AI-Guided Sarcopenia Risk Assessment and Detection

NCT07426159 | Active Not Recruiting

• 1 other identifier: 0011903-1
• Study Type: interventional
• Target: 120
• Locations: 1 country
• Sites: 1

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.

Conditions

Sarcopenia; Age-related Muscle Loss

Keywords

Muscle mass; Muscle function; Predictive biomarkers; Sarcopenia; Risk Assessment

Outcome Measures

Primary Outcomes (3)

• Accuracy of AI-Based Sarcopenia Risk Prediction Model

  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.

  Baseline to end of follow-up (up to 12 months)

• Change in MRI-Derived Thigh Muscle Volume

  Mean change in thigh skeletal muscle volume assessed by 3-Tesla MRI (Siemens Prisma) using standardized segmentation analysis. Unit of Measure: cm³

  Baseline to 12 weeks

• Change in Handgrip Strength (kg)

  Mean change in maximal handgrip strength measured using a Jamar dynamometer (best of three trials). Unit of Measure: kg

  Baseline to 12 weeks

Secondary Outcomes (5)

• Change in Appendicular Lean Mass Index (ALM/height²) Measured by DXA

  Baseline to 12 weeks

• Change in Resting Metabolic Rate (kcal/day)

  Baseline to 12 weeks

• Change in Gut Microbiome Diversity

  Baseline to 12 weeks

• Change in Short Physical Performance Battery (SPPB) Total Score

  Baseline to 12 weeks

• Change in Quality of Life Assessed by SF-36

  Baseline to 12 weeks

Study Arms (1)

AI-Guided Personalized Exercise and Nutrition Intervention

EXPERIMENTAL

All participants undergo comprehensive baseline profiling and receive a 12-week personalized, AI-guided exercise and nutrition intervention designed to improve muscle mass, muscle function, and metabolic health. Individualized recommendations are generated using a machine learning-based sarcopenia risk prediction model and are dynamically adjusted based on physiological responses and wearable sensor data. Participants are stratified by sarcopenia risk (low, moderate, high) but all receive the same adaptive intervention framework.

Behavioral: Personalized AI-Guided Exercise and Nutrition

Interventions

Personalized AI-Guided Exercise and Nutrition BEHAVIORAL

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.

AI-Guided Personalized Exercise and Nutrition Intervention

Eligibility Criteria

• Age: 50 Years - 70 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• Tel Aviv University: lead

Study Sites (1)

Sylvan Adams Sport Institute

Tel Aviv, 69978, Israel

Location

MeSH Terms

Conditions

Sarcopenia

Interventions

Nutritional Status

Condition Hierarchy (Ancestors)

Muscular Atrophy; Neuromuscular Manifestations; Neurologic Manifestations; Nervous System Diseases; Atrophy; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Signs and Symptoms

Intervention Hierarchy (Ancestors)

Nutritional Physiological Phenomena; Diet, Food, and Nutrition; Physiological Phenomena; Health Status; Demography; Population Characteristics

Study Officials

• Yftach Gepner

  Tel Aviv University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator - Professor

Model Details: 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.

Study Record Dates

• First Submitted: February 9, 2026
• First Posted: February 23, 2026
• Study Start: February 1, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: February 23, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

IL (1)