Diabetes Obes Metab. 2023 Apr 11. doi: 10.1111/dom.15084. Online ahead of print.
ABSTRACT
AIM: The lack of longitudinal metabolomics data and the statistical techniques to analyze 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.
METHODS: We used the 6th, 7th, and 8th follow-up data from 2013, 2015, and 2017 among the Korea Association REsource (KARE) cohort data. Semi-targeted metabolite analysis was performed using ultra performance liquid chromatography/triple quadrupole-mass spectrometry (UPLC/TQ-MS) systems.
RESULTS: Since 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.
CONCLUSIONS: To treat patients with early-stage insulin resistance and dyslipidemia when metabolic disorders are still reversible, our findings may contribute to a greater understanding of how metabolomics could be used in disease intervention strategies in the early stages of T2D. This article is protected by copyright. All rights reserved.
PMID:37041660 | DOI:10.1111/dom.15084
