Journal Article

MetSCORE: a molecular metric to evaluate the risk of metabolic syndrome based on serum NMR metabolomics

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

2024

Overview

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: BioMed Central,Springer Nature B.V,BMC
• Subject: Adult / Aged / Angiology / Biomarkers - blood / Biomarkers - urine / Blood pressure / Cardiology / Cardiometabolic Risk Factors / Cluster analysis / Diabetes / Dyslipidemia / Female / Humans / Hyperglycemia / Hypertension / Insulin resistance / Lipoproteins / Lipoproteins - blood / Liver / Magnetic Resonance Spectroscopy / Male / Medicine / Medicine & Public Health / Metabolic disorders / Metabolic syndrome / Metabolic Syndrome - blood / Metabolic Syndrome - diagnosis / Metabolic Syndrome - epidemiology / Metabolic Syndrome - urine / Metabolites / Metabolomics / Middle Aged / NMR / Nuclear magnetic resonance / Obesity / Phenotypes / Predictive Value of Tests / Prognosis / Risk Assessment / Risk Factors / Spectrum analysis / Urine / Young Adult