Metabolomic profiles of current cigarette smokers

Abstract

Smoking-related biomarkers for lung cancer and other diseases are needed to enhance early detection strategies and to provide a science base for tobacco product regulation. An untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UHPLC-Q-TOF MS) totaling 957 assays was used in a novel experimental design where 105 current smokers smoked two cigarettes 1 h apart. Blood was collected immediately before and after each cigarette allowing for within-subject replication. Dynamic changes of the metabolomic profiles from smokers' four blood samples were observed and biomarkers affected by cigarette smoking were identified. Thirty-one metabolites were definitively shown to be affected by acute effect of cigarette smoking, uniquely including menthol-glucuronide, the reduction of glutamate, oleamide, and 13 glycerophospholipids. This first time identification of a menthol metabolite in smokers' blood serves as proof-of-principle for using metabolomics to identify new tobacco-exposure biomarkers, and also provides new opportunities in studying menthol-containing tobacco products in humans. Gender and race differences also were observed. Network analysis revealed 12 molecules involved in cancer, notably inhibition of cAMP. These novel tobacco-related biomarkers provide new insights to the effects of smoking which may be important in carcinogenesis but not previously linked with tobacco-related diseases. © 2016 Wiley Periodicals, Inc.

Keywords: UHPLC-QTOF-MS; menthol; metabolomics; plasma; smokers.

Fig. 1

(a) Study design for the cross-sectional study of smokers. (b) Distribution of the estimated measurement error in CV (%) before filtering. Each dot represents a single peak. The dot size and color corresponds to the CV value: the larger the dot, and the brighter the color of the dot, the larger the CV value of the individual peak.

Fig. 2

Scatter plots representation of significant features regulated by (a) the first cigarette, and (b) the second cigarette. Significant features were selected by paired t-tests with threshold 0.05. The red dots represent features above the threshold. All p values are transformed by -log10 so that the more significant features (with smaller p values) were plotted higher on the graph. (c) Venn diagram representing metabolites significantly increased or decreased after 1^st and 2^nd cigarette.

Fig. 3

Significant metabolites and their putative identifications. (a) Venn-diagram represents the significant metabolites influenced by cigarette smoking from the ANCOVA model; (b) Pairwise correlations from pre-1 cigarettes for the significant metabolites. Abbreviations: phosphatidylethanolamine (PE), phosphatidylcholines (PC), lysophosphatidylcholines (LysoPC), lysophosphatidylethanolamine (LysoPE).

Fig. 4

(a) Levels of menthol-glucuronide across all time points. Blue dots and lines represent menthol smokers and red dots and lines represent non-menthol smokers. Each error bar was constructed using 1 standard error from the mean. Differences in (b) Race, (c) Gender, and among (d) Menthol cigarette smokers and (e) Non-menthol cigarette smokers on the significant metabolites after both cigarette smoking. All differences in fold change were significant at p < 0.05 level.

Fig. 5

Two networks associated with cigarette smoking. Green nodes represented metabolites decreased in level in our study. Twelve molecules known as biomarkers for cancer were outlined in magenta.