Author:
Gil-Redondo Rubén,Conde Ricardo,Bruzzone Chiara,Seco Maria Luisa,Bizkarguenaga Maider,González-Valle Beatriz,de Diego Angela,Laín Ana,Habisch Hansjörg,Haudum Christoph,Verheyen Nicolas,Obermayer-Pietsch Barbara,Margarita Sara,Pelusi Serena,Verde Ignacio,Oliveira Nádia,Sousa Adriana,Zabala-Letona Amaia,Santos-Martin Aida,Loizaga-Iriarte Ana,Unda-Urzaiz Miguel,Kazenwadel Jasmin,Berezhnoy Georgy,Geisler Tobias,Gawaz Meinrad,Cannet Claire,Schäfer Hartmut,Diercks Tammo,Trautwein Christoph,Carracedo Arkaitz,Madl Tobias,Valenti Luca,Spraul Manfred,Lu Shelly C.,Embade Nieves,Mato José M.,Millet Oscar
Abstract
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.
Publisher
Springer Science and Business Media LLC