AI Models Identify Hidden Cardiac Arrest Risk in Routine Patient Data
Key Takeaways:
- Researchers have built AI models that analyse electronic health records and electrocardiograms to identify people at elevated risk of sudden cardiac arrest, which kills more than 400,000 Americans each year.
- In a real-world group of nearly 40,000 patients, the combined model correctly flagged 153 of 228 high-risk people who later experienced cardiac arrest, narrowing risk prediction from 1 in 1,000 to 1 in 100.
- The models also surfaced modifiable contributors such as electrolyte disorders, substance use and medication interactions, pointing to practical opportunities for clinicians to intervene.
A new approach to an unpredictable emergency
Researchers have developed artificial intelligence (AI) models capable of analysing electronic health records (EHR) and electrocardiograms to pinpoint people in the general population who face a heightened risk of sudden cardiac arrest. The condition is responsible for more than 400,000 deaths each year in the United States and carries a survival rate of just 10%, making any tool capable of forecasting it a meaningful step forward.
The work represents a notable advance in anticipating an event that is widely considered difficult, if not impossible, to predict, and which often strikes people who have no previously known heart disease.
“Using artificial intelligence applications and health records data, the prediction of cardiac arrest in the general population is feasible,” said Dr Neal Chatterjee, the study’s lead investigator and a cardiologist at the University of Washington School of Medicine.
The paper was published on 11 May in JACC: Advances, a journal of the American College of Cardiology. Additional co-senior authors are affiliated with Massachusetts General Hospital and the Broad Institute of MIT and Harvard.
How the models were built
The investigation drew on a test population of roughly 1.7 million patients enrolled in a large healthcare system in the United States. The team built three separate AI models, each trained on a distinct dataset. The first, referred to as “EKG-only,” relied solely on electrocardiogram readings. The second, “EHR-only,” weighed 156 clinical features drawn from patients’ health records. The third combined both EKG and EHR data into a single integrated model.
The researchers developed and validated their models across three distinct patient groups.
Training cohort
The models were initially trained using data from 993 people who had experienced out-of-hospital cardiac arrest between 2013 and 2021, alongside 5,479 control patients matched for age and sex who had not. This stage allowed the AI to learn which patterns in EHR entries and EKG readings were linked to a higher risk of cardiac arrest.
Testing cohort
To confirm that the models could reliably distinguish between high- and low-risk indicators, the researchers applied them to a separate group consisting of 463 cardiac arrest cases from 2022 to 2023 and 2,979 control patients. The risk associations identified in this testing group closely mirrored those established during training.
Real-world cohort
The final stage involved 39,911 people who had received EKGs during 2021, regardless of their health status. The researchers examined the records of those within this group who went on to experience cardiac arrest over the following two years, assessing how closely their profiles aligned with the risk patterns identified by the models.
Within this real-world group, the combined EHR-EKG model accurately predicted 153 of 228 people who were classified as high-risk and who later went on to experience a cardiac arrest.
Bringing theoretical risk into focus
The shift in predictive precision is one of the study’s most striking outcomes.
“With these models, we’re able to enrich risk prediction from about 1 in 1,000 down to 1 in 100,” Chatterjee said. “If your doctor were to tell you that your risk of cardiac arrest is 1 in 100, that would catch your attention. We’re bringing a theoretical risk into focus.”
Another encouraging finding concerned the performance of the EKG-based model on its own. AI-enhanced analysis of electrocardiograms alone demonstrated strong predictive ability, only modestly behind the two models that drew on EHR data.
“The 12-lead EKG is a low-cost tool that might stratify patients’ risk for cardiac arrest in any community around the world,” Chatterjee said.
Risk factors beyond traditional cardiology
The study also surfaced risk factors that lie outside the conventional cardiovascular picture. Contributors flagged by the models included electrolyte disorders, substance use and interactions between medications, all of which are often addressable through clinical attention.
“We show some relatively low hanging fruit … modifiable risk factors,” Chatterjee noted. “A model that flags a patient as high-risk might prompt somebody taking care of a patient to review their medical history and their medications.”
