[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100640704":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":20,"locations":10,"responsibleParty":30,"collaborators":34,"id":38,"slug":39,"hasResults":40,"nctId":41,"briefTitle":42,"officialTitle":43,"acronym":44,"eligibilityCriteria":45,"healthyVolunteers":46,"sex":47,"minAge":48,"maxAge":49,"enrollmentInfo":50,"targetDuration":10,"studyType":53,"phases":10,"briefSummary":54,"conditions":55,"keywords":58,"overallStatus":78,"whyStopped":10,"lastUpdateSubmitDate":79,"lastUpdatePostDateStruct":80,"startDateStruct":83,"completionDateStruct":85,"leadSponsor":87,"locationsCount":10},{"fullName":5,"class":6},"IMDEA Food","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Food_iSense Analytics Cohort",null,"This cohort includes adults aged 18-70 years without diagnosed diabetes who undergo continuous glucose monitoring (CGM) for 14 days using a FreeStyle Libre 3 sensor. Participants complete structured dietary records, provide meal photographs for AI-based food recognition, and answer validated nutrition and physical-activity questionnaires. Anthropometry, body composition, blood pressure, and recent clinical history are collected at study visits. At the end of monitoring, fasting blood and first-morning urine samples are obtained for biochemical and molecular analyses. No therapeutic intervention is administered; instead, the study characterizes natural glucose-response patterns (\"glucotypes\") under free-living conditions and evaluates how diet, lifestyle, and metabolic traits relate to glycemic dynamics to support future precision-nutrition strategies.",[13],"Device: Continuous glucose monitoring using a wearable sensor (flash interstitial glucose monitor)",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"DEVICE","Continuous glucose monitoring using a wearable sensor (flash interstitial glucose monitor)","The intervention consists of applying and wearing a 14-day continuous glucose monitoring (CGM) device that captures interstitial glucose every minute under free-living conditions. This wearable flash sensor is used exclusively for passive data collection; it does not provide insulin delivery, therapeutic adjustments, or real-time clinical management. What distinguishes this intervention is its integration into a multimodal data-capture system: participants simultaneously complete structured dietary records, submit standardized meal photographs for AI-based food recognition, and undergo detailed phenotyping. The CGM data are then processed through the study's proprietary GLIA algorithm to derive individualized glucose-response patterns (\"glucotypes\"). This combination of high-frequency glucose monitoring, dietary image analytics, and machine-learning modeling differentiates the device's use from typical clinical or self-management applications in other studies.",[9],[21,26],{"name":22,"role":23,"phone":24,"phoneExt":10,"email":25},"Lidia Daimiel Ruiz, Senior Researcher","CONTACT","+34655250563","lidia.daimiel@nutricion.imdea.org",{"name":27,"role":23,"phone":28,"phoneExt":10,"email":29},"Víctor de la O Pascual, Junior Researcher","+34648749288","victor.delao@nutricion.imdea.org",{"type":31,"investigatorFullName":32,"investigatorTitle":33,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Lidia Daimiel Ruiz","Principal Investigator",[35],{"name":36,"class":37},"Abbott Laboratories (Pak) Ltd.","UNKNOWN","100640704","food-i-sense-analytics-integrating-ai-into-continuous-glucose-monitoring-data-analysis-for-precision-nutrition-100640704",false,"NCT07626658","Food-i-Sense Analytics: Integrating AI Into Continuous Glucose Monitoring Data Analysis for Precision Nutrition.","Food_i Sense Analytics: Integrando la Inteligencia Artificial Con la monitorización Continua de la Glucosa Para la nutrición de precisión","Food_i Sense","Inclusion Criteria:\n\n* Adults aged 18 to 70 years.\n* Willing and able to undergo 14 days of continuous glucose monitoring (CGM) using a wearable sensor.\n* Able to maintain stable dietary habits during the monitoring period.\n* Able and willing to complete dietary records, including two structured 3-day food logs.\n* Able and willing to photograph all meals during the 14-day monitoring period following instructions provided.\n* Able to keep a record of physical activity as instructed.\n* No previous diagnosis of diabetes or other serious metabolic disorders.\n* Sufficient commitment and availability to attend all study visits (screening, baseline evaluation, final evaluation).\n* Capable of providing written informed consent.