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

Autor: Hartstra AV; Department of Internal and Vascular Medicine Amsterdam University Medical Centers Amsterdam the Netherlands., de Groot PF; Department of Internal and Vascular Medicine Amsterdam University Medical Centers Amsterdam the Netherlands., Mendes Bastos D; Department of Internal and Vascular Medicine Amsterdam University Medical Centers Amsterdam the Netherlands., Levin E; Department of Internal and Vascular Medicine Amsterdam University Medical Centers Amsterdam the Netherlands., Serlie MJ; Department of Endocrinology and Metabolism Amsterdam University Medical Centers Amsterdam the Netherlands., Soeters MR; Department of Endocrinology and Metabolism Amsterdam University Medical Centers Amsterdam the Netherlands., Pekmez CT; Department of Nutrition, Exercise and Sports University of Copenhagen Copenhagen Denmark., Dragsted LO; Department of Nutrition, Exercise and Sports University of Copenhagen Copenhagen Denmark., Ackermans MT; Endocrine Laboratory, Department of Clinical Chemistry Amsterdam University Medical Centers Amsterdam the Netherlands., Groen AK; Department of Internal and Vascular Medicine Amsterdam University Medical Centers Amsterdam the Netherlands.; Department of Laboratory Medicine, University of Groningen University Medical Center Groningen the Netherlands., Nieuwdorp M; Department of Internal and Vascular Medicine Amsterdam University Medical Centers Amsterdam the Netherlands.
Jazyk: angličtina
Zdroj: Obesity science & practice [Obes Sci Pract] 2020 Feb 07; Vol. 6 (3), pp. 340-349. Date of Electronic Publication: 2020 Feb 07 (Print Publication: 2020).
DOI: 10.1002/osp4.402
Abstrakt: 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 insulin-regulated 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.
Competing Interests: None to declare.
(© 2020 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.)
Databáze: MEDLINE
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