In an innovative study featured in Nature Health, researchers delved into over 400,000 Reddit posts from nearly 70,000 users spanning more than five years. Their analysis revealed various symptoms frequently discussed by users, including some that merit further scientific scrutiny, such as menstrual irregularities and temperature-related issues like chills and hot flashes.
According to Sharath Chandra Guntuku, Research Associate Professor in Computer and Information Science at Penn Engineering and the study's senior author, "Some side effects, such as nausea, are already known, indicating that our method is capturing genuine signals. The symptoms reported by patients, often unprompted, are crucial for clinicians to consider."
Lyle Ungar, a Professor in CIS and co-author of the study, noted that social media can offer insights into patient concerns that may not be raised during medical appointments.
"Clinical trials typically identify the most severe side effects," Ungar explained. "However, they may overlook symptoms that are significant to patients. While social media isn't always representative, a large volume of posts can highlight additional concerns."
AI and Reddit Unveil Emerging GLP-1 Concerns
The researchers clarified that their findings do not establish a causal link between the medications and the symptoms discussed online. Instead, they indicate patterns that may warrant further exploration.
Neil Sehgal, the study's first author and a doctoral student in CIS, stated, "We can't definitively say that GLP-1s are causing these symptoms. Yet, nearly 4% of Reddit users reported menstrual irregularities, a figure likely higher in a female-only sample, indicating a signal worth investigating."
This research builds on years of examining online discussions for insights into drug side effects. Ungar has been involved in similar projects since 2011, focusing on user-generated content for adverse drug reactions.
"Online patient communities function like a neighborhood grapevine," Ungar remarked. "Individuals using these medications share experiences in real time, often missing from doctors' visits or official reports."
As social media platforms have evolved, researchers find these discussions increasingly valuable for health-related information, despite the growing complexity of data collection and analysis.
"While clinical trials are the gold standard, they are inherently slow," Guntuku noted. "This method can accelerate the process, which is crucial when a drug transitions from niche to mainstream rapidly."
Large Language Models Enhance Side Effect Detection
A significant challenge in analyzing online health discussions is scale. Users express symptoms in diverse ways, complicating comparisons with standardized medical terminology.
The advent of large language models like GPT and Gemini has transformed this landscape. Researchers now leverage these AI systems to process extensive online discussions more effectively and consistently.
"Large language models enable quicker analysis with a level of standardization that was previously challenging," Sehgal explained.
While Reddit users may not fully represent the general population, many reported symptoms align with known side effects of semaglutide and tirzepatide. Approximately 44% of users mentioned at least one side effect, predominantly gastrointestinal issues.
Unexpected Symptoms Reported by GLP-1 Users
Notably, researchers identified symptoms that might not be adequately represented in current drug labeling or standard adverse event reporting systems. Nearly 4% of users reported reproductive symptoms, including irregular menstrual cycles and heavy bleeding. Others mentioned temperature-related symptoms such as chills and hot flashes.
Fatigue emerged as a common complaint, ranking as the second most reported symptom, even though it is less frequently highlighted in clinical trials.
"These medications interact with the hypothalamus, which regulates various hormones," explained Jena Shaw Tronieri, Senior Research Investigator at Penn's Center for Weight and Eating Disorders. "While this doesn't confirm causation, it suggests that reports of menstrual and temperature changes warrant systematic study."
Researchers Aim to Broaden Analysis Beyond Reddit
The research team aspires to encourage scientists and healthcare providers to pay closer attention to the side effects discussed online. "These concerns are evidently on patients' minds," Sehgal emphasized.
Plans are underway to expand the analysis beyond Reddit and English-speaking communities to explore whether similar patterns exist across other social media platforms globally.
Ultimately, the researchers believe that AI-assisted analysis of social media conversations could be pivotal in identifying emerging concerns about medications and wellness trends much sooner than traditional methods allow. This approach could revolutionize how we monitor health products, particularly those rapidly gaining popularity.
This study was conducted at the University of Pennsylvania School of Engineering and Applied Science. The authors report no outside funding. Tronieri has received an investigator-initiated grant from Novo Nordisk and consulting fees from Currax Pharmaceuticals, LLC. The other authors report no conflicts of interest.