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Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers
- Source :
- Bioanalysis. 8(19)
- Publication Year :
- 2016
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
Adult
Male
Nicotine
Magnetic Resonance Spectroscopy
Time Factors
metabonomics/metabolomics
Lipoproteins
Clinical Biochemistry
Pulmonary disease
Disease
Biology
Bioinformatics
Analytical Chemistry
03 medical and health sciences
0302 clinical medicine
medicine
Cluster Analysis
Humans
Metabolomics
xenobiotics
General Pharmacology, Toxicology and Pharmaceutics
Adverse effect
Saliva
Chromatography, High Pressure Liquid
Chromatography, Reverse-Phase
Principal Component Analysis
Metabolic biomarkers
Smoking
General Medicine
Middle Aged
Lipids
Medical Laboratory Technology
Metabolic pathway
030104 developmental biology
Linear Models
biomarker
Female
030217 neurology & neurosurgery
Biomarkers
medicine.drug
Subjects
Details
- ISSN :
- 17576199
- Volume :
- 8
- Issue :
- 19
- Database :
- OpenAIRE
- Journal :
- Bioanalysis
- Accession number :
- edsair.doi.dedup.....e6cab9a35a03ba03a274cd61db93fcd0