RecruitingNCT07461805T1DCUpdated Mar 10, 2026

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

An observational study in Type 1 Diabetes Mellitus, sponsored by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau. Recruiting at 1 site in Spain. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-03-10.

Sponsored by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau · Observational

From the registry’s dates

Started Nov 2025; still recruiting 11 months later.

Study type: Observational
Model: Case-only
Time perspective: Other
Enrollment: 800
Ages: 18 Years and older
Sex: All

Study 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.

Read the 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.

Conditions studied

Type 1 Diabetes Mellitus

Browse trials for

Diabetes Mellitus, Type 1 3,522

Keywords

Artificial intelligence
glucometry
Clusters
Type 1 diabetes mellitus

In context

Diabetes Mellitus, Type 1

3,522 studies on the registry are indexed under Diabetes Mellitus, Type 1; 577 are open to participants now.

This study's planned enrollment of 800 is above the median of 120 across 748 observational studies indexed under Diabetes Mellitus, Type 1.

Browse Diabetes Mellitus, Type 1 studies →

Lead sponsor

Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau is the lead sponsor of 335 studies on the registry; 64 are open to participants now.

Counted across the registry records on this site, refreshed daily.

Who can participate

Ages eligible: 18 Years and older
Sexes eligible: All
Accepts healthy volunteers: No
Sampling method: Non-probability sample

Study population

The study population consists of individuals with type 1 diabetes (T1D) cared for at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau.

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

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.

Study design

Observational model: Case-only
Time perspective: Other
Enrollment: 800 participants (estimated)
Target follow-up: 4 Years
Patient registry: Yes

Groups and cohorts

People with type 1 diabetes mellitus
Individuals with type 1 diabetes mellitus (T1D) cared for at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau.

What researchers measure

Primary outcomes

Type 1 diabetes clusters
Differentiated groups of people with type 1 diabetes defined through the analysis of clinical, analytical, and glucometric variables.
Time frame: Subgroups defined based on data from the year 2024.

Secondary outcomes

Cluster stability over time
Cluster 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
Time frame: 2024 - 2027
Acute and chronic diabetes complications
Presence of acute complications (such as severe hypoglycemia) and chronic complications (such as retinopathy, nephropathy, and neuropathy) across the different clusters.
Time frame: 2024-2027
Glycemic control: mean glucose
Mean glucose reported in mg/dL
Time frame: 2024-2027
Glycemic control: GMI (glucose management indicator)
GMI (glucose management indicator) reported in percentage (%)
Time frame: 2024-2027
Glycemic control: CV (coefficient of variation)
CV (coefficient of variation) reported in percentage (%)
Time frame: 2024-2027
Glycemic control: time in range
Time 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%
Time frame: 2024-2027
HbA1c
Lab or point-of-care HbA1c
Time frame: 2024-2027
Lipid profile
Laboratory mesured total cholesterol, triglycerids, LDL anb HDL
Time frame: 2024-2027
Creatinine
creatinine Laboratory measure. Units mg/dL
Time frame: 2024-2027
Estimated glomerular filtration rate
laboratory estimated glomerular filtration rate. Units mL/min/1.73 m2
Time frame: 2024-2027
Albuminuria
Albuminuria, laboratory measure. Units mg/g
Time frame: 2024-2027
Antihypertensive treatment
Use of Antihypertensive treatment and its relationship with the clusters.
Time frame: 2024-2027
Hypolipemiant treatment: use
Use of hypolipemiant medication (yes/no)
Time frame: 2024-2027
Hypolipemiant treatment amongst clusters
Association between use of hypolipemiant treatment and the clusters.
Time frame: 2024-2027
Insulin treatment: type
Type of insulin therapy: multiple daily injections, continuous subcutaneous insulin infusion systems, hybrid closed-loop systems
Time frame: 2024-2027
Insulin treatment: association with the clusters
Association with the type of insulin therapy and the clusters
Time frame: 2024-2027
Anthropometric variables: weight
Weight in kilograms and its relationship with the clusters. Weight and height will be combined to report BMI in kg/m\^2.
Time frame: 2024-2027
Anthropometric variables: height
Height in centimeters and its relationship with the clusters. Weight and height will be combined to report BMI in kg/m\^2.
Time frame: 2024-2027
Anthropometric variables: waist circumference
Waist circumference in centimeters and its relationship with the clusters.
Time frame: 2024-2027
Substance use: tobacco
Tobacco consumption and its relationship with the clusters. Tobacco use will be reported: active tobacco use, past tobacco use, never smoker, unknown.
Time frame: 2024-2027
Substance use: alcohol
Acohol 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.
Time frame: 2024-2027
Age at diagnosis
Patient age at diabetes diagnosis and its relationship with the clusters.
Time frame: 2024-2027
Disease duration
Diabetes duration and its relationship with the clusters.
Time frame: 2024-2027
Pregnancy
Active pregnancy and its relationship with the clusters.
Time frame: 2024-2027
Parity status in women
Parity status in women and its relationship with the clusters.
Time frame: 2024-2027
Menstrual cycle phase
Menstrual cycle phase and its relationship with the clusters.
Time frame: 2024-2027
Reproductive stage in women
Reproductive stage in women and its relationship with the clusters. Reproductive stage will be reported as: Reproductive, Perimenopausal, Postmenopausal, Unknown
Time frame: 2024-2027
Patient-reported variables
Patient-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.
Time frame: 2024-2027
Patient-reported variables and its association with the clusters
Correlation between patient reported health-related quality of life and the association with the clusters.
Time frame: 2024-2027
Sociodemographic variables
Date of birth
Time frame: 2024-2027
Sociodemographic variables
Sex assigned at birth
Time frame: 2024-2027
Sociodemographic variables
Race/ethnic background reported as: White, Mediterranean or Hispanic, African or Caribbean, South Asian (Indian, Pakistani, Bangladeshi, or other Asian), East or Southeast Asian (Chinese, Japanese, or Southeast Asian), Arab or North African (including Egyptian), Unknown
Time frame: 2024-2027

