Metabolomic Profiling Revealed Potential Biomarkers in Patients With Moyamoya Disease

Abstract

Metabolomics is increasingly used to observe metabolic patterns and disease-specific metabolic biomarkers. However, serum metabolite analysis of moyamoya disease (MMD) is rarely reported. We investigated serum metabolites in MMD and compared them with those of healthy controls (HCs) using a non-targeted gas chromatography-mass spectrometry (GC-MS) approach to identify metabolic biomarkers associated with MMD. Forty-one patients with MMD diagnosed by cerebral angiography and 58 HCs were recruited for our study. Comparative analyses (univariate, multivariate, correlation, heatmaps, receiver operating characteristi curves) were performed between MMD patients and HCs. Twenty-five discriminating serum metabolic biomarkers between MMD patients and HCs were identified. Compared with HCs, MMD patients had higher levels of phenol, 2-hydroxybutyric acid, L-isoleucine, L-serine, glycerol, pelargonic acid, L-methionine, myristic acid, pyroglutamic acid, palmitic acid, palmitoleic acid, stearic acid, octadecanamide, monoglyceride (MG) (16:0/0:0/0:0), and MG (0:0/18:0/0:0), and lower levels of L-alanine, L-valine, urea, succinic acid, L-phenylalanine, L-threonine, L-tyrosine, edetic acid, and oleamide. These metabolic biomarkers are involved in several pathways and are closely associated with the metabolism of amino acids, lipids, carbohydrates, and carbohydrate translation. A GC-MS-based metabolomics approach could be useful in the clinical diagnosis of MMD. The identified biomarkers may be helpful to develop an objective diagnostic method for MMD and improve our understanding of MMD pathogenesis.

Keywords: biomarkers; gas chromatography–mass spectrometry; metabolomics; moyamoya disease; serum.

FIGURE 1

A representative gas chromatography–mass spectrometry (GC–MS) total ion chromatogram (TIC) of the quality control (QC) serum sample.

FIGURE 2

Multivariate statistical analysis between the control group and moyamoya disease (MMD) group. (A) Principal components analysis (PCA) scores plot; (B) partial least squares discriminant analysis (PLS-DA) scores plot; (C) orthogonal PLS-DA (OPLS-DA) scores plot; and (D) statistical validation of the OPLS-DA model through 200 × permutation testing.

FIGURE 3

(A) Correlation analysis of the differential metabolites in moyamoya disease (MMD) patients and healthy controls (HCs). (B) Heat map for identified metabolites in MMD patients and HCs. The color of each section is proportional to the significance of change of metabolites (red, upregulated; blue, downregulated). Rows, samples; columns, metabolites.

FIGURE 4

(A) Receiver operating characteristic (ROC) curves were for the sets of biomarker metabolites. (B) Summary of pathway analysis with MetaboAnalyst 4.0. (a) Aminoacyl-tRNA biosynthesis; (b) valine, leucine, and isoleucine biosynthesis; (c) propanoate metabolism; (d) phenylalanine metabolism; (e) cysteine and methionine metabolism; (f) alanine, aspartate, and glutamate metabolism; (g) phenylalanine, tyrosine, and tryptophan biosynthesis; (h) tyrosine metabolism; and (i) glycerolipid metabolism.

FIGURE 5

Schematic diagram of the proposed metabolic pathways in moyamoya disease (MMD) compared to healthy controls (HCs). Red and blue represent up- and downregulated metabolites, respectively.