The PCA function (Principal Component Analysis (PCA)) outputs a set of principal components, and each principal component is a linear combination of the set of original predictors.
In the PCA example output table pca_health_ev_scaled (see Output), the first-ranked principal component is:
-0.082 * age + 0.387 * bmi + (-0.0935) * bloodpressure + 0.042 * glucose …
The PCA_Plot function uses these coefficients from (the output table of the PCA function) to compute a principal component score for each observation. If the PCA function returned n principal components, the PCA_Plot function calculates n scores for each observation. That is, the n principal components replace the original, larger set of predictors for subsequent analyses.