Comparative analysis of lung cancer risk factors between never-smokers and heavy smokers: a nationwide population-based study in Korea

Introduction

Lung cancer remains one of the most significant health concerns worldwide (1). In Korea, it was one of the most common cancers (32,149/259,999) and the leading cause of cancer-related deaths (18,379/81,567) in 2021 (2). Although tobacco smoking is a major risk factor for lung cancer (3), the national prevalence of tobacco smoking among adults has declined in recent years (from 24.1% in 2013 to 17.7% in 2022) (4). Despite this reduction, more than one-third of individuals with newly diagnosed lung cancer were never-smokers (36.4% in 2014) in Korea (5). In contrast, approximately two-thirds (63.6%) of individuals with newly diagnosed lung cancer had a history of smoking in 2014 (5), reflecting the historically high prevalence of tobacco smoking (35.1% overall and 66.3% among males in 1998) (4).

Previous large randomized trials have demonstrated that low-dose computed tomography (LDCT) screening among high-risk populations—either current smokers or former smokers with at least 15 pack-years of smoking history—significantly reduces lung cancer-related mortality (6,7). Korea implemented the world’s first nationwide LDCT screening program as part of the national health examination in 2019. Individuals aged 54–74 years with ≥30 pack-years of smoking history (either current smokers or former smokers who had quit within the last 15 years) undergo annual LDCT. This program has been associated with an overall decrease in one-year mortality [−3.21%, 95% confidence interval (CI): −4.84% to −1.58%] and lung cancer-related mortality (−2.69%, 95% CI: −4.24% to −1.13%), with more pronounced effects observed in older individuals and those living in nonmetropolitan areas (8). These findings suggest that certain subgroups of heavy smokers may benefit disproportionately from lung cancer screening programs.

However, current screening programs eligibility relies largely on age and smoking history (e.g., pack-years and years since quitting); thus, this smoking history-based approach may fail to identify a substantial proportion of high-risk individuals who have never smoked (9,10). Given the increasing proportion of never-smokers lung cancer, targeted screening strategies for this population are needed (11). Although previous epidemiologic studies have identified several risk factors for lung cancer in never-smokers, most have focused on a single domain of risk factors (12,13) or included only females (14,15).

In this context, identifying risk factors for lung cancer beyond smoking history may contribute to the further development of lung cancer risk prediction models. In the present study, we examined risk factors for lung cancer separately in never-smokers and heavy smokers with ≥30 pack-years of smoking history. We developed multivariable regression models that incorporated demographic information, lifestyle behaviors, and comorbidities, all of which can be obtained from national health insurance and health examination data. Our findings could inform the development of a lung cancer prediction model applicable to both never-smokers and heavy smokers using health examination data. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0212/rc).

Methods

Data source

Study data were obtained from the Korean National Health Information Database, a comprehensive nationwide database managed by the National Health Insurance Service (NHIS). This resource includes demographic profiles, health service use, insurance claims, health examination records, and mortality information for the entire Korean population since 2001 (16,17). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

The national health examination program has been implemented for preventive care and early disease detection since 1995, providing biennial examinations to most adults (adult employees aged ≥19 years and adults aged ≥40 years), particularly annual examinations for manual laborers. The program includes questionnaires, laboratory testing, anthropometric measurements, and imaging such as chest radiography (17-19).

Study population

From individuals who completed the 2012 (the index year) national health examination, 40% (4,910,068 persons) were randomly sampled. From this cohort, we identified two population subsets: (I) never-smokers and (II) heavy smokers with ≥30 pack-years of smoking history.

For the never-smoker subset, we identified 2,927,449 individuals with no history of tobacco smoking or less than 100 cigarettes smoked in their lifetime. For the heavy smoker subset, we included only individuals aged ≥40 years to ensure the possibility of ≥30 pack-years of smoking history (assuming smoking began in mid-adolescence at one pack per day, 30 pack-years would be reached by the mid-40s). We then identified 301,583 individuals meeting this criterion.

Participants were excluded if they had (I) prior insurance claims [International Classification of Diseases, 10th Revision, International Classification of Diseases (ICD)-10 codes C00-C97] or critical illness registration (special code V193) for cancer before the health examination; (II) insufficient data; or (III) cancer claims or critical illness registration within one year of baseline (1-year lag period), thereby minimizing detection bias.

After exclusions, 2,650,867 never-smokers and 269,320 heavy smokers were included. Follow-up began one year after the index date and continued until December 2022, with endpoints being lung cancer diagnosis, censoring, or death. The mean follow-up durations were 9.27±0.96 and 8.87±1.72 years, respectively. During follow-up, we identified newly developed lung cancers in 16,540 never-smokers and 9,895 heavy smokers (Figure 1).

