Blueberries are rich in polyphenols, particularly anthocyanins, which have been associated with beneficial cardiometabolic effects. However, the molecular mechanisms underlying their effects during the postprandial response remain challenging and are often investigated within individual biological layers. Integrating multiple omics datasets can reveal coordinated molecular responses and interactions that may otherwise remain undetected 1. This study integrated circulating metabolomic and transcriptomic profiles to identify molecular signatures associated with blueberry co-consumption following a high-fat, high-sugar (HFHS) meal. In a double-blind randomised crossover trial (n=12), healthy males consumed an HFHS meal with or without 30 g freeze-dried blueberry powder (HFHS+B). Plasma and peripheral blood mononuclear cells (PBMCs) were collected under fasting (-2 and 0 h) and every 2 h throughout the 6 h postprandial period for metabolomic and transcriptomic profiling, respectively. The two molecular datasets were integrated using the mixOmics DIABLO framework 2 to identify correlated features and gene-metabolite signatures differentiating the meal conditions. Integration revealed strong coordination between transcriptomic and metabolomic responses, with the first latent component showing the highest cross-omics correlation (r=0.83) and clear separation between HFHS and HFHS+B. The primary discriminating signature included immune- and metabolism-related genes, particularly IL1R1, TLR4, IRS1 and STAT3, alongside metabolites including quinic acid, hippuric acid, catechol, arabinose and acylcarnitines. Hierarchical clustering of the integrated signature resolved two major sample clusters largely corresponding to the HFHS and HFHS+B interventions, indicating that meal discrimination reflected coordinated variation across both molecular layers. Network analysis further revealed extensive gene–metabolite connectivity, with CPT1A, TCL1A and CLEC6A among highly connected transcriptomic features and several metabolites associated with polyphenol metabolism and microbial biotransformation connected to genes involved in immune and metabolic regulation. Collectively, these integrated patterns suggest that blueberry co-consumption was associated with coordinated shifts in immune- and metabolism-related genes and connected polyphenol-associated metabolites, consistent with modulation of immune and metabolic responses elicited by the HFHS challenge. Blueberry co-consumption was associated with coordinated changes across transcriptomic and metabolomic layers, involving interconnected immune, metabolic and polyphenol-related features. These findings provide new insight into how blueberry consumption may modulate the molecular response to an HFHS challenge and highlight the value of multi-omics integration for characterising complex postprandial responses.
Keywords: multi-omics; metabolomics; postprandial response; transcriptomics
1 Nourazarain, A. & Vaziri, Y. Genes Nutr 20, 30 (2025).
2 Singh, A., Shannon, C. P. & al, e. Bioinformatics 35, 3055-3062 (2019).