Factor Analysis of Metabolic Syndrome Components in an Iranian Non-Diabetic Adult Population: A Population-Based Study from the North of Iran.

Objectives: The aim of this study was to explore the underlying latent factors that can explain the observed variation of components of metabolic syndrome (MetS) in Iranian non-diabetic adult population.

Methods: The researchers performed an exploratory factor analysis (EFA) of metabolic syndrome components, including body mass index (BMI), waist circumference (WC), systolic (SBP) and diastolic blood pressure (DBP), triglyceride (TG), high density lipoprotein (HDL), and Fasting blood sugar (FBS). These observed variables were measured from a representative sample of 841 non-diabetic participants in a cross-sectional population-based study of adults aged 20 to 70 years in the North of Iran.

Results: Three factors were extracted by EFA in both genders. In males, the 3 generated factors were, 1) blood pressure factor underlying systolic and diastolic blood pressure, 2) obesity factor manifested by BMI and WC, 3) lipid/glucose factor underlying TG, HDL and FBS that explained 23.9%, 23.0% and 18.4% of variance in the observed data, respectively, in males. However, in females, BMI and WC were revealed as obesity factors, and systolic and diastolic blood pressure were characterized as hypertension factor, and TG, HDL and FBS appeared to be loaded on lipid/glucose factor, similar to males, and designated 25.6%, 25.4%, and 15.8% of the variance, respectively. Triglyceride and FBS were positively loaded, whereas HDL was loaded negatively with similar loading pattern in both genders. Overall, these 3 underlying latent factors explained 65.3% of the variance of observed clinical data sets in males and 66.8% in females. When TG and HDL were replaced by TG to HDL ratio and also SBP and DBP by mean arterial pressure (MAP), the two-factor model was generated in both genders.

Conclusions: The 2-and 3-factor models were characterized indicating a single pathogenesis that could not explain the unified clustering of MetS in non-diabetic adults.