Food_i Sense Analytics: Integrando la Inteligencia Artificial Con la monitorización Continua de la Glucosa Para la nutrición de precisión
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 471
研究概览
简要总结
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
- Train and validate the GLIA algorithm** in a large and diverse sample of adults.
- Study how glucotypes relate to health indicators**, such as blood pressure, body composition, cholesterol, or lifestyle.
- 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.
详细描述
The Food_iSense Analytics (FiS) study is an observational, cross-sectional protocol designed to advance precision nutrition through the integration of continuous glucose monitoring (CGM), artificial intelligence (AI), and comprehensive phenotyping. The project builds upon preliminary work using data from the ENSATI and TEMPUS studies, where the research team developed GLIA, an AI-driven algorithm capable of generating individualized glucotypes-patterns of glycemic behavior that reflect the dynamic response of glucose to daily living conditions and meal intake.
Scientific Background and Rationale Although individuals without diagnosed diabetes may exhibit blood glucose values within standard reference intervals, the shape, duration, and variability of glucose excursions reflect underlying physiological regulation and may reveal early signs of metabolic dysfunction. Research has demonstrated high inter-individual variability in glycemic responses to identical meals, suggesting that dietary guidelines must move toward personalization.
The introduction of CGM devices (FreeStyle Libre 3) allows for high-resolution temporal data capturing minute-to-minute changes in interstitial glucose. However, traditional CGM metrics (mean glucose, time in range, coefficient of variation) do not sufficiently capture the full complexity of glucose dynamics.
GLIA addresses this limitation by extracting multidimensional features that quantify:
Peak morphology: slope, amplitude, recovery time, decay kinetics. Variability features: short- and long-term variability indexes, glycemic volatility, post-prandial oscillation density.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 70 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
- •Able and willing to photograph all meals during the 14-day monitoring period following instructions provided.
- •Able to keep a record of physical activity as instructed.
- •No previous diagnosis of diabetes or other serious metabolic disorders.
- •Sufficient commitment and availability to attend all study visits (screening, baseline evaluation, final evaluation).
- •Capable of providing written informed consent.
排除标准
- •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.
- •Participants with skin conditions or allergies that prevent safe use of a CGM sensor.
- •Current participation in another clinical trial that could affect study results.
- •Use of medications that significantly alter glucose metabolism or interfere with CGM accuracy.
- •Inability to attend all scheduled study visits or complete required records (diet logs, photos, questionnaires).
- •Any condition judged by the investigators to make the participant unsuitable for the study or unable to provide informed consent.
研究者
Lidia Daimiel Ruiz
Principal Investigator
IMDEA Food