Open questions for clinical practice
While the results demonstrate that predicting cardiac arrest risk at the population level is achievable, Chatterjee was careful to note that the next stage of inquiry involves working out what clinicians should actually do once a patient is flagged.
“We need to figure out which follow-on studies to pursue to understand what we do with this patient information. What screening, what surveillance, what intervention is warranted?”
Limitations of the study
Several constraints temper the findings. All of the data was drawn from a single healthcare system, leaving open the question of whether the models would perform similarly across populations with different demographic profiles or patterns of care. The real-world group was also restricted to people who had received an EKG, and these individuals may differ in important ways from those who had not undergone such testing. In addition, the AI-enhanced interpretations of EKGs could reflect biases tied to demographics or to the way care is delivered.
Funding and support
The research received support from the National Institutes of Health (K23HL169839, R01 HL160003, R01 HL168889, K24 HL153669, R01HL092577, R01HL157635), the American Heart Association (23CDA1050571, 961045), the European Union (MAESTRIA 965286) and the Foundation Leducq (24CVD01). Chatterjee is supported through a philanthropic donation from Kevin and Ann Harrang and through the John and Cookie Laughlin Endowed Professorship.
Source: UW Medicine
AI and Ultrasound Data May Transform Detection of Advanced Heart Failure
Key Takeaways:
- A new artificial intelligence approach can estimate a key heart failure metric using routine ultrasound images and electronic health records
- The method may help identify people with advanced heart failure who are currently missed due to limited access to specialised testing
- Early results show approximately 85% accuracy, suggesting strong potential for real-world clinical use
A new approach to a persistent diagnostic challenge
Applying artificial intelligence to cardiac ultrasound data may offer a more accessible way to identify people with advanced heart failure, according to a new study led by researchers from Weill Cornell Medicine, Cornell Tech, Cornell Ann S. Bowers College of Computing and Information Science, Columbia University Vagelos College of Physicians and Surgeons, and NewYork-Presbyterian.
Advanced heart failure is typically diagnosed using cardiopulmonary exercise testing (CPET). While effective, this method requires specialised equipment and trained personnel and is usually limited to large medical centres. As a result, many people do not receive timely or appropriate diagnosis and care.
In the United States alone, an estimated 200,000 people are living with advanced heart failure. However, only a small proportion are properly identified each year, in part due to these diagnostic limitations.
The new study, published on 3 March in npj Digital Medicine, explored whether artificial intelligence could help overcome this bottleneck by using more widely available clinical data.
Predicting peak VO2 without specialised testing
The research team developed an artificial intelligence model capable of predicting peak oxygen consumption, known as peak VO2. This measure is a central output of CPET and a key indicator of heart failure severity and patient risk.
Instead of relying on exercise testing, the model uses routinely collected data, including cardiac ultrasound images and information from electronic health records. These sources are already embedded in standard clinical care, making the approach potentially scalable across a wide range of healthcare settings.
“This opens up a promising pathway for more efficient assessment of patients with advanced heart failure using data sources that are already embedded in routine care,” said study senior author Dr. Fei Wang, associate dean for AI and data science and the Frances and John L. Loeb Professor of Medical Informatics at Weill Cornell Medicine.
A collaborative effort across disciplines
The study represents a highly collaborative effort involving experts in artificial intelligence, informatics, and clinical cardiology. Alongside Dr. Wang’s team, key contributors included Dr. Deborah Estrin, associate dean for impact at Cornell Tech, and Dr. Nir Uriel, director of advanced heart failure and cardiac transplantation at NewYork-Presbyterian.
The work forms part of the broader Cardiovascular AI Initiative, a joint effort between Cornell, Columbia, and NewYork-Presbyterian aimed at advancing the use of artificial intelligence in heart failure diagnosis and management.
“Initially we put together a group of more than 40 heart failure specialists and asked them to tell us where they thought AI could best be applied,” said Dr. Uriel.
One of the most promising opportunities identified was the use of artificial intelligence to analyse cardiac ultrasound data in order to detect advanced heart failure earlier and more accurately.
