PDF(Pubmed)
Abstract：
BACKGROUND: Metabolic syndrome (MetS) is a cluster of medical conditions and risk factors correlating with insulin resistance that increase the risk of developing cardiometabolic health problems. The specific criteria for diagnosing MetS vary among different medical organizations but are typically based on the evaluation of abdominal obesity, high blood pressure, hyperglycemia, and dyslipidemia. A unique, quantitative and independent estimation of the risk of MetS based only on quantitative biomarkers is highly desirable for the comparison between patients and to study the individual progression of the disease in a quantitative manner.
METHODS: We used NMR-based metabolomics on a large cohort of donors (n = 21,323; 37.5% female) to investigate the diagnostic value of serum or serum combined with urine to estimate the MetS risk. Specifically, we have determined 41 circulating metabolites and 112 lipoprotein classes and subclasses in serum samples and this information has been integrated with metabolic profiles extracted from urine samples.
RESULTS: We have developed MetSCORE, a metabolic model of MetS that combines serum lipoprotein and metabolite information. MetSCORE discriminate patients with MetS (independently identified using the WHO criterium) from general population, with an AUROC of 0.94 (95% CI 0.920-0.952, p < 0.001). MetSCORE is also able to discriminate the intermediate phenotypes, identifying the early risk of MetS in a quantitative way and ranking individuals according to their risk of undergoing MetS (for general population) or according to the severity of the syndrome (for MetS patients).
CONCLUSIONS: We believe that MetSCORE may be an insightful tool for early intervention and lifestyle modifications, potentially preventing the aggravation of metabolic syndrome.
METHODS: We used NMR-based metabolomics on a large cohort of donors (n = 21,323; 37.5% female) to investigate the diagnostic value of serum or serum combined with urine to estimate the MetS risk. Specifically, we have determined 41 circulating metabolites and 112 lipoprotein classes and subclasses in serum samples and this information has been integrated with metabolic profiles extracted from urine samples.
RESULTS: We have developed MetSCORE, a metabolic model of MetS that combines serum lipoprotein and metabolite information. MetSCORE discriminate patients with MetS (independently identified using the WHO criterium) from general population, with an AUROC of 0.94 (95% CI 0.920-0.952, p < 0.001). MetSCORE is also able to discriminate the intermediate phenotypes, identifying the early risk of MetS in a quantitative way and ranking individuals according to their risk of undergoing MetS (for general population) or according to the severity of the syndrome (for MetS patients).
CONCLUSIONS: We believe that MetSCORE may be an insightful tool for early intervention and lifestyle modifications, potentially preventing the aggravation of metabolic syndrome.
摘要：
背景：代谢综合征（MetS）是一组与胰岛素抵抗相关的疾病和危险因素，增加了发生心脏代谢健康问题的风险。诊断MetS的具体标准因不同医疗机构而异，但通常基于对腹部肥胖的评估，高血压,高血糖症,和血脂异常。一个独特的，仅基于定量生物标志物的MetS风险的定量和独立估计对于患者之间的比较和以定量方式研究疾病的个体进展是非常理想的.
方法：我们在一大群供体（n=21,323；37.5%女性）中使用基于NMR的代谢组学研究血清或血清与尿液联合评估MetS风险的诊断价值。具体来说,我们已经确定了血清样本中的41种循环代谢物和112种脂蛋白类别和亚类，这些信息已与从尿液样本中提取的代谢谱相结合。
结果：我们开发了MetSCORE，结合血清脂蛋白和代谢物信息的MetS代谢模型。MetSCORE将MetS患者（使用WHO标准独立识别）与普通人群区分开来，AUROC为0.94（95%CI0.920-0.952，p<0.001）。MetSCORE还能够区分中间表型，以定量方式识别MetS的早期风险,并根据个体接受MetS的风险(对于普通人群)或根据综合征的严重程度(对于MetS患者)对个体进行排名。
结论：我们认为MetSCORE可能是早期干预和生活方式改变的有见地工具，有可能预防代谢综合征的恶化。
方法：我们在一大群供体（n=21,323；37.5%女性）中使用基于NMR的代谢组学研究血清或血清与尿液联合评估MetS风险的诊断价值。具体来说,我们已经确定了血清样本中的41种循环代谢物和112种脂蛋白类别和亚类，这些信息已与从尿液样本中提取的代谢谱相结合。
结果：我们开发了MetSCORE，结合血清脂蛋白和代谢物信息的MetS代谢模型。MetSCORE将MetS患者（使用WHO标准独立识别）与普通人群区分开来，AUROC为0.94（95%CI0.920-0.952，p<0.001）。MetSCORE还能够区分中间表型，以定量方式识别MetS的早期风险,并根据个体接受MetS的风险(对于普通人群)或根据综合征的严重程度(对于MetS患者)对个体进行排名。
结论：我们认为MetSCORE可能是早期干预和生活方式改变的有见地工具，有可能预防代谢综合征的恶化。
