Head-to-head trial compares weight loss drugs
Weill Cornell Medicine
Tirzepatide (trade name Zepbound) promoted greater weight loss in individuals with obesity than did semaglutide (trade name Wegovy) in a clinical trial that compared the safety and efficacy of the injectable drugs. In the 72-week trial—led by an investigator at Weill Cornell Medicine and NewYork-Presbyterian and conducted with the University of Texas McGovern Medical School, the David Geffen School of Medicine at the University of California, Los Angeles, the University College Dublin and Eli Lilly—participants taking tirzepatide lost about 50 pounds—or 20.2% of their body weight—compared with those on semaglutide, who lost an average of 33 pounds or 13.7% of their baseline weight.
The results of the SURMOUNT-5 phase 3b study, published May 11 in the New England Journal of Medicine, showed that when both drugs are administered at their maximum doses, participants receiving tirzepatide were more likely to reach weight loss targets and saw a greater reduction in waist circumference than those on semaglutide.
In some ways, the outcome was not a surprise. “The results are consistent with—in fact, almost identical to—what we’ve seen in trials in which these drugs were evaluated independently,” said Dr. Louis Aronne, director of the Comprehensive Weight Control Center and the Sanford I. Weill Professor of Metabolic Research at Weill Cornell Medicine and principal investigator of SURMOUNT-5. In 2022, for example, Dr. Aronne led a study showing that a 72-week course of tirzepatide at its maximum dosage reduced body weight by 20.9%; a similar study published in 2021 reported a 14.9% loss with semaglutide after 68 weeks.
A Head-to-Head Comparison
The benefit of this study—a randomized, controlled trial of 751 people with obesity but without type 2 diabetes—is that the drugs could be compared head-to-head. “Doctors, insurance companies and patients are always asking, ‘which drug is more effective?’” said Dr. Aronne, who is also an internist specializing in diabetes and obesity at NewYork-Presbyterian/Weill Cornell Medical Center. “This study allowed us to do a direct comparison.” However, the trial was not conducted as a blinded analysis—the gold standard for minimizing bias in clinical trials. Because these drugs are administered via labeled auto-injection devices, participants knew which medication they were receiving.
Eli Lilly, the company that produces tirzepatide, sponsored the study, which was conducted at 32 sites across the United States and Puerto Rico. All participants received counseling regarding diet and exercise, and the side effects associated with both drugs were very similar. For example, about 44% of individuals in each treatment arm experienced nausea and 25% reported abdominal pain.
Nearly one-third (32%) of the people who took tirzepatide achieved a body-weight reduction of at least 25%, compared with 16% of those who received semaglutide. The improved performance is likely linked to tirzepatide’s dual mechanism of action, said Dr. Aronne. Whereas semaglutide works by activating receptors for a hormone called glucagon-like peptide 1, or GLP-1, tirzepatide mimics not only GLP-1 but an additional hormone, glucose-dependent insulinotropic peptide (GIP). Together, these actions reduce hunger, lower blood-glucose levels, and affect fat cell metabolism.
“The pathways that regulate weight are incredibly complicated,” Dr. Aronne said. Targeting multiple mechanisms may pave the way to additive weight loss. Trials are underway to determine whether tirzepatide, like semaglutide, also reduces the risk of cardiovascular events, such as heart attack and stroke.
The Next Generation
Dr. Aronne and his colleagues are currently testing the next generation of weight-loss drugs, including compounds such as Eli Lilly’s retatrutide, dubbed “triple G” for the three hormones it mimics: GLP-1, GIP and glucagon. In addition to possibly being more effective, drugs like retatrutide could also potentially benefit a broader population.
“Even though drugs like tirzepatide and semaglutide work really well, better than anything we have ever seen, we still have people who don't respond to them,” said Dr. Aronne. “So, moving forward, we want to keep trying to do better.”
Dr. Louis Aronne is a paid consultant and advisory board member for Eli Lilly and Company, the study sponsor and the manufacturer of Zepbound (tirzepatide). Dr. Aronne also serves as a paid advisory board member for Novo Nordisk, the manufacturer of Wegovy (semaglutide).
Journal
New England Journal of Medicine
Machine learning uncovers social risk clusters linked to suicide across U.S.
