Abstract
Using dietary biomarkers in nutritional epidemiological studies may better capture exposure and improve the level at which diet-disease associations can be established and explored. Here, we aimed to identify and evaluate reproducibility of novel biomarkers of reported habitual food intake using targeted and non-targeted metabolomic blood profiling in a large twin cohort. Reported intakes of 71 food groups, determined by FFQ, were assessed against 601 fasting blood metabolites in over 3500 adult female twins from the TwinsUK cohort. For each metabolite, linear regression analysis was undertaken in the discovery group (excluding MZ twin pairs discordant [≥1 SD apart] for food group intake) with each food group as a predictor adjusting for age, batch effects, BMI, family relatedness and multiple testing (1.17x10-6 = 0.05/[71 food groups x 601 detected metabolites]). Significant results were then replicated (non-targeted: P<0.05; targeted: same direction) in the MZ discordant twin group and results from both analyses meta-analyzed. We identified and replicated 180 significant associations with 39 food groups (P<1.17x10-6), overall consisting of 106 different metabolites (74 known and 32 unknown), including 73 novel associations. In particular we identified trans-4-hydroxyproline as a potential marker of red meat intake (0.075[0.009]; P = 1.08x10-17), ergothioneine as a marker of mushroom consumption (0.181[0.019]; P = 5.93x10-22), and three potential markers of fruit consumption (top association: apple and pears): including metabolites derived from gut bacterial transformation of phenolic compounds, 3-phenylpropionate (0.024[0.004]; P = 1.24x10-8) and indolepropionate (0.026[0.004]; P = 2.39x10-9), and threitol (0.033[0.003]; P = 1.69x10-21). With the largest nutritional metabolomics dataset to date, we have identified 73 novel candidate biomarkers of food intake for potential use in nutritional epidemiological studies. We compiled our findings into the DietMetab database (http://www.twinsuk.ac.uk/dietmetab-data/), an online tool to investigate our top associations.
Highlights
Measurement of dietary intakes in epidemiological settings has traditionally relied on subjective assessment of food intake, which may have resulted in inconsistencies in analyses of associations between specific foods or nutrients and disease endpoints
Recent metabolomics studies have successfully used non-targeted approaches to identify dietary biomarkers in blood in US cohorts, including subjects from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial where 39 potential dietary biomarkers for multiple food groups were identified [1], and subjects from the African Americans in the Atherosclerosis Risk in Communities Study where 39 metabolites were associated with alcohol intake [2] and 48 metabolites to food intakes [2]
73 of our known blood metabolite-diet associations have never been identified in large nutritional metabolomics studies before
Summary
Measurement of dietary intakes in epidemiological settings has traditionally relied on subjective assessment of food intake, which may have resulted in inconsistencies in analyses of associations between specific foods or nutrients and disease endpoints. These methods allow us to rank order intakes in large population groups and make comparisons between extreme intake levels, more objective measures, capturing absorption and metabolism in vivo are required to further understand the impact of dietary intake and its subsequent metabolism on health. Using ours (TwinsUK) and the Cooperative health research in the Region of Augsburg (KORA) datasets [10,11], over 400 blood metabolites were associated with 145 metabolic loci, extending the number of potential loci where metabolism, diet and genetics may interact
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