Statistical analysis
We performed an intention-to-treat analysis of stool SCFA (butyrate, propionate and acetate) levels in mmol kgWe generated volcano plots based on a Student’s UMAP ordination (the umap-learn library in Python) of the specimens was generated after calculating the cosine distance between specimens based on the median-centered plasma metabolite data, specifying ten nearest neighbors. The result was four distinct clusters that could be readily identified by HDBSCAN (minimum sample of 1 and minimum cluster size of 5). Generalized linear modeling was used to determine the compounds most associated with at least one cluster. The number of specimens per cluster was established for each time point and each cohort.Python v.3.9 with umap-learn, statmodels and scikit-learn libraries were used for the data analysis, with all parameters noted in the 