Ranges of phenotypic flexibility in healthy subjects

Abstract

BackgroundA key feature of metabolic health is the ability to adapt upon dietary perturbations. A systemic review defined an optimal nutritional challenge test, the “PhenFlex test” (PFT). Recently, it has been shown that the PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Furthermore, it was demonstrated that quantification of PFT response was more sensitive as compared to fasting markers in demonstrating reduced phenotypic flexibility in metabolically impaired type 2 diabetes subjects.MethodsThis study aims to demonstrate that quantification of PFT response can discriminate between different states of health within the healthy range of the population. Therefore, 100 healthy subjects were enrolled (50 males, 50 females) ranging in age (young, middle, old) and body fat percentage (low, medium, high), assuming variation in phenotypic flexibility. Biomarkers were selected to quantify main processes which characterize phenotypic flexibility in response to PFT: flexibility in glucose, lipid, amino acid and vitamin metabolism, and metabolic stress. Individual phenotypic flexibility was visualized using the “health space” by representing the four processes on the health space axes. By quantifying and presenting the study subjects in this space, individual phenotypic flexibility was visualized.ResultsUsing the “health space” visualization, differences between groups as well as within groups from the healthy range of the population can be easily and intuitively assessed. The health space showed a different adaptation to the metabolic PhenFlex test in the extremes of the recruited population; persons of young age with low to normal fat percentage had a markedly different position in the health space as compared to persons from old age with normal to high fat percentage.ConclusionThe results of the metabolic PhenFlex test in conjunction with the health space reliably assessed health on an individual basis. This quantification can be used in the future for personalized health quantification and advice.Electronic supplementary materialThe online version of this article (10.1186/s12263-017-0589-8) contains supplementary material, which is available to authorized users.

Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss‐mediated metabolic improvements

“…Challenge tests began at 9 AM after a 12-h fasting period. The MMTT was a high-fat, high-glucose, high-calorie drink (400 ml) consisting of 320 ml tap water, 75 g glucose, 60 g palm oil, 20 g Protifar as a milk protein concentrate (Nutricia, Utrecht, The Netherlands), and 0.5 g artificial vanilla aroma resulting in a shake with 33, 59, and 8% of energy from carbohydrates, lipids, and proteins, respectively (7,8). The drink was ingested within 5 min, and blood was collected in heparin-coated tubes at 0, 60, 120, 240, 360, and 480 min.…”

“…Monitoring timeresolved metabolic changes underlying the disturbance and regaining of homeostasis, rather than static measurements, ABBREVIATIONS: AT, adipose tissue; AUC, area under the curve; BCAA, branched-chain amino acid; GGT, g-glutamyl transpeptidase; LC-MS/MS, liquid chromatography-tandem mass spectrometry; lyso-PC, lysophosphatidylcholine; MMTT, mixed meal tolerance test; NEFA, nonesterified fatty acid; PC, phosphatidylcholine; PCA, principal components analysis; PLS-DA, partial least squares-discriminant analysis such as after an overnight fast, may thus deliver more predictive parameters of the health status (3,4). In fact, the responses to a lifestyle intervention or the effects of a metabolic disease can be more comprehensively evaluated when dietary challenges are used (5)(6)(7)(8). The ability to adapt to a challenge such as an oral glucose tolerance test or a mixed meal tolerance test (MMTT) has been coined phenotypic flexibility (4,9).…”

Weight loss moderately affects the mixed meal challenge response of the plasma metabolome and transcriptome of peripheral blood mononuclear cells in abdominally obese subjects

“…The use of a mixed-meal challenge, comprising protein, glucose and lipids, has been proposed to target multiple organs and more broadly encompass phenotypic flexibility (Stroeve et al 2015 ; Wopereis et al 2017 ). By challenging metabolic regulatory processes by means of a dietary challenge, dynamic changes in nutrient metabolism might be uncovered, allowing better exploration of the individual capacity to cope with metabolic stressors (Krug et al 2012 ; Broek et al 2017 ).…”

Next Generation Health Claims Based on Resilience: The Example of Whole-Grain Wheat

“…The next step would be the subsequent integration of these combined data [ 52 ], which will yield a quantified degree of resilience of an individual which can be compared to others [ 50 , 53 , 54 , 55 , 56 , 57 ]. This approach will deliver us a next generation of biomarkers [ 8 ] or early biomarkers of disease or effects ( Figure 1 adapted from [ 58 ]).…”