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Characterization of Type 1 Diabetes Subgroup: An Artificial Intelligence Analysis of Clinical and Glucometric Features

Caracterización de Subgrupos de Personas Con Diabetes Tipo 1: análisis de características clínicas y glucométricas Utilizando Una aproximación de Inteligencia Artificial

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07461805
Acronym
T1DC
Enrollment
800
Registered
2026-03-10
Start date
2025-11-04
Completion date
2028-12-01
Last updated
2026-03-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Type 1 Diabetes Mellitus

Keywords

Artificial intelligence, glucometry, Clusters, Type 1 diabetes mellitus

Brief summary

The goal of this observational study is to characterize different subgroups among patients with type 1 diabetes. The main research question is: Are there distinct subtypes among people with type 1 diabetes? Participants will be invited to take part in the study by allowing access to their health data. They will not be required to undergo any additional examinations, tests, visits, or interventions.

Detailed description

Study Description Main Objective The primary objective of this study is to characterize subgroups of individuals with type 1 diabetes (T1D) based on clinical and glucometric features using an artificial intelligence (AI) approach. Secondary objectives Evaluate cluster stability over time (1, 2, and 3 years); assess cluster utility for predicting complications; analyze the contribution of different clinical variables to cluster characterization and its evolution over time; and model endpoints such as diabetes-related complications. Study Design This is an ambispective observational study. Disease Under Study Type 1 Diabetes Mellitus. Methodology This ambispective observational study will use information extracted from participants' electronic medical records and glucometric data obtained from the corresponding monitoring platforms. The data will be analyzed using artificial intelligence techniques to identify patterns and potential subgroups within the type 1 diabetes population. Study Population and Sample Size The study population includes individuals with type 1 diabetes (T1D) who are being followed at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau. As this is an exploratory study, no formal sample size calculation is required. Approximately 800 patients are expected to be included.

Interventions

None listed

Sponsors

Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
Lead SponsorOTHER
Sociedad Española de Diabetes
CollaboratorNETWORK
Associació Catalana de Diabetis
CollaboratorUNKNOWN

Study design

Observational model
CASE_ONLY
Time perspective
OTHER

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Individuals with type 1 diabetes (T1D) aged 18 years or older. * T1D individuals expected to have regular follow-up at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau. * Users of continuous glucose monitoring (CGM) systems for at least the last 6 months of 2024. * Willingness and ability to provide written informed consent to participate in the study (by the patient or his/her representative).

Exclusion criteria

* Presence of severe comorbidities or medical conditions that, in the investigator's judgment, could interfere with participation in the study or the interpretation of results. This circumstance is expected to be exceptional, as the study aims to be as inclusive as possible.

Design outcomes

Primary

MeasureTime frameDescription
Type 1 diabetes clustersSubgroups defined based on data from the year 2024.Differentiated groups of people with type 1 diabetes defined through the analysis of clinical, analytical, and glucometric variables.

Secondary

MeasureTime frameDescription
Insulin treatment: type2024-2027Type of insulin therapy: multiple daily injections, continuous subcutaneous insulin infusion systems, hybrid closed-loop systems
Estimated glomerular filtration rate2024-2027laboratory estimated glomerular filtration rate. Units mL/min/1.73 m2
Albuminuria2024-2027Albuminuria, laboratory measure. Units mg/g
Antihypertensive treatment2024-2027Use of Antihypertensive treatment and its relationship with the clusters.
Hypolipemiant treatment: use2024-2027Use of hypolipemiant medication (yes/no)
Hypolipemiant treatment amongst clusters2024-2027Association between use of hypolipemiant treatment and the clusters.
Cluster stability over time2024 - 2027Cluster stability over time determined using the Jaccard index as a reliability criterion: persistence of clusters over 1, 2, and 3 years. The Jaccard index (JI) measures the degree of similarity between two sets, regardless of the type of elements. It takes values between 0 and 1, with the latter corresponding to complete equality between both sets
Acute and chronic diabetes complications2024-2027Presence of acute complications (such as severe hypoglycemia) and chronic complications (such as retinopathy, nephropathy, and neuropathy) across the different clusters.
Glycemic control: mean glucose2024-2027Mean glucose reported in mg/dL
Glycemic control: GMI (glucose management indicator)2024-2027GMI (glucose management indicator) reported in percentage (%)
Glycemic control: CV (coefficient of variation)2024-2027CV (coefficient of variation) reported in percentage (%)
Glycemic control: time in range2024-2027Time in range expressed as percentage: * % of time in glucose range 70-180 mg/dl (TIR) \>70% * % of time in glucose range 70-140 mg/dl (TTIR) \>70% * % of time \<70 mg/dl (TBR1) \<4% * % of time \<54 mg/dl (TBR2) \<1% * % of time \>180 mg/dl (TAR1) \<25% * % of time \>250 mg/dl (TAR2) \<5%
HbA1c2024-2027Lab or point-of-care HbA1c
Lipid profile2024-2027Laboratory mesured total cholesterol, triglycerids, LDL anb HDL
Creatinine2024-2027creatinine Laboratory measure. Units mg/dL
Anthropometric variables: weight2024-2027Weight in kilograms and its relationship with the clusters. Weight and height will be combined to report BMI in kg/m\^2.
Anthropometric variables: height2024-2027Height in centimeters and its relationship with the clusters. Weight and height will be combined to report BMI in kg/m\^2.
Anthropometric variables: waist circumference2024-2027Waist circumference in centimeters and its relationship with the clusters.
Substance use: tobacco2024-2027Tobacco consumption and its relationship with the clusters. Tobacco use will be reported: active tobacco use, past tobacco use, never smoker, unknown.
Substance use: alcohol2024-2027Acohol consumption and its relationship with the clusters. Alcohol consumption will be reported as: Low risk consumption, Risk consumption (\>10 grams of alcohol in women, \>20g of alcohol in men), known active alcohol disorder, Passed alcohol disorder, Unknown.
Age at diagnosis2024-2027Patient age at diabetes diagnosis and its relationship with the clusters.
Disease duration2024-2027Diabetes duration and its relationship with the clusters.
Pregnancy2024-2027Active pregnancy and its relationship with the clusters.
Parity status in women2024-2027Parity status in women and its relationship with the clusters.
Menstrual cycle phase2024-2027Menstrual cycle phase and its relationship with the clusters.
Reproductive stage in women2024-2027Reproductive stage in women and its relationship with the clusters. Reproductive stage will be reported as: Reproductive, Perimenopausal, Postmenopausal, Unknown
Patient-reported variables2024-2027Patient-reported health-related quality of life will be assessed using a validated questionnaire for patients with type 1 diabetes. The Spanish version of the Diabetes Quality of Life questionnaire (EsDQOL) will be used. The score obtained from the questionnaire ranges from 0 to 100, where 0 represents the lowest possible quality of life and 100 the highest possible.
Patient-reported variables and its association with the clusters2024-2027Correlation between patient reported health-related quality of life and the association with the clusters.
Insulin treatment: association with the clusters2024-2027Association with the type of insulin therapy and the clusters
Sociodemographic variables2024-2027Date of birth

Countries

Spain

Contacts

CONTACTEva Safont, MD
esafont@santpau.cat+34686203964
CONTACTRosa M Corcoy, MD, PhD
rcorcoy@santpau.cat+34686203964
PRINCIPAL_INVESTIGATORRosa M Corcoy

Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Mar 11, 2026