Aim: Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events.
Results: A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC-MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed.
Conclusion: Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment.
Keywords: biomarker; metabonomics/metabolomics; xenobiotics.