PHOENIX — Current genetic screening guidelines fail to identify most people with an inherited condition known as familial hypercholesterolemia that can cause dangerously high cholesterol and early heart disease, a Mayo Clinic study found. 

The condition often passes silently through families for generations. It is highly treatable, yet people who remain undiagnosed are at greater risk for heart attacks and strokes. 

Cardiovascular disease remains the leading cause of death in the United States, affecting millions of adults each year. It includes conditions such as coronary artery disease, heart failure and stroke. One of its key risk factors is high cholesterol. 

The study, published in Circulation: Genomic and Precision Medicine, suggests that routine screening could identify the majority of people with the inherited condition and, ultimately, save lives. 

Study reveals missed diagnoses 

Researchers found that nearly 90% of those with familial hypercholesterolemia would not have been flagged for standard genetic testing and were unaware they had the condition until DNA testing in a Mayo Clinic population-based research study identified them. About 1 in 5 had already developed coronary artery disease. 

"Our findings expose a blind spot in current national guidelines, which rely on cholesterol levels and family history to determine who should receive genetic testing," says Niloy Jewel Samadder, M.D., lead author and a Mayo Clinic gastroenterologist and cancer geneticist at the Mayo Clinic Comprehensive Cancer Center. "If we can find those at risk of cardiovascular disease early, we can treat it early and change its course and likely save lives." 

Familial hypercholesterolemia is one of the most common genetic conditions, affecting an estimated 1 in 200 to 250 people worldwide. It causes very high levels of low-density lipoprotein (LDL) cholesterol — the "bad" cholesterol — from birth.  

"Our findings expose a blind spot in current national guidelines, which rely on cholesterol levels and family history to determine who should receive genetic testing. If we can find those at risk of cardiovascular disease early, we can treat it early and change its course and likely save lives." 

The study analyzed data from exome sequencing, a form of genetic testing that reads the protein-coding regions of the genome — where most disease-causing variants are found. The research included more than 84,000 participants across Mayo Clinic sites in Arizona, Florida and Minnesota through the Tapestry DNA research study, part of the institution's effort to integrate genomics into everyday patient care.  

The research team identified 419 people with genetic variants known to cause familial hypercholesterolemia. They found that nearly 75% of those individuals would not have met current clinical criteria for genetic testing based on their cholesterol levels or family history. This represents a missed opportunity for disease prevention.  

Integrating genetics into preventive care 

Dr. Samadder says the next step is to bring genetic screening into routine care to identify high-risk patients earlier and start treatment sooner. 

The work is part of Mayo Clinic's Precure strategic priority, which aims to predict and prevent serious diseases before they advance. Through innovative technologies and population-based studies, Precure is designed to bring prevention-focused care directly to patients sooner.  

For a complete list of authors, disclosures and funding, review the study.    

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About Mayo Clinic  
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.  

Media contact:

• Terri Malloy, Mayo Clinic Communications, newsbureau@mayo.edu

The post Most people with a genetic condition that causes significantly high cholesterol go undiagnosed, Mayo Clinic study finds   appeared first on Mayo Clinic News Network.

ROCHESTER, Minn. — At Mayo Clinic, cardiologists Peter Noseworthy, M.D., and John Giudicessi, M.D., Ph.D., are uncovering the earliest signs of a genetic heart condition that often strikes without warning. Their work shows that early detection, and even prevention, may be possible long before symptoms appear. 

The condition, arrhythmogenic right ventricular cardiomyopathy, weakens the molecules that hold heart cells together. As those bonds deteriorate, healthy muscle is replaced with scar tissue and fat, leaving the heart weaker.  

The disease often advances silently. Sometimes the first sign is when a person suddenly collapses during exercise. For many, a diagnosis comes only after the heart is already damaged. Detecting it earlier could spare patients from heart failure, the need for a transplant or sudden death. 

"We spend so much time managing the consequences of this disease — ablations, transplants, repeated hospitalizations. It's a much better paradigm to ask: What can we do to prevent this in the first place?" 

"We spend so much time managing the consequences of this disease — ablations, transplants, repeated hospitalizations," Dr. Noseworthy says. "It's a much better paradigm to ask: What can we do to prevent this in the first place?" 

Leading a new era of heart care 

Dr. Noseworthy leads Mayo Clinic's Division of Heart Rhythm Services, where he cares for patients with heart rhythm disorders and helps guide innovation strategies across the institution. Much of his research centers on using digital tools and artificial intelligence (AI) to detect problems earlier and improve patient care. 