How the AI model works
The research team developed a multi-modal, multi-instance machine learning model designed to process multiple types of clinical data simultaneously. These included:
- Moving ultrasound images of the heart
- Waveform imagery showing heart valve motion and blood flow
- Structured and unstructured data from electronic health records
“The close interaction between clinicians and AI researchers on this project ended up driving the development of new AI techniques that would not have been explored otherwise,” said Dr. Estrin. “So, this was a case of medicine shaping the future of AI – not just AI shaping the future of medicine.”
Training and validation
The model was trained using deidentified data from 1,000 people with heart failure treated at NewYork-Presbyterian/Columbia University Irving Medical Center.
Once trained, it was tested on a separate group of 127 people with heart failure from three additional NewYork-Presbyterian campuses. The goal was to assess how accurately the model could predict peak VO2 and identify individuals at high risk.
Strong early performance
The results demonstrated a level of accuracy that exceeds previous artificial intelligence approaches for predicting peak VO2.
Using a standard performance metric for risk prediction, the model achieved an overall accuracy of approximately 85%. This suggests that the tool could effectively distinguish between people at higher and lower risk of advanced heart failure in clinical settings.
Implications for clinical practice
If validated in further studies, this approach could significantly expand access to advanced heart failure assessment. By removing the need for specialised exercise testing in some cases, clinicians may be able to identify high-risk individuals earlier and initiate appropriate treatment sooner.
“If we can use this approach to identify many advanced heart failure patients who would not be identified otherwise, then this will change our clinical practice and significantly improve patient outcomes and quality of life,” Dr. Uriel said.
Next steps towards clinical adoption
The research team is now planning prospective clinical studies to further evaluate the model. These studies will be essential for regulatory approval, including review by the U.S. Food and Drug Administration, and for eventual integration into routine clinical workflows.
The work was partially supported by funding from NewYork-Presbyterian as part of the Cardiovascular AI Initiative. As with other research at Weill Cornell Medicine, relationships with external organisations are disclosed publicly to ensure transparency.
Source: Weill Cornell Medicine
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Apple Watch Use in Older Adults Linked to Fourfold Increase in Atrial Fibrillation Detection, Study Finds
Key Takeaways:
- Older adults using an Apple Watch were four times more likely to be diagnosed with atrial fibrillation than those receiving standard care.
- Over half of the people diagnosed in the smartwatch group had no outward symptoms and were identified through watch alerts.
- Researchers suggest that smartwatch screening could reduce stroke risk and healthcare costs by accelerating diagnosis.
Smartwatches and fitness trackers have increasingly incorporated electrocardiogram functionality, allowing users to record heart rhythm data from their wrists. Among these devices, the Apple Watch has emerged as one of the most advanced consumer wearables in this space, capable of detecting irregular heart rhythms and, in some models, identifying indicators associated with raised blood pressure.
While these features have received certification from the US Food and Drug Administration for consumer use, the Apple Watch is not formally approved as a medical diagnostic device for clinical settings. Its purpose is to alert individuals to possible irregularities, prompting them to seek professional medical assessment for confirmation and formal diagnosis. In some cases, such alerts have led to early intervention, including reports of potentially life-saving outcomes.
New research now strengthens the case for wearable technology as a supportive diagnostic tool, particularly in detecting atrial fibrillation – a common heart rhythm disorder that can significantly increase the risk of stroke if left untreated.
Study design and participant profile
The research was conducted by investigators at Amsterdam University Medical Center. The team enrolled 437 adults aged over 65 who were considered to be at elevated risk of stroke.
Participants were divided into two groups:
- 219 individuals were provided with an Apple Watch for heart rhythm monitoring
- 218 individuals received standard care without smartwatch monitoring
All participants were assessed for atrial fibrillation, a condition that can be intermittent and frequently asymptomatic, making it challenging to detect through routine clinical encounters alone.
