MetaboVariation 2.0: Multivariate analysis for identifying metabolite variation at the individual level

Variations in individuals’ metabolic profiles are the result of their genetic makeup and environmental and lifestyle factors. To address the challenge of identifying these intra-individual variations at the individual level, we introduce “MetaboVariation 2.0”, a multivariate Bayesian generalised linear model designed to flag individuals with intra-individual variations in metabolite levels across repeated measurements. MetaboVariation 2.0 builds upon the previous univariate MetaboVariation approach by incorporating dependencies between metabolites, offering a more comprehensive assessment of individual metabolic variations. While simultaneously considering all metabolites, MetaboVariation 2.0 flags an individual when their observed metabolite levels deviate from their individual-level posterior predictive interval at a time point. A series of simulation studies were conducted to evaluate the performance of MetaboVariation 2.0. In addition it was applied to a metabolomics data set. The efficacy of this approach was validated through a series of simulation studies. These simulations demonstrated that the multivariate model outperformed its predecessor, particularly in scenarios where the dependencies between the metabolites were positive. The model showed lower mean absolute differences between correlation matrices of metabolite levels from replicate datasets and the original simulated data, indicating improved accuracy in capturing the metabolic dependencies. In addition, analysis of plasma metabolite levels from 164 individuals with 20 metabolites measured across four time points was performed to detect individuals with intra-individual variations. MetaboVariation 2.0 revealed intra-individual variations in 15.2% of the individuals, with 20% or more of their metabolites showing variations beyond their 97.5% posterior predictive intervals in at least one time point. In conclusion, MetaboVariation 2.0 accounts for the inherent dependencies between different metabolites, offering a full view of an individual’s metabolic profile which is an important advancement for assessment of individual-level metabolite variation. A software implementation of this approach is freely available through the “MetaboVariation” R package, promoting its accessibility and use in broader metabolomics research.