A Post-Marketing Safety Study of Dorzagliatin in Patients With Type 2 Diabetes
NCT06263348
Type2diabetes
Beijing, Beijing Municipality, China
View Trial DetailsNCT Number: NCT05609266
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.
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Notify Me28 year–62 year
All sexes
Observational
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.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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
Umeå University
Other
Validation of Non-invasive Risk Models for Prediction of Incident Type 2 Diabetes in a Swedish Population
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