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. 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. 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. 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.

中文翻译：

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通过尿液代谢组学检测代谢综合征的分子特征

代谢综合征 (MetS) 是一种多病的长期病症，没有双方同意的医学定义和基于相容症状的诊断。在这里，我们研究了尿液中 MetS 的分子特征。我们使用基于 NMR 的代谢组学研究了一个欧洲队列，其中包括来自 11,754 名个体（18-75 岁，41% 女性）的尿液样本，旨在填充 MetS 的所有中间条件，从没有任何风险因素的受试者到发育不良的个体MetS（4–5%，取决于定义）。从尿液光谱中整合一组量化的代谢物以获得代谢模型（每个定义一个），以区分患有 MetS 的个体。MetS 进展会产生尿液代谢组的连续和单调变化，以相关代谢物（总共 17 种，包括葡萄糖、脂质、芳香族氨基酸、水杨酸、麦芽糖醇、三甲胺 N-氧化物和对甲酚硫酸盐）的上调或下调为特征，其中一些代谢物与 MetS 相关首次。这种代谢特征完全基于从尿液光谱中提取的信息，为 MetS 定义增加了一个分子维度，并用于生成可以识别 MetS 受试者的模型（AUROC 值介于 0.83 和 0.87 之间）。该签名特别适合为健康受试者和 MetS 患者之间的界面中的条件增加意义。衰老和非酒精性脂肪肝也是可能增加 MetS 概率的危险因素，但不会直接干扰该综合征的代谢鉴别。