Fourfold increase in diagnoses
Among those using the Apple Watch, 21 individuals were diagnosed with atrial fibrillation and subsequently received medical care. Notably, 57 per cent of those diagnosed in the smartwatch group had no outward symptoms beyond what was indicated on their device.
In contrast, only five individuals in the standard care group were diagnosed with atrial fibrillation. All of these individuals were symptomatic at the time of diagnosis.
Overall, smartwatch monitoring identified four times as many people who were ultimately diagnosed with the condition compared with standard care alone.
These findings suggest that wearable devices may play a significant role in uncovering otherwise silent arrhythmias in older adults at increased stroke risk.
Clinical and economic implications
Atrial fibrillation is a well-established risk factor for stroke. Early detection allows for timely intervention, including anticoagulation therapy where appropriate, which can substantially reduce the likelihood of stroke.
Cardiologist Michael Winter of Amsterdam University Medical Center advocated for the integration of smartwatch technology into clinical pathways. He argued that the financial benefits could outweigh the initial expense of the devices.
He stated that the savings in medical services would “offset the initial cost of the device”.
Winter further explained:
“Using smartwatches with PPG and ECG functions aids doctors in diagnosing individuals unaware of their arrhythmia, thereby expediting the diagnostic process,” said Winter. “Our findings suggest a potential reduction in the risk of stroke, benefiting both patients and the healthcare system by reducing costs.”
By accelerating diagnosis in people who might otherwise remain undiagnosed until a serious event occurs, smartwatch-assisted monitoring may reduce both clinical burden and long-term healthcare expenditure.
Wearables as an adjunct – not a replacement
Despite these promising findings, the Apple Watch remains a supplementary tool rather than a replacement for clinical evaluation. Alerts generated by the device require follow-up assessment by healthcare professionals to confirm diagnosis and determine appropriate management.
However, for older adults at elevated risk of stroke, especially those who may not experience noticeable symptoms, wearable ECG and photoplethysmography technology may provide an additional layer of protection.
As consumer health technology continues to evolve, research such as this indicates that smartwatches could increasingly bridge the gap between everyday life and preventive cardiovascular care, supporting earlier detection of conditions that might otherwise go unnoticed.
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Heart Disease Now Affects Nearly Half of US Adults, as Obesity and Diabetes Continue to Rise
Key Takeaways:
- Nearly half of adults in the United States are now living with cardiovascular disease, with prevalence projected to rise further as obesity, diabetes, and hypertension increase.
- New data highlight worsening cardiometabolic risk factors, including declining blood pressure and glycaemic control, alongside rising concerns around sleep health, physical inactivity, and nicotine exposure.
- The report underscores the urgent need for prevention-focused, equitable approaches to cardiovascular, kidney, and metabolic health across the life course.
A comprehensive annual snapshot of cardiovascular health
The 2026 Heart Disease and Stroke Statistics Report from the American Heart Association, published in the journal Circulation, provides an updated and wide-ranging overview of heart disease, stroke, and cardiovascular risk factors. Updated annually, the report integrates the most recent data, adds new thematic chapters, and removes outdated material to reflect the evolving cardiovascular health landscape.
The latest edition draws on a year-long collaborative effort involving volunteers, scientists, clinicians, government representatives, and AHA staff. It includes an expanded chapter on nicotine and tobacco use and exposure, alongside a new chapter focused on cardiovascular, kidney, and metabolic (CKM) syndrome. Together, these additions reflect growing recognition of the interconnected nature of cardiometabolic risk factors and their cumulative impact on population health.
Cardiovascular health trajectories and nicotine exposure
According to the report, several major cardiometabolic conditions are projected to rise substantially by 2050 among adults in the United States. Hypertension prevalence is expected to reach 61 percent, diabetes 26.8 percent, and obesity 60.6 percent. In contrast, hypercholesterolaemia is the only major risk factor projected to decline, falling from 45 percent to 24 percent.
Most core health behaviours are projected to worsen over time. An important exception is sleep, where inadequate sleep duration is expected to increase. Evidence from a 2010 to 2022 meta-analysis showed that people with ideal cardiovascular health experienced a 74 percent lower risk of cardiovascular disease events compared with those with poor cardiovascular health.
