The Impact of Inflammation on the Incidence of Different Pathological Types of Lung Cancer: The Kailuan Study

The Impact of Inflammation on the Incidence of Different Pathological Types of Lung Cancer: The Kailuan Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Inflammation on the Incidence of Different Pathological Types of Lung Cancer: The Kailuan Study Songlin Li, Shuohua Chen, Qian Zhang, Xinhong Zhang, Jie Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7418683/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Prior lung cancer studies on inflammatory indicators have examined single subtypes or aggregated types; however, there have been no systematic comparisons among major subtypes [adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), small cell lung cancer (SCLC)]. The accuracy of novel indicators of incident lung cancer risk [systemic inflammation response index (SIRI) and aggregate index of systemic inflammation (AISI)] remains unexamined. Objective We aimed to assess associations of 15 inflammatory indicators (including SIRI and AISI) with both long-term and 10-year incident lung cancer risks, stratified by the pathological subtype. Methods and Results A prospective cohort of 99,925 cancer-free participants (mean age, 51.9 ± 12.7 years) was followed for 14.9 ± 3.1 years, identifying 1,804 incident lung cancers (317 LUAD, 215 LUSC, 138 SCLC, and 1,134 others). Multivariable analyses (using Cox or Fine-Gray models) showed that for each 1-Z score increase in several biomarkers, there was an associated increase in the long term hazard for lung cancer: Overall lung cancer HR increases : WBC (1.106), monocytes (1.058), lymphocytes (1.046), neutrophils (1.081), CLR (1.030), SIRI (1.535), AISI (1.068, stronger in men); LUSC HR increases : monocytes (1.102), MLR (1.026), PLR (1.063), SIRI (1.773), AISI (1.147), MHR (1.027). Long term Survival was not found to be associated with LUAD but showed an inverse association with SCLC [PLR (0.668)]. Notably, SIRI showed the strongest associations (+ 53.5% overall; +77.3% LUSC). Ten-year risk associations were generally stronger than long-term for most markers across subtypes. Conclusion Elevated inflammation elevates long-term overall lung cancer risk (particularly in men) and LUSC risk but not LUAD risk. PLR inversely associates with SCLC. Most indicators increase 10-year risk across all subtypes, with SIRI showing the strongest association. lung cancer inflammation pathological types prospective study Figures Figure 1 Introduction Lung cancer remains the leading cause of cancer-related mortality globally. GLOBOCAN 2022 estimates indicate 2.48 million new cases (12.4% of total cancer incidence) and 1.81 million deaths (18.7% of cancer deaths) annually [ 1 ]. Despite advances with targeted therapies and immune checkpoint inhibitors, the 5-year survival rate remains below 20% [ 2 ]. Therefore, developing effective prevention strategies is critical for improving outcomes. Chronic systemic inflammation is a well-established driver of lung cancer pathogenesis and progression, highlighting its translational significance [ 3 ]. Routine blood test-derived inflammatory indicators, including high-sensitivity C-reactive protein (hs-CRP) [ 4 , 5 ], white blood cell count (WBC) [ 6 , 7 ], the neutrophil-to-lymphocyte ratio (NLR) [ 8 ], and the platelet-to-lymphocyte ratio (PLR) [ 9 ], are associated with lung cancer risk and prognosis. Novel composite indices integrating multi-dimensional pathophysiology, such as the lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR) [ 10 ] and C-reactive protein-to-lymphocyte ratio (CLR) [ 11 , 12 ], show potentially stronger predictive value for incidence and prognosis. However, significant knowledge gaps persist. Existing studies typically focus on single pathological subtypes or aggregate all lung cancers, lacking systematic comparisons of differential inflammatory effects across major subtypes [adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and small cell lung cancer (SCLC)] within a unified cohort. Studies have shown that an elevated systemic inflammation response index (SIRI) correlates with increased mortality in non-small cell lung cancer (NSCLC) [ 13 ] and an elevated aggregate index of systemic inflammation (AISI) associates with mortality across cancers broadly [ 14 ]. However, the associations of these indices with incident lung cancer risk, particularly when stratified by subtype, remain unexplored. To address these gaps, we leveraged the large-scale prospective Kailuan cohort to comprehensively assess associations of 15 inflammatory indicators, including SIRI and AISI, with incident lung cancer risk. This study specifically aims to elucidate differential associations across major pathological subtypes (LUAD, LUSC, and SCLC), providing novel evidence for subtype-specific prevention and treatment strategies. Materials and Methods Study Subjects The Kailuan Study (ChiCTR2000029767) is a prospective cohort established within the company-based community of Kailuan Group in Tangshan, China. The initial health examinations were conducted at Kailuan General Hospital and its affiliated medical institutions during 2006–2007, targeting both active and retired employees of the group. Subsequently, systematic health examinations have been conducted biennially (with the 8th follow-up round completed through December 31, 2022), accumulating a total of 188,573 participants in the study cohort. The study protocol was approved by the Ethics Committee of Kailuan General Hospital, with written informed consent obtained from all participants in accordance with the Declaration of Helsinki. Inclusion required completion of the 2006 baseline examination with available inflammation biomarker data. Exclusion criteria comprised pre-existing cancer diagnosis and missing inflammatory indicator measurements(Fig. 1 ). Data Collection Inflammatory Indicators Fifteen inflammatory indicators were analyzed: 1) Hematological parameters: high-sensitivity C-reactive protein (hs-CRP), WBC, monocytes, lymphocytes, and neutrophils, platelets; 2) Cellular ratios: NLR, monocyte-to-lymphocyte ratio (MLR), PLR, and hs-CRP-to-lymphocyte ratio (CLR); 3) Systemic inflammation indices: systemic immune-inflammation index (SII = [neutrophils × platelets]/lymphocytes), SIRI [(neutrophils × monocytes)/lymphocytes], and AISI [(neutrophils × monocytes × platelets)/lymphocytes]; 4) Lipid-related ratios: monocyte-to-HDL-C ratio (MHR), neutrophil-to-HDL-C ratio (NHR), and lymphocyte-to-HDL-C ratio (LHR). All indicators were standardized as Z-scores using the formula Z = (X - µ)/σ, where X represents the original value, µ the sample mean, and σ the standard deviation. Specimen Collection and Laboratory Testing Fasting venous blood samples collected after overnight fasting for 8–12 h were processed within 2 h. Complete blood count analysis using EDTA-anticoagulated samples was performed on Sysmex XN-9000 analyzers (Sysmex Corporation, Japan). Serum hs-CRP was measured using particle-enhanced immunonephelometry (BN ProSpec system, Siemens Healthineers, Germany). High-density lipoprotein cholesterol (HDL-C) was quantified by direct method on Hitachi 7600-020 analyzers (Hitachi High-Technologies, Japan). The central laboratory implemented blinded testing protocols with routine internal quality control and external quality assessment after established procedures [ 15 – 17 ]. Covariates Using structured questionnaires, trained personnel collected data on demographic characteristics (age and sex), socioeconomic factors (education level categorized as high school or above; income level dichotomized at ≥ 1000 CNY/month), behavioral factors (smoking status defined as current smoking of ≥ 1 cigarette/day), and medical history. Physician-measured blood pressure followed standardized protocols using mercury sphygmomanometers (Omron HBP-1300 devices after 2014). Hypertension was defined as a systolic blood pressure of ≥ 140 mmHg, a diastolic blood pressure of ≥ 90 mmHg, physician-diagnosed hypertension, or antihypertensive medication use[ 18 ]. Diabetes mellitus was defined as fasting blood glucose ≥ 7.0 mmol/L, physician-diagnosed diabetes, or glucose-lowering medication use[ 19 ]. Family history of cancer was defined as any cancer diagnosis in either parent. Outcomes The primary outcome was incident lung cancer (ICD-10: C34) identified through the Kailuan Medical Insurance Database with verification via hospital medical records. Cases were classified into pathological subtypes: adenocarcinoma, squamous cell carcinoma, small cell carcinoma, and other/unspecified. Follow-up continued until lung cancer diagnosis, death from non-lung cancer causes, loss to follow-up, or study termination (December 31, 2023). Statistical Analysis Continuous variables are presented as mean ± standard deviation for normally distributed data or median (interquartile range) for non-normally distributed data, with between-group comparisons performed using ANOVA or Mann–Whitney U tests as appropriate. Categorical variables are expressed as frequencies (percentages) and compared using χ² or Fisher's exact tests. Multivariable Cox proportional hazards regression models were used to evaluate associations between inflammatory indicators and long-term/10-year incident lung cancer risk (overall and by pathological subtype); subdistribution hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Fine-Gray competing risk models for subtypes subject to competing risks. All models were adjusted for sex, age, smoking history, education, income, hypertension, diabetes, and family cancer history. Based on primary findings, we conducted analyses stratified by sex, age (< 60 vs. ≥60 years), and smoking history to test subgroup effects, and we performed sensitivity analyses, including (a) the exclusion of participants diagnosed with lung cancer within 12 months of enrollment to mitigate potential lead-time bias and (b) additional adjustment for hs-CRP and WBC (when not the primary exposure variables) in primary models. All analyses were performed using SAS 9.4 (SAS Institute Inc.) with statistical significance defined as a two-tailed P value of < 0.05. Results Baseline Characteristics of Study Participants A cohort of 99,925 participants was analyzed to investigate the association between inflammatory indicators and incident lung cancer. After a mean follow-up period of 14.85 ± 3.07 years, 1,804 individuals developed lung cancer (cumulative incidence: 1.33%), including 317 LUADs, 215 LUSCs, 138 SCLCs, and 1,134 cases of other pathological types. The cohort comprised 79,824 males (79.8%; mean age, 51.85 ± 12.67 years). Compared to the non-cancer group, cancer group participants were significantly older and showed significantly elevated levels of hs-CRP, WBC, monocytes, neutrophils, CLR, SIRI, and AISI. This group also had a significantly higher proportion of males, individuals with hypertension, those reporting a family history of cancer, smokers, and individuals with higher education level (all, P < 0.05; Table 1 ). Table 1 Baseline characteristics of patients with and without lung cancer Variables Total (N = 999,25) Non-lung cancer (N = 98,121) Lung cancer (N = 1,804) P Age, years 51.85 ± 12.67 51.74 ± 12.69 57.55 ± 10.31 < 0.001 Women 20101(20.12) 19900(20.28) 