Ignite Creation Date:
2025-12-24 @ 4:20 PM
Ignite Modification Date:
2025-12-25 @ 2:16 PM
Study NCT ID:
NCT05609266
Status:
COMPLETED
Last Update Posted:
2022-12-02
First Post:
2022-11-01
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Validation of Existing Diabetes Risk Models in a Swedish Population
Sponsor:
Organization:
Raw JSON
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Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019 Nov;157:107843. doi: 10.1016/j.diabres.2019.107843. Epub 2019 Sep 10.'}, {'pmid': '29496507', 'type': 'BACKGROUND', 'citation': 'Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018 Apr;138:271-281. doi: 10.1016/j.diabres.2018.02.023. Epub 2018 Feb 26.'}, {'pmid': '18502303', 'type': 'BACKGROUND', 'citation': 'Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, Li H, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson TJ, Gerzoff RB, Roglic G, Hu Y, Bennett PH. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. 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Epub 2017 Aug 23.'}, {'pmid': '25986661', 'type': 'BACKGROUND', 'citation': 'Herman WH, Ye W, Griffin SJ, Simmons RK, Davies MJ, Khunti K, Rutten GE, Sandbaek A, Lauritzen T, Borch-Johnsen K, Brown MB, Wareham NJ. Early Detection and Treatment of Type 2 Diabetes Reduce Cardiovascular Morbidity and Mortality: A Simulation of the Results of the Anglo-Danish-Dutch Study of Intensive Treatment in People With Screen-Detected Diabetes in Primary Care (ADDITION-Europe). Diabetes Care. 2015 Aug;38(8):1449-55. doi: 10.2337/dc14-2459. Epub 2015 May 18.'}, {'pmid': '29549417', 'type': 'BACKGROUND', 'citation': 'Sortso C, Komkova A, Sandbaek A, Griffin SJ, Emneus M, Lauritzen T, Simmons RK. Effect of screening for type 2 diabetes on healthcare costs: a register-based study among 139,075 individuals diagnosed with diabetes in Denmark between 2001 and 2009. Diabetologia. 2018 Jun;61(6):1306-1314. doi: 10.1007/s00125-018-4594-2. Epub 2018 Mar 16.'}, {'pmid': '24622666', 'type': 'BACKGROUND', 'citation': 'Kengne AP, Beulens JW, Peelen LM, Moons KG, van der Schouw YT, Schulze MB, Spijkerman AM, Griffin SJ, Grobbee DE, Palla L, Tormo MJ, Arriola L, Barengo NC, Barricarte A, Boeing H, Bonet C, Clavel-Chapelon F, Dartois L, Fagherazzi G, Franks PW, Huerta JM, Kaaks R, Key TJ, Khaw KT, Li K, Muhlenbruch K, Nilsson PM, Overvad K, Overvad TF, Palli D, Panico S, Quiros JR, Rolandsson O, Roswall N, Sacerdote C, Sanchez MJ, Slimani N, Tagliabue G, Tjonneland A, Tumino R, van der A DL, Forouhi NG, Sharp SJ, Langenberg C, Riboli E, Wareham NJ. Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol. 2014 Jan;2(1):19-29. doi: 10.1016/S2213-8587(13)70103-7. Epub 2013 Oct 8.'}, {'pmid': '18689695', 'type': 'BACKGROUND', 'citation': 'Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, Fumeron F, Froguel P, Vaxillaire M, Cauchi S, Ducimetiere P, Eschwege E. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2008 Oct;31(10):2056-61. doi: 10.2337/dc08-0368. Epub 2008 Aug 8.'}, {'pmid': '20170456', 'type': 'BACKGROUND', 'citation': 'Chen L, Magliano DJ, Balkau B, Colagiuri S, Zimmet PZ, Tonkin AM, Mitchell P, Phillips PJ, Shaw JE. AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010 Feb 15;192(4):197-202. doi: 10.5694/j.1326-5377.2010.tb03507.x.'}, {'pmid': '10867715', 'type': 'BACKGROUND', 'citation': 'Griffin SJ, Little PS, Hales CN, Kinmonth AL, Wareham NJ. Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes Metab Res Rev. 2000 May-Jun;16(3):164-71. doi: 10.1002/1520-7560(200005/06)16:33.0.co;2-r.'}, {'pmid': '12610029', 'type': 'BACKGROUND', 'citation': 'Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003 Mar;26(3):725-31. doi: 10.2337/diacare.26.3.725.'}, {'pmid': '19297312', 'type': 'BACKGROUND', 'citation': 'Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ. 2009 Mar 17;338:b880. doi: 10.1136/bmj.b880.'}, {'pmid': '18515811', 'type': 'BACKGROUND', 'citation': 'Rahman M, Simmons RK, Harding AH, Wareham NJ, Griffin SJ. A simple risk score identifies individuals at high risk of developing Type 2 diabetes: a prospective cohort study. Fam Pract. 2008 Jun;25(3):191-6. doi: 10.1093/fampra/cmn024. Epub 2008 May 30.'}, {'pmid': '20515896', 'type': 'BACKGROUND', 'citation': 'Rosella LC, Manuel DG, Burchill C, Stukel TA; PHIAT-DM team. A population-based risk algorithm for the development of diabetes: development and validation of the Diabetes Population Risk Tool (DPoRT). J Epidemiol Community Health. 2011 Jul;65(7):613-20. doi: 10.1136/jech.2009.102244. Epub 2010 Jun 1.'}, {'pmid': '16043747', 'type': 'BACKGROUND', 'citation': 'Schmidt MI, Duncan BB, Bang H, Pankow JS, Ballantyne CM, Golden SH, Folsom AR, Chambless LE; Atherosclerosis Risk in Communities Investigators. Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study. Diabetes Care. 2005 Aug;28(8):2013-8. doi: 10.2337/diacare.28.8.2013.'}, {'pmid': '17327313', 'type': 'BACKGROUND', 'citation': 'Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Mohlig M, Pfeiffer AF, Spranger J, Thamer C, Haring HU, Fritsche A, Joost HG. 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Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years. Ann Intern Med. 2009 Jun 2;150(11):741-51. doi: 10.7326/0003-4819-150-11-200906020-00002.'