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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. [ABSTRACT FROM AUTHOR]

Stroke remains the second leading cause of mortality worldwide, and the third leading cause of death and morbidity combined, affecting more than 12 million people every year. Stroke pathophysiology results from complex interactions of several risk factors related to age, family history, gender, lifestyle, and the presence of cardiovascular and metabolic diseases. Despite all the evidence, it is not possible to fully prevent stroke onset. In recent years, there has been an exploration of innovative methodologies for metabolite analysis aimed at identifying novel stroke biomarkers. Utilizing Nuclear Magnetic Resonance (NMR) spectroscopy, we investigated small molecule variations in urine across different stages of stroke risk. The Framingham Stroke Risk Score was used in people over 63 years of age living in long-term care facilities (LTCFs) to calculate the probability of suffering a stroke: low stroke risk (LSR, control), moderate stroke risk (MSR), and high stroke risk (HSR). Univariate statistical analysis showed that urinary 4-hydroxyphenylacetate levels increased while glycolate levels decreased across the different stroke risk groups, from the LSR to the HSR groups. Trimethylamine N-oxide (TMAO) had average concentration values that were significantly higher in elderly people in the HSR group, while trigonelline levels were significantly lower in the MSR group. These metabolic markers can be used for early detection and to differentiate stages of stroke risk more efficiently. [ABSTRACT FROM AUTHOR]