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

Food_i Sense Analytics: Integrando la Inteligencia Artificial Con la monitorización Continua de la Glucosa Para la nutrición de precisión

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Prediabetes (Insulin Resistance, Impaired Glucose Tolerance); Artificial Intelligence Mobile Application
• Intervention / Treatment: Device: Continuous glucose monitoring using a wearable sensor (flash interstitial glucose monitor)

Detailed Description

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.

Chrononutrition-related features: differences in glycemic control across circadian windows (morning/afternoon/evening), alignment with habitual eating patterns.

Derived metrics: composite indexes generated via principal component analysis (PCA) and clustering.

Using machine learning and unsupervised clustering with bootstrapping, GLIA identifies stable glucose response phenotypes. These glucotypes are then examined in relation to health indicators, dietary patterns, and predictive models of individual glycemic responses.

Study Structure and Workflow Overview

The study consists of three in-person visits across approximately 21 days, during which participants undergo:

Initial assessment (demographics, anthropometrics, medical history, baseline health measures).

Continuous 14-day CGM period with detailed dietary monitoring using:

Two structured 3-day dietary records Automated AI-based food image recognition Mediterranean diet and ultraprocessed food questionnaires Physical activity questionnaires

Final assessment including biological sample collection (fasting blood and first-morning urine), updated anthropometry, and final quality check of all dietary records.

The final dataset incorporates more than 140 nutritional variables per food item, combined with high-resolution glucose time-series data, clinical phenotype, and multiple molecular biomarkers.

Registry-Related Quality Procedures and Data Governance Although this study is not a patient registry in the classical sense, the research team implements registry-grade data management procedures due to the scale, multidimensionality, and long-term value of the dataset. The following subsections reflect the registry-quality framework.

1. Quality Assurance Plan

   A comprehensive quality assurance (QA) plan governs all activities from recruitment to data analysis. Key components include:

   Standardized training of all personnel (dietitians, research nurses, data managers).

   Calibration schedules for anthropometric devices (stadiometer, scale, bioimpedance instruments) and for blood pressure monitors.

   Daily consistency checks of CGM data uploads. Protocol deviation logs documenting missing measurements, device issues, and participant non-compliance.

   Internal monthly audits performed by the IMDEA Quality Office to verify protocol adherence.

   Independent external audit capability is maintained, though audits are not routinely scheduled unless required by funders or ethics committees.

2. Data Validation and Automated Data Checks

   Incoming data are processed through a multi-stage validation pipeline:

   1. Range and plausibility checks Automatic filtering flags values outside expected biological ranges (e.g., impossible BMI, extreme macronutrient percentages, duplicate CGM timestamps).

   2. Internal consistency checks

      Cross-field validation identifies inconsistencies, such as:

      caloric intake mismatching macronutrient totals, dietary patterns incompatible with photographed meals, anthropometric values inconsistent across visits.

   3. Technical consistency

   CGM streams are checked for:

   signal dropouts longer than 15 minutes, abrupt shifts indicating sensor displacement, unrealistic glucose kinetics (rise/decay rates).

   Problematic sections are annotated but not deleted, preserving data integrity for sensitivity analyses.

3. Source Data Verification (SDV)

   To ensure data accuracy and representativeness:

   Dietary records are cross-validated against food photographs and questionnaire responses.

   Medical history and medication data are verified against documents participants bring to Visit 2 (e.g., lab reports ≤ 6 months old).

   Blood pressure and anthropometry undergo dual measurement with two assessors performing random checks on 10% of sessions.

   CGM data are compared with participant logs describing sensor issues, physical activity peaks, and atypical meals.

   All verification steps follow Good Clinical Practice (GCP) documentation practices.

4. Data Dictionary and Variable Coding

The study uses an extensive data dictionary organized into modules:

Sociodemographic module

Definitions, coding schemes (e.g., ISCED for education), universe and skip patterns.

Clinical phenotype module

Standard coding for medical conditions using ICD-10 when applicable. Definitions of metabolic syndrome markers and derived variables.

Anthropometry and body composition

Measurement rules, rounding conventions, and device-specific calibrations.

CGM module

Time-series format, sampling interval, sensor metadata, derived features, preprocessing rules.

