Abstract
Improvement of diet at the population level is a cornerstone of national and international strategies for reducing chronic disease burden. A critical challenge in generating robust data on habitual dietary intake is accurate exposure assessment. Self-reporting instruments (e.g., food frequency questionnaires, dietary recall) are subject to reporting bias and serving size perceptions, while weighed dietary assessments are unfeasible in large-scale studies. However, secondary metabolites derived from individual foods/food groups and present in urine provide an opportunity to develop potential biomarkers of food intake (BFIs). Habitual dietary intake assessment in population surveys using biomarkers presents several challenges, including the need to develop affordable biofluid collection methods, acceptable to participants that allow collection of informative samples. Monitoring diet comprehensively using biomarkers requires analytical methods to quantify the structurally diverse mixture of target biomarkers, at a range of concentrations within urine. The present article provides a perspective on the challenges associated with the development of urine biomarker technology for monitoring diet exposure in free-living individuals with a view to its future deployment in “real world” situations. An observational study (n = 95), as part of a national survey on eating habits, provided an opportunity to explore biomarker measurement in a free-living population. In a second food intervention study (n = 15), individuals consumed a wide range of foods as a series of menus designed specifically to achieve exposure reflecting a diversity of foods commonly consumed in the UK, emulating normal eating patterns. First Morning Void urines were shown to be suitable samples for biomarker measurement. Triple quadrupole mass spectrometry, coupled with liquid chromatography, was used to assess simultaneously the behavior of a panel of 54 potential BFIs. This panel of chemically diverse biomarkers, reporting intake of a wide range of commonly-consumed foods, can be extended successfully as new biomarker leads are discovered. Towards validation, we demonstrate excellent discrimination of eating patterns and quantitative relationships between biomarker concentrations in urine and the intake of several foods. In conclusion, we believe that the integration of information from BFI technology and dietary self-reporting tools will expedite research on the complex interactions between dietary choices and health.
Highlights
There is a rich history of nutrition research spanning many decades, much of which has had at its core a need for accurate information on dietary intake for investigation of the links between exposure to individual food/food groups and specific health outcomes
A major component of our strategy to develop urine biomarker technology to monitor habitual diet was the need for a biobank of urine samples from a food intervention trial that was designed to provide comprehensive exposure to foods commonly consumed in the UK
Key food groups were identified initially from The Eatwell Guide [77]; the most commonly eaten foods were identified within each disaggregated food group using estimates of intakes of foods by the UK population from the UK National Diet and Nutrition Survey (NDNS) [21]
Summary
There is a rich history of nutrition research spanning many decades, much of which has had at its core a need for accurate information on dietary intake for investigation of the links between exposure to individual food/food groups and specific health outcomes. Large-scale nutritional epidemiological projects and nutrition surveys involving freeliving individuals consuming their habitual diet rely almost totally on self-reporting of dietary exposure. Long-established tools to collect self-reported quantitative dietary information include Food Frequency Questionnaires (FFQs), diet diaries, and dietary recall methodology [3]. For a panel of dietary biomarkers to have any significant utility, it is essential that its coverage is as comprehensive as Abbreviations: AUC, area under the ROC (Receiver Operator Characteristic) curve; BFI, biomarker of food intake; CRN, Clinical Research Network; FFQ, Food Frequency Questionnaire; FMV, First Morning Void; HESI, heated electrospray ionisation; HILIC, Hydrophilic Interaction Liquid Chromatography; HPLC, high-performance liquid chromatography; IAN-AF, Portuguese National Food, Nutrition and Physical Activity Survey; ISRCTN, International Standard Randomised Controlled Trials Number; LC-QQQ-MS, liquid chromatography triple quadrupole mass spectrometry; LoD, limit of detection; logP, partition coefficients; LoQ, limit of quantification; MACCS, Molecular ACCess System; MAIN, Metabolomics at Aberystwyth, Imperial and Newcastle; MDS, multidimensional scaling; MRC, Medical Research Council; MRM, multiple reaction monitoring; MS, Mass Spectrometry; NDNS, National Diet and Nutrition Survey; PCA, Principal Components Analysis; QC, Quality Control; RF, Random Forest; RI, refractive index; ROC, Receiver Operator Characteristic; RP, reverse phase; RSD, relative standard deviation; SG, specific gravity; SRM, Selected Reaction Monitoring; UHPLC, Ultra High Performance Liquid Chromatography
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