201(11.14) < 0.001 Men 79824(79.88) 78221(79.72) 1603(88.86) High school or above, n(%) 19877(19.89) 19654(20.03) 223(12.36) 1000 yuan/moth, n(%) 93455(93.53) 91749(93.51) 1706(94.57) 0.069 Current smoking, n(%) 33487(33.51) 32726(33.35) 761(42.18) < 0.001 Family history of cancer, n(%) 4616(4.62) 4513(4.60) 103(5.71) 0.026 Hypertension, n(%) 43842(43.87) 42985(43.81) 857(47.51) 0.002 Diabetes, n(%) 9114(9.12) 8946(9.12) 168(9.31) 0.775 Hs-CRP, mg/L 0.80(0.30–2.10) 0.80(0.30–2.10) 0.95(0.35–2.41) 0.006 White blood cell, 10 3 /µL 6.62 ± 1.74 6.62 ± 1.74 6.80 ± 1.81 < 0.001 Monocytes, 10 3 /µL 0.42 ± 0.35 0.42 ± 0.35 0.46 ± 0.61 0.001 Lymphocytes, 10 3 /µL 2.32 ± 0.95 2.32 ± 0.95 2.37 ± 0.95 0.063 Neutrophils, 10 3 /µL 3.92 ± 1.37 3.92 ± 1.37 4.05 ± 1.41 < 0.001 Platelet, 10 9 /L 200.00(168.00–237.00) 200.00(168.00–237.00) 198.00(163.00–235.00) 0.016 HDL-C, mmol/L 1.55 ± 0.40 1.55 ± 0.40 1.55 ± 0.41 0.772 NLR 1.86 ± 1.48 1.86 ± 1.49 1.87 ± 0.86 0.626 MLR 0.17(0.13–0.22) 0.17(0.13–0.22) 0.17(0.13–0.23) 0.471 PLR 90.42(72.07–113.16) 90.43(72.11–113.18) 87.66(68.57–111.18) 0.507 CLR 0.36(0.14–1.01) 0.36(0.13–1.01) 0.42(0.16–1.23) < 0.001 SII 335.19(247.68–451.86) 335.16(247.78–451.56) 336.67(242.53–464.88) 0.999 SIRI 0.62(0.41–0.92) 0.62(0.41–0.92) 0.65(0.43–0.97) 0.007 AISI 152.37 ± 117.68 152.24 ± 117.53 158.91 ± 125.13 0.021 MHR 0.24(0.17–0.35) 0.24(0.17–0.35) 0.25(0.17–0.36) 0.367 LHR 1.66 ± 2.76 1.66 ± 2.78 1.65 ± 1.08 0.857 NHR 2.79 ± 2.98 2.79 ± 3.00 2.81 ± 1.39 0.754 Abbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol. The participants’ baseline characteristics in total and by the incident of lung cancer were presented as mean ± standard deviation (SD) and median with interquartile range (IQR) for normally and non-normally distributed continuous variables, respectively, and as numbers with percentages for categorical variables. The distinctions in attributes among groups were scrutinized using the Chi-squared (χ 2 ) test or Fisher’s Exact Test for categorical variables, and the Kruskal-Wallis test for continuous variables, respectively. Significant differences in baseline characteristics across pathological lung cancer subtypes are detailed in Supplementary Table 1 (all, P < 0.05). These differences encompassed age, sex, education level, smoking history, family history of cancer, hypertension prevalence, and levels of hs-CRP, WBC, monocytes, neutrophils, platelets, CLR, SIRI, and AISI. Specifically, patients with adenocarcinoma presented with the youngest age, the highest proportion of females, the highest incidence of a family history of cancer, the lowest neutrophil levels, and the highest platelet levels. Patients with squamous cell carcinoma had the highest proportion of males and smokers, along with the highest levels of WBC and monocytes and the highest AISI. Patients with SCLC had the lowest proportion of individuals with high school or higher education, the highest hypertension prevalence, and the highest SIRI. Finally, patients with other pathological types had the highest hs-CRP and CLR levels. Inflammation and the Long-Term Risk of Overall Lung Cancer Multivariable-adjusted Cox regression analysis revealed significant positive associations of WBC, monocytes, lymphocytes, neutrophils, CLR, SIRI, and AISI with overall lung cancer risk, with hazard increases of 10.6%, 5.8%, 4.6%, 8.1%, 3.0%, 53.5%, and 6.8% each 1 Z-score increment, respectively (Table 2 ). Except for the association of CLR with overall lung cancer risk, all associations remained robust after excluding participants diagnosed within 12 months of enrollment (Supplementary Table 2). Following additional adjustment for hs-CRP and WBC, the associations of WBC, monocytes, lymphocytes, and SIRI with incident lung cancer risk persisted (Supplementary Table 3). Sex-stratified analysis suggested potential sex differences: among males, most indicators significantly increased incident lung cancer risk (WBC: +10.4%, monocytes: +6.1%, lymphocytes: +4.9%, neutrophils: +7.7%, CLR: +3.1%, SIRI: +55.6%, AISI: +6.9%), whereas elevated hs-CRP was associated with a 16.1% risk reduction in females. In age- and smoking status-stratified analyses, inflammation consistently influenced incident lung cancer risk without significant heterogeneity across age groups or smoking status (Table 3 ). Table 2 The HR(95%CI) between inflammation and the incidence risk of lung cancer. Variables Lung cancer Lung squamous cell carcinomas Lung adenocarcinoma Small cell lung cancer Other pathological types of lung cancer Hs-CRP 1.019(0.974–1.066) 1.125(0.991–1.276) 0.907(0.803–1.026) 0.926(0.769–1.114) 1.034(0.980–1.090) WBC 1.106(1.057–1.157) 1.094(0.952–1.257) 1.084(0.974–1.206) 1.071(0.912–1.257) 1.116(1.057–1.178) Monocytes 1.058(1.031–1.086) 1.102(1.062–1.143) 1.016(0.926–1.115) 1.045(0.998–1.094) 1.031(1.001–1.061) Lymphocytes 1.046(1.010–1.084) 1.036(0.959–1.119) 0.996(0.914–1.085) 1.074(0.980–1.178) 1.055(1.022–1.089) Neutrophils 1.081(1.035–1.129) 1.067(0.941–1.209) 1.077(0.980–1.184) 1.064(0.915–1.238) 1.083(1.028–1.141) NLR 0.996(0.940–1.055) 1.012(0.956–1.070) 0.992(0.902–1.092) 0.947(0.755–1.190) 0.993(0.940–1.051) MLR 1.012(0.985–1.038) 1.026(1.014–1.038) 1.002(0.962–1.043) 0.946(0.791–1.131) 1.010(0.995–1.026) PLR 0.992(0.909–1.083) 0.929(0.590–1.462) 1.006(0.995–1.017) 0.999(0.893–1.117) 0.933(0.741–1.174) CLR 1.030(1.005–1.057) 1.063(1.035–1.092) 1.027(0.957–1.102) 0.668(0.450–0.992) 1.017(0.991–1.043) SII 1.004(0.973–1.036) 1.009(0.977–1.042) 1.004(0.990–1.020) 0.992(0.835–1.178) 1.004(0.991–1.017) SIRI 1.535(1.179–1.997) 1.773(1.345–2.337) 0.522(0.090–3.030) 0.966(0.428–2.179) 1.638(1.262–2.129) AISI 1.068(1.022–1.117) 1.147(1.013–1.299) 1.062(0.956–1.179) 1.092(0.935–1.277) 1.047(0.990–1.108) MHR 1.013(0.991–1.035) 1.027(1.015–1.039) 0.900(0.685–1.184) 0.953(0.846–1.074) 1.014(1.000-1.027) LHR 1.002(0.957–1.049) 0.990(0.912–1.074) 0.882(0.688–1.131) 1.012(0.968–1.057) 1.009(0.992–1.026) NHR 1.006(0.970–1.044) 1.004(0.949–1.063) 0.986(0.912–1.066) 1.009(0.965–1.054) 1.008(0.992–1.023) Abbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol. Model adjusted for age, sex, current smoking, education level, income level, hypertension, diabetes and family history of cancer. Table 3 Stratification analysis: the HR(95%CI) between inflammation and the incidence risk of lung cancer Variables Sex Age Smoking status Men Wmen ≥ 60 years old < 60 years old Current smoking Never smoking Hs-CRP 1.039(0.992–1.089) 0.839(0.709–0.993) 1.022(0.954–1.094) 1.077(1.016–1.142) 1.039(0.964–1.120) 1.011(0.956–1.070) WBC 1.104(1.053–1.157) 1.135(0.983–1.310) 1.076(0.997–1.162) 1.089(1.029–1.152) 1.139(1.065–1.217) 1.081(1.017–1.149) Monocytes 1.061(1.035–1.088) 0.987(0.843–1.156) 1.072(1.032–1.114) 1.039(0.997–1.083) 1.048(1.009–1.089) 1.071(1.031–1.112) Lymphocytes 1.049(1.011–1.089) 1.023(0.902–1.160) 1.055(0.995–1.119) 1.023(0.970–1.078) 1.068(1.012–1.128) 1.031(0.983–1.082) Neutrophils 1.077(1.028–1.128) 1.110(0.987–1.249) 1.048(0.970–1.132) 1.085(1.028–1.145) 1.087(1.019–1.160) 1.078(1.016–1.143) NLR 0.975(0.903–1.054) 1.022(0.971–1.075) 0.928(0.818–1.052) 1.023(0.985–1.064) 0.965(0.852–1.094) 1.005(0.960–1.052) MLR 1.013(0.979–1.048) 1.010(0.967–1.055) 1.022(0.986–1.059) 1.004(0.954–1.056) 1.035(0.896–1.196) 1.011(0.983–1.039) PLR 0.939(0.793–1.112) 1.002(0.963–1.043) 0.983(0.788–1.225) 1.002(0.916–1.096) 0.838(0.627–1.119) 1.002(0.964–1.041) CLR 1.031(1.005–1.057) 0.982(0.802–1.203) 1.021(0.977–1.066) 1.050(1.025–1.075) 1.031(0.998–1.065) 1.032(0.991–1.074) SII 1.009(0.955–1.066) 1.002(0.959–1.048) 0.995(0.915–1.082) 1.016(0.971–1.063) 0.995(0.834–1.186) 1.005(0.976–1.036) SIRI 1.556(1.133–2.137) 1.493(0.923–2.416) 2.660(1.853–3.819) 1.144(0.682–1.920) 1.546(0.611–3.912) 1.516(1.154–1.992) AISI 1.069(1.020–1.121) 1.061(0.914–1.231) 1.003(0.926–1.087) 1.084(1.026–1.145) 1.067(0.998–1.140) 1.073(1.010–1.141) MHR 1.022(0.989–1.057) 1.007(0.962–1.054) 1.084(1.018–1.154) 1.002(0.950–1.056) 1.034(0.901–1.185) 1.012(0.989–1.035) LHR 1.003(0.952–1.056) 1.001(0.903–1.109) 1.005(0.945–1.069) 0.988(0.904–1.080) 1.017(1.017–1.078) 0.987(0.906–1.075) NHR 1.003(0.961–1.047) 1.037(0.945–1.139) 1.001(0.943–1.063) 1.008(0.951–1.068) 1.026(0.963–1.092) 0.998(0.942–1.057) Abbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol. Model adjusted for age (exclude age stratified analysis), sex (exclude sex stratified analysis), current smoking (exclude smoking stratified analysis), education level, income level, hypertension, diabetes and family history of cancer. Inflammation and the Long-Term Risk of LUSC In LUSC, each 1 Z-score increase in monocytes, MLR, PLR, SIRI, AISI, and MHR was significantly associated with elevated risks of 10.2%, 2.6%, 6.3%, 77.3%, 14.7%, and 2.7%, respectively (Table 2 ). Except for the association of AISI with incident lung cancer risk, all associations remained robust after excluding participants diagnosed within 12 months of enrollment (Supplementary Table 2). After additional adjustment for hs-CRP and WBC, the associations of monocytes, MLR, SIRI, and MHR with incident LUSC risk persisted (Supplementary Table 3). Analyses stratified by sex, age, and smoking status revealed no significant differences in inflammation-associated LUSC risk across these subgroups (Supplementary Table 4). Inflammation and the Long-Term Risk of LUAD Multivariable Cox regression revealed no significant association between inflammatory markers and overall LUAD risk (Table 2 ). However, stratified analyses identified specific associations. Among men, each 1 Z-score increase in WBC was associated with a 12.8% elevated risk. Among participants aged < 60 years, each 1 Z-score increase in CLR was associated with a 4.6% increased risk. No significant risk associations were observed across smoking strata (Supplementary Table 5). Inflammation and the Long-Term Risk of SCLC For SCLC, each 1 Z-score increase in PLR was associated with a 33.2% reduced risk (Table 2 ). Stratified analyses revealed subgroup-specific patterns. Among women, elevated NLR (-76.1%) and SII (-80.2%) were associated with significantly reduced risk. In participants aged < 60 years, higher monocyte levels (+ 5.7%) were associated with increased risk. Among non-smokers, elevated monocytes (+ 6.7%) and lymphocytes (+ 9.7%) were associated with increased risk (Supplementary Table 6). Inflammation and the Long-Term Risk of Other Pathological Lung Cancer Types For other pathological types of lung cancer, each 1 Z-score increase in WBC (+ 11.6%), monocytes (+ 3.1%), lymphocytes (+ 5.5%), neutrophils (+ 8.3%), SIRI (+ 63.8%), and MHR (+ 1.4%) was significantly associated with elevated risk (Table 2 ). These associations remained robust after excluding participants diagnosed within 12 months of enrollment (Supplementary Table 2). Following additional adjustment for hs-CRP and WBC, the associations of WBC, SIRI, and MHR with incident risk of other pathological types persisted (Supplementary Table 3). Analyses stratified by sex, age, and smoking status revealed no differences in inflammation-associated risk between sexes for these other pathological types (Supplementary Table 7). Inflammation and the 10-Year Risk of Lung Cancer In addition, we assessed the 10-year incident risk of lung cancer (Table 4 ). For overall lung cancer, each 1 Z-score increase in