Figure 1 Study flowchart of participants inclusion process.

Measurements

The study outcome was newly developed lung cancer, identified from insurance claim data (ICD-10 codes C33 and C34) and the critical illness registry (special code V193), a combination known to yield high diagnostic accuracy in Korea (11,20).

Anthropometric and lifestyle information was derived from health examination records. Body mass index (BMI) was categorized using Korean BMI classifications: <18.5, 18.5–22.9, 23–24.9, 25–29.9, and ≥30 kg/m² (21). Smoking variables included smoking status (former vs. current) and cumulative exposure categorized as 30–39, 40–49, or ≥50 pack years (one pack-year: smoking one pack of cigarettes per day for one year). Alcohol intake was categorized as none, mild (males: 1–29.9 g/day; females: 1–19.9 g/day), or heavy (males: ≥30 g/day; females: ≥20 g/day). Regular exercise was defined as moderate activity >30 minutes at least five times weekly or vigorous activity >20 minutes at least three times weekly (22). Income level was categorized into quartiles (Q1 = lowest and Q4 = highest), with medical aid beneficiaries (lowest 3%) included in Q1.

Comorbidities were identified based on insurance claims within one year prior to the health examination and baseline health examination data, as follows: (I) impaired fasting glucose (IFG)—fasting serum glucose of 100–125 mg/dL in a health examination; (II) diabetes mellitus (DM)—insurance claim for ICD-10 codes E11–14 with a prescription for hypoglycemic medication, or fasting serum glucose ≥126 mg/dL; (III) hypertension—insurance claim for ICD-10 codes I10–13 or I15 with a prescription for antihypertensive medication, or blood pressure ≥140/90 mmHg during the health examination; (IV) dyslipidemia—insurance claim for ICD-10 code E78 with a prescription for lipid-lowering medication, or total cholesterol ≥240 mg/dL; (V) chronic kidney disease (CKD)—insurance claim for ICD-10 codes N18–N19 or estimated glomerular filtration rate <60 mL/min/1.73 m² (modification of diet in renal disease equation).

Depression, a major mental disorder linked to health behaviors such as smoking and alcohol intake (23), was defined as ≥1 insurance claim for ICD-10 codes F32–F33 within one year prior to the health examination. History of tuberculosis (TB) and chronic obstructive pulmonary disease (COPD), both associated with increased lung cancer risk (11), were defined as follows—TB: ≥3 insurance claims for ICD-10 codes A15–A19 between 2002 and the health examination date; and COPD: ≥3 insurance claims for ICD-10 codes J41–J44 between 2009 and the health examination date.

Statistical analysis

Continuous variables were presented as means ± standard deviations and were compared using the Student’s t-test. Categorical variables were summarized as counts (percentages) and were compared using the χ² test. Lung cancer incidence rates were calculated as the number of newly diagnosed lung cancer cases divided by the number of person-years at risk, expressed per 1,000 person-years. A multivariable Cox proportional hazards model was used to evaluate the effects of risk factors on time to lung cancer development, with additional stratification by age and sex. All P values were two-tailed, and statistical significance was defined as P<0.05. Analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, United States).

Results

Baseline characteristics of the study population

Table 1 presents the baseline characteristics of the never-smoker subset. The mean age was 49.2±14.3 years, and 72.5% were female. Compared with the non-lung cancer group, the lung cancer group was older (61.9±11.5 vs. 49.1±14.3 years) and had a higher proportion of males (34.4% vs. 27.4%). Approximately two-thirds (67.3%) were non-drinkers.

Table 2 presents the baseline characteristics of the heavy smoker subset. The mean age was 58.0±9.0 years, and 98.8% were male. The lung cancer group was older (64.7±8.0 vs. 57.7±9.0 years) than the non-lung cancer group. While 17.8% of the overall heavy smoker population had a smoking history of ≥50 pack-years, this proportion increased to 29.8% in the lung cancer group. Approximately one-third (32.8%) were non-drinkers.

Risk of lung cancer in never-smokers and heavy smokers

Tables 3,4 show the risk factors for lung cancer in never-smokers and heavy smokers, respectively. Among sociodemographic factors, older age, male sex [never-smokers—adjusted hazard ratio (aHR) 1.56, 95% CI: 1.51–1.62; heavy smokers—aHR 1.64, 95% CI: 1.37–1.96], and low income were associated with increased lung cancer risk. BMI demonstrated an inverse association with lung cancer risk, and low BMI was associated with increased risk in heavy smokers (aHR 1.19, 95% CI: 1.07–1.31). Current smoking and pack-year history exhibited a significant dose-response relationship with lung cancer risk, even among heavy smokers (current smoking with ≥50 pack-years—aHR 2.49, 95% CI: 2.31–2.67). Among comorbidities, a history of TB (never-smokers—aHR 2.20, 95% CI: 2.06–2.35; heavy smokers—aHR 1.83, 95% CI: 1.70–1.97) and COPD (never-smokers—aHR 1.62, 95% CI: 1.55–1.70; heavy smokers—aHR 1.91, 95% CI: 1.82–2.00) had particularly strong associations with lung cancer risk. Additional multivariate analysis with interaction terms for smoking status showed no noticeable difference from the original analysis, only except for sex owing to considerable difference in sexual distribution between never-smokers and heavy smokers (Table S1).

