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Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in most western countries. Current NAFLD diagnosis methods (e.g., liver biopsy analysis or imaging techniques) are poorly suited as tests for such a prevalent condition, from both a clinical and financial point of view. The present work aims to demonstrate the potential utility of serum metabolic profiling in defining phenotypic biomarkers that could be useful in NAFLD management. A parallel animal model/human NAFLD exploratory metabolomics approach was employed, using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) to analyze 42 serum samples collected from nondiabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-methyltransferase knockout (GNMT-KO) NAFLD mouse model. Multivariate statistical analysis of the data revealed a series of common biomarkers that were significantly altered in the NAFLD (GNMT-KO) subjects in comparison to their normal liver counterparts (WT). Many of the compounds observed could be associated with biochemical perturbations associated with liver dysfunction (e.g., reduced Creatine) and inflammation (e.g., eicosanoid signaling). This differential metabolic phenotyping approach may have a future role as a supplement for clinical decision making in NAFLD and in the adaption to more individualized treatment protocols.
Male, Mice, Knockout, Principal Component Analysis, Glycine N-Methyltransferase, Lipid Metabolism, Mass Spectrometry, [SDV] Life Sciences [q-bio], Fatty Liver, Disease Models, Animal, Mice, Non-alcoholic Fatty Liver Disease, Multivariate Analysis, Disease Progression, Animals, Humans, Metabolomics, Biomarkers, Chromatography, Liquid
Male, Mice, Knockout, Principal Component Analysis, Glycine N-Methyltransferase, Lipid Metabolism, Mass Spectrometry, [SDV] Life Sciences [q-bio], Fatty Liver, Disease Models, Animal, Mice, Non-alcoholic Fatty Liver Disease, Multivariate Analysis, Disease Progression, Animals, Humans, Metabolomics, Biomarkers, Chromatography, Liquid
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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