Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, United States; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, United States.
J Nutr. 2024 Nov;154(11):3235-3245. doi: 10.1016/j.tjnut.2024.08.029. Epub 2024 Sep 18.
There is a need to understand the underlying biological mechanisms through which ultra-processed foods negatively affect health. Proteomics offers a valuable tool with which to examine different aspects of ultra-processed foods and their impact on health.
The aim of this study was to identify protein biomarkers of usual ultra-processed food consumption and assess their relation to the incidence of coronary heart disease (CHD), chronic kidney disease (CKD), and all-cause mortality risk.
A total of 9361 participants from the Atherosclerosis Risk in Communities visit 3 (1993-1995) were included. Dietary intake was assessed using a 66-item food-frequency questionnaire and the processing levels were categorized on the basis of the Nova classification. Plasma proteins were detected using an aptamer-based proteomic assay. We used multivariable linear regressions to examine the association between ultra-processed food and proteins, and Cox proportional hazard models to identify associations between ultra-processed food-related proteins and health outcomes. Models extensively controlled for sociodemographic characteristics, health behaviors, and clinical factors.
Eight proteins (6 positive, 2 negative) were identified as significantly associated with ultra-processed food consumption. Over a median follow-up of 22 y, there were 1276, 3084, and 5127 cases of CHD, CKD, and death, respectively. Three, 5, and 3 ultra-processed food-related proteins were associated with each outcome, respectively. One protein (β-glucuronidase) was significantly associated with a higher risk of all 3 outcomes, and 3 proteins (receptor-type tyrosine-protein phosphatase U, C-C motif chemokine 25, and twisted gastrulation protein homolog 1) were associated with a higher risk of 2 outcomes.
We identified a panel of protein biomarkers that were significantly associated with ultra-processed food consumption. These proteins may be considered potential biomarkers for ultra-processed food intake and may elucidate the biological processes through which ultra-processed foods impact health outcomes.
需要了解超加工食品对健康产生负面影响的潜在生物学机制。蛋白质组学提供了一种有价值的工具,可以用来检查超加工食品的不同方面及其对健康的影响。
本研究旨在确定超加工食品常规摄入的蛋白质生物标志物,并评估其与冠心病(CHD)、慢性肾脏病(CKD)和全因死亡率风险的关系。
共纳入 9361 名来自动脉粥样硬化风险社区研究 3 期(1993-1995 年)的参与者。采用 66 项食物频率问卷评估膳食摄入量,并根据 Nova 分类对加工水平进行分类。使用基于适配体的蛋白质组学检测方法检测血浆蛋白。我们使用多变量线性回归来研究超加工食品与蛋白质之间的关系,使用 Cox 比例风险模型来识别与超加工食品相关的蛋白质与健康结果之间的关系。模型广泛控制了社会人口特征、健康行为和临床因素。
鉴定出 8 种(6 种阳性,2 种阴性)与超加工食品消费显著相关的蛋白质。在中位随访 22 年期间,分别有 1276、3084 和 5127 例 CHD、CKD 和死亡发生。有 3、5 和 3 种超加工食品相关蛋白分别与每种结局相关。一种蛋白(β-葡糖苷酸酶)与所有 3 种结局的风险增加显著相关,3 种蛋白(受体型酪氨酸蛋白磷酸酶 U、C-C 基序趋化因子 25 和扭曲原肠胚形成蛋白同源物 1)与 2 种结局的风险增加相关。
我们确定了一组与超加工食品消费显著相关的蛋白质生物标志物。这些蛋白质可以被认为是超加工食品摄入的潜在生物标志物,并可能阐明超加工食品影响健康结果的生物学过程。