Ignite Creation Date:
2026-03-26 @ 3:14 PM
Ignite Modification Date:
2026-03-31 @ 4:09 AM
Study NCT ID:
NCT07426159
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2026-02-23
First Post:
2026-02-09
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-Guided Sarcopenia Risk Assessment and Detection
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D055948', 'term': 'Sarcopenia'}], 'ancestors': [{'id': 'D009133', 'term': 'Muscular Atrophy'}, {'id': 'D020879', 'term': 'Neuromuscular Manifestations'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D001284', 'term': 'Atrophy'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D009752', 'term': 'Nutritional Status'}], 'ancestors': [{'id': 'D009747', 'term': 'Nutritional Physiological Phenomena'}, {'id': 'D000066888', 'term': 'Diet, Food, and Nutrition'}, {'id': 'D010829', 'term': 'Physiological Phenomena'}, {'id': 'D006304', 'term': 'Health Status'}, {'id': 'D003710', 'term': 'Demography'}, {'id': 'D011154', 'term': 'Population Characteristics'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'SINGLE_GROUP', 'interventionModelDescription': '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.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 120}}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2026-02-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-15', 'studyFirstSubmitDate': '2026-02-09', 'studyFirstSubmitQcDate': '2026-02-15', 'lastUpdatePostDateStruct': {'date': '2026-02-23', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-23', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of AI-Based Sarcopenia Risk Prediction Model', 'timeFrame': 'Baseline to end of follow-up (up to 12 months)', 'description': '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.'}, {'measure': 'Change in MRI-Derived Thigh Muscle Volume', 'timeFrame': 'Baseline to 12 weeks', 'description': 'Mean change in thigh skeletal muscle volume assessed by 3-Tesla MRI (Siemens Prisma) using standardized segmentation analysis.\n\nUnit of Measure: cm³'}, {'measure': 'Change in Handgrip Strength (kg)', 'timeFrame': 'Baseline to 12 weeks', 'description': 'Mean change in maximal handgrip strength measured using a Jamar dynamometer (best of three trials).\n\nUnit of Measure: kg'}], 'secondaryOutcomes': [{'measure': 'Change in Appendicular Lean Mass Index (ALM/height²) Measured by DXA', 'timeFrame': 'Baseline to 12 weeks', 'description': '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).\n\nUnit of Measure: kg/m²'}, {'measure': 'Change in Resting Metabolic Rate (kcal/day)', 'timeFrame': 'Baseline to 12 weeks', 'description': 'Mean change in resting metabolic rate measured by indirect calorimetry using the Cosmed Quark RMR system under standardized fasting conditions.\n\nUnit of Measure: kcal/day'}, {'measure': 'Change in Gut Microbiome Diversity', 'timeFrame': 'Baseline to 12 weeks', 'description': 'Mean change in gut microbiome diversity assessed using 16S rRNA gene sequencing from extracted microbial DNA and calculated using the Shannon diversity index.'}, {'measure': 'Change in Short Physical Performance Battery (SPPB) Total Score', 'timeFrame': 'Baseline to 12 weeks', 'description': 'Mean change in total score of the Short Physical Performance Battery (SPPB), assessing lower extremity function.\n\nUnit of Measure: Scale score (0-12)'}, {'measure': 'Change in Quality of Life Assessed by SF-36', 'timeFrame': 'Baseline to 12 weeks', 'description': 'Change in health-related quality of life assessed using the 36-Item Short Form Health Survey (SF-36).\n\nUnit of Measure: SF-36 scale score (0-100)'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Muscle mass', 'Muscle function', 'Predictive biomarkers', 'Sarcopenia', 'Risk Assessment'], 'conditions': ['Sarcopenia', 'Age-related Muscle Loss']}, 'descriptionModule': {'briefSummary': '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.\n\nIn 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.', 'detailedDescription': 'Background:\n\nSarcopenia, 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.\n\nObjectives:\n\n* Develop and validate an AI-driven model integrating muscle composition, functional performance, and metabolic biomarkers to predict sarcopenia risk.\n* Implement a personalized, adaptive intervention combining exercise and nutrition, guided by AI predictions and real-time monitoring.\n* Evaluate the effectiveness of this intervention on muscle mass, functional performance, and metabolic health in older adults.\n\nMethods:\n\nPhase 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.\n\nPhase 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.\n\nSignificance and Innovation:\n\nThis 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.\n\nAnticipated Outcomes:\n\n* AI-based sarcopenia screening tools for early detection and risk stratification.\n* Personalized exercise and nutrition protocols tailored to individual risk and physiology.\n* A scalable, data-driven intervention framework suitable for clinical or home-based deployment.\n\nEnhanced understanding of heterogeneous responses to sarcopenia interventions.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '70 Years', 'minimumAge': '50 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Men and women aged 50-70 years\n* At risk for sarcopenia based on muscle mass and/or muscle function screening\n* Able to participate in supervised exercise training\n* Willing to comply with study procedures and provide written informed consent\n\nExclusion Criteria:\n\n* Participation in structured exercise or weight loss programs within the past 6 months\n* Unstable body weight (\\>±5%) in the past 6 months\n* Current smoking or smoking within the past 6 months\n* Pregnancy, breastfeeding, or post-menopause\n* Contraindications to MRI (e.g., implanted devices, tattoos, permanent makeup)\n* Severe cardiopulmonary disease (e.g., recent myocardial infarction, unstable angina)\n* Musculoskeletal or neuromuscular conditions limiting exercise participation\n* Cognitive impairment\n* Chronic diseases including cancer, diabetes, thyroid disease, hypertension, or chronic renal failure\n* Use of medications affecting metabolism\n* Secondary liver disease (viral, autoimmune, alcoholic, or drug-induced)\n* Alcohol intake \\>20 g/day (women) or \\>30 g/day (men)'}, 'identificationModule': {'nctId': 'NCT07426159', 'acronym': 'SARC-AI', 'briefTitle': 'AI-Guided Sarcopenia Risk Assessment and Detection', 'organization': {'class': 'OTHER', 'fullName': 'Tel Aviv University'}, 'officialTitle': 'AI-Driven Integration of Muscle Mass and Muscle Function: A Novel Approach to Sarcopenia Risk Assessment and Intervention', 'orgStudyIdInfo': {'id': '0011903-1'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'AI-Guided Personalized Exercise and Nutrition Intervention', 'description': '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.\n\nParticipants are stratified by sarcopenia risk (low, moderate, high) but all receive the same adaptive intervention framework.', 'interventionNames': ['Behavioral: Personalized AI-Guided Exercise and Nutrition']}], 'interventions': [{'name': 'Personalized AI-Guided Exercise and Nutrition', 'type': 'BEHAVIORAL', 'description': '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.', 'armGroupLabels': ['AI-Guided Personalized Exercise and Nutrition Intervention']}]}, 'contactsLocationsModule': {'locations': [{'zip': '69978', 'city': 'Tel Aviv', 'country': 'Israel', 'facility': 'Sylvan Adams Sport Institute', 'geoPoint': {'lat': 32.08088, 'lon': 34.78057}}], 'overallOfficials': [{'name': 'Yftach Gepner', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Tel Aviv University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Tel Aviv University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator - Professor', 'investigatorFullName': 'Gepner Yftach', 'investigatorAffiliation': 'Tel Aviv University'}}}}