Principal Component Analysis of Categorized Parameters Used in the Study of Diabetes Mellitus And Dyslipidaemia in Association with Carotid Intima Medial Thickness
Background: Objective: The present study was attempted for multivariate analysis through principal component analysis for carotid intima medial thickness (CIMT) as dependent variable compared to different independent variables used as parameters in diabetes and dyslipidaemia.
Methods: The biochemical and obese data of total 75 patients were taken from earlier study. The data of six biochemical markers of DM and dyslipidaemia along with one obese marker and CIMT were analysed. Data were analysed for Pearson’s-Spearman correlation coefficients matrix for the relationships between CIMT and parameters of DM as well as dyslipidaemia. Principal component analysis (PCA) was performed to reduce the variables into a smaller number of uncorrelated predictor variables. Individual PC scores were generated from their risk factors loadings for DM and dyslipidaemia separately.
Results: The DM parameters PC loadings scores, which were generated from the individual PC scores, PC-1 (97.10% of the original variation explained) resulted in relatively high loadings for FBS and PPBS, moderately loading for HbA1c, lower loadings for urea and creatinine and least loading for BMI while PC-2 (1.79% of the variation explained) was negatively loaded for the BMI and PPBS but positively loaded for FBS, creatinine, urea and HbA1c. The dyslipidaemia parameters PC loadings scores, which were also generated from the individual PC scores for dyslipidaemia, PC-1 (83.93% of the original variation explained) resulted in relatively high loadings for TC, TG and VLDL while strongly negatively loaded for the BMI and LDL-C and HDL-C but positive loaded for TC, TG and VLDL while PC-2 (7.56% of the variation explained) was strongly negatively loaded for the HDL-C and VLDL but positively loaded for LDL-C followed by TC and BMI but least loading for TG.
Conclusions: This research work may be suitable tool to presents a significant step towards determining complex parameters and clinical biomarkers in DM and dyslipidaemia in association with CIMT that may ultimately play a role in CVD.
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