A molecular signature for the metabolic syndrome by urine metabolomics

Abstract

Background: Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine.

Methods: We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18-75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4-5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS.

Results: MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N-oxide, and p-cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome.

Conclusions: Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome.

Keywords: Metabolic syndrome; NMR spectroscopy; NMR-metabolomics; Precision medicine; Urine.

Fig. 1

Univariate and Multivariate analyses for the MetS subtypes. A PCA for the mean profiles for the 16 conditions under consideration. Each condition contains (or not) the risk factor according to Table 1. Color ellipses indicates clusters for subjects with: diabetes (green), hypertension (purple), both factors (yellow) or none of the two (blue). B Heatmap for the different conditions as compared to the apparently healthy condition (0000). The conditions (in the abscise axis) and the bins/metabolites (in the ordinate axis) have been sorted according to cluster analysis. The relevant bins that contributed to the heatmap have been assigned to the corresponding metabolite, as indicated. The fold change is colour-coded according to the bar legend. For each condition, the statistical significance of the variation with respect to apparently healthy individuals is determined by the p-value, shown inside the squares. C Spearman correlation distances to the healthy condition for all the conditions. Colours represent the distance to the apparently healthy (0000) condition, as indicated in the legend. The lines connect adjacent conditions. MetS definition according to WHO, EGIR and AACE is represented by squares and triangles; definition from NCEP:ATPIII and Harmonized is represented by squares, triangles and rhombus; definition by IDF is represented by squares and rhombus. 4-HPPA: 4-hydroxyphenylpyruvic acid; TMAO: trimethylamine N-oxide. The orange ellipse embraces all the conditions that would correspond to MetS according to our metabolic definition

Fig. 2

Probability distribution of the MetS models. A–C Receiving Operating Characteristic (ROC) curves for the three definitions under consideration: WHO, EGIR and AACE (A), NCEP:ATPIII and Harmonized (B), and IDF (C). D–F Smoothed histograms (kernel density based) showing the probability distributions of the MetS model applied to the full cohort for the three definitions under consideration: WHO, EGIR, and AACE (D), NCEP:ATPIII and Harmonized (E), and IDF (F). Red and green colours indicate that the sample has/doesn't have MetS according to the given definition, as indicated

Fig. 3

The effect of senior and NASH populations in MetS. A Probability distributions of suffering MetS calculated from the metabolic model for: general population (individuals with 0000, green), senior population with no risk factors (light green), senior population with 1RF (orange); population with MetS (blue). B Probability distributions of suffering MetS calculated from the metabolic model for: general population (individuals with 0000, green), MetS population (according to WHO definition, purple), NASH without MetS (orange), and NASH with MetS (blue)

Fig. 4

A molecular signature for MetS. All the risk factors that contribute to MetS have at least one metabolite in urine that is altered and contributes to the MetS metabotype. Such characteristic metabotype has been used to create a metabolic model to predict the probability of suffering MetS from the NMR analysis of a urine sample. Red and blue arrows correspond to up- and down-regulated metabolites in urine respectively. Created with BioRender.com