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Type 1 Diabetes MellitusT1DCNCT07461805

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

An observational study of Type 1 Diabetes Mellitus, run by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau at 1 site in Spain.

Recruiting

Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts
Official record
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Trial 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. Read more about this study

Who is it for?
Ages 18 years and older
Conditions the study is for
Type 1 Diabetes Mellitus
How many people
800 participants
Where
1 siteSpain
Study dates
4 Nov 2025to Dec 2028
How long will I take part?
4 Years
Phase
Observational
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

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.

Who may be able to join

  • 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).

Who cannot join

  • 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.

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Eva Safont, MD contact

+34686203964 ext. 5661

esafont@santpau.cat

Rosa M Corcoy, MD, PhD contact

+34686203964 ext. 5661

rcorcoy@santpau.cat

Spain1 site, 1 recruiting

  • Hospital de la Santa Creu i Sant Pau, Barcelona, Barcelona 08041

    Barcelona, Barcelona, 08025

    Rosa Corcoy, MD PhD, +34 935565661, rcorcoy@santpau.cat

    Eva Safont, MD, +34 935565661, esafont@santpau.cat

    Recruiting

Other trials in Spain

About this study

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

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.

Keywords
Artificial intelligence, glucometry, Clusters, Type 1 diabetes mellitus
Medical subject headings
Diabetes Mellitus, Type 1

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Study groups in NCT07461805
Study groupRoleWhat they receive
People with type 1 diabetes mellitusIndividuals with type 1 diabetes mellitus (T1D) cared for at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau.

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Type 1 diabetes clusters

    Differentiated groups of people with type 1 diabetes defined through the analysis of clinical, analytical, and glucometric variables.

    Measured: Subgroups defined based on data from the year 2024.

Secondary outcomes (32)

  1. 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

    Measured: 2024 - 2027

  2. 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.

    Measured: 2024-2027

  3. Glycemic control: mean glucose

    Mean glucose reported in mg/dL

    Measured: 2024-2027

  4. Glycemic control: GMI (glucose management indicator)

    GMI (glucose management indicator) reported in percentage (%)

    Measured: 2024-2027

  5. Glycemic control: CV (coefficient of variation)

    CV (coefficient of variation) reported in percentage (%)

    Measured: 2024-2027

  6. 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%

    Measured: 2024-2027

  7. HbA1c

    Lab or point-of-care HbA1c

    Measured: 2024-2027

  8. Lipid profile

    Laboratory mesured total cholesterol, triglycerids, LDL anb HDL

    Measured: 2024-2027

  9. Creatinine

    creatinine Laboratory measure. Units mg/dL

    Measured: 2024-2027

  10. Estimated glomerular filtration rate

    laboratory estimated glomerular filtration rate. Units mL/min/1.73 m2

    Measured: 2024-2027

  11. Albuminuria

    Albuminuria, laboratory measure. Units mg/g

    Measured: 2024-2027

  12. Antihypertensive treatment

    Use of Antihypertensive treatment and its relationship with the clusters.

    Measured: 2024-2027

  13. Hypolipemiant treatment: use

    Use of hypolipemiant medication (yes/no)

    Measured: 2024-2027

  14. Hypolipemiant treatment amongst clusters

    Association between use of hypolipemiant treatment and the clusters.

    Measured: 2024-2027

  15. Insulin treatment: type

    Type of insulin therapy: multiple daily injections, continuous subcutaneous insulin infusion systems, hybrid closed-loop systems

    Measured: 2024-2027

  16. Insulin treatment: association with the clusters

    Association with the type of insulin therapy and the clusters

    Measured: 2024-2027

  17. 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.

    Measured: 2024-2027

  18. 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.

    Measured: 2024-2027

  19. Anthropometric variables: waist circumference

    Waist circumference in centimeters and its relationship with the clusters.

    Measured: 2024-2027

  20. 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.

    Measured: 2024-2027

  21. 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.

    Measured: 2024-2027

  22. Age at diagnosis

    Patient age at diabetes diagnosis and its relationship with the clusters.

    Measured: 2024-2027

  23. Disease duration

    Diabetes duration and its relationship with the clusters.

    Measured: 2024-2027

  24. Pregnancy

    Active pregnancy and its relationship with the clusters.

    Measured: 2024-2027

  25. Parity status in women

    Parity status in women and its relationship with the clusters.

    Measured: 2024-2027

  26. Menstrual cycle phase

    Menstrual cycle phase and its relationship with the clusters.

    Measured: 2024-2027

  27. Reproductive stage in women

    Reproductive stage in women and its relationship with the clusters. Reproductive stage will be reported as: Reproductive, Perimenopausal, Postmenopausal, Unknown

    Measured: 2024-2027

  28. 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.

    Measured: 2024-2027

  29. Patient-reported variables and its association with the clusters

    Correlation between patient reported health-related quality of life and the association with the clusters.

    Measured: 2024-2027

  30. Sociodemographic variables

    Date of birth

    Measured: 2024-2027

  31. Sociodemographic variables

    Sex assigned at birth

    Measured: 2024-2027

  32. 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

    Measured: 2024-2027

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted10 Mar 2026
  2. Study start4 Nov 2025
  3. Primary completionJun 2028 (estimated)
  4. Study completionDec 2028 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Case only
Time perspective
Other
Enrolment
800 participants (estimated)
Target duration
4 Years
IDs, dates and publications
ClinicalTrials.gov ID
NCT07461805
Sponsor's protocol ID
IIBSP-GIA-2024-115
Acronym
T1DC
Status last verified
Nov 2025
First submitted
14 Nov 2025
Last updated
10 Mar 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
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.

Publications

  • Goldstein A, Shahar Y, Weisman Raymond M, Peleg H, Ben-Chetrit E, Ben-Yehuda A, Shalom E, Goldstein C, Shiloh SS, Almoznino G. Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study. Bioengineering (Basel). 2024 Jan 19;11(1):97. doi: 10.3390/bioengineering11010097.

    PubMed 38275577

  • Celeux G, Govaert G. Gaussian parsimonious clustering models. Pattern Recognit. 1995 May;28(5):781-93.

  • 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

Questions about this trial

What is this clinical trial studying?

An observational study of Type 1 Diabetes Mellitus, run by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau at 1 site in Spain.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: looking for participants now, as of the sponsor's last update on 10 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Contact Eva Safont, MD (+34686203964 ext. 5661, esafont@santpau.cat), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Type 1 diabetes clusters (time frame: Subgroups defined based on data from the year 2024.). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07461805, which also lists 13 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Mar 2026.

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