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

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

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

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

Bifidobacterium species associated with breastfeeding produce aromatic lactic acids in the infant gut

Breastfeeding profoundly shapes the infant gut microbiota, which is critical for early life immune development, and the gut microbiota can impact host physiology in various ways, such as through the production of metabolites. However, few breastmilk-dependent microbial metabolites mediating host–microbiota interactions are currently known. Here, we demonstrate that breastmilk-promoted Bifidobacterium species convert aromatic amino acids (tryptophan, phenylalanine and tyrosine) into their respective aromatic lactic acids (indolelactic acid, phenyllactic acid and 4-hydroxyphenyllactic acid) via a previously unrecognized aromatic lactate dehydrogenase (ALDH). The ability of Bifidobacterium species to convert aromatic amino acids to their lactic acid derivatives was confirmed using monocolonized mice. Longitudinal profiling of the faecal microbiota composition and metabolome of Danish infants (n = 25), from birth until 6 months of age, showed that faecal concentrations of aromatic lactic acids are correlated positively with the abundance of human milk oligosaccharide-degrading Bifidobacterium species containing the ALDH, including Bifidobacterium longum, B. breve and B. bifidum. We further demonstrate that faecal concentrations of Bifidobacterium-derived indolelactic acid are associated with the capacity of these samples to activate in vitro the aryl hydrocarbon receptor (AhR), a receptor important for controlling intestinal homoeostasis and immune responses. Finally, we show that indolelactic acid modulates ex vivo immune responses of human CD4+ T cells and monocytes in a dose-dependent manner by acting as an agonist of both the AhR and hydroxycarboxylic acid receptor 3 (HCA3). Our findings reveal that breastmilk-promoted Bifidobacterium species produce aromatic lactic acids in the gut of infants and suggest that these microbial metabolites may impact immune function in early life