Using machine learning technology, a new study has identified three distinct profiles describing social and economic factors that are associated with a higher risk of suicide. Scientists at Weill Cornell Medicine and Columbia University Vagelos College of Physicians and Surgeons led the research that showed suicide rates vary significantly across the three clusters and that the patterns differ geographically across the United States.
The findings, published May 12 in Nature Mental Health, could facilitate more effective prevention strategies and thereby help counter the substantial rise in suicide rates over the past two decades in the U.S.
This is the first study to use unsupervised machine learning to analyze a comprehensive set of social determinants of health such as poverty, poor housing, lack of access to health care, harmful environmental exposures and social factors like high family stress, which can all contribute to suicide risk. While prior prevention efforts largely targeted individual or clinical risk factors, this research emphasizes the importance of broader, community-level social conditions.
Unsupervised machine learning can process massive data sets without labels or guidance to discover hidden patterns and relationships unbiased by researchers’ assumptions or partial data. This method allowed the researchers to characterize the overall social and environmental landscape in 3,018 counties, based on 284 social determinants of health. Three distinct clusters were identified which the researchers correlated with suicide rates from 2009 to 2019, after controlling for sex, age and race/ethnicity.
“Our findings could help public health workers develop more tailored interventions that address the specific and different social determinants of health profiles that each community faces to more effectively lower suicide rates,” said lead author Dr. Yunyu Xiao, assistant professor of population health sciences and psychiatry at Weill Cornell Medicine. Dr. Yuan Meng, a postdoctoral associate in population health sciences, also contributed to the analysis.
Clusters Based on Social Determinants of Health
One of the clusters, called “REMOTE,” affected people living in remote rural or mountainous areas that often rely on coal or other energy sources contributing to pollution. These individuals tended to be elderly and living in areas with aged, low-quality housing and abandoned homes. Suicide deaths in this cluster predominantly involved men and frequently included firearms.
The second cluster, “COPE,” included individuals experiencing complex family dynamics and severe environmental and social stressors. For example, single parents or grandparents raising their grandchildren created different family structures, many in poverty. Predominantly living in the southern U.S., these communities face harsher environmental factors, including extreme heat. Suicide deaths in this cluster were more common among middle-aged white individuals.
People living in racially and ethnically diverse metropolitan areas on the East and West Coasts were more likely to be part of the third cluster, called “DIVERSE.” Many of these communities have large immigrant populations with extreme income inequality, high housing costs, poorer air quality and difficulties accessing health care despite the presence of hospitals and clinicians in their area. Suicide deaths associated with this cluster were higher among women, youth, and Black or Hispanic individuals.
“This research moves beyond the idea that suicide is only an individual or medical issue,” Dr. Xiao said. “Instead, it shows that the places we live—shaped by history, policy, and economics—play a powerful role in shaping who is at risk.”
Tailored Prevention Strategies
“Our findings suggest suicide prevention approaches based on modifying social determinants of health must be region- and population-specific, rather than applying the same intervention strategies across the United States,” said senior author Dr. John Mann, Paul Janssen Professor of Translational Neuroscience in Psychiatry and Radiology at Columbia University and the New York State Psychiatric Institute.
Interventions for the REMOTE cluster, for example, may focus on reducing social isolation, increasing access to mental health care and addressing gun-related risks, Dr. Xiao suggested. In contrast, community-based interventions addressing economic stress and substance use, including alcohol and opioid overdoses, may help the COPE cluster. For the DIVERSE cluster, improving culturally adapted mental health programs, increasing health care accessibility and adapting measures to enhance air quality could have a positive impact.
By tracking changes over time, the researchers were also able to identify factors that reduced suicide rates previously, such as Medicaid expansion in certain counties, which improved health care access and affordability. These areas saw a decrease in suicides.
Next, Dr. Xiao and her colleagues will see if they can connect data on regional social determinants of health, suicide rates and electronic health records to gain a clearer picture of the factors driving suicide clusters. They also hope to test specific interventions targeting each of the three clusters.
Journal
Nature Mental Health
Article Title
Machine learning to investigate policy-relevant social determinants of health and suicide rates in the United States
Article Publication Date
12-May-2025
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