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. 2023 Sep 25;13(1):16007.
doi: 10.1038/s41598-023-40485-y.

Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling

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Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling

Marjan Nouri-Keshtkar et al. Sci Rep. .

Abstract

Many factors can lead to an increase in the prevalence of metabolic syndrome (MetS) in different populations. Using an advanced structural equation model (SEM), this study is aimed to determine the most important risk factors of MetS, as a continuous latent variable, using a large number of males and females. We also aimed to evaluate the interrelations among the associated factors involved in the development of MetS. This study used data derived from the Fasa PERSIAN cohort study, a branch of the PERSIAN cohort study, for participants aged 35 to 70 years with 10,138 males and females. SEM was used to evaluate the direct and indirect effects, as well as gender effects of influencing factors. Results from the SEM showed that in females most changes in MetS are described by waist circumference (WC), followed by hypertension (HP) and triglyceride (TG), while in males most changes in MetS are described by WC, followed by TG then fasting blood glucose (FBG). Results from the SEM confirmed the gender effects of social status on MetS, mediated by sleep and controlled by age, BMI, ethnicity and physical activity. This study also shows that the integration of TG and WC within genders could be useful as a screening criterion for MetS in our study population.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Hypothesized structural equation model for significant relationships between MetS and associated factors. Oval circles indicate latent variables which are not measured directly. MetS was implemented as a latent variable with five domains as predictors including BMI body mass index, PA Physical activity, HP Hypertension, WC Waist circumference, HDL high density lipoprotein cholesterol, TG triglycerides, GL glucose. Social status was implemented as a latent variable with two domains as predictors including Education and Employment Status. Sleep was implemented as a latent variable with two domains as predictors including sleep duration and sleep quality.

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