Study locations

1 of 1 sites recruiting

Hospital de la Santa Creu i Sant Pau, Barcelona, Barcelona 08041
Barcelona, Barcelona 08025, Spain
- Rosa Corcoy, MD PhD · Contact · rcorcoy@santpau.cat · +34 935565661
- Eva Safont, MD · Contact · esafont@santpau.cat · +34 935565661
- Rosa Corcoy, MD PhD · Principal investigator
- Eva Safont, MD · Sub investigator
- Ana Chico, MD PhD · Sub investigator
- Alex Mesa, MD PhD · Sub investigator
- Helena Sardà, MD · Sub investigator
- Lilian C Mendoza, MD PhD · Sub investigator
- Natalia Mangas, RN · Sub investigator
- Dídac Mauricio, MD PhD · Sub investigator
- Santiago Martinez, MD · Sub investigator
- Jose M Cubero, MD PhD · Sub investigator
- Romina Miranda, PhD · Sub investigator
- Bogdan Vlacho, PhD · Sub investigator
Recruiting

References and documents

Publications

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Sammouda R, El-Zaart A. An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method. Comput Intell Neurosci. 2021 Nov 15;2021:4553832. doi: 10.1155/2021/4553832. eCollection 2021. PubMed 34819951 ↗
Vigers T, Chan CL, Snell-Bergeon J, Bjornstad P, Zeitler PS, Forlenza G, Pyle L. cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data. PLoS One. 2019 Oct 11;14(10):e0216851. doi: 10.1371/journal.pone.0216851. eCollection 2019. PubMed 31603912 ↗
Kovatchev B, Lobo B. Clinically Similar Clusters of Daily Continuous Glucose Monitoring Profiles: Tracking the Progression of Glycemic Control Over Time. Diabetes Technol Ther. 2023 Aug;25(8):519-528. doi: 10.1089/dia.2023.0117. PubMed 37130300 ↗
Tobias DK, Merino J, Ahmad A, Aiken C, Benham JL, Bodhini D, Clark AL, Colclough K, Corcoy R, Cromer SJ, Duan D, Felton JL, Francis EC, Gillard P, Gingras V, Gaillard R, Haider E, Hughes A, Ikle JM, Jacobsen LM, Kahkoska AR, Kettunen JLT, Kreienkamp RJ, Lim LL, Mannisto JME, Massey R, Mclennan NM, Miller RG, Morieri ML, Most J, Naylor RN, Ozkan B, Patel KA, Pilla SJ, Prystupa K, Raghavan S, Rooney MR, Schon M, Semnani-Azad Z, Sevilla-Gonzalez M, Svalastoga P, Takele WW, Tam CH, Thuesen ACB, Tosur M, Wallace AS, Wang CC, Wong JJ, Yamamoto JM, Young K, Amouyal C, Andersen MK, Bonham MP, Chen M, Cheng F, Chikowore T, Chivers SC, Clemmensen C, Dabelea D, Dawed AY, Deutsch AJ, Dickens LT, DiMeglio LA, Dudenhoffer-Pfeifer M, Evans-Molina C, Fernandez-Balsells MM, Fitipaldi H, Fitzpatrick SL, Gitelman SE, Goodarzi MO, Grieger JA, Guasch-Ferre M, Habibi N, Hansen T, Huang C, Harris-Kawano A, Ismail HM, Hoag B, Johnson RK, Jones AG, Koivula RW, Leong A, Leung GKW, Libman IM, Liu K, Long SA, Lowe WL Jr, Morton RW, Motala AA, Onengut-Gumuscu S, Pankow JS, Pathirana M, Pazmino S, Perez D, Petrie JR, Powe CE, Quinteros