Ignite Creation Date:
2026-03-26 @ 3:19 PM
Ignite Modification Date:
2026-03-31 @ 3:58 AM
Study NCT ID:
NCT07461805
Status:
RECRUITING
Last Update Posted:
2026-03-10
First Post:
2025-11-14
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Characterization of Type 1 Diabetes Subgroup: An Artificial Intelligence Analysis of Clinical and Glucometric Features
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003922', 'term': 'Diabetes Mellitus, Type 1'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 800}, 'targetDuration': '4 Years', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-11-04', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2028-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-06', 'studyFirstSubmitDate': '2025-11-14', 'studyFirstSubmitQcDate': '2026-03-06', 'lastUpdatePostDateStruct': {'date': '2026-03-10', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-10', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2028-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Type 1 diabetes clusters', 'timeFrame': 'Subgroups defined based on data from the year 2024.', 'description': 'Differentiated groups of people with type 1 diabetes defined through the analysis of clinical, analytical, and glucometric variables.'}], 'secondaryOutcomes': [{'measure': 'Cluster stability over time', 'timeFrame': '2024 - 2027', 'description': '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'}, {'measure': 'Acute and chronic diabetes complications', 'timeFrame': '2024-2027', 'description': 'Presence of acute complications (such as severe hypoglycemia) and chronic complications (such as retinopathy, nephropathy, and neuropathy) across the different clusters.'}, {'measure': 'Glycemic control: mean glucose', 'timeFrame': '2024-2027', 'description': 'Mean glucose reported in mg/dL'}, {'measure': 'Glycemic control: GMI (glucose management indicator)', 'timeFrame': '2024-2027', 'description': 'GMI (glucose management indicator) reported in percentage (%)'}, {'measure': 'Glycemic control: CV (coefficient of variation)', 'timeFrame': '2024-2027', 'description': 'CV (coefficient of variation) reported in percentage (%)'}, {'measure': 'Glycemic control: time in range', 'timeFrame': '2024-2027', 'description': 'Time in range expressed as percentage:\n\n* % of time in glucose range 70-180 mg/dl (TIR) \\>70%\n* % of time in glucose range 70-140 mg/dl (TTIR) \\>70%\n\n * % of time \\<70 mg/dl (TBR1) \\<4%\n * % of time \\<54 mg/dl (TBR2) \\<1%\n * % of time \\>180 mg/dl (TAR1) \\<25%\n * % of time \\>250 mg/dl (TAR2) \\<5%'}, {'measure': 'HbA1c', 'timeFrame': '2024-2027', 'description': 'Lab or point-of-care HbA1c'}, {'measure': 'Lipid profile', 'timeFrame': '2024-2027', 'description': 'Laboratory mesured total cholesterol, triglycerids, LDL anb HDL'}, {'measure': 'Creatinine', 'timeFrame': '2024-2027', 'description': 'creatinine Laboratory measure. Units mg/dL'}, {'measure': 'Estimated glomerular filtration rate', 'timeFrame': '2024-2027', 'description': 'laboratory estimated glomerular filtration rate. Units mL/min/1.73 m2'}, {'measure': 'Albuminuria', 'timeFrame': '2024-2027', 'description': 'Albuminuria, laboratory measure. Units mg/g'}, {'measure': 'Antihypertensive treatment', 'timeFrame': '2024-2027', 'description': 'Use of Antihypertensive treatment and its relationship with the clusters.'}, {'measure': 'Hypolipemiant treatment: use', 'timeFrame': '2024-2027', 'description': 'Use of hypolipemiant medication (yes/no)'}, {'measure': 'Hypolipemiant treatment amongst clusters', 'timeFrame': '2024-2027', 'description': 'Association between use of hypolipemiant treatment and the clusters.'}, {'measure': 'Insulin treatment: type', 'timeFrame': '2024-2027', 'description': 'Type of insulin therapy: multiple daily injections, continuous subcutaneous insulin infusion systems, hybrid closed-loop systems'}, {'measure': 'Insulin treatment: association with the clusters', 'timeFrame': '2024-2027', 'description': 'Association with the type of insulin therapy and the clusters'}, {'measure': 'Anthropometric variables: weight', 'timeFrame': '2024-2027', 'description': 'Weight in kilograms and its relationship with the clusters. Weight and height will be combined to report BMI in kg/m\\^2.'}, {'measure': 'Anthropometric variables: height', 'timeFrame': '2024-2027', 'description': 'Height in centimeters and its relationship with the clusters. Weight and height will be combined to report BMI in kg/m\\^2.'}, {'measure': 'Anthropometric variables: waist circumference', 'timeFrame': '2024-2027', 'description': 'Waist circumference in centimeters and its relationship with the clusters.'}, {'measure': 'Substance use: tobacco', 'timeFrame': '2024-2027', 'description': 'Tobacco consumption and its relationship with the clusters. Tobacco use will be reported: active tobacco use, past tobacco use, never smoker, unknown.'}, {'measure': 'Substance use: alcohol', 'timeFrame': '2024-2027', 'description': '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.'}, {'measure': 'Age at diagnosis', 'timeFrame': '2024-2027', 'description': 'Patient age at diabetes diagnosis and its relationship with the clusters.'}, {'measure': 'Disease duration', 'timeFrame': '2024-2027', 'description': 'Diabetes duration and its relationship with the clusters.'}, {'measure': 'Pregnancy', 'timeFrame': '2024-2027', 'description': 'Active pregnancy and its relationship with the clusters.'}, {'measure': 'Parity status in women', 'timeFrame': '2024-2027', 'description': 'Parity status in women and its relationship with the clusters.'}, {'measure': 'Menstrual cycle phase', 'timeFrame': '2024-2027', 'description': 'Menstrual cycle phase and its relationship with the clusters.'}, {'measure': 'Reproductive stage in women', 'timeFrame': '2024-2027', 'description': 'Reproductive stage in women and its relationship with the clusters. Reproductive stage will be reported as: Reproductive, Perimenopausal, Postmenopausal, Unknown'}, {'measure': 'Patient-reported variables', 'timeFrame': '2024-2027', 'description': '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.'