Age-related Muscle Loss, Sarcopenia
Conditions
Keywords
Muscle mass, Muscle function, Predictive biomarkers, Sarcopenia, Risk Assessment
Brief summary
Sarcopenia, the age-related decline in muscle mass and function, is a major contributor to frailty, disability, and mortality in older adults. Current diagnostic tools assess muscle quantity or function separately and lack predictive biomarkers, limiting early detection and personalized management. This study proposes an AI-driven framework that integrates multimodal physiological, metabolic, and functional data with wearable sensor monitoring to improve sarcopenia risk assessment and guide individualized interventions. In Phase 1, we will analyze a large retrospective dataset of 3,500 adults to identify early predictors of sarcopenia and develop a machine learning-based risk stratification model. Phase 2 will test a 12-week personalized exercise and nutrition intervention in 120 participants, using real-time sensor data and AI-guided adjustments to optimize outcomes. This integrative approach aims to advance early detection, precision intervention, and long-term muscle health in aging populations.
Detailed description
Background: Sarcopenia, defined by the progressive loss of skeletal muscle mass and function, poses significant risks for falls, disability, metabolic dysfunction, and mortality in older adults. Current clinical diagnostics rely on static measures of muscle strength or mass, often missing early-stage or subclinical decline. Moreover, conventional interventions, such as resistance training and increased protein intake, show high inter-individual variability in outcomes due to factors like baseline muscle phenotype, metabolic status, genetics, and gut microbiome composition. Emerging technologies, including wearable sensors, high-throughput metabolic profiling, and AI/ML approaches, provide an opportunity to create predictive, individualized frameworks for sarcopenia risk assessment and management. Objectives: * Develop and validate an AI-driven model integrating muscle composition, functional performance, and metabolic biomarkers to predict sarcopenia risk. * Implement a personalized, adaptive intervention combining exercise and nutrition, guided by AI predictions and real-time monitoring. * Evaluate the effectiveness of this intervention on muscle mass, functional performance, and metabolic health in older adults. Methods: Phase 1: Retrospective analysis of multimodal data from 3,500 adults, including muscle composition (DXA, MRI), functional tests (grip strength, chair rise), metabolic markers, and microbiome profiles. AI/ML models will be trained to predict sarcopenia risk and identify key predictive features. Validation will occur using a subset of newly recruited participants under standard care. Phase 2: A 12-week prospective intervention in 120 adults aged 50-70, stratified into sarcopenia risk groups based on Phase 1 predictions. Participants will receive AI-guided personalized exercise (resistance and aerobic) and nutrition plans, monitored via wearable sensors and a mobile app. Data collection includes MRI and DXA for muscle composition, functional performance tests, metabolic and inflammatory biomarkers, microbiome profiling, and self-reported outcomes. Intervention response will be analyzed using mixed-effects models and ML to identify predictors of efficacy. Significance and Innovation: This study integrates AI-driven risk prediction with personalized, real-time adaptive interventions, addressing current diagnostic and therapeutic gaps in sarcopenia care. By combining muscle structure, function, metabolic, behavioral, and microbiome data, it enables early detection of muscle decline, individualized management, and improved adherence. The framework has potential for broad clinical translation, digital health integration, and future commercialization as a scalable AI-based sarcopenia platform. Anticipated Outcomes: * AI-based sarcopenia screening tools for early detection and risk stratification. * Personalized exercise and nutrition protocols tailored to individual risk and physiology. * A scalable, data-driven intervention framework suitable for clinical or home-based deployment. Enhanced understanding of heterogeneous responses to sarcopenia interventions.
Interventions
BEHAVIORALPersonalized AI-Guided Exercise and Nutrition
Participants complete 12 weeks of supervised resistance and aerobic training combined with personalized nutrition support. Exercise prescriptions (3 resistance sessions/week; 2-3 aerobic sessions/week) and dietary guidance (including protein targets) are individualized using AI models and wearable data. A mobile app provides real-time feedback and monitoring, with biweekly safety check-ins.
Sponsors
Tel Aviv University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
PREVENTION
Masking
NONE
Intervention model description
Single-arm interventional study evaluating a 12-week AI-guided personalized exercise and nutrition program in adults at risk for sarcopenia. Participants undergo comprehensive baseline profiling followed by individualized resistance and aerobic training combined with personalized dietary guidance. Recommendations are dynamically adapted using wearable sensor data and machine learning-based risk stratification. Outcomes assess changes in muscle mass, muscle function, and metabolic health.
Eligibility
Sex/Gender
ALL
Age
50 Years to 70 Years
Healthy volunteers
Yes
Inclusion criteria
* Men and women aged 50-70 years * At risk for sarcopenia based on muscle mass and/or muscle function screening * Able to participate in supervised exercise training * Willing to comply with study procedures and provide written informed consent
Exclusion criteria
* Participation in structured exercise or weight loss programs within the past 6 months * Unstable body weight (\>±5%) in the past 6 months * Current smoking or smoking within the past 6 months * Pregnancy, breastfeeding, or post-menopause * Contraindications to MRI (e.g., implanted devices, tattoos, permanent makeup) * Severe cardiopulmonary disease (e.g., recent myocardial infarction, unstable angina) * Musculoskeletal or neuromuscular conditions limiting exercise participation * Cognitive impairment * Chronic diseases including cancer, diabetes, thyroid disease, hypertension, or chronic renal failure * Use of medications affecting metabolism * Secondary liver disease (viral, autoimmune, alcoholic, or drug-induced) * Alcohol intake \>20 g/day (women) or \>30 g/day (men)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of AI-Based Sarcopenia Risk Prediction Model | Baseline to end of follow-up (up to 12 months) | Predictive performance of an artificial intelligence-based model to identify current and future risk of sarcopenia using multimodal baseline data, including body composition, muscle function, metabolic biomarkers, and wearable-derived measures. |
| Change in MRI-Derived Thigh Muscle Volume | Baseline to 12 weeks | Mean change in thigh skeletal muscle volume assessed by 3-Tesla MRI (Siemens Prisma) using standardized segmentation analysis. Unit of Measure: cm³ |
| Change in Handgrip Strength (kg) | Baseline to 12 weeks | Mean change in maximal handgrip strength measured using a Jamar dynamometer (best of three trials). Unit of Measure: kg |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in Appendicular Lean Mass Index (ALM/height²) Measured by DXA | Baseline to 12 weeks | Mean change in appendicular lean mass index (ALM divided by height squared) measured using whole-body dual-energy X-ray absorptiometry (DXA; Hologic QDR 4500A). Unit of Measure: kg/m² |
| Change in Resting Metabolic Rate (kcal/day) | Baseline to 12 weeks | Mean change in resting metabolic rate measured by indirect calorimetry using the Cosmed Quark RMR system under standardized fasting conditions. Unit of Measure: kcal/day |
| Change in Gut Microbiome Diversity | Baseline to 12 weeks | Mean change in gut microbiome diversity assessed using 16S rRNA gene sequencing from extracted microbial DNA and calculated using the Shannon diversity index. |
| Change in Short Physical Performance Battery (SPPB) Total Score | Baseline to 12 weeks | Mean change in total score of the Short Physical Performance Battery (SPPB), assessing lower extremity function. Unit of Measure: Scale score (0-12) |
| Change in Quality of Life Assessed by SF-36 | Baseline to 12 weeks | Change in health-related quality of life assessed using the 36-Item Short Form Health Survey (SF-36). Unit of Measure: SF-36 scale score (0-100) |
Countries
Israel
Contacts
PRINCIPAL_INVESTIGATORYftach Gepner
Tel Aviv University
Outcome results
None listed