ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Endocrine Abstracts (2017) 50 MTE2 | DOI: 10.1530/endoabs.50.MTE2

Modernazing dietary assessment by use of metabolic profiling

Isabel Garcia-Perez, Joram M Posma, Rachel Gibson, Edward S Chambers, Elaine Holmes & Gary Frost

Imperial College London, London, UK.

A major limitation of nutritional science is the objective assessment of dietary intake in free-living populations. Monitoring individuals’ response to policy recommendations is based on self-reported dietary assessment tools, which are known to have high misreporting rates estimated at 30–88%. We have developed a novel analytical pipeline capable to classify people into consumers of a healthy or unhealthy diet based on urinary metabolic patterns, without relying on recorded food intake.

Here we aim to apply this methodology based on metabolic profiling to objectively monitor adherence to diet guidelines for free living people over time.

Methods: We conducted a randomised controlled clinical trial. 19 volunteers attended to a clinical research unit to follow four-dietary intervention representing 25, 50, 75 and 100% of adherence to WHO-healthy eating recommendations to increase fruits, vegetables, carbohydrates, dietary fibre and to decrease total fats, sugars, and salt, etc. A cohort of 20 volunteers collected spot urine samples once a week for 6 months and a matching 24-h food diaries for each day of the sample collection. Metabolic profiles were measured by 1H-NMR spectroscopy.

Results: Analysis of 1H-NMR spectroscopy data indicated significant differences in the urinary metabolic profiles of the four diets. These were used to predict the healthiness of the dietary habits of free-living people and tracking adherence to healthy eating recommendations over time.

Conclusions: This study demonstrates that a urinary metabolic profile developed in a highly controlled environment, independent of recorded food intake, can classify people into consumers of a healthy or unhealthy diet based on urinary metabolic patterns. This can be used for the objective monitoring of adherence over time to healthful diets in a population setting.

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