Dataset Information

Serum Metabolic Profiles of Chinese Women With Perimenopausal Obesity Explored by the Untargeted Metabolomics Approach.

ABSTRACT: By far, no study has focused on observing the metabolomic profiles in perimenopause-related obesity. This study attempted to identify the metabolic characteristics of subjects with perimenopause obesity (PO). Thirty-nine perimenopausal Chinese women, 21 with PO and 18 without obesity (PN), were recruited in this study. A conventional ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UHPLC-QTOF/MS) followed by principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used as untargeted metabolomics approaches to explore the serum metabolic profiles. Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were used to identify the related metabolic pathways. A total of 46 differential metabolites, along with seven metabolic pathways relevant to PO were identified, which belonged to lipid, amino acids, carbohydrates, and organic acids. As for amino acids, we found a significant increase in l-arginine and d-ornithine in the positive ion (POS) mode and l-leucine, l-valine, l-tyrosine, and N-acetyl-l-tyrosine in the negative ion (NEG) mode and a significant decrease in l-proline in the POS mode of the PO group. We also found phosphatidylcholine (PC) (16:0/16:0), palmitic acid, and myristic acid, which are associated with the significant upregulation of lipid metabolism. Moreover, the serum indole lactic acid and indoleacetic acid were upregulated in the NEG mode. With respect to the metabolic pathways, the d-arginine and d-ornithine metabolisms and the arginine and proline metabolism pathways in POS mode were the most dominant PO-related pathways. The changes of metabolisms of lipid, amino acids, and indoleacetic acid provided a pathophysiological scenario for Chinese women with PO. We believe that the findings of this study are helpful for clinicians to take measures to prevent the women with PO from developing severe incurable obesity-related complications, such as cardiovascular disease and stroke.

SUBMITTER: Ding S

PROVIDER: S-EPMC8498571 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Cigarette smoking is an important environmental risk factor for cardiovascular events of hypertension (HTN). Existing studies have provided evidence supporting altered gut microbiota by cigarette smoking, especially in hypertensive patients. Metabolic biomarkers play a central role in the functional potentials of the gut microbiome but are poorly characterized in hypertensive smokers. To explore whether serum metabolomics signatures and compositions of HTN patients were varied in smokers, and investigate their connecting relationship to gut microbiota, the serum metabolites were examined in untreated hypertensive patients using untargeted liquid chromatography-mass spectrometry (LC/MS) analysis. Results: A dramatic difference and clear separation in community features of circulating metabolomics members were seen in smoking HTN patients compared with the non-smoking controls, according to partial least squares discrimination analysis (PLS-DA) and orthogonal partial least squares discrimination analysis (OPLS-DA). Serum metabolic profiles and compositions of smoking patients with HTN were significantly distinct from the controls, and were characterized by enrichment of 12-HETE, 7-Ketodeoxycholic acid, Serotonin, N-Stearoyl tyrosine and Deoxycholic acid glycine conjugate, and the depletion of Tetradecanedioic acid, Hippuric acid, Glyceric acid, 20-Hydroxyeicosatetraenoic acid, Phenylpyruvic acid and Capric acid. Additionally, the metabolome displayed prominent functional signatures, with a majority proportion of the metabolites identified to be discriminating between groups distributed in Starch and sucrose metabolism, Caffeine metabolism, Pyruvate metabolism, Glycine, serine and threonine metabolism, and Phenylalanine metabolic pathways. Furthermore, the observation of alterations in metabolites associated with intestinal microbial taxonomy indicated that these metabolic members might mediate the effects of gut microbiome on the smoking host. Indeed, the metabolites specific to smoking HTNs were strongly organized into co-abundance networks, interacting with an array of clinical parameters, including uric acid (UA), low-denstiy lipoprotein cholesterol (LDLC) and smoking index. Conclusions: In conclusion, we demonstrated disparate circulating blood metabolome composition and functional potentials in hypertensive smokers, showing a linkage between specific metabolites in blood and the gut microbiome.

