Lee Chiyoung, Cao Jiepin, Eagen-Torkko Meghan, Mohammed Selina A
School of Nursing & Health Studies, University of Washington Bothell, 18115 Campus Way NE, Bothell, WA, 98011, USA.
Section for Health Equity, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA.
SSM Popul Health. 2023 Feb 7;22:101358. doi: 10.1016/j.ssmph.2023.101358. eCollection 2023 Jun.
The findings to date indicate that adverse childhood experiences (ACEs) increase the risk of cardiovascular disease (CVD) in later life. We demonstrate how network analysis, a statistical method that estimates complex patterns of associations between variables, can be used to model ACEs and CVD. The main goal is to explore the differential impacts of ACE components on CVD outcomes, conditioned on other ACEs and important covariates using network analysis. We also sought to determine which ACEs are most synergistically correlated and subsequently cluster together to affect CVD risk.
Our analysis was based on cross-sectional data from the 2020 Behavioral Risk Factor Surveillance System, which included 31,242 adults aged 55 or older (54.6% women, 79.8% whites, mean age of 68.7 ± 7.85 years). CVD outcomes included angina/coronary heart disease (CHD) and stroke prevalence. Mixed graphical models were estimated using the R-package including all variables simultaneously to elucidate their one-to-one inter-relationships. Next, we conducted Walktrap cluster detection on the estimated networks using the R-package All analyses were stratified by gender to examine group differences.
In the network for men, the variable "household incarceration" was most strongly associated with stroke. For women, the strongest connection was between "physical abuse" and stroke, followed by "sexual abuse" and angina/CHD. For men, angina/CHD and stroke were clustered with several CVD risk factors, including depressive disorder, diabetes, obesity, physical activity, and smoking, and further clustered with components of household dysfunction (household substance abuse, household incarceration, and parental separation/divorce). No clusters emerged for women.
Specific ACEs associated with CVDs across gender may be focal points for targeted interventions. Additionally, findings from the clustering method (especially for men) may provide researchers with valuable information on potential mechanisms linking ACEs with cardiovascular health, in which household dysfunction plays a critical role.
迄今为止的研究结果表明,童年不良经历(ACEs)会增加成年后患心血管疾病(CVD)的风险。我们展示了网络分析(一种估计变量间复杂关联模式的统计方法)如何用于对ACEs和CVD进行建模。主要目标是利用网络分析,探究在其他ACEs和重要协变量的条件下,ACE各组成部分对CVD结局的不同影响。我们还试图确定哪些ACEs具有最强的协同相关性,并随后聚集在一起影响CVD风险。
我们的分析基于2020年行为危险因素监测系统的横断面数据,该数据包括31242名55岁及以上的成年人(54.6%为女性,79.8%为白人,平均年龄68.7±7.85岁)。CVD结局包括心绞痛/冠心病(CHD)和中风患病率。使用R软件包估计混合图形模型,同时纳入所有变量以阐明它们的一一对应关系。接下来,我们使用R软件包对估计出的网络进行Walktrap聚类检测。所有分析均按性别分层,以检验组间差异。
在男性网络中,变量“家庭监禁”与中风的关联最为紧密。对于女性而言,“身体虐待”与中风之间的联系最为紧密,其次是“性虐待”与心绞痛/冠心病。对于男性,心绞痛/冠心病和中风与几种CVD危险因素聚集在一起,包括抑郁症、糖尿病、肥胖、身体活动和吸烟,并进一步与家庭功能障碍的组成部分(家庭药物滥用、家庭监禁以及父母分居/离婚)聚集在一起。女性未出现聚类情况。
不同性别中与CVD相关的特定ACEs可能是有针对性干预的重点。此外,聚类方法的结果(尤其是对男性而言)可能为研究人员提供有关ACEs与心血管健康之间潜在机制的有价值信息,其中家庭功能障碍起着关键作用。