By Nancy Lapid
(Reuters) -U.S. researchers are beginning to identify clinical characteristics that distinguish “super responders” to GLP-1 weight-loss drugs like Wegovy and Zepbound from patients who lose only moderate amounts of weight at best, according to a report published online ahead of peer review.
The massive data analysis may eventually help personalize treatment decisions for the drugs already being used by millions of patients. Individual patients’ health status before starting treatment can also help guide drug selection, the analysis suggests.
“If I am a clinician seeing patients, I need to know what medicine will best benefit my patient,” said study leader Venky Soundararajan of Massachusetts-based data analysis company nference. “I also need to know… what benefits and what side effects are they likely to have” given their unique medical history.
Researchers analyzed 14 million doctors’ notes and 15 million clinical data entries from more than 135,000 patients with and without diabetes who each took only one GLP-1 drug. They found that roughly 12.5% were “super responders” who lost more than 15% of their weight in the year after starting treatment.
Another 35% were considered moderate responders, having lost 5% to 15% in the first year. The largest group – the minimal responders, accounting for 47% – lost less than 5% of their body weight, while an additional 5% lost roughly 5% of their weight but gained it back again within a year.
The trajectories of weight loss were very diverse, Soundararajan said. “But when you break it down by the brands, you can see the medicines are getting better and better over time in lowering the percentage of patients who still continue to be in that minimal weight-loss group.”
With Eli Lilly’s Zepbound and Mounjaro, for example, 23% to 28% of patients fall into the minimal weight-loss group, compared to 30% to 43% with Novo Nordisk’s Wegovy and Ozempic. With earlier-generation GLP-1s like Lilly’s Trulicity and Novo’s Saxenda and Victoza, 46% to 63% of patients fell into the minimal weight-loss category, the study found.
AI TOOLS ANALYZE MEDICAL CONDITIONS
Using artificial intelligence tools, the researchers analyzed not only weight-loss outcomes but also the presence and absence of 1,300 different medical conditions before and after treatment.
For example, the pre-treatment presence of muscle stiffness without knee pain or osteoarthritis increased the probability a patient prescribed Zepbound would become a super responder.
That suggests patients with obesity-related muscular dysfunction but preserved joint health may be particularly likely to achieve exceptional weight-loss outcomes with tirzepatide, the active ingredient in Zepbound and Mounjaro, the researchers said.
“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, Soundararajan said.
Patients with sciatica saw improvements if they were prescribed Wegovy, the researchers also found.
“If you have melanoma, you are very likely to respond to Wegovy. If you have actinic keratosis, you are highly likely to respond to Mounjaro. If you have aged osteoporosis, you are very likely to respond to Ozempic,” Soundararajan said of weight-loss prospects.
Regardless of whether they received a Lilly or Novo drug, patients with sinus pressure beforehand reported improvements afterward.
Given that any individual patient is likely to have a cluster of medical conditions, the researchers say their next step is to develop an algorithm that yields scores to indicate the likely benefit and risk for each drug under different circumstances and test that in prospective studies.
“These signals will continue to get more and more and more refined as data is collected from more and more patients,” Soundararajan said.
(Reporting by Nancy Lapid; Editing by Bill Berkrot)
