Poster Theater Flash Session
The objective of the study is to determine whether features of individuals’ baseline gut microbiota modify the effect of a resistant starch (RS) intervention on post-prandial glycemic response and other metabolic markers that have been linked to gut microbial action (i.e. short-chain fatty acids (SCFA), bile acids, breath hydrogen/methane).
Methods : Metabolic responses and 16S rRNA gene data generated from a double-blind, placebo controlled, crossover clinical trial of RS and regular wheat will be used to investigate whether baseline features of the microbiota are correlated with inter-individual differences in the effect of RS supplementation in individuals. Women and men consumed 3 or 4 rolls per day, respectively, made from RS (14-18g total dietary fiber, TDF) or conventional wheat (4-5.5g TDF) for 7 days during each arm of the trial duration. Linear mixed models of glycemic response and features of the gut microbiota hypothesized to modify the effect of the intervention as well as covariates (e.g. gender, habitual fiber intake) will be used to determine microbiota features associated with improvement in glycemic response as a result of RS supplementation.
Results : A total of 30 healthy adults ages 40-65 will be studied and 25 have completed the study thus far. Of the metabolic variables analyzed to date, a significant amount of inter-individual variability in the magnitude and direction of postprandial glycemia and breath hydrogen responses to RS wheat supplementation. For example, the intra-class correlations (ICC) of post-prandial glucose and insulin area under the curve (AUC) were high, 42.07% and 52.86%, respectively. However, there was a significant overall effect of RS wheat on glycemic response such that postprandial insulin during RS supplementation was lower than during regular wheat consumption (p=0.004).
The results of this study show a high degree of inter-individual variability in metabolic response to fiber (RS) supplementation, suggesting the presence of individual factors that modify the effect of the intervention. Future studies should incorporate this analysis into their statistical plan to validate these findings and contribute to the current literature on personalized nutrition and the gut microbiota. This will enable us to predict and potentially modify metabolic response to dietary components.
Funding Sources : Institute for Innovation and Health (IIFH) and Arcadia Biosciences