Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics?

Gaëlle Favé, Manfred Beckmann, John Draper, John C. Mathers

Research output: Contribution to journalArticlepeer-review

71 Citations (SciVal)


The diet is an important environmental exposure, and its measurement is an essential component of much health-related research. However, conventional tools for measuring dietary exposure have significant limitations being subject to an unknown degree of misreporting and dependent upon food composition tables to allow estimation of intakes of energy, nutrients and non-nutrient food constituents. In addition, such tools may be inappropriate for use with certain groups of people. As an alternative approach, the recent techniques of metabolite profiling or fingerprinting, which allows simultaneous monitoring of multiple and dynamic components of biological fluids, may provide metabolic signals indicative of food intake. Samples can be analysed through numerous analytical platforms, followed by multivariate data analysis. In humans, metabolomics has been applied successfully in pharmacology, toxicology and medical screening, but nutritional metabolomics is still in its infancy. Biomarkers of a small number of specific foods and nutrients have been developed successfully but less targeted and more high-throughput methods, that do not need prior knowledge of which signals might be discriminatory, and which may allow a more global characterisation of dietary intake, remain to be tested. A proof a principle project (the MEDE Study) is currently underway in our laboratories to test the hypothesis that high-throughput, non-targeted metabolite fingerprinting using flow injection electrospray mass spectrometry can be applied to human biofluids (blood and urine) to characterise dietary exposure in humans.
Original languageEnglish
Pages (from-to)135-141
Number of pages7
JournalGenes and Nutrition
Issue number2
Publication statusPublished - 01 Jun 2009


  • Dietary exposure
  • Human studies
  • Mass spectrometry
  • Metabolite fingerprinting
  • Nutritional metabolomics
  • Multivariate supervised classifications


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