WBC, monocytes, neutrophils, CLR, SIRI, and AISI was associated with elevated 10-year risks of 12.0%, 7.1%, 8.9%, 3.8%, 75.7%, and 8.7%, respectively. The magnitude of risk increase for these inflammatory markers consistently exceeded that observed for long-term risk. In LUSC, nearly all inflammatory indicators showed positive associations with substantially higher 10-year risk when compared with long-term risk elevations. For each 1 Z-score increment, WBC (+ 33.8%), monocytes (+ 11.3%), lymphocytes (+ 9.0%), neutrophils (+ 21.9%), NLR (+ 2.1%), MLR (+ 3.3%), CLR (+ 7.3%), SII (+ 119.9%), SIRI (+ 32.8%), MHR (+ 3.3%), and NHR (+ 2.4%) significantly increased the 10-year risk. For LUAD, only monocytes showed significance (+ 7.2% each 1 Z-score). In SCLC, only PLR showed an association (+ 1.8% each 1 Z-score). Among other subtypes, for each 1 Z-score increment, WBC (+ 10.5%), neutrophils (+ 8.5%), MLR (+ 1.5%), SIRI (+ 79.6%), and MHR (+ 1.7%) elevated the 10-year risk, with these increases similarly surpassing long-term risk levels. Table 4 Stratification analysis: the HR(95%CI) between inflammation and the incidence risk of lung squamous cell carcinomas Variables Sex Age Smoking status Men Wmen ≥ 60 years old < 60 years old Current smoking Never smoking Hs-CRP 1.133(0.998–1.286) 0.740(0.401–1.368) 0.949(0.728–1.239) 1.227(1.076–1.398) 1.046(0.856–1.278) 1.190(1.010–1.401) WBC 1.076(0.935–1.238) 1.840(1.073–3.155) 1.061(0.763–1.476) 1.076(0.919–1.260) 1.145(0.958–1.369) 1.024(0.819–1.281) Monocytes 1.101(1.060–1.143) 1.182(1.100–1.270) 1.150(1.129–1.172) 1.061(1.021–1.103) 1.081(1.039–1.126) 1.144(1.116–1.173) Lymphocytes 1.021(0.932–1.117) 1.196(1.126–1.271) 1.017(0.811–1.275) 1.022(0.922–1.132) 1.079(1.001–1.163) 0.961(0.789–1.170) Neutrophils 1.063(0.936–1.209) 1.159(0.781–1.721) 1.015(0.739–1.394) 1.071(0.932–1.230) 1.087(0.948–1.247) 1.040(0.830–1.303) NLR 1.016(0.979–1.055) 0.288(0.015–5.353) 0.944(0.644–1.383) 1.033(0.985–1.082) 0.947(0.706–1.269) 1.020(0.997–1.044) MLR 1.030(1.014–1.045) 0.991(0.812–1.208) 1.036(1.011–1.062) 1.021(1.004–1.038) 1.078(0.990–1.174) 1.026(1.012–1.039) PLR 0.955(0.627–1.454) 0.321(0.006–17.994) 1.004(0.980–1.030) 0.899(0.508–1.593) 0.747(0.322–1.735) 1.006(0.978–1.036) CLR 1.064(1.036–1.093) 0.267(0.037–1.931) 1.053(0.981–1.130) 1.069(1.037–1.101) 1.056(1.026–1.086) 1.092(1.036–1.151) SII 1.016(0.970–1.064) 0.775(0.034–17.857) 0.972(0.555–1.702) 1.017(0.974–1.061) 0.935(0.625-1.400) 1.014(0.996–1.032) SIRI 1.798(1.350–2.397) 1.258(0.624–2.535) 3.569(2.053–6.206) 1.292(0.966–1.728) 2.179(1.312–3.620) 1.651(1.169–2.332) AISI 1.139(1.001–1.294) 1.581(1.129–2.214) 1.015(0.806–1.278) 1.167(1.011–1.347) 1.113(0.965–1.283) 1.194(0.971–1.470) MHR 1.038(1.016–1.061) 1.014(1.005–1.024) 1.131(1.046–1.223) 1.023(1.008–1.038) 1.076(0.999–1.159) 1.027(1.014–1.040) LHR 0.965(0.814–1.144) 1.033(1.016–1.050) 1.013(0.937–1.095) 0.965(0.797–1.170) 1.010(0.948–1.077) 0.919(0.636–1.327) NHR 0.996(0.915–1.085) 1.075(1.016–1.137) 0.983(0.761–1.271) 1.009(0.939–1.084) 1.015(0.906–1.138) 0.999(0.922–1.082) Abbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol. Model adjusted for age (exclude age stratified analysis), sex (exclude sex stratified analysis), current smoking (exclude smoking stratified analysis), education level, income level, hypertension, diabetes and family history of cancer. Discussion This study included 99,925 participants with a mean follow-up period of 14.85 ± 3.07 years. Key findings were as follows: Most inflammatory indicators showed positive associations with lung cancer risk (both long-term and 10-year risk), and the strength of inflammation–lung cancer associations showed heterogeneity across pathological subtypes and sex. To our knowledge, this is the first report linking elevated SIRI and AISI with increased incident lung cancer risk. Our analysis revealed that each 1 Z-score increase, most inflammatory indicators elevated long-term overall lung cancer risk by 3.0–53.5%. This finding remained robust after excluding cases diagnosed within 1 year of follow-up. Previous studies [ 20 – 23 ] have confirmed associations between elevated inflammatory markers (e.g., WBC, CLR, and SII) and increased lung cancer risk. Therese et al. reported that elevated SII and NLR increased lung cancer risk by 69% and 53%, respectively, in the UK Biobank cohort [ 21 ]. In addition, another UK Biobank study documented a HR of 1.14 (95% CI: 1.08–1.20) for lung cancer per WBC increase [ 22 ]. Our results are consistent with these findings. Furthermore, we observed that elevated levels of nearly all studied inflammatory indicators were associated with increased 10-year lung cancer risk across all pathological subtypes. Notably, the magnitude of 10-year risk elevation generally exceeded that of long-term risk. Thus, lung cancer prevention strategies should prioritize assessment and management of both long-term and 10-year risk. In addition to traditional markers (e.g., WBC and monocytes), composite indicators (e.g., SII, NLR, SIRI, AISI) should be incorporated into risk monitoring. Second, we observed pathological subtype-specific associations. Elevated inflammatory indicators (i) significantly increased long-term risk in overall lung cancer, LUSC, and other subtypes, (ii) showed an inverse association in SCLC, and (iii) showed no significant association in LUAD. A prior study reported a 51% increased LUSC risk when WBC > 0.57*10 9 /L compared with the lowest WBC group[ 24 ]. However, most inflammation–lung cancer studies focused on patient prognosis or risk stratification [ 9 , 11 , 25 – 27 ]. To our knowledge, only one study investigated the association between the lymphocyte-to-monocyte ratio [ 20 ] and incident lung cancer without pathological subtype analysis. In addition, we identified sex-based heterogeneity; inflammatory elevations increased lung cancer risk by 3.1–55.6% in males, whereas elevated hs-CRP was associated with a 16.1% risk reduction in females. Although sex interaction was not statistically significant ( P interaction >0.05), HR magnitudes suggested stronger associations in males. A previous UK Biobank study also found that WBC > 9.3*10 9 /L increased incident lung cancer risk by 195% and 115% in male and female current smokers, respectively [ 6 ], which is consistent with our observations. Therefore, precision prevention strategies should account for pathological subtypes and prioritize male populations. To our knowledge, previous studies have not investigated the association of SIRI and AISI with incident lung cancer risk, with limited reports on their prognostic value in cancers like pancreatic, colorectal, breast, and esophageal cancer [ 28 – 32 ]. This may be the first report to demonstrate that elevated SIRI significantly increases incident risk in overall lung cancer (+ 53.5%), LUSC (+ 77.3%), and other subtypes (+ 63.8%), with SIRI showing the strongest association among all studied markers. Elevated AISI increased risk in overall lung cancer (+ 6.8%) and LUSC (+ 14.7%). Importantly, these associations persisted in the 10-year risk analysis, with SIRI and AISI elevations increasing 10-year overall lung cancer risk by 75.7% and 8.7%, respectively. Consequently, individuals with elevated SIRI and AISI (particularly SIRI) require heightened vigilance for lung cancer risk, particularly during the next decade of their lives. Our study has several advantages. First, the large sample size ensured the reliability of our results. The prospective design minimized the potential for recall bias and guaranteed comprehensive data for our analysis. Second, our study provides the first comprehensive comparison of the associations between 15 inflammatory biomarkers and incident lung cancer, and is the first to investigate the impact of SIRI and AISI on lung cancer development. Third, we stratified analyses by pathological subtypes of lung cancer to examine the relationship between inflammation and each subtype. Fourth, we further adjusted for coexisting inflammatory variables as covariates to ensure our results were not confounded by other inflammatory markers. Fifth, the robustness of our findings was validated through stratified and sensitivity analyses. However, we acknowledge the following limitations. First, although our new model incorporated additional blood biochemical indicators, several tumor markers (such as CEA and CYFRA21-1) were not included due to lack of testing. Second, the analysis may have overlooked certain unmeasured confounders. Third, the Kailuan Study lacks ethnic diversity, limiting generalizability of findings to other ethnic groups. Finally, discrepancies between pathological subtypes and actual diagnoses may exist due to limited availability of historical pathological examinations. Conclusion This study shows that elevated levels of multiple inflammatory indicators significantly increase both long-term and 10-year incident lung cancer risks. The associations were particularly pronounced in LUSC and other pathological subtypes and were also stronger in males. Among these indicators, SIRI showed the strongest association with incident risk. We believe this is the first evidence linking the novel composite inflammatory markers SIRI and AISI to overall incident lung cancer risk. Declarations Acknowledgments The authors thank all the investigators, staff, and participants of the Kailuan study for their valuable contributions. We thank Medjaden Inc. for scientific editing of this manuscript. Authors’ contributions SL, GC, and SC contributed to the conception and design of the study. QZ and XZ performed the analysis. JL and HZ contributed to the interpretation of data. SL and YD wrote the initial draft of the manuscript. QZ, XZ, and JL revised it critically for important intellectual content. All authors read and approved the final manuscript. Data sharing Data described in the manuscript, code book, and analytic code will be made available upon request pending approval by the authors. Ethics approval and consent to participants The Kailuan study was approved by the Ethics Committee of the Kailuan Medical Group and followed the Declaration of Helsinki. All participants provided signed informed consent. Clinical trial number Not applicable. Consent for publication Not applicable. Conflicts of Interest The authors declare that no competing interests exist. Funding This research project is funded by Medical Science Research Project of Hebei, project number: 20261085. 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1","display":"","copyAsset":false,"role":"figure","size":19061,"visible":true,"origin":"","legend":"\u003cp\u003eInclusion and exclusion flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7418683/v1/e831dfa37a72b22ad380697f.png"},{"id":104401777,"identity":"0bc1bbb9-c245-41e9-bd33-471130adb807","added_by":"auto","created_at":"2026-03-11 12:13:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1656244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7418683/v1/1e146f65-d1d8-4487-8f08-7d675a291a28.pdf"},{"id":92476641,"identity":"d97e0c53-bf28-422e-a7d7-7201f9e76a0a","added_by":"auto","created_at":"2025-09-30 07:22:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57399,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7418683/v1/fd27e1cbe803bc25b8d649b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Inflammation on the Incidence of Different Pathological Types of Lung Cancer: The Kailuan Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality globally. GLOBOCAN 2022 estimates indicate 2.48\u0026nbsp;million new cases (12.4% of total cancer incidence) and 1.81\u0026nbsp;million deaths (18.7% of cancer deaths) annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances with targeted therapies and immune checkpoint inhibitors, the 5-year survival rate remains below 20% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, developing effective prevention strategies is critical for improving outcomes.