\n\nExclusion Criteria:\n\n* Diagnosed diabetes mellitus or other serious metabolic disorders.\n* History of severe gastrointestinal, cardiovascular, or other medical conditions that may interfere with stable diet or physical activity during the study.\n* Pregnant or breastfeeding women.\n* Inability or unwillingness to comply with continuous glucose monitoring (CGM) procedures for 14 days.\n* Participants with skin conditions or allergies that prevent safe use of a CGM sensor.\n* Current participation in another clinical trial that could affect study results.\n* Use of medications that significantly alter glucose metabolism or interfere with CGM accuracy.\n* Inability to attend all scheduled study visits or complete required records (diet logs, photos, questionnaires).\n* Any condition judged by the investigators to make the participant unsuitable for the study or unable to provide informed consent.",true,"ALL","18 Years","70 Years",{"count":51,"type":52},471,"ESTIMATED","OBSERVATIONAL","This study aims to improve how we understand and manage blood sugar responses in adults without diabetes. Even in people who appear healthy, blood sugar levels after meals can behave in different ways. These patterns may help predict future risk of diseases such as type 2 diabetes or other cardiometabolic problems.\n\nTo study this, researchers at IMDEA Nutrition have developed a computer algorithm called GLIA, which uses artificial intelligence (AI) to analyze continuous glucose monitoring (CGM) data. The goal is to classify people into different \"glucotypes\", meaning typical patterns of how their blood sugar behaves throughout the day. These glucotypes could help tailor dietary recommendations in the future.\n\nGoals of the study\n\n1. Train and validate the GLIA algorithm\\*\\* in a large and diverse sample of adults.\n2. Study how glucotypes relate to health indicators\\*\\*, such as blood pressure, body composition, cholesterol, or lifestyle.\n3. Predict how each person responds to different foods\\*\\*, to support personalized nutrition advice.\n\nWho can participate?\n\nAdults 18-70 years old who:\n\n* Do not\\*have diagnosed diabetes or serious metabolic disease.\n* Agree to wear a glucose sensor for 14 days.\n* Can keep stable eating habits and record diet and physical activity.\n\nWhat participation involves\n\nThe study lasts 3 weeks and includes 3 visits:\n\nVisit 1 - Screening (20 min):\n\n* Review of eligibility criteria.\n* Explanation of the study.\n* Signing informed consent.\n* Visit 2 - Initial assessment (45 min)\n* Collection of personal and health information.\n* Measurements: weight, height, waist, body composition, blood pressure.\n* Placement of a FreeStyle Libre 3 CGM sensor.\n* Instructions for:\n* Completing two 3-day food records (one each week).\n* Taking photos of all meals.\n* Reporting physical activity.\n\nContinuous monitoring (14 days)\n\nVisit 3 - Final evaluation (45 min)\n\n* Review of diet records.\n* Repeat measurements.\n* Blood and urine samples are collected for metabolic and molecular analyses.\n\nMeal photos are analyzed using an AI-based food recognition model. The system identifies foods and estimates nutrients (macronutrients, vitamins, minerals, glycemic index, etc.). This helps researchers understand how meals relate to blood sugar patterns.\n\nPotential benefits: Although participants may not receive direct health benefits, the study will:\n\n* Improve understanding of how healthy people process glucose.\n* Help identify early risk markers for metabolic diseases.\n* Contribute to developing \\*\\*personalized nutrition tools\\*\\* based on individual glucose responses.\n\nRisks: are minimal and mainly include:\n\n* Mild skin irritation from the CGM sensor.\n* Temporary discomfort from blood draw.",[56,57],"Prediabetes (Insulin Resistance, Impaired Glucose Tolerance)","Artificial Intelligence Mobile Application",[59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77],"Continuous glucose monitoring (CGM)","Glucose dynamics","Glucose phenotyping","Glucotypes","Artificial intelligence","Nutrition","Machine-learning","Glucose patterns","Precision nutrition","Chrononutrition","Glycemic variability","Nutritional pattern","Glycemic response modeling","Personalized dietary recommendations","Multimodal metabolic phenotyping","Wearable glucose sensors","AI-driven health monitoring","Cardiometabolic health","Adults Without Diabetes","NOT_YET_RECRUITING","2026-06-02",{"date":81,"type":82},"2026-06-04","ACTUAL",{"date":84,"type":52},"2026-07",{"date":86,"type":52},"2028-12",{"name":5,"class":6}]