Evaluation of Diabetes Risk Models Applied to the Prediction of Incident Dysglycaemia in African and European Populations
Journal article, 2026
Aims: We evaluated overall and sex-specific performance of established diabetes risk models for predicting incident dysglycaemia in African and European populations. Materials and Methods: We externally evaluated the African Diabetes Risk model, Finnish Diabetes Risk model, Epidemiological Study on the Insulin Resistance Syndrome model and the personal and clinical Framingham models. Evaluation was performed in three prospective cohorts: the Research on Obesity and Diabetes among African Migrants (RODAM-Pros; migrant and non-migrant Ghanaians; n = 1607), the Middle-aged Soweto cohort (MASC; urban Black South Africans; n = 515) and the Västerbotten Intervention Programme (VIP; Sweden; n = 3044). Dysglycaemia was defined according to American Diabetes Association criteria. Discrimination (c-statistic) and calibration were assessed before and after recalibration. Results: Dysglycaemia developed in 17.7% of RODAM-Pros participants, 35.0% of MASC participants and 27.5% of VIP participants, with median follow-up of 6.7, 6.7 and 9.9 years, respectively. All models demonstrated poor-to-modest discrimination (c-statistic < 0.7). Most models substantially underestimated risk. Recalibration eliminated systematic miscalibration and reduced calibration error to ≤ 2.3% in RODAM-Pros and VIP and ≤ 3.4% in MASC. Performance varied by sex and cohort. Conclusions: Existing diabetes risk prediction models can be applied to predict incident dysglycaemia in both Sub-Saharan African and European populations, but only after recalibration to local risk levels.
calibration
sub-Saharan Africa
ethnicity
discrimination
prediction
incident dysglycaemia
Author
Melony Fortuin de Smidt
Umeå University
Patrik Wennberg
Umeå University
Hannah Maike Albers
University of Bonn
Charles Agyemang
University of Amsterdam
Felix P. Chilunga
University of Amsterdam
Rikard Landberg
Wallenberg Lab.
Chalmers, Life Sciences, Food and Nutrition Science
Lisa K. Micklesfield
University of Witwatersrand
Samuel N. Darko
Kwame Nkrumah University of Science and Technology
J. Otten
Umeå University
Ellis Owusu-Dabo
Kwame Nkrumah University of Science and Technology
Clemens Wittenbecher
SciLifeLab
Chalmers, Life Sciences, Food and Nutrition Science
Tommy Olsson
Stellenbosch University
Umeå University
Julia H. Goedecke
Umeå University
South African Medical Research Council
University of Witwatersrand
Ina Danquah
University of Bonn
Umeå University
Diabetes, Obesity and Metabolism
1462-8902 (ISSN) 1463-1326 (eISSN)
Vol. In PressOmics approach for personalised prevention of type 2 diabetes mellitus for African and European Populations, OPTIMA
VINNOVA (2022-00547), 2022-11-21 -- 2025-12-31.
The metabolomics imprint of controlled modification of carbohydrate quality elucidates long-term impact on cardiometabolic disease risk and facilitates precision prevention approaches
Swedish Research Council (VR) (2022-01529), 2023-01-01 -- 2026-12-31.
Subject Categories (SSIF 2025)
Public Health, Global Health and Social Medicine
Endocrinology and Diabetes
DOI
10.1111/dom.71285
PubMed
42681823