Abstract
Metabolomics represents an emerging discipline concerned with comprehensive assessment of small molecule endogenous metabolites in biological systems and provides a powerful approach insight into the mechanisms of diseases. Type 2 diabetes (T2D), called the burden of the 21st century, is growing with an epidemic rate. However, its precise molecular mechanism has not been comprehensively explored. In this study, we applied urinary metabolomics based on the UPLC/MS integrated with pattern recognition approaches to discover differentiating metabolites, to characterize and explore metabolic pathway disruption in an experimental model for high-fat-diet induced T2D. Six differentiating urinary metabolites were found in the negative mode, and two (2-(4-hydroxy-3-methoxy-phenyl) acetaldehyde sulfate, 2-phenylethanol glucuronide) of which were identified involving the key metabolic pathways linked to pentose and glucuronate interconversions, starch, sucrose metabolism and tyrosine metabolism. Our study provides new insight into pathophysiologic mechanisms and may enhance the understanding of T2D pathogenesis.
Highlights
The prevalence of diet-induced obesity is increasing globally, and posing significant health problems for millions of people in tne world
A partial least squares-discriminant analysis (PLS-DA) method was established and employed to identify biomarkers which were related to type 2 diabetes (T2D) development
We illustrate how metabolomics can be utilized to explore the mechanisms of T2D which affect different ‘key pathway’
Summary
The prevalence of diet-induced obesity is increasing globally, and posing significant health problems for millions of people in tne world. T2D, represents one of the most significant global health problems because it is associated with a large economic burden on the health systems of many countries [1]. New platform metabolomics, focused on a holistic investigation of living systems to external stimuli based on the global metabolite profiles in biological samples, provides variation of whole metabolic networks for characterizing pathological states, as well giving mechanistic insight into the biochemical effects of the drugs [4]. Metabolomics technologies bring a wealth of opportunity to develop new biomarkers which are important tools for identifying diseases, predicting their progression and determining the effectiveness, and doses of therapeutic interventions [5]
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