AI-Guided Sarcopenia Risk Assessment and Detection (SARC-AI)

February 15, 2026 updated by: Gepner Yftach, Tel Aviv University

AI-Driven Integration of Muscle Mass and Muscle Function: A Novel Approach to Sarcopenia Risk Assessment and Intervention

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Estimated)

120

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Israel
      • Tel Aviv, Israel, 69978
        • Sylvan Adams Sport Institute

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-Guided Personalized Exercise and Nutrition Intervention

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
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of AI-Based Sarcopenia Risk Prediction Model
Time Frame: 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.
Baseline to end of follow-up (up to 12 months)
Change in MRI-Derived Thigh Muscle Volume
Time Frame: 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
Baseline to 12 weeks
Change in Handgrip Strength (kg)
Time Frame: Baseline to 12 weeks

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

Unit of Measure: kg
Baseline to 12 weeks

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in Appendicular Lean Mass Index (ALM/height²) Measured by DXA
Time Frame: 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
Baseline to 12 weeks
Change in Resting Metabolic Rate (kcal/day)
Time Frame: 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
Baseline to 12 weeks
Change in Gut Microbiome Diversity
Time Frame: 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.
Baseline to 12 weeks
Change in Short Physical Performance Battery (SPPB) Total Score
Time Frame: 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)
Baseline to 12 weeks
Change in Quality of Life Assessed by SF-36
Time Frame: 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)
Baseline to 12 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Tel Aviv University

Investigators

  • Principal Investigator: Yftach Gepner, Tel Aviv University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 1, 2026

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

February 9, 2026

First Submitted That Met QC Criteria

February 15, 2026

First Posted (Actual)

February 23, 2026

Study Record Updates

Last Update Posted (Actual)

February 23, 2026

Last Update Submitted That Met QC Criteria

February 15, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 0011903-1

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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