Effects of smoking and smoking cessation on human serum metabolite profile: results from the KORA cohort study

Abstract

Background: Metabolomics helps to identify links between environmental exposures and intermediate biomarkers of disturbed pathways. We previously reported variations in phosphatidylcholines in male smokers compared with non-smokers in a cross-sectional pilot study with a small sample size, but knowledge of the reversibility of smoking effects on metabolite profiles is limited. Here, we extend our metabolomics study with a large prospective study including female smokers and quitters.

Methods: Using targeted metabolomics approach, we quantified 140 metabolite concentrations for 1,241 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) human cohort at two time points: baseline survey conducted between 1999 and 2001 and follow-up after seven years. Metabolite profiles were compared among groups of current smokers, former smokers and never smokers, and were further assessed for their reversibility after smoking cessation. Changes in metabolite concentrations from baseline to the follow-up were investigated in a longitudinal analysis comparing current smokers, never smokers and smoking quitters, who were current smokers at baseline but former smokers by the time of follow-up. In addition, we constructed protein-metabolite networks with smoking-related genes and metabolites.

Results: We identified 21 smoking-related metabolites in the baseline investigation (18 in men and six in women, with three overlaps) enriched in amino acid and lipid pathways, which were significantly different between current smokers and never smokers. Moreover, 19 out of the 21 metabolites were found to be reversible in former smokers. In the follow-up study, 13 reversible metabolites in men were measured, of which 10 were confirmed to be reversible in male quitters. Protein-metabolite networks are proposed to explain the consistent reversibility of smoking effects on metabolites.

Conclusions: We showed that smoking-related changes in human serum metabolites are reversible after smoking cessation, consistent with the known cardiovascular risk reduction. The metabolites identified may serve as potential biomarkers to evaluate the status of smoking cessation and characterize smoking-related diseases.

Figure 1

Flow diagram illustrating the analysis strategy. CS: current smokers; FS: former smokers; NS: never smokers.

Figure 2

Heat maps of smoking-related metabolites in (A) men and (B) women. The heat map shows mean residues of smoking-related metabolites in CS, FS and NS and the reversibility after smoking cessation. The color of each cell in the heat map represents the relative mean concentration of each metabolite in NS, FS or CS. The number of samples in each group is provided. The bar besides the metabolite names indicates the reversibility of these metabolites after smoking cessation. aa: diacyl-; ae: acyl-alkyl-; C0: carnitine; CS: current smokers; FS: former smokers; lysoPC: acyl-phosphatidylcholine; NS: never smokers; PC: phosphatidylcholine; SM (OH): hydroxysphingomyeline.

Figure 3

Metabolite concentration variations in relation to smoking cessation time. Taking NS as baseline, figures show the mean residuals of metabolites in different groups of CS and FS, giving the trend of metabolite variation with cessation time. FS were grouped by stratified cessation time (≤10, 11 to 20, 21 to 30, 31 to 40, 41+). Residuals were calculated by linear regression model (regression of metabolite concentration on age, body mass index and alcohol consumption). aa: diacyl-; ae: acyl-alkyl-; CS: current smokers; FS: former smokers; NS: never smokers; PC: phosphatidylcholine.

Figure 4

Changes of smoking-related metabolites in current, former and never smokers in KORA S4 → F4. Taking the NS as baseline, the concentration change of each metabolite is shown as the adjusted mean residue in KORA S4 and F4 in all three groups (CS, FS and NS). Only metabolites with significant prospective change in KORA S4 → F4 are shown in the figure. Residuals were calculated from a linear regression model (regression of metabolite concentration on age, body mass index and alcohol consumption). aa: diacyl-; ae: acyl-alkyl-; CS: current smokers; FS: former smokers; lysoPC: acyl-phosphatidylcholine; NS: never smokers; PC: phosphatidylcholine.

Figure 5

Pathway analyses of smoking-related metabolites. Figure shows enrichment and impact of smoking-related metabolites in Kyoto Encyclopedia of Genes and Genomes pathways. The enrichment scores are shown on y-axis, which was calculated as the negative logarithm of the P-value from an enrichment test. The x-axis indicates the structural impact with a score from 0 to 1 of the smoking-related metabolites in the enriched pathways.

Figure 6

Protein-metabolite networks and pathways of the smoking-related metabolites and genes. (A) Network linking metabolites and proteins encoded by smoking-related genes with maximum one intermediate. Node color indicates the reversibility after smoking cessation. (B, C) Effects of smoking on arginine and glutamate as well as on lipid metabolism. Metabolites are in regular font, protein coding genes are in italic, gender-specific gene (CPS1) is in bold italic font. aa: diacyl-; ae: acyl-alkyl-; APOA5: apolipoprotein A-V; BDH: 3-hydroxybutyrate dehydrogenase, type 1; cPLA2: cytosolic phospholipase A2; CS: current smokers; FS: former smokers; GIIC sPLA2: phospholipase A2, membrane associated; LRAT: lecithin retinol acyltransferase; LYPLA1: lysophospholipase I; lysoPC: acyl-phosphatidylcholine; NOS1: nitric oxide synthase 1; NS: never smokers; PC: phosphatidylcholine; PLA2G10: group 10 secretory phospholipase A2; SCGB1A1: uteroglobin; SDH: serine dehydratase; SLC3A2: solute carrier family 3 member 2