\u003c/p\u003e\u003cp\u003eChronic systemic inflammation is a well-established driver of lung cancer pathogenesis and progression, highlighting its translational significance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Routine blood test-derived inflammatory indicators, including high-sensitivity C-reactive protein (hs-CRP) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], white blood cell count (WBC) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the neutrophil-to-lymphocyte ratio (NLR) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and the platelet-to-lymphocyte ratio (PLR) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], are associated with lung cancer risk and prognosis. Novel composite indices integrating multi-dimensional pathophysiology, such as the lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and C-reactive protein-to-lymphocyte ratio (CLR) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], show potentially stronger predictive value for incidence and prognosis.\u003c/p\u003e\u003cp\u003eHowever, significant knowledge gaps persist. Existing studies typically focus on single pathological subtypes or aggregate all lung cancers, lacking systematic comparisons of differential inflammatory effects across major subtypes [adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and small cell lung cancer (SCLC)] within a unified cohort. Studies have shown that an elevated systemic inflammation response index (SIRI) correlates with increased mortality in non-small cell lung cancer (NSCLC) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and an elevated aggregate index of systemic inflammation (AISI) associates with mortality across cancers broadly [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the associations of these indices with incident lung cancer risk, particularly when stratified by subtype, remain unexplored.\u003c/p\u003e\u003cp\u003eTo address these gaps, we leveraged the large-scale prospective Kailuan cohort to comprehensively assess associations of 15 inflammatory indicators, including SIRI and AISI, with incident lung cancer risk. This study specifically aims to elucidate differential associations across major pathological subtypes (LUAD, LUSC, and SCLC), providing novel evidence for subtype-specific prevention and treatment strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Subjects\u003c/h2\u003e\u003cp\u003eThe Kailuan Study (ChiCTR2000029767) is a prospective cohort established within the company-based community of Kailuan Group in Tangshan, China. The initial health examinations were conducted at Kailuan General Hospital and its affiliated medical institutions during 2006\u0026ndash;2007, targeting both active and retired employees of the group. Subsequently, systematic health examinations have been conducted biennially (with the 8th follow-up round completed through December 31, 2022), accumulating a total of 188,573 participants in the study cohort. The study protocol was approved by the Ethics Committee of Kailuan General Hospital, with written informed consent obtained from all participants in accordance with the Declaration of Helsinki. Inclusion required completion of the 2006 baseline examination with available inflammation biomarker data. Exclusion criteria comprised pre-existing cancer diagnosis and missing inflammatory indicator measurements(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eInflammatory Indicators\u003c/h2\u003e\u003cp\u003eFifteen inflammatory indicators were analyzed: 1) Hematological parameters: high-sensitivity C-reactive protein (hs-CRP), WBC, monocytes, lymphocytes, and neutrophils, platelets; 2) Cellular ratios: NLR, monocyte-to-lymphocyte ratio (MLR), PLR, and hs-CRP-to-lymphocyte ratio (CLR); 3) Systemic inflammation indices: systemic immune-inflammation index (SII = [neutrophils \u0026times; platelets]/lymphocytes), SIRI [(neutrophils \u0026times; monocytes)/lymphocytes], and AISI [(neutrophils \u0026times; monocytes \u0026times; platelets)/lymphocytes]; 4) Lipid-related ratios: monocyte-to-HDL-C ratio (MHR), neutrophil-to-HDL-C ratio (NHR), and lymphocyte-to-HDL-C ratio (LHR). All indicators were standardized as Z-scores using the formula Z = (X - \u0026micro;)/σ, where X represents the original value, \u0026micro; the sample mean, and σ the standard deviation.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpecimen Collection and Laboratory Testing\u003c/h3\u003e\n\u003cp\u003eFasting venous blood samples collected after overnight fasting for 8\u0026ndash;12 h were processed within 2 h. Complete blood count analysis using EDTA-anticoagulated samples was performed on Sysmex XN-9000 analyzers (Sysmex Corporation, Japan). Serum hs-CRP was measured using particle-enhanced immunonephelometry (BN ProSpec system, Siemens Healthineers, Germany). High-density lipoprotein cholesterol (HDL-C) was quantified by direct method on Hitachi 7600-020 analyzers (Hitachi High-Technologies, Japan). The central laboratory implemented blinded testing protocols with routine internal quality control and external quality assessment after established procedures [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eUsing structured questionnaires, trained personnel collected data on demographic characteristics (age and sex), socioeconomic factors (education level categorized as high school or above; income level dichotomized at \u0026ge;\u0026thinsp;1000 CNY/month), behavioral factors (smoking status defined as current smoking of \u0026ge;\u0026thinsp;1 cigarette/day), and medical history. Physician-measured blood pressure followed standardized protocols using mercury sphygmomanometers (Omron HBP-1300 devices after 2014). Hypertension was defined as a systolic blood pressure of \u0026ge;\u0026thinsp;140 mmHg, a diastolic blood pressure of \u0026ge;\u0026thinsp;90 mmHg, physician-diagnosed hypertension, or antihypertensive medication use[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Diabetes mellitus was defined as fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, physician-diagnosed diabetes, or glucose-lowering medication use[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Family history of cancer was defined as any cancer diagnosis in either parent.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOutcomes\u003c/h2\u003e\u003cp\u003eThe primary outcome was incident lung cancer (ICD-10: C34) identified through the Kailuan Medical Insurance Database with verification via hospital medical records. Cases were classified into pathological subtypes: adenocarcinoma, squamous cell carcinoma, small cell carcinoma, and other/unspecified. Follow-up continued until lung cancer diagnosis, death from non-lung cancer causes, loss to follow-up, or study termination (December 31, 2023).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data or median (interquartile range) for non-normally distributed data, with between-group comparisons performed using ANOVA or Mann\u0026ndash;Whitney U tests as appropriate. Categorical variables are expressed as frequencies (percentages) and compared using χ\u0026sup2; or Fisher's exact tests. Multivariable Cox proportional hazards regression models were used to evaluate associations between inflammatory indicators and long-term/10-year incident lung cancer risk (overall and by pathological subtype); subdistribution hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Fine-Gray competing risk models for subtypes subject to competing risks. All models were adjusted for sex, age, smoking history, education, income, hypertension, diabetes, and family cancer history. Based on primary findings, we conducted analyses stratified by sex, age (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years), and smoking history to test subgroup effects, and we performed sensitivity analyses, including (a) the exclusion of participants diagnosed with lung cancer within 12 months of enrollment to mitigate potential lead-time bias and (b) additional adjustment for hs-CRP and WBC (when not the primary exposure variables) in primary models. All analyses were performed using SAS 9.4 (SAS Institute Inc.) with statistical significance defined as a two-tailed \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of Study Participants\u003c/h2\u003e\u003cp\u003eA cohort of 99,925 participants was analyzed to investigate the association between inflammatory indicators and incident lung cancer. After a mean follow-up period of 14.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07 years, 1,804 individuals developed lung cancer (cumulative incidence: 1.33%), including 317 LUADs, 215 LUSCs, 138 SCLCs, and 1,134 cases of other pathological types. The cohort comprised 79,824 males (79.8%; mean age, 51.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67 years). Compared to the non-cancer group, cancer group participants were significantly older and showed significantly elevated levels of hs-CRP, WBC, monocytes, neutrophils, CLR, SIRI, and AISI. This group also had a significantly higher proportion of males, individuals with hypertension, those reporting a family history of cancer, smokers, and individuals with higher education level (all, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of patients with and without lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;999,25)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-lung cancer\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;98,121)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLung cancer\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1,804)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20101(20.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19900(20.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e201(11.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79824(79.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78221(79.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1603(88.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or above, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19877(19.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19654(20.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223(12.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u0026thinsp;\u0026gt;\u0026thinsp;1000 yuan/moth, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93455(93.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91749(93.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1706(94.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33487(33.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32726(33.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e761(42.