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

Lung cancer is one of the most common cancers and a leading cause of cancer-related death worldwide [1, 2]. In 2018, there were 2.09 million new cases and 1.76 million deaths of lung cancer worldwide, accounting for 11.6% and 18.4% of all cancer cases and deaths, respectively [3]. In particular, non-small cell lung cancer (NSCLC), the most common type of lung cancer, accounts for approximately 85% of all lung cancer cases [4]. Due to the increasing burden of NSCLC, it is necessary to identify more potential risk factors associated with NSCLC so as to develop individualized prevention strategies.

Obesity, usually defined as body mass index (BMI) ≥ 30 kg/m², is becoming an increasingly common global health problem [5]. The global prevalence of obesity in adults increased steadily between 1975 and 2016, from 3 to 11% in men and 6 to 15% in women [6]. Several epidemiological studies have demonstrated that a higher BMI is associated with a lower risk of NSCLC in European and Asian populations [2, 7], which was also confirmed by a recent meta-analyses with a sample size of 7,310,130 participants [8]. However, most of these studies only used BMI at a single time point instead of considering the role of longitudinal BMI trajectories across the life course. In addition, a number of studies have shown that BMI trajectory from normal weight to obesity was associated with the risk of multiple cancers, including prostate, colorectal, oesophageal, gastric cardia adenocarcinoma, and even lung cancer [9‐11].

Although environmental risk factors (e.g. BMI) are the main risk factors for NSCLC [12], genetic susceptibility is also an important contributor [13]. The heritability of lung cancer in European and Asian populations is estimated to be 12–21% [14, 15]. Previous genome-wide association studies (GWAS) identified more than 80 susceptibility variants associated with lung cancer in European and Asian populations, mainly NSCLC, as it is the main type of lung cancer; however, these variants could only explain a small proportion of the overall genetic variance [16, 17]. Interestingly, there is accumulating evidence that gene-environment interactions may be responsible for the missing heritability of cancer and act together with environmental risk factors in the pathogenesis of cancer [18, 19].

However, it remained unclear whether there was evidence to support the joint association between BMI trajectories and genetic variants on NSCLC incidence. In this study, we comprehensively investigated the relationship between BMI trajectories and NSCLC risk in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. In addition, we applied a genome-wide interaction analysis to further assess the effect of different BMI trajectories in participants stratified according to genetic variants on NSCLC risk, which can provide novel insights into the pathophysiology of NSCLC.

There were 138,110 individuals in the prospective cohort study (Table 1). In total, 2641 NSCLC patients with a mean age of 64.34 years (SD = 5.20) were confirmed, including 2343 (88.72%) whites and 298 (11.28%) non-whites (184 blacks, 32 Hispanics, 63 Asians, and 19 others) populations. Compared with non-NSCLC individuals, NSCLC was more common among participants who were male (HR = 0.61, 95% CI: 0.56 to 0.66, P < 2×10⁻¹⁶), older (HR = 1.06, 95% CI: 1.05 to 1.07, P < 2×10⁻¹⁶), non-Hispanic Blacks (HR = 1.58, 95% CI: 1.36 to 1.84, P = 2.32×10⁻⁹), and current (HR = 24.22, 95% CI: 20.93 to 28.03, P < 2×10⁻¹⁶) or ever smoker (HR = 6.94, 95% CI: 6.01 to 8.01, P < 2×10⁻¹⁶); had a family history of lung cancer (HR = 1.83, 95% CI: 1.66 to 2.03, P < 2×10⁻¹⁶); had a low level of education (HR = 0.63, 95% CI: 0.58 to 0.69, P < 2×10⁻¹⁶); had a history of diabetes (HR = 1.25, 95% CI: 1.09 to 1.44, P = 0.001); and were divorced, separated, or widowed (HR = 1.41, 95% CI: 1.30 to 1.54, P = 1.08×10⁻¹⁴).

