Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults

Abstract

Background Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored. Objectives We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs). Methods This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R. Results We identified 5 DPs and 7 BCPs. The “bread, margarine, and processed meat” and “fruiting vegetables and vegetable oils” DPs were positively associated with the “amino acids” (β = 0.35; 95% CI: 0.02, 0.69; P = 0.03) and “fatty acids” MPs (β = 0.45; 95% CI: 0.16, 0.74; P = 0.01), respectively. The “tea and miscellaneous” was inversely associated with the “amino acids” (β = −0.28; 95% CI: −0.52, −0.05; P = 0.02) and “amino acid derivatives” MPs (β = −0.21; 95% CI: −0.39, −0.02; P = 0.03). One BCP negatively modified the association between the “bread, margarine, and processed meat” DP and the “amino acids” MP (P-interaction = 0.01). Conclusions In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP–MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria.

Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake

“…However, the fact that we were able to replicate a considerable number of consistent associations suggest that these factors have minimal impact on the food-metabolite associations that we reported in this study. As shown by others [ 36 ], the complexity of jointly consumed foods and their relationship with correlated metabolites indicates that exploring the relationship of patterns of intake of food groups and metabolite patterns may provide important insights into the relationship between food intake and metabolites. Further, future studies should consider sex-specific differences in metabolism by exploring sex-specific relationships of foods with metabolites.…”

Advances in dietary pattern analysis in nutritional epidemiology

“…Hence, an approach that maps the association of DPs with aggregated correlated metabolites or taxa would potentially yield higher statistical power. The study by Oluwagbemigun and colleagues underscores the importance of this approach where [55][56][57] structured patterns of correlations present in each of the dietary, metabolome, and microbiome compositional domains were identified using TT and possible DP-metabolite patterns and DP-microbiome patterns associations were fitted in linear regression models [50]. This strategy is appealing because it fully explores the complex multivariate structure of the metabolome and microbiome data sets and ensures that grouping of metabolites is beyond chemical similarities or grouping of bacteria is beyond taxonomic relationships.…”

“…Example of these novel methods, which have been applied in our research fields, includes the sparse PLS [91] and the sparse RRR [92]. The foregoing suggests that the additional information offered by the metabolome and the gut microbiome would be more exploited in exploratory DP when compared Mediterranean DP associated with a metabolite score [84] Three DPs related to 26 bacteria [70], Mediterranean diet associated with several bacteria [72], Mediterranean DP and Healthy Food DP related to several taxa [73], Two DPs related to several bacterial taxa [87] Three DPs associated with enterotypes [81], unprocessed foods and processed food groups associated with phylogenetically grouped bacterial taxa [86], Two DPs related to two enterotypes [87] Exploratory DPs Seven PCA-derived DPs related to ratio of 363 metabolites [64], Three Cluster analysis-derived DPs related to plasma fatty acid profiles and metabolomic data [65], Seven PCA-derived DPs related to 163 metabolites [67], sparse PCA-derived meat and vegetable DPs associated with 130 metabolites [81] Five TT-derived DPs related to eight TT-derived metabolite patterns [50], DPs explaining a maximum variation in the concentration of the seven classes of chemically similar metabolites were derived by RRR [66], Two PCAderived DPs correlated with two PCA-derived metabolite patterns [74], RRR-derive DP associated with branched chain amino acids [79], RRR-derive DP associated with 853 metabolites [82] Three cluster analysisderived DPs related to seven bacteria [69], factor analysis-derived DPs associated with several bacteria taxa [85], Three PCA-derived DP associated with 24 bacterial taxa [86] two DPs were related to several bacteria taxa [89] Five TT-derived DPs related to seven TT-derived groups of bacteria [50] to hypothesis-driven DP analysis. Indeed, using a large part of the metabolome and the gut microbiome data and metabotyping [93] and enterotyping [94] populations into generally valid and biologically meaningful homogeneous subgroups would facilitate the integration of the metabolome and the gut microbiome in DP analysis.…”

Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

“…Human serum spectra collected by FTIR spectroscopy showed higher heterogeneity than mouse spectra, which might be in part caused by several confounding factors [ 62 ]. For instance, varying nutrition habits may be reflected in the metabolic patterns in serum, regardless of the immunological state of the individual [ 63 , 64 , 65 ]. Different diets as well as other environmental factors shape the composition of the gut microbiome, which in turn produces a broad variety of metabolites, such as short-chain fatty acids, flavones, indol- and phenyl derivatives, that are detectable in serum [ 43 , 66 , 67 , 68 ].…”