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily history of cancer, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4616(4.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4513(4.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103(5.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43842(43.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42985(43.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e857(47.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9114(9.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8946(9.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168(9.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHs-CRP, mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.80(0.30\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80(0.30\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95(0.35\u0026ndash;2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200.00(168.00\u0026ndash;237.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200.00(168.00\u0026ndash;237.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198.00(163.00\u0026ndash;235.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.17(0.13\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17(0.13\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17(0.13\u0026ndash;0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.42(72.07\u0026ndash;113.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.43(72.11\u0026ndash;113.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.66(68.57\u0026ndash;111.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.36(0.14\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36(0.13\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42(0.16\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e335.19(247.68\u0026ndash;451.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e335.16(247.78\u0026ndash;451.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e336.67(242.53\u0026ndash;464.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62(0.41\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62(0.41\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65(0.43\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152.37\u0026thinsp;\u0026plusmn;\u0026thinsp;117.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152.24\u0026thinsp;\u0026plusmn;\u0026thinsp;117.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e158.91\u0026thinsp;\u0026plusmn;\u0026thinsp;125.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.24(0.17\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.24(0.17\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25(0.17\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol.\u003c/p\u003e\u003cp\u003eThe participants\u0026rsquo; baseline characteristics in total and by the incident of lung cancer were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and median with interquartile range (IQR) for normally and non-normally distributed continuous variables, respectively, and as numbers with percentages for categorical variables. The distinctions in attributes among groups were scrutinized using the Chi-squared (χ\u003csup\u003e2\u003c/sup\u003e) test or Fisher\u0026rsquo;s Exact Test for categorical variables, and the Kruskal-Wallis test for continuous variables, respectively.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSignificant differences in baseline characteristics across pathological lung cancer subtypes are detailed in Supplementary Table\u0026nbsp;1 (all, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These differences encompassed age, sex, education level, smoking history, family history of cancer, hypertension prevalence, and levels of hs-CRP, WBC, monocytes, neutrophils, platelets, CLR, SIRI, and AISI. Specifically, patients with adenocarcinoma presented with the youngest age, the highest proportion of females, the highest incidence of a family history of cancer, the lowest neutrophil levels, and the highest platelet levels. Patients with squamous cell carcinoma had the highest proportion of males and smokers, along with the highest levels of WBC and monocytes and the highest AISI. Patients with SCLC had the lowest proportion of individuals with high school or higher education, the highest hypertension prevalence, and the highest SIRI. Finally, patients with other pathological types had the highest hs-CRP and CLR levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eInflammation and the Long-Term Risk of Overall Lung Cancer\u003c/h2\u003e\u003cp\u003eMultivariable-adjusted Cox regression analysis revealed significant positive associations of WBC, monocytes, lymphocytes, neutrophils, CLR, SIRI, and AISI with overall lung cancer risk, with hazard increases of 10.6%, 5.8%, 4.6%, 8.1%, 3.0%, 53.5%, and 6.8% each 1 Z-score increment, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Except for the association of CLR with overall lung cancer risk, all associations remained robust after excluding participants diagnosed within 12 months of enrollment (Supplementary Table\u0026nbsp;2). Following additional adjustment for hs-CRP and WBC, the associations of WBC, monocytes, lymphocytes, and SIRI with incident lung cancer risk persisted (Supplementary Table\u0026nbsp;3). Sex-stratified analysis suggested potential sex differences: among males, most indicators significantly increased incident lung cancer risk (WBC: +10.4%, monocytes: +6.1%, lymphocytes: +4.9%, neutrophils: +7.7%, CLR: +3.1%, SIRI: +55.6%, AISI: +6.9%), whereas elevated hs-CRP was associated with a 16.1% risk reduction in females. In age- and smoking status-stratified analyses, inflammation consistently influenced incident lung cancer risk without significant heterogeneity across age groups or smoking status (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe HR(95%CI) between inflammation and the incidence risk of lung cancer.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLung cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLung squamous cell carcinomas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLung adenocarcinoma\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSmall cell lung cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOther pathological types of lung cancer\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHs-CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.019(0.974\u0026ndash;1.066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.125(0.991\u0026ndash;1.276)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.907(0.803\u0026ndash;1.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.926(0.769\u0026ndash;1.114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.034(0.980\u0026ndash;1.090)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.106(1.057\u0026ndash;1.157)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.094(0.952\u0026ndash;1.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.084(0.974\u0026ndash;1.206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.071(0.912\u0026ndash;1.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.116(1.057\u0026ndash;1.178)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.058(1.031\u0026ndash;1.086)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.102(1.062\u0026ndash;1.143)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.016(0.926\u0026ndash;1.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.045(0.998\u0026ndash;1.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.031(1.001\u0026ndash;1.061)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.046(1.010\u0026ndash;1.084)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.036(0.959\u0026ndash;1.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.996(0.914\u0026ndash;1.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.074(0.980\u0026ndash;1.178)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.055(1.022\u0026ndash;1.089)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.081(1.035\u0026ndash;1.129)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.067(0.941\u0026ndash;1.209)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.077(0.980\u0026ndash;1.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.064(0.915\u0026ndash;1.238)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.083(1.028\u0026ndash;1.141)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.996(0.940\u0026ndash;1.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.012(0.956\u0026ndash;1.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.992(0.902\u0026ndash;1.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.947(0.755\u0026ndash;1.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.993(0.940\u0026ndash;1.051)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.012(0.985\u0026ndash;1.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.026(1.014\u0026ndash;1.038)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.002(0.962\u0026ndash;1.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.946(0.791\u0026ndash;1.131)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.010(0.995\u0026ndash;1.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.992(0.909\u0026ndash;1.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.929(0.590\u0026ndash;1.462)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.006(0.995\u0026ndash;1.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.999(0.893\u0026ndash;1.117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.933(0.741\u0026ndash;1.174)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.030(1.005\u0026ndash;1.057)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.063(1.035\u0026ndash;1.092)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.027(0.957\u0026ndash;1.102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.668(0.450\u0026ndash;0.992)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.017(0.991\u0026ndash;1.043)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.004(0.973\u0026ndash;1.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.009(0.977\u0026ndash;1.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.004(0.990\u0026ndash;1.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.992(0.835\u0026ndash;1.178)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.004(0.991\u0026ndash;1.017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.535(1.179\u0026ndash;1.997)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.773(1.345\u0026ndash;2.337)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.522(0.090\u0026ndash;3.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.966(0.428\u0026ndash;2.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.638(1.262\u0026ndash;2.129)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.068(1.022\u0026ndash;1.117)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.147(1.013\u0026ndash;1.299)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.062(0.956\u0026ndash;1.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.092(0.935\u0026ndash;1.277)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.047(0.990\u0026ndash;1.108)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.013(0.991\u0026ndash;1.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.027(1.015\u0026ndash;1.039)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.900(0.685\u0026ndash;1.