Structural equation modelling for predicting the relative contribution of each component in the metabolic syndrome status change

• Autor(a) principal: Teixeira, José Eduardo
• Data de Publicação: 2022
• Outros Autores: Bragada, José A., Bragada, João P., Coelho, Joana, Pinto, Isabel, Reis, Luís P., Fernandes, Paula Odete, Morais, J.E., Magalhães, Pedro
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10198/25342
• topic: Metabolic syndrome; Multilevel modelling; Prediction; Progression; Public health

Understanding the factor weighting in the development of metabolic syndrome (MetS) may help to predict the progression for cardiovascular and metabolic diseases. Thus, the aim of this study was to develop a confirmatory model to describe and explain the direct and indirect effect of each component in MetS status change. A total of 3581 individuals diagnosed with MetS, aged 18–102 years, were selected between January 2019 and December 2020 from a community-representative sample of Portuguese adults in a north-eastern Portuguese region to test the model’s goodness of fit. A structural equation modelling (SEM) approach and a two-way ANOVA (age × body composition) were performed to compare the relative contribution of each MetS component using joint interim statement (JIS). Waist circumference (β = 0.189–0.373, p < 0.001), fasting glucose (β = 0.168–0.199, p < 0.001) and systolic blood pressure (β = 0.140–0.162, p < 0.001) had the highest direct effect on the change in MetS status in the overall population and concerning both sexes. Moreover, diastolic blood pressure (DBP), triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c) had a low or non-significant effect. Additionally, an indirect effect was reported for age and body composition involving the change in MetS status. The findings may suggest that other components with higher specificity and sensitivity should be considered to empirically validate the harmonised definition of MetS. Current research provides the first multivariate model for predicting the relative contribution of each component in the MetS status change, specifically in Portuguese adults.