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.
Adults aged 18 to 70 years.
Willing and able to undergo 14 days of continuous glucose monitoring (CGM) using a wearable sensor.
Able to maintain stable dietary habits during the monitoring period.
Able and willing to complete dietary records, including two structured 3-day food logs.
Diagnosed diabetes mellitus or other serious metabolic disorders.
History of severe gastrointestinal, cardiovascular, or other medical conditions that may interfere with stable diet or physical activity during the study.
Pregnant or breastfeeding women.
Inability or unwillingness to comply with continuous glucose monitoring (CGM) procedures for 14 days.
The study population consists of community-dwelling adults aged 18-70 years, recruited from the general population through public advertisements, university campus postings, pharmacies, and primary care centers. Participants represent a broad, non-clinical community sample without diagnosed diabetes or severe metabolic disease. Recruitment is open to individuals living independently and able to maintain their usual daily routines. The population reflects a heterogeneous mix of sociodemographic backgrounds to ensure variability in dietary habits, lifestyle patterns, and glucose-response profiles.
Cohort
Cross-sectional
Continuous glucose monitoring using a wearable sensor (flash interstitial glucose monitor)
DeviceThe 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.
Glucotype Classification Derived From Continuous Glucose Monitoring Data
The primary outcome is the glucotype assigned to each participant based on analysis of the 14-day continuous glucose monitoring (CGM) trace. Glucotypes reflect individualized patterns of glucose dynamics, capturing peak shape, amplitude, recovery, variability, and chrononutrition-related fluctuations. The classification is generated using the GLIA machine-learning algorithm, which incorporates preprocessing (normalization, artifact detection), multivariate feature extraction (including peak morphology descriptors, temporal patterns, and variability metrics), and unsupervised clustering with stability assessment. The outcome quantifies each participant's predominant glucose-response phenotype under free-living conditions and serves as the foundation for assessing associations with dietary intake, metabolic traits, and predictive modeling of glycemic responses.
Body mass index
BMI is calculated as weight (kg) divided by height (m²). It provides an estimate of overall body size and is used to characterize participants' cardiometabolic phenotype. Measurements are taken using calibrated scales and stadiometers according to standardized anthropometric procedures.
Waist circunference
Waist circumference (cm) is measured at the midpoint between the last rib and the iliac crest using a flexible tape. It reflects central adiposity and is used to evaluate abdominal fat distribution, a key cardiometabolic risk marker. Two measurements are averaged.
Body Fat Percentage
Body fat percentage is obtained using bioelectrical impedance analysis (BIA). It quantifies total adiposity and contributes to understanding metabolic status and its association with glucotype patterns.
Muscle mass
Muscle mass (kg and %) is determined using segmental BIA. This measure helps characterize body composition and assess relationships between lean mass and glucose-response patterns.
Energy Intake
Total daily energy intake (kcal/day) is estimated from two structured 3-day dietary records and from AI-assisted analysis of meal photographs. Energy intake is used to evaluate how habitual consumption relates to individual glucotype patterns and postprandial glucose responses.
Macronutrient Distribution
Daily intake of carbohydrates, proteins, and fats (grams and % of total energy) derived from dietary records and AI-classified meal images. This variable helps determine whether macronutrient balance is associated with distinct glucotypes or glucose-response dynamics.
Micronutrient Intake
Intake of vitamins, minerals, and bioactive compounds is estimated through validated food-composition tables linked to dietary records and image-based nutrient extraction. This variable assesses whether micronutrient patterns differ across glucotypes or influence glucose peaks and variability.
Mediterranean Diet Adherence (MEDAS Score)
MEDAS is a validated 14-item questionnaire quantifying adherence to the Mediterranean dietary pattern. Scores range from 0 to 14, greater scores indicating greater adherence to Mediterranean diet. Scores are examined in relation to glucotypes to determine whether adherence to this dietary style predicts more favorable glucose-response profiles.
Click on the lead sponsor to view all of their trials.
IMDEA Food
Lead sponsor
Abbott Laboratories (Pak) Ltd.
Collaborator
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