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.953(0.846\u0026ndash;1.074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.014(1.000-1.027)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.002(0.957\u0026ndash;1.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.990(0.912\u0026ndash;1.074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.882(0.688\u0026ndash;1.131)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.012(0.968\u0026ndash;1.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.009(0.992\u0026ndash;1.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.006(0.970\u0026ndash;1.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.004(0.949\u0026ndash;1.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.986(0.912\u0026ndash;1.066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.009(0.965\u0026ndash;1.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.008(0.992\u0026ndash;1.023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol.\u003c/p\u003e\u003cp\u003eModel adjusted for age, sex, current smoking, education level, income level, hypertension, diabetes and family history of cancer.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStratification analysis: the HR(95%CI) between inflammation and the incidence risk of lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWmen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60 years old\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60 years old\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCurrent smoking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNever smoking\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHs-CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.039(0.992\u0026ndash;1.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.839(0.709\u0026ndash;0.993)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.022(0.954\u0026ndash;1.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.077(1.016\u0026ndash;1.142)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.039(0.964\u0026ndash;1.120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.011(0.956\u0026ndash;1.070)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.104(1.053\u0026ndash;1.157)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.135(0.983\u0026ndash;1.310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.076(0.997\u0026ndash;1.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.089(1.029\u0026ndash;1.152)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.139(1.065\u0026ndash;1.217)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.081(1.017\u0026ndash;1.149)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.061(1.035\u0026ndash;1.088)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.987(0.843\u0026ndash;1.156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.072(1.032\u0026ndash;1.114)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.039(0.997\u0026ndash;1.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.048(1.009\u0026ndash;1.089)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.071(1.031\u0026ndash;1.112)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.049(1.011\u0026ndash;1.089)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.023(0.902\u0026ndash;1.160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.055(0.995\u0026ndash;1.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.023(0.970\u0026ndash;1.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.068(1.012\u0026ndash;1.128)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.031(0.983\u0026ndash;1.082)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.077(1.028\u0026ndash;1.128)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.110(0.987\u0026ndash;1.249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.048(0.970\u0026ndash;1.132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.085(1.028\u0026ndash;1.145)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.087(1.019\u0026ndash;1.160)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.078(1.016\u0026ndash;1.143)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.975(0.903\u0026ndash;1.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.022(0.971\u0026ndash;1.075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.928(0.818\u0026ndash;1.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.023(0.985\u0026ndash;1.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.965(0.852\u0026ndash;1.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.005(0.960\u0026ndash;1.052)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.013(0.979\u0026ndash;1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.010(0.967\u0026ndash;1.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.022(0.986\u0026ndash;1.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.004(0.954\u0026ndash;1.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.035(0.896\u0026ndash;1.196)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.011(0.983\u0026ndash;1.039)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.939(0.793\u0026ndash;1.112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.002(0.963\u0026ndash;1.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.983(0.788\u0026ndash;1.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.002(0.916\u0026ndash;1.096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.838(0.627\u0026ndash;1.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.002(0.964\u0026ndash;1.041)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.031(1.005\u0026ndash;1.057)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.982(0.802\u0026ndash;1.203)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.021(0.977\u0026ndash;1.066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.050(1.025\u0026ndash;1.075)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.031(0.998\u0026ndash;1.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.032(0.991\u0026ndash;1.074)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.009(0.955\u0026ndash;1.066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.002(0.959\u0026ndash;1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.995(0.915\u0026ndash;1.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.016(0.971\u0026ndash;1.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.995(0.834\u0026ndash;1.186)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.005(0.976\u0026ndash;1.036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.556(1.133\u0026ndash;2.137)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.493(0.923\u0026ndash;2.416)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.660(1.853\u0026ndash;3.819)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.144(0.682\u0026ndash;1.920)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.546(0.611\u0026ndash;3.912)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.516(1.154\u0026ndash;1.992)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.069(1.020\u0026ndash;1.121)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.061(0.914\u0026ndash;1.231)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.003(0.926\u0026ndash;1.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.084(1.026\u0026ndash;1.145)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.067(0.998\u0026ndash;1.140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.073(1.010\u0026ndash;1.141)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.022(0.989\u0026ndash;1.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.007(0.962\u0026ndash;1.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.084(1.018\u0026ndash;1.154)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.002(0.950\u0026ndash;1.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.034(0.901\u0026ndash;1.185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.012(0.989\u0026ndash;1.035)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.003(0.952\u0026ndash;1.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.001(0.903\u0026ndash;1.109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.005(0.945\u0026ndash;1.069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.988(0.904\u0026ndash;1.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.017(1.017\u0026ndash;1.078)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.987(0.906\u0026ndash;1.075)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.003(0.961\u0026ndash;1.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.037(0.945\u0026ndash;1.139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.001(0.943\u0026ndash;1.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.008(0.951\u0026ndash;1.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.026(0.963\u0026ndash;1.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.998(0.942\u0026ndash;1.057)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol.\u003c/p\u003e\u003cp\u003eModel adjusted for age (exclude age stratified analysis), sex (exclude sex stratified analysis), current smoking (exclude smoking stratified analysis), education level, income level, hypertension, diabetes and family history of cancer.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInflammation and the Long-Term Risk of LUSC\u003c/h2\u003e\u003cp\u003eIn LUSC, each 1 Z-score increase in monocytes, MLR, PLR, SIRI, AISI, and MHR was significantly associated with elevated risks of 10.2%, 2.6%, 6.3%, 77.3%, 14.7%, and 2.7%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Except for the association of AISI with incident lung cancer risk, all associations remained robust after excluding participants diagnosed within 12 months of enrollment (Supplementary Table\u0026nbsp;2). After additional adjustment for hs-CRP and WBC, the associations of monocytes, MLR, SIRI, and MHR with incident LUSC risk persisted (Supplementary Table\u0026nbsp;3). Analyses stratified by sex, age, and smoking status revealed no significant differences in inflammation-associated LUSC risk across these subgroups (Supplementary Table\u0026nbsp;4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInflammation and the Long-Term Risk of LUAD\u003c/h2\u003e\u003cp\u003eMultivariable Cox regression revealed no significant association between inflammatory markers and overall LUAD risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, stratified analyses identified specific associations. Among men, each 1 Z-score increase in WBC was associated with a 12.8% elevated risk. Among participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, each 1 Z-score increase in CLR was associated with a 4.6% increased risk. No significant risk associations were observed across smoking strata (Supplementary Table\u0026nbsp;5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eInflammation and the Long-Term Risk of SCLC\u003c/h2\u003e\u003cp\u003eFor SCLC, each 1 Z-score increase in PLR was associated with a 33.2% reduced risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Stratified analyses revealed subgroup-specific patterns. Among women, elevated NLR (-76.1%) and SII (-80.2%) were associated with significantly reduced risk. In participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, higher monocyte levels (+\u0026thinsp;5.7%) were associated with increased risk. Among non-smokers, elevated monocytes (+\u0026thinsp;6.7%) and lymphocytes (+\u0026thinsp;9.7%) were associated with increased risk (Supplementary