[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"biological-age\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:biological-age":30},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,52,78],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":33,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":40,"lastUpdatePostDateStruct":41,"startDateStruct":44,"completionDateStruct":46,"leadSponsor":48,"locationsCount":51},"100640034","sheba-healthspan-research-population-sharp-trial---sheba-longevity-center-diagnostic-and-intervention-protocol-to-lower-biological-age-in-older-adults-100640034",false,"NCT07596576","Sheba Healthspan Research Population (SHARP) Trial - Sheba Longevity Center Diagnostic and Intervention Protocol to Lower Biological Age in Older Adults","Sheba Healthspan Research Population (SHARP) Trial","SHARP","Inclusion Criteria:\n\n* Healthy older adults \\>50 years old men and women\n\nExclusion Criteria:\n\n1. A former diagnostic of a significant cognitive reduction (MoCa score \\\u003C 24 ).\n2. Former diagnosis of neurodegenerative disease as Alzheimer's disease, Parkinson's disease, Lewy body dementia.\n3. Former diagnosis of psychiatric disease.\n4. In the past two years, underwent chemotherapy or radiation therapy.",true,"ALL","50 Years",{"count":21,"type":22},1500,"ESTIMATED","INTERVENTIONAL",[25],"NA","Background: Population aging is accelerating rapidly in Israel and worldwide, necessitating adaptation of the healthcare system and considering new approaches that serve the needs of older adult populations.\n\nWorking hypothesis and aims: We hypothesize that a personalized health and behavior intervention program will decrease the biological age as assessed by several biological aging clocks and improve functional and cognitive performance among older adults.\n\nMethods: We propose to conduct a randomized study among healthy community-dwelling elderly subjects (\\>50 years old). The study will include an extensive aging assessment and imaging protocol (baseline assessment), including comprehensive physical, functional, sensory, cognitive, and mental assessment. Each participant in the intervention group will receive a personalized intervention program based on an integrative systems approach analysis. In addition, a uniquely developed application will track compliance and monitor physiological data through a provided wearable device. The control group will be assessed at baseline without receiving an intervention program. Each participant will visit the center aging after 6 months for a blood test and after 12 months for a second extensive diagnostic protocol, similar to the baseline assessment protocol. About 1,500 subjects will be recruited to participate in the study.\n\nExpected results: Obtaining data at two points will allow us to examine efficiency and compliance with a personalized intervention program based on integrative systems analysis models. We expect biological age, general well-being, and various clinical and psychosocial outcomes in the intervention group will decrease and improve compared to the control group.\n\nStudy importance and relevance: The obtained results may help establish evidence-based healthy aging diagnostics protocols and an effective personalized intervention program that might be applied, with proper modifications, to national healthcare organizations for the general older adult population. In addition, to provides a scientific basis on which policymakers and intervention programs can rely to develop national guidelines for promoting extended health.",[28,29,30,31,32],"Healthy Aging","Healthspan","Biological Age","Intrinsic Capacity","Immunaging",[34,35,36,37,38],"healthy longevity","healthspan","geromedicine","biological age","health trajectories","RECRUITING","2026-05-18",{"date":42,"type":43},"2026-05-19","ACTUAL",{"date":45,"type":43},"2024-03-01",{"date":47,"type":22},"2027-12-01",{"name":49,"class":50},"Sheba Medical Center","OTHER_GOV",1,{"id":53,"slug":54,"hasResults":11,"nctId":55,"briefTitle":56,"officialTitle":57,"acronym":4,"eligibilityCriteria":58,"healthyVolunteers":11,"sex":18,"minAge":59,"maxAge":60,"enrollmentInfo":61,"targetDuration":4,"studyType":63,"phases":4,"briefSummary":64,"conditions":65,"keywords":4,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":68,"lastUpdatePostDateStruct":69,"startDateStruct":71,"completionDateStruct":73,"leadSponsor":75,"locationsCount":51},"100591752","accelerated-biological-aging-is-associated-with-increased-risk-of-t2dm-in-the-masld-population-100591752","NCT06984510","Accelerated