No evidence of departure from the proportional hazard assumption in Cox models for NSCLC (P = 0.166) was found. Cox proportional hazards model showed that a higher BMI at 20 years, 50 years, and the time of enrolment (baseline) were associated with a decreased risk of NSCLC (HR = 0.88, P = 0.001; HR = 0.70, P < 2×10⁻¹⁶; HR = 0.75, P < 2×10⁻¹⁶, respectively), and similar findings were observed in categorical BMI (decreased risk in overweight and obesity, Additional file 1: Table S3). Further, we identified four distinct BMI trajectories by the latent class growth model (Fig. 1). Compared with participants with a stable normal BMI in their adulthood (n = 47,982, 34.74%), the risk of NSCLC decreased in participants who progressed from a normal BMI to an overweight BMI at baseline (n = 64,498, 46.70%, HR = 0.77, 95% CI: 0.70 to 0.84, P = 3.80×10⁻⁹), who progressed from a normal BMI to an obese BMI at baseline (n = 21,259, 15.39%, HR = 0.60, 95% CI: 0.53 to 0.69, P = 5.42×10⁻¹³), and who were overweight at the onset of adulthood and became obese at baseline (n = 4371, 3.16%, HR = 0.54, 95% CI: 0.40 to 0.74, P = 9.33×10⁻⁵). Interestingly, the NSCLC risk decreased gradually across all three BMI trajectories (HR for trend = 0.78, 95%CI: 0.74 to 0.83, P = 2×10⁻¹⁶) compared with subjects who maintained a normal BMI. Sensitivity analyses showed that the primary model retained a stable association between BMI trajectories and NSCLC risk (Additional file 1: Table S4). Furthermore, stratified analyses by sex, smoking status, and histological type showed almost no significant heterogeneity in the effect of age-specific BMI and BMI trajectories on NSCLC risk, although the P value for the heterogeneity test was less than 0.05 among those with BMI < 18.5 at baseline stratified by sex (Additional file 1: Figures S1-S3).

Nineteen GWAS-identified SNPs were used to construct the PRS and examine the potential effect of BMI trajectories on NSCLC risk according to the genetic variants. The characteristics of 13,365 individuals from the GWAS are shown in Additional file 1: Appendix S1. Nineteen GWAS-identified SNPs associated with lung cancer were used to construct the sPRS and wPRS (Additional file 1: Table S2). Furthermore, compared with the low tertiles of sPRS_GWAS, the middle and high tertiles of sPRS_GWAS were associated with a higher probability of NSCLC (HR = 1.13, 95% CI: 1.12 to 1.59, P = 0.001; HR = 1.56, 95% CI: 1.34 to 1.82, P = 1.62×10⁻⁸, respectively) (Additional file 1: Table S5). Similar results were obtained for wPRS_GWAS, indicating that a higher PRS_GWAS was associated with an increased risk of NSCLC. However, there was no significant interaction between BMI trajectories and PRS_GWAS with the NSCLC risk (P_sPRS= 0.863 and P_wPRS= 0.704; Additional file 1: Figure S4). Similar findings were observed for age-specific BMI (Additional file 1: Tables S6-S7).

GWIA was subsequently performed to investigate the effect of the genome-wide interaction between each SNP and BMI trajectories on the NSCLC risk. A Manhattan plot was constructed to show the significant SNPs that interacted with BMI trajectories (Additional file 1: Figure S5). Four independent SNPs reached statistically suggestive significance [34] instead of genome-wide significance in GWIA, which were also confirmed in the bootstrap and permutation tests (Additional file 1: Table S8). Among the four SNPs, rs79297227 with the lowest P value (1.01×10⁻⁷) located in SLC16A7 (12q14.1) showed a statistically suggestively significant interaction with the BMI trajectories, and the remaining three SNPs, including rs2336652 near CLASP2 (3p22.3, P = 3.92×10⁻⁷), rs16018 in CACNA1A (19p13.2, P = 3.92×10⁻⁷), and rs4726760 near BRAF (7q34, P = 9.19×10⁻⁷) interacted with the BMI trajectories in terms of the NSCLC risk. Similar results were obtained from the analysis stratified by genotype (Table 2). Figure 2B displays the cumulative incidence of NSCLC stratified by GWIA-identified SNPs by the log-rank test. In the sensitivity analysis, a significant interaction was observed between four SNPs and the BMI trajectories by additionally adjusting for occupation and family history of any cancer or performing other sensitivity analyses (almost P < 1.0×10⁻⁴, Additional file 1: Table S9). MR sensitivity analyses showed that the correlation direction between BMI trajectories and NSCLC risk was consistent with the above analysis, although no meaningful differences in these results were observed, with no evidence of directional pleiotropy (Additional file 1: Tables S10-S11). For the functional annotation, the search for cis-eQTLs at the four loci detected by GWIA showed that SNP rs4726760 at 7q34 was a strong cis-eQTL for BRAF (P = 0.011, β = 0.073) in the lung tissue. No cis-eQTL was found at the other three loci (rs16018, P = 0.070, β = 0.128; rs2336652, P = 0.854, β = −0.015; rs79297227, P = 0.376, β = −0.042) (Additional file 1: Figure S6A). SNP rs16018 is located on chromosome 19p13.2 in calcium voltage-gated channel subunit alpha1 A (CACNA1A), which is a protein-coding gene involved in calcium channel regulation; SNP rs2336652 at 3p22.3 is located near cytoplasmic linker-associated protein 2 (CLASP2),which is significantly expressed in lung tissue and promotes the stability of microtubules; and SNP rs79297227 at 12q14.1 is located in the solute carrier family 16 member 7 (SLC16A7), which is not only significantly expressed in lung tissues (Additional file 1: Figure S6B) but also expressed in various types of malignant tumours.

GWIA-based PRS of the four SNPs above was constructed to evaluate the cumulative interaction with BMI trajectories on NSCLC risk (Fig. 3). Although a significant association was identified between BMI trajectories and a higher NSCLC risk among the individuals with high tertiles of wPRS_GWIA (HR for trend =1.30, 95% CI = 1.10–1.54), interestingly, BMI trajectories were also associated with a decreased risk of NSCLC among individuals with a low (0.54, 0.47–0.62) or intermediate tertiles of wPRS_GWIA (0.85, 0.72–0.99), indicating an obvious interaction between the GWIA-based wPRS_GWIA and BMI trajectories. Similar findings were observed for age-specific BMI (Additional file 1: Table S12). The interaction between BMI trajectories and PRS_GWIA with the NSCLC risk was significant (P_sPRS = 6.61×10⁻⁵ and P_wPRS = 3.80×10⁻¹⁶; Additional file 1: Figure S4). In addition, individuals with low or intermediate tertiles of wPRS_GWIA experienced a gradually decreased cancer risk across the BMI trajectories from normal to normal, normal to overweight, overweight to obese, and normal to obese, while the high tertiles of wPRS_GWIA were just the opposite after adjustment for age, sex, race, family history of lung cancer, education, smoking, personal history of diabetes, current marital status, study centre, and first 10 principal components (Fig. 3A, B). Stratification analyses for wPRS_GWIA showed that associations between BMI trajectories and NSCLC risk were heterogeneous (I² = 73.09%, P for heterogeneity < 0.001, Fig. 3B). Similar results were also observed in sPRS_GWIA (Additional file 1: Figure S4CD, Table S13).

In this multi-centre study, four distinct trajectories of BMI were identified during adulthood, finding that subjects who progressed from a normal BMI at the onset of adulthood to overweight or obesity at baseline (compared to maintaining a stable BMI) had a lower risk of developing NSCLC in this PLCO cohort (Fig. 2A). In addition, interaction analysis provided evidence that the association between BMI trajectories and NSCLC risk slightly differed according to genetic variation at SNPs rs4726760, rs16018, rs2336652, and rs79297227.

The results of this study suggested that the BMI trajectory from normal weight to overweight or obesity was associated with protective effects against NSCLC development, which was consistent with previous epidemiology studies [1, 2, 41‐43]. Several hypotheses have been postulated to explain the relationship between leanness and a higher risk of lung cancer. For example, smoking, as a dominant risk factor for lung cancer, usually leads to lower body weight, which may explain the observed inverse BMI-lung cancer association. However, several large prospective studies show a negative association between BMI and lung cancer risk, and this association persists after excluding up to 10 years of follow-up, suggesting that it is not entirely due to smoking [44]. Moreover, never-smokers were more likely to have a stable normal BMI trajectory according to a stratified analysis of smoking status, although never-smokers in each BMI trajectory group accounted for about 50% of our analysis. Likewise, it has been suggested that weight loss represents a preclinical event prior to the clinical manifestation of lung cancer [45]. However, our sensitivity analysis suggested that BMI trajectories resulting in overweight or obesity were associated with a lower risk of lung cancer, even excluding patients who developed the disease during the first, second, or fourth year of follow-up. Interestingly, interaction analysis of PRS_GWIA with BMI trajectories on NSCLC risk indicated that BMI progressed from normal to overweight or obesity was associated with higher NSCLC risk among individuals with the high tertiles of wPRS_GWIA or sPRS_GWIA. Specifically, they experienced a gradually increased NSCLC risk across the BMI trajectories from normal to normal, normal to overweight, overweight to obese, and normal to obese, although the low or intermediate tertiles of wPRS_GWIA or sPRS_GWIA were just the opposite (Fig. 3). In addition, those identified SNPs were located in or near genes that might be involved in biological pathways leading to lung cancer. The gene BRAF near rs4726760 provides instructions for making a protein that helps transmit chemical signals from outside the cell to the nucleus. This protein is a component of the extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) pathway, which regulates several important cell functions including cellular proliferation, differentiation, migration, and apoptosis. Chemical signalling through this pathway is essential for normal development before birth. BRAF also is an oncogene. When mutated, oncogenes have the potential to cause normal cells to become cancerous [46]. BRAF mutations are seen in 3–5% of NSCLC cases [47]. It is generally believed that obese people eat nutrient-rich foods, and studies have found that nutrients (antioxidants) can significantly inhibit the MAPK signalling pathway to reduce the inflammation response related to the risk of cancer [48]. The MAPK pathway plays an important role in the differentiation of adipocytes [49], and ERK is essential for the transcription of gene CCATT/enhancer binding protein α/β/δ and peroxisome proliferator-activated receptor gamma (PPARγ), key factors of adipocyte differentiation. When the ERK signalling pathway is activated, PPARγ is phosphorylated and transcriptional activity is reduced, which inhibits adipocyte differentiation [50]. Decreased adipocyte differentiation reduces the accumulation of adipocytes, thereby reducing the incidence of inflammation that may be related to pathological obesity (Fig. 2C).

