For each mouse, the behavioral and bodyweight analyses from days 100 and 114 of treatment were assembled into a vector, with a total of 31 values/vector/mouse. To avoid bias from behavioral measurements with a high range, all values were normalized (by subtracting the mean of each behavior and dividing by the standard deviation). PCA was performed using the Python scikit‐learn package. Clustering of mouse treatment groups was quantified using a silhouette score, as described previously (Cunningham et al, 2017). Briefly, for each point in a treatment group, this silhouette score (s) was defined as s = (b−a)/max(a,b), where ‘a’ is the mean distance between the point and other points in the same group and ‘b’ is the mean distance between the point and other points in the next nearest group.