### INTRODUCTION

### METHODS

### Study subjects and data set

### Measures of observed variables

^{2}. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured with the patient in the setting position two times on a home visit before and after a 10-minute rest with the use of a digital sphygmomanometer. The average of these two measures is used in the analyses. Mean arterial pressure (MAP) was calculated with the equation: MA

*P*=DBP+⅓ (SBP-DBP). The 10- to 12-hour overnight fasting blood samples were taken from invited individuals at the central laboratory of Ayatollah Rohani Hospital. Triglycerides (TGs), high density lipoprotein cholesterol (HDL-C), and FBG were measured with use of the enzymatic method. We used the Adult Treatment Panel III (ATP III) report of the National Cholesterol Education Program (NCEP) criteria to define MetS [3]. According to these criteria, the presence of at least three of the following five criteria indicate the presence of the MetS: abdominal obesity as measured by WC (>102 cm in men and >88 cm in women), TG >150 mg/dL, HDL-C (<40 mg/dL in men and <50 mg/dL in women), SBP/DBP >130/85 mm Hg, and FBG >110 mg/dL. However, in our confirmatory factor analysis, the continuous measurements of these components plus BMI were applied.

### Factor structure of conceptual hypothesized models

### Statistical analysis

### RESULTS

*P*=0.001). Among the components of MetS, low HDL-C (82.4%), abnormal TG (42.2%), and abnormal WC (39.7%) values were the most common abnormalities in our study population. A significant difference of higher metabolic abnormality was observed in women compared with men (Table 1). Table 2 summarizes the fit statistics of the hypothesized models that were evaluated in this study according to sex. Model 1A is characterized as a single-factor model with reduced dimension of observed variables such that TG and HDL-C were replaced by single-dimension observed data as TG:HDL-C and DBP and SBP by MAP. In model 1B, all observed variables were considered as a single factor without reducing the dimension of data. All fitting indexes met the criterion of goodness of fit in both sexes in model 1A, but the other single-factor model (model 1B) used the original observed data; all fitting indexes were poor, in particular, the high value of RMSEA=0.20 was observed. Model 2 characterized by two correlated latent constructs, BP, and metabolic factors; the summary fitting statistics have improved significantly compared with model 1B. Except for the significant

*P*value of the chi-square test, all fitting indexes met the required criteria in both sexes. However, a lower standardized regression coefficient of HDL-C on the lipid factor was observed. Model 3 (three-factor model) yielded a better improvement of fitting indexes than model 2; in particular, RMERA reached 0.023 in men and the

*P*value of the chi-square test was not significant (i.e., the observed data are progressively fit with the hypothesized model) and a relatively high correlation was apparent between the two constructs of obesity and metabolic factors, but in women, again, all fitting statistics met the criteria except a significant

*P*value of the chi-square test was revealed. The results show that the correlations among three underlying constructs were greater in women than in men, but, surprisingly, a low positive standardized regression coefficient between HDL-C and metabolic factor was observed in women. The standardized loading coefficients and the correlation between underlying constructs in two- and three-factor models are presented in Figs. 1 and 2 according to sex, respectively. Additionally, the standardized loading coefficients of the single-factor model are shown in Figs. 3 and 4. In all figures, one side arrow shows the standardized loading coefficients of observed variables on the latent construct as regression coefficients regardless of the scale used, and two side arrows indicate the correlation structure between different constructs. Additionally, the four-factor model was locally underidentified; the loading estimate for a factor or latent construct with only one indicator as observed variable cannot be mathematically derived because in our data insulin resistance factor had only one observed variable, FBG.

### DISCUSSION

*P*value, perhaps due to a larger sample size. Few published studies evaluated and compared the fit indexes of different competitive models of MetS by using SEM approach, but many used only EFA to designate factors associated with MetS. Our findings are in accordance with those reported by Shah et al. [23] in a nondiabetic United States population with rather similar summary fitting indexes. Similar to our results, a single-factor model was not fit with the observed data, but a two-correlated factor model suggested by Hanley et al. [12] fit the observed data well. The four-factor model, as suggested by previous EFA, might be more plausible as shown by Shah et al. [23]. The fitting criteria were improved, but our observed data for insulin resistance were sparse and we had only one observed variable as measured by FBG as an indicator of insulin resistance and thus the SEM were unidentifiable.