A, Jain R, Ray D, Ried-Larsen M, Saeed Z, Santhakumar V, Kanbour S, Sarkar S, Monaco GSF, Scholtens DM, Selvin E, Sheu WH, Speake C, Stanislawski MA, Steenackers N, Steck AK, Stefan N, Stoy J, Taylor R, Tye SC, Ukke GG, Urazbayeva M, Van der Schueren B, Vatier C, Wentworth JM, Hannah W, White SL, Yu G, Zhang Y, Zhou SJ, Beltrand J, Polak M, Aukrust I, de Franco E, Flanagan SE, Maloney KA, McGovern A, Molnes J, Nakabuye M, Njolstad PR, Pomares-Millan H, Provenzano M, Saint-Martin C, Zhang C, Zhu Y, Auh S, de Souza R, Fawcett AJ, Gruber C, Mekonnen EG, Mixter E, Sherifali D, Eckel RH, Nolan JJ, Philipson LH, Brown RJ, Billings LK, Boyle K, Costacou T, Dennis JM, Florez JC, Gloyn AL, Gomez MF, Gottlieb PA, Greeley SAW, Griffin K, Hattersley AT, Hirsch IB, Hivert MF, Hood KK, Josefson JL, Kwak SH, Laffel LM, Lim SS, Loos RJF, Ma RCW, Mathieu C, Mathioudakis N, Meigs JB, Misra S, Mohan V, Murphy R, Oram R, Owen KR, Ozanne SE, Pearson ER, Perng W, Pollin TI, Pop-Busui R, Pratley RE, Redman LM, Redondo MJ, Reynolds RM, Semple RK, Sherr JL, Sims EK, Sweeting A, Tuomi T, Udler MS, Vesco KK, Vilsboll T, Wagner R, Rich SS, Franks PW. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat Med. 2023 Oct;29(10):2438-2457. doi: 10.1038/s41591-023-02502-5. Epub 2023 Oct 5. PubMed 37794253 ↗
Somolinos-Simon FJ, Garcia-Saez G, Tapia-Galisteo J, Corcoy R, Elena Hernando M. Cluster analysis of adult individuals with type 1 diabetes: Treatment pathways and complications over a five-year follow-up period. Diabetes Res Clin Pract. 2024 Sep;215:111803. doi: 10.1016/j.diabres.2024.111803. Epub 2024 Jul 30. PubMed 39089589 ↗
Kahkoska AR, Nguyen CT, Jiang X, Adair LA, Agarwal S, Aiello AE, Burger KS, Buse JB, Dabelea D, Dolan LM, Imperatore G, Lawrence JM, Marcovina S, Pihoker C, Reboussin BA, Sauder KA, Kosorok MR, Mayer-Davis EJ. Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood. BMJ Open Diabetes Res Care. 2020 Jan;8(1):e000886. doi: 10.1136/bmjdrc-2019-000886. PubMed 32049631 ↗
Misra S, Wagner R, Ozkan B, Schon M, Sevilla-Gonzalez M, Prystupa K, Wang CC, Kreienkamp RJ, Cromer SJ, Rooney MR, Duan D, Thuesen ACB, Wallace AS, Leong A, Deutsch AJ, Andersen MK, Billings LK, Eckel RH, Sheu WH, Hansen T, Stefan N, Goodarzi MO, Ray D, Selvin E, Florez JC; ADA/EASD PMDI; Meigs JB, Udler MS. Precision subclassification of type 2 diabetes: a systematic review. Commun Med (Lond). 2023 Oct 5;3(1):138. doi: 10.1038/s43856-023-00360-3. PubMed 37798471 ↗
Ahlqvist E, Storm P, Karajamaki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P, Wessman Y, Shaat N, Spegel P, Mulder H, Lindholm E, Melander O, Hansson O, Malmqvist U, Lernmark A, Lahti K, Forsen T, Tuomi T, Rosengren AH, Groop L. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018 May;6(5):361-369. doi: 10.1016/S2213-8587(18)30051-2. Epub 2018 Mar 5. PubMed 29503172 ↗
