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

Abstract

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.

Keywords: challenge meal; insulin resistance; nutrition; nutritional phenotypic flexibility; omics; oral glucose tolerance test (OGTT); oral lipid tolerance test (OLTT); postprandial physiology; type 2 diabetes.

Figure 1

Frequently reported effects of challenge meals on postprandial key metabolic pathways as identified by metabolomics approaches and blunted responses characteristic of a reduced phenotypic flexibility in populations with increased cardiometabolic risk compared to healthy individuals. The metabolite response to the challenge meal is presented as the evolution of concentration (C) over time (t). For details on the metabolites and experimental designs, see Table 1. AA: amino acids; BCAA: branched-chain amino acid; FFA: free fatty acids; and OGTT: oral glucose tolerance test.

Figure 2

Selection of statistical methods that can be applied to metabolomics data obtained from challenge test studies with different experimental designs: Balanced parallel designs (A), unbalanced, parallel and longitudinal designs (B), unbalanced, cross-over and longitudinal designs (C). AMO-PLS: ANOVA-Multiblock Orthogonal Partial Least Squares, APCA: ANOVA-PCA, ASCA: ANOVA-Simultaneous Component Analysis, C: Metabolite concentration, LiMM-PCA: Linear Mixed Models-PCA, n_individuals: number of individuals included in the experiment, n_metabolite: number of metabolites quantified in the experiment, PARAFAC: Parallel Factor Analysis and t: Time.