Metabolic and Genetic Markers Improve Prediction of Incident Type 2 Diabetes: A Nested Case-Control Study in Chinese

Abstract

CONTEXT: It is essential to improve the current predictive ability for type 2 diabetes (T2D) risk. OBJECTIVE: We aimed to identify novel metabolic markers for future T2D in Chinese individuals of Han ethnicity and to determine whether the combined effect of metabolic and genetic markers improves the accuracy of prediction models containing clinical factors. METHODS: A nested case-control study containing 220 incident T2D patients and 220 age- and sex- matched controls from normoglycemic Chinese individuals of Han ethnicity was conducted within the Wuxi Non-Communicable Disease cohort with a 12-year follow-up. Metabolic profiling detection was performed by high-performance liquid chromatography‒mass spectrometry (HPLC-MS) by an untargeted strategy and 20 single nucleotide polymorphisms (SNPs) associated with T2D were genotyped using the Iplex Sequenom MassARRAY platform. Machine learning methods were used to identify metabolites associated with future T2D risk. RESULTS: We found that abnormal levels of 5 metabolites were associated with increased risk of future T2D: riboflavin, cnidioside A, 2-methoxy-5-(1H-1, 2, 4-triazol-5-yl)- 4-(trifluoromethyl) pyridine, 7-methylxanthine, and mestranol. The genetic risk score (GRS) based on 20 SNPs was significantly associated with T2D risk (OR = 1.35; 95% CI, 1.08-1.70 per SD). The area under the receiver operating characteristic curve (AUC) was greater for the model containing metabolites, GRS, and clinical traits than for the model containing clinical traits only (0.960 vs 0.798, P = 7.91 × 10-16). CONCLUSION: In individuals with normal fasting glucose levels, abnormal levels of 5 metabolites were associated with future T2D. The combination of newly discovered metabolic markers and genetic markers could improve the prediction of incident T2D.