Factor Analysis of Cardiovascular Risk Clustering in Pediatric Metabolic Syndrome

1280 Words 6 Pages
The first article is Factor Analysis of Cardiovascular Risk Clustering in Pediatric Metabolic Syndrome: CASPIAN Study. This article examined multiple variable factors to determine if children who have metabolic syndrome (MetS) were more prong to have coronary artery disease. Kelishadi, Ardalan, Adeli, Motaghian, Majdzadeh, Delavari, and Namazi (2007) investigated data from over 4,800 nationally represented school students aged 6-18 years. In order to find out if there was a correlation within the data, the authors used factor analysis with a principle components analysis and Varimax rotation to determine the forthright risk factors that are linked to coronary artery disease.
The results show that MetS was found to be present in just
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The results indicate that the following factors were tied to type two diabeates. The first trait was the “metabolic” factor which consist of positive loadings of BMI, waist circumference, 2-h glucose, log triglyceride, log PAI-1, inverse loadings of log S(i) + 1, and HDL. The second trait was the “inflammation” factor which consist of positive loadings of body mass index (BMI), waist size, fibrinogen, log CRP, and inverse loading of log S(i) + 1. Finally, the third trait was the “blood pressure” factor, which consists of positive loadings of systolic and diastolic blood pressure. The authors made an assumption to validate their research, which states that chronic inflammation was associated with insulin resistance and it weakens components of the metabolic syndrome.
The final article is Factor Analysis and Linear Regression Model (LRM) of Metal Speciation and Physico-Chemical Characters of Groundwater Samples. This article examines a unique approach that looks at the correlation between different variables that are sources of pollution in groundwater for north Chennai (India). Kumaresan and Riyazuddin (2008) studied a data set, which contained 43 different variables. The authors then used factor analysis, which contained a linear regression model and a correlation analysis to

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