Reconstruction And Analysis Of Correlation Networks Based On Gc-Ms Metabolomics Data For Hypercholesterolemia

Background and aims: Hypercholesterolemia is characterized by the elevation of plasma total cholesterol level, especially low-density lipoprotein (LDL) cholesterol. This disease is usually caused by a mutation in genes such as LDL receptor, apolipoprotein B, or proprotein convertase subtilisin/kexin type 9. However, a considerable number of patients with hypercholesterolemia do not have any mutation in these candidate genes. In this study, we examined the difference in the metabolic level between patients with hyper-cholesterolemia and healthy subjects, and screened the potential biomarkers for this disease.Methods: Analysis of plasma metabolomics in hypercholesterolemia patients and healthy controls was performed by gas chromatography-mass spectrometry and metabolic correlation networks were constructed using Gephi-0.9.2.Results: First, metabolic profile analysis confirmed the distinct metabolic footprints between the patients and the healthy ones. The potential biomarkers screened by orthogonal partial least-squares discrimination analysis included L-lactic acid, cholesterol, phosphoric acid, D-glucose, urea, and D-allose (Variable importance in the projection > 1). Second, arginine and methionine metabolism were significantly perturbed in hypercholesterolemia patients. Finally, we identified that L-lactic acid, L-lysine, L-glutamine, and L-cysteine had high scores of centrality parameters in the metabolic correlation network.Conclusion: Plasma L-lactic acid could be used as a sensitive biomarker for hypercholesterolemia. In addition, arginine biosynthesis and cysteine and methionine metabolism were profoundly altered in patients with hypercholesterolemia. (C) 2021 Elsevier Inc. All rights reserved.