Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Am J Clin Nutr. 2019 Oct 1;110(4):984-992. doi: 10.1093/ajcn/nqz169.
Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.
Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18-45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)-defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level.
There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance.
These exploratory metabolomics results support beneficial effects of a low-glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.
低升糖负荷的饮食模式,其特点是摄入全谷物、豆类、水果和蔬菜,与多种慢性疾病风险降低有关。
我们利用一项随机、对照、交叉喂养试验的样本,评估了与高精制谷物和添加糖饮食模式(RG)相比,28 天低升糖全谷物饮食模式(WG)对代谢谱的影响。对 80 名健康参与者(男性 40 名,女性 40 名)的空腹血浆样本进行基于 LC-MS 的靶向代谢组学分析,年龄 18-45 岁。使用线性混合模型评估个体代谢物在两种饮食之间的反应差异。京都基因与基因组百科全书(KEGG)定义的途径和 2 种新的数据驱动分析用于考虑途径水平的差异。
在>98%的所有血浆样本中都有可检测信号的 121 种代谢物。在第 28 天,有 18 种代谢物在两种饮食之间存在显著差异(错误发现率(FDR)<0.05)。与 RG 饮食相比,WG 饮食后肌醇、对羟苯丙酮酸、瓜氨酸、精氨酸、13-羟基十八碳二烯酸、谷氨酰胺和草酰乙酸水平更高,而褪黑素、甜菜碱、肌酸、乙酰胆碱、天冬氨酸、羟脯氨酸、甲基组氨酸、色氨酸、半胱氨酸、肉碱和三甲胺水平更低。使用 KEGG 定义的途径进行的分析显示,两种饮食之间色氨酸代谢存在统计学上的显著差异,犬尿氨酸和褪黑素与血清 C 反应蛋白浓度呈正相关。在代谢物和网络水平上使用新的数据驱动方法发现,参与支链氨基酸(BCAA)降解、三甲胺-N-氧化物生成和脂肪酸β氧化的代谢物之间存在相关性(FDR<0.1),WG 饮食后代谢谱更为有利。较高的 BCAA 和三甲胺与稳态模型评估-胰岛素抵抗呈正相关。
这些探索性代谢组学结果支持低升糖负荷的饮食模式(其特点是全谷物、豆类、水果和蔬菜)与高精制谷物和添加糖的饮食模式相比,对炎症和能量代谢途径有有益的影响。这项试验在 clinicaltrials.gov 上注册为 NCT00622661。