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Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease.

ABSTRACT: Coronary heart disease (CHD) is one of the leading causes of deaths globally. Identification of serum metabolic biomarkers for its early diagnosis is thus much desirable. Serum samples were collected from healthy controls (n = 86) and patients with CHD (n = 166) and subjected to untargeted and targeted metabolomics analyses. Subsequently, potential biomarkers were detected and screened, and a clinical model was developed for diagnosing CHD. Four dysregulated metabolites, namely PC(17:0/0:0), oxyneurine, acetylcarnitine, and isoundecylic acid, were identified. Isoundecylic acid was not found in Human Metabolome Database, so we could not validate differences in its relative abundance levels. Further, the clinical model combining serum oxyneurine, triglyceride, and weight was found to be more robust than that based on PC(17:0/0:0), oxyneurine, and acetylcarnitine (AUC = 0.731 vs. 0.579, sensitivity = 83.0 vs. 75.5%, and specificity = 64.0 vs. 46.5%). Our findings indicated that serum metabolomics is an effective method to identify differential metabolites and that serum oxyneurine, triglyceride, and weight appear to be promising biomarkers for the early diagnosis of CHD.

SUBMITTER: Jiang H

PROVIDER: S-EPMC8667077 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease.

Jiang Huali H Li Li L Chen Weijie W Chen Benfa B Li Heng H Wang Shanhua S Wang Min M Luo Yi Y

Frontiers in physiology 20211129

Coronary heart disease (CHD) is one of the leading causes of deaths globally. Identification of serum metabolic biomarkers for its early diagnosis is thus much desirable. Serum samples were collected from healthy controls (<i>n</i> = 86) and patients with CHD (<i>n</i> = 166) and subjected to untargeted and targeted metabolomics analyses. Subsequently, potential biomarkers were detected and screened, and a clinical model was developed for diagnosing CHD. Four dysregulated metabolites, namely PC( ...[more]

PMID: 34912241

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Project description:Hypertensive nephropathy (HN), caused by long-term poorly controlled hypertension, is the second common cause of end-stage renal disease after diabetes mellitus, but the pathogenesis of HN is unclear. The purpose of this study was to identify the biological pathways involved in the progression of HN and bile acid (BA)-related biomarkers, and to analyze the role of bile acids in HN. Download gene microarray data from Gene Expression Omnibus. Differentially expressed genes (DEGs) associated with HN were identified, and then DEGs were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. A protein-protein interaction (PPI) network was established using DEGs to identify BA-related hub genes in combination with bile acid identical targets. An animal model of early hypertensive nephropathy was established using SHR and the concentrations of 39 bile acids were measured quantitatively in the renal cortex to screen for significantly different concentrations and to analyze the correlation between bile acid concentrations and blood pressure. A total of 398 DEGs were screened. The results of enrichment analysis identified multiple biological pathways associated with hypertension, nephropathy and bile acids. Combining PPI network and bile acid-related targets, three BA-related hub genes (APOE, ALB, SERPINA1) were identified. Quantitative analysis of bile acids revealed significant differences in the concentrations of seven bile acids (DCA, CDCA, UDCA, UCA, CA, TDCA, TCDCA). The concentrations of these bile acids showed a positive correlation with blood pressure values in SHR, with CA, DCA and TDCA showing a stronger correlation and specificity with blood pressure in SHR. Three BA-related hub genes (APOE, ALB, SERPINA1) may be involved in the early stages of HN. The concentrations of multiple bile acids were significantly elevated in the early stages of HN, with CA, DCA and TDCA being more correlated and specific with blood pressure and having higher diagnostic value. These BA-related hub genes and BAs may be involved in disease progression in the early stages of HN.

Dataset Information

Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease.

Publications

Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease.

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