Dataset Information

A Metabolomic Signature of Glucagon Action in Healthy Individuals With Overweight/Obesity.

ABSTRACT:

Context

Glucagon is produced and released from the pancreatic alpha-cell to regulate glucose levels during periods of fasting. The main target for glucagon action is the liver, where it activates gluconeogenesis and glycogen breakdown; however, glucagon is postulated to have other roles within the body.

Objective

We sought to identify the circulating metabolites that would serve as markers of glucagon action in humans.

Methods

In this study (NCT03139305), we performed a continuous 72-hour glucagon infusion in healthy individuals with overweight/obesity. Participants were randomized to receive glucagon 12.5 ng/kg/min (GCG 12.5), glucagon 25 ng/kg/min (GCG 25), or a placebo control. A comprehensive metabolomics analysis was then performed from plasma isolated at several time points during the infusion to identify markers of glucagon activity.

Results

Glucagon (GCG 12.5 and GCG 25) resulted in significant changes in the plasma metabolome as soon as 4 hours following infusion. Pathways involved in amino acid metabolism were among the most affected. Rapid and sustained reduction of a broad panel of amino acids was observed. Additionally, time-dependent changes in free fatty acids and diacylglycerol and triglyceride species were observed.

Conclusion

These results define a distinct signature of glucagon action that is broader than the known changes in glucose levels. In particular, the robust changes in amino acid levels may prove useful to monitor changes induced by glucagon in the context of additional glucagon-like peptide-1 or gastric inhibitory polypeptide treatment, as these agents also elicit changes in glucose levels.

SUBMITTER: Vega RB

PROVIDER: S-EPMC8317630 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and aims: Youth populations with overweight/obesity (OW/OB) exhibit heterogeneity in cardiometabolic health phenotypes (e.g. fasting glucose, serum triglycerides, etc.). The underling mechanisms for those differences are still unclear. This study aimed to analyze the whole blood transcriptome profile (RNA-seq) of children with metabolic healthy overweight/obesity (MHO) and metabolic unhealthy overweight/obesity (MUO) phenotypes. Methods: 27 children with OW/OB (10.14 ± 1.3 years, 59% boys) from the ActiveBrains project were included. MHO was defined as having none of the following criteria for metabolic syndrome: elevated fasting glucose, high serum triglycerides, low high-density lipoprotein cholesterol, and high systolic or diastolic blood pressure, while MUO was defined as presenting one or more of these criteria. Inflammatory markers interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), epidermal growth factor (EGF) and, vascular endothelial growth factor A (VEGF) were additionally determined. Total-blood RNA was analyzed by 5’-end RNA sequencing. Two different bioinformatics approaches were performed: 1) Differential gene expression analyses using Limma R/Bioconductor software package (analyses adjusted by sex and maturational status) and 2) weighted gene coexpression network analyses (WGCNA). Results: Children with MHO had lower values of pro-inflammatory cytokines TNF-α and IL-6 compared to MUO (p < 0.05). 40 genes were differentially expressed (FDR < 0.05) in children with MHO compared to MUO. WGCNA analysis identified 23 gene coexpression modules (i.e. clusters of genes) with low preservation (Zsummary < 2) in children with MHO compared to MUO. Differential and WGCNA gene expression patterns identified 32 genes linked to metabolism, mitochondrial and immune functions. Conclusions: Whole blood transcriptome analysis revealed a distinct pattern of gene expression in children with MHO compared to MUO children. The identified gene expression patterns related to metabolism, mitochondrial and immune functions can promote a better understanding of cardiovascular disease prognosis in individuals with MHO.