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• W3139374080 abstract "Current treatment strategies for type 2 diabetes are still characterized by a number of shortcomings including side effects and lack of long-term effectiveness when using pharmaceuticals, absence of personalized guidelines for lifestyle interventions, and insufficient knowledge on the role of gut microbiota to justify fecal transplants. Moreover, current diagnostic tests have limited sensitivity and specificity, with no opportunities for large-scale screening. A better understanding of the underlying mechanisms of type 2 diabetes is thus desired to make progress in disease management and diagnosis.This study implemented a strategy of fecal metabolomics to assess the pathology of type 2 diabetes. Hereby, feces was targeted as this specimen can be obtained non-invasively and captures the interactions between the host, gut microbiota, diet, and other exposomal factors. Methodologies were established for both the polar and non-polar (lipidome) metabolome fraction. The lipidomics methodology covers all eight lipid classes (defined by LIPID MAPS) whereas the method for polar metabolomics targets ten different polar to medium-polar classes. Both methods apply generic extraction and mass detection through UHPLC-Q-ExactiveTM Orbitrap MS and were used to map the fecal metabolome, thereby considering individuals without (n=22) and with hyperglycemia (n=17) (UZ Ghent EC 2016/0673). Fecal fingerprints enclosed 7061 lipophilic and 10214 polar species, whereby associated intensity data matrices were subjected to multivariate statistics. Valid OPLS-DA models (p-values ≤ 1.4e-7) were generated by which the fingerprints of healthy individuals and diabetes patients could be compared. Hereby, significant discrepancies were noted within both metabolome fractions, leading to the selection of 96 lipids and 105 polar metabolites with discriminating power. The use of any resulting biomarkers may lead to better and personalized disease management in terms of prediction, diagnosis, treatment, and follow-up. Moreover, based on revealed metabolic networks, alternative treatment targets may arise. Eventually, coupling these data to metagenome data may support fecal transplants. Acknowledgements : VLAIO" @default.
• W3139374080 created "2021-03-29" @default.
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• W3139374080 date "2017-08-22" @default.
• W3139374080 modified "2023-09-26" @default.
• W3139374080 title "INTEGRATING THE INFLUENCE OF HOST, DIET AND MICROBIOME VIA FECAL METABOLOMICS AND LIPIDOMICS TO REVEAL METABOLIC PERTURBATIONS IN TYPE 2 DIABETES" @default.
• W3139374080 doi "https://doi.org/10.14748/ssp.v4i1.3973" @default.
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