2.5. Factor and Structural Equation Modelling Analyses
impaired systolic cardiac function, diabetes
HYPERTENSION, ISCHAEMIC HEART DISEASE, DIABETES
In the first step using variable data at baseline, Confirmatory Factor Analysis (CFA) was used to reduce the complexity of all the 19 measured variables (age, NT-proBNP, CRP, copeptin, MR-proADM, TWEAK, endostatin, cathepsin, MMP1, ST2, hypertension, diabetes, ischaemic heart disease, impaired systolic cardiac function, ICAM-1, adiponectin, leptin, SCF and OPG). Next, CFA was used to reduce the complexity of the five biomarkers ICAM-1, adiponectin, SCF, OPG and leptin into a lower number of latent, underlying variables or factors.In a second step, Structural Equation Modelling (SEM) and modum LISREL were used to explore the structural relations between the five biomarkers and age at baseline.The goodness of fit of the SEM model was examined with Chi-Square, the Root Mean Square Error of Approximation (RMSEA) and the Comparative Fit Index (CFI). A non-significant Chi-Square, an RMSEA < 0.05 and a CFI > 0.95 indicated a good model fit [Firstly, all variables were analysed and, thereafter, the five selected ageing biomarkers. The factor and modelling analyses reflect the natural variation in the biomarkers in the total group at baseline.