Challenges Associated With the Design and Deployment of Food Intake Urine Biomarker Technology for Assessment of Habitual Diet in Free-Living Individuals and Populations:A Perspective

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. (c) Copyright (c) 2020 Beckmann, Wilson, Lloyd, Torres, Goios, Willis, Lyons, Phillips, Mathers and Draper

Validation of a new software eAT24 used to assess dietary intake in the adult Portuguese population

Objective: The aim of the current study was to evaluate the accuracy of the new software eAT24 used to assess dietary intake in the National Food, Nutrition and Physical Activity Survey (IAN-AF) against urinary biomarkers: N (nitrogen), K (potassium) and Na (sodium). Design: We conducted a cross-sectional study. Two non-consecutive 24-h dietary recalls (24-HDR) were applied, and a 24-h urine sample was collected. We examined differences between estimates from dietary and urine measures, Pearson correlation coefficients were calculated and the Bland-Altman plots were drawn. Multiple linear regression was used to evaluate the factors associated with the difference between estimates. Setting: Sub-sample from the Portuguese IAN-AF sampling frame. Participants: Ninety-five adults (men and women) aged 18-84 years. Results: The estimated intake calculated using the dietary recall data was lower than that estimated from urinary excretion for the three biomarkers studied (protein 94·3 v. 100·4 g/d, K 3212 v. 3416 mg/d and Na 3489 v. 4003 mg/d). Considering 2 d of recall, the deattenuated correlation coefficients were 0·33, 0·64 and 0·26 for protein, K and Na, respectively. For protein, differences between dietary and urinary estimates varied according to BMI (β = -1·96, P = 0·017). The energy intake and 24-h urine volume were significantly associated with the difference between estimates for protein (β = 0·03, P < 0·001 and β = -0·02, P = 0·002, respectively), K (β = 0·71, P < 0·001 and β = -0·42, P = 0·040, respectively) and Na (β = 1·55, P < 0·001 and β = -0·81, P = 0·011, respectively). Conclusions: The new software eAT24 performed well in estimating protein and K intakes, but lesser so in estimating Na intake, using two non-consecutive 24-HDR.The IAN-AF 2015–2016 was developed by a consortium: Carla Lopes, Andreia Oliveira, Milton Severo, Faculty of Medicine, University of Porto; Duarte Torres, Sara Rodrigues, Faculty of Nutrition and Food Sciences, University of Porto; Elisabete Ramos, Sofia Vilela, EPIUnit, Institute of Public Health, University of Porto; Sofia Guiomar, Luísa Oliveira, National Health Institute Doutor Ricardo Jorge; Violeta Alarcão, Paulo Nicola, Institute of Preventive Medicine and Public Health, Faculty of Medicine, University of Lisbon; Jorge Mota, CIAFEL, Faculty of Sports, University of Porto; Pedro Teixeira, Faculty of Human Kinetics, CIPER, University of Lisbon; Simão Soares, SilicoLife, Lda, Portugal; Lene Frost Andersen, Faculty of Medicine, University of Oslo. The current study had institutional support from the General Directorate of Health, the Regional Health Administration Departments, the Central Administration of the Health System and from the European Food Safety Authority (CFT/EFSA/DCM/2012/01-C03). The researchers acknowledge all these institutions and persons involved in all phases of the survey, as well as participants. Financial support: The current study has received funding from the EEA Grants Program, Public Health Initiatives (PT06 – 000088SI3). The EEA Grant Program had no role in the design, analysis or writing of the current article