Abstract
SummaryObjectiveInsulin 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.DesignLiquid 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 [6,6‐2H2]glucose and [1,1,2,3,3‐2H5]glycerol. This additionally enabled measuring insulin‐regulated lipolysis, which combined with metabolomics, to the knowledge of this research group, has not been reported on before.ResultsSeveral 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.ConclusionThis 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.
Highlights
Obesity is often accompanied by metabolic disorders such as dyslipidemia and insulin resistance, both part of the metabolic syndrome, which in turn is a major risk factor for type 2 diabetes (T2DM), cardiovascular pathology, non-alcoholic fatty liver disease and different types of cancer.[1,2] Insulin resistance develops prior to the beginning of overt T2DM, making its early detection of vital clinical importance
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 hyperinsulinemic euglycemic clamp (HIEC) with stable isotopes [6,6-2H2] glucose and [1,1,2,3,3-2H5]glycerol
Summary
Obesity is often accompanied by metabolic disorders such as dyslipidemia and insulin resistance, both part of the metabolic syndrome, which in turn is a major risk factor for type 2 diabetes (T2DM), cardiovascular pathology, non-alcoholic fatty liver disease and different types of cancer.[1,2] Insulin resistance develops prior to the beginning of overt T2DM, making its early detection of vital clinical importance. Direct accurate evaluation of insulin resistance in relation to fasting insulin and (compensatory) hyperinsulinemia is currently only possible using complex, invasive and time-consuming examinations such as the two-step stable isotope based hyperinsulinemic euglycemic clamp (HIEC), a method which is regarded as the gold standard. An interesting developing research field in this respect is untargeted metabolic profiling, which enables the detection of in principle thousands of plasma metabolites.[3] These metabolites are products that reflect levels of cellular (dys)function. As they are influenced by both environmental (dietary) and biological (genetic) factors, plasma metabolites may provide insight into the balance of genotype and phenotype of T2DM. Due to the unbiased nature that characterizes metabolomic platforms, they can provide a tool to unveil novel underlying mechanisms of insulin resistance, metabolic syndrome and its dire consequences in humans with obesity
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