Study Identifies Which Patients With Obesity Respond Best to GLP-1-Based Treatment
Key Takeaways:
- A Mayo Clinic study has identified a distinct biological subtype of obesity – a form of the “hungry gut” phenotype – that responds especially well to tirzepatide.
- People in this subgroup lost an average of 21.5% of their body weight after six months on tirzepatide, roughly double the 11.7% seen in other subtypes.
- The lower appetite-hormone levels traced back to reduced hormone production in the intestine rather than differences in the gut microbiome.
A step towards precision medicine for obesity
Why do some people lose a substantial amount of weight on GLP-1-based medications while others see far more modest results? A new Mayo Clinic study offers a potential answer, identifying a distinct biological subtype of obesity that responds especially well to tirzepatide – a medication that mimics two naturally occurring hormones involved in appetite and blood sugar regulation. The finding moves the field a step closer to precision medicine for obesity, where treatment is matched to an individual’s underlying biology rather than applied uniformly.
The research, published in the journal Gastroenterology, points to a future in which clinicians could predict, rather than simply hope, that a given therapy will work for a given person.
What the researchers found
The team studied 483 adults living with obesity and identified three distinct biological types of the disease. About one in four participants produced lower levels of GLP-1 and other hormones that help people feel full after eating.
This subgroup saw markedly better results on treatment. Patients in this group lost an average of 21.5% of their body weight after six months of tirzepatide, compared with 11.7% for patients in the other groups – losing nearly twice as much weight over the same period.
“Obesity is a complex disease driven by different biological mechanisms,” says senior author Andres Acosta, M.D., Ph.D., a gastroenterologist and the Delaney Family Director of the Nutrition Obesity Research Program at Mayo Clinic in Minnesota. “Our findings suggest we can begin identifying which patients are most likely to respond to specific therapies rather than treating obesity as a single disease.”
Understanding the “hungry gut” subtype
The subgroup that responded so strongly to tirzepatide shares a recognisable biological signature: people in this group produce lower levels of natural appetite-regulating hormones, experience faster stomach emptying and report greater hunger after meals.
Researchers describe this as a form of the “hungry gut” obesity phenotype, which is characterised by an abnormal duration of fullness. Rather than eating unusually large amounts at any one sitting, people with hungry-gut obesity may eat normal portion sizes but find themselves snacking more frequently, because the sense of fullness does not last as long as it should.
Because tirzepatide acts on the same appetite pathways that are underactive in this group, it appears especially well suited to addressing the biology that drives their eating patterns.
Why the underlying biology matters
The study also sheds light on why hormone levels differ in this subgroup. The researchers found that the reduced hormone levels were associated with decreased hormone production in the intestine itself, rather than with differences in the gut microbiome. That distinction offers new insight into the biology underlying this subtype and helps explain where the difference in treatment response originates.
Identifying the right therapy sooner could carry significant long-term benefits. Because obesity increases the risk of diabetes, heart disease, certain cancers and many other serious chronic conditions, matching people to the treatment most likely to help them – rather than relying on a one-size-fits-all approach – could improve long-term health outcomes.
What this means for clinical practice
The findings support growing efforts to personalise obesity treatment based on an individual’s biology. For clinicians, interpreting studies like this one increasingly depends on a firm grasp of how GLP-1-based medications act on appetite hormones and gastric emptying in the first place – the kind of grounding offered by CPD courses such as the College of Contemporary Health’s GLP-1RAs in Focus, which examines why some people respond more strongly to these treatments than others. Still, the authors are careful to note the limits of the current work. They caution that prospective studies are needed before this approach can be incorporated into routine clinical practice.
Even so, the results represent an important step towards more precise, individualised treatment for obesity – and towards a future in which people are guided to the therapy most likely to work for them from the outset.
Studies like this one land almost weekly, and making sense of them starts with understanding how GLP-1 receptor agonists actually work – from gut hormones and appetite regulation to why some people respond far more strongly than others. CCH’s GLP-1RAs in Focus – Why Drugs Like Ozempic Work is a two-hour, CPD-accredited online course created by Prof. Mike Bewick and Nigel Hinchliffe that builds exactly this foundation, whether or not you prescribe. Explore the course and interpret the next headline with confidence.
Source: Mayo Clinic
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Mayo Clinic Study Uses AI and CT Imaging to Identify Midlife Risk of Falls
Key Takeaways:
- Artificial intelligence applied to routine abdominal CT imaging can identify adults at increased risk of falls as early as midlife.
- Muscle density, a marker of muscle quality, is a far stronger predictor of fall risk than muscle size.
- Abdominal muscle health and core strength appear to play an important role in physical function and fall prevention across adulthood.
AI reveals early markers of fall risk
Researchers at Mayo Clinic have demonstrated that artificial intelligence applied to abdominal imaging can help predict which adults are at higher risk of falling, even from middle age onwards. The study, published in Mayo Clinic Proceedings: Digital Health, highlights abdominal muscle quality as a key predictor of future falls among adults aged 45 years and older.
Falls remain a leading cause of injury, particularly in older populations. However, the research team found that early indicators of fall risk may already be visible in CT scans that many people undergo for unrelated clinical reasons. This raises the possibility of identifying and addressing fall risk much earlier in the life course.
Using CT imaging beyond its original purpose
Working alongside radiology bioinformatics specialists, the researchers examined whether AI-derived measurements from abdominal CT scans could uncover subtle physical changes associated with falls. These measurements included fat distribution, muscle size, muscle density and indicators of bone quality.