Dr. Giudicessi, a genetic cardiologist, focuses on inherited heart rhythm conditions and how genetic changes influence who develops disease. His work bridges patient care and laboratory research, including first-in-human clinical trials that explore the potential of gene therapy. 

Together, they turned to two powerful data sources: the genetic code that shapes the heart and the electrical signals that guide its rhythm. 

"So much of medicine is reactionary — we wait for something bad to happen. This work is ushering in the tools to push against disease and to identify it early." 

Pinpointing risk in genes and rhythm  

Their search for answers started in Mayo Clinic's new Research Data Atlas, which unites decades of genetic, clinical and biospecimen data. Within it, they identified people carrying disease-causing mutations in PKP2, the gene most often linked to this disease.  

About 1 in 2,000 people carry a mutation in the PKP2 gene, but many never develop the disease. The challenge is knowing who will — and stepping in early enough to change the outcome. That question drives Dr. Giudicessi's work. 

"So much of medicine is reactionary — we wait for something bad to happen," Dr. Giudicessi says. "This work is ushering in the tools to push against disease and to identify it early." 

To spot those early rhythm changes, the team drew on an AI model developed by Mayo Clinic cardiologist Ammar Killu, M.B.B.S., and his team.  

Analyzing electrocardiograms from patients with a PKP2 mutation, the AI helped the researchers identify faint patterns that marked the first signs of disease.  

"This research shows how AI can help us identify really subtle changes that may facilitate earlier diagnosis and then allow us to move beyond individual cases to understand patterns across populations."

"This research shows how AI can help us identify really subtle changes that may facilitate earlier diagnosis and then allow us to move beyond individual cases to understand patterns across populations," Dr. Killu says. "It's a powerful example of how we can scale early detection to reach more patients before disease takes hold." 

Those insights led to a next step. Guided by the AI findings, Dr. Noseworthy and Dr. Giudicessi provided smartwatches to people who were showing early signs of disease. The devices help track daily activity levels — important data, since strenuous exercise can accelerate the disease, while even small adjustments may help slow it. 

The researchers are also exploring gene therapy for PKP2. Still in early testing, the approach may one day restore the missing gene and keep the disease from advancing. 

Building medicine's next chapter 

Their work is part of a larger shift at Mayo Clinic: predicting and preventing disease before it takes hold. That vision drives Precure, an initiative to intercept serious conditions early enough to alter their course. With tools like the Research Data Atlas, investigators can study health patterns across large populations, detect risk sooner and potentially guide people to care before disease advances. 

For Dr. Noseworthy, Dr. Giudicessi and Dr. Killu, the vision is bigger than a single study. They are helping build the infrastructure to move medicine earlier in the course of disease, where illness can be intercepted at scale. 

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About Mayo Clinic
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.  

Media contact:

• Terri Malloy, Mayo Clinic Communications, newsbureau@mayo.edu

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ROCHESTER, Minn. — A genomic test co-developed by Mayo Clinic and SkylineDx can identify whether people with melanoma are at low or high risk for cancer in their lymph nodes — a finding that could guide treatment decisions and help some people avoid lymph node biopsy surgery. The study results are published in JAMA Surgery.

In the largest prospective study of its kind, about 93% of people classified as low risk had no cancer in their lymph nodes, while about 25% in the high-risk group did. The multicenter clinical trial enrolled 1,761 people with early- or intermediate-stage melanoma at nine U.S. cancer centers between 2021 and 2024. 

Decoding the tumor’s genomic blueprint

The test measures the activity of eight genes in a melanoma tumor and combines that data with a person's age and tumor thickness to estimate the chance that cancer has reached the lymph nodes. The Merlin CP-GEP Test analyzes tissue from the tumor already collected during an initial biopsy, so no additional procedure or visit is required for the test.

Sentinel lymph node biopsy is performed under anesthesia to remove one or a few lymph nodes and check for microscopic cancer. The procedure usually requires a second incision and can have side effects, yet nearly 80% of people who undergo the surgery have no cancer in their lymph nodes.

"Surgery will always be central to cancer care, but this study shows that sentinel lymph node surgery might be avoided for selected patients with melanoma," says first author Tina Hieken, M.D., a surgical oncologist at the Mayo Clinic Comprehensive Cancer Center and co-principal investigator of the study. "This test lets us use a patient's own tumor biology to guide care with true precision."

