Zhimin Ma1,2, Ditian Li3, Siyan Zhan4, Feng Sun4, Chaonan Xu1,2, Yunfeng Wang1,2, Xinghua Yang5,6. 1. School of Public Health, Capital Medical University, Beijing, China. 2. Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China. 3. Mailman School of Public Health, Columbia University, New York, NY, USA. 4. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China. 5. School of Public Health, Capital Medical University, Beijing, China. xinghuay@sina.com. 6. Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China. xinghuay@sina.com.
Abstract
PURPOSE: We aimed to use a structural equation model (SEM) to determine the interrelations between various risk factors, including latent variables, involved in the development of metabolic syndrome(MetS). METHODS: This study used data derived from the MJ Longitudinal Health Check-up Population Database for participants aged 20 to 70 years, who were asymptomatic for MetS at enrollment and were followed up for 5 years. A SEM was applied to investigate the attributions of MetS and the interrelations between different risk factors. RESULTS: Socioeconomic status (SES), living habits, components of metabolic syndrome (COMetS), and blood pressure had a diverse impact on the onset of MetS, directly and (or) indirectly. When investigating the latent risk factors and the interrelations between different risk factors. The standardized total effect (the sum of the direct and indirect effects, βt) of SES, living habits, blood pressure and COMetS on the onset of MetS was 0.084, -0.179, 0.154, and 0.353, respectively. SES, as a distal risk factor, directly influenced living habits, blood pressure, and COMetS with standardized regression coefficients (βr) of -0.079 (P < 0.001), 0.200 (P < 0.001), and -0.163 (P < 0.001) respectively. Unfavorable living habits exerted an inverse effect on blood pressure and COMetS (βr = -0.101, P < 0.001; βr = -0.463, P < 0.001), which was an important path way for developing MetS. CONCLUSIONS: These results demonstrate that individuals with a higher level of SES are susceptible to high blood pressure and are at increased risk for MetS. Additionally, there is a decrease in exercise and an increase in smoking and consumption of alcohol corresponded to an increase in metabolic risk factors.
PURPOSE: We aimed to use a structural equation model (SEM) to determine the interrelations between various risk factors, including latent variables, involved in the development of metabolic syndrome(MetS). METHODS: This study used data derived from the MJ Longitudinal Health Check-up Population Database for participants aged 20 to 70 years, who were asymptomatic for MetS at enrollment and were followed up for 5 years. A SEM was applied to investigate the attributions of MetS and the interrelations between different risk factors. RESULTS: Socioeconomic status (SES), living habits, components of metabolic syndrome (COMetS), and blood pressure had a diverse impact on the onset of MetS, directly and (or) indirectly. When investigating the latent risk factors and the interrelations between different risk factors. The standardized total effect (the sum of the direct and indirect effects, βt) of SES, living habits, blood pressure and COMetS on the onset of MetS was 0.084, -0.179, 0.154, and 0.353, respectively. SES, as a distal risk factor, directly influenced living habits, blood pressure, and COMetS with standardized regression coefficients (βr) of -0.079 (P < 0.001), 0.200 (P < 0.001), and -0.163 (P < 0.001) respectively. Unfavorable living habits exerted an inverse effect on blood pressure and COMetS (βr = -0.101, P < 0.001; βr = -0.463, P < 0.001), which was an important path way for developing MetS. CONCLUSIONS: These results demonstrate that individuals with a higher level of SES are susceptible to high blood pressure and are at increased risk for MetS. Additionally, there is a decrease in exercise and an increase in smoking and consumption of alcohol corresponded to an increase in metabolic risk factors.
Entities:
Keywords:
Metabolic syndrome; Risk factors; Socioeconomic status; Structural equation model
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