Association of weight change after smoking cessation on the risk of cancer in patients with allergic diseases: a nationally representative cohort study

Association of weight change after smoking cessation on the risk of cancer in patients with allergic diseases: a nationally representative cohort 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 Article Association of weight change after smoking cessation on the risk of cancer in patients with allergic diseases: a nationally representative cohort study Hahyeon Cho, Soonho Ha, Seogsong Jeong, Hwamin Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5265057/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 Relationships between allergies and cancer are complex and depend on various factors. However, little is known regarding the effects of weight change after smoking cessation on the risk of cancer in patients with allergic diseases. This study investigated 26,614 patients with asthma, allergic rhinitis, and atopic dermatitis from the NHIS-HEALS cohort, who had biennial health screenings between 2005 and 2008. All patients were followed up from 2009 until the date of cancer diagnosis, death, or 2013, whichever earliest. Allergic diseases patients who quit smoking and gained weight have a significantly higher cancer risk than non-smokers with stable weight (adjusted hazard ratio [aHR], 1.59; 95% confidence interval [CI], 1.11–2.27). This risk is particularly elevated in older adults (≥ 65 years, aHR, 2.06; 95% CI, 1.29–3.29), men (aHR, 1.53; 95% CI, 1.05–2.22), and those with multiple comorbidities (aHR, 1.77; 95% CI, 1.24–2.52). Moreover, varying effects of weight gain on lung cancer (aHR, 2.28; 95% CI, 1.21–4.28). These findings highlight the importance of personalized weight management strategies to maximize the benefits of smoking cessation in patients with allergic diseases. Comprehensive public health strategies that address both smoking cessation and weight control may be essential to reduce cancer risk among patients with allergic diseases. Health sciences/Health care Health sciences/Risk factors Health sciences/Diseases/Cancer Allergic diseases Cancer Smoking habit change Weight change after smoking cessation cancer immunology Smoking-related cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Allergic diseases, such as asthma, allergic rhinitis, and atopic dermatitis, along with cancer, are major contributors to the global social burden 1,2 . Allergic diseases, characterized by chronic inflammation and immune dysregulation, are increasingly prevalent worldwide, leading to substantial social and economic burdens 3–5 . This rising prevalence, combined with new discoveries in cancer immunology, has heightened the interest in understanding the relationship between allergic diseases and cancer 3 . Concurrently, advancements in cancer immunology have uncovered novel insights into the role of immune cells, which play crucial roles in allergic responses, in tumorigenesis 4 . The dual roles of immune cells in allergic diseases and cancer has been reported 6 . M1 macrophages, cytotoxic CD8 + immune and natural killer cells suppress tumors, whereas M2 macrophages are associated with tumor promotion 7,8 . Mast cells and eosinophils, which are involved in allergic responses, influence cancer development in complex ways 9,10 . Similarly, immunoglobulin E antibodies may exert anti-cancer effects, whereas immunoglobulin G4 antibodies can promote immune tolerance, potentially aiding tumor growth 10,11 . Regulatory cytokines and epigenetic changes further add to this complexity, as they can either suppress or promote tumors depending on the conditions 12,13 . Understanding these interactions is crucial for elucidating the relationship between allergic diseases and cancer 14,15 . Although smoking is a well-known risk factor for both cancer and the worsening of allergic diseases, many individuals suffering from allergic conditions continue to smoke 16 . Smoking can exacerbate inflammatory processes and induce long-term changes in innate and adaptive immune responses, leading to a persistent inflammatory state that aggravates disease outcomes and contributes to an increased cancer risk 17 . The negative effects of smoking persist despite smoking cessation, with long-term effects on the immune system 18 . Weight gain after smoking cessation may be concerning 19 . Although smoking cessation is beneficial in reducing cancer risk, the associated weight gain can lead to metabolic changes that exacerbate inflammatory conditions 20,21 . This issue is particularly significant for patients with allergic diseases because the combined effects of weight gain and the residual impact of smoking may potentially increase the risk of cancer development 22–24 . Herein, this study is conducted to evaluate the effects of changes in smoking status and subsequent weight gain increased the cancer risk in patients with allergic diseases using the 2005–2013 National Health Insurance Service-Health Screening Cohort (NHIS-HEALS), which is designed to represent the entire South Korean population on