Turning molecular insight into clinical impact

Melanoma is the deadliest form of skin cancer. While early-stage disease can often be treated successfully, once melanoma spreads to the lymph nodes, the risk of recurrence increases. Determining whether the cancer has reached the lymph nodes is a key step in guiding treatment.

"Melanoma progression is driven by subtle molecular processes that we're only beginning to understand," says Alexander Meves, M.D., a dermatologist at the Mayo Clinic Comprehensive Cancer Center who led earlier validation studies of the test. "This work translates that biology into tools that can improve care."

Researchers are now studying how incorporating the test into melanoma care might help healthcare professionals understand the risk of recurrence and guide follow-up care.

For a complete list of authors, disclosures and funding information, review the study.

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About Mayo Clinic Comprehensive Cancer Center 
Designated as a comprehensive cancer center by the National Cancer Institute, Mayo Clinic Comprehensive Cancer Center is defining the cancer center of the future, focused on delivering the world's most exceptional patient-centered cancer care for everyone. At Mayo Clinic Comprehensive Cancer Center, a culture of innovation and collaboration is driving research breakthroughs in cancer detection, prevention and treatment to change lives.

About Mayo Clinic
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.

Media contact:

• Julie Ferris-Tillman, Ph.D., Mayo Clinic Communications, newsbureau@mayo.edu

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During the Undiagnosed Hackathon, which was hosted by the Wilhelm Foundation in collaboration with Mayo Clinic, each ring of the bell became a symbol of discovery and global unity. 

Inside Mayo Clinic, a bell rang six times, each chime signaling that a diagnosis was discovered for another person with a rare and undiagnosed condition. For at least eight more families, scientists believe answers are within reach. 

These breakthroughs unfolded during the first U.S.-based Undiagnosed Hackathon, held Sept. 21–23 — an intensive, three-day effort to solve 29 rare disease cases that had resisted explanation. The event drew nearly 130 researchers, clinicians and data scientists from 28 countries, reflecting a global spirit united in the pursuit of answers. 

The hackathon was founded by the Wilhelm Foundation, established by Helene and Mikk Cederroth of Sweden after losing three children to an undiagnosed condition. Their commitment to international collaboration created the model for events like this, tackling the rarest and most complex diseases. 

Laptops, whiteboards and hope 

In hackathon rooms, scientists leaned over laptops, combing through millions of data points from DNA, RNA and the chemical signals that control genes. Whiteboards were filled with gene names and arrows mapping possible pathways. At one table, a group debated whether a rare mutation explained symptoms. Across the room, others scoured medical literature. Every clue was weighed against the rest. 

Confirming a diagnosis meant more than spotting a mutation. In many ways, DNA tells a story: a typo, a missing page, a chapter read out of order, an extra paragraph. The challenge was proving whether the story matched the patient's symptoms. Piece by piece, teams layered evidence until they could call it a diagnosis — and ring the bell. 

When one diagnosis reaches further 

Each solved case carried weight beyond the room. A single diagnosis can redefine a condition worldwide, guiding care for others with similar symptoms. To build on that progress, hackathon data and tools will remain open through the year's end, with biweekly meetings planned to pursue the unsolved cases. 

"The sun has set on working one genetic test at a time for complex patient cases," says Dr. Eric Klee, the Everett J. and Jane M. Hauck Midwest Associate Director of Research and Innovation and co-leader of the hackathon. "We can now see the bigger picture, and I look forward to the day when every diagnosis leads to action." 

His co-leader, Dr. Cherisse Marcou, assistant professor and co-director of the Clinical Genomics laboratory echoed the sentiment: "When people and ideas come together, barriers fall. This week we witnessed that truth, fueled by the tireless commitment of everyone devoted to this cause." 

Rare diseases affect an estimated 350 million people worldwide, yet only about 40 percent of cases yield to existing diagnostics, and even fewer have treatment options. 

Searching for treatments 

Finding a diagnosis is often the first breakthrough. The next challenge is turning knowledge into care. Sometimes Mayo scientists test existing drugs to see if one approved for another condition might help. In others, they study patient-derived cells to understand how a mutation alters function and whether a drug might counter it. Artificial intelligence also helps clinicians sift through vast libraries of potential drugs. The goal: ensure that a genetic answer is not the end of the story, but the beginning of care. 

A global lens on rare disease

The momentum of the hackathon carried into Mayo's Rare Disease Symposium, where the conversation widened to global challenges in diagnosis and treatment. 

Keynote speaker Dr. Salman Kirmani of Aga Khan University in Pakistan, and a former Mayo Clinic physician, reminded the audience that for much of the world, even inconclusive testing remains out of reach.

