【营养前沿】代谢组学研究获得进展,精准营养更靠谱了吗?
创作:JIAN 剑 审核:JIAN 剑 2017年03月27日
  • 从英国临床研究部门的数据库中招募健康志愿者;
  • 根据世界卫生组织健康膳食指南设计4种渐变的膳食干预方式;
  • 搜集尿样,用质子核磁共振法鉴定与膳食模式相关的差异性代谢产物,建立“膳食-尿液代谢产物特征”模型,并利用现有队列研究验证模型的可靠性;
  • 评价:针对自由生活人群的膳食分析是营养学的研究难点,代谢组学是对传统评价工具的补充而非替代;
  • 开发、验证和完善更多评价方法,获得膳食的多维信息,是实现精准营养的前提。
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Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial

基于代谢表型分析受试者的饮食模式:一项随机、对照、交叉临床实验

10.1016/S2213-8587(16)30419-3

2017-01-13, Article

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BACKGROUND: Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, the concentrations of which can be affected by food intake. We hypothesised that metabolic profiles of urine samples developed under controlled feeding conditions reflect dietary intake and can be used to model and classify dietary patterns of free-living populations. METHODS: In this randomised, controlled, crossover trial, we recruited healthy volunteers (aged 21-65 years, BMI 20-35 kg/m2) from a database of a clinical research unit in the UK. We developed four dietary interventions with a stepwise variance in concordance with the WHO healthy eating guidelines that aim to prevent non-communicable diseases (increase fruits, vegetables, whole grains, and dietary fibre; decrease fats, sugars, and salt). Participants attended four inpatient stays (72 h each, separated by at least 5 days), during which they were given one dietary intervention. The order of diets was randomly assigned across study visits. Randomisation was done by an independent investigator, with the use of opaque, sealed, sequentially numbered envelopes that each contained one of the four dietary interventions in a random order. Participants and investigators were not masked from the dietary intervention, but investigators analysing the data were masked from the randomisation order. During each inpatient period, urine was collected daily over three timed periods: morning (0900-1300 h), afternoon (1300-1800 h), and evening and overnight (1800-0900 h); 24 h urine samples were obtained by pooling these samples. Urine samples were assessed by proton nuclear magnetic resonance (1H-NMR) spectroscopy, and diet-discriminatory metabolites were identified. We developed urinary metabolite models for each diet and identified the associated metabolic profiles, and then validated the models using data and samples from the INTERMAP UK cohort (n=225) and a healthy-eating Danish cohort (n=66). This study is registered with ISRCTN, number ISRCTN43087333. FINDINGS: Between Aug 13, 2013, and May 18, 2014, we contacted 300 people with a letter of invitation. 78 responded, of whom 26 were eligible and invited to attend a health screening. Of 20 eligible participants who were randomised, 19 completed all four 72 h study stays between Oct 2, 2013, and July 29, 2014, and consumed all the food provided. Analysis of 1H-NMR spectroscopy data indicated that urinary metabolic profiles of the four diets were distinct. Significant stepwise differences in metabolite concentrations were seen between diets with the lowest and highest metabolic risks. Application of the derived metabolite models to the validation datasets confirmed the association between urinary metabolic and dietary profiles in the INTERMAP UK cohort (p<0·0001) and the Danish cohort (p<0·0001). INTERPRETATION: Urinary metabolite models developed in a highly controlled environment can classify groups of free-living people into consumers of diets associated with lower or higher non-communicable disease risk on the basis of multivariate metabolite patterns. This approach enables objective monitoring of dietary patterns in population settings and enhances the validity of dietary reporting.

First Authors:
Isabel Garcia-Perez, Joram M Posma, Elaine Holmes, Gary Frost

Correspondence Authors:
Elaine Holmes

All Authors:
Isabel Garcia-Perez,Joram M Posma,Rachel Gibson,Edward S Chambers,Tue H Hansen,Henrik Vestergaard,Torben Hansen,Manfred Beckmann,Oluf Pedersen,Paul Elliott,Jeremiah Stamler,Jeremy K Nicholson,John Draper,John C Mathers,Elaine Holmes,Gary Frost

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