Risk of lung cancer stratified by age and sex

We performed stratified analyses by age (cutoff value: 65 years) in both subsets. Overall patterns of lung cancer risk factors were consistent across age groups. The inverse association between BMI and lung cancer was more pronounced in younger individuals, whereas the effect of alcohol intake varied between age groups. The associations of TB history and COPD with lung cancer risk were stronger in younger individuals (Figure 2; Tables S2,S3).

Figure 2 Risks of lung cancer stratified by age. (A) Never-smokers aged 20–64 years. (B) Never-smokers aged ≥65 years. (C) Heavy smokers aged 40–64 years. (D) Heavy smokers aged ≥65 years. Q1, Q2, Q3, and Q4 represent the income quartiles, corresponding to each quartile category from the lowest to the highest income group. BMI, body mass index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; IFG, impaired fasting glucose; MA, medical aid; P-Y, pack-years; TB, tuberculosis.

We then conducted stratified analyses by sex in both subsets, although statistical power was limited in the female heavy smoker subset. In never-smokers, certain comorbidities, including DM, hypertension, and CKD, showed an inverse association with lung cancer risk in females. The associations of TB history and COPD with lung cancer risk were stronger in males (Figure 3; Tables S4,S5).

Figure 3 Risks of lung cancer stratified by sex. (A) Male never-smokers. (B) Female never-smokers. (C) Male heavy smokers. (D) Female heavy smokers. Q1, Q2, Q3, and Q4 represent the income quartiles, corresponding to each quartile category from the lowest to the highest income group. BMI, body mass index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; IFG, impaired fasting glucose; MA, medical aid; P-Y, pack-years; TB, tuberculosis.

Discussion

In this study, we separately investigated risk factors for lung cancer in never-smokers and heavy smokers. Across both populations, advancing age, male sex, and lower income consistently increased lung cancer risk. Alcohol use and smoking demonstrated dose-response relationships, whereas BMI showed an inverse association with lung cancer development. Several comorbidities, such as DM and hypertension, also contributed to elevated risk, suggesting a potential association of metabolic dysfunction in tumor development. Previous TB and COPD exhibited particularly strong associations with lung cancer, emphasizing a potential link between chronic respiratory conditions and lung cancer. Age- and sex-stratified analyses further demonstrated that these associations tended to be more prominent among men and younger individuals.

Historically, Korea has had a high prevalence of tobacco smoking among adult males. National data indicate that male smoking prevalence peaked at 79.3% in 1980, and even in 1998, men smoked over ten times more frequently than women (males: 66.3% vs. females: 6.5%) (4). Assuming a pattern of smoking one pack per day for over 30 years, our study population likely reflects smoking behaviors dating back to at least the 1980s. In our findings, current heavy smokers had approximately twofold higher lung cancer risk than former heavy smokers, and cumulative exposure showed a strong dose-response effect. These results reaffirm that tobacco smoking is a major risk factor for lung cancer and suggest that individuals with persistent heavy smoking may warrant more intensive surveillance, potentially including shorter LDCT screening intervals or extension of the upper screening age limit beyond 74 years (8). Our findings also reinforce that smoking cessation should be recommended regardless of cumulative exposure.

Among chronic respiratory conditions, TB and COPD have been recognized as significant risk factors for lung cancer (24,25). In our analysis, these conditions showed stronger associations with lung cancer risk than most lifestyle factors and other comorbidities, with the association between TB history and lung cancer being particularly pronounced in never-smokers. Long-standing post-TB changes, such as fibrosis, chronic inflammation, and structural lung damage, may create a microenvironment conducive to carcinogenesis through persistent oxidative stress and aberrant tissue repair (26). Because current screening programs largely depend on age and smoking history, individuals with little or no smoking exposure but with prior TB or chronic lung disease may remain unidentified despite substantial risk (9,10). Korea’s historically high prevalence of TB means that many older adults may have post-TB lung conditions (27,28). While prior meta-analyses have suggested that the TB-lung cancer association is most apparent within two years of TB diagnosis (potentially reflecting surveillance bias) (29), our study addressed this concern by (I) capturing TB history up to 10 years prior to baseline and (II) excluding lung cancer cases diagnosed within one year after the index date. In never-smokers, post-TB lung conditions may approximate the risk conferred by substantial smoking exposure, suggesting that TB history could be considered as a screening criterion in this group, pending further research (11).

