Associations between metabolomic signatures of persistent smoking effects and lung cancer risk among former smokers

BACKGROUND: Smoking-related metabolic perturbations may persist long after smoking cessation. We aimed to develop a metabolite signature capturing persistent smoking effect (MSPS) and prospectively assess its association with lung cancer (LC) risk among former smokers. METHODS: Global untargeted metabolomics data from 1732 participants of the Shanghai Men's Health Study (SMHS) and Southern Community Cohort Study (SCCS) were used to construct MSPS. Linear regression and Elastic net regression were applied to select persistent smoking-related metabolites and develop MSPS. We evaluated the association of MSPS with LC via conditional logistic regression among 229 case-control pairs nested in former smokers of the SCCS and SMHS. RESULTS: We identified 130 metabolites significantly associated with current smoking status (PFDR<0.05). After excluding tobacco-specific metabolites, we selected 16 metabolites associated with former smoking (6 lipids, 6 xenobiotics, 2 amino acids, and 2 carbohydrates) to derive the MSPS. The MSPS showed a significant association with increased LC risk [Odds ratio (OR), 95% confidence interval (CI):1.44 (1.15-1.81) per standard deviation (SD)], independent of smoking pack-years and quitting duration. The association was consistently seen in Whites (OR=1.91, 95%CI:1.19-3.07 per SD), Blacks (OR=1.66, 95%CI:1.07-2.56 per SD), and Asians (OR=1.15, 95%CI:0.81-1.63 per SD). CONCLUSIONS: We developed an MSPS that was robustly associated with LC risk independent of smoking history for former smokers. If validated, the MSPS can be applied to identify former smokers at high risk of developing LC. IMPACT: The MSPS provides a novel tool to stratify LC risk among former smokers, beyond current guidelines based on smoking history.