented in current drug labels or conventional adverse event reports.

Nearly 4% of users who reported side effects described reproductive symptoms. These included changes in menstruation such as bleeding between periods, heavy bleeding, and irregular menstrual cycles.

Users also described changes involving body temperature, including chills, feeling unusually cold, hot flashes, and symptoms resembling a fever.

Fatigue was another notable finding. It was the second most frequently reported complaint in the Reddit data, even though relatively few clinical trials reported fatigue often enough for it to reach established reporting thresholds.

Why Menstrual and Temperature Changes Are Interesting

One possible reason these reports caught researchers’ attention involves the hypothalamus, a small but extremely important region of the brain. Among its many jobs, the hypothalamus helps regulate hunger, hormones, reproduction and body temperature.

“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.”

The researchers are not proposing that this biological connection proves GLP-1 drugs are responsible. Instead, it offers another reason to test the patient-reported patterns more carefully through controlled research.

Turning Online Conversations Into Research Leads

For now, the team hopes the results encourage scientists and clinicians to pay closer attention to symptoms that patients repeatedly discuss online.

“They’re clearly on patients’ minds, and that’s worth paying attention to,” says Sehgal.

The researchers also want to broaden their analysis beyond Reddit and beyond English-language communities. Doing so could help determine whether the same patterns emerge among different groups of people and on different social media platforms.

“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.

In the longer term, rapid AI analysis of online patient conversations could potentially become an early detection system for emerging health concerns involving drugs, supplements and wellness products.

That could be particularly valuable for substances that become popular online faster than conventional research can keep up. Loosely regulated or unregulated products, including injectable peptides, can spread quickly through communities on Reddit, TikTok and other platforms. Discussions among users may therefore provide some of the earliest indications of unexpected effects.

“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.

This study was conducted at the University of Pennsylvania School of Engineering and Applied Science. The authors report no outside funding. Tronieri reports receiving an investigator-initiated grant, on behalf of the University of Pennsylvania, from Novo Nordisk and receiving consulting fees from Currax Pharmaceuticals, LLC. The other authors report no conflicts of interest.