ArticleActa diabetologica2025
A 14-year prospective cohort study of type 2 diabetes development in Dutch healthy adults of South Asian origin: risk factors and the association with metabolic syndrome and HOMA-IR.
Article in Acta diabetologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Multi-ancestry polygenic risk scores: toward equitable prediction of type 2 diabetes and its complications.Acta diabetologica · 2026Article
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5 authors.
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Abstract
objectiveType 2 Diabetes (T2D) imposes a disproportionate burden on the South Asian population. Their phenotype is characterized by heightened insulin resistance, even in individuals without overt T2D. Commonly used screening tools underestimate the T2D incidence in this population. The Metabolic syndrome (MetS) and the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) are indicators of insulin resistance; however, their predictive value for the development of T2D remains unexplored.
methodsAmong 698 initially enrolled healthy South Asian adults aged 30 to 65 in a Rotterdam-based cardiovascular disease prevention study, 270 participants were included after a 14-year follow-up. At baseline, an extensive history, physical examination, and metabolic screening were taken. A follow-up assessment of incident T2D was conducted. Multivariable logistic regression models calculated odds ratios (ORs) for MetS, its components, and HOMA-IR and adjusted for confounders.
results33 (12.2%) of participants developed T2D. The presence of MetS at baseline showed an adjusted OR of 2.6, (95% confidence interval (CI) 1.2-5.7, p = 0.02) for incident T2D. Fasting plasma glucose was the most strongly associated component of MetS (OR 3.0, CI 1.1-8.6, p = 0.04) HOMA-IR was also associated and showed an OR of 1.2 per point increase (CI 1.0-1.4, p=0.05).
conclusionsMetS and FPG were the most important predictors of T2D development in this South Asian cohort. These results underscore the value of diverse variables in T2D detection and give insight into which screening tools for T2D prediction should be used in this high-risk population.
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