Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake

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.

Keywords: biomarkers; dietary assessment methods; dietary intake; metabolites; untargeted metabolomics.

Figure 1

Flow chart describing the selection process in the systematic literature search.

Figure 2

Confirmed food-metabolite associations for the food groups: (a) Coffee, (b) fish, (c) chocolate, and (d) wine. The figure represents the percentage change in metabolites for one gram change of (a) coffee, (b) fish, (c) alcohol, and (d) chocolate, respectively, and the subpathways of the metabolites; the estimates in percentage were generated from linear regression models with natural log-transformed metabolites as the dependent variables and (a) coffee, (b) fish, (c) alcohol, and (d) chocolate as the independent variable. * No pure compound was available, but we are confident in its identity. Abbreviations: AAMU, 5-acetylamino-6-amino-3-methyluracil; CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropionate; FA, fatty acid; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; GPC, glycerophosphochcline; Leu, Ile, and Val, Leucine, isoleucine and valine metabolism; PUFAs (n-3 and n-6), polyunsaturated fatty acid (n-3 and n-6).