Biological Aging is Associated With Increased Risk of T2DM in the MASLD Population","Accelerated Biological Aging is Associated With Increased Risk of T2DM in the MASLD Population: a Cohort Study","Inclusion Criteria:\n\n1. Abdominal ultrasound data available in annual health check-up records\n2. At least one of the following five metabolic indicators recorded annually:\n\n(1) Body mass index (BMI) or waist circumference (2) Blood pressure (3) Serum triglycerides (4) High-density lipoprotein cholesterol (HDL-C) (5) Fasting plasma glucose or glycated hemoglobin (HbA1c)\n\nExclusion Criteria:\n\n1. Age \\\u003C20 or \\>90 years\n2. Other causes of hepatic steatosis (e.g., alcoholic liver disease or hepatitis B infection)\n3. Type 2 diabetes mellitus at baseline\n4. Missing baseline data for any of the following variables: systolic blood pressure, albumin, alkaline phosphatase, blood urea nitrogen, creatinine, glycated hemoglobin (HbA1c), total cholesterol, lymphocyte percentage, white blood cell count, mean corpuscular volume, uric acid, fasting plasma glucose, and red cell distribution width (RDW)","20 Years","90 Years",{"count":62,"type":22},2720,"OBSERVATIONAL","The association between biological aging and type 2 diabetes mellitus (T2DM) incidence in individuals with and without metabolic dysfunction-associated steatotic liver disease (MASLD) remains unclear.We assessed biological age by calculating phenotypic age (PhenoAge), Klemera-Doubal method (KDMAge), and homeostatic dysregulation (HDAge). To examine the association of biological ageing with the risk of T2DM, cox regression models were conducted. Furthermore, we applied survival analysis, restricted cubic spline models and population attributable fraction (PAF) to further evaluate the association between biological ageing and T2DM incidence.",[66,67,30],"Type 2 Diabetes Mellitus (T2DM)","Metabolic Dysfunction-Associated Steatotic Liver Disease","2025-05-30",{"date":70,"type":43},"2025-06-04",{"date":72,"type":43},"2025-04-01",{"date":74,"type":22},"2025-12-31",{"name":76,"class":77},"Ningbo No. 1 Hospital","OTHER",{"id":79,"slug":80,"hasResults":11,"nctId":81,"briefTitle":82,"officialTitle":83,"acronym":4,"eligibilityCriteria":84,"healthyVolunteers":17,"sex":18,"minAge":85,"maxAge":86,"enrollmentInfo":87,"targetDuration":4,"studyType":63,"phases":4,"briefSummary":89,"conditions":90,"keywords":91,"overallStatus":39,"whyStopped":4,"lastUpdateSubmitDate":72,"lastUpdatePostDateStruct":95,"startDateStruct":97,"completionDateStruct":99,"leadSponsor":100,"locationsCount":102},"100576914","ai-driven-prediction-of-biological-age-with-ehr-100576914","NCT06791486","AI-Driven Prediction of Biological Age With EHR","Predicting Biological Age Using Electronic Health Records: An AI-Based Approach","Inclusion Criteria:\n\n1. Patients with comprehensive and accessible EHR data, including medical history, laboratory results, treatment data, imaging data (if available), and lifestyle factors (e.g., smoking, physical activity, diet).\n2. Patients with no significant cognitive impairments that would prevent them from providing informed consent or participating in the study.\n3. All participants must provide informed consent for the use of their medical data for research purposes.\n\nExclusion Criteria:\n\n1. Patients with incomplete or missing critical EHR data such as medical history, laboratory results, or treatment data that are necessary for predicting biological age.\n2. atients with severe cognitive disorders (e.g., dementia, significant mental disabilities) who are unable to provide informed consent or participate meaningfully in the study.\n3. Patients with terminal illnesses or those with limited life expectancy where biological age predictions may not be relevant for the purposes of the study.","0 Years","100 Years",{"count":88,"type":22},1000000,"This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.",[30],[30,92,93,94],"electronic health records","AI prediction","aging",{"date":96,"type":43},"2025-04-02",{"date":98,"type":43},"2023-03-01",{"date":96,"type":22},{"name":101,"class":77},"The Eye Hospital of Wenzhou Medical University",4]