Bernard Lauren, Chen Jingsha, Kim Hyunju, Wong Kari E, Steffen Lyn M, Yu Bing, Boerwinkle Eric, Rebholz Casey M
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Curr Dev Nutr. 2023 Mar 24;7(4):100067. doi: 10.1016/j.cdnut.2023.100067. eCollection 2023 Apr.
Dietary consumption has traditionally been studied through food intake questionnaires. Metabolomics can be used to identify blood markers of dietary protein that may complement existing dietary assessment tools.
We aimed to identify associations between 3 dietary protein sources (total protein, animal protein, and plant protein) and serum metabolites using data from the Atherosclerosis Risk in Communities Study.
Participants' dietary protein intake was derived from a food frequency questionnaire administered by an interviewer, and fasting serum samples were collected at study visit 1 (1987-1989). Untargeted metabolomic profiling was performed in 2 subgroups (subgroup 1: = 1842; subgroup 2: = 2072). Multivariable linear regression models were used to assess associations between 3 dietary protein sources and 360 metabolites, adjusting for demographic factors and other participant characteristics. Analyses were performed separately within each subgroup and meta-analyzed with fixed-effects models.
In this study of 3914 middle-aged adults, the mean (SD) age was 54 (6) y, 60% were women, and 61% were Black. We identified 41 metabolites significantly associated with dietary protein intake. Twenty-six metabolite associations overlapped between total protein and animal protein, such as pyroglutamine, creatine, 3-methylhistidine, and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid. Plant protein was uniquely associated with 11 metabolites, such as tryptophan betaine, 4-vinylphenol sulfate, -δ-acetylornithine, and pipecolate.
The results of 17 of the 41 metabolites (41%) were consistent with those of previous nutritional metabolomic studies and specific protein-rich food items. We discovered 24 metabolites that had not been previously associated with dietary protein intake. These results enhance the validity of candidate markers of dietary protein intake and introduce novel metabolomic markers of dietary protein intake.
传统上通过食物摄入问卷来研究饮食消费情况。代谢组学可用于识别饮食蛋白质的血液标志物,这可能补充现有的饮食评估工具。
我们旨在利用社区动脉粥样硬化风险研究的数据,确定3种饮食蛋白质来源(总蛋白质、动物蛋白和植物蛋白)与血清代谢物之间的关联。
参与者的饮食蛋白质摄入量来自访谈员管理的食物频率问卷,并在研究访视1(1987 - 1989年)时采集空腹血清样本。在2个亚组(亚组1:n = 1842;亚组2:n = 2072)中进行非靶向代谢组学分析。使用多变量线性回归模型评估3种饮食蛋白质来源与360种代谢物之间的关联,并对人口统计学因素和其他参与者特征进行调整。在每个亚组内分别进行分析,并使用固定效应模型进行荟萃分析。
在这项对3914名中年成年人的研究中,平均(标准差)年龄为54(6)岁,60%为女性,61%为黑人。我们确定了41种与饮食蛋白质摄入量显著相关的代谢物。总蛋白质和动物蛋白之间有26种代谢物关联重叠,如焦谷氨酸、肌酸、3 - 甲基组氨酸和3 - 羧基 - 4 - 甲基 - 5 - 丙基 - 2 - 呋喃丙酸。植物蛋白与11种代谢物独特相关,如色氨酸甜菜碱、4 - 乙烯基苯酚硫酸盐、δ - 乙酰鸟氨酸和哌可酸。
41种代谢物中的17种(41%)结果与先前的营养代谢组学研究和特定富含蛋白质食物的结果一致。我们发现了24种先前未与饮食蛋白质摄入量相关的代谢物。这些结果提高了饮食蛋白质摄入量候选标志物的有效性,并引入了饮食蛋白质摄入量的新型代谢组学标志物。