Nicotine exposure remains a major concern. People who smoke have a mortality risk three times higher than those who have never smoked. While smoking prevalence among adults in the United States has declined, the use of e-cigarettes has increased sharply. National Health Interview Survey data from 2017 to 2023 indicate that e-cigarette use has quadrupled over this period.
Physical activity and sleep health
Levels of physical activity remain suboptimal across age groups and regions. Only one in five children and adolescents aged 6 to 17 years achieved at least 60 minutes of daily physical activity. Globally, around one-third of adults across 163 countries did not meet recommended activity levels.
Sleep health has emerged as a significant cardiovascular risk factor. Data from the National Health and Nutrition Examination Survey covering 2017 to 2020 showed that 30 percent of adults experienced at least one hour of sleep debt, defined as the difference between sleep duration on workdays and free days. Observational analyses linked poor sleep with higher odds of type 2 diabetes, hypercholesterolaemia, and hypertension.
Obesity, lipids, blood pressure, and diabetes
Obesity prevalence continues to rise among both children and adults in the United States. Estimates from the Global Burden of Diseases, Injuries, and Risk Factors study indicated that in 2021 more than 15 million children aged 5 to 14 years, 21 million young people aged 15 to 24 years, and 172 million adults aged 25 years or older were living with overweight or obesity.
While the prevalence of high total cholesterol has decreased, low-density lipoprotein cholesterol remains a major driver of cardiovascular mortality. Global data from 2021 attributed a cardiovascular disease mortality rate of 43.7 per 100,000 people to elevated low-density lipoprotein cholesterol.
Hypertension prevalence remained broadly stable between 2013 and 2023. However, blood pressure control worsened, declining from 54.1 percent in 2013 to 2014 to 48.3 percent in 2017 to 2020. Some improvement was observed among non-Hispanic Black adults between 2017 to 2020 and 2021 to 2023.
Diabetes prevalence also remains high. Between 2021 and 2023, an estimated 29.5 million adults had diagnosed diabetes, 96 million had prediabetes, and 9.6 million were living with undiagnosed diabetes. Among people with diagnosed diabetes, glycated haemoglobin levels increased significantly from 2017 to 2020 and again from 2021 to 2023, while overall glycaemic control rates declined.
Kidney disease, CKM syndrome, and pregnancy outcomes
The burden of kidney disease has risen markedly over the past two decades. The prevalence of end-stage kidney disease nearly doubled between 2002 and 2019, before stabilising in subsequent years. Across 114 cohort studies, both albuminuria and reduced kidney function were consistently associated with increased risk of kidney failure and mortality.
Data from NHANES between 2011 and 2020 suggest that approximately 90 percent of adults in the United States were in stage 1 or higher of CKM syndrome. People from underrepresented ethnic and racial groups experienced a disproportionately higher burden of advanced CKM stages. More advanced stages were strongly associated with increased cardiovascular disease mortality.
The report also highlights links between cardiometabolic health and pregnancy outcomes. In Japan, pregnant individuals with higher healthy lifestyle scores before pregnancy had around a one-third lower risk of adverse pregnancy outcomes compared with those with the lowest scores. Although maternal mortality rates declined across all ethnic and racial groups between 2021 and 2022, persistent disparities remain.
Cardiovascular disease, stroke, dementia, and congenital conditions
Overall cardiovascular disease prevalence reached nearly 49 percent among adults aged 20 years or older, based on NHANES data from 2021 to 2023. Prevalence increased with age in both women and men. At the population level, stronger adherence to healthy dietary patterns was associated with lower cardiovascular disease risk.
Stroke incidence declined between 1993 and 2015 among both Black and White adults, although rates remained consistently higher in Black populations. Dementia prevalence among older adults decreased between 2011 and 2021, though findings varied depending on study design and population. Evidence from selected intervention studies suggested that high-intensity training may help slow cognitive decline.
