Abstract

ObjectiveUsing structural equation modeling, this study is one of the first efforts aimed at assessing influential factors causing gender differences in the association between health-related quality of life (HRQoL) and metabolic syndrome.MethodsA sample of 950 adults, from Tehran Lipid and Glucose Study were recruited for this cross sectional study in 2005–2007. Health-related quality of life was assessed using the Iranian version of SF-36. Metabolic syndrome components (MetSCs) and physical and mental HRQoL were considered as continuous latent constructs explaining the variances of their observed components. Structural equation modeling was performed to examine the association between the constructs of MetSCs and the physical and mental HRQoL within the two gender groups.ResultsBased on the primary hypothesis, MetSCs and HRQoL were fitted in a model. The negative effect of MetSCs on HRQoL was found to be significant only in the physical domain and only in women. The proportion of all the cardio-metabolic risk factors as well as subscales of physical HRQoL that have been explained via the two constructs of MetSCs and HRQoL, respectively, were significantly higher in women. Physical activity in both men (β = 3.19, p<0.05) and women (β = 3.94, p<0.05), age (β = -3.28, p<0.05), education (β = 2.63, p<0.05) only in women and smoking (β = 2.28, p<0.05) just in men, directly affected physical HRQoL. Regarding the mental domain, physical activity (β = 3.37, p<0.05) and marital status (β = 3.44, p<0.05) in women and age (β = 2.01, p<0.05) in men were direct effective factors. Age and education in women as well as smoking in men indirectly affected physical HRQoL via MetSCs.ConclusionGender differences in the association between MetSCs and physical HRQoL could mostly be attributed to the different structures of both MetSCs and physical HRQoL constructs in men and women. Age and smoking are the most important socio-behavioral factors which could affect this gender-specific association in the mental domain.

Highlights

  • As a chronic condition manifested by insulin resistance, central obesity, dyslipidemia, and hypertension, the metabolic syndrome (MetS) is associated with increased risk of a range of non-communicable diseases such as type 2 diabetes and cardiovascular diseases (CVDs) [1]

  • The proportion of all the cardio-metabolic risk factors as well as subscales of physical health-related quality of life (HRQoL) that have been explained via the two constructs of Metabolic syndrome components (MetSCs) and HRQoL, respectively, were significantly higher in women

  • Gender differences in the association between MetSCs and physical HRQoL could mostly be attributed to the different structures of both MetSCs and physical HRQoL constructs in men and women

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Summary

Introduction

As a chronic condition manifested by insulin resistance, central obesity, dyslipidemia, and hypertension, the metabolic syndrome (MetS) is associated with increased risk of a range of non-communicable diseases such as type 2 diabetes and cardiovascular diseases (CVDs) [1]. Another study emphasized the different effects of socio-behavioral factors, e.g. educational level, physical activity, quality of sleep and doctor visits, on the personal health judgment of Swedish male and female subjects [9]; similar to these psycho-social effects, there is more evidence showing different influential patterns of cardio-metabolic risk factors on HRQoL in men and women [3]; findings similar to a previous study have demonstrated that sex-specific influential patterns of cardiovascular risk factors affect the incidence of cardiovascular outcomes [10]. Considering these psycho-biological differences, it may be possible to hypothesize that different associations between MetS and HRQoL in men and women could be due to: (i) Substantial gender differences of HRQoL and MetS as two multi-component concepts, (ii) different influential patterns of MetS components on HRQoL in each gender and (iii) different effects of socio-behavioral factors in the association between HRQoL and MetS in both genders

Methods
Results
Conclusion
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