Jacobsen LM, Sherr JL, Considine E, Chen A, Peeling SM, Hulsmans M, Charleer S, Urazbayeva M, Tosur M, Alamarie S, Redondo MJ, Hood KK, Gottlieb PA, Gillard P, Wong JJ, Hirsch IB, Pratley RE, Laffel LM, Mathieu C; ADA/EASD PMDI. Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review. Commun Med (Lond). 2023 Oct 5;3(1):132. doi: 10.1038/s43856-023-00358-x. PubMed 37794113 ↗
Battaglia M, Ahmed S, Anderson MS, Atkinson MA, Becker D, Bingley PJ, Bosi E, Brusko TM, DiMeglio LA, Evans-Molina C, Gitelman SE, Greenbaum CJ, Gottlieb PA, Herold KC, Hessner MJ, Knip M, Jacobsen L, Krischer JP, Long SA, Lundgren M, McKinney EF, Morgan NG, Oram RA, Pastinen T, Peters MC, Petrelli A, Qian X, Redondo MJ, Roep BO, Schatz D, Skibinski D, Peakman M. Introducing the Endotype Concept to Address the Challenge of Disease Heterogeneity in Type 1 Diabetes. Diabetes Care. 2020 Jan;43(1):5-12. doi: 10.2337/dc19-0880. Epub 2019 Nov 21. PubMed 31753960 ↗
Diabetes Control and Complications Trial Research Group; Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, Rand L, Siebert C. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993 Sep 30;329(14):977-86. doi: 10.1056/NEJM199309303291401. PubMed 8366922 ↗

Individual participant data

Plan to share: Undecided — We may publish anonymized patient data on the Universitat Autònoma de Barcelona website, ensuring that no information allowing re-identification is included, such as name, ID number, telephone number, postal or email address, social security number, exact date of birth, exact place of birth, or occupation.

Updates

Tracking since Sep 25, 2026

No changes since tracking began. The registry record was last updated on Mar 10, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗

Registry details

Key details

Study ID: NCT07461805
Lead sponsor: Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
Collaborators: Sociedad Española de Diabetes, Associació Catalana de Diabetis
Responsible party: Sponsor
First posted: Mar 10, 2026
Start date: Nov 4, 2025
Primary completion: Jun 2028 (estimated)
Completion: Dec 2028 (estimated)
Last update: Mar 10, 2026

Study contacts

Eva Safont, MD

Contact

esafont@santpau.cat

+34686203964 ext. 5661

Rosa M Corcoy, MD, PhD

Contact

rcorcoy@santpau.cat

+34686203964 ext. 5661

Rosa M Corcoy

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

Oversight

Data monitoring committee: No
FDA-regulated drug: No
FDA-regulated device: No

View the source record on ClinicalTrials.gov ↗

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