title: Bayesian modeling of complex metabolic pathways.
abstract: Many chronic diseases are the result of a complex sequence of biochemical reactions involving exposures to various environmental agents, metabolized by a number of different genes. Routine epidemiologic analyses of such associations have tended to rely on standard contingency table or logistic regression methods, typically focusing on one variable at a time or pairwise combinations. We consider two statistical alternatives to this approach, one based on Bayesian model averaging, one based on pharmacokinetic modeling of the biochemical pathways. These approaches are illustrated using data from a case-control study of colorectal polyps in relation to tobacco smoking and consumption of well done red meat, both viewed as sources of heterocyclic amines and polycyclic aromatic hydrocarbons. The new analyses are structured in a manner that attempts to take advantage of prior knowledge of the metabolism of these classes of compounds and the various genes that regulate these pathways.
authors: Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089-9011, USA. dconti@usc.edu
authors: David V
authors: DV
authors: Conti
authors: Victoria
authors: V
authors: Cortessis
authors: John
authors: J
authors: Molitor
authors: Duncan C
authors: DC
authors: Thomas
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D008660:Metabolism / Q000502:physiology*; D008954:Models, Biological