Metabolomic markers of healthy dietary patterns in US postmenopausal women

Abstract

Background Healthy diet patterns are associated with lower risk of cancer and other chronic diseases. Metabolomics has the potential to expand dietary biomarker development to include dietary patterns, which may provide a complement or alternative to self-reported diet. Objective This study examined the correlation of serum untargeted metabolomic markers with 4 diet pattern scores—the alternate Mediterranean diet score (aMED), alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Healthy Eating Index (HEI)-2015—and used multivariate methods to identify discriminatory metabolites for each pattern. Methods Among 1367 US postmenopausal women with serum metabolomic data in the Cancer Prevention Study-II Nutrition Cohort, we conducted partial correlation analysis, adjusted for demographic and lifestyle variables, to examine cross-sectional correlations between serum metabolomic markers and healthy diet pattern scores. In a randomly selected “training” set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discriminated the top from bottom diet score quintiles. Combinations of metabolites with a variable importance in projection (VIP) score ≥2.5 were tested for predictability in the “testing” set based on the use of receiver operating characteristic curves. Results Out of 1186 metabolites, 32 unique metabolites were considered discriminatory based on a VIP score ≥2.5 in the training dataset with some overlap across scores (aMED = 16; AHEI = 17; DASH = 13; HEI = 12). Spearman partial correlation analyses, applying a cut-point (|r| ≥ 0.15) and Bonferroni correction (P < 1.05 × 10−5), identified similar key metabolites. The top 5 metabolites for each pattern mostly distinguished high compared with low scores; 4 of the 5 (fish-derived) metabolites were the same for aMED and AHEI, 2 of which were identified for HEI; 4 DASH metabolites were unique. Conclusions Metabolomic methods that used a split-sample approach identified potential biomarkers for 4 healthy diet patterns. Similar metabolites across scores reflect fish consumption in healthy dietary patterns. These findings should be replicated in independent populations.

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study

“…Similar to our investigation, two prior studies comparing hypothesis-driven dietary patterns reported just a modest number of unique dietary pattern and metabolite associations [49,50]. A higher degree of similarities across metabolomic signatures of healthy dietary patterns might indicate a generalized metabolic response of an overall healthy dietary pattern.…”

“…A higher degree of similarities across metabolomic signatures of healthy dietary patterns might indicate a generalized metabolic response of an overall healthy dietary pattern. Prior studies have reported that a large proportion of the metabolites associated with dietary patterns are lipids [49][50][51]. Lipids represented 63% of the metabolites associated with all three dietary patterns.…”

Identification and Reproducibility of Plasma Metabolomic Biomarkers of Habitual Food Intake in a US Diet Validation Study

“…It holds a great promise in nutritional epidemiology as an increasing number of food biomarkers have been identified and could be used to facilitate diet assessment in future research [ 1 ]. Several large metabolomics analyses conducted in cohort studies with biospecimens have identified biomarkers of habitual food intakes [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ] or dietary patterns [ 15 , 16 ]. In our previous metabolomics analysis of 91 food groups and 1186 serum metabolites among 1369 nonsmoking postmenopausal women in the Cancer Prevention Study II (CPS-II) Nutrition Cohort, we identified 379 diet–metabolite associations with 199 metabolites as putative food biomarkers of 42 food groups/items (one metabolite could be biomarker of multiple food groups/items) [ 8 ].…”

Differential Effects of Typical Korean Versus American-Style Diets on Gut Microbial Composition and Metabolic Profile in Healthy Overweight Koreans: A Randomized Crossover Trial

“…Multi-omics technologies that include genomics, transcriptomics, proteomics, and metabolomics are leading us toward a new approach for nutrition research [22]. In particular, metabolomics reflects the most current biological status of an individual by comprehensively analyzing and quantifying metabolites in samples, such as biofluids and feces [23,24]. This information could help clarify the molecular mechanisms of the effects of diet on health and disease as the closest link to phenotype [25,26].…”