Numerous studies have reported that diets rich in phenolic compounds are beneficial to immune-metabolic health, yet these effects are heterogeneous and the underlying mechanisms are poorly understood. To investigate the inter-individual variability of the immune-metabolic response to raspberry consumption, whole-blood RNAseq data from 24 participants receiving 280 g/d of raspberries for 8 weeks were used for the identification of responsiveness subgroups by using partial least squares-discriminant analysis (PLSDA) and hierarchical clustering. Transcriptomic-based clustering regrouped participants into two distinct subgroups of 13 and 11 participants, so-called responders and non-responders, respectively. Following raspberry consumption, a significant decrease in triglycerides, cholesterol and C-reactive protein levels were found in responders, as compared to non-responders. Two major gene expression components of 100 and 220 genes were identified by sparse PLSDA as those better discriminating responders from non-responders, and functional analysis identified pathways related to cytokine production, leukocyte activation and immune response as significantly enriched with most discriminant genes. As compared to non-responders, the plasma lipidomic profile of responders was characterized by a significant decrease in triglycerides and an increase in phosphatidylcholines following raspberry consumption. Prior to the intervention, a distinct metagenomic profile was identified by PLSDA between responsiveness subgroups, and the Firmicutes-to-Bacteroidota ratio was found significantly lower in responders, as compared to non-responders. Findings point to this transcriptomic-based clustering approach as a suitable tool to identify distinct responsiveness subgroups to raspberry consumption. This approach represents a promising framework to tackle the issue of inter-individual variability in the understanding of the impact of foods on immune-metabolic health.
Trial registration: ClinicalTrials.gov NCT03620617.
Keywords: Gene expression; Gut microbiota; Immunity; Multi-omics; Raspberry; Transcriptomic-based clustering.
Copyright © 2022. Published by Elsevier Inc.