Esko Tõnu, Hirschhorn Joel N, Feldman Henry A, Hsu Yu-Han H, Deik Amy A, Clish Clary B, Ebbeling Cara B, Ludwig David S
Center for Basic and Translational Obesity Research.
Estonian Genome Center, University of Tartu, Tartu, Estonia.
Am J Clin Nutr. 2017 Mar;105(3):547-554. doi: 10.3945/ajcn.116.144428. Epub 2017 Jan 11.
Clinical nutrition research often lacks robust markers of compliance, complicating the interpretation of clinical trials and observational studies of free-living subjects. We aimed to examine metabolomics profiles in response to 3 diets that differed widely in macronutrient composition during a controlled feeding protocol. Twenty-one adults with a high body mass index (in kg/m; mean ± SD: 34.4 ± 4.9) were given hypocaloric diets to promote weight loss corresponding to 10-15% of initial body weight. They were then studied during weight stability while consuming 3 test diets, each for a 4-wk period according to a crossover design: low fat (60% carbohydrate, 20% fat, 20% protein), low glycemic index (40% carbohydrate, 40% fat, 20% protein), or very-low carbohydrate (10% carbohydrate, 60% fat, 30% protein). Plasma samples were obtained at baseline and at the end of each 4-wk period in the fasting state for metabolomics analysis by using liquid chromatography-tandem mass spectrometry. Statistical analyses included adjustment for multiple comparisons. Of 333 metabolites, we identified 152 whose concentrations differed for ≥1 diet compared with the others, including diacylglycerols and triacylglycerols, branched-chain amino acids, and markers reflecting metabolic status. Analysis of groups of related metabolites, with the use of either principal components or pathways, revealed coordinated metabolic changes affected by dietary composition, including pathways related to amino acid metabolism. We constructed a classifier using the metabolites that differed between diets and were able to correctly identify the test diet from metabolite profiles in 60 of 63 cases (>95% accuracy). Analyses also suggest differential effects by diet on numerous cardiometabolic disease risk factors. Metabolomic profiling may be used to assess compliance during clinical nutrition trials and the validity of dietary assessment in observational studies. In addition, this methodology may help elucidate mechanistic pathways linking diet to chronic disease risk. This trial was registered at clinicaltrials.gov as NCT00315354.
临床营养研究常常缺乏衡量依从性的可靠指标,这使得对自由生活受试者的临床试验和观察性研究的解读变得复杂。我们旨在研究在一项控制饮食方案中,对三种宏量营养素组成差异很大的饮食产生反应的代谢组学特征。21名体重指数较高(单位:kg/m²;均值±标准差:34.4±4.9)的成年人接受了低热量饮食以促进体重减轻,减轻幅度相当于初始体重的10 - 15%。然后在体重稳定期对他们进行研究,期间他们按照交叉设计食用三种试验饮食,每种饮食为期4周:低脂饮食(60%碳水化合物、20%脂肪、20%蛋白质)、低血糖指数饮食(40%碳水化合物、40%脂肪、20%蛋白质)或极低碳水化合物饮食(10%碳水化合物、60%脂肪、30%蛋白质)。在空腹状态下,于基线以及每个4周周期结束时采集血浆样本,采用液相色谱 - 串联质谱法进行代谢组学分析。统计分析包括对多重比较的校正。在333种代谢物中,我们鉴定出152种代谢物,其浓度在至少一种饮食与其他饮食之间存在差异,包括二酰甘油和三酰甘油、支链氨基酸以及反映代谢状态的标志物。使用主成分或代谢途径对相关代谢物组进行分析,揭示了受饮食组成影响的协同代谢变化,包括与氨基酸代谢相关的途径。我们使用饮食之间存在差异的代谢物构建了一个分类器,在63例中有60例能够根据代谢物谱正确识别试验饮食(准确率>95%)。分析还表明不同饮食对众多心血管代谢疾病风险因素有不同影响。代谢组学分析可用于评估临床营养试验期间的依从性以及观察性研究中饮食评估的有效性。此外,这种方法可能有助于阐明饮食与慢性病风险之间的作用机制途径。该试验已在clinicaltrials.gov上注册,注册号为NCT00315354。