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.
摘要：
传统上,个性化营养是在个人层面提供的。然而,通过识别代谢型或代谢相似个体群体,在群体层面提供量身定制的饮食建议的概念已经出现.尽管这种个性化营养的方法看起来很有希望，需要进一步的工作来在更广泛的人群中研究这一概念。因此,本研究的目的是:(1)在欧洲人群中确定代谢型;(2)为这些代谢型制定有针对性的饮食建议方案.使用Food4Me研究(n1607)的数据，k-means聚类分析显示，基于27个代谢标记，包括胆固醇，单个脂肪酸和类胡萝卜素。簇2被确定为代谢健康的代谢型，因为这些个体的Omega-3指数最高（6·56（sd1·29）％），类胡萝卜素（2·15（SD0·71）µm）和最低的总饱和脂肪水平。根据其脂肪酸谱，簇1的特征是代谢不健康的簇。使用决策树方法为每个集群开发有针对性的饮食建议解决方案。通过与个性化饮食建议进行比较来测试该方法，由营养学家提供给Food4Me研究参与者（n180）。在有针对性的和个性化的方法之间观察到极好的一致性,在相同饮食信息的递送水平下平均匹配82%。未来的工作应该确定这种提出的方法是否可以在医疗保健环境中使用，快速高效地提供量身定制的饮食建议解决方案。
