Targeted High Performance Liquid Chromatography Tandem Mass Spectrometry-based Metabolomics differentiates metabolic syndrome from obesity

Abstract

Both obesity and the metabolic syndrome are risk factors for type 2 diabetes and cardiovascular disease. Identification of novel biomarkers are needed to distinguish metabolic syndrome from equally obese individuals in order to direct them to early interventions that reduce their risk of developing further health problems. We utilized mass spectrometry-based targeted metabolic profiling of 221 metabolites to evaluate the associations between metabolite profiles and established metabolic syndrome criteria (i.e. elevated waist circumference, hypertension, elevated fasting glucose, elevated triglycerides, and low high-density lipoprotein cholesterol) in plasma samples from obese men ( n = 29; BMI = 35.5 ± 5.2 kg/m²) and women ( n = 40; 34.9 ± 6.7 kg/m²), of which 26 met the criteria for metabolic syndrome (17 men and 9 women). Compared to obese individuals without metabolic syndrome, univariate statistical analysis and partial least squares discriminant analysis showed that a specific group of metabolites from multiple metabolic pathways (i.e. purine metabolism, valine, leucine and isoleucine degradation, and tryptophan metabolism) were associated with the presence of metabolic syndrome. Receiver operating characteristic curves generated based on the PLS-DA models showed excellent areas under the curve (0.85 and 0.96, for metabolites only model and enhanced metabolites model, respectively), high specificities (0.86 and 0.93), and good sensitivities (0.71 and 0.91). Moreover, principal component analysis revealed that metabolic profiles can be used to further differentiate metabolic syndrome with 3 versus 4-5 metabolic syndrome criteria. Collectively, these findings support targeted metabolomics approaches to distinguish metabolic syndrome from obesity alone, and to stratify metabolic syndrome status based on the number of criteria met. Impact statement We utilized mass spectrometry-based targeted metabolic profiling of 221 metabolites to evaluate the associations between metabolite profiles and established MetS criteria. To our best knowledge, the findings of this study provide the first evidence that metabolic profiles can be used to differentiate participants with MetS from similarly obese individuals who do not meet established criteria of MetS. Furthermore, the study demonstrated that within MetS participants, their unique metabolic profiles correlated to the number of criteria used for MetS determination. Taken together, this metabolic profiling approach can potentially serve as a novel tool for MetS detection and monitoring, and provide useful metabolic information for future interventions targeting obesity and MetS.

Keywords: HPLC-MS/MS; metabolic pathways; metabolic syndrome; obesity; targeted metabolic profiling.

Figure 1

Volcano plot showing the P value (y-axis) and the fold changes (x-axis) of the metabolites detected in this study. Cutoff P value of 0.01 was used. The metabolites above the horizontal line with significant P value are alanine, trans-4-hydroxyproline, N-alpha-acetyl-l-lysine, leucine/isoleucine, glutamic acid, creatine, cysteine, kynurenine, inosine-5′-diphosphate, urate, inosine, tryptophan, methionine, d-glucosamine-6-sulfate, (2R, 3R) – (−)-2, 3-butanediol. (A color version of this figure is available in the online journal.)

Figure 2

A metabolome view showing all impacted metabolic pathways in comparison between MetS and Obese non-MetS groups in this study. (a) Purine metabolism; (b) valine, leucine and isoleucine degradation; (c) aminoacyl-tRNA biosynthesis; (d) tryptophan metabolism; (e) cysteine and methionine metabolism; (f) lysine degradation; (g) pyrimidine metabolism; (h) arginine and proline metabolism; (i) glycine, serine and threonine metabolism; (j) taurine and hypotaurine metabolism; (k) alanine, aspartate and glutamate metabolism; (l) pantothenate and CoA biosynthesis. (A color version of this figure is available in the online journal.)

Figure 3

ROC (upper panel) and MCCV (lower panel) using metabolites with p < 0.005 in comparison of MetS group versus Obese non-MetS in the metabolites only model. Seven metabolites are used, AUROC = 0.85, sensitivity = 0.71 and specificity = 0.86. True: true class model; random: random permutation model. (A color version of this figure is available in the online journal.)

Figure 4

ROC (upper panel) and MCCV (lower panel) using p < 0.005 in comparison of MetS group versus Obese non-MetS, metabolites and clinical characteristics (plasma glucose, plasma TG, HDL) combined model. AUROC = 0.96, sensitivity = 0.91, specificity = 0.93. True: true class model; random: random permutation model. (A color version of this figure is available in the online journal.)

Figure 5

PCA plots showing detailed comparison of MetS subgroups (participants with 3 factors versus 4 and 5 factors). F: participants with 4 and 5 risk factors; T: participants with only 3 risk factors. (a) Score plot showing separation of the two subgroups of MetS participants based on their metabolic profile of selected metabolites. (b) Loading plot showing contributions of each metabolites in the separation of two subgroups. (A color version of this figure is available in the online journal.)