Type2diabetesNCT05609266
Validation of Existing Diabetes Risk Models in a Swedish Population
An observational study of Type2diabetes, run by Umeå University.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 28 to 62 yearsHealthy volunteers can take part
- Conditions the study is for
- Type2diabetes
- How many people
- 115,642 participants
- Where
- No sites listed yet
- Study dates
- Jan 1990to Dec 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
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 28 to 62 years
- All sexes
- Healthy volunteers accepted
- Adult
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.
Who may be able to join
- At least one visit within the Västerbotten Intervention program
Who cannot join
- 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
About this study
Official title: 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.
Read the 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.
- Conditions
- Type2diabetes
- Keywords
- Non-invasive prediction model, Incident diabetes, Validation
What is being measured
The primary outcome is the main question the study is designed to answer.
Incident type 2 diabetes
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
Measured: 10-years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Nov 2022
- Study startJan 1990
- Primary completionDec 2020
- Study completionDec 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (30)Papers the sponsor has linked to this study.
- Other trials for Type2diabetesStudies of the same condition, open and under way.
- More trials run by Umeå UniversityThe 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 115,642 participants (actual)
Sponsor and investigators
- Lead sponsor
- Umeå University
- Sponsor type
- University, hospital or other
- Collaborators
- University of Cambridge
- Responsible party
- Sponsor
- Investigators
- Olov Rolandsson, MD, Principal investigator, Umeå University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05609266
- Sponsor's protocol ID
- 2022-VIPRisk_Valexist
- Status last verified
- Nov 2022
- First submitted
- 1 Nov 2022
- Last updated
- 2 Dec 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Publications
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.
PubMed 31518657Cho 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.
PubMed 29496507Li 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.
PubMed 18502303Gillies 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. doi: 10.1136/bmj.39063.689375.55. Epub 2007 Jan 19.
PubMed 17237299Simmons 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.
PubMed 28831539Herman 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.
PubMed 25986661Sortso 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.
PubMed 29549417Kengne 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.
PubMed 24622666Balkau 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.
PubMed 18689695Chen 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.
PubMed 20170456Griffin 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.
PubMed 10867715Lindstrom 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.
PubMed 12610029Hippisley-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.
PubMed 19297312Rahman 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.
PubMed 18515811Rosella 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.
PubMed 20515896
Study at a glance
From the registry record, last updated by the sponsor on 2 Dec 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of Non-invasive Risk Models for Prediction of Incident Type 2 Diabetes in a Swedish Population
- ClinicalTrials.gov ID
- NCT05609266
- Acronym / sponsor ID
- 2022-VIPRisk_Valexist
- Conditions
- Type2diabetes
- Keywords
- Non-invasive prediction model, Incident diabetes, Validation
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 115,642 participants (actual)
- Lead sponsor
- Umeå UniversityUniversity, hospital or other
- Collaborators
- University of Cambridge
- Study start
- Jan 1990 (actual)
- Primary completion
- Dec 2020 (actual)
- Study completion
- Dec 2020 (actual)
- First posted
- 8 Nov 2022 (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 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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 28 to 62 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- 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.
Key inclusion criteria (1 listed)
- At least one visit within the Västerbotten Intervention program
Key exclusion criteria (1 listed)
- 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
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
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Dec 2022.
- Background: PubMed 31518657Saeedi 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.
- Background: PubMed 29496507Cho 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.
- Background: PubMed 18502303Li 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.
- Background: PubMed 17237299Gillies 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. doi: 10.1136/bmj.39063.689375.55. Epub 2007 Jan 19.
- Background: PubMed 28831539Simmons 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.
- Background: PubMed 25986661Herman 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.
- Background: PubMed 29549417Sortso 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.
- Background: PubMed 24622666Kengne 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.
- Background: PubMed 18689695Balkau 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.
- Background: PubMed 20170456Chen 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.
- Background: PubMed 10867715Griffin 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.
- Background: PubMed 12610029Lindstrom 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.
- WHO Definition, diagnosis and classification of diabetes mellitus and its complicationsLink provided by the sponsor.
- Individual participant dataSharing planned: No. 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.
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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. This study is not recruiting at the moment.
Olov Rolandsson, MD principal investigator
Umeå University
Questions about this trial
What is this clinical trial studying?
An observational study of Type2diabetes, run by Umeå University.
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 1 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: finished, as of the sponsor's last update on 2 Dec 2022. Status can change between updates, so confirm with the study contact before making plans.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incident type 2 diabetes (time frame: 10-years). 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 NCT05609266, which also lists 30 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 2 Dec 2022.