Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations

Abstract

Background: Metabolomics is an emerging field with the potential to advance nutritional epidemiology; however, it has not yet been applied to large cohort studies.

Objectives: Our first aim was to identify metabolites that are biomarkers of usual dietary intake. Second, among serum metabolites correlated with diet, we evaluated metabolite reproducibility and required sample sizes to determine the potential for metabolomics in epidemiologic studies.

Design: Baseline serum from 502 participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was analyzed by using ultra-high-performance liquid-phase chromatography with tandem mass spectrometry and gas chromatography-mass spectrometry. Usual intakes of 36 dietary groups were estimated by using a food-frequency questionnaire. Dietary biomarkers were identified by using partial Pearson's correlations with Bonferroni correction for multiple comparisons. Intraclass correlation coefficients (ICCs) between samples collected 1 y apart in a subset of 30 individuals were calculated to evaluate intraindividual metabolite variability.

Results: We detected 412 known metabolites. Citrus, green vegetables, red meat, shellfish, fish, peanuts, rice, butter, coffee, beer, liquor, total alcohol, and multivitamins were each correlated with at least one metabolite (P < 1.093 × 10(-6); r = -0.312 to 0.398); in total, 39 dietary biomarkers were identified. Some correlations (citrus intake with stachydrine) replicated previous studies; others, such as peanuts and tryptophan betaine, were novel findings. Other strong associations included coffee (with trigonelline-N-methylnicotinate and quinate) and alcohol (with ethyl glucuronide). Intraindividual variability in metabolite levels (1-y ICCs) ranged from 0.27 to 0.89. Large, but attainable, sample sizes are required to detect associations between metabolites and disease in epidemiologic studies, further emphasizing the usefulness of metabolomics in nutritional epidemiology.

Conclusions: We identified dietary biomarkers by using metabolomics in an epidemiologic data set. Given the strength of the associations observed, we expect that some of these metabolites will be validated in future studies and later used as biomarkers in large cohorts to study diet-disease associations. The PLCO trial was registered at clinicaltrials.gov as NCT00002540.

FIGURE 1.

Many more metabolites are statistically associated with food groups by using the less conservative FDR method, as opposed to the Bonferroni method, for multiple testing correction. Only food groups with significant Bonferroni-corrected correlations are included. Each food group shown has at least one significant metabolite at an FDR of 0.01, and many more metabolites are found as the stringency of the FDR threshold is relaxed. FDR, false discovery rate.