Longitudinal study investigating serum metabolites and their association with type 2 diabetes risk in a Korean population

Abstract

AbstractAimThe lack of longitudinal metabolomics data and the statistical techniques to analyse them has limited the understanding of the metabolite levels related to type 2 diabetes (T2D) onset. Thus, we carried out logistic regression analysis and simultaneously proposed new approaches based on residuals of multiple logistic regression and geometric angle‐based clustering for the analysis in T2D onset‐specific metabolic changes.Materials and methodsWe used the sixth, seventh and eighth follow‐up data from 2013, 2015 and 2017 among the Korea Association REsource (KARE) cohort data. Semi‐targeted metabolite analysis was performed using ultraperformance liquid chromatography/triple quadrupole‐mass spectrometry systems.ResultsAs the results from the multiple logistic regression and a single metabolite in a logistic regression analysis varied dramatically, we recommend using models that consider potential multicollinearity among metabolites. The residual‐based approach particularly identified neurotransmitters or related precursors as T2D onset‐specific metabolites. By using geometric angle‐based pattern clustering studies, ketone bodies and carnitines are observed as disease‐onset specific metabolites and separated from others.ConclusionTo treat patients with early‐stage insulin resistance and dyslipidaemia when metabolic disorders are still reversible, our findings may contribute to a greater understanding of how metabolomics could be used in disease intervention strategies during the early stages of T2D.