• Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
• Department of Public Health and Preventive Medicine, Kangda College of Nanjing Medical University, Lianyungang, 222000, China.
• Section of Biostatistics, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, ØsterFarimagsgade 5, 1353, Copenhagen, Denmark.
• Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
• Department of Critical Care Medicine, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, 214023, Jiangsu, China.
• Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
• Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China. fengchen@njmu.edu.cn.
• Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China. yzhao@njmu.edu.cn.

BACKGROUND: Body mass index (BMI) has been found to be associated with a decreased risk of non-small cell lung cancer (NSCLC); however, the effect of BMI trajectories and potential interactions with genetic variants on NSCLC risk remain unknown.

METHODS: Cox proportional hazards regression model was applied to assess the association between BMI trajectory and NSCLC risk in a cohort of 138,110 participants from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. One-sample Mendelian randomization (MR) analysis was further used to access the causality between BMI trajectories and NSCLC risk. Additionally, polygenic risk score (PRS) and genome-wide interaction analysis (GWIA) were used to evaluate the multiplicative interaction between BMI trajectories and genetic variants in NSCLC risk.

RESULTS: Compared with individuals maintaining a stable normal BMI (n = 47,982, 34.74%), BMI trajectories from normal to overweight (n = 64,498, 46.70%), from normal to obese (n = 21,259, 15.39%), and from overweight to obese (n = 4,371, 3.16%) were associated with a decreased risk of NSCLC (hazard ratio [HR] for trend = 0.78, P < 2×10^-16). An MR study using BMI trajectory associated with genetic variants revealed no significant association between BMI trajectories and NSCLC risk. Further analysis of PRS showed that a higher GWAS-identified PRS (PRS_GWAS) was associated with an increased risk of NSCLC, while the interaction between BMI trajectories and PRS_GWAS with the NSCLC risk was not significant (P_sPRS= 0.863 and P_wPRS= 0.704). In GWIA analysis, four independent susceptibility loci (P < 1×10^-6) were found to be associated with BMI trajectories on NSCLC risk, including rs79297227 (12q14.1, located in SLC16A7, P_interaction = 1.01×10^-7), rs2336652 (3p22.3, near CLASP2, P_interaction = 3.92×10^-7), rs16018 (19p13.2, in CACNA1A, P_interaction = 3.92×10^-7), and rs4726760 (7q34, near BRAF, P_interaction = 9.19×10^-7). Functional annotation demonstrated that these loci may be involved in the development of NSCLC by regulating cell growth, differentiation, and inflammation.

CONCLUSIONS: Our study has shown an association between BMI trajectories, genetic factors, and NSCLC risk. Interestingly, four novel genetic loci were identified to interact with BMI trajectories on NSCLC risk, providing more support for the aetiology research of NSCLC.

TRIAL REGISTRATION: http://www.

CLINICALTRIALS: gov , NCT01696968 .