Dietary variables

Food items mapped to validated food composition databases for Spain. Nutrient coding includes standardized units and upper/lower expected ranges.

Molecular biomarkers

Units, assay platforms, storage conditions, and laboratory reference values.

The dictionary includes more than 500 entries and is version-controlled.

SOPs govern all operational and analytic tasks, including:

Recruitment and informed consent CGM application, monitoring, and removal Anthropometric and BIA measurement Blood/urine sample collection, processing, aliquoting, and storage Data entry and double-entry verification for paper forms Food image capture guidelines and data upload protocol Use of the AI food-recognition system and manual override procedures Documentation of adverse events related to CGM or blood collection Dataset freeze procedures and change control for analysis scripts Secure storage, encryption, and pseudonymization workflows

SOPs are reviewed periodically and updated as needed.

6. Sample Size Assessment

Using synthetic data generated during algorithm development, investigators determined that approximately 392 complete datasets provide adequate clustering stability:

Maximization of the Silhouette score (~0.67 at n=392). Near-minimal Davies-Bouldin index at ~392-588 samples. Robust PCA component stability at ≥350 participants.

Accounting for a 20% potential data-loss rate, 471 participants are targeted for recruitment.

7. Missing Data Plan Missingness may arise from incomplete dietary records, sensor failures, lost images, or participant withdrawal.

The plan includes:

Classification of missingness as MCAR, MAR, or MNAR. Short-gap interpolation for CGM data gaps ≤ 20 minutes using cubic spline or Kalman smoothing.

Exclusion of unusable segments (e.g., sensor warm-up periods). Multiple imputation (MI) for covariates such as weight or blood pressure if only one visit yields valid measures.

Complete-case sensitivity analyses to confirm robustness of imputed models.

Retention strategies minimize missing data, but high technical data density ensures analytic viability even with partial loss.

8. Statistical Analysis Plan (SAP) The SAP covers both algorithm validation and hypothesis-generating analyses. 8.1 Preprocessing

CGM normalization to individual baselines. PCA for dimensionality reduction. Unsupervised clustering (k-means and hierarchical) with bootstrapping. Feature engineering for peak characteristics and chrononutrition variables.

8.2 Glucotype generation and validation

Internal validation with Silhouette, Calinski-Harabasz, and Davies-Bouldin metrics.

Stability assessment through subsampling and perturbation tests.

8.3 Association analyses

ANOVA/Kruskal-Wallis for between-glucotype comparisons. Logistic and linear regression adjusting for key confounders. MANOVA for overall cardiometabolic profiles.

8.4 Dietary modelling

Multiple Correspondence Analysis (MCA) for dietary pattern extraction. Regression models linking nutrient intake to glucotype. Machine learning models (Random Forest, SVM, XGBoost) to predict meal-level glucose responses.

8.5 Predictive simulations Agent-based modelling is used to simulate hypothetical diet changes and predict personalized glucose responses under various nutritional scenarios.

8.6 Software and reproducibility Analyses are performed in Python and R, with scripts managed through version control, containerization (Docker), and reproducibility logs.

Conclusion The FiS protocol integrates advanced CGM analytics, AI-driven phenotyping, and detailed dietary and biochemical profiling to map the diversity of glucose regulation in adults without diabetes. The study applies rigorous registry-level quality procedures, including automated data validation, structured data dictionaries, SOPs, and a comprehensive statistical analysis plan. The resulting dataset and validated algorithm will support future work in personalized nutrition, early risk detection, and tailored dietary interventions.

• Study Type: Observational
• Enrollment (Estimated): 471

Contacts and Locations

• Study Contact: Name: Lidia Daimiel Ruiz, Senior Researcher; Phone Number: +34655250563; Email: lidia.daimiel@nutricion.imdea.org
• Study Contact Backup: Name: Víctor de la O Pascual, Junior Researcher; Phone Number: +34648749288; Email: victor.delao@nutricion.imdea.org

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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.

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Food_iSense Analytics Cohort; 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.

