Abstract
Previous cross-sectional metabolomics studies have identified many potential dietary biomarkers, mostly in blood. Few studies examined urine samples although urine is preferred for dietary biomarker discovery. Furthermore, little is known regarding the reproducibility of urinary metabolomic biomarkers over time. We aimed to identify urinary metabolomic biomarkers of diet and assess their reproducibility over time. We conducted a metabolomics analysis among 648 racially/ethnically diverse men and women in the Diet Assessment Sub-study of the Cancer Prevention Study-3 cohort to examine the correlation between >100 food groups/items [101 by a food frequency questionnaire (FFQ), and 105 by repeated 24 h diet recalls (24HRs)] and 1391 metabolites measured in 24 h urine sample replicates, six months apart. Diet–metabolite associations were examined by Pearson’s partial correlation analysis. Biomarkers were evaluated for prediction accuracy assessed using area under the curve (AUC) calculated from the receiver operating characteristic curve and for reproducibility assessed using intraclass correlation coefficients (ICCs). A total of 1708 diet–metabolite associations were identified after Bonferroni correction for multiple comparisons and restricting correlation coefficients to >0.2 or <−0.2 (1570 associations using the FFQ and 933 using 24HRs), 513 unique metabolites correlated with 79 food groups/items. The median ICCs of the 513 putative biomarkers was 0.53 (interquartile range 0.42–0.62). In this study, with comprehensive dietary data and repeated 24 h urinary metabolic profiles, we identified a large number of diet–metabolite correlations and replicated many found in previous studies. Our findings revealed the promise of urine samples for dietary biomarker discovery in a large cohort study and provide important information on biomarker reproducibility, which could facilitate their utilization in future clinical and epidemiological studies.
Highlights
Nutritional epidemiological studies have significantly advanced understanding of the relationships between diet and chronic diseases and have led to dietary guidelines for disease prevention in recent decades [1,2,3]
Most studies rely on self-reported dietary data, such as those collected from food frequency questionnaires (FFQs), which involve systematic and random measurement errors that could result in underestimated risk estimates [4]
We extended our previous research to urine by utilizing the resources from the CPS-3 Diet Assessment Substudy (DAS) including the post-study FFQ, repeated 24 h diet recalls (24HRs) and two 24 h urine samples collected six months apart
Summary
Nutritional epidemiological studies have significantly advanced understanding of the relationships between diet and chronic diseases and have led to dietary guidelines for disease prevention in recent decades [1,2,3]. Robust and reliable objective dietary biomarkers are important to estimate dietary intake or calibrate self-reported dietary data, holding promise to advancing research on diet and cancer and other health outcomes; such dietary biomarkers are limited to a few nutrients and do not exist for most foods and dietary patterns. Several large metabolomics analyses conducted in cohort studies employing a cross-sectional study design have identified hundreds of potential biomarkers of habitual food intakes [6,7,8,9,10,11,12,13] or dietary patterns [14,15]. Our previous metabolomics analyses of blood samples from the Cancer Prevention Study-II (CPS-II) Nutrition Cohort [6] and CPS-3 [13] have identified more than 200 putative food-related metabolic markers, many of which replicated findings from other population and feeding studies. Little is known about urinary metabolomic biomarker reproducibility over time [18]
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