- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05609266
Validation of Existing Diabetes Risk Models in a Swedish Population
November 29, 2022 updated by: Umeå University
Validation of Non-invasive Risk Models for Prediction of Incident Type 2 Diabetes in a Swedish Population
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.
Study Overview
Status
Completed
Conditions
Detailed Description
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.
Study Type
Observational
Enrollment (Actual)
115642
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
26 years to 60 years (Adult)
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Sampling Method
Non-Probability Sample
Study Population
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.
Description
Inclusion Criteria:
• At least one visit within the Västerbotten Intervention program
Exclusion Criteria:
• 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
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Incident type 2 diabetes
Time Frame: 10-years
|
Determined by a fasting capillary plasma glucose of ≥7 mmol/L or a 2-hour capillary plasma glucose of ≥12.2 mmol/L, or self-reported
|
10-years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Investigators
- Principal Investigator: Olov Rolandsson, MD, Umeå University
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- 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.
- 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.
- 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.
- Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R; IDF Diabetes Atlas Committee. 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.
- 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. Lancet. 2008 May 24;371(9626):1783-9. doi: 10.1016/S0140-6736(08)60766-7.
- Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.
- Gillies CL, Abrams KR, Lambert PC, Cooper NJ, Sutton AJ, Hsu RT, Khunti K. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ. 2007 Feb 10;334(7588):299. Epub 2007 Jan 19. Review.
- Simmons RK, Griffin SJ, Lauritzen T, Sandbaek A. Effect of screening for type 2 diabetes on risk of cardiovascular disease and mortality: a controlled trial among 139,075 individuals diagnosed with diabetes in Denmark between 2001 and 2009. Diabetologia. 2017 Nov;60(11):2192-2199. doi: 10.1007/s00125-017-4299-y. Epub 2017 Aug 23.
- 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.
- Sortsø C, Komkova A, Sandbæk 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.
- 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, Mühlenbruch K, Nilsson PM, Overvad K, Overvad TF, Palli D, Panico S, Quirós JR, Rolandsson O, Roswall N, Sacerdote C, Sánchez MJ, Slimani N, Tagliabue G, Tjønneland 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. Erratum in: Lancet Diabetes Endocrinol. 2014 Apr;2(4):e11. Erratum in: Lancet Diabetes Endocrinol. 2014 Apr;2(4):e11.
- Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, Fumeron F, Froguel P, Vaxillaire M, Cauchi S, Ducimetière P, Eschwège 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.
- 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. Erratum In: Med J Aust. 2010 Mar 1;192(5):274.
- 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.
- 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.
- 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.
- 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.
- 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.
- Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Möhlig M, Pfeiffer AF, Spranger J, Thamer C, Häring HU, Fritsche A, Joost HG. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care. 2007 Mar;30(3):510-5.
- Abbasi A, Peelen LM, Corpeleijn E, van der Schouw YT, Stolk RP, Spijkerman AM, van der A DL, Moons KG, Navis G, Bakker SJ, Beulens JW. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012 Sep 18;345:e5900. doi: 10.1136/bmj.e5900.
- Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D'Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007 May 28;167(10):1068-74. doi: 10.1001/archinte.167.10.1068.
- Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW. 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.
- 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. Review.
- 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.
- Norberg M, Wall S, Boman K, Weinehall L. The Vasterbotten Intervention Programme: background, design and implications. Glob Health Action. 2010 Mar 22;3. doi: 10.3402/gha.v3i0.4643.
- Norberg M, Blomstedt Y, Lonnberg G, Nystrom L, Stenlund H, Wall S, Weinehall L. Community participation and sustainability--evidence over 25 years in the Vasterbotten Intervention Programme. Glob Health Action. 2012 Dec 17;5:1-9. doi: 10.3402/gha.v5i0.19166.
- Feldman AL, Griffin SJ, Fhärm E, Norberg M, Wennberg P, Weinehall L, Rolandsson O. Screening for type 2 diabetes: do screen-detected cases fare better? Diabetologia. 2017 Nov;60(11):2200-2209. doi: 10.1007/s00125-017-4402-4. Epub 2017 Aug 23.
- 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. Review.
- 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.
- 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.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
January 1, 1990
Primary Completion (Actual)
December 1, 2020
Study Completion (Actual)
December 1, 2020
Study Registration Dates
First Submitted
November 1, 2022
First Submitted That Met QC Criteria
November 1, 2022
First Posted (Actual)
November 8, 2022
Study Record Updates
Last Update Posted (Actual)
December 2, 2022
Last Update Submitted That Met QC Criteria
November 29, 2022
Last Verified
November 1, 2022
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 2022-VIPRisk_Valexist
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
No
IPD Plan 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.
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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