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.

Figure 1

Representative ¹H nuclear magnetic resonance (NMR) spectra of serum from patients with metabolic syndrome (MetS) and healthy controls (δ 0.5–5.0 and δ 6.0–9.0). 3‐HB, 3‐hydroxybutyrate; AA, acetoacetate; DMA, dimethylamine; EA, ethanolamine; G, glycerol; Glc, glucose; Gln, glutamine; Glu, glutamate; L5: very low‐density lipoprotein (VLDL), –CH2–CH2‐C = O; L6: lipid, –CH2–CH = CH–; Lac, lactate; Leu, leucine; m‐I, myo‐Inositol; Sar, sarcosine; Suc, succinate; Val, valine.

Figure 2

Score plots for patients with metabolic syndrome (MetS) and healthy controls. (a) The principal component analysis (PCA) score plot shows preliminary separation between patients with MetS and healthy controls. (b) The orthogonal partial least squares discriminant analysis (OPLS‐DA) score plot shows clear separation between patients with MetS and healthy controls. Red blocks represent data from patients with MetS; yellow triangles represent data from healthy controls.

Figure 3

Statistical validation of the orthogonal partial least squares discriminant analysis (OPLS‐DA) model in 200 random permutation tests. Green dots are the R2 values and blue blocks are the Q2 values. When the abscissa equals 0, R2 = (0.0, 0.0928) and Q2 = (0.0, −0.303). The model was confirmed to be of good quality.

Figure 4

Pathway analysis of patients with metabolic syndrome (MetS) and healthy controls. The top 15 pathways with major changes (P < 0.05) were identified. Bubble color represents the P value: deeper colors represent smaller P values, indicating larger differences. The size of the bubble represents the impact of the pathway during topological analysis. Larger size represents higher impact.