"Most families are not just undiagnosed. They are untested," he said. He noted another barrier: genomic reference databases remain overwhelmingly Eurocentric, skewing interpretations and limiting accuracy. "Data diversity is not a matter of fairness alone," he argued. "It is a diagnostic tool. Without it, families remain invisible." 

Hope rings out 

From hackathon rooms to the symposium stage, the message was the same: progress against rare disease depends on collaboration across borders and disciplines. 

Each time the bell rang, scientists cheered and exchanged hugs, then quickly returned to their work. Beyond the celebration, the bell stood as a symbol of what science can achieve when pursued together. 

The post Sounds of discovery ring at the Undiagnosed Hackathon appeared first on Mayo Clinic News Network.

ROCHESTER, Minn. — Mayo Clinic researchers have developed artificial intelligence (AI) tools that help identify which children with asthma face the highest risk of serious asthma exacerbation and acute respiratory infections. The study, published in the Journal of Allergy and Clinical Immunology, found the tools can detect those risks as early as age 3. 

The work is part of Mayo Clinic's Precure strategic priority, which aims to predict and prevent serious diseases before they advance. Through innovative technologies and population-based studies, precure is designed to bring prevention-focused care directly to patients sooner. 

Toll of childhood asthma

Asthma affects nearly 6 million U.S. children and is a leading cause of missed school, emergency visits and hospital stays, according to the Centers for Disease Control and Prevention. Respiratory infections are the most common trigger of asthma attacks, but symptoms vary widely and change over time. That makes it hard for clinicians to know which children are most vulnerable, a gap these AI tools are designed to help address. 

"This study takes us a step closer to precision medicine in childhood asthma, where care shifts from reactive care for advanced severe asthma to prevention and early detection of high-risk patients," says Young Juhn, M.D., M.P.H., professor of pediatrics at Mayo Clinic and senior author of the study. Dr. Juhn directs several Mayo Clinic research programs, including the AI Program of Mayo Clinic Children's, the Precision Population Science Lab and the HOUSES socioeconomic health program. 

New AI tools for early detection

For the study, researchers examined electronic health records from more than 22,000 children born between 1997 and 2016 in southeastern Minnesota. To interpret the data at scale, they developed multiple artificial intelligence tools that use machine learning and natural language processing to extract details from doctors' notes. 

The tools captured information such as symptoms and family history, allowing the team to apply two widely used diagnostic checklists for asthma in young children: the Predetermined Asthma Criteria and the Asthma Predictive Index. These checklists are how clinicians assess signs such as recurring wheezing, coughs or allergic conditions. Children who met the criteria on both lists formed a distinct subgroup at higher risk for serious complications. 

Asthma risk revealed by age 3

When researchers compared this subgroup with other children in the study, the differences were clear. By age 3, the subgroup members were experiencing pneumonia more than twice as often and influenza nearly three times as often. They also had the highest rates of asthma attacks requiring steroids, emergency visits or hospitalization. Respiratory syncytial virus (RSV) infection was also more common in this group during their first three years of life.  

Children in this subgroup were more likely to have a family history of asthma, eczema, allergic rhinitis or food allergies. Further, their laboratory tests from a previous study showed signs of allergic inflammation — including higher eosinophil counts, allergen-specific IgE and periostin, which reflect type 2 inflammation — as well as impaired lung function. Together, the findings point to a high-risk asthma subtype that makes some children more vulnerable to acute respiratory infections and asthma exacerbation. 

Next steps

The research team plans to test the tools in broader clinical settings as well as more diverse populations and health systems. They aim to combine the tools with biological data to refine how asthma subtypes are defined and treated early. 

The team is also planning a study to explore a compound that could calm overactive immune responses linked to asthma. By using lab-grown cell models, known as organoids, they hope to find ways to detect and prevent childhood asthma earlier and on a larger scale.      

This research was supported by a National Institutes of Health–funded R01 grant. For a complete list of authors, disclosures and funding, review the study.   

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About Mayo Clinic 
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news. 

Media contact:  

• Terri Malloy, Mayo Clinic Communications, newsbureau@mayo.edu 

The post Mayo Clinic’s AI tools help predict severe asthma risks in young children  appeared first on Mayo Clinic News Network.

Mayo Clinic researchers have developed a genetic test that can help predict how people will respond to weight loss medications such as GLP-1s.

The test estimates an individual's calories to satiation (CTS) — how much food it takes for a person to feel full — and links this biological trait to treatment success. The findings, published in Cell Metabolism, represent a promising step toward more personalized and effective treatments for people living with obesity.

