Dataset Information

Serum Metabolomic Profiles Associated With Untreated Metabolic Syndrome Patients in the Chinese Population.

ABSTRACT: Metabolomics is a promising technology for elucidating the mechanisms of metabolic syndrome (MetS). However, measurements in patients with MetS under different conditions vary. Metabolomics experiments in different populations and pathophysiological conditions are, therefore, indispensable. We performed a serum metabolomics investigation in untreated patients with MetS in the Chinese population. Untreated patients with MetS were recruited to this study. Metabolites were measured using a traditional ¹ H nuclear magnetic resonance (NMR) experiment followed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Key metabolic pathways were identified by searching the Kyoto Encyclopedia of Genes and Genomes Pathway Database. A total of 28 patients with MetS and 30 healthy subjects were enrolled. All patients were untreated because they were unaware of or neglected to treat their MetS. By ¹ H NMR, we identified 49 known substances. Following PCA and OPLS-DA, 36 metabolites were confirmed to be closely associated with MetS compared with the control group; 33 metabolites were increased, whereas 3 metabolites were reduced. Importantly, 14 metabolites that changed in the serum of these untreated patients with MetS were previously unreported. Pathway analysis revealed the top 15 metabolic pathways associated with untreated MetS, which included 3 amino acid metabolic pathways. Our data suggest that untreated patients exhibit a worse pathophysiologic manifestation, which may result in more rapid progression of MetS. Thus, we propose that health education be reinforced to improve the public's knowledge, attitude, and practice regarding MetS. The rates of "untreated" patients due to unawareness and neglect must be reduced immediately.

SUBMITTER: Li Y

PROVIDER: S-EPMC7719370 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Serum Metabolomic Profiles Associated With Untreated Metabolic Syndrome Patients in the Chinese Population.

Li Yuanyuan Y Wang Yongfa Y Zhuang Yaodong Y Zhang Ping P Chen Shujiao S Asakawa Tetsuya T Gao Bizhen B

Clinical and translational science 20200616 6

Metabolomics is a promising technology for elucidating the mechanisms of metabolic syndrome (MetS). However, measurements in patients with MetS under different conditions vary. Metabolomics experiments in different populations and pathophysiological conditions are, therefore, indispensable. We performed a serum metabolomics investigation in untreated patients with MetS in the Chinese population. Untreated patients with MetS were recruited to this study. Metabolites were measured using a traditio ...[more]

PMID: 32543029

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Project description:PurposePrevious studies have shown that serum carcinoembryonic antigen (CEA) is independently associated with metabolic syndrome (MetS). However, these studies were mainly cross-sectional analyses, and cause was not clarified. In the present study, two bidirectional cohort studies were conducted to investigate the bidirectional associations between CEA and MetS using a Chinese male sample cohort.MethodsThe initial longitudinal cohort included 9629 Chinese males enrolled from January 2010 to December 2015. Two bidirectional cohorts were conducted in the study: subcohort A (from CEA to MetS, n = 6439) included participants without MetS at baseline to estimate the risk of developing incident MetS; subcohort B (from MetS to CEA, n = 8533) included participants without an elevated CEA level (Hyper-CEA) at baseline to examine the risk of developing incident Hyper-CEA. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models.ResultsIn subcohort A, the incidence densities of MetS among participants with and without Hyper-CEA were 84.56 and 99.28 per 1000 person-years, respectively. No significant effects of Hyper-CEA on incident MetS were observed in subcohort A (HR, 0.89; 95% CI, 0.71 to 1.12; P = 0.326). In subcohort B, a higher incidence density of Hyper-CEA was found among participants with MetS (33.42 and 29.13 per 1000 person-years for those with and without MetS, respectively). For nonsmoking participants aged > 65 years, MetS increased the risk of incident Hyper-CEA (HR, 1.87; 95% CI, 1.09 to 3.20; P = 0.022).ConclusionFor the direction of CEA on incident MetS, no significant association was observed. For the direction of MetS on incident Hyper-CEA, MetS in nonsmoking elderly men could increase the risk of incident Hyper-CEA, while this association was not found in other stratified participants. The clinical implications of the association between CEA and MetS should be interpreted with caution.

Project description:The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value < 0.05). Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models, respectively, that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median area under the receiver operating characteristic curve (AUC) = 0.772 for the metabolite level model; median AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline in the later years (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.

Dataset Information

Serum Metabolomic Profiles Associated With Untreated Metabolic Syndrome Patients in the Chinese Population.

Publications

Serum Metabolomic Profiles Associated With Untreated Metabolic Syndrome Patients in the Chinese Population.

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