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Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study

  • Clare B. O’Donovan (a1), Marianne C. Walsh (a1), Clara Woolhead (a1), Hannah Forster (a1), Carlos Celis-Morales (a2), Rosalind Fallaize (a3), Anna L. Macready (a3), Cyril F. M. Marsaux (a4), Santiago Navas-Carretero (a5) (a6), S. Rodrigo San-Cristobal (a5), Silvia Kolossa (a7), Lydia Tsirigoti (a8), Christina Mvrogianni (a8), Christina P. Lambrinou (a8), George Moschonis (a8), Magdalena Godlewska (a9), Agnieszka Surwillo (a9), Iwona Traczyk (a10), Christian A. Drevon (a11), Hannelore Daniel (a7), Yannis Manios (a8), J. Alfredo Martinez (a5) (a6) (a12), Wim H. M. Saris (a4), Julie A. Lovegrove (a3), John C. Mathers (a2), Michael J. Gibney (a1), Eileen R. Gibney (a1) and Lorraine Brennan (a1)...
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.

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Corresponding author
* Corresponding author: L. Brennan, email lorraine.brennan@ucd.ie
References
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1. Ronteltap, A, van Trijp, H, Berezowska, A, et al. (2013) Nutrigenomics-based personalised nutritional advice: in search of a business model? Genes Nutr 8, 153163.
2. Gibney, MJ & Walsh, MC (2013) The future direction of personalised nutrition: my diet, my phenotype, my genes. Proc Nutr Soc 72, 219225.
3. Brennan, L (2008) Session 2: Personalised nutrition. Metabolomic applications in nutritional research. Proc Nutr Soc 67, 404408.
4. Kaput, J (2008) Nutrigenomics research for personalized nutrition and medicine. Curr Opin Biotechnol 19, 110120.
5. O’Donovan, CB, Walsh, MC, Gibney, MJ, et al. (2015) Can metabotyping help deliver the promise of personalised nutrition? Proc Nutr Soc 75, 106114.
6. Morris, C, O’Grada, C, Ryan, M, et al. (2013) Identification of differential responses to an oral glucose tolerance test in healthy adults. PLOS ONE 8, e72890.
7. Erro, R, Vitale, C, Amboni, M, et al. (2013) The heterogeneity of early Parkinson’s disease: a cluster analysis on newly diagnosed untreated patients. PLOS ONE 8, e70244.
8. Richette, P, Clerson, P, Perissin, L, et al. (2013) Revisiting comorbidities in gout: a cluster analysis. Ann Rheum Dis 74, 142147.
9. Viniol, A, Jegan, N, Hirsch, O, et al. (2013) Chronic low back pain patient groups in primary care – a cross sectional cluster analysis. BMC Musculoskelet Disord 14, 294.
10. Castaldi, PJ, Dy, J, Ross, J, et al. (2014) Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema. Thorax 69, 415422.
11. Haldar, P, Pavord, ID, Shaw, DE, et al. (2008) Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med 178, 218224.
12. Moore, WC, Meyers, DA, Wenzel, SE, et al. (2010) Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program. Am J Respir Crit Care Med 181, pp. 315323.
13. Newby, C, Heaney, LG, Menzies-Gow, A, et al. (2014) Statistical cluster analysis of the British Thoracic Society Severe refractory Asthma Registry: clinical outcomes and phenotype stability. PLOS ONE 9, e102987.
14. Park, HW, Song, WJ, Kim, SH, et al. (2015) Classification and implementation of asthma phenotypes in elderly patients. Ann Allergy Asthma Immunol 114, 1822.
15. Clayton, TA, Lindon, JC, Cloarec, O, et al. (2006) Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 440, pp. 10731077.
16. Winnike, JH, Li, Z, Wright, FA, et al. (2010) Use of pharmaco-metabonomics for early prediction of acetaminophen-induced hepatotoxicity in humans. Clin Pharmacol Ther 88, 4551.
17. van Bochove, K, van Schalkwijk, DB, Parnell, LD, et al. (2012) Clustering by plasma lipoprotein profile reveals two distinct subgroups with positive lipid response to fenofibrate therapy. PLOS ONE 7, pp. e38072.
18. O’Sullivan, A, Gibney, MJ, Connor, AO, et al. (2011) Biochemical and metabolomic phenotyping in the identification of a vitamin D responsive metabotype for markers of the metabolic syndrome. Mol Nutr Food Res 55, 679690.
19. Heinzmann, SS, Merrifield, CA, Rezzi, S, et al. (2011) Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J Proteome Res 11, 643655.
20. Vazquez-Fresno, R, Llorach, R, Perera, A, et al. (2016) Clinical phenotype clustering in cardiovascular risk patients for the identification of responsive metabotypes after red wine polyphenol intake. J Nutr Biochem 28, 114120.
21. Botelho, PB, Fioratti, CO, Abdalla, DS, et al. (2010) Classification of individuals with dyslipidaemia controlled by statins according to plasma biomarkers of oxidative stress using cluster analysis. Br J Nutr 103, 256265.
22. O’Donovan, CB, Walsh, MC, Nugent, AP, et al. (2015) Use of metabotyping for the delivery of personalised nutrition. Mol Nutr Food Res 59, 377385.
23. Celis-Morales, C, Livingstone, KM, Marsaux, CF, et al. (2015) Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr 10, 450.
24. Celis-Morales, C, Livingstone, KM, Marsaux, CF, et al. (2016) Effect of personalized nutrition on health-related behaviour change: evidence from the Food4me European randomized controlled trial. Int J Epidemiol 46, 578588.
25. Forster, H, Fallaize, R, Gallagher, C, et al. (2014) Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 16, e150.
26. Fallaize, R, Forster, H, Macready, AL, et al. (2014) Online dietary intake estimation: reproducibility and validity of the Food4Me food frequency questionnaire against a 4-day weighed food record. J Med Internet Res 16, e190.
27. Celis-Morales, C, Livingstone, KM, Woolhead, C, et al. (2015) How reliable is internet-based self-reported identity, socio-demographic and obesity measures in European adults? Genes Nutr 10, 476.
28. Rosal, MC, Ebbeling, CB, Lofgren, I, et al. (2001) Facilitating dietary change: the patient-centered counseling model. J Am Diet Assoc 101, 332341.
29. Forster, H, Walsh, MC, O’Donovan, CB, et al. (2016) A dietary feedback system for the delivery of consistent personalized dietary advice in the web-based multicenter Food4Me Study. J Med Internet Res 18, e150.
30. McDade, TW, Williams, S & Snodgrass, JJ (2007) What a drop can do: dried blood spots as a minimally invasive method for integrating biomarkers into population-based research. Demography 44, 899925.
31. McDade, TW (2014) Development and validation of assay protocols for use with dried blood spot samples. Am J Hum Biol 26, 19.
32. Chan, BC, Laws, RA, Williams, AM, et al. (2012) Is there scope for community health nurses to address lifestyle risk factors? the community nursing SNAP trial. BMC Nurs 11, 4.
33. Chan, BC, Jayasinghe, UW, Christl, B, et al. (2013) The impact of a team-based intervention on the lifestyle risk factor management practices of community nurses: outcomes of the community nursing SNAP trial. BMC Health Serv Res 13, 54.
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British Journal of Nutrition
  • ISSN: 0007-1145
  • EISSN: 1475-2662
  • URL: /core/journals/british-journal-of-nutrition
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