Stanford Medicine Center for Longevity and Healthy Aging
Climb Everest. Swim with the sharks. Virtual reality is helping older Americans check off their bucket lists. 
We are rewriting the playbook on how genetic cancer risk gets communicated 
Track your microbiome over time to unlock a personalized blueprint for longevity 
Machine learning is catching dementia before it catches you 
Virtual Reality is revolutionizing how we treat hoarding disorder 
Loneliness is hiding in plain sight. Artificial Intelligence is teaching us to see it 
Detecting peripheral artery disease very early — regardless of skin color 
Solving longevity’s biggest challenges — our community partners won’t let us settle for anything less
Highlights
Keynote address:
Lloyd Minor, MD, Dean of the Stanford University School of Medicine
Dr. Periyakoil receives an “Award of Honor” from Fiji’s Ambassador to the United StatesDr. Periyakoil receives an “Award of Honor” from Fiji’s Ambassador to the United States
Center-Funded Research in Action
Scaling Clinical AI Across a National Health System
Dr. Tamang’s work on Deploying a National Clinical Text Processing Infrastructure highlights a major translational achievement: the first nationwide NLP system deployed within a U.S. federal healthcare system. The platform processes over one million clinical notes daily and enables extraction of patient-centered insights that support suicide and overdose prevention, functional assessment, care coordination, quality measurement, and chronic disease management. This work stands out for its real-world scale and impact, establishing a national infrastructure that advances population health and drives new research.
Lab-grown Muscle Launched into Space to Learn What Happens to Astronauts
Dr. Huang’s work explores how muscle regeneration declines with aging, disease, and exposure to microgravity, with the goal of identifying countermeasures to support healthy aging. Leveraging experiments conducted aboard the International Space Station, her team developed an innovative muscle-on-a-chip platform to model regeneration in microgravity. This research establishes a novel, space-enabled platform for studying muscle biology and accelerating therapeutic discovery for age-related muscle decline.
Epigenetic Biomarkers for Predicting Post-Operative Delirium
Dr. Shinozaki’s research aims to transform how clinicians predict and manage post-operative delirium—a serious and often underdiagnosed condition in older adults that can lead to long-term cognitive decline and increased mortality. By analyzing molecular profiles from elderly patients undergoing high-risk procedures such as hip fracture surgery, the team seeks to identify biomarkers that can signal delirium risk before symptoms emerge. This work has the potential to enable earlier detection, targeted prevention strategies, and improved outcomes.
Redefining Healthy Aging Through Multi-Omics and Microbiome Dynamics
Dr. Zhou and the team show that healthy aging is a dynamic and highly individual process, with key changes happening at different stages of life rather than a steady decline. Their work also finds that the human microbiome is both stable and unique to each person, and may play a role in diseases such as pulmonary arterial hypertension. Together, this research supports a more personalized approach to aging and highlights new opportunities to improve long-term health and longevity.
Predicting Movement to Prevent Falls
Dr. Kennedy and the team have developed a wearable sensor approach that can predict a person’s walking path and how their body will move in real time. This capability makes it possible to identify potential trip hazards before they occur and opens the door to early fall detection and prevention. The work also supports future innovations such as smart assistive devices and lower-limb exoskeletons that can respond to a person’s movement, helping improve safety, mobility, and independence in older adults.
Empowering Patients Through Person-Centered Communication
Dr. Cano’s work focuses on advancing person-centered approaches to health behavior change by improving how patients—particularly those from underserved communities—engage with their care. Through her research, she highlights the importance of empowering individuals to ask questions, navigate the healthcare system, and actively participate in decision-making. Her work emphasizes integrating peer support and strengthening communication between patients and providers to improve outcomes, including smoking cessation and long-term health behaviors.
Center News
2024 Visionary Award by the American Academy of Hospice and Palliative Medicine (AAHPM)
Dr. VJ Periyakoil, Professor of Medicine and Associate Dean of Research, received the 2024 Visionary Award by the American Academy of Hospice and Palliative Medicine (AAHPM). Every five years, AAHPM calls upon its extensive membership of over 5,000 professionals to nominate visionaries who have significantly shaped the landscape of palliative care. “This program recognizes extraordinary individuals who continue to enhance the delivery of care for seriously ill patients and have brought true innovation to our field,” noted Wendy-Jo Toyama, MBA FASAE, AAHPM CEO. Dr. Periyakoil’s groundbreaking contributions at Stanford University and her tireless efforts in advancing the field have earned her this distinguished accolade.
Machine learning prediction of mild cognitive impairment and its progression to Alzheimer’s disease
Health Science Report: Effective screening for mild cognitive impairment (MCI) as a risk factor for developing Alzheimer’s disease is a crucial step in helping aging population with their needs Early detection and automated screening for MCI and dementia could offer opportunities for deliberate study and recruitment into trials for developing other potentially useful therapeutics or interventions.
The study was first published in October 2023. The results illustrate that it is possible to predict MCI onset and AD progression with moderate levels of accuracy, which suggests an opportunity for population-wide screening mechanisms to identify patients at potential risk, who could then undergo more specific evaluation to consider early treatment or recruitment into clinical trials.