Interpreting principal component values

The dataset is made available through the following link: https://drive.google.com/file/d/1GmJWk8vFc6KC-TrrvGceaHW2HE4SZn6e/view?usp=sharing

R code used:
library(factoextra)
library(ggplot2)
library(corrplot)
diab=read.table("diab.txt",header=T)
head(diab)
dim(diab)
diab.pca=prcomp(diab,scale=T)
var=get_pca_var(diab.pca)
var
eig.val=get_eigenvalue(diab.pca)
eig.val
fviz_eig(diab.pca, addlabels = T, ylim = c(0, 30))
fviz_pca_var(diab.pca, col.var = "blue")
fviz_contrib(diab.pca,choice = "var", axes = 1)
fviz_contrib(diab.pca, choice = "var", axes = 2)
fviz_contrib(diab.pca, choice = "var", axes = 3)
fviz_contrib(diab.pca, choice = "var", axes = 4)
corrplot(var$cos2, is.corr=FALSE)
X11()
corrplot(var$contrib, is.corr=F)
diab.pca

principal component analysisPCAinterpreting PCA