We also found that males had a higher risk of lung cancer than females. Possible explanations include: (I) greater direct and secondhand exposure to tobacco smoke among males (30); (II) sex-related biological differences in responses to smoke exposure, with males exhibiting stronger inflammatory and immune responses (31); and (III) higher occupational exposure to carcinogenic agents in male-dominant industries, such as construction, manufacturing, and agriculture (32), potentially compounded by sex-specific physiological differences in response to occupational exposures (33). These factors may collectively contribute to elevated lung cancer risk in both never-smoking and heavy-smoking men.

Age-stratified analyses revealed stronger lung cancer risks in older males, whereas incidence rates were similar between males and females in younger adults. In Korea, females are at greater risk for early-onset lung cancer, particularly in younger age groups (age 20–29 years—aHR 1.83, 95% CI: 1.53–2.17; age 30–39 years—aHR 1.49, 95% CI: 1.36–1.64) (20). Lung cancer in young adults (≤35 years) is distinct, with a high proportion of adenocarcinoma (45%) and a greater prevalence of driver gene mutations (34). Estrogen, including exogenous sources, may promote cancer cell proliferation (35), which could partly explain the relatively high occurrence of lung cancer in younger-aged females. Unfortunately, because the ICD-10 coding system does not classify lung cancer by histologic type, we could not analyze subtype-specific risks.

Although higher BMI is generally associated with a higher risk for most cancers, an inverse relationship between BMI and lung cancer risk—often referred to as the “obesity paradox”—has been reported (36). Our findings align with this pattern, but we also observed higher lung cancer risk in heavy smokers with low BMI, likely reflecting the impact of smoking on body weight (37). Nicotine increases energy expenditure and suppresses appetite, leading to weight loss (38). Additionally, tobacco-related respiratory diseases such as COPD and bronchiectasis involve chronic systemic inflammation, which can further increase catabolic energy expenditure (39,40). The inverse BMI-lung cancer association was more pronounced in younger and male participants, underscoring the complex metabolic interplay between BMI and lung cancer risk.

Metabolic disorders such as DM, hypertension, and dyslipidemia were significantly associated with lung cancer risk, particularly in younger or male individuals, suggesting metabolic contributions to carcinogenesis (41,42). Notably, CKD showed an inverse relationship with lung cancer risk, most evident in older adults. Given that CKD is generally regarded as a cancer risk factor, this result should be interpreted cautiously, as it may reflect immortal time bias (43). Depression has also been linked to cancer risk through chronic inflammation and unhealthy behaviors (23).

This study has some limitations. First, among heavy smokers, the proportion of female participants was low (1.2%), limiting generalizability to women in this group. Second, several potentially important determinants, such as family history, secondhand smoke exposure, ambient air pollution, and occupational exposures, were unavailable in the source database. Third, as an insurance claim-based retrospective observational study, observed associations may not be causal, and residual biases, including immortal time bias and reverse causality, remain possible. Particularly, the non-lung cancer group represents individuals who had not yet developed lung cancer during follow-up and could potentially develop lung cancer with longer observation. Therefore, prospective, large-scale cohort studies are needed to further clarify lung cancer risk factors beyond smoking history. Finally, this study aimed to comparatively analyze how various lung cancer risk factors manifest between heavy smokers and never-smokers, rather than to determine actual lung cancer incidence rates.

Conclusions

In conclusion, we identified several risk factors for lung cancer in never-smokers and heavy smokers, spanning sociodemographic, lifestyle, and comorbidity domains. Lung cancer risk increased with age, male sex, and low income. Alcohol intake and tobacco smoking showed a dose–response relationship with lung cancer risk, while BMI had an inverse association. Among heavy smokers, current smokers with high cumulative exposure were at greatest risk. Histories of TB and COPD had particularly strong associations with lung cancer risk. Age- and sex-stratified findings underscore the importance of tailoring prevention and screening strategies. These results may contribute to the development of lung cancer prediction models applicable to both never-smokers and heavy smokers.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0212/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0212/prf

Funding: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-NR077159), the Bio&Medical Technology Development Program of the NRF funded by the MSIT (Nos. RS-2022-NR067421 and RS-2023-00222687), and the National Institute of Health Research Project (No. 2024ER080602).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0212/coif). S.W.L. reports receiving support from the National Research Foundation of Korea (NRF) grant funded by the Korea government, the Bio&Medical Technology Development Program of the NRF funded by the MSIT, and the National Institute of Health Research Project. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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