Project description:PurposeGastric cancer is a common tumor of the digestive system. Identification of potential molecules associated with gastric cancer progression and validation of potential biomarkers for gastric cancer diagnosis are very important. Thus, the aim of our study was to determine the serum metabolic characteristics of the serum of patients with chronic gastritis (CG) or gastric cancer (GC) and validate candidate biomarkers for disease diagnosis.Experimental designA total of 123 human serum samples from patients with CG or GC were collected for untargeted metabolomic analysis via UHPLC-Q-TOF/MS to determine characteristics of the serum. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and heat map were used for multivariate analysis. In addition, commercial databases were used to identify the pathways of metabolites. Differential metabolites were identified based on a heat map with a t-test threshold (p < 0.05), fold-change threshold (FC > 1.5 or FC < 2/3) and variable importance in the projection (VIP >1). Then, differential metabolites were analyzed by receiver operating characteristic (ROC) curve to determine candidate biomarkers. All samples were analyzed for fasting lipid profiles.ResultsAnalysis of serum metabolomic profiles indicated that most of the altered metabolic pathways in the three groups were associated with lipid metabolism (p < 0.05) and lipids and lipid-like molecules were the predominating metabolites within the top 100 differential metabolites (p < 0.05, FC > 1.5 or FC < 2/3, and VIP >1). Moreover, differential metabolites, including hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine had high diagnostic performance according to PLS-DA. In addition, fasting lipid profile analysis showed the serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (Apo-A1) were decreased concomitant to the progression of the progression of the disease compared with those in the control group (p < 0.05).ConclusionsThus, this study demonstrated that lipid metabolism may influence the development of CG to GC. Hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine were selected as candidate diagnostic markers for CG and GC.

Project description:BackgroundAcute kidney injury (AKI) is one of the most common clinical emergencies characterized by rapid progression, difficulty in early diagnosis, and high mortality. Currently, there are no effective AKI early diagnostic methods and treatments. Therefore, identifying new mechanisms of AKI have become urgent for development new targets for early diagnosis and treatment of AKI in the current clinical setting.MethodsIn this study, systematic analysis and comparison of serum metabolic profiles of clinical AKI patients, chronic kidney disease (CKD) patients, and healthy subjects were performed using untargeted metabolomics. Moreover, the first spatial metabolomic analysis of kidney tissues in an AKI mouse model using MALDI-TOF MS technology was conducted. Differentially expressed metabolites were identified using a comprehensive, publicly available database. The metabolic data obtained were evaluated using principal component analysis, (orthogonal) partial least squares discriminant analysis, and metabolic pathway analysis to explore the unique serum metabolic profile of the patients, as well as to characterize the spatial distribution of differential metabolites in the kidneys of AKI mice.ResultsSignificant changes in the metabolite levels of amino acids, carnitine, and lipids were observed in the AKI and CKD groups versus the healthy population, suggesting that kidney injury may lead to abnormalities in various metabolic pathways, such as amino acids, fatty acids, and lipids. The significant difference between the AKI and CKD groups were found for the first time in these indexes including amino acid, carnitine, fatty acid, and lipid levels. Additionally, spatial metabolomics results revealed that amino acid, carnitine, organic acid, and fatty acid metabolites were more likely significantly altered in the renal cortex, while lipid metabolites were both differentially distributed in the cortex and medulla of the AKI group.ConclusionAbnormalities in the serum metabolism of amino acids, carnitine, and lipids in patients with kidney diseases, such as AKI and CKD, are closely associated with the physiological dysfunction of kidney injury. Metabolic differences between patients with AKI and CKD were compared for the first time, showing that fatty acid oxidative inhibition was more severe in patients with AKI. Furthermore, spatial metabolomics has revealed metabolic reprogramming with tissue heterogeneity in AKI mice model. Our study provides valuable information in the molecular pathological features of AKI in the kidney tissues.

Dataset Information

Serum Metabolic Profiles of Chinese Women With Perimenopausal Obesity Explored by the Untargeted Metabolomics Approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets