Associations of genetic risk, BMI trajectories, and the risk of non-small cell lung cancer: a population-based cohort study

Dongfang You, Danhua Wang, Yaqian Wu, Xin Chen, Fang Shao, Yongyue Wei, Ruyang Zhang, Theis Lange, Hongxia Ma, Hongyang Xu, Zhibin Hu, David C Christiani, Hongbing Shen, Feng Chen, Yang Zhao, Dongfang You, Danhua Wang, Yaqian Wu, Xin Chen, Fang Shao, Yongyue Wei, Ruyang Zhang, Theis Lange, Hongxia Ma, Hongyang Xu, Zhibin Hu, David C Christiani, Hongbing Shen, Feng Chen, Yang Zhao

Abstract

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 (PRSGWAS) was associated with an increased risk of NSCLC, while the interaction between BMI trajectories and PRSGWAS with the NSCLC risk was not significant (PsPRS= 0.863 and PwPRS= 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, Pinteraction = 1.01×10-7), rs2336652 (3p22.3, near CLASP2, Pinteraction = 3.92×10-7), rs16018 (19p13.2, in CACNA1A, Pinteraction = 3.92×10-7), and rs4726760 (7q34, near BRAF, Pinteraction = 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 .

Keywords: Body mass index; Genome-wide interaction study; Non-small cell lung cancer; Trajectory.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
The latent class growth model of BMI trajectories in the PLCO study. A BMI changes for each participant in each trajectory group across three analysed age points (ages of 20 years, 50 years, and baseline). B Each trajectory was calculated at any of the three analysed age points (ages of 20 years, 50 years, and baseline). HR and 95% CI were estimated by Cox proportional hazards regression model with the adjustment for age, sex, race, family history of lung cancer, education, smoking, personal history of diabetes, current marital status, and study centre
Fig. 2
Fig. 2
Stratifications analysis for the interaction effects between BMI trajectories and GWIA-identified SNPs on NSCLC risk. A The identified four BMI trajectories from the onset of adulthood to the baseline. B Cumulative incidence of NSCLC stratified by GWIA-identified SNPs. P-value was derived from the Log-rank test. C Pathway of the gene (BRAF)-BMI trajectories interaction effect on the risk of NSCLC
Fig. 3
Fig. 3
Interaction analysis and stratification analysis of BMI trajectories and the PRS constructed by four GWIA-identified SNPs on NSCLC risk. A, BwPRSGWIA were weighted according to the strength of their association with lung cancer. C, DsPRSGWIA were calculated by simple counting. P value for interaction was derived from multivariate-adjusted Cox proportional hazards regression model. PRS, polygenic risk score; GWIA, genome wide interaction analysis; SNP, single nucleotide polymorphism; HR, hazard ratio; CI, confidence interval

References

    1. Rauscher GH, Mayne ST, Janerich DT. Relation between body mass index and lung cancer risk in men and women never and former smokers. Am J Epidemiol. 2000;152(6):506–513. doi: 10.1093/aje/152.6.506.
    1. Smith L, Brinton LA, Spitz MR, Lam TK, Park Y, Hollenbeck AR, et al. Body mass index and risk of lung cancer among never, former, and current smokers. J Natl Cancer Inst. 2012;104(10):778–789. doi: 10.1093/jnci/djs179.
    1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. doi: 10.3322/caac.21492.
    1. Ettinger DS, Wood DE, Akerley W, Bazhenova LA, Borghaei H, Camidge DR, et al. Non-small cell lung cancer, Version 6.2015. J Natl Compr Cancer Netw. 2015;13(5):515–524. doi: 10.6004/jnccn.2015.0071.
    1. Alvarnas A, Alvarnas J. Obesity and cancer risk: a public health crisis. Am J Manag Care. 2019;25(11 Spec No.):Sp332–Spsp3.
    1. Chen C, Ye Y, Zhang Y, Pan XF, Pan A. Weight change across adulthood in relation to all cause and cause specific mortality: prospective cohort study. BMJ. 2019;367:l5584. doi: 10.1136/bmj.l5584.
    1. Yu D, Zheng W, Johansson M, Lan Q, Park Y, White E, et al. Overall and central obesity and risk of lung cancer: a pooled analysis. J Natl Cancer Inst. 2018;110(8):831–842. doi: 10.1093/jnci/djx286.
    1. Duan P, Hu C, Quan C, Yi X, Zhou W, Yuan M, et al. Body mass index and risk of lung cancer: Systematic review and dose-response meta-analysis. Sci Rep. 2015;5:16938. doi: 10.1038/srep16938.
    1. Zheng R, Du M, Zhang B, Xin J, Chu H, Ni M, et al. Body mass index (BMI) trajectories and risk of colorectal cancer in the PLCO cohort. Br J Cancer. 2018;119(1):130–132. doi: 10.1038/s41416-018-0121-y.
    1. Petrick JL, Kelly SP, Liao LM, Freedman ND, Graubard BI, Cook MB. Body weight trajectories and risk of oesophageal and gastric cardia adenocarcinomas: a pooled analysis of NIH-AARP and PLCO Studies. Br J Cancer. 2017;116(7):951–959. doi: 10.1038/bjc.2017.29.
    1. Abdel-Rahman O. Pre-diagnostic body mass index trajectory in relationship to lung cancer incidence and mortality; findings from the PLCO trial. Expert Rev Respir Med. 2019:1–7.
    1. Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, et al. Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000;343(2):78–85. doi: 10.1056/NEJM200007133430201.
    1. Brennan P, Hainaut P, Boffetta P. Genetics of lung-cancer susceptibility. Lancet Oncol. 2011;12(4):399–408. doi: 10.1016/S1470-2045(10)70126-1.
    1. Dai J, Shen W, Wen W, Chang J, Wang T, Chen H, et al. Estimation of heritability for nine common cancers using data from genome-wide association studies in Chinese population. Int J Cancer. 2017;140(2):329–336. doi: 10.1002/ijc.30447.
    1. Sampson JN, Wheeler WA, Yeager M, Panagiotou O, Wang Z, Berndt SI, et al. Analysis of heritability and shared heritability based on genome-wide association studies for thirteen cancer types. J Natl Cancer Inst. 2015;107(12):djv279. doi: 10.1093/jnci/djv279.
    1. Bosse Y, Amos CI. A decade of GWAS results in lung cancer. Cancer Epidemiol Biomark Prev. 2018;27(4):363–379. doi: 10.1158/1055-9965.EPI-16-0794.
    1. Dai J, Lv J, Zhu M, Wang Y, Qin N, Ma H, et al. Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations. Lancet Respir Med. 2019;7(10):881–891. doi: 10.1016/S2213-2600(19)30144-4.
    1. Vineis P, Pearce N. Missing heritability in genome-wide association study research. Nat Rev Genet. 2010;11(8):589. doi: 10.1038/nrg2809-c2.
    1. Manuck SB, McCaffery JM. Gene-environment interaction. Annu Rev Psychol. 2014;65:41–70. doi: 10.1146/annurev-psych-010213-115100.
    1. Malaiyandi V, Sellers EM, Tyndale RF. Implications of CYP2A6 genetic variation for smoking behaviors and nicotine dependence. Clinical Pharmacology & Therapeutics. 2005;77(3):145–158. doi: 10.1016/j.clpt.2004.10.011.
    1. Oken MM, Hocking WG, Kvale PA, Andriole GL, Buys SS, Church TR, et al. Screening by chest radiograph and lung cancer mortality: the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial. Jama. 2011;306(17):1865–1873. doi: 10.1001/jama.2011.1591.
    1. Gohagan JK, Prorok PC, Hayes RB, Kramer BS, Prostate LC. Ovarian Cancer Screening Trial Project T. The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial of the National Cancer Institute: history, organization, and status. Control Clin Trials. 2000;21(6 Suppl):251S–272S. doi: 10.1016/S0197-2456(00)00097-0.
    1. Kelly SP, Graubard BI, Andreotti G, Younes N, Cleary SD, Cook MB. Prediagnostic body mass index trajectories in relation to prostate cancer incidence and mortality in the PLCO Cancer Screening Trial. J Natl Cancer Inst. 2017;109(3).
    1. Armstrong H, Carraro N, Amanda T, Gaudreau P, Louvet B. Latent class growth mdelling: a tutorial. 2009.
    1. Koning M, Hoekstra T, de Jong E, Visscher TL, Seidell JC, Renders CM. Identifying developmental trajectories of body mass index in childhood using latent class growth (mixture) modelling: associations with dietary, sedentary and physical activity behaviors: a longitudinal study. BMC Public Health. 2016;16(1):1128. doi: 10.1186/s12889-016-3757-7.
    1. Lampousi AM, Moller J, Liang Y, Berglind D, Forsell Y. Latent class growth modelling for the evaluation of intervention outcomes: example from a physical activity intervention. J Behav Med. 2021;44(5):622–629. doi: 10.1007/s10865-021-00216-y.
    1. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–138. doi: 10.1146/annurev.clinpsy.121208.131413.
    1. Tryka KA, Hao L, Sturcke A, Jin Y, Wang ZY, Ziyabari L, et al. NCBI’s Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 2014;42(Database issue):D975–D979. doi: 10.1093/nar/gkt1211.
    1. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007;39(10):1181–1186. doi: 10.1038/ng1007-1181.
    1. Huyghe JR, Bien SA, Harrison TA, Kang HM, Chen S, Schmit SL, et al. Discovery of common and rare genetic risk variants for colorectal cancer. Nat Genet. 2019;51(1):76–87. doi: 10.1038/s41588-018-0286-6.
    1. Wang ML, Gu DY, Du ML, Xu Z, Zhang SZ, Zhu LJ, et al. Common genetic variation in ETV6 is associated with colorectal cancer susceptibility. Nat Commun. 2016;7.
    1. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5(6):e1000529. doi: 10.1371/journal.pgen.1000529.
    1. McKay JD, Hung RJ, Han Y, Zong X, Carreras-Torres R, Christiani DC, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet. 2017;49(7):1126–1132. doi: 10.1038/ng.3892.
    1. de Vries PS, Brown MR, Bentley AR, Sung YJ, Winkler TW, Ntalla I, et al. Multiancestry genome-wide association study of lipid levels incorporating gene-alcohol interactions. Am J Epidemiol. 2019;188(6):1033–1054. doi: 10.1093/aje/kwz005.
    1. Battle A, Brown CD, Engelhardt BE, Montgomery SB. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–213. doi: 10.1038/nature24277.
    1. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. doi: 10.1038/nature14177.
    1. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–330. doi: 10.1038/nature14248.
    1. Ward LD, Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016;44(D1):D877–D881. doi: 10.1093/nar/gkv1340.
    1. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515–526. doi: 10.1093/biomet/81.3.515.
    1. Hellstrom A, Nilsson AK, Wackernagel D, Pivodic A, Vanpee M, Sjobom U, et al. Effect of enteral lipid supplement on severe retinopathy of prematurity: a randomized clinical trial. JAMA Pediatr. 2021;175(4):359–367. doi: 10.1001/jamapediatrics.2020.5653.
    1. Lam VK, Bentzen SM, Mohindra P, Nichols EM, Bhooshan N, Vyfhuis M, et al. Obesity is associated with long-term improved survival in definitively treated locally advanced non-small cell lung cancer (NSCLC) Lung Cancer. 2017;104:52–57. doi: 10.1016/j.lungcan.2016.11.017.
    1. Greenlee H, Unger JM, LeBlanc M, Ramsey S, Hershman DL. Association between body mass index and cancer survival in a pooled analysis of 22 clinical trials. Cancer Epidemiol Biomark Prev. 2017;26(1):21–29. doi: 10.1158/1055-9965.EPI-15-1336.
    1. Shepshelovich D, Xu W, Lu L. Body mass index (BMI), BMI change, and overall survival in patients with SCLC and NSCLC: a pooled analysis of the International Lung Cancer Consortium (Vol 14, pg 1594, 2019) J Thorac Oncol. 2019;14(11):2024. doi: 10.1016/j.jtho.2019.09.003.
    1. Wood AM, Jonsson H, Nagel G, Haggstrom C, Manjer J, Ulmer H, et al. The inverse association of body mass index with lung cancer: exploring residual confounding, metabolic aberrations and within-person variability in smoking. Cancer Epidemiol Biomark Prev. 2021;30(8):1489–1497. doi: 10.1158/1055-9965.EPI-21-0058.
    1. Ross PJ, Ashley S, Norton A, Priest K, Waters JS, Eisen T, et al. Do patients with weight loss have a worse outcome when undergoing chemotherapy for lung cancers? Br J Cancer. 2004;90(10):1905–1911. doi: 10.1038/sj.bjc.6601781.
    1. Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, et al. Tracking the evolution of non-small-cell lung cancer. N Engl J Med. 2017;376(22):2109–21. doi: 10.1056/NEJMoa1616288.
    1. Marchetti A, Felicioni L, Malatesta S, Grazia Sciarrotta M, Guetti L, Chella A, et al. Clinical features and outcome of patients with non-small-cell lung cancer harboring BRAF mutations. J Clin Oncol. 2011;29(26):3574–3579. doi: 10.1200/JCO.2011.35.9638.
    1. Su X, Shen Z, Yang Q, Sui F, Pu J, Ma J, et al. Vitamin C kills thyroid cancer cells through ROS-dependent inhibition of MAPK/ERK and PI3K/AKT pathways via distinct mechanisms. Theranostics. 2019;9(15):4461–4473. doi: 10.7150/thno.35219.
    1. Bost F, Aouadi M, Caron L, Binétruy B. The role of MAPKs in adipocyte differentiation and obesity. Biochimie. 2005;87(1):51–56. doi: 10.1016/j.biochi.2004.10.018.
    1. Tang QQ, Grønborg M, Huang H, Kim JW, Otto TC, Pandey A, et al. Sequential phosphorylation of CCAAT enhancer-binding protein beta by MAPK and glycogen synthase kinase 3beta is required for adipogenesis. Proc Natl Acad Sci U S A. 2005;102(28):9766–9771. doi: 10.1073/pnas.0503891102.
    1. Wang CY, Lai MD, Phan NN, Sun Z, Lin YC. Meta-analysis of public microarray datasets reveals voltage-gated calcium gene signatures in clinical cancer patients. PLoS One. 2015;10(7):e0125766. doi: 10.1371/journal.pone.0125766.
    1. Lang F, Föller M, Lang KS, Lang PA, Ritter M, Gulbins E, et al. Ion channels in cell proliferation and apoptotic cell death. J Membr Biol. 2005;205(3):147–157. doi: 10.1007/s00232-005-0780-5.
    1. Zemel MB, Miller SL. Dietary calcium and dairy modulation of adiposity and obesity risk. Nutr Rev. 2004;62(4):125–131. doi: 10.1111/j.1753-4887.2004.tb00034.x.
    1. Akhmanova A, Hoogenraad CC, Drabek K, Stepanova T, Dortland B, Verkerk T, et al. Clasps are CLIP-115 and -170 associating proteins involved in the regional regulation of microtubule dynamics in motile fibroblasts. Cell. 2001;104(6):923–935. doi: 10.1016/S0092-8674(01)00288-4.
    1. Tai AL, Mak W, Ng PK, Chua DT, Ng MY, Fu L, et al. High-throughput loss-of-heterozygosity study of chromosome 3p in lung cancer using single-nucleotide polymorphism markers. Cancer Res. 2006;66(8):4133–4138. doi: 10.1158/0008-5472.CAN-05-2775.
    1. Halestrap AP. The SLC16 gene family – structure, role and regulation in health and disease. Mol Asp Med. 2013;34(2-3):337–349. doi: 10.1016/j.mam.2012.05.003.
    1. Zhang G, Zhang Y, Dong D, Wang F, Ma X, Guan F, et al. MCT1 regulates aggressive and metabolic phenotypes in bladder cancer. J Cancer. 2018;9(14):2492–2501. doi: 10.7150/jca.25257.
    1. Wu DH, Liang H, Lu SN, Wang H, Su ZL, Zhang L, et al. miR-124 suppresses pancreatic ductal adenocarcinoma growth by regulating monocarboxylate transporter 1-mediated cancer lactate metabolism. Cell Physiol Biochem. 2018;50(3):924–935. doi: 10.1159/000494477.
    1. Sanità P, Capulli M, Teti A, Galatioto GP, Vicentini C, Chiarugi P, et al. Tumor-stroma metabolic relationship based on lactate shuttle can sustain prostate cancer progression. BMC Cancer. 2014;14:154. doi: 10.1186/1471-2407-14-154.
    1. Kim EJ, Hoffmann TJ, Nah G, Vittinghoff E, Delling F, Marcus GM. Coffee consumption and incident tachyarrhythmias reported behavior, Mendelian randomization, and their interactions. JAMA Intern Med. 2021;181(9):1185–1193. doi: 10.1001/jamainternmed.2021.3616.
    1. Hales CM, Fryar CD, Carroll MD, Freedman DS, Aoki Y, Ogden CL. Differences in obesity prevalence by demographic characteristics and urbanization level among adults in the United States, 2013-2016. Jama-Journal of the American Medical Association. 2018;319(23):2419–2429. doi: 10.1001/jama.2018.7270.
    1. Cornelis MC, Munafo MR. Mendelian randomization studies of coffee and caffeine consumption. Nutrients. 2018;10(10).

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