Evaluation of Diabetes Risk Models Applied to the Prediction of Incident Dysglycaemia in African and European Populations
Artikel i vetenskaplig tidskrift, 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
Författare
Melony Fortuin de Smidt
Umeå universitet
Patrik Wennberg
Umeå universitet
Hannah Maike Albers
Universität Bonn
Charles Agyemang
Universiteit Van Amsterdam
Felix P. Chilunga
Universiteit Van Amsterdam
Rikard Landberg
Wallenberg Lab.
Chalmers, Life sciences, Livsmedelsvetenskap
Lisa K. Micklesfield
University of Witwatersrand
Samuel N. Darko
Kwame Nkrumah University of Science and Technology
J. Otten
Umeå universitet
Ellis Owusu-Dabo
Kwame Nkrumah University of Science and Technology
Clemens Wittenbecher
SciLifeLab
Chalmers, Life sciences, Livsmedelsvetenskap
Tommy Olsson
Universiteit Stellenbosch
Umeå universitet
Julia H. Goedecke
Umeå universitet
South African Medical Research Council
University of Witwatersrand
Ina Danquah
Universität Bonn
Umeå universitet
Diabetes, Obesity and Metabolism
1462-8902 (ISSN) 1463-1326 (eISSN)
Vol. In PressIndividspecifika rekommendationer för prevention av typ 2 diabetes i afraikanska och europeiska populationer
VINNOVA (2022-00547), 2022-11-21 -- 2025-12-31.
Fingeravtryck i metabolomet från en kontrollerad modifiering av kolhydratkvalitet i kosten belyser långsiktig påverkan på kardiometabol sjukdomsrisk och ger nya verktyg för precisionsprevention
Vetenskapsrådet (VR) (2022-01529), 2023-01-01 -- 2026-12-31.
Ämneskategorier (SSIF 2025)
Folkhälsovetenskap, global hälsa och socialmedicin
Endokrinologi och diabetes
DOI
10.1111/dom.71285
PubMed
42681823