Congenital cardiovascular defects were estimated to affect around 1 in 80 babies in high-income regions of North America. Globally, survival into adulthood among people born with congenital heart disease improved substantially between 1990 and 2019. Population-based analyses linked limited prenatal care, neighbourhood deprivation, and air pollution to increased risk of heart defects, poorer outcomes, and delayed diagnosis.
Heart rhythm disorders, cardiac arrest, and heart failure
Heart rhythm disorders and heart failure continue to contribute significantly to cardiovascular morbidity. Atrial fibrillation affected an estimated 10.55 million adults in the United States, representing 4.48 percent of the adult population.
Patterns of cardiac arrest have also shifted. Opioid-related out-of-hospital cardiac arrests accounted for less than 1 percent of cases in 2000 but rose to between 7 percent and 14 percent by 2023. Coronary heart disease prevalence was estimated at 5.2 percent among adults aged 20 years or older between 2021 and 2023. Over the same period, heart failure prevalence increased from 6.7 million people in 2017 to 2020 to 7.7 million in 2021 to 2023.
A growing burden with global implications
Taken together, the 2026 Heart Disease and Stroke Statistics Report paints a picture of a growing cardiovascular disease burden affecting around half of the adult population. Despite major advances in diagnostics, prevention strategies, and treatment options, ageing populations, widening health inequalities, and rising cardiometabolic risk factors continue to place increasing pressure on healthcare systems.
The report emphasises the need for coordinated, prevention-led approaches that prioritise early intervention and equitable access to care. Without sustained action across policy, healthcare, and community settings, current trends are likely to continue, with profound long-term health and economic consequences.
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Metformin May Help Prevent Recurrence of Atrial Fibrillation After Ablation in Adults with Obesity
Key Takeaways:
- Adults with obesity who took metformin after AFib ablation had fewer recurrences of atrial fibrillation compared with those receiving standard care alone.
- 78% of participants receiving metformin remained free of AFib episodes lasting 30 seconds or more, compared with 58% in the standard care group.
- Researchers suggest further large-scale studies are needed to confirm whether diabetes medications such as metformin or GLP-1 receptor agonists could support heart rhythm stability in people with obesity who do not have diabetes.
Metformin shows promise beyond diabetes treatment
People with atrial fibrillation (AFib) and obesity may experience fewer episodes of irregular heart rhythm after undergoing ablation if they take the diabetes medication metformin in addition to standard care, according to a preliminary presentation of late-breaking science at the American Heart Association’s (AHA) Scientific Sessions 2025, held from 7 to 10 November in New Orleans. The annual meeting is a leading international forum for sharing new research and clinical advances in cardiovascular medicine.
“Lifestyle and risk factor modification efforts are essential to treating AFib and, according to the results of our study, could be aided by taking metformin,” said Dr Amish Deshmukh, lead author and clinical assistant professor of medicine at the University of Michigan in Ann Arbor.
AFib, characterised by an irregular and often rapid heartbeat, is the most common form of heart rhythm disorder. According to the AHA, it can lead to blood clots, stroke, heart failure, or other cardiovascular complications.
Metformin, a long-established and low-cost generic medication, helps regulate blood glucose levels and is most often prescribed to people with Type 2 diabetes. It is widely regarded as a first-line treatment due to its safety, affordability, and efficacy.
Exploring metformin’s role in reducing AFib recurrence
Previous research has indicated that people with diabetes and obesity who take metformin tend to have a lower risk of developing AFib compared with those using other antidiabetic medications. In laboratory studies, metformin has shown direct effects on cardiac cells, including the reduction of abnormal heart rhythms. Building on this evidence, researchers sought to determine whether metformin could help reduce the recurrence of AFib in people with obesity or overweight following catheter ablation.
The Metformin as an Adjunctive Therapy to Catheter Ablation of Atrial Fibrillation (META-AF) study enrolled 99 adults with AFib who were either overweight or obese. All participants underwent catheter ablation – a procedure that targets and removes small areas of heart tissue responsible for irregular electrical activity – and were then randomly assigned to receive either standard care alone or standard care plus metformin.