Table\u0026nbsp;6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eInflammation and the Long-Term Risk of Other Pathological Lung Cancer Types\u003c/h2\u003e\u003cp\u003eFor other pathological types of lung cancer, each 1 Z-score increase in WBC (+\u0026thinsp;11.6%), monocytes (+\u0026thinsp;3.1%), lymphocytes (+\u0026thinsp;5.5%), neutrophils (+\u0026thinsp;8.3%), SIRI (+\u0026thinsp;63.8%), and MHR (+\u0026thinsp;1.4%) was significantly associated with elevated risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These associations remained robust after excluding participants diagnosed within 12 months of enrollment (Supplementary Table\u0026nbsp;2). Following additional adjustment for hs-CRP and WBC, the associations of WBC, SIRI, and MHR with incident risk of other pathological types persisted (Supplementary Table\u0026nbsp;3). Analyses stratified by sex, age, and smoking status revealed no differences in inflammation-associated risk between sexes for these other pathological types (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eInflammation and the 10-Year Risk of Lung Cancer\u003c/h2\u003e\u003cp\u003eIn addition, we assessed the 10-year incident risk of lung cancer (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For overall lung cancer, each 1 Z-score increase in WBC, monocytes, neutrophils, CLR, SIRI, and AISI was associated with elevated 10-year risks of 12.0%, 7.1%, 8.9%, 3.8%, 75.7%, and 8.7%, respectively. The magnitude of risk increase for these inflammatory markers consistently exceeded that observed for long-term risk. In LUSC, nearly all inflammatory indicators showed positive associations with substantially higher 10-year risk when compared with long-term risk elevations. For each 1 Z-score increment, WBC (+\u0026thinsp;33.8%), monocytes (+\u0026thinsp;11.3%), lymphocytes (+\u0026thinsp;9.0%), neutrophils (+\u0026thinsp;21.9%), NLR (+\u0026thinsp;2.1%), MLR (+\u0026thinsp;3.3%), CLR (+\u0026thinsp;7.3%), SII (+\u0026thinsp;119.9%), SIRI (+\u0026thinsp;32.8%), MHR (+\u0026thinsp;3.3%), and NHR (+\u0026thinsp;2.4%) significantly increased the 10-year risk. For LUAD, only monocytes showed significance (+\u0026thinsp;7.2% each 1 Z-score). In SCLC, only PLR showed an association (+\u0026thinsp;1.8% each 1 Z-score). Among other subtypes, for each 1 Z-score increment, WBC (+\u0026thinsp;10.5%), neutrophils (+\u0026thinsp;8.5%), MLR (+\u0026thinsp;1.5%), SIRI (+\u0026thinsp;79.6%), and MHR (+\u0026thinsp;1.7%) elevated the 10-year risk, with these increases similarly surpassing long-term risk levels.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStratification analysis: the HR(95%CI) between inflammation and the incidence risk of lung squamous cell carcinomas\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWmen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60 years old\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60 years old\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCurrent smoking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNever smoking\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHs-CRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.133(0.998\u0026ndash;1.286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.740(0.401\u0026ndash;1.368)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.949(0.728\u0026ndash;1.239)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.227(1.076\u0026ndash;1.398)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.046(0.856\u0026ndash;1.278)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.190(1.010\u0026ndash;1.401)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.076(0.935\u0026ndash;1.238)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.840(1.073\u0026ndash;3.155)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.061(0.763\u0026ndash;1.476)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.076(0.919\u0026ndash;1.260)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.145(0.958\u0026ndash;1.369)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.024(0.819\u0026ndash;1.281)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.101(1.060\u0026ndash;1.143)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.182(1.100\u0026ndash;1.270)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.150(1.129\u0026ndash;1.172)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.061(1.021\u0026ndash;1.103)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.081(1.039\u0026ndash;1.126)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.144(1.116\u0026ndash;1.173)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.021(0.932\u0026ndash;1.117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.196(1.126\u0026ndash;1.271)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.017(0.811\u0026ndash;1.275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.022(0.922\u0026ndash;1.132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.079(1.001\u0026ndash;1.163)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.961(0.789\u0026ndash;1.170)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.063(0.936\u0026ndash;1.209)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.159(0.781\u0026ndash;1.721)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.015(0.739\u0026ndash;1.394)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.071(0.932\u0026ndash;1.230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.087(0.948\u0026ndash;1.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.040(0.830\u0026ndash;1.303)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.016(0.979\u0026ndash;1.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.288(0.015\u0026ndash;5.353)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.944(0.644\u0026ndash;1.383)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.033(0.985\u0026ndash;1.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.947(0.706\u0026ndash;1.269)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.020(0.997\u0026ndash;1.044)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.030(1.014\u0026ndash;1.045)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.991(0.812\u0026ndash;1.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.036(1.011\u0026ndash;1.062)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.021(1.004\u0026ndash;1.038)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.078(0.990\u0026ndash;1.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.026(1.012\u0026ndash;1.039)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.955(0.627\u0026ndash;1.454)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.321(0.006\u0026ndash;17.994)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.004(0.980\u0026ndash;1.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.899(0.508\u0026ndash;1.593)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.747(0.322\u0026ndash;1.735)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.006(0.978\u0026ndash;1.036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.064(1.036\u0026ndash;1.093)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.267(0.037\u0026ndash;1.931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.053(0.981\u0026ndash;1.130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.069(1.037\u0026ndash;1.101)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.056(1.026\u0026ndash;1.086)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.092(1.036\u0026ndash;1.151)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.016(0.970\u0026ndash;1.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.775(0.034\u0026ndash;17.857)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.972(0.555\u0026ndash;1.702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.017(0.974\u0026ndash;1.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.935(0.625-1.400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.014(0.996\u0026ndash;1.032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.798(1.350\u0026ndash;2.397)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.258(0.624\u0026ndash;2.535)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.569(2.053\u0026ndash;6.206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.292(0.966\u0026ndash;1.728)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.179(1.312\u0026ndash;3.620)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.651(1.169\u0026ndash;2.332)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAISI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.139(1.001\u0026ndash;1.294)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.581(1.129\u0026ndash;2.214)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.015(0.806\u0026ndash;1.278)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.167(1.011\u0026ndash;1.347)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.113(0.965\u0026ndash;1.283)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.194(0.971\u0026ndash;1.470)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.038(1.016\u0026ndash;1.061)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.014(1.005\u0026ndash;1.024)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.131(1.046\u0026ndash;1.223)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.023(1.008\u0026ndash;1.038)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.076(0.999\u0026ndash;1.159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.027(1.014\u0026ndash;1.040)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.965(0.814\u0026ndash;1.144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.033(1.016\u0026ndash;1.050)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.013(0.937\u0026ndash;1.095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.965(0.797\u0026ndash;1.170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.010(0.948\u0026ndash;1.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.919(0.636\u0026ndash;1.327)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.996(0.915\u0026ndash;1.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.075(1.016\u0026ndash;1.137)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.983(0.761\u0026ndash;1.271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.009(0.939\u0026ndash;1.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.015(0.906\u0026ndash;1.138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.999(0.922\u0026ndash;1.082)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: NLR, neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; PLR, platelet to lymphocyte ratio; CLR, hs-CRP to lymphocyte ratio; SII, neutrophil*platelet/lymphocyte; SIRI, neutrophil*monocyte/lymphocyte; AISI, neutrophil*monocyte*platelet/lymphocyte; MHR, monocyte/high-density lipoprotein cholesterol; LHR, lymphocyte/high-density lipoprotein cholesterol; NHR, neutrophil/high-density lipoprotein cholesterol.\u003c/p\u003e\u003cp\u003eModel adjusted for age (exclude age stratified analysis), sex (exclude sex stratified analysis), current smoking (exclude smoking stratified analysis), education level, income level, hypertension, diabetes and family history of cancer.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study included 99,925 participants with a mean follow-up period of 14.