A Scoping Review of Epigenetic Signatures of Diet and Diet-related Metabolites: Insights from Epigenome-Wide Association Studies and Their Implications for Cardiometabolic Health and Diseases

Abstract

Epigenome-wide association studies (EWASs) have emerged as a powerful approach to investigate how dietary exposures shape the epigenome and subsequently influence metabolic and cardiovascular health. A growing number of EWAS have examined the effects of various dietary factors, including overall dietary patterns, specific food groups, micronutrients, and food-related metabolites, on DNA methylation (DNAm) across diverse populations. In this review, we map the landscape of nutritional EWAS, identifying the types of dietary exposures studied, the genomic regions where epigenetic signals emerge, and overarching trends across studies. Across studies, consistent associations were reported at nine CpG sites in genes such as AHRR, CPT1A, and FADS2, particularly in relation to fatty acid consumption, and certain diet patterns. Biological pathways enriched included fatty acid metabolism and the PPAR signaling pathway. In conclusion, our review identified a pattern of epigenetic convergence that may underlie diet-related disease risk. While promising, key knowledge gaps were also noted, including limited longitudinal follow-up, unclear causal pathways, and underrepresentation of ethnic diversity. Moving forward, we highlighted several complementary approaches for translating nutritional EWAS findings into actionable public health and precision nutrition strategies, including integrating multi-omics, mediation analyses, and population-wide epigenetic risk profiling.

Keywords: Cardiometabolic Disease; Chronic Disease; DNA Methylation; Diet; Dietary Patterns; Epigenetics; Epigenome-Wide Association Study; Metabolomics; Nutritional Biomarkers; Precision Nutrition.

Figure 1

Conceptual framework linking dietary patterns, dietary metabolites, and DNA methylation to chronic disease outcomes. This diagram illustrates the hypothesized relationships between subjective measures of diet (dietary patterns) and more objective biomarkers of intake and exposure (dietary metabolites and DNA methylation) in relation to health consequences such as coronary artery disease (CAD), type 2 diabetes (T2D), aging, and other chronic conditions. We hypothesize that dietary patterns may show weaker and less robust associations with outcomes due to recall bias and imprecision. In contrast, dietary metabolites, as more objective indicators of recent intake, may be more strongly and immediately associated with health outcomes. Finally, DNA methylation captures the long-term biological responses to exposure, potentially reflecting the cumulative effects of diet and other environmental factors. Arrows indicate hypothesized directions of influence, with DNA methylation and dietary metabolites acting as intermediates or biomarkers linking diet to disease.

Figure 2

Common CpG Sites Associated with Multiple Dietary Exposures. This figure summarizes the significant CpG–diet associations identified in studies from Table 1. Each row corresponds to a specific dietary exposure (eg, AHEI, cream, butter), while blue dots mark individual CpG sites that show evidence of association with that exposure. Only CpGs that are associated with more than one exposure are shown to highlight shared epigenetic signals. To the right of each row, a horizontal bar represents the total number of shared CpG associations for that exposure. The bars help summarize the relative density of shared epigenetic signals across different exposures.

Figure 3

KEGG pathway enrichment analysis for genes identified from nutritional EWASs. Dot plots show the top enriched KEGG pathways based on gene ratio identified in (A) both dietary and metabolite EWASs, or Table 2 and (B) metabolite EWASs, or Table S2. The x-axis represents the Gene Ratio (the proportion of input genes associated with each pathway). Dot color indicates the adjusted p-value (p.adjust), with red denoting higher significance. Dot size reflects the number of genes mapped to each pathway. Pathways such as Fatty acid metabolism and PPAR signaling pathway were enriched in both groups, whereas unique enrichment patterns (eg, Insulin resistance) highlight additional biological processes linked to epigenetic signals of metabolites.