Responses to High-Fat Challenges Varying in Fat Type in Subjects with Different Metabolic Risk Phenotypes: A Randomized Trial

Abstract

BackgroundThe ability of subjects to respond to nutritional challenges can reflect the flexibility of their biological system. Nutritional challenge tests could be used as an indicator of health status but more knowledge on metabolic and immune responses of different subjects to nutritional challenges is needed. The aim of this study was to compare the responses to high-fat challenges varying in fat type in subjects with different metabolic risk phenotypes.Methodology/Principal FindingsIn a cross-over design 42 men (age 50–70 y) consumed three high-fat shakes containing saturated fat (SFA), monounsaturated fat (MUFA) or n-3 polyunsaturated (PUFA). Men were selected on BMI and health status (lean, obese or obese diabetic) and phenotyped with MRI for adipose tissue distribution. Before and 2 and 4 h after shake consumption blood was drawn for measurement of expression of metabolic and inflammation-related genes in peripheral blood mononuclear cells (PBMCs), plasma triglycerides (TAG), glucose, insulin, cytokines and ex vivo PBMC immune response capacity. The MUFA and n-3 PUFA challenge, compared to the SFA challenge, induced higher changes in expression of inflammation genes MCP1 and IL1β in PBMCs. Obese and obese diabetic subjects had different PBMC gene expression and metabolic responses to high-fat challenges compared to lean subjects. The MUFA challenge induced the most pronounced TAG response, mainly in obese and obese diabetic subjects.Conclusion/SignificanceThe PBMC gene expression response and metabolic response to high-fat challenges were affected by fat type and metabolic risk phenotype. Based on our results we suggest using a MUFA challenge to reveal differences in response capacity of subjects.Trial RegistrationClinicalTrials.gov NCT00977262

Meal rich in carbohydrate, but not protein or fat, reveals adverse immunometabolic responses associated with obesity

“…In fact, MNC transcriptome better reflects such postprandial pro-inflammatory responses compared to plasma cytokines levels [18]. However, the total amount of fat intake and fatty acid composition of the meal are important determinants of postprandial inflammatory response; and inconsistencies in results of previous studies [19, 20] could be attributed to variations in these factors. Van Djik et al administered HF meals with different fatty acid composition and showed that differences in the postprandial MNC immune responses between lean, obese non-diabetic, and obese diabetic subjects were dependent on the fatty acid composition of the meal [19].…”

Peripheral blood mononuclear cells as a source to detect markers of homeostatic alterations caused by the intake of diets with an unbalanced macronutrient composition

“…This down-regulation could be reflecting immune impairment due to the increased fat intake, as has been previously reported [38,39]. The effect on immune system is dependent not only on the content but also on the type of fat [28,38,40,41]. It has been demonstrated that immunosuppressive effects on both innate and adaptive immunity are higher for unsaturated fat, mainly polyunsaturated fatty acids (PUFAs) [41][42][43], while consumption of saturated fat results in a proinflammatory gene expression profile in PBMCs [40].…”

“…In addition, in recent years, their relevance for nutritional studies has been demonstrated [24][25][26][27]. PBMCs are known to reflect responses to nutrients and diets which occur in different tissues with an important role in the control of energy metabolism [24][25][26]28]. Based on this information, we hypothesize that gene expression changes in these cells could reflect metabolic effects (adaptive or harmful) of unbalanced diets.…”

Use of nutrigenomics endpoints in dietary interventions

“…Such an approach may make better use of complex biomarkers. As such, it may be important to take into account various processes that contribute to an organism's ability to adapt (6,7) . Complex biomarkers can be studied in a better manner by nutrigenomics technology.…”