O'Donovan Clare B, Walsh Marianne C, Woolhead Clara, Forster Hannah, Celis-Morales Carlos, Fallaize Rosalind, Macready Anna L, Marsaux Cyril F M, Navas-Carretero Santiago, Rodrigo San-Cristobal S, Kolossa Silvia, Tsirigoti Lydia, Mvrogianni Christina, Lambrinou Christina P, Moschonis George, Godlewska Magdalena, Surwillo Agnieszka, Traczyk Iwona, Drevon Christian A, Daniel Hannelore, Manios Yannis, Martinez J Alfredo, Saris Wim H M, Lovegrove Julie A, Mathers John C, Gibney Michael J, Gibney Eileen R, Brennan Lorraine
1School of Agriculture & Food Science,Institute of Food & Health,University College Dublin,Dublin 4,Ireland.
2Human Nutrition Research Centre,Institute of Cellular Medicine,Newcastle University,Newcastle NE4 5PL,UK.
Br J Nutr. 2017 Oct;118(8):561-569. doi: 10.1017/S0007114517002069.
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.
传统上，个性化营养是在个体层面提供的。然而，通过识别代谢型或代谢相似个体组在群体层面提供量身定制的饮食建议这一概念已经出现。尽管这种个性化营养方法看起来很有前景，但仍需要进一步开展工作，以在更广泛的人群中检验这一概念。因此，本研究的目的是：（1）在欧洲人群中识别代谢型，以及（2）为这些代谢型制定有针对性的饮食建议方案。利用来自Food4Me研究的数据（n = 1607），k均值聚类分析显示，基于包括胆固醇、单个脂肪酸和类胡萝卜素在内的27种代谢标志物，存在三个代谢上不同的聚类。聚类2被确定为代谢健康的代谢型，因为这些个体具有最高的Omega-3指数（6.56（标准差1.29）%）、类胡萝卜素（2.15（标准差0.71）µm）和最低的总饱和脂肪水平。基于其脂肪酸谱，聚类1被表征为代谢不健康的聚类。使用决策树方法为每个聚类制定了有针对性的饮食建议方案。通过与营养学家为Food4Me研究参与者（n = 180）提供的个性化饮食建议进行比较，对该方法进行了测试。在提供相同饮食信息的层面上，目标方法与个性化方法之间观察到了极好的一致性，平均匹配度为82%。未来的工作应确定这种提议的方法是否可用于医疗保健环境，以便快速有效地提供量身定制的饮食建议方案。