"Patients deserve treatments that reflect their biology, not just their body size," says Andres Acosta, M.D., Ph.D., a gastroenterologist at Mayo Clinic and senior author of the study. "This test helps us deliver the right medication to the right person from the start."

Beyond body size

Obesity is a chronic, complex disease that affects more than 650 million adults worldwide. It stems from a mix of genetic, environmental and behavioral factors that vary from person to person. This complexity helps explain why people respond differently to weight-loss interventions. Yet treatment decisions often rely on simple measures such as body mass index (BMI) rather than the biological processes that drive weight gain and weight loss.

To uncover these processes, Dr. Acosta has focused on satiation, the physiological signal that tells the body it has eaten enough. In 2021, he and his colleagues defined a series of obesity phenotypes to describe eating patterns. For example, some people with obesity tend to eat very large meals ("hungry brain"), while others may eat average portions but snack frequently throughout the day ("hungry gut").

In this study, the researchers studied satiation in nearly 800 adults with obesity by inviting them to partake in an all-you-can-eat meal of lasagna, pudding and milk until they felt "Thanksgiving full." The results revealed striking variation: Some participants stopped after 140 calories whereas others consumed more than 2,000. On average, men consumed more calories than women.

The team investigated possible explanations for this variability. Several factors, including body weight, height, percentage of body fat, waist-to-hip ratio and age — as well as appetite-related hormones such as ghrelin and leptin — played a small role. But none accounted for the huge range in calorie intake. So the researchers turned to genetics.

Using machine learning, the researchers combined variants in 10 genes known to influence food intake into a single metric called the CTS-GRS (Calories to Satiation Genetic Risk Score). The score, calculated from a blood or saliva sample, provides a personalized estimate of a person's expected satiation threshold.

Matching genes to medications

Mayo Clinic researchers then calculated this CTS-GRS metric in clinical trials of two FDA-approved medications: a first-generation weight loss drug, phentermine-topiramate (brand name Qsymia), and a newer GLP-1 drug, liraglutide (Saxenda). They found that:

• People with a high satiation threshold lost more weight on phentermine-topiramate. This drug may help control portion size and reduce large-meal overeating (hungry brain).
• People with a low satiation threshold responded better to liraglutide. This drug may reduce overall hunger and frequency of eating (hungry gut).

"With one genetic test, we can predict who is most likely to succeed on two different medications," says Dr. Acosta. "That means more cost-effective care and better outcomes for patients."

The team has conducted additional studies to predict response to semaglutide, another GLP-1 medication (sold under the brand names Ozempic and Wegovy), and results are expected soon. They are working to expand the test by incorporating data from the microbiome and metabolome, as well as developing models to predict common side effects such as nausea and vomiting.

Conflict of interest or disclosure: The CTS-GRS technology was licensed to Phenomix Sciences, a Mayo Clinic innovation commercialization partner.  The technology is already being used in 300 clinics in the U.S. 

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At the first U.S. 'Undiagnosed Hackathon,' scientists from around the world will team up at Mayo Clinic to solve unsolved medical mysteries. 

Young Julian Limon clutches his blanket wherever he goes, a source of comfort during hospital stays, procedures and tests. At 17 months, he has not yet reached walking or talking milestones. His brittle hair and unexplained neurological symptoms compound his challenges. He has endured pneumonia and other respiratory illnesses, and his weak immune system leaves him vulnerable. Despite extensive evaluations and genetic testing, Julian's condition remains a mystery.

This September, Julian's family will travel to Mayo Clinic in Minnesota to take part in the Undiagnosed Hackathon, a global effort to solve rare diseases that have long gone unexplained. 

The Hackathon was inspired by Helene and Mikk Cederroth, founders of the Wilhelm Foundation, who lost two young sons and a daughter to an undiagnosed condition. Their grief became a call to action. Over the past two decades, they've built a global network of scientists, clinicians and advocates committed to finding answers. 

An unprecedented collaboration

Over three days at Mayo Clinic, more than 125 scientists, clinicians and AI experts will gather for the first U.S.-based Undiagnosed Hackathon. They will come from 30 countries across six continents. Their goal: to uncover answers for Julian and 28 others whose conditions have eluded diagnosis. 

Having families in person at the Hackathon allows researchers to observe traits and ask questions that data alone can't capture. 

"If you put a molecular biologist next to a bioinformatician next to a clinician who have come from different parts of the world, each will bring a unique lens to the same investigation shaped by their training and lived experience," Dr. Klee says. "That's how breakthroughs happen." 