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07 years. Key findings were as follows: Most inflammatory indicators showed positive associations with lung cancer risk (both long-term and 10-year risk), and the strength of inflammation\u0026ndash;lung cancer associations showed heterogeneity across pathological subtypes and sex. To our knowledge, this is the first report linking elevated SIRI and AISI with increased incident lung cancer risk.\u003c/p\u003e\u003cp\u003eOur analysis revealed that each 1 Z-score increase, most inflammatory indicators elevated long-term overall lung cancer risk by 3.0\u0026ndash;53.5%. This finding remained robust after excluding cases diagnosed within 1 year of follow-up. Previous studies [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] have confirmed associations between elevated inflammatory markers (e.g., WBC, CLR, and SII) and increased lung cancer risk. Therese et al. reported that elevated SII and NLR increased lung cancer risk by 69% and 53%, respectively, in the UK Biobank cohort [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, another UK Biobank study documented a HR of 1.14 (95% CI: 1.08\u0026ndash;1.20) for lung cancer per WBC increase [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our results are consistent with these findings. Furthermore, we observed that elevated levels of nearly all studied inflammatory indicators were associated with increased 10-year lung cancer risk across all pathological subtypes. Notably, the magnitude of 10-year risk elevation generally exceeded that of long-term risk. Thus, lung cancer prevention strategies should prioritize assessment and management of both long-term and 10-year risk. In addition to traditional markers (e.g., WBC and monocytes), composite indicators (e.g., SII, NLR, SIRI, AISI) should be incorporated into risk monitoring.\u003c/p\u003e\u003cp\u003eSecond, we observed pathological subtype-specific associations. Elevated inflammatory indicators (i) significantly increased long-term risk in overall lung cancer, LUSC, and other subtypes, (ii) showed an inverse association in SCLC, and (iii) showed no significant association in LUAD. A prior study reported a 51% increased LUSC risk when WBC\u0026thinsp;\u0026gt;\u0026thinsp;0.57*10\u003csup\u003e9\u003c/sup\u003e/L compared with the lowest WBC group[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, most inflammation\u0026ndash;lung cancer studies focused on patient prognosis or risk stratification [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To our knowledge, only one study investigated the association between the lymphocyte-to-monocyte ratio [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and incident lung cancer without pathological subtype analysis. In addition, we identified sex-based heterogeneity; inflammatory elevations increased lung cancer risk by 3.1\u0026ndash;55.6% in males, whereas elevated hs-CRP was associated with a 16.1% risk reduction in females. Although sex interaction was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u003csub\u003einteraction\u003c/sub\u003e \u0026gt;0.05), HR magnitudes suggested stronger associations in males. A previous UK Biobank study also found that WBC\u0026thinsp;\u0026gt;\u0026thinsp;9.3*10\u003csup\u003e9\u003c/sup\u003e/L increased incident lung cancer risk by 195% and 115% in male and female current smokers, respectively [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which is consistent with our observations. Therefore, precision prevention strategies should account for pathological subtypes and prioritize male populations.\u003c/p\u003e\u003cp\u003eTo our knowledge, previous studies have not investigated the association of SIRI and AISI with incident lung cancer risk, with limited reports on their prognostic value in cancers like pancreatic, colorectal, breast, and esophageal cancer [\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This may be the first report to demonstrate that elevated SIRI significantly increases incident risk in overall lung cancer (+\u0026thinsp;53.5%), LUSC (+\u0026thinsp;77.3%), and other subtypes (+\u0026thinsp;63.8%), with SIRI showing the strongest association among all studied markers. Elevated AISI increased risk in overall lung cancer (+\u0026thinsp;6.8%) and LUSC (+\u0026thinsp;14.7%). Importantly, these associations persisted in the 10-year risk analysis, with SIRI and AISI elevations increasing 10-year overall lung cancer risk by 75.7% and 8.7%, respectively. Consequently, individuals with elevated SIRI and AISI (particularly SIRI) require heightened vigilance for lung cancer risk, particularly during the next decade of their lives.\u003c/p\u003e\u003cp\u003eOur study has several advantages. First, the large sample size ensured the reliability of our results. The prospective design minimized the potential for recall bias and guaranteed comprehensive data for our analysis. Second, our study provides the first comprehensive comparison of the associations between 15 inflammatory biomarkers and incident lung cancer, and is the first to investigate the impact of SIRI and AISI on lung cancer development. Third, we stratified analyses by pathological subtypes of lung cancer to examine the relationship between inflammation and each subtype. Fourth, we further adjusted for coexisting inflammatory variables as covariates to ensure our results were not confounded by other inflammatory markers. Fifth, the robustness of our findings was validated through stratified and sensitivity analyses.\u003c/p\u003e\u003cp\u003eHowever, we acknowledge the following limitations. First, although our new model incorporated additional blood biochemical indicators, several tumor markers (such as CEA and CYFRA21-1) were not included due to lack of testing. Second, the analysis may have overlooked certain unmeasured confounders. Third, the Kailuan Study lacks ethnic diversity, limiting generalizability of findings to other ethnic groups. Finally, discrepancies between pathological subtypes and actual diagnoses may exist due to limited availability of historical pathological examinations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that elevated levels of multiple inflammatory indicators significantly increase both long-term and 10-year incident lung cancer risks. The associations were particularly pronounced in LUSC and other pathological subtypes and were also stronger in males. Among these indicators, SIRI showed the strongest association with incident risk. We believe this is the first evidence linking the novel composite inflammatory markers SIRI and AISI to overall incident lung cancer risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the investigators, staff, and participants of the Kailuan study for their valuable contributions. We thank Medjaden Inc. for scientific editing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSL, GC, and SC contributed to the conception and design of the study. QZ and XZ\u0026nbsp;performed the analysis. JL and HZ contributed to the interpretation of data. SL and YD wrote the initial draft of the manuscript. QZ, XZ, and JL revised it critically for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData described in the manuscript, code book, and analytic code will be made available upon request pending approval by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kailuan study was approved by the Ethics Committee of the Kailuan Medical Group and followed the Declaration of Helsinki. All participants provided signed informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research project is funded by Medical Science Research Project of Hebei, project number: 20261085.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMenyhart O, Fekete JT, Győrffy B. Inflammation and Colorectal Cancer: A Meta-Analysis of the Prognostic Significance of the Systemic Immune-Inflammation Index (SII) and the Systemic Inflammation Response Index (SIRI). Int J Mol Sci. 2024;25(15):8441.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang S, Cheng T. Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis. Ann Med. 2024;56(1):2337729.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu Z, Zhang Z, Gu C. Prognostic and clinicopathological impact of systemic inflammation response index (SIRI) on patients with esophageal cancer: a meta-analysis. Syst Rev. 2025;14(1):104.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung cancer, inflammation, pathological types, prospective study","lastPublishedDoi":"10.21203/rs.3.rs-7418683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7418683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePrior lung cancer studies on inflammatory indicators have examined single subtypes or aggregated types; however, there have been no systematic comparisons among major subtypes [adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), small cell lung cancer (SCLC)]. The accuracy of novel indicators of incident lung cancer risk [systemic inflammation response index (SIRI) and aggregate index of systemic inflammation (AISI)] remains unexamined.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eWe aimed to assess associations of 15 inflammatory indicators (including SIRI and AISI) with both long-term and 10-year incident lung cancer risks, stratified by the pathological subtype.\u003c/p\u003e\u003ch2\u003eMethods and Results\u003c/h2\u003e\u003cp\u003eA prospective cohort of 99,925 cancer-free participants (mean age, 51.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 years) was followed for 14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 years, identifying 1,804 incident lung cancers (317 LUAD, 215 LUSC, 138 SCLC, and 1,134 others). Multivariable analyses (using Cox or Fine-Gray models) showed that for each 1-Z score increase in several biomarkers, there was an associated increase in the long term hazard for lung cancer: \u003cem\u003eOverall lung cancer HR increases\u003c/em\u003e: WBC (1.106), monocytes (1.058), lymphocytes (1.046), neutrophils (1.081), CLR (1.030), SIRI (1.535), AISI (1.068, stronger in men); \u003cem\u003eLUSC HR increases\u003c/em\u003e: monocytes (1.102), MLR (1.026), PLR (1.063), SIRI (1.773), AISI (1.147), MHR (1.027). Long term Survival was not found to be associated with LUAD but showed an inverse association with SCLC [PLR (0.668)]. Notably, SIRI showed the strongest associations (+\u0026thinsp;53.5% overall; +77.3% LUSC). Ten-year risk associations were generally stronger than long-term for most markers across subtypes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElevated inflammation elevates long-term overall lung cancer risk (particularly in men) and LUSC risk but not LUAD risk. PLR inversely associates with SCLC. Most indicators increase 10-year risk across all subtypes, with SIRI showing the strongest association.\u003c/p\u003e","manuscriptTitle":"The Impact of Inflammation on the Incidence of Different Pathological Types of Lung Cancer: The Kailuan Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:22:41","doi":"10.21203/rs.3.rs-7418683/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4600bdb7-2ed7-4134-9136-f9e9aebdb45d","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T11:42:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:22:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7418683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7418683","identity":"rs-7418683","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}