Identification of 102 Correlations between Serum Metabolites and Habitual Diet in a Metabolomics Study of the Prostate, Lung, Colorectal, and Ovarian Cancer Trial. Issue 4 (18th December 2019)

• Record Type: Journal Article
• Authors: Mazzilli, Kaitlyn M; McClain, Kathleen M; Lipworth, Loren; Playdon, Mary C; Sampson, Joshua N; Clish, Clary B; Gerszten, Robert E; Freedman, Neal D; Moore, Steven C
• Is Part Of: Journal of nutrition. Volume 150:Issue 4(2020)
• Page Start: 694
• Page End: 703
• Publication Date: 2019-12-18
• Subjects: metabolites -- dietary questionnaire -- biomarkers -- food -- metabolomics; Nutrition -- Periodicals; Diet -- Periodicals; 613.205
• DOI: 10.1093/jn/nxz300
• Languages: English
• ISSNs: 0022-3166

Abstract

ABSTRACT: Background: Metabolomics has proven useful for detecting objective biomarkers of diet that may help to improve dietary measurement. Studies to date, however, have focused on a relatively narrow set of lipid classes. Objective: The aim of this study was to uncover candidate dietary biomarkers by identifying serum metabolites correlated with self-reported diet, particularly metabolites in underinvestigated lipid classes, e.g. triglycerides and plasmalogens. Methods: We assessed dietary questionnaire data and serum metabolite correlations from 491 male and female participants aged 55–75 y in an exploratory cross-sectional study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Self-reported intake was categorized into 50 foods, food groups, beverages, and supplements. We examined 522 identified metabolites using 2 metabolomics platforms (Broad Institute and Massachusetts General Hospital). Correlations were identified using partial Pearson's correlations adjusted for age, sex, BMI, smoking status, study site, and total energy intake [Bonferroni-corrected level of 0.05/(50 × 522) = 1.9 × 10 −6 ]. We assessed prediction of dietary intake by multiple-metabolite linear models with the use of 10-fold crossvalidation least absolute shrinkage and selection operator (LASSO) regression. Results: Eighteen foods, beverages, and supplements were correlated with ≥1 serum metabolite at the Bonferroni-corrected significance threshold, for a total of 102 correlations. Of these, only 5 have been reported previously, to our knowledge. Our strongest correlations were between citrus and proline betaine ( r = 0.55), supplements and pantothenic acid ( r = 0.46), and fish and C40:9 phosphatidylcholine (PC) ( r = 0.35). The multivariate analysis similarly found reasonably large correlations between metabolite profiles and citrus ( r = 0.59), supplements ( r = 0.57), and fish ( r = 0.44). Conclusions: Our study of PLCO participants identified many novel food-metabolite associations and replicated 5 previous associations. These candidate biomarkers of diet may help to complement measures of self-reported diet in nutritional epidemiology studies, though further validation work is still needed.