McCullough, M. L. et al. Metabolomic markers of healthy dietary patterns in US postmenopausal women. The American Journal of Clinical Nutrition 109, 1439-1451 (2019).

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.

Keywords: biomarkers; diet; diet patterns; diet scores; metabolomics; postmenopausal women; prospective cohort.

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