Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism
Plasma metabolomics profiles suggest beneficial effects of a low-glycemic load dietary pattern on inflammation and energy metabolism
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DOI：
10.1093/ajcn/nqz169
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发表时间：
2019-10-01
影响因子：
7.1
通讯作者：
Lampe, Johanna W.
中科院分区：
文献类型：
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作者：
Navarro, Sandi L.;Tarkhan, Aliasghar;Lampe, Johanna W.
背景资料：低血糖负荷的饮食模式，其特点是食用全谷物，豆类，水果和蔬菜，与几种慢性疾病的风险降低。方法：使用来自随机对照交叉喂养试验的样本，我们评估了低血糖全谷物饮食模式（WG）与高精制谷物和添加糖饮食模式（RG）对代谢谱的影响。28 d。对来自80名18-45岁的健康参与者（n = 40名男性，n = 40名女性）的空腹血浆样本进行基于LC-MS的靶向代谢组学分析。使用线性混合模型评价饮食对单个代谢物的反应差异。京都基因和基因组百科全书（KEGG）定义的途径和2个新的数据驱动的分析进行考虑在途径水平上的差异。在第28天，18种代谢物在饮食之间显著不同[错误发现率（FDR）< 0.05]。肌醇，羟苯丙酮酸，瓜氨酸，鸟氨酸，13-羟基十八碳二烯酸，谷氨酰胺，草酰乙酸后，WG饮食高于RG饮食后，而褪黑激素，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，胱胺，肉毒碱，和三甲胺较低。使用KEGG定义的途径分析显示，饮食之间色氨酸代谢存在统计学显著差异，犬尿氨酸和褪黑激素与血清C反应蛋白浓度呈正相关。在代谢物和网络水平上的新型数据驱动方法发现了参与支链氨基酸（BCAA）降解，三甲胺-N-氧化物生产和脂肪酸β氧化（FDR < 0.1）的代谢物之间的相关性，这些代谢物在饮食之间存在差异，WG饮食后检测到更有利的代谢特征。较高的支链氨基酸和三甲胺与稳态模型评估胰岛素resistance.Conclusions：这些探索性的代谢组学结果支持有益的影响，低血糖负荷的饮食模式，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相比，炎症和能量代谢途径。
Background: Low-glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases.Methods: 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.Results: 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 beta 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.Conclusions: 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.