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.

Objective:The obesity rate among middle-aged and young adults in China is increasing annually,and the incidence of cardiovascular diseases is becoming more prevalent in younger populations.However,it has not yet been reported whether obesity is associated with early vascular aging(EVA).This study aims to explore the correlation between obesity and EVA in middle-aged and young adult health check-up populations,providing a reference for the prevention of cardiovascular diseases. Methods:A total of 15 464 middle-aged and young adults aged 18-59 who completed brachial-ankle pulse wave velocity(baPWV)test in the Third Xiangya Hospital of Central South University from January to December 2020 were included.Among them,1 965 individuals with normal blood pressure and no cardiovascular risk factors were selected as the healthy population.The baPWV thresholds for determining EVA in each age group for males and females were calculated based on the baPWV values of the healthy population.The number and percentage of individuals meeting the EVA criteria in the middle-aged and young adult health check-up populations were statistically analyzed by age and gender.The differences in obesity indicators[visceral adiposity index(VAI),body mass index(BMI),waist circumference(WC)]between the EVA and non-EVA groups for males and females were compared.Using EVA as the dependent variable,VAI,BMI,and WC were included as independent variables in a Logistic model to analyze the correlation between each obesity indicator and EVA before and after adjusting for other influencing factors.Furthermore,the correlation between each obesity indicator and EVA in each age group was analyzed. Results:In the health check-up populations,the detection rate of EVA in different age groups was 1.65%-10.92%for males,and 1.16%-10.50%for females,the detection rate of EVA increased with age in both males and females.Except for the 40-<50 age group,the EVA detection rate was higher in males than in females in all other age groups.Regardless of gender,obesity indicators VAI,BMI,and WC were significantly higher in the EVA group than in the non-EVA group(all P<0.01).Before and after adjusting for other influencing factors,VAI and WC were both correlated with EVA(both P<0.05).BMI was a risk factor for EVA before adjusting for other influencing factors(P<0.01),but after adjustment,the correlation between BMI and EVA was not statistically significant(P=0.05).After adjusting for other influencing factors,the correlation between VAI and EVA was statistically significant in the 18-<40 and 50-<60 age groups(both P<0.05),while the correlation between BMI and WC with EVA was not statistically significant(both P>0.05).In the 40-<50 age group,the correlation between VAI and BMI with EVA was not statistically significant(both P>0.05),but the correlation between WC and EVA was statistically significant(P<0.01). Conclusion:VAI is closely related to the occurrence of EVA in middle-aged and young adults aged 18-<40 and 50-<60 years,while WC is closely related to the occurrence of EVA in those aged 40-<50 years.