Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA 02130, USA.
South Australian Health and Medical Research Institute, Infection and Immunity Theme, Adelaide 5000, Australia.
Nutrients. 2021 Apr 19;13(4):1364. doi: 10.3390/nu13041364.
The represents the array of dietary, lifestyle, and demographic factors to which an individual is exposed. Individual components of the exposome, or groups of components, are recognized as influencing many aspects of human physiology, including cardiometabolic health. However, the influence of the whole exposome on health outcomes is poorly understood and may differ substantially from the sum of its individual components. As such, studies of the complete exposome are more biologically representative than fragmented models based on subsets of factors. This study aimed to model the system of relationships underlying the way in which the diet, lifestyle, and demographic components of the overall exposome shapes the cardiometabolic risk profile. The current study included 36,496 US Veterans enrolled in the VA Million Veteran Program (MVP) who had complete assessments of their diet, lifestyle, demography, and markers of cardiometabolic health, including serum lipids, blood pressure, and glycemic control. The cohort was randomly divided into training and validation datasets. In the training dataset, we conducted two separate exploratory factor analyses (EFA) to identify common factors among exposures (diet, demographics, and physical activity) and laboratory measures (lipids, blood pressure, and glycemic control), respectively. In the validation dataset, we used multiple normal regression to examine the combined effects of exposure factors on the clinical factors representing cardiometabolic health. The mean ± SD age of participants was 62.4 ± 13.4 years for both the training and validation datasets. The EFA revealed 19 Exposure Common Factors and 5 Physiology Common Factors that explained the observed (measured) data. Multivariate regression in the validation dataset revealed the structure of associations between the Exposure Common Factors and the Physiology Common Factors. For example, we found that the factor for fruit consumption was inversely associated with the factor summarizing total cholesterol and low-density lipoprotein cholesterol (LDLC, = 0.008), and the latent construct describing light levels of physical activity was inversely associated with the blood pressure latent construct ( < 0.0001). We also found that a factor summarizing that participants who frequently consume whole milk are less likely to frequently consume skim milk, was positively associated with the latent constructs representing total cholesterol and LDLC as well as systolic and diastolic blood pressure ( = 0.0006 and <0.0001, respectively). Multiple multivariable-adjusted regression analyses of exposome factors allowed us to model the influence of the exposome as a whole. In this metadata-rich, prospective cohort of US Veterans, there was evidence of structural relationships between diet, lifestyle, and demographic exposures and subsequent markers of cardiometabolic health. This methodology could be applied to answer a variety of research questions about human health exposures that utilize electronic health record data and can accommodate continuous, ordinal, and binary data derived from questionnaires. Further work to explore the potential utility of including genetic risk scores and time-varying covariates is warranted.
暴露组代表个体所接触的饮食、生活方式和人口统计学因素的数组。暴露组的各个组成部分或组成部分的群组被认为会影响人类生理学的许多方面,包括心脏代谢健康。然而,整个暴露组对健康结果的影响还知之甚少,并且可能与单个组成部分的总和大不相同。因此,基于因素子集的完整暴露组的研究比基于碎片化模型的研究更具生物学代表性。本研究旨在建立饮食、生活方式和人口统计学因素的整体暴露组塑造心脏代谢风险特征的方式背后的系统关系模型。本研究纳入了 36496 名参与 VA 百万退伍军人计划(MVP)的美国退伍军人,他们的饮食、生活方式、人口统计学和心脏代谢健康标志物(包括血清脂质、血压和血糖控制)均得到了全面评估。该队列被随机分为训练数据集和验证数据集。在训练数据集中,我们分别进行了两次探索性因素分析(EFA),以确定暴露(饮食、人口统计学和体力活动)和实验室测量(脂质、血压和血糖控制)之间的共同因素。在验证数据集中,我们使用多元正态回归来检验暴露因素对代表心脏代谢健康的临床因素的综合影响。训练数据集和验证数据集的参与者的平均年龄分别为 62.4±13.4 岁。EFA 揭示了 19 个暴露共同因素和 5 个生理共同因素,这些因素解释了观察到的(测量的)数据。验证数据集中的多元回归揭示了暴露共同因素与生理共同因素之间的关联结构。例如,我们发现水果消费因素与总胆固醇和低密度脂蛋白胆固醇(LDLC)的综合因素呈负相关( = 0.008),描述低水平体力活动的潜在结构与血压潜在结构呈负相关( < 0.0001)。我们还发现,一个综合参与者经常食用全脂牛奶的因素与代表总胆固醇和 LDLC 以及收缩压和舒张压的潜在结构呈正相关( = 0.0006 和 <0.0001,分别)。对暴露组因素进行多次多变量调整回归分析使我们能够对暴露组作为一个整体的影响进行建模。在这个元数据丰富的、前瞻性的美国退伍军人队列中,饮食、生活方式和人口统计学暴露与随后的心脏代谢健康标志物之间存在结构关系的证据。这种方法可用于回答有关利用电子健康记录数据的人类健康暴露的各种研究问题,并可以适应来自问卷的连续、有序和二进制数据。进一步探索包括遗传风险评分和时变协变量的潜在效用是值得的。