Abstract

Poor dietary choices are major risk factors for obesity and non-communicable diseases, which places an increasing burden on healthcare systems worldwide. To monitor the effectiveness of healthy eating guidelines and strategies, there is a need for objective measures of dietary intake in community settings. Metabolites derived from specific foods present in urine samples can provide objective biomarkers of food intake (BFIs). Whilst the majority of biomarker discovery/validation studies have investigated potential biomarkers for single foods only, this study considered the whole diet by using menus that delivered a wide range of foods in meals that emulated conventional UK eating patterns. Fifty-one healthy participants (range 19–77 years; 57% female) followed a uniquely designed, randomized controlled dietary intervention, and provided spot urine samples suitable for discovery of BFIs within a real-world context. Free-living participants prepared and consumed all foods and drinks in their own homes and were asked to follow the protocols for meal consumption and home urine sample collection. This study also assessed the robustness, and impact on data quality, of a minimally invasive urine collection protocol. Overall the study design was well-accepted by participants and concluded successfully without any drop outs. Compliance for urine collection, adherence to menu plans, and observance of recommended meal timings, was shown to be very high. Metabolome analysis using mass spectrometry coupled with data mining demonstrated that the study protocol was well-suited for BFI discovery and validation. Novel, putative biomarkers for an extended range of foods were identified including legumes, curry, strongly-heated products, and artificially sweetened, low calorie beverages. In conclusion, aspects of this study design would help to overcome several current challenges in the development of BFI technology. One specific attribute was the examination of BFI generalizability across related food groups and across different preparations and cooking methods of foods. Furthermore, the collection of urine samples at multiple time points helped to determine which spot sample was optimal for identification and validation of BFIs in free-living individuals. A further valuable design feature centered on the comprehensiveness of the menu design which allowed the testing of biomarker specificity within a biobank of urine samples.

Highlights

  • The amount and pattern of foods and beverages consumed influence gene expression [1] and are major determinants of multiple health outcomes [2]

  • We have shown recently that the metabolome of spot urine samples collected, stored and transported as described in this manuscript is stable with negligible microbial growth at 4◦C and, that inclusion of preservatives has no impact on data quality [36]

  • The majority (85%) of those who expressed an interest in Study 2 and were screened for eligibility (69%) had learned about it through local community and internal university advertising and contributed 66% of enrolled participants

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Summary

Introduction

The amount and pattern of foods and beverages consumed influence gene expression [1] and are major determinants of multiple health outcomes [2]. Despite this centrality in the atiology of health and disease, the estimation of habitual dietary intake remains difficult [3]. Conventional tools based on dietary self-report are tedious and time-consuming for both study participants and researchers. Whilst the development of digital tools to assist with dietary recording may reduce the workload for respondents and researchers [6], use of such tools does not eliminate the subjectivity and biases inherent in approaches based on self-report. To improve measurements of dietary intake, there is a need to develop strategies for the objective identification, validation and deployment of suitable biomarkers [7]

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