Metabolomic markers of healthy dietary patterns in US postmenopausal women. Issue 5 (3rd May 2019)

• Record Type: Journal Article
• Authors: McCullough, Marjorie L; Maliniak, Maret L; Stevens, Victoria L; Carter, Brian D; Hodge, Rebecca A; Wang, Ying
• Is Part Of: American journal of clinical nutrition. Volume 109:Issue 5(2019)
• Page Start: 1439
• Page End: 1451
• Publication Date: 2019-05-03
• Subjects: metabolomics -- diet patterns -- diet scores -- diet -- biomarkers -- prospective cohort -- postmenopausal women; Diet therapy -- Periodicals; Nutrition -- Periodicals; Dietetics -- Periodicals; 613.205
• DOI: 10.1093/ajcn/nqy385
• Languages: English
• ISSNs: 0002-9165

Abstract

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. Objectives: 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, 34 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; 3 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.