}, {'pmid': '21902820', 'type': 'BACKGROUND', 'citation': 'Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011 Sep 8;9:103. doi: 10.1186/1741-7015-9-103.'}, {'pmid': '25563062', 'type': 'BACKGROUND', 'citation': 'Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 2015 Jan 6;13:1. doi: 10.1186/s12916-014-0241-z.'}, {'pmid': '25560730', 'type': 'BACKGROUND', 'citation': 'Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. 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Diabetologia. 2017 Nov;60(11):2200-2209. doi: 10.1007/s00125-017-4402-4. Epub 2017 Aug 23.'}, {'pmid': '24473060', 'type': 'BACKGROUND', 'citation': 'Chen Y, Copeland WK, Vedanthan R, Grant E, Lee JE, Gu D, Gupta PC, Ramadas K, Inoue M, Tsugane S, Tamakoshi A, Gao YT, Yuan JM, Shu XO, Ozasa K, Tsuji I, Kakizaki M, Tanaka H, Nishino Y, Chen CJ, Wang R, Yoo KY, Ahn YO, Ahsan H, Pan WH, Chen CS, Pednekar MS, Sauvaget C, Sasazuki S, Yang G, Koh WP, Xiang YB, Ohishi W, Watanabe T, Sugawara Y, Matsuo K, You SL, Park SK, Kim DH, Parvez F, Chuang SY, Ge W, Rolland B, McLerran D, Sinha R, Thornquist M, Kang D, Feng Z, Boffetta P, Zheng W, He J, Potter JD. Association between body mass index and cardiovascular disease mortality in east Asians and south Asians: pooled analysis of prospective data from the Asia Cohort Consortium. BMJ. 2013 Oct 1;347:f5446. doi: 10.1136/bmj.f5446.'}, {'pmid': '20106935', 'type': 'BACKGROUND', 'citation': 'Lee KJ, Carlin JB. Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation. Am J Epidemiol. 2010 Mar 1;171(5):624-32. doi: 10.1093/aje/kwp425. Epub 2010 Jan 27.'}, {'pmid': '21225900', 'type': 'BACKGROUND', 'citation': 'White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011 Feb 20;30(4):377-99. doi: 10.1002/sim.4067. Epub 2010 Nov 30.'}, {'pmid': '20010215', 'type': 'BACKGROUND', 'citation': 'Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010 Jan;21(1):128-38. doi: 10.1097/EDE.0b013e3181c30fb2.'}], 'seeAlsoLinks': [{'url': 'https://apps.who.int/iris/handle/10665/66040', 'label': 'WHO Definition, diagnosis and classification of diabetes mellitus and its complications'}]}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to validate existing non-invasive diabetes clinical prediction models in a Swedish population. The main question it aims to answer is: how well 11 existing models will perform in predicting incident type 2 diabetes in participants from the Västerbotten Intervention programme (VIP). Participants in VIP are residents of Västerbotten that are invited for a comprehensive health screening at 30- (until 1995), 40-, 50-, and 60-years of age.', 'detailedDescription': 'Several type 2 diabetes risk prediction models have been developed but how it will perform in a Swedish population is not known. No diabetes risk prediction model is routinely used in Sweden. The aim of this study is therefore, to validate 11 non-invasive models and to evaluate the performance to predict incident type 2 diabetes in a Swedish population. A population-based cohort from the Västerbotten Intervention programme (VIP) from 1990 to 2020 will be the validation sample. Incident type 2 diabetes within 10-years of follow-up, will be determined by oral glucose tolerance test or through self-reports. A self-administered questionnaire is completed, and anthropometric, clinical, and biochemical measures are obtained at each of the health screening visits. In the statistical analysis the overall performance of the models will be compared using the Brier score. In addition. discrimination and calibration of all the models will be evaluated. Recalibration of models will be done.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '62 Years', 'minimumAge': '28 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Inhabitants from the Västerbotten region in Sweden that have been invited to participate in the Västerbotten Intervention Program for a comprehensive health screening at 30 (stopped in 1995) 40, 50 and 60 years of age.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n• At least one visit within the Västerbotten Intervention program\n\nExclusion Criteria:\n\n• Prevalent diabetes at first visit defined by a fasting capillary plasma glucose \\>7mmol/L, a 2-hour capillary plasma glucose of ≥12.2 mmol/L or self-reported history of diabetes'}, 'identificationModule': {'nctId': 'NCT05609266', 'briefTitle': 'Validation of Existing Diabetes Risk Models in a Swedish Population', 'organization': {'class': 'OTHER', 'fullName': 'Umeå University'}, 'officialTitle': 'Validation of Non-invasive Risk Models for Prediction of Incident Type 2 Diabetes in a Swedish Population', 'orgStudyIdInfo': {'id': '2022-VIPRisk_Valexist'}}, 'contactsLocationsModule': {'overallOfficials': [{'name': 'Olov Rolandsson, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Umeå University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'IPD will not be made available due to the protection of privacy and data sharing constraints. Applications to access the data may be submitted to the Biobank Research Unit at Umeå University.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Umeå University', 'class': 'OTHER'}, 'collaborators': [{'name': 'University of Cambridge', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}