Precision medicine in diabetes prediction: Exploring a subgroup-specific biomarker strategy for risk stratification.

Abstract

Introduction: The early detection of high-risk individuals is crucial to delay and reduce the incidence of type 2 diabetes. In this study, we aimed to explore the performance of a novel subgroup-specific biomarker strategy in the prediction of incident diabetes.

Materials and methods: In the Taiwan Lifestyle Cohort Study, adult subjects without diabetes were included and followed for the incidence of diabetes in 2006-2019. The biomarkers measured included blood secretogranin III (SCG3), vascular adhesion protein-1 (VAP-1), fibrinogen-like protein 1 (FGL1), angiopoietin-like protein 6 (ANGPTL6), and angiopoietin-like protein 4 (ANGPTL4).

Results: Among the 1,287 subjects, 12.2% developed diabetes during a 6 year follow-up. Blood VAP-1 was significantly associated with incident diabetes in the overall population (HR = 0.724, P < 0.05), participants under 65 years old (HR = 0.685, P < 0.05), those with a BMI of ≥24 kg/m² (HR = 0.673, P < 0.05), and females (HR = 0.635, P < 0.05). Blood ANGPTL6 was significantly correlated with incident diabetes in participants aged 65 and older (HR = 0.314, P < 0.05), and blood SCG3 was associated with incident diabetes in those with a BMI of <24 kg/m² (HR = 1.296, P < 0.05). Two subgroup-specific biomarker strategies were developed. The gender and BMI-specific biomarker strategy, using traditional risk factors and blood SCG3 or VAP-1 in different subgroups, could improve prediction performance, especially the specificity and positive prediction value, compared with the whole-population strategy using only traditional risk factors or traditional risk factors plus blood VAP-1.

Conclusion: Gender- and BMI-specific biomarker strategy can improve the prediction of incident diabetes. A subgroup-specific biomarker strategy is a novel approach in the prediction of incident diabetes.