Statistical analysis
The statistical analyses were performed using R version 4.0.3 (Following NMR analyses, separate data matrices of metabolite abundances for lipid species, small molecule species and amino acids were generated and each analysed separately as targeted panels of metabolites. Each metabolite panel was first filtered to remove missing data points and/or those with negative abundance values, followed by log transformation, centering and scaling to bring metabolite abundances within the same dynamic range. Exploratory visualisations were then generated using principal component analyses to assess for any obvious effects of the feeding regimens on metabolite abundances. To determine differences in metabolite abundance between feeding regimens, across time and interactions between feed and time, analyses were performed using a moderated empirical Bayesian mixed effects model approach with the limma (version 3.18) R package, with the primary outcome being a difference in metabolite profiles between armsLinear mixed effect modelling was chosen over standard statistical approaches (e.g. repeated measures ANOVA) as it deals better with designs where the repeated measure is continuous (i.e. in this case, time), where the design may become unbalanced (e.g. due to attrition) and where non-independence of data due to clustering within individuals exists, as is likely to be the case here. In addition we used limma to generate empirical Bayes estimates of metabolite-wise variance