Assessing individual metabolic responsiveness to a lipid challenge using a targeted metabolomic approach

Abstract

The development of assessment techniques with immediate clinical applicability is a priority for resolving the growing epidemic in metabolic disease. Many imbalances in diet-dependent metabolism are not detectable in the fasted state. Resolving the high inter-individual variability in response to diet requires the development of techniques that can detect metabolic dysfunction at the level of the individual. The intra- and inter-individual variation in lipid metabolism in response to a standardized test meal was determined. Following an overnight fast on three different days, three healthy subjects consumed a test meal containing 40% of their daily calories. Plasma samples were collected at fasting, and 1, 3, 6, and 8 h after the test meal. Plasma fatty acid (FA) concentrations within separated lipid classes and lipoprotein fractions were measured at each time point. The intra-individual variation within each subject across three days was lower than the inter-individual differences among the three subjects for over 50% of metabolites in the triacylglycerol (TG), FA, and phosphatidylcholine (PC) lipid classes at 6 h, and for 25-50% of metabolites across lipid classes at 0, 1, 3, and 8 h. The consistency of response within individuals was visualized by principal component analysis (PCA) and confirmed by ANOVA. Three representative metabolites that discriminated among the three individuals in the apolipoprotein B (ApoB) fraction, TG16:1n7, TG18:2n6, and PC18:3n3, are discussed in detail. The postprandial responses of individuals were unique within metabolites that were individual discriminators (ID) of metabolic phenotype. This study shows that the targeted metabolomic measurement of individual metabolic phenotype in response to a specially formulated lipid challenge is possible even without lead-in periods, dietary and lifestyle control, or intervention over a 3-month period in healthy free-living individuals.

Fig. 1

Score plot of principal component analysis (PCA) of plasma lipid metabolome. PC1, principal component 1; PC2, principal component 2. Each point/triangle represents a specific time point (0, 1, 3, 6, and 8 h) on a specific day (1, 2, 3) for each subject (A, B, C) such that A2.0 is the 0 h time point on day 2 for subject A, B3.6 is the 6 h time point on day 3 for subject B, C2.1 is the 1 h time point on day 2 for subject C, and so on

Fig. 2

Percentage of individual discriminators (ID) in each lipid class. The percentage of ID (metabolites for which the intra-individual variation across three days was lower than the inter-individual variation among subjects as assessed by one-way ANOVA) is shown for each lipid class in descending order for each time point. TG triacylglycerol, CE cholesterol ester, DG diacylglycerol, FA free fatty acids, LY lysophosphatidylcholine, PC phosphatidylcholine, PE phosphatidylethanolamine

Fig. 3

Postprandial response curves of 18:2n6 in TG lipid class. The concentrations of 18:2n6TG at each time point are shown in nmol FA/g. The lines connecting the time point measurements represent the 3 different days on which measurements were made

Fig. 4

Postprandial variation in 16:1n7 in TG lipid class. The concentrations of 16:1n7TG at each time point are shown in nmol FA/g. The lines connecting the time point measurements represent the three different days on which measurements were made

Fig. 5

Postprandial variation in 18:3n3 in phosphatidylcholine (PC) lipid class. The concentrations of 18:3n3PC at each time point are shown in nmol FA/g. The lines connecting the time point measurements represent the three different days on which measurements were made