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.
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.
Spain1 site, 1 recruiting
- 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
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.
- Conditions
- Type 1 Diabetes Mellitus
- 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 group | Role | What they receive |
|---|---|---|
| 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 is being measured
The primary outcome is the main question the study is designed to answer.
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)
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
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
Glycemic control: mean glucose
Mean glucose reported in mg/dL
Measured: 2024-2027
Glycemic control: GMI (glucose management indicator)
GMI (glucose management indicator) reported in percentage (%)
Measured: 2024-2027
Glycemic control: CV (coefficient of variation)
CV (coefficient of variation) reported in percentage (%)
Measured: 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%
Measured: 2024-2027
HbA1c
Lab or point-of-care HbA1c
Measured: 2024-2027
Lipid profile
Laboratory mesured total cholesterol, triglycerids, LDL anb HDL
Measured: 2024-2027
Creatinine
creatinine Laboratory measure. Units mg/dL
Measured: 2024-2027
Estimated glomerular filtration rate
laboratory estimated glomerular filtration rate. Units mL/min/1.73 m2
Measured: 2024-2027
Albuminuria
Albuminuria, laboratory measure. Units mg/g
Measured: 2024-2027
Antihypertensive treatment
Use of Antihypertensive treatment and its relationship with the clusters.
Measured: 2024-2027
Hypolipemiant treatment: use
Use of hypolipemiant medication (yes/no)
Measured: 2024-2027
Hypolipemiant treatment amongst clusters
Association between use of hypolipemiant treatment and the clusters.
Measured: 2024-2027
Insulin treatment: type
Type of insulin therapy: multiple daily injections, continuous subcutaneous insulin infusion systems, hybrid closed-loop systems
Measured: 2024-2027
Insulin treatment: association with the clusters
Association with the type of insulin therapy and the clusters
Measured: 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.
Measured: 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.
Measured: 2024-2027
Anthropometric variables: waist circumference
Waist circumference in centimeters and its relationship with the clusters.
Measured: 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.
Measured: 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.
Measured: 2024-2027
Age at diagnosis
Patient age at diabetes diagnosis and its relationship with the clusters.
Measured: 2024-2027
Disease duration
Diabetes duration and its relationship with the clusters.
Measured: 2024-2027
Pregnancy
Active pregnancy and its relationship with the clusters.
Measured: 2024-2027
Parity status in women
Parity status in women and its relationship with the clusters.
Measured: 2024-2027
Menstrual cycle phase
Menstrual cycle phase and its relationship with the clusters.
Measured: 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
Measured: 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.
Measured: 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.
Measured: 2024-2027
Sociodemographic variables
Date of birth
Measured: 2024-2027
Sociodemographic variables
Sex assigned at birth
Measured: 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
Measured: 2024-2027
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Mar 2026
- Study start4 Nov 2025
- Primary completionJun 2028 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for Type 1 Diabetes MellitusStudies of the same condition, open and under way.
- More trials run by Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant PauThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
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
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Sociedad Española de Diabetes, Associació Catalana de Diabetis
- Responsible party
- Sponsor
- Investigators
- Rosa M Corcoy, Principal investigator, Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
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 38275577Celeux 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 34819951Vigers 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 31603912Kovatchev 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 37130300Tobias 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 37794253Somolinos-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 39089589Kahkoska 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 32049631Misra 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 37798471Ahlqvist 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 29503172Jacobsen 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 37794113Battaglia 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 31753960Diabetes 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
Study at a glance
From the registry record, last updated by the sponsor on 10 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Caracterización de Subgrupos de Personas Con Diabetes Tipo 1: análisis de características clínicas y glucométricas Utilizando Una aproximación de Inteligencia Artificial
- ClinicalTrials.gov ID
- NCT07461805
- Acronym / sponsor ID
- T1DC / IIBSP-GIA-2024-115
- Conditions
- Type 1 Diabetes Mellitus
- Keywords
- Artificial intelligence, glucometry, Clusters, Type 1 diabetes mellitus
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 800 participants (estimated)
- Lead sponsor
- Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant PauUniversity, hospital or other
- Collaborators
- Sociedad Española de Diabetes, Associació Catalana de Diabetis
- Study start
- 4 Nov 2025 (actual)
- Primary completion
- Jun 2028 (estimated)
- Study completion
- Dec 2028 (estimated)
- First posted
- 10 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Other
- Target follow-up
- 4 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- 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.
Key inclusion criteria (4 listed)
- 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).
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Type 1 diabetes clusters
Time frame: Subgroups 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.
32 secondary outcomes
Cluster stability over time
Time frame: 2024 - 2027
Acute and chronic diabetes complications
Time frame: 2024-2027
Glycemic control: mean glucose
Time frame: 2024-2027
Glycemic control: GMI (glucose management indicator)
Time frame: 2024-2027
Glycemic control: CV (coefficient of variation)
Time frame: 2024-2027
Glycemic control: time in range
Time frame: 2024-2027
HbA1c
Time frame: 2024-2027
Lipid profile
Time frame: 2024-2027
Creatinine
Time frame: 2024-2027
Estimated glomerular filtration rate
Time frame: 2024-2027
Albuminuria
Time frame: 2024-2027
Antihypertensive treatment
Time frame: 2024-2027
Hypolipemiant treatment: use
Time frame: 2024-2027
Hypolipemiant treatment amongst clusters
Time frame: 2024-2027
Insulin treatment: type
Time frame: 2024-2027
Insulin treatment: association with the clusters
Time frame: 2024-2027
Anthropometric variables: weight
Time frame: 2024-2027
Anthropometric variables: height
Time frame: 2024-2027
Anthropometric variables: waist circumference
Time frame: 2024-2027
Substance use: tobacco
Time frame: 2024-2027
Substance use: alcohol
Time frame: 2024-2027
Age at diagnosis
Time frame: 2024-2027
Disease duration
Time frame: 2024-2027
Pregnancy
Time frame: 2024-2027
Parity status in women
Time frame: 2024-2027
Menstrual cycle phase
Time frame: 2024-2027
Reproductive stage in women
Time frame: 2024-2027
Patient-reported variables
Time frame: 2024-2027
Patient-reported variables and its association with the clusters
Time frame: 2024-2027
Sociodemographic variables
Time frame: 2024-2027
Sociodemographic variables
Time frame: 2024-2027
Sociodemographic variables
Time frame: 2024-2027
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Mar 2026.
- Background: PubMed 38275577Goldstein 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.
- BackgroundCeleux G, Govaert G. Gaussian parsimonious clustering models. Pattern Recognit. 1995 May;28(5):781-93.
- Background: PubMed 34819951Sammouda 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.
- Background: PubMed 31603912Vigers 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.
- Background: PubMed 37130300Kovatchev 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.
- Background: PubMed 37794253Tobias 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.
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- Individual participant dataSharing planned: 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Rosa M Corcoy principal investigator
Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
1 site in 1 country: see every site and its status.
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.