Unlocking hidden clues with advanced tools 

Among the international team are Mayo Clinic's Dr. Cherisse Marcou, assistant professor and co-director of the Clinical Genomics laboratory, and Dr. Eric Klee, the Everett J. and Jane M. Hauck Midwest Associate Director of Research and Innovation. After participating in last year’s Undiagnosed Hackathon in the Netherlands, they return with momentum to co-lead this year’s event. 

Working with global colleagues, they’ll explore DNA, RNA and other signals using tools that reveal what standard tests can miss. This includes examining long DNA stretches, studying RNA to see which genes are active and identifying chemical changes that turn genes on or off — a process called methylation.

This complex approach, known as omics, combines layers of biological information to better understand how the body works and why disease occurs. Bringing multiple omics together is more like a moving picture than a still photo, where hidden patterns emerge. Artificial intelligence will help scientists integrate these layers and interpret the results.

Breaking silos to spark breakthroughs 

"I come from a place where many families are not afforded the access to the latest and greatest diagnostic testing options in their diagnostic journey," Dr. Marcou says. "To be part of something that brings hope worldwide is deeply personal."

The idea behind the Hackathon is bringing people together who might not otherwise work side by side. 

"If you put a molecular biologist next to a bioinformatician next to a clinician who have come from different parts of the world, each will bring a unique lens to the same investigation shaped by their training and lived experience," Dr. Klee says. "That's how breakthroughs happen." 

Fueled by passion, and personal connection 

Now in its third year, the Hackathon has become a global engine for rare disease discovery. The Cederroths have co-led every one. 

"They've poured their lives into this mission," Dr. Marcou says. "Their energy is transformative. You leave the Hackathon changed." 

For Dr. Marcou, the work is personal. She grew up in the Bahamas, where access to advanced diagnostics is limited. 

"I come from a place where many families are not afforded the access to the latest and greatest diagnostic testing options in their diagnostic journey," she says. "To be part of something that brings hope worldwide is deeply personal." 

Dr. Marcou clinically interprets and decodes genomic data to deliver real-time insights for patients every day and has been involved in the development of AI tools at Mayo Clinic to advance this work. Dr. Klee, a leader in rare disease research, is building the Research Data Atlas to accelerate discoveries by unifying Mayo Clinic's extensive research data. 

Hope for families, and ripple effects worldwide

The Hackathon's goal is ambitious: solve as many cases as possible. Last year, 10 of 26 participants received diagnoses, with promising leads for nine more. One person's diagnosis can also unlock recognition, testing and potential treatment options for others with the same condition. 

"Our ultimate goal is to find answers for all our participants. That said, if we can find an answer for even one person, that would be amazing. If we find answers for 10 or 12 participants, that would be incredible," Dr. Klee says. "And for the participants where a clear answer eludes us, we hope to find strong leads that guide future research and testing for others." 

The Hackathon doesn't end when the event does. The findings must be clinically confirmed before they become diagnoses. For those who receive answers, the next goal is treatment, if one exists. For cases that remain unsolved, the work continues. 

It's also a powerful exchange of knowledge. Collaborators from places with fewer resources gain exposure to advanced techniques, while all experts have the opportunity to learn new approaches from those working alongside them. 

"It's peer-to-peer learning at its best," Dr. Marcou says. "We're all better for it." 

Julian's diagnostic journey

Even after long days of doctor visits and tests, Julian still breaks into bright smiles. He is working with physical therapists to build strength as his family continues to hope for a diagnosis. 

"I feel incredibly grateful that we'll have so many experts looking closely at Julian," says his mother, Jasmine Limon. "I just want to know what we're facing so we can give him the best possible care." 

At its heart, the Hackathon is where some of the world's brightest minds gather around families like Julian's, determined to give all they can and to open new doors in medicine. 

The post When diagnosis hits a wall, this global hackathon opens new doors  appeared first on Mayo Clinic News Network.

While doctors are often focused on monitoring the health and vital signs of others, a new study had some tuning in to their own health and vital statistics as well. The results suggest that doing so may offer doctors real benefits to their own well-being, in a scalable way. 

Physicians who wore a smartwatch and had access to their personal health data — including information on their heart rate, sleep, breathing patterns and physical activity — reported greater resilience and 54% saw a reduction in the overall odds of burnout compared to those who did not receive a study smartwatch, according to new research published in JAMA Network Open. Mayo Clinic investigators conducted the study in collaboration with the University of Colorado School of Medicine.

