Correlation of plasma metabolites with glucose and lipid fluxes in human insulin resistance
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Correlation of plasma metabolites with glucose and lipid fluxes in human insulin resistance. / Hartstra, Annick V; de Groot, Pieter F; Mendes Bastos, Diogo; Levin, Evgeni; Serlie, Mireille J; Soeters, Maarten R; Pekmez, Ceyda Tugba; Dragsted, Lars Ove; Ackermans, Mariette T; Groen, Albert K; Nieuwdorp, Max.
In: Obesity Science and Practice, Vol. 6, No. 3, 2020, p. 340-349.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Correlation of plasma metabolites with glucose and lipid fluxes in human insulin resistance
AU - Hartstra, Annick V
AU - de Groot, Pieter F
AU - Mendes Bastos, Diogo
AU - Levin, Evgeni
AU - Serlie, Mireille J
AU - Soeters, Maarten R
AU - Pekmez, Ceyda Tugba
AU - Dragsted, Lars Ove
AU - Ackermans, Mariette T
AU - Groen, Albert K
AU - Nieuwdorp, Max
N1 - CURIS 2020 NEXS 084
PY - 2020
Y1 - 2020
N2 - 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 [6,6-2H2]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. 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.
AB - 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 [6,6-2H2]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. 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.
KW - Citrulline
KW - Human insulin resistance
KW - Plasma metabolites
KW - Stable isotope hyperinsulinemic clamp
UR - http://www.scopus.com/inward/record.url?scp=85079197932&partnerID=8YFLogxK
U2 - 10.1002/osp4.402
DO - 10.1002/osp4.402
M3 - Journal article
C2 - 32523723
AN - SCOPUS:85079197932
VL - 6
SP - 340
EP - 349
JO - Obesity Science & Practice
JF - Obesity Science & Practice
SN - 2055-2238
IS - 3
ER -
ID: 238007990