title: Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure.
abstract: In this study, we propose a novel statistical framework for detecting progressive changes in molecular traits as response to a pathogenic stimulus. In particular, we propose to employ Bayesian hierarchical models to analyse changes in mean level, variance and correlation of metabolic traits in relation to covariates. To illustrate our approach we investigate changes in urinary metabolic traits in response to cadmium exposure, a toxic environmental pollutant. With the application of the proposed approach, previously unreported variations in the metabolism of urinary metabolites in relation to urinary cadmium were identified. Our analysis highlights the potential effect of urinary cadmium on the variance and correlation of a number of metabolites involved in the metabolism of choline as well as changes in urinary alanine. The results illustrate the potential of the proposed approach to investigate the gradual effect of pathogenic stimulus in molecular traits.
authors.forename: Beatriz Valcarcel
authors.initials: BV
authors.lastname: Salamanca
authors.forename: Timothy M D
authors.initials: TM
authors.lastname: Ebbels
authors.forename: Maria De
authors.initials: MD
authors.lastname: Iorio
mesh_terms: D001499:Bayes Theorem; D002104:Cadmium / Q000633:toxicity*; D004787:Environmental Pollution; D006801:Humans; D008962:Models, Theoretical; D014551:Urinary Tract / Q000187:drug effects / Q000378:metabolism*
chemical_list: D002104:Cadmium