}, {'measure': 'Patient-reported variables and its association with the clusters', 'timeFrame': '2024-2027', 'description': 'Correlation between patient reported health-related quality of life and the association with the clusters.'}, {'measure': 'Sociodemographic variables', 'timeFrame': '2024-2027', 'description': 'Date of birth'}, {'measure': 'Sociodemographic variables', 'timeFrame': '2024-2027', 'description': 'Sex assigned at birth'}, {'measure': 'Sociodemographic variables', 'timeFrame': '2024-2027', 'description': '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'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial intelligence', 'glucometry', 'Clusters', 'Type 1 diabetes mellitus'], 'conditions': ['Type 1 Diabetes Mellitus']}, 'referencesModule': {'references': [{'pmid': '38275577', 'type': 'BACKGROUND', 'citation': '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.'}, {'type': 'BACKGROUND', 'citation': 'Celeux G, Govaert G. Gaussian parsimonious clustering models. Pattern Recognit. 1995 May;28(5):781-93.'}, {'pmid': '34819951', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '31603912', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '37130300', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '37794253', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '39089589', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '32049631', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '37798471', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '29503172', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '37794113', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '31753960', 'type': 'BACKGROUND', 'citation': '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.'}, {'pmid': '8366922', 'type': 'BACKGROUND', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to characterize different subgroups among patients with type 1 diabetes. The main research question is:\n\nAre there distinct subtypes among people with type 1 diabetes?\n\nParticipants 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.', 'detailedDescription': "Study Description\n\nMain 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.\n\nSecondary 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.\n\nStudy Design This is an ambispective observational study.\n\nDisease Under Study Type 1 Diabetes Mellitus.\n\nMethodology 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.\n\nStudy 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Individuals with type 1 diabetes (T1D) aged 18 years or older.\n* T1D individuals expected to have regular follow-up at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau.\n* Users of continuous glucose monitoring (CGM) systems for at least the last 6 months of 2024.\n* Willingness and ability to provide written informed consent to participate in the study (by the patient or his/her representative).\n\nExclusion Criteria:\n\n* 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."}, 'identificationModule': {'nctId': 'NCT07461805', 'acronym': 'T1DC', 'briefTitle': 'Characterization of Type 1 Diabetes Subgroup: An Artificial Intelligence Analysis of Clinical and Glucometric Features', 'organization': {'class': 'OTHER', 'fullName': "Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau"}, 'officialTitle': 'Caracterización de Subgrupos de Personas Con Diabetes Tipo 1: análisis de características clínicas y glucométricas Utilizando Una aproximación de Inteligencia Artificial', 'orgStudyIdInfo': {'id': 'IIBSP-GIA-2024-115'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'People with type 1 diabetes mellitus', 'description': 'Individuals with type 1 diabetes mellitus (T1D) cared for at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '08025', 'city': 'Barcelona', 'state': 'Barcelona', 'status': 'RECRUITING', 'country': 'Spain', 'contacts': [{'name': 'Rosa Corcoy, MD PhD', 'role': 'CONTACT', 'email': 'rcorcoy@santpau.cat', 'phone': '+34 935565661'}, {'name': 'Eva Safont, MD', 'role': 'CONTACT', 'email': 'esafont@santpau.cat', 'phone': '+34 935565661'}, {'name': 'Rosa Corcoy, MD PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Eva Safont, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Ana Chico, MD PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Alex Mesa, MD PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Helena Sardà, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Lilian C Mendoza, MD PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Natalia Mangas, RN', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Dídac Mauricio, MD PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Santiago Martinez, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Jose M Cubero, MD PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Romina Miranda, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Bogdan Vlacho, PhD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Hospital de la Santa Creu i Sant Pau, Barcelona, Barcelona 08041', 'geoPoint': {'lat': 41.38879, 'lon': 2.15899}}], 'centralContacts': [{'name': 'Eva Safont, MD', 'role': 'CONTACT', 'email': 'esafont@santpau.cat', 'phone': '+34686203964', 'phoneExt': '5661'}, {'name': 'Rosa M Corcoy, MD, PhD', 'role': 'CONTACT', 'email': 'rcorcoy@santpau.cat', 'phone': '+34686203964', 'phoneExt': '5661'}], 'overallOfficials': [{'name': 'Rosa M Corcoy', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': "Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau"}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': '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.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau", 'class': 'OTHER'}, 'collaborators': [{'name': 'Sociedad Española de Diabetes', 'class': 'NETWORK'}, {'name': 'Associació Catalana de Diabetis', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}