AI Predicts Who Will Truly Benefit From Weight Loss Drugs Like wegovy adn Zepbound
Table of Contents
A new analysis of millions of patient records is paving the way for personalized medicine in the burgeoning market for GLP-1 weight loss drugs, possibly maximizing benefits and minimizing side effects for individuals. Researchers are beginning to identify the clinical characteristics that distinguish “super responders” – those who experience dramatic weight loss – from patients who see more modest results.
“We need to know which drugs will be most helpful to your patients,” said Venky Soundarajan, director of research at nference. “We also need to know what benefits and what side effects are likely to occur given the patient’s unique medical history.”
The Spectrum of Response to GLP-1 Medications
The analysis revealed a wide range of responses to GLP-1 medications. Approximately 12.5% of patients were classified as “super responders,” achieving more than 15% body weight loss within one year of starting treatment. An additional 35% experienced an “intermediate response,” losing between 5% and 15% of their body weight in the first year.However, nearly half of the patients (47%) were considered “least responders,” losing less than 5% of their body weight, with another 5% initially losing around 5% only to regain it within a year.
While weight loss trajectories varied, the data indicates newer drugs are improving outcomes. “But when you break it down by brand, you see that over time the drugs are getting better at reducing the proportion of patients who remain in the lowest weight loss group,” Soundarajan explained. Eli Lilly’s Zepbound and Munjaro showed that 23% to 28% of patients fell into the minimal weight loss category, a similar rate to Novo Nordisk’s offerings. In contrast, Wegovy and Ozempic had 30% to 43% of patients in the minimal weight loss group. Older GLP-1 treatments like Lilly’s Trulicity and Novo’s Saxenda and Victoza demonstrated even lower success rates, with 46% to 63% of patients experiencing minimal weight loss.
The researchers leveraged artificial intelligence to analyze weight loss outcomes in relation to the presence of 1,300 different medical conditions, both before and after treatment. This revealed surprising correlations. For example, patients prescribed Zepbound with pre-existing knee pain or muscle stiffness without osteoarthritis were more likely to become super responders, suggesting a benefit for those with obesity-related muscle dysfunction but preserved joint health.
However, certain conditions appeared to hinder success with Zepbound. “If you have knee pain, osteoarthritis, chest pain, sleep apnea, or fibromyalgia, you are less likely to be a Zepbound super responder in terms of weight loss,” Soundarajan stated.
Conversely,the analysis showed positive correlations with other medications. Researchers found that patients with sciatica experienced symptom enhancement while taking Wegovy. Furthermore, patients with melanoma were more likely to respond to Wegovy, those with actinic keratosis to Munjaro, and individuals with age-related osteoporosis to Ozempic. Notably, patients across all drug brands reported improvements in sinus pressure.
The Future of Personalized Weight Loss Treatment
Given the complexity of individual health profiles, the researchers are now focused on developing an algorithm that can predict drug efficacy and potential risks. This algorithm would assign scores based on a patient’s medical history, guiding treatment decisions.
“These signals will become increasingly sophisticated as data is collected from more and more patients,” Soundarajan concluded, highlighting the potential for a future where weight loss medication is tailored to the individual, maximizing benefits and improving patient outcomes.
