University of Hertfordshire

From the same journal

By the same authors

  • Clare B O'Donovan
  • Marianne C Walsh
  • Clara Woolhead
  • Hannah Forster
  • Carlos Celis-Morales
  • Anna L Macready
  • Cyril F M Marsaux
  • Santiago Navas-Carretero
  • S Rodrigo San-Cristobal
  • Silvia Kolossa
  • Lydia Tsirigoti
  • Christina Mvrogianni
  • Christina P Lambrinou
  • George Moschonis
  • Magdalena Godlewska
  • Agnieszka Surwillo
  • Iwona Traczyk
  • Christian A Drevon
  • Hannelore Daniel
  • Yannis Manios
  • J Alfredo Martinez
  • Wim H M Saris
  • Julie A Lovegrove
  • John C Mathers
  • Michael J Gibney
  • Eileen R Gibney
  • Lorraine Brennan
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Original languageEnglish
Number of pages9
Pages (from-to)561-569
JournalBritish Journal of Nutrition
Journal publication date28 Oct 2017
Volume118
Issue8
Early online date23 Oct 2017
DOIs
Publication statusPublished - 28 Oct 2017

Abstract

Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.

Notes

© The Authors 2017

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