Construction and analysis of the structural equation model for the influencing factors of endothelial function of the brachial artery
10.3760/cma.j.cn115624-20220130-00073
- VernacularTitle:肱动脉内皮功能影响因素的结构方程模型构建及分析
- Author:
Ting PENG
1
;
Rujia MIAO
;
Linlin ZHAO
;
Chunxiang QIN
;
Nini CHEN
;
Jie PENG
;
Qun ZHAO
;
Wenzhao YAO
;
Ting YUAN
;
Jiangang WANG
Author Information
1. 中南大学湘雅三医院健康管理科，长沙 410013
- Keywords:
Brachial artery flow-mediated dilatation;
Metabolic syndrome;
Physical examination;
Questionnaire
- From:
Chinese Journal of Health Management
2022;16(7):464-470
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To examine the influencing factors of endothelial injury using the structural equation model (SEM).Methods:A total of 6 861 asymptomatic individuals free of cardiovascular disease underwent health examinations at the health management center of the third Xiangya hospital, Central South University from May 2015 to August 2020. And collected their questionnaire and checkup data. Spearman′s rank correlation coefficient was used to analyze metabolic factors and brachial artery flow-mediated dilatation (FMD). Exploratory factor analysis (3 430/6 861) and confirmatory factor analysis (3 431/6 861) were conducted on the diet items. An SEM was constructed using the diet pattern data, cardiovascular risk factors and FMD, and using multi-path regression analysis to determine the correlation between the indicators.Results:Based on the factor analysis, diet items were divided into three patterns: healthy food, meat, and supplementary food. The SEM indicated that age ( β=0.27) and blood pressure ( β=0.12) had obvious effects on low FMD. Triglyceride ( β=0.03), fasting blood glucose ( β=0.04), and body mass index ( β=0.08) were positively correlated with low FMD. On the upstream, healthy food was negatively correlated with blood pressure ( β=-0.04) and body mass index ( β=-0.04), meat was positively correlated with triglyceride ( β=0.33), blood pressure ( β=0.06), fasting blood glucose ( β=0.20), and body mass index ( β=0.16), and supplementary food was negatively correlated with fasting blood glucose ( β=-0.30). This was the only pattern that was directly correlated with FMD ( β=0.05). Conclusions:SEM is an effective method to analyze the influence of various risk factors on the population and the relationship between individual indicators. This study revealed direct and indirect correlations between age, diet pattern, cardiovascular-metabolic risk, and FMD impairment. Comprehensive control of dietary patterns and metabolic indicators could prevent and improve early cardiovascular injury.
