Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Am J Clin Nutr. 2018 Aug 1;108(2):243-255. doi: 10.1093/ajcn/nqy099.
The Dietary Approaches to Stop Hypertension (DASH) dietary pattern is recommended for cardiovascular disease risk reduction. Assessment of dietary intake has been limited to subjective measures and a few biomarkers from 24-h urine collections.
The aim of the study was to use metabolomics to identify serum compounds that are associated with adherence to the DASH dietary pattern.
We conducted untargeted metabolomic profiling in serum specimens collected at the end of 8 wk following the DASH diet (n = 110), the fruit and vegetables diet (n = 111), or a control diet (n = 108) in a multicenter, randomized clinical feeding study (n = 329). Multivariable linear regression was used to determine the associations between the randomized diets and individual log-transformed metabolites after adjustment for age, sex, race, education, body mass index, and hypertension. Partial least-squares discriminant analysis (PLS-DA) was used to identify a panel of compounds that discriminated between the dietary patterns. The area under the curve (C statistic) was calculated as the cumulative ability to distinguish between dietary patterns. We accounted for multiple comparisons with the use of the Bonferroni method (0.05 of 818 metabolites = 6.11 × 10-5).
Serum concentrations of 44 known metabolites differed significantly between participants randomly assigned to the DASH diet compared with both the control diet and the fruit and vegetables diet, which included an amino acid, 2 cofactors and vitamins (n = 2), and lipids (n = 41). With the use of PLS-DA, component 1 explained 29.4% of the variance and component 2 explained 12.6% of the variance. The 10 most influential metabolites for discriminating between the DASH and control dietary patterns were N-methylproline, stachydrine, tryptophan betaine, theobromine, 7-methylurate, chiro-inositol, 3-methylxanthine, methyl glucopyranoside, β-cryptoxanthin, and 7-methylxanthine (C statistic = 0.986).
An untargeted metabolomic platform identified a broad array of serum metabolites that differed between the DASH diet and 2 other dietary patterns. This newly identified metabolite panel may be used to assess adherence to the DASH dietary pattern. This trial was registered at http://www.clinicaltrials.gov as NCT03403166.
为了降低心血管疾病风险，推荐采用 DASH（停止高血压的饮食方法）饮食模式。目前对饮食摄入的评估仅限于主观测量和一些 24 小时尿液收集的生物标志物。
本研究旨在使用代谢组学来确定与 DASH 饮食模式依从性相关的血清化合物。
我们在一项多中心、随机临床喂养研究（n=329）中，在 DASH 饮食（n=110）、水果和蔬菜饮食（n=111）或对照饮食（n=108）结束后 8 周收集血清标本进行非靶向代谢组学分析。多变量线性回归用于确定随机饮食与个体经对数转换后的代谢物之间的关联，调整因素包括年龄、性别、种族、教育程度、体重指数和高血压。偏最小二乘判别分析（PLS-DA）用于识别区分饮食模式的化合物组合。曲线下面积（C 统计量）被计算为区分饮食模式的累积能力。我们使用 Bonferroni 方法（818 个代谢物中的 0.05=6.11×10-5）对多重比较进行了校正。
与对照饮食和水果及蔬菜饮食相比，随机分配至 DASH 饮食的参与者的血清中 44 种已知代谢物的浓度存在显著差异，这些代谢物包括一种氨基酸、2 种辅助因子和维生素（n=2）以及脂质（n=41）。使用 PLS-DA，成分 1 解释了 29.4%的方差，成分 2 解释了 12.6%的方差。用于区分 DASH 和对照饮食模式的 10 种最有影响力的代谢物为 N-甲基脯氨酸、斯替维林、色氨酸甜菜碱、可可碱、7-甲基尿酸、手性肌醇、3-甲基黄嘌呤、甲基吡喃葡萄糖苷、β-隐黄质和 7-甲基黄嘌呤（C 统计量=0.986）。
非靶向代谢组学平台确定了 DASH 饮食与其他两种饮食模式之间存在差异的广泛血清代谢物。这个新发现的代谢物组合可能被用于评估 DASH 饮食模式的依从性。本试验在 http://www.clinicaltrials.gov 注册，注册号为 NCT03403166。
