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Introduction: Ginseng berry (GB) has previously been demonstrated to improve systemic insulin resistance and regulate hepatic glucose metabolism and steatosis in mice with diet-induced obesity (DIO)., Objectives: In this study, the role of GB in metabolism was assessed using metabolomics analysis on the total liver metabolites of DIO mice., Methods: Metabolomic profiling was performed using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF/MS) of liver tissue from mice on a 12-wk normal chow diet (NC), high-fat diet (HFD), and HFD supplemented with 0.1% GB (HFD + GB). The detected metabolites, its pathways, and functions were analyzed through partial least square discriminant analysis (PLS-DA), the small molecular pathway database (SMPDB), and MetaboAnalyst 5.0., Results: The liver metabolite profiles of NC, HFD, and GB-fed mice (HFD + GB) were highly compartmentalized. Metabolites involved in major liver functions, such as mitochondrial function, gluconeogenesis/glycolysis, fatty acid metabolism, and primary bile acid biosynthesis, showed differences after GB intake. The metabolites that showed significant correlations with fasting blood glucose (FBG), insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) were highly associated with mitochondrial membrane function, energy homeostasis, and glucose metabolism. Ginseng berry intake increased the levels of metabolites involved in mitochondrial membrane function, decreased the levels of metabolites related to glucose metabolism, and was highly correlated with metabolic phenotypes., Conclusion: This study demonstrated that long-term intake of GB changed the metabolite of hepatosteatotic livers in DIO mice, normalizing global liver metabolites involved in mitochondrial function and glucose metabolism and indicating the potential mechanism of GB in ameliorating hyperglycemia in DIO mice., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The present study aimed to identify key metabolites related to weight reduction in humans by studying the metabolic profiles of sera obtained from 34 participants who underwent dietary intervention with black soybean peptides (BSP) for 12 weeks. This research is a sequel to our previous work in which the effects of BSP on BMI and blood composition of lipid were investigated. Sera of the study were subjected to ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), and the data were analyzed using partial least-squares discriminate analysis (PLS-DA) score plots. Body mass index and percent body fat of the test group were reduced. Levels of betaine, benzoic acid, pyroglutamic acid, pipecolic acid, N-phenylacetamide, uric acid, l-aspartyl-l-phenylalanine, and lysophosphatidyl cholines (lysoPCs) (C18:1, C18:2, C20:1, and C20:4) showed significant increases. Levels of l-proline, valine, l-leucine/isoleucine, hypoxanthine, glutamine, l-methionine, phenylpyruvic acid, several carnitine derivatives, and lysoPCs (C14:0, PC16:0, C15:0, C16:0, C17:1, C18:0, and C22:0) were significantly decreased. In particular, lysoPC 16:0 with a VIP value of 12.02 is esteemed to be the most important metabolite for evaluating the differences between the 2 serum samples. Our result confirmed weight-lowering effects of BSP, accompanied by favorable changes in metabolites in the subjects' blood. Therefore, this research enables us to better understand obesity and increases the predictability of the obesity-related risk by studying metabolites present in the blood.

Liver and serum metabolites of obese and lean mice fed on high fat or normal diets were analyzed using ultraperformance liquid chromatography-quadrupole-time-of-flight mass spectrometry, gas chromatography-mass spectrometry, and partial least-squares-discriminant analysis (PLS-DA). Obese and lean groups were clearly discriminated from each other on PLS-DA score plot and major metabolites contributing to the discrimination were assigned as lipid metabolites (fatty acids, phosphatidylcholines (PCs), and lysophosphatidylcholines (lysoPCs)), lipid metabolism intermediates (betaine, carnitine, and acylcarnitines), amino acids, acidic compounds, monosaccharides, and serotonin. A high-fat diet increased lipid metabolites but decreased lipid metabolism intermediates and the NAD/NADH ratio, indicating that abnormal lipid and energy metabolism induced by a high-fat diet resulted in fat accumulation via decreased β-oxidation. In addition, this study revealed that the levels of many metabolites, including serotonin, betaine, pipecolic acid, and uric acid, were positively or negatively related to obesity-associated diseases. On the basis of these metabolites, we proposed a metabolic pathway related to high-fat diet-induced obesity. These metabolites can be used to better understand obesity and related diseases induced by a hyperlipidic diet. Furthermore, the level changes of these metabolites can be used to assess the risk of obesity and the therapeutic effect of obesity management.

Abstract: Changes in the water-soluble metabolites of meju during fermentation were analysed by ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF MS), and the resultant data were statistically processed by partial least squares-discriminant analysis (PLS-DA). Various metabolites, including amino acids, small peptides, nucleosides, urea cycle intermediates, and organic acids, which are responsible for the unique taste and nutritional and functional quality of fermented soy foods, were clearly altered by increasing the fermentation period. Changes in these metabolites allowed discrimination among meju samples with different fermentation periods (0, 10, 20, 40, and 60d) on a PLS-DA score plot, and the fermentation was mainly processed between 10 and 40d of fermentation. Twenty-two metabolites (phenylalanine, glutamic acid, leucine, adenine, citrulline, arginine, glutamine, γ-aminobutyric acid, proline, acetylornithine, valine, pipecolic acid, methionine, citric acid, xanthine, tyrosine, isoleucine, Glu-Tyr, Ser-Pro, tryptophan, Glu-Phe, and Leu-Val-Pro-Pro) with high PLS-DA values of over 1.00 were determined as the major compounds contributing to the discrimination of meju samples. These metabolites, which were positively related to the sensory quality of meju, can be used as fermentation biomarkers for the production of meju and to construct the meju fermentation metabolic pathway. Therefore, our results indicate that monitoring the changes in metabolites during meju fermentation might be important for producing meju-related foods with good nutritional and sensory quality. [Copyright &y& Elsevier]

A previous genome-wide association study (GWAS) on obese/overweight Korean women reported five new genetic loci associated with the basal metabolic rate (BMR) and body mass index (BMI), NRG3, OR8U8, BCL2L2-PABPN1, PABPN1, and SLC22A17. This metabolite GWAS (mGWAS) aimed to identify the key metabolites and metabolic pathways regulated by these genes. Potential metabolic pathways associated with leanness and obesity were identified by detecting metabolites in association with GWAS. Waist circumference, lean body mass, and body fat mass were strongly associated with BMI rather than BMR. However, plasma triglyceride and total cholesterol were significantly higher in obese individuals with low BMR than in lean individuals with high BMR. Upon analyzing NRG3, BCL2L2-PABPN1, and SLC22A17, uric acid, succinic acid, arginine, uridine, and aspartic acid were the metabolites positively associated with obesity. Uric acid and arginine were both identified through general metabolomics targeting of obesity genes classified on the basis of BMI or BMR. Metabolites associated with disruption in beta-oxidation, lipid metabolism, branched-chain amino acid and aromatic amino acid catabolism, the urea cycle, and purine/pyrimidine metabolism play important roles in obesity classified on the basis of either BMI or BMR in middle-aged Korean women. These results further the current understanding of obesity and the predictability of obesity-related risks using mGWAS. [ABSTRACT FROM AUTHOR]