title: Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Metabometrix Ltd, Bioincubator, Prince Consort Road, South Kensington, London, SW7 2BP, UK.
authors.forename: Manuja R
authors.initials: MR
authors.lastname: Kaluarachchi
authors.affiliation: Metabometrix Ltd, Bioincubator, Prince Consort Road, South Kensington, London, SW7 2BP, UK.
authors.forename: Claire L
authors.initials: CL
authors.lastname: Boulangé
authors.affiliation: Metabometrix Ltd, Bioincubator, Prince Consort Road, South Kensington, London, SW7 2BP, UK.
authors.forename: Isabel
authors.initials: I
authors.lastname: Garcia-Perez
authors.affiliation: Metabometrix Ltd, Bioincubator, Prince Consort Road, South Kensington, London, SW7 2BP, UK.
authors.forename: John C
authors.initials: JC
authors.lastname: Lindon
authors.affiliation: British American Tobacco, Research & Development, Regents Park Road, Southampton, SO15 8TL, UK.
authors.forename: Emmanuel F
authors.initials: EF
authors.lastname: Minet
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000097:blood* / Q000652:urine; D002851:Chromatography, High Pressure Liquid; D056148:Chromatography, Reverse-Phase; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D016014:Linear Models; D008055:Lipids / Q000032:analysis / Q000097:blood; D008074:Lipoproteins / Q000737:chemistry; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D009538:Nicotine / Q000097:blood / Q000378:metabolism / Q000652:urine; D025341:Principal Component Analysis; D012463:Saliva / Q000737:chemistry; D012907:Smoking; D013997:Time Factors
chemical_list: D015415:Biomarkers; D008055:Lipids; D008074:Lipoproteins; D009538:Nicotine
keywords: biomarker; metabonomics/metabolomics; xenobiotics