Identification of Insulin Resistance Biomarkers in Metabolic Syndrome Detected by UHPLC-ESI-QTOF-MS

Abstract

Metabolic syndrome (MetS) is a disorder characterized by a group of factors that can increase the risk of chronic diseases, including cardiovascular diseases and type 2 diabetes mellitus (T2D). Metabolomics has provided new insight into disease diagnosis and biomarker identification. This cross-sectional investigation used an untargeted metabolomics-based technique to uncover metabolomic alterations and their relationship to pathways in normoglycemic and prediabetic MetS participants to improve disease diagnosis. Plasma samples were collected from drug-naive prediabetic MetS patients (n = 26), normoglycemic MetS patients (n = 30), and healthy (normoglycemic lean) subjects (n = 30) who met the inclusion criteria for the study. The plasma samples were analyzed using highly sensitive ultra-high-performance liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). One-way ANOVA analysis revealed that 59 metabolites differed significantly among the three groups (p < 0.05). Glutamine, 5-hydroxy-L-tryptophan, L-sorbose, and hippurate were highly associated with MetS. However, 9-methyluric acid, sphinganine, and threonic acid were highly associated with prediabetes/MetS. Metabolic pathway analysis showed that arginine biosynthesis and glutathione metabolism were associated with MetS/prediabetes, while phenylalanine, D-glutamine and D-glutamate, and lysine degradation were highly impacted in MetS. The current study sheds light on the potential diagnostic value of some metabolites in metabolic syndrome and the role of their alteration on some of the metabolic pathways. More studies are needed in larger cohorts in order to verify the implication of the above metabolites on MetS and their diagnostic value.

Discovery and Validation of Potential Serum Biomarkers with Pro-Inflammatory and DNA Damage Activities in Ulcerative Colitis: A Comprehensive Untargeted Metabolomic Study

“…Unlike targeted metabolomics, which only focuses on specific substances, untargeted metabolomics collects as much information as possible and has a wider coverage of substances. Disturbances found in local and systemic metabolism can not only provide biomarkers for the clinical diagnosis of disease but may also be new targets for the treatment of disease [ 15 , 16 , 17 , 18 ].…”

Plasma metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients

“…The dried sample was then either stored in a –80ºC freezer for further use or dissolved in solvent for LC-MS/MS analysis. Metabolites were analyzed by HPLC-Q-TOF MS/MS using the auto MS/MS positive scan mode as per described in our recent publications [ 18 , 19 ]. Briefly, samples were chromatographically separated using a Hamilton ® Intensity Solo 2 C18 column (100 mm x 2.1 mm x 1.8 μm) and eluted using 0.1% formic acid in water (A) and 0.1% formic acid in ACN (B) using the following gradient: at a flow rate of 0.250 mL/min 1% B from 0–2 min, then gradient elution to 99% B from 2–17 min, held at 99% B from 17–20 min, then re-equilibrated to 1% B from 20–30 min using a flow rate of 0.350 mL/min.…”

MOTS-c Functionally Prevents Metabolic Disorders

“…Insulin resistance is defined as impaired signal transduction and biological actions in response to insulin stimulation, resulting in a decrease in the ability of insulin to increase glucose uptake and utilization [ 16 ]. Insulin resistance is a risk factor for non-alcoholic steatohepatitis, metabolic syndrome, and type 2 diabetes (T2D) [ 17 , 18 , 19 ]. MOTS-c has been demonstrated to increase insulin sensitivity in mice by preventing fat accumulation through reducing the pathways of sphingolipid metabolism, monoacylglycerol metabolism, and dicarboxylate metabolism [ 20 ].…”