"Advancing care starts with caring for those who deliver it. We're shaping a future where the well-being of our workforce is integral to the care we deliver."   - Colin West, M.D., Ph.D., Medical Director of Employee Well-Being at Mayo Clinic

Physician well-being is essential not only to personal health, but also to the quality of care patients receive. It's tied to job performance, patient safety, access to care and workforce sustainability.  

That’s why Mayo Clinic and others are prioritizing strategies to strengthen and sustain the well-being of healthcare professionals. 

How the smartwatch trial was designed and conducted 

The 12-month trial was conducted at Mayo Clinic and the University of Colorado School of Medicine. It included 184 physicians across specialties such as primary care, surgery, neurology and oncology. Researchers randomly assigned about half of the participants to wear a smartwatch for the full 12 months, while they gave the other half the watch during the study’s second half.

All participants received brief newsletters with general tips on smartwatch use and reminders to sync their devices. These resources aimed to support awareness of the tools and encourage engagement with personal health data.

Physicians in both study groups wore the device more than 70% of the time during the trial. Participants also completed validated well-being surveys at the beginning and end of the study. 

Participants could view their health data through a mobile app but were not prompted to take specific actions in response to it. Researchers say even this passive approach may help support well-being.  

Designing smarter tools for a healthy workforce

The study was co-designed and led by Arjun Athreya, Ph.D., an electrical and computer engineer in Mayo Clinic's Department of Molecular Pharmacology and Experimental Therapeutics; Colin West, M.D., Ph.D., medical director of Employee Well-Being at Mayo Clinic; and study Principal Investigator Liselotte Dyrbye, M.D., M.H.P.E., senior associate dean for faculty and chief well-being officer at the University of Colorado School of Medicine.  

"We're entering an era where wearable technology, when paired with thoughtful design and artificial intelligence methods that use the data, could help personalize well-being strategies in clinical settings," Dr. Athreya says. "This study shows we can support healthcare professionals with passive monitoring digital technologies with innovative engagement strategies to provide potentially helpful data without adding burden to their day."  

The researchers say this approach can offer timely support as part of a broader physician well-being strategy. 

"While this is an individually focused intervention, it offers an evidence-based way to support physicians in the short term, complementing longer-term efforts aimed at addressing systemic contributors to physician stress," says Dr. Dyrbye. 

Caring for caregivers: A vision for the future

Next steps for the researchers include evaluating long-term outcomes of the smartwatch project. They also plan to explore whether this approach can support other healthcare professionals.

"Advancing care starts with caring for those who deliver it," says Dr. West. "We’re shaping a future where the well-being of our workforce is integral to the care we deliver."   

The Physicians Foundation, Mayo Clinic's Center for Individualized Medicine, and the University of Colorado School of Medicine partly funded the study. Review the study for a complete list of authors, disclosures and funding details. 

The post Mayo Clinic smartwatch study reveals new path to boosting physician well-being appeared first on Mayo Clinic News Network.

(Video animation shows blood stem cells dividing and multiplying. Getty Images).

Deep inside the body, a slow-growing cluster of mutated blood cells can form. This cluster, found in 1 in 5 older adults, can raise the risk of leukemia and heart disease, often without warning. 

To better understand this hidden risk, Mayo Clinic researchers have developed an artificial intelligence (AI) tool to help investigators uncover how it contributes to disease risk and progression.

In a study published in Genomics, Proteomics & Bioinformatics, the tool showed promising results in identifying early signs of this condition, known as clonal hematopoiesis of indeterminate potential, or CHIP.

When blood cells mutate

CHIP starts in the bone marrow, where blood stem cells make the cells that keep organs working, oxygen flowing and the immune system strong. But if one of those cells acquires a mutation in a gene linked to blood cancer, it can multiply abnormally, forming a cluster of mutated cells that gradually expands. 

This can cause CHIP, a condition with no symptoms that researchers link to higher rates of death, especially from heart disease. Because its effects vary, CHIP is hard to track and often goes undetected for years. 

CHIP makes leukemia more than 10 times more likely and raises the risk of heart disease up to four times, even in healthy adults. Finding it earlier could help guide proactive monitoring or preventive care.

A new tool for early detection 

The new tool, called UNISOM — short for UNIfied SOmatic calling and Machine learning — was developed by Shulan Tian, Ph.D., under the leadership of Eric Klee, Ph.D., co-senior author of the study and the Everett J. and Jane M. Hauck Midwest Associate Director of Research and Innovation.  