Standard care included lifestyle education focused on physical activity, nutrition, sleep, and management of comorbidities. Participants in the metformin group received the medication in addition to these measures.
Key findings: Fewer AFib episodes with metformin
Over the 12 months following ablation, the analysis revealed:
- 78% of those taking metformin experienced no AFib episodes lasting 30 seconds or longer, compared with 58% of those receiving usual care.
- 6% of participants in the metformin group required a repeat ablation or electric cardioversion (a procedure to restore normal rhythm) versus 16% in the usual care group.
- 8% of participants in the metformin group had recurrent AFib during rhythm monitoring, compared with 16% in the usual care group.
- Antiarrhythmic medication was required by 8% of participants in the metformin group versus 18% in usual care.
- Weight changes were minimal across both groups, consistent with previous findings that metformin produces little or no weight reduction in people without diabetes.
“Treatment with metformin in people with obesity who do not have diabetes and are undergoing AFib ablation seems to lower the likelihood of recurrent AFib or atrial arrhythmias after a single procedure,” Dr Deshmukh said. “While most people tolerated the medication well, a significant number stopped taking it due to side effects or because they felt well and did not want to add another medication to their regimen.”
Could other diabetes medications offer similar benefits?
The findings raise further questions about whether other diabetes or weight management drugs – particularly GLP-1 receptor agonists – may also help prevent AFib recurrence in people with obesity who do not have diabetes.
Obesity is a well-established risk factor for AFib. People living with obesity often experience more frequent or recurrent episodes of the condition following catheter ablation. According to the American Heart Association’s 2025 Heart Disease and Stroke Statistics, more than six million people in the United States currently live with AFib.
“I would suggest conducting a larger study to investigate metformin and other diabetes treatments,” Dr Deshmukh added. “We know that many of these medications offer cardiovascular benefits, and we are starting to gain a better understanding of how they might specifically benefit patients with arrhythmias. A study comparing various medications would be valuable to confirm our findings and also to address questions about tolerability, the feasibility of long-term use, and costs.”
Study design and limitations
The META-AF study was conducted at the University of Michigan between 2021 and 2025. It involved 99 adults with an average age of 63 years; 70% were men, and most were white. Among participants, 70% were classified as obese and the remainder as overweight. About 22% had previously undergone ablation, and 46% experienced AFib that stopped spontaneously within a week.
Participants with Type 1 or Type 2 diabetes were excluded, although 40% met criteria for prediabetes (HbA1c between 5.7% and 6.4%). Individuals taking diabetes medications or those for whom metformin posed risks were also excluded.
All participants received anticoagulant therapy to reduce the risk of stroke. The ablation targeted pulmonary vein tissue, a common source of AFib triggers.
The study was open-label, meaning participants knew which treatment they were receiving. Forty-nine participants were assigned to the metformin group and fifty to standard care. After a three-month healing period post-ablation, and once the metformin dose was gradually increased to its maximum, participants were monitored for recurrent AFib lasting at least 30 seconds. Researchers measured the AFib burden – the proportion of time spent in AFib – at three months and twelve months using clinical monitoring data, handheld devices, pacemakers, and defibrillators.
A notable limitation was participant withdrawal: 12 of the 49 people assigned to metformin discontinued treatment due to side effects or because they felt improved and preferred to stop the medication. The small sample size and single-centre design also limit generalisability to other populations or ablation techniques.
Disclosures and context
The study’s co-authors, funding, and disclosures are listed in the abstract presented at the AHA meeting.
The AHA emphasises that statements and conclusions from conference presentations reflect only the authors’ views and do not necessarily represent official policy or position. Abstracts presented at the Association’s scientific sessions are reviewed for scientific merit but are not peer-reviewed publications. Therefore, these findings are considered preliminary until published in a peer-reviewed journal.
The Association notes that over 85% of its funding derives from non-corporate sources, including individual donations, foundations, estates, investments, and educational material sales. Corporate donations are accepted under strict policies that prevent any influence on scientific content or policy positions.
