Precision Medicine and Metabolism Laboratory, CIC bioGUNE, BRTA, CIBERehd, Bizkaia Technology Park, Bld. 800, 48160, Derio, Bizkaia, Spain.
OSARTEN Kooperativa Elkartea, 20500, Arrasate-Mondragón, Spain.
Cardiovasc Diabetol. 2021 Jul 28;20(1):155. doi: 10.1186/s12933-021-01349-9.
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.
代谢综合征（MetS）是一种多系统的慢性疾病，目前尚无共识的医学定义，其诊断依据是相关症状。在此，我们研究了尿液中代谢综合征的分子特征。
我们采用基于 NMR 的代谢组学方法，对一个包括 11754 名个体（年龄 18-75 岁，女性占 41%）的欧洲队列进行了尿液样本研究。该队列设计旨在涵盖代谢综合征的所有中间状态，从无任何危险因素的个体到已发展为代谢综合征的个体（根据不同定义，占比为 4-5%）。我们从尿液光谱中提取了一组定量代谢物，以获得代谢模型（每种定义一个），用于区分代谢综合征患者和非患者。
代谢综合征的进展会导致尿液代谢组连续、单调地变化，其特征是相关代谢物的上调或下调（共 17 种，包括葡萄糖、脂质、芳香族氨基酸、水杨尿酸、麦芽糖醇、三甲胺 N-氧化物和对甲酚硫酸盐），其中一些代谢物以前与代谢综合征有关。这种代谢特征完全基于从尿液光谱中提取的信息，为代谢综合征的定义增加了分子维度，并用于生成可识别代谢综合征患者的模型（AUROC 值在 0.83 至 0.87 之间）。该特征特别适用于为健康个体和代谢综合征患者之间的中间状态赋予意义。衰老和非酒精性脂肪肝也是可能增加代谢综合征概率的风险因素，但它们不会直接干扰该综合征的代谢鉴别。
通过 NMR 光谱研究尿液代谢组学揭示了一组与代谢综合征进展同时演变的代谢物，这些代谢物被用于推导和验证代谢综合征的分子定义，并区分健康个体和代谢综合征之间的中间状态。