UNISOM helps clinicians identify CHIP-related mutations in standard genetic datasets, opening new avenues for research and discovery. In the past, that level of detection required more complex and advanced sequencing methods. 

"Detecting disease at its earliest molecular roots is one of the most meaningful advances we can make in medicine," says Dr. Klee. "UNISOM is just one of many examples of how we're translating genomic science into innovative tools that support timely and informed care." 

UNISOM helped researchers detect nearly 80% of CHIP mutations using whole-exome sequencing, which analyzes the protein-coding regions of DNA.  

The team also tested UNISOM on whole-genome sequencing data from the Mayo Clinic Biobank, which captures nearly all of a person's genetic code. In that data, it detected early signs of CHIP, including mutations present in fewer than 5% of blood cells. Standard techniques often miss these small but important changes.

"We're engineering a path from genomic discovery to clinical decision-making," says Dr. Tian, the co-senior author and a bioinformatician at Mayo Clinic. "It's rewarding to help bring these discoveries closer to clinical care, where they can inform decisions and support more precise treatment." 

Next, the team plans to apply UNISOM to larger and more diverse datasets to support research and expand its use in clinical practice. 

Review the study for a complete list of authors, disclosures and funding.   

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ROCHESTER, Minn. — Mayo Clinic researchers have discovered a key reason some cancer patients relapse after receiving chimeric antigen receptor T-cell therapy, or CAR-T cell therapy. Over time, the engineered immune cells age and lose their ability to fight cancer.  

Published in Molecular Cancer, the study identifies this aging process, known as senescence, as a previously unrecognized mechanism of CAR-T failure.   

The researchers also showed that senescence is influenced by how CAR-T cells are engineered. Certain intracellular features — such as how the cell recognizes cancer and how strongly it activates — can overwork the cells. The researchers found that if the activation signal is too intense or prolonged, it can push CAR-T cells into premature aging.  

The discovery may guide the development of next-generation CAR-T therapies that last longer and are more effective across a broader range of cancers.  

"This is one of the most clinically relevant discoveries we've made because it doesn't just explain the cause of relapse, it gives us a biological target to possibly prevent it," says Saad Kenderian, M.B., Ch.B., a principal investigator and hematologist at Mayo Clinic.  

CAR-T therapy reprograms a patient's own immune cells to recognize and destroy cancer. It has led to long-term remission for patients, including some with aggressive or treatment-resistant diseases. But many patients eventually relapse, and the causes have remained poorly understood. 

Modeling CAR-T cell stress over time 

To investigate why CAR-T therapy can fail, the Mayo team developed a novel lab model that simulates long-term biological stress, offering a clearer view of how the engineered cells behave after infusion. Over time, some CAR-T cells lost their ability to multiply and attack cancer. Specifically, they showed hallmark signs of senescence, including distinct genetic changes.  

The researchers found that senescence occurred more often in CAR-T cells built with a signaling feature, known as 4-1BB, which affects how the cells respond to cancer. In comparison, cells designed with an alternative domain, called CD28, were less affected by aging. These cells activate more quickly and persist for a shorter time, reducing the cumulative stress that drives senescence.

The researchers confirmed the results in multiple laboratory models and validated them in patient samples.

Engineering CAR-T cells for longevity 

That discovery was driven in part by the work of Ismail Can, Ph.D., who helped lead the molecular analysis behind the finding. 

"Efforts to make CAR-T cell therapy more durable will likely fail without fully understanding the reasons behind CAR-T cell failure. This study represents a significant step toward understanding why CAR-T cells fail," says Dr. Can, first author of the study and a senior research fellow at Mayo Clinic’s T Cell Engineering Laboratory. "By identifying the early molecular triggers of senescence, we can begin to refine CAR-T design to potentially improve long-term function and reduce relapse." 

The findings highlight a new direction for CAR-T research, with potential implications not only for blood cancers but also for expanding cell therapy into solid tumors.  

The study builds on Dr. Kenderian's broader efforts to identify resistance mechanisms and design more durable and personalized immunotherapies.   

This work was supported in part by Mayo Clinic Comprehensive Cancer Center, the Eagles 5th District Cancer Telethon Funds for Cancer Research, the State of Minnesota, and benefactors Georgia and Michael Michelson. For a complete list of authors, disclosures and funding information, review the study.    

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About Mayo Clinic 
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news. 

Media contact:  

• Sharon Theimer, Mayo Clinic Communications, newsbureau@mayo.edu 

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