research purposes. Results Table 1 presents the descriptive characteristics of the study participants. This study included 26,614 participants with newly diagnosed allergic diseases. The mean age of the participants was 58.1 years, and 52.4% were women. Regarding smoking status, the vast majority were non-smokers (82.8%), followed by sustained smokers (10.9%), those who had quit smoking (3.5%), and a smaller proportion of smokers who had relapsed after quitting (2.8%). The median body mass index (BMI) was 24.3, 23.8, 24.2, and 23.9 kg/m 2 in the non-smokers, patients who quit smoking, smoking relapsers, and sustained smokers groups, respectively Table 1 Baseline characteristics of patients with newly diagnosed allergic diseases. Characteristic Non-smokers (n = 22,041) Smoking relapsers (n = 734) Patients who quit smoking (n = 929) Sustained smokers (n = 2,910) Age, years 58.4 (± 9.4) 57.6 (± 9.6) 58.0 (± 9.7) 56.1 (± 9.1) Sex, women 13,654 (61.9) 67 (9.1) 85 (9.2) 137 (4.7) Household income, upper half 14,447 (65.6) 506 (68.9) 632 (68.0) 2,041 (70.1) Body mass index, kg/m2 24.3 (± 2.9) 23.8 (± 3.1) 24.2 (± 2.9) 23.9 (± 2.9) Total cholesterol, mg/dL 200.0 (± 36.9) 197.4 (± 37.8) 199.3 (± 40.7) 197.1 (± 35.9) Exercise None 10,704 (48.6) 305 (41.6) 489 (52.6) 1,331 (45.7) 1–2 times/week 5,632 (25.6) 248 (33.8) 248 (26.7) 1,020 (35.1) 3–4 times/week 3,207 (14.6) 108 (14.7) 110 (11.8) 328 (11.3) ≥ 5 times/week 2,498(11.3) 73 (10.0) 82 (8.8) 231 (7.9) Alcohol consumption, non-drinker 15,770 (71.6) 248 (33.8) 546 (58.8) 886 (30.5) Hypertension 4,542 (20.6) 141 (19.2) 134 (14.4) 460 (15.8) Diabetes mellitus 1,214 (5.5) 39 (5.3) 52 (5.6) 196 (6.7) Charlson comorbidity index 0–1 8,363 (37.9) 308 (42.0) 385 (41.4) 1,342 (46.1) ≥ 2 13,678 (62.1) 426 (58.0) 544 (58.6) 1,568 (53.9) Table 2 illustrates the significant association between smoking cessation, BMI gain, and increased risk of developing cancer in patients with allergic diseases. Among individuals who quit smoking and subsequently experienced an increase in BMI, the risk of cancer was significantly higher than that among non-smokers with a stable BMI, with an adjusted hazard ratio [aHR] of 1.59 (95% confidence interval [CI] 1.11–2.27). Sensitivity analyses were performed to confirm the robustness of the associations among smoking cessation, BMI gain, and cancer risk. The first sensitivity analysis, considering at least 2 days of hospitalization, revealed a similar association with an aHR of 1.64 (95% CI, 1.09–2.47). To further validate these findings and account for potential confounding factors, a second sensitivity analysis was conducted by excluding the 1-year latent period. After this 1-year washout period, the association between smoking cessation + BMI gain and incident cancer risk remained significant, with an aHR of 2.39 (95% CI, 1.40–4.05). Table 2 Effects of smoking and BMI change in patients with allergic diseases with incident cancer risk. Non-smokers (n = 22,041) Smoking relapsers (n = 734) Patients who quit smoking (n = 929) Sustained smokers (n = 2,910) BMI stable (n = 17,399) BMI gain (n = 4,519) BMI stable (n = 581) BMI gain (n = 164) BMI stable (n = 715) BMI gain (n = 301) BMI stable (n = 2,354) BMI gain (n = 553) PY 102,025 25,051 3,457 778 3,828 1,385 13,969 2,851 Event 1,438 371 55 13 71 35 235 66 Incidence/1,000 PY 14.1 23.6 24.0 25.6 26.0 28.5 27.8 28.8 HR (95% CI) 1.00 (reference) 1.05 (0.93–1.17) 1.12 (0.86–1.47) 1.18 (0.68–2.04) 1.31 (1.03–1.66) 1.79 (1.28–2.50) 1.19 (1.04–1.37) 1.64 (1.28–2.10) aHR (95% CI) a 1.00 (reference) 0.99 (0.88–1.11) 0.97 (0.74–1.27) 0.96 (0.55–1.66) 1.11 (0.87–1.42) 1.47 (1.05–2.07) 1.10 (0.95–1.27) 1.42 (1.11–1.83) aHR (95% CI) b 1.00 (reference) 1.01 (0.90–1.13) 0.84 (0.64–1.11) 0.60 (0.34–1.07) 0.95 (0.74–1.23) 1.59 (1.11–2.26) 0.90 (0.77–1.04) 0.82 (0.64–1.05) aHR (95% CI) c 1.00 (reference) 1.01 (0.90–1.13) 0.84 (0.64–1.11) 0.60 (0.34–1.08) 0.95 (0.74–1.23) 1.59 (1.11–2.27) 0.90 (0.77–1.04) 0.82 (0.64–1.05) aHR (95% CI) d 1.00 (reference) 1.01 (0.89–1.15) 0.82 (0.60–1.10) 0.59 (0.32–1.07) 0.98 (0.75–1.28) 1.64 (1.09–2.47) 0.85 (0.72–1.01) 0.92 (0.71–1.20) aHR (95% CI) e 1.00 (reference) 0.98 (0.78–1.23) 0.97 (0.60–1.57) 2.11 (1.18–3.77) 0.98 (0.62–1.55) 2.39 (1.40–4.05) 1.41 (1.11–1.78) 1.30 (0.90–1.90) HR calculated using the Cox proportional hazards model. a Adjusted for age and sex. b Adjusted for age, sex, household income, body mass index, hypertension, diabetes mellitus, smoking, alcohol consumption, moderate-to-vigorous physical activity, and Charlson comorbidity index. c Adjusted for age, sex, household income, body mass index, hypertension, diabetes mellitus, smoking, alcohol consumption, moderate-to-vigorous physical activity, Charlson comorbidity index, and total cholesterol. d 1 years of latent period washed out for sensitivity analysis. e More than two hospital admissions. Acronyms: BMI, body mass index; PY, person-year; HR, hazard ratio; CI, confidence interval; aHR, adjusted hazard ratio. Subgroup analyses were conducted for cancer events by stratifying the patients by’ age and, sex. For participants aged