Abstract
Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.
Highlights
Dietary intake can be assessed with various methods, all of which have strengths and limitations.Self-reported methods, such as questionnaires, are relatively easy to implement, but reporting bias and measurement error are major challenges [1,2]
Recent advances in omic analytical techniques, metabolomics, have offered a data-driven approach for the development of dietary intake biomarkers that allows the assessment of many potential biomarkers at the same time [4]
The present study aimed to confirm blood metabolite-food associations reported in previous studies and to identify novel metabolomics-based biomarkers of food group intake in a general population sample from Northern Germany
Summary
Dietary intake can be assessed with various methods, all of which have strengths and limitations. Self-reported methods, such as questionnaires, are relatively easy to implement, but reporting bias and measurement error are major challenges [1,2]. Suboptimal estimation of actual dietary intake and inconsistent findings for diet-disease associations might be due to measurement errors [3]. Biomarkers are considered more objective compared to self-report methods. The traditional approach in development of dietary intake biomarkers is to measure one or more hypothesis-driven biomarkers at a time. Recent advances in omic analytical techniques, metabolomics, have offered a data-driven approach for the development of dietary intake biomarkers that allows the assessment of many potential biomarkers at the same time [4]
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