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Characterizing poorly controlled type 2 diabetes using 1H-NMR metabolomics

Abstract

ntroduction: The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited. Objective: This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort. Method: Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites. Results: The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage. Conclusion: These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance.

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Journal Article, Faculty of Natural and Agricultural Sciences, Human Metabolomics-- Potchefstroom Campus

Citation

Van Reenen, Mari. et al. 2024. Characterizing poorly controlled type 2 diabetes using 1H-NMR metabolomics. Metabolomics, (2024), 20:54, [https://doi.org/10.1007/s11306-024-02127-w]

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