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Assessed continuously over 14 days of CGM wear, with glucotype classification calculated after completion of the full 14-day glucose-monitoring period for each participant.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Measured during Visit 2 and Visit 3 across the 14-day monitoring period.
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.	Assessed during Visit 2 and Visit 3 within the 14-day monitoring period.
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.	Measured during Visit 2 and Visit 3 across the 14-day monitoring period.
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.	Measured during Visit 2 and Visit 3 within the 14-day CGM period.
Visceral Fat Index	The visceral fat index is derived from BIA and represents estimated deep abdominal fat levels. Elevated visceral fat is associated with insulin resistance and cardiometabolic risk, making it relevant to glucotype interpretation.	Measured during Visit 2 and Visit 3 over the 14-day monitoring period.
Resting Metabolic Rate (RMR)	RMR is estimated via BIA and expressed in kilocalories per day. It reflects basal energy expenditure and may relate to individualized glucose variability and metabolic patterns.	Measured during Visit 2 and Visit 3 during the 14-day CGM period.
Blood Pressure (Systolic and Diastolic)	Blood pressure is measured in mmHg using a validated automatic device (three readings averaged). It provides an indicator of cardiovascular status to explore associations with glucose-response phenotypes.	Measured during Visit 2 and Visit 3 within the 14-day monitoring period.
Fasting Glucose	Fasting plasma glucose (mg/dL) is obtained from venous blood after an overnight fast. It reflects baseline glycemic control and is used to characterize metabolic status in relation to glucotypes.	Collected once during Visit 3 after completion of the 14-day monitoring period.
Hemoglobin A1c (HbA1c)	HbA1c (%) measures average glucose control over the prior 2-3 months. Although participants are non-diabetic, HbA1c helps assess subtle alterations in glycemic regulation associated with glucotypes.	Measured during Visit 3 following the 14-day CGM period.
Total Cholesterol	Total cholesterol (mg/dL) is quantified from fasting blood samples to evaluate lipid status. Its relationship with glucotype-derived metabolic phenotypes will be explored.	Assessed during Visit 3 after completion of the 14-day monitoring period.
LDL Cholesterol	LDL (mg/dL) is calculated with the Friedewald formula	Measured during Visit 3 following the 14-day CGM period.
HDL Cholesterol	HDL cholesterol is assessed from fasting blood samples and reflects protective lipid status. Associations with glucose-response patterns will be evaluated.	Measured during Visit 3 after the 14-day monitoring period.
Triglycerides	Fasting triglycerides (mg/dL) are measured from venous blood obtained during the end-of-study visit. Triglyceride concentration reflects circulating lipid metabolism and is an important cardiometabolic risk indicator. This outcome assesses whether triglyceride levels differ across glucotypes or relate to glucose-response features such as postprandial peak amplitude or glycemic variability. Analyses will explore associations using regression models adjusted for demographic and clinical covariates.	Assessed during Visit 3 after completion of the 14-day monitoring period.

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Assessed across the 14-day CGM monitoring period, combining two 3-day diet records and all photographed meals.
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.	Assessed across the 14-day CGM monitoring period, using two 3-day diet records plus continuous meal-photo submissions.
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.	Assessed throughout the 14-day CGM period, based on both 3-day diet logs and all meal photographs.
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.	MEDAS questionnaire completed during Visit 2, reflecting usual diet during the 14-day monitoring period.
Intake of Ultraprocessed Foods (sQ-HPF Score)	The sQ-HPF questionnaire produces a score reflecting frequency and quantity of ultraprocessed-food consumption. This measure evaluates how ultraprocessed-food intake relates to glucose variability, peak magnitude, and assigned glucotype.	Assessed once during Visit 2, representing dietary habits during the 14-day CGM period.
Physical Activity Levels	Evaluated as mets/min/week with the REGICOR questionnaire to evaluate wether physical activity correlates with glucose-response patterns.	Collected during Visit 2 and matched to CGM data collected over the subsequent 14-day monitoring period.

Collaborators and Investigators

• Sponsor: IMDEA Food
• Collaborators: Abbott Laboratories (Pak) Ltd.