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GLP-1 Receptor Agonists May Reduce Rheumatoid Arthritis Activity and Cardiovascular Risk in People with Obesity
Key Takeaways:
- GLP-1 receptor agonists were linked to reduced rheumatoid arthritis (RA) disease activity and improved cardiovascular risk factors in people with obesity or overweight.
- Significant improvements were observed in inflammation markers, weight, and cholesterol levels over 12 months of treatment.
- Findings suggest GLP-1RAs could offer dual benefits by addressing both metabolic and inflammatory disease processes in RA.
GLP-1RAs show promise in managing RA and cardiometabolic health
The use of glucagon-like peptide-1 receptor agonists (GLP-1RAs) was associated with improvements in both rheumatoid arthritis (RA) disease activity and cardiovascular risk factors among people living with overweight or obesity, according to new research published in ACR Open Rheumatology.
Rheumatoid arthritis frequently coexists with obesity, which is known to exacerbate systemic inflammation, increase disease activity, and reduce response to treatment. Although GLP-1RAs are well established for treating obesity, type 2 diabetes, and for reducing cardiovascular risk, their potential role in inflammatory diseases such as RA has not been clearly defined.
Study overview
Researchers at the University of California, Los Angeles (UCLA) conducted a single-centre, retrospective observational study involving people with RA and a body mass index (BMI) of at least 27 kg/m². Participants were prescribed either semaglutide (oral or subcutaneous) or tirzepatide (subcutaneous) between 2018 and 2024.
Out of 554 screened individuals, 229 met the inclusion criteria. Of these, 173 took the prescribed GLP-1RA and formed the treatment cohort, while 42 individuals served as controls, having been prescribed but not initiating therapy. Participants were evaluated at 3-month intervals for up to 12 months.
Baseline characteristics
Both groups were broadly similar in terms of BMI, RA duration, and the use of conventional, biologic, or targeted synthetic disease-modifying antirheumatic drugs (DMARDs). However, diabetes (49% vs 14%) and hypertension (57% vs 38%) were more common in the treatment group.
The mean baseline BMI was 37.1 kg/m² among those receiving treatment and 35.3 kg/m² among control individuals. A greater proportion of participants in the treatment group were White (71% vs 47%).
Improvements in RA and cardiometabolic outcomes
People who took GLP-1RAs showed significantly greater improvements in several clinical measures compared with those who did not initiate therapy:
- RA disease activity scores: decreased by −0.03 versus an increase of +0.21 in controls (P = .03)
- Visual analogue scale (VAS) pain scores: decreased by −0.6 cm versus an increase of +1.3 cm in controls (P < .001)
- Weight: reduced by −6.2 kg versus −1.7 kg (P < .001)
- Total cholesterol: decreased by −10.3 mg/dL versus +0.3 mg/dL (P = .04)
- HbA1c: reduced by −0.4% versus +0.1% (P = .03)
Within the treatment group, significant reductions were also noted in inflammatory and lipid parameters, including:
- Erythrocyte sedimentation rate (ESR): −5.4 mm/hr (P = .004)
- C-reactive protein (CRP): −0.9 mg/dL (P = .004)
- Low-density lipoprotein (LDL) cholesterol: −7.3 mg/dL (P = .002)
- Triglycerides: −10.5 mg/dL (P = .004)
Tolerability and limitations
Adverse effects were relatively common, with 29% of participants discontinuing treatment, most frequently due to gastrointestinal symptoms or insurance-related issues.
Sensitivity analyses that accounted for comorbidities and serostatus did not significantly alter the study’s outcomes. However, researchers noted several limitations, including the single-centre, retrospective design, reliance on chart abstraction rather than validated disease activity indices, and the possibility of residual confounding.
Clinical implications
“Going forward, clinicians may consider integrating GLP-1RAs into the treatment regimens for patients with [RA and obesity], not only to target obesity-related complications but also possibly to target the underlying inflammatory disease process,” the authors concluded.
Some study authors disclosed affiliations with biotechnology, pharmaceutical, and medical device companies. A full list of disclosures is available in the original publication.
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