The SNP rs16018, a member of the family of voltage-gated calcium channels, is located in the gene CACNA1A which is upregulated in numerous types of cancer including lung cancer [51]. The roles of calcium channels in various cell functions including mitogenesis, cell proliferation, differentiation, inflammation, and metastasis are well recognized [52]. Through calmodulin, intracellular calcium (Ca²⁺) levels regulate many different kinases, phosphatases, cyclases, esterases, and ion channels. Increased intracellular Ca²⁺ levels are correlated with cell proliferation, leading to inflammation and promoting carcinogenesis [51]. Subjects with a higher BMI may have sufficient nutritional status, and current studies have demonstrated that people with higher intake of nutrients (e.g. high dietary calcium) can modulate circulating calcitriol, thereby regulating intracellular Ca²⁺ levels [53], maintaining the balance of intracellular and extracellular Ca²⁺ concentrations and reducing the risk of lung cancer.

The SNP rs2336652, located near CLASP2, interacts with cytoplasmic linker protein, binds to microtubules, and has microtubule-stabilizing effects [54]. Increasing microtubule instability may cause genetic instability, and altered expression of CLASP2 may induce genetic instability and contribute to the development of lung cancer [55]. The variant rs79297227 is associated with the expression of SLC16A7. The SLC16A family of monocarboxylate transporters is a subfamily of solute carriers that transport monocarboxylate molecules, including L-lactate and pyruvate, across cell membranes [56]. Aberrant expression of SLC16A gene family members occurs in various types of malignant tumours and regulates cell migration, invasion, and proliferation [57‐59].

MR analysis revealed non-significant associations between genetic polymorphisms affecting BMI and NSCLC. Although MR is considered a powerful tool to infer causality from nature’s randomization, it cannot completely avoid bias and confounders; thus, the results of MR studies warrant a cautious interpretation [60]. For example, BMI is strongly affected by smoking status, age, sex, and ethnicity [61]. However, confounding could not result in the genetic variant, and it is possible that attenuation of a protective effect against NSCLC has been caused by adjustment for mediators actually along the causal pathway or associated with collider bias [62]. In the end, the use of BMI variants in MR as proxies for BMI trajectories had inherent limitations due to the lack of previous GWAS studies on BMI trajectories, and insufficient PLCO genetic data despite the large sample in the PLCO cohort.

Our study had several strengths. First, this study was performed in a multi-centre, large sample size cohort. Second, we not only investigated the association between BMI trajectories and the NSCLC risk but also evaluated the interaction between BMI trajectories and genetic variants in the development of NSCLC. Third, we identified four novel and functionally plausible GWIA-based SNPs, which located near genes that paly critical roles in cell growth, differentiation, and inflammation and were mechanistically linked to BMI and NSCLC genesis. However, limitations of this study have also been identified. Similar to nearly all epidemiologic study on the subject, BMI at age 20 and 50 were obtained from individual’s self-report. However, that information was obtained before the subsequent development of the outcomes of interest, so recall bias could not have been operative. Second, a substantial number of exclusions could limit generalizability, while it constrained our study cohort to those with complete data available that should help mitigate against threats to internal validity. Third, residual for unmeasured confounding cannot be excluded even exhaustive adjustment was performed in the multivariable analyses. And conclusions from further Mendelian randomization, which purportedly provides a methodologic approach for causality inference, should also be treated with caution. Fourth, our findings have not been validated by other larger-sample epidemiological studies, especially the limited sample size of the PLCO GWAS data. Finally, additional functional studies are warranted to elucidate the mechanisms underlying the effects of these loci and BMI trajectories interactions on NSCLC risk.

Our study found that genetic susceptibility may modify the effect of BMI trajectories on the development of NSCLC by regulating cell growth, differentiation and inflammation. Further larger or multi-ethnicity studies should be conducted to validate our findings.