Their analysis showed that muscle density, rather than muscle size, was most strongly associated with fall risk. Muscle density reflects muscle quality and the degree of fat infiltration within muscle tissue, whereas muscle size simply measures overall volume.
Muscle density matters more than muscle size
“Muscle size is just a measure of how big your muscles are,” says lead author Jennifer St. Sauver, an epidemiologist at Mayo Clinic in Rochester. “Muscle density is different; on a CT scan, it’s a measure of how ‘dark’ and homogenous the muscles are.”
Dr. St. Sauver explains that more homogenous muscles tend to be denser and contain less fat. This distinction is clinically meaningful, as muscle quality is more closely linked to physical strength and function than size alone.
“Previous studies have suggested that muscle density, not size, is more strongly associated with physical strength and function,” she says. “Our results support the idea that we should be focusing on muscle density, not muscle size, when we try to understand physical function.”
Strong associations seen even in midlife
While the research team anticipated finding associations between poorer abdominal muscle measures and falls among older adults, they were surprised by how pronounced these relationships were in middle-aged adults. The strength of the association suggests that meaningful declines in muscle quality may begin earlier than traditionally recognised and that these changes can significantly predict future fall risk.
“Leg muscles have been associated with physical function, but our findings show that abdominal muscles also play a significant role,” Dr. St. Sauver says.
Implications for lifelong core strength
The findings reinforce the importance of maintaining core strength and muscle quality throughout adulthood, not only in later life. According to the researchers, prioritising abdominal muscle health may offer long-term benefits for balance, stability and physical independence.
“One of the most important messages from this research is to keep your abdominal muscles in the best shape possible,” Dr. St Sauver says. “Doing so may provide benefits that start in midlife and continue well into older adulthood.”
Together, these results suggest that AI-enhanced imaging could one day support earlier identification of people at increased risk of falls, allowing preventative strategies to be introduced well before injuries occur.
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Mayo Clinic Launches AI-Powered Nurse Virtual Assistant to Support Frontline Care
Key Takeaways:
- Mayo Clinic nurses have led the design and development of an in-house, AI-powered Nurse Virtual Assistant to streamline access to clinical information.
- The tool consolidates patient summaries, evidence-based guidelines, and clinical policies into one tab within the electronic health record, reducing administrative burden.
- More than 9,600 nurses across Mayo Clinic’s inpatient and emergency departments are now using the system, with ongoing feedback ensuring it continues to evolve with nursing practice.
Streamlining access to critical information
High-quality care depends on timely access to electronic health records, clinical policies, and evidence-based practice guidelines. However, navigating multiple systems to retrieve this information can be time-consuming for nurses, detracting from direct patient care.
To solve this challenge, Mayo Clinic’s Department of Nursing led a multidisciplinary initiative to create Nurse Virtual Assistant – a generative artificial intelligence (AI) tool developed entirely in-house by nurses for nurses. Integrated into Mayo Clinic’s electronic health record (EHR) system, the tool presents essential information within a single tab, making it easier to retrieve and act upon.
Nurses can view a curated, nurse-specific patient summary and access direct links to key evidence-based resources, including Lippincott procedures, IV administration guidelines, and Mayo Clinic’s clinical policy library – all in one place.
This streamlined interface allows nurses to spend less time searching and more time focusing on what matters most: the person receiving care.
Augmenting – not replacing – human connection
Mayo Clinic emphasises that Nurse Virtual Assistant is designed to support, rather than replace, the expertise and human presence that nurses bring to clinical practice.
“It is an amazing tool,” says Nick Flynn, a registered nurse in the Emergency Department at Mayo Clinic Hospital in Arizona. Flynn highlights the value of consolidated patient data from inpatient stays, outpatient visits, and phone calls:
“You have easy access to a history of their illness, and that is available just moments after they arrive.”
Nurse-driven innovation from concept to rollout
The project began in 2024 as part of Mayo Clinic’s strategy to ease administrative pressures in a rapidly digitising healthcare environment. Crucially, nurses were involved at every stage – from conceptualisation to design and testing – ensuring the tool meets real-world clinical needs.
Early-access users played an active role in shaping the system’s features, providing feedback that directly informed improvements.
Brendon Bloomfield, a registered nurse in Psychiatric Acute Care at Mayo Clinic Hospital – Rochester, explains:
“To see a concept I was passionate about, AI-enhanced communication, actually get built – and to be invited to help shape it – reinforces that frontline nurses’ voices matter and that we have the power to influence the future of care.”
The solution underwent a research study approved by an Institutional Review Board before scaling to more than 9,600 nurses across inpatient and emergency department units.
Evolving with nursing practice
Nurse Virtual Assistant has been released as a Minimum Lovable Product – a version that not only solves an immediate problem but is designed to be engaging and impactful for end users. Nurses can submit feedback directly through the tool, allowing the solution to continuously evolve with frontline input.
Enhancements already implemented include improved search result accuracy, refined content layouts, and new functionality based on user suggestions.
Privacy, security, and compliance at the core
Built to the highest standards of data privacy, the Nurse Virtual Assistant is a patent-pending solution developed in full compliance with HIPAA regulations and other applicable requirements. This ensures that patient information remains secure while enabling timely and efficient access for clinical teams.
Shaping the future of nursing care
Mayo Clinic’s Chief Nursing Officer, Ryannon Frederick, sees the innovation as a milestone in supporting nursing practice:
“Nurse Virtual Assistant is an example of how Mayo Clinic nurses are driving innovation and shaping the future of care. By reducing administrative burden, we allow nurses to focus on the most important part of their work: caring for patients with skill, compassion and presence.”
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