Please, if someone know any clue for my problem, help me.

I'm running logistic regression for outcome on SBP(systolic blood pressure), DBP(diastolic blood pressure), BMI , and other covariates.

Because SBP, BMI didn't show linear relationship with logit of outcome, I changed these continuous variables to categorical variable by quintile.

Below is the models I tried.

Model1 : Logistic outcome covariates BMI SBP

Model2 : Logistic outcome covariates BMI DBP

Model3 : Logistic outcome covariates BMI SBP DBP

Model4 : Logistic outcome covariates BMI SBP DBP SBP*DBP

This is model 1, model 2

This is model 3

Because in model 3, there was a significance change in 2nd 3rd of SBP and 3rd 4th of DBP compared with model 1, model2, so I guess 'Is there any collinearity between SBP and DBP?'

But all VIF values were below 3.

So then i would like to check interaction between SBP and DBP.

So I made interaction term using categorical SBP and DBP, it resulted in 16 level variable.

This is model 4

All levels of interaction term were not significant, but SBP and DBP lost it's significance in most of the level.

So I have some question about my analysis.

(1) Do I have to include interaction term into the model or not ? Do you think is there any collinearity or interaction between SBP and DBP?

(2) How can I interpret difference between model 1,2 vs model 3.

(3) Can only small VIF value confirm absence of multicollinearity? I ask this question cause condition index was 88.68. How low VIF and high condition index are present same time?

Please help me....

Best