Correlation of plasma metabolites with glucose and lipid fluxes in human insulin resistance

Abstract

Objective: Insulin resistance develops prior to the onset of overt type 2 diabetes, making its early detection vital. Direct accurate evaluation is currently only possible with complex examinations like the stable isotope-based hyperinsulinemic euglycemic clamp (HIEC). Metabolomic profiling enables the detection of thousands of plasma metabolites, providing a tool to identify novel biomarkers in human obesity.Design: Liquid chromatography mass spectrometry-based untargeted plasma metabolomics was applied in 60 participants with obesity with a large range of peripheral insulin sensitivity as determined via a two-step HIEC with stable isotopes [6,6-2 H 2 ] glucose and [1,1,2,3,3-2 H 5 ]glycerol. This additionally enabled measuring insulinregulated lipolysis, which combined with metabolomics, to the knowledge of this research group, has not been reported on before.Results: Several plasma metabolites were identified that significantly correlated with glucose and lipid fluxes, led by plasma (gamma-glutamyl)citrulline, followed by betaine, beta-cryptoxanthin, fructosyllysine, octanylcarnitine, sphingomyelin (d18:0/18:0, d19:0/17:0) and thyroxine. Subsequent machine learning analysis showed that a panel of these metabolites derived from a number of metabolic pathways may be used to predict insulin resistance, dominated by non-essential amino acid citrulline and its metabolite gamma-glutamylcitrulline. Conclusion:This approach revealed a number of plasma metabolites that correlated reasonably well with glycemic and lipolytic flux parameters, measured using gold standard techniques. These metabolites may be used to predict the rate of glucose disposal in humans with obesity to a similar extend as HOMA, thus providing potential novel biomarkers for insulin resistance. K E Y W O R D Scitrulline, human insulin resistance, plasma metabolites, stable isotope hyperinsulinemic clamp

The metabolic signatures of cardiometabolic diseases: Does the shared metabotype offer new therapeutic targets?

“…Many of these biomarkers are very similar to metabolic syndrome metabolomic biomarkers, and they indicate the presence of IR and inflammation 38,35,71,92–94 . Metabolic biomarkers of the G‐S‐T pathway such as betaine, serine, and threonine, as well as arginine–proline metabolism metabolites, such as citrulline, ornithine, arginine, and proline, are highly correlated with obesity 38,35,71 . In addition, metabolites of glycolysis and the gluconeogenesis pathway, such as glucose, lactate, and pyruvate as well as fructose and mannose pathway metabolites such as fructose and mannose, are implicated in obesity 92,93,35,94 .…”

“…Obesity metabolic biomarkers were investigated in several metabolomic studies. Many of these biomarkers are very similar to metabolic syndrome metabolomic biomarkers, and they indicate the presence of IR and inflammation 38,35,71,92–94 . Metabolic biomarkers of the G‐S‐T pathway such as betaine, serine, and threonine, as well as arginine–proline metabolism metabolites, such as citrulline, ornithine, arginine, and proline, are highly correlated with obesity 38,35,71 .…”

“…Similar to metabolic syndrome, IR is a distinctive feature of obesity which associates obesity with other IR disorders 52,53,48,54,89,90,38 . Obesity is one of the crucial risk factors of T2DM and CVDs.…”

“…The metabolomic approach has been applied to identify metabolic traits of CMDs 31–41 . Moreover, it was also been applied in nutrimetabolomic and pharmacometabolomic investigations to identify CMDs dietary interventions and pharmacologic treatments metabotypes, respectively 42–46 .…”

Metabolite Profile of Treatment-Naive Metabolic Syndrome Subjects in Relation to Cardiovascular Disease Risk

“…This syndrome increases the chance of developing type 2 diabetes (T2D) and cardiovascular diseases (CVD) [ 4 , 5 ], as well as other diseases including non-alcoholic fatty liver disease (NAFLD) and chronic kidney disease [ 6 , 7 ]. Previous research focusing on the metabolic profile related to insulin sensitivity deterioration already indicated that a noteworthy amount of hepatic and peripheral insulin resistance variation can be explained by metabolite profiles, especially metabolites from the lipid and amino acid super families [ 8 , 9 ]. A systematic review on metabolite profiles in patients with prediabetes and diabetes also found that lipid-associated metabolites such as phospholipids but also amino acid-related metabolites such as aromatic and branched-chain amino acids are associated with the incidence of progressed prediabetes and T2D [ 10 ].…”