Food-i-Sense Analytics: Integrating AI Into Continuous Glucose Monitoring Data Analysis for Precision Nutrition.

Brief Summary

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. To 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. Goals of the study 1. Train and validate the GLIA algorithm\*\* in a large and diverse sample of adults. 2. Study how glucotypes relate to health indicators\*\*, such as blood pressure, body composition, cholesterol, or lifestyle. 3. Predict how each person responds to different foods\*\*, to support personalized nutrition advice. Who can participate? Adults 18-70 years old who: * Do not\*have diagnosed diabetes or serious metabolic disease. * Agree to wear a glucose sensor for 14 days. * Can keep stable eating habits and record diet and physical activity. What participation involves The study lasts 3 weeks and includes 3 visits: Visit 1 - Screening (20 min): * Review of eligibility criteria. * Explanation of the study. * Signing informed consent. * Visit 2 - Initial assessment (45 min) * Collection of personal and health information. * Measurements: weight, height, waist, body composition, blood pressure. * Placement of a FreeStyle Libre 3 CGM sensor. * Instructions for: * Completing two 3-day food records (one each week). * Taking photos of all meals. * Reporting physical activity. Continuous monitoring (14 days) Visit 3 - Final evaluation (45 min) * Review of diet records. * Repeat measurements. * Blood and urine samples are collected for metabolic and molecular analyses. Meal 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. Potential benefits: Although participants may not receive direct health benefits, the study will: * Improve understanding of how healthy people process glucose. * Help identify early risk markers for metabolic diseases. * Contribute to developing \*\*personalized nutrition tools\*\* based on individual glucose responses. Risks: are minimal and mainly include: * Mild skin irritation from the CGM sensor. * Temporary discomfort from blood draw.

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