Untargeted Metabolomics as a Screening Tool for Estimating Compliance to a Dietary Pattern
Untargeted Metabolomics as a Screening Tool for Estimating Compliance to a Dietary Pattern
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DOI：
10.1021/pr400964s
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发表时间：
2014-03-01
影响因子：
4.4
通讯作者：
Dragsted, Lars O.
中科院分区：
文献类型：
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作者：
Andersen, Maj-Britt S.;Rinnan, Asmund;Dragsted, Lars O.
人们对研究复杂饮食的营养效应越来越感兴趣。对于这样的研究，饮食依从性的测量是一个挑战，因为目前可用的依从性标记物只涵盖饮食的有限方面。在本研究中，进行了一项非靶向代谢组学方法研究，其中181名参与者被用来开发尿液依从性测量，以区分两种饮食模式。一项平行干预随机分为新北欧饮食(NND)或丹麦平均饮食(ADD)，为期6个月。在整个研究期间密切监测饮食摄入量，并在研究期间多次收集24小时尿样以及称重的饮食记录。采用UPLC-QTOE-MS对尿样进行分析，采用偏最小二乘判别分析和特征选择相结合的方法，对214份尿样的数据建立了依从性模型。优化的模型包含52种代谢物，在包含139个样本的验证集中具有19%的错误分类率。模型中确定的代谢物是柑橘、含有可可的产品和鱼等个别食物的标志，以及更一般的饮食特征，如水果和蔬菜的高摄入量或热处理食物的高摄入量。ADD饮食比NND饮食更容易分类，可能是因为NND食物组成的季节性变化以及NND受试者依从性较低的迹象。总而言之，非靶向代谢组学是一种很有前途的方法，可以开发涵盖复杂饮食中最重要的区别代谢物的遵从性措施。
There is a growing interest in studying the nutritional effects of complex diets. For such studies, measurement of dietary compliance is a challenge because the currently available compliance markers cover only limited aspects of a diet. In the present study, an untargeted metabolomics approach study was carried out in which 181 participants were was used to develop a compliance measure in urine to distinguish between two dietary patterns. A parallel intervention randomized to follow either a New Nordic Diet (NND) or an Average Danish Diet (ADD) for 6 months. Dietary intakes were closely monitored over the whole study period, and 24 h urine samples as well as weighed dietary records were collected several times during the study. The urine samples were analyzed by UPLC-qTOE-MS, and a partial least-squares discriminant analysis with feature selection was applied to develop a compliance model based on data from 214 urine samples. The optimized model included 52 metabolites and had a misclassification rate of 19% in a validation set containing 139 samples. The metabolites identified in the model were markers of individual foods such as citrus, cocoa-containing products, and fish as well as more general dietary traits such as high fruit and vegetable intake or high intake of heat-treated foods. It was easier to classify the ADD diet than the NND diet probably due to seasonal variation in the food composition of NND and indications of lower compliance among the NND subjects. In conclusion, untargeted metabolomics is a promising approach to develop compliance measures that cover the most important discriminant metabolites of complex diets.