SarcopeniaSARC-AINCT07426159
AI-Guided Sarcopenia Risk Assessment and Detection
A study testing Personalized AI-Guided Exercise and Nutrition in people with Sarcopenia and Age-related Muscle Loss, run by Tel Aviv University at 1 site in Israel.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Sarcopenia, Age-related Muscle Loss
- What is being tested?
- Personalized AI-Guided Exercise and Nutrition
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 120 participants
- Where
- 1 siteIsrael
- Study dates
- 1 Feb 2026to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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)
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- 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)
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Sarcopenia, Age-related Muscle Loss
- Being tested
- Personalized AI-Guided Exercise and Nutrition
- Keywords
- Muscle mass, Muscle function, Predictive biomarkers, Sarcopenia, Risk Assessment
- Medical subject headings
- Sarcopenia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Personalized AI-Guided Exercise and NutritionBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Behavioral: Personalized AI-Guided Exercise and Nutrition |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Measured: 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
Measured: Baseline to 12 weeks
Secondary outcomes (5)
Change in Appendicular Lean Mass Index (ALM/height²) Measured by DXA
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
Measured: Baseline to 12 weeks
Change in Resting Metabolic Rate (kcal/day)
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
Measured: Baseline to 12 weeks
Change in Gut Microbiome Diversity
Mean change in gut microbiome diversity assessed using 16S rRNA gene sequencing from extracted microbial DNA and calculated using the Shannon diversity index.
Measured: Baseline to 12 weeks
Change in Short Physical Performance Battery (SPPB) Total Score
Mean change in total score of the Short Physical Performance Battery (SPPB), assessing lower extremity function.
Unit of Measure: Scale score (0-12)
Measured: Baseline to 12 weeks
Change in Quality of Life Assessed by SF-36
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)
Measured: Baseline to 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Feb 2026
- Study start1 Feb 2026
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SarcopeniaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Not applicable
- Intervention model
- Single group
- 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.
- Masking
- None (open label)
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tel Aviv University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Gepner Yftach, Tel Aviv University
- Investigators
- Yftach Gepner, Principal investigator, Tel Aviv University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07426159
- Sponsor's protocol ID
- 0011903-1
- Acronym
- SARC-AI
- Status last verified
- Feb 2026
- First submitted
- 9 Feb 2026
- Last updated
- 23 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Driven Integration of Muscle Mass and Muscle Function: A Novel Approach to Sarcopenia Risk Assessment and Intervention
- ClinicalTrials.gov ID
- NCT07426159
- Acronym / sponsor ID
- SARC-AI / 0011903-1
- Conditions
- Sarcopenia, Age-related Muscle Loss
- Keywords
- Muscle mass, Muscle function, Predictive biomarkers, Sarcopenia, Risk Assessment
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Tel Aviv UniversityUniversity, hospital or other
- Study start
- 1 Feb 2026 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 23 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Prevention
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- 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.
| Arm | Type | Receives |
|---|---|---|
| AI-Guided Personalized Exercise and Nutrition Intervention | Experimental | Behavioral: Personalized AI-Guided Exercise and NutritionAll 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. |
Who may qualify
- Ages
- 50 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (12 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Personalized AI-Guided Exercise and NutritionParticipants 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. | Behavioural | AI-Guided Personalized Exercise and Nutrition Intervention |
Primary outcome
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.
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
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
5 secondary outcomes
Change in Appendicular Lean Mass Index (ALM/height²) Measured by DXA
Time frame: Baseline to 12 weeks
Change in Resting Metabolic Rate (kcal/day)
Time frame: Baseline to 12 weeks
Change in Gut Microbiome Diversity
Time frame: Baseline to 12 weeks
Change in Short Physical Performance Battery (SPPB) Total Score
Time frame: Baseline to 12 weeks
Change in Quality of Life Assessed by SF-36
Time frame: Baseline to 12 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Yftach Gepner principal investigator
Tel Aviv University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Personalized AI-Guided Exercise and Nutrition in people with Sarcopenia and Age-related Muscle Loss, run by Tel Aviv University at 1 site in Israel.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 12 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 23 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI-Based Sarcopenia Risk Prediction Model (time frame: Baseline to end of follow-up (up to 12 months)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Feb 2026.