Global testing of shifts in metabolic phenotype

Gut liver brain axis in diseases: the implications for therapeutic interventions

Gut-liver-brain axis is a three-way highway of information interaction system among the gastrointestinal tract, liver, and nervous systems. In the past few decades, breakthrough progress has been made in the gut liver brain axis, mainly through understanding its formation mechanism and increasing treatment strategies. In this review, we discuss various complex networks including barrier permeability, gut hormones, gut microbial metabolites, vagus nerve, neurotransmitters, immunity, brain toxic metabolites, β-amyloid (Aβ) metabolism, and epigenetic regulation in the gut-liver-brain axis. Some therapies containing antibiotics, probiotics, prebiotics, synbiotics, fecal microbiota transplantation (FMT), polyphenols, low FODMAP diet and nanotechnology application regulate the gut liver brain axis. Besides, some special treatments targeting gut-liver axis include farnesoid X receptor (FXR) agonists, takeda G protein-coupled receptor 5 (TGR5) agonists, glucagon-like peptide-1 (GLP-1) receptor antagonists and fibroblast growth factor 19 (FGF19) analogs. Targeting gut-brain axis embraces cognitive behavioral therapy (CBT), antidepressants and tryptophan metabolism-related therapies. Targeting liver-brain axis contains epigenetic regulation and Aβ metabolism-related therapies. In the future, a better understanding of gut-liver-brain axis interactions will promote the development of novel preventative strategies and the discovery of precise therapeutic targets in multiple diseases.

Systematic Review and Quantitative Data Synthesis of Peripheral Blood Mononuclear Cell Transcriptomics Reveals Consensus Gene Expression Changes in Response to a High Fat Meal

SCOPE

Metabolic flexibility is essential for a healthy response to a high fat meal, and is assessed by measuring postprandial changes in blood markers including peripheral blood mononuclear cells (PBMCs; lymphocytes and monocytes). However, there is no clear consensus on postprandial gene expression and protein changes in these cells.

METHOD AND RESULTS

The study systematically reviews the literature reporting transcriptional and proteomic changes in PBMCs after consumption of a high fat meal. After re-analysis of the raw data to ensure equivalence between studies, ≈85 genes are significantly changed (defined as in the same direction in ≥3 studies) with about half involved in four processes: inflammation/oxidative stress, GTP metabolism, apoptosis, and lipid localization/transport. For meals consisting predominantly of unsaturated fatty acids (UFA), notable additional processes are phosphorylation and glucocorticoid response. For saturated fatty acids (SFA), genes related to migration/angiogenesis and platelet aggregation are also changed.

CONCLUSION

Despite differences in study design, common gene changes are identified in PBMCs following a high fat meal. These common genes and processes will facilitate definition of the postprandial transcriptome as part of the overall postcibalome, linking all molecules and processes that change in the blood after a meal.

Investigating the Postprandial Metabolome after Challenge Tests to Assess Metabolic Flexibility and Dysregulations Associated with Cardiometabolic Diseases

This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.