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

Factor Analysis of Metabolic Syndrome Components in a Population-Based Study in the South of Iran (PERSIAN Kharameh Cohort Study)

Background:

We aimed to estimate the exploratory factor analysis (EFA) of metabolic syndrome components based on variables including gender, BMI, and age groups in a population-based study with large sample size.

Methods:

This study was conducted on 10663 individuals 40-70 yr old in Phase 1 of the Persian Kharameh cohort study conducted in 2014–2017. EFA of the metabolic syndrome components, including waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), high-density lipoprotein (HDL) and fasting blood sugar (FBS), was performed on all participants by gender, BMI (Body Mass Index), and age groups.

Results:

EFA results in the whole population based on eigenvalues greater than one showed two factors explaining 56.06% of the total variance. Considering factor loadings higher than 0.3, the first factor included: DBP, SBP, and WC, named as hypertension factor. The second factor also included TG, negative-loaded HDL, FBS, and WC, named as lipid factor. Almost similar patterns were extracted based on subgroups.

Conclusion:

MetS is a multi-factorial syndrome. Both blood pressure and lipid had a central role in this study and obesity was an important factor in both ones. Hypertension, having the highest factor loading, can generally be a valuable screening parameter for cardiovascular and metabolic risk assessment.

Factor Analysis of Metabolic Syndrome and Its Relationship with the Risk of Cardiovascular Disease in Ethnic Populations in Rural Xinjiang, China

Background

This cohort study created a risk equation of CVD for the Uyghur and Kazakh ethnic groups with metabolic syndrome (MetS) in Xinjiang and its associated factors, evaluated the model’s feasibility, and provided theoretical support for the prevention and early diagnosis of CVD.

Methods

A total of 5655 participants from Xinyuan and Jiashi counties in Xinjiang from 2010 to 2012 were selected, including 3770 and 1885 training and validation samples, respectively. A factor analysis was performed on 975 patients with MetS in the training sample, whereas potential factors related to CVD were extracted from 21 MetS biomarkers. Cox regression was used to create and verify a CVD-risk prediction model based on training samples. The receiver operating characteristic curve was drawn to evaluate the model’s prediction efficiency.

Results

The cumulative incidence of CVD was 9.20% (training sample, 9.12%; validation sample, 9.36%). Nine potential factors were extracted from the training sample population with MetS to predict the CVD risk: lipid (hazard ratio [HR], 1.205), obesity (HR, 1.047), liver function (HR, 1.042), myocardial enzyme (HR, 1.008), protein (HR, 1.024), blood pressure (HR, 1.027), liver enzyme (HR, 1.012), renal metabolic (HR, 1.015), and blood glucose (HR, 1.010). The area under the curve of the training and validation samples was 0.841 (95% confidence interval [CI], 0.821–0.861) and 0.889 (95% CI, 0.870–0.909), respectively.

Conclusion

The CVD prediction model created with nine potential factors in patients with MetS in Kazakh and Uyghur has a good predictive power.

Exploratory factor analysis of gender-based metabolic syndrome components: Results from the PERSIAN Guilan cohort study (PGCS)

BACKGROUND

One of the important issues related to metabolic syndrome is the underlying factor that remains controversial. The purpose of this study was estimating exploratory factor analysis (EFA) to reveal underlying factors that may explain the observed variants of metabolic syndrome (MetS) components in a population-based study.

METHODS

In this cross-sectional study, the target population consisted of 10,520 individuals aged 35-70 years from Phase 1 of the PERSIAN Guilan cohort study conducted between 2014 and 2017. Exploratory factor analysis (EFA) of components of the metabolic syndrome, including waist circumference (WC), systolic (SBP) and diastolic (DBP) blood pressure, triglyceride (TG), high-density lipoprotein (HDL) and fasting blood glucose (f-Glc) was performed across the population as well as by gender.

RESULTS

EFA results in the whole population based on eigen values > 1 showed two factors that explain 55.46% of the total variance. Taking factor loadings above 0.3, the first factor included systolic blood pressure, diastolic blood pressure, and waist circumference - called the blood pressure factor. Also, the second factor included triglycerides, negative-loaded HDL, and fasting blood glucose, which was named as lipid factor. In terms of gender, the first factor was similar to the whole population pattern, but in the second factor, in addition to the two components of blood lipids, waist size for men and in fasting blood glucose for women was launched.

CONCLUSION

Hypertension and lipids were substantial factors, and obesity is an important factor in this study. Hypertension, having the highest factor load, can generally be a valuable screening parameter for cardiovascular and metabolic risk assessment.