ArticleActa diabetologica2025
Metabolomic analyses of multiple biologic matrices reveal metabolic heterogeneity in diabetic complications.
Article in Acta diabetologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimsType 2 diabetes mellitus (T2DM) arises from a complex interplay of genetic and environmental factors. Patients with T2DM are susceptible to hyperglycemia-related complications that can impair organ function, underscoring the need to explore the metabolic profiles of affected organs.
methodsIn this study, a comprehensive metabolomic analysis was conducted on the serum, kidney, and heart tissues from a rat model of diabetic complications (DC). Pattern recognition and multivariate statistical analyses were applied to identify the potential biomarkers of DC, and metabolic network analysis served to understand the specific metabolic pathways associated with DC.
resultsFourteen significantly altered metabolites were identified in serum, 20 in the kidney, and 14 in the heart. The corresponding metabolic pathways included mineral absorption, mTOR signaling pathway, taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, ABC transporters, glucagon signaling pathway, protein degradation and uptake, galactose metabolism, purine metabolism, nicotinic acid and nicotinamide metabolism, and glycolysis and gluconeogenesis. Differential metabolite network analysis revealed instinct metabolic patterns among the serum, kidney, and heart. Notably, the serum's metabolic correlation patterns were found to be somewhat similar to those observed in the kidney, whereas the heart exhibited less pronounced metabolite correlations compared to the other two biological matrices.
conclusionsThese findings provide insights into the mechanism underlying the development of diabetic complications. The integration of metabolomics and biological network analyses into diabetes research can potentially revolutionize the field by revealing novel biomarkers for early detection and personalized treatment of diabetes and its associated complications.
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