Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake

Towards nutrition with precision: unlocking biomarkers as dietary assessment tools

Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.

Calibration of citrus intake assessed by food frequency questionnaires using urinary proline betaine in an observational study setting

BACKGROUND

Whether observational study can be employed to establish calibration equations for self-reported dietary intake using food biomarkers is unknown.

OBJECTIVES

This study aims to demonstrate the feasibility of obtaining calibration equations based on food biomarkers and 7-d diet records (7DDRs) to correct measurement errors of food frequency questionnaires (FFQs) in an observational study setting.

METHODS

The study population consisted of 669 males and 749 females from the Women's and Men's Lifestyle Validation Studies. In the training set, the biomarker-predicted intake derived by regressing 7DDR-assessed intake on urinary proline betaine concentration was regressed on the FFQ-assessed intake to obtain the calibration equations. The regression coefficients were applied to the test set to calculate the calibrated FFQ intake. We examined total citrus as well as individual citrus fruits/beverages.

RESULTS

Urinary proline betaine was moderately correlated with orange juice intake (Pearson correlation [r] = 0.53 for 7DDR and 0.48 for FFQ) but only weakly correlated with intakes of orange (r = 0.12 for 7DDR and 0.15 for FFQ) and grapefruit (r = 0.14 for 7DDR and 0.09 for FFQ). The FFQ-assessed citrus intake was systematically higher than the 7DDR-assessed intake, and after calibrations, the mean calibrated FFQ measurements were almost identical to 7DDR assessments. In the test set, the mean intake levels from 7DDRs, FFQs, and calibrated FFQs were 62.5, 75.3, and 63.2 g/d for total citrus; 41.6, 42.5, and 41.9 g/d for orange juice; 11.8, 24.3, and 12.3 g/d for oranges; and 8.3, 9.3, and 8.6 g/d for grapefruit, respectively. We observed larger differences between calibrated FFQ and 7DDR assessments at the extreme ends of intake, although, on average, good agreements were observed for all citrus except grapefruit.

CONCLUSIONS

Our 2-step calibration approach has the potential to be adapted to correct systematic measurement error for other foods/nutrients with established food biomarkers in a cost effective way.

Workplace food purchases, dietary intake, and gut microbial metabolites in a secondary analysis of the ChooseWell 365 study

BACKGROUND

Dietary choices can affect human health through alterations in gut microbial metabolism, and gut microbial metabolites could serve as biomarkers for disease risk conferred by dietary intake. However, self-reported dietary intake may not reflect true intake.

OBJECTIVES

We identified circulating metabolites, including gut microbiome-related metabolites, associated with adherence to a healthy diet in the ChooseWell 365 study. In this randomized clinical trial, the dietary choices of hospital employees were assessed over 24 mo using not only 24-h dietary recalls but also electronic records of hospital cafeteria purchases.

METHODS

Plasma metabolites were profiled from 470 participants. Two targeted metabolomics methods were developed and implemented to expand detection coverage for metabolites related to gut microbial activity. Linear regression models were used to associate metabolites with Healthy Purchasing Scores (HPSs) derived from cafeteria purchases and Healthy Eating Index-2015 (HEI-15) scores derived from dietary recalls.

RESULTS

Fourteen metabolites were concordantly associated with the HPS and HEI-15 scores in multivariable models adjusted for age, gender, and race, including the gut microbiome-related metabolites indole-3-propionic acid (HPS, β: 0.16, 95% CI: 0.07, 0.26, P = 7.32 × 10-4; HEI-15, β: 0.16, 95% CI: 0.07, 0.25, P = 6.79 × 10-4), hippuric acid (HPS, β: 0.11, 95% CI: 0.02, 0.21, P = 1.97 × 10-2; HEI-15, β: 0.10, 95% CI: 0.01, 0.19, P = 3.14 × 10-2), and indoxyl sulfate (HPS, β = -0.13, 95% CI: -0.23, -0.03, P = 8.21 × 10-3; HEI-15, β: -0.12, 95% CI: -0.22, -0.03, P = 8.50 × 10-3). These gut microbial metabolites were associated with the intake of specific food groups, such as whole fruits. These metabolites were also associated with clinical variables, including blood pressure, diabetes or prediabetes, and body mass index.

CONCLUSIONS

In a secondary analysis of the ChooseWell 365 study, associations between circulating gut microbiome-related metabolites and a healthy diet were confirmed using both objective and subjective measures of consumption. Accurate identification of diet-associated metabolites may help guide dietary or microbiome-based interventions aimed at disease prevention.