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This research focused on establishing a hierarchy concerning the influence of various biological markers and body composition parameters on preventing, diagnosing and managing Metabolic Syndrome (MetS). Our cross-sectional cohort study included 104 subjects without any atherosclerotic antecedent pathology, organized in two groups (with and without MetS). All participants underwent clinical and anthropometric measurements, DEXA investigation and blood tests for all MetS criteria, together with adiponectin, leptin, insulin, uric acid and CRP. Based on mathematical logic, we calculated a normalized sensitivity score to compare the predictive power of biomarkers and parameters associated with MetS, upon the prevalence of MetS. Patients with MetS report higher levels of uric acid (p = 0.02), CRP (p = 0.012) and lower levels of adiponectin (p = 0.025) than patients without MetS. The top three biological markers with the highest predictive power of the prevalence of the disease are HDL, insulin, and adiponectin:leptin ratio, and the top three body composition parameters are trunk fat-free percentage, waist-height ratio and trunk fat percentage. Their high sensitivity scores differentiate them from all the other markers analysed in the study. Our findings report relevant scores for estimating the importance of cardiometabolic risks in the prevalence of MetS. The high rank of protective markers, HDL and trunk fat-free percentage, suggest that positive effects have a stronger association with the prevalence of MetS, than negative ones do. Therefore, this risk stratification study provides important support for prevention and management programs regarding MetS.

Host-microbe co-metabolism via MCAD generates circulating metabolites including hippuric acid

Intravenous fat induces changes in PUFA and their bioactive metabolites: Comparison between Japanese and Australian preterm infants - ScienceDirect

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Estimation of Fraction Metabolized by Cytochrome P450 Enzymes Using Long-Term Cocultured Human Hepatocytes

Individual variability in human blood metabolites identifies age-related differences

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication - ScienceDirect

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