The purpose of this study was to apply the structural equation modeling (SEM) to compare the fitness of different competing models (one, two, and three factors) of the metabolic syndrome (MetS) in Iranian adult population.
Data are given on the cardiometabolic risk factors of 841 individuals with nondiabetic adults from a cross-sectional population-based study of glucose, lipids, and MetS in the north of Iran. The three conceptual hypothesized models (single factor, two correlated factors, and three correlated latent factors) were evaluated by using confirmatory factor analysis with the SEM approach. The summary statistics of correlation coefficients and the model summary fitting indexes were calculated.
The findings show that a single-factor model and a two-correlated factor model had a poorer summary fitting index compared with a three-correlated factor model. All fitting criteria met the conceptual hypothesized three-correlated factor model for both sexes. However, the correlation structure between the three underlying constructs designating the MetS was higher in women than in men.
These results indicate the plausibility of the pathophysiology and etiology of MetS being multifactorial, rather than a single factor, in a nondiabetic Iranian adult population.
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The second derivative of the finger photoplethysmogram (SDPTG) is an indicator of arterial stiffness. The present study was conducted to clarify the factor structure of indices of the SDPTG in combination with components of the metabolic syndrome (MetS), to elucidate the significance of the SDPTG among various cardiovascular risk factors.
The SDPTG was determined in the second forefinger of the left hand in 1,055 male workers (mean age, 44.2±6.4 years). Among 4 waves of SDPTG components, the ratios of the height of the "a" wave to that of the "b" and "d" waves were expressed as b/a and d/a, and used as SDPTG indices for the analysis.
Principal axis factoring analysis was conducted using age, SDPTG indices, components of MetS, and the serum levels of C-reactive protein (CRP) and uric acid. Three factors were extracted, and the SDPTG indices were categorized in combination with age as the third factor. Metabolic components and the SDPTG indices were independently categorized. These three factors explained 44.4% of the total variation. Multiple logistic regression analysis revealed age, d/a, serum uric acid, serum CRP, and regular exercise as independent determinants of the risk of MetS. The odds ratios (95% confidence intervals) were 1.08 (1.04 to 1.11), 0.10 (0.01 to 0.73), 1.24 (1.06 to 1.44), 3.59 (2.37 to 5.42), and 0.48 (0.28 to 0.82), respectively.
The SDPTG indices were categorized in combination with age, and they differed in characteristics from components of MetS or inflammatory markers. In addition, this cross-sectional study also revealed decrease of the d/a as a risk factor for the development of MetS.
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Screening Test on Metabolic Syndrome Using Electro Interstitial Scan Instrument
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