TY - JOUR
T1 - Metabotyping for the development of tailored dietary advice solutions in a European population
T2 - the Food4Me study
AU - O'Donovan, Clare B.
AU - Walsh, Marianne C.
AU - Woolhead, Clara
AU - Forster, Hannah
AU - Celis-Morales, Carlos
AU - Fallaize, Rosalind
AU - Macready, Anna L.
AU - Marsaux, Cyril F. M.
AU - Navas-Carretero, Santiago
AU - Rodrigo San-Cristobal, S.
AU - Kolossa, Silvia
AU - Tsirigoti, Lydia
AU - Mvrogianni, Christina
AU - Lambrinou, Christina P.
AU - Moschonis, George
AU - Godlewska, Magdalena
AU - Surwillo, Agnieszka
AU - Traczyk, Iwona
AU - Drevon, Christian A.
AU - Daniel, Hannelore
AU - Manios, Yannis
AU - Alfredo Martinez, J.
AU - Saris, Wim H. M.
AU - Lovegrove, Julie A.
AU - Mathers, John C.
AU - Gibney, Michael J.
AU - Gibney, Eileen R.
AU - Brennan, Lorraine
PY - 2017/10/28
Y1 - 2017/10/28
N2 - 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) mu m) 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.
AB - 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) mu m) 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.
KW - Personalised nutrition
KW - Dried blood spots
KW - Cluster analyses
KW - Metabotyping
KW - Targeted nutrition
KW - PERSONALIZED NUTRITION
KW - CLUSTER-ANALYSIS
KW - ASTHMA PHENOTYPES
KW - IDENTIFICATION
KW - CLASSIFICATION
KW - BIOMARKERS
KW - MODEL
U2 - 10.1017/S0007114517002069
DO - 10.1017/S0007114517002069
M3 - Article
C2 - 29056103
SN - 0007-1145
VL - 118
SP - 561
EP - 569
JO - British Journal of Nutrition
JF - British Journal of Nutrition
IS - 8
ER -