Publications and helpful links

General Publications

• Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.
• Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, Snyder M. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018 Jul 24;16(7):e2005143. doi: 10.1371/journal.pbio.2005143. eCollection 2018 Jul.
• Klonoff DC, Nguyen KT, Xu NY, Gutierrez A, Espinoza JC, Vidmar AP. Use of Continuous Glucose Monitors by People Without Diabetes: An Idea Whose Time Has Come? J Diabetes Sci Technol. 2023 Nov;17(6):1686-1697. doi: 10.1177/19322968221110830. Epub 2022 Jul 20.
• Hengist A, Ong JA, McNeel K, Guo J, Hall KD. Imprecision nutrition? Intraindividual variability of glucose responses to duplicate presented meals in adults without diabetes. Am J Clin Nutr. 2025 Jan;121(1):74-82. doi: 10.1016/j.ajcnut.2024.10.007. Epub 2024 Dec 2.
• Mao Y, Tan KXQ, Seng A, Wong P, Toh SA, Cook AR. Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning. Health Data Sci. 2022 Apr 27;2022:9892340. doi: 10.34133/2022/9892340. eCollection 2022.
• van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A, van der Kallen CJH, Wesselius A, Schram MT, Henry RMA, Dagnelie PC, de Galan BE, Bekers O, Stehouwer CDA, Meex SJR, Brouwers MCGJ. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study. PLoS One. 2021 Jun 24;16(6):e0253125. doi: 10.1371/journal.pone.0253125. eCollection 2021.
• Barrea L, Verde L, Colao A, Mandarino LJ, Muscogiuri G. Medical nutrition therapy for the management of type 2 diabetes mellitus. Nat Rev Endocrinol. 2025 Dec;21(12):769-782. doi: 10.1038/s41574-025-01161-5. Epub 2025 Aug 15.
• Safiri S, Karamzad N, Kaufman JS, Bell AW, Nejadghaderi SA, Sullman MJM, Moradi-Lakeh M, Collins G, Kolahi AA. Prevalence, Deaths and Disability-Adjusted-Life-Years (DALYs) Due to Type 2 Diabetes and Its Attributable Risk Factors in 204 Countries and Territories, 1990-2019: Results From the Global Burden of Disease Study 2019. Front Endocrinol (Lausanne). 2022 Feb 25;13:838027. doi: 10.3389/fendo.2022.838027. eCollection 2022.

Study record dates

Study Major Dates

• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): May 1, 2028
• Study Completion (Estimated): December 1, 2028

Study Registration Dates

• First Submitted: May 27, 2026
• First Submitted That Met QC Criteria: June 2, 2026
• First Posted (Actual): June 4, 2026

Study Record Updates

• Last Update Posted (Actual): June 4, 2026
• Last Update Submitted That Met QC Criteria: June 2, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Keywords: Nutrition; Precision nutrition; Artificial intelligence; Glycemic variability; Machine-learning; Chrononutrition; Cardiometabolic health; Continuous glucose monitoring (CGM); Glucose dynamics; Glucose phenotyping; Glucotypes; Glucose patterns; Nutritional pattern; Glycemic response modeling; Personalized dietary recommendations; Multimodal metabolic phenotyping; Wearable glucose sensors; AI-driven health monitoring; Adults Without Diabetes
• Additional Relevant MeSH Terms: Endocrine System Diseases; Metabolic Diseases; Glucose Metabolism Disorders; Diabetes Mellitus; Hyperinsulinism; Hyperglycemia; Nutritional and Metabolic Diseases; Insulin Resistance; Prediabetic State; Glucose Intolerance
• Other Study ID Numbers: IMD PI-078

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: De-identified individual participant data will be made available to qualified researchers upon reasonable request. All shared datasets will undergo double codification, meaning two independent pseudonymization layers are applied before external release: one ID replacing personal identifiers within IMDEA Nutrition, and a second external-use ID generated solely for data sharing. No key linking either code to participant identities will be shared. Data will be accessible only for ethically approved scientific purposes and after signing a data-sharing agreement outlining permitted use, data-security requirements, and obligations to prevent re-identification. Access will be provided through secure, controlled-transfer procedures.
• IPD Sharing Time Frame: Individual participant data (IPD) and accompanying documentation will become available not before 12 months after completion of the final data analysis, anticipated to begin once all primary and secondary outcomes are fully evaluated. Data will remain accessible to qualified researchers for a minimum of 5 years following the initial release. After this period, continued availability will depend on dataset relevance, ethical approvals, and storage capacity. Access will be granted only through controlled procedures and under a signed data-sharing agreement ensuring secure use and strict protection against re-identification.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: Yes