Addressing the pitfalls when designing intervention studies to discover and validate biomarkers or habitual dietary intake

Authors Organisations
Type Article
Original languageEnglish
Article number72
JournalMetabolomics
Volume15
Issue number5
Early online date02 May 2019
DOI
Publication statusPublished - 02 May 2019
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Abstract

Introduction
Dietary exposure monitoring within populations is reliant on self-reported measures such as Food Frequency Questionnaires and diet diaries. These methods often contain inaccurate information due to participant misreporting, non-compliance and bias. Urinary metabolites derived from individual foods could provide additional objective indicators of dietary exposure. For biomarker approaches to have utility it is essential that they cover a wide-range of commonly consumed foods and the methodology works in a real-world environment.

Objectives
To test that the methodology works in a real-world environment and to consider the impact of the major sources of likely variance; particularly complex meals, different food formulations, processing and cooking methods, as well as the dynamics of biomarker duration in the body.

Methods
We designed and tested a dietary exposure biomarker discovery and validation strategy based on a food intervention study involving free-living individuals preparing meals and collecting urine samples at home. Two experimental periods were built around three consecutive day menu plans where all foods and drinks were provided (n = 15 and n = 36).

Results
The experimental design was validated by confirming known consumption biomarkers in urinary samples after the first menu plan. We tested biomarker performance with different food formulations and processing methods involving meat, wholegrain, fruits and vegetables.

Conclusion
It was demonstrated that spot urine samples, together with robust dietary biomarkers, despite major sources of variance, could be used successfully for dietary exposure monitoring in large epidemiological studies.

Keywords

  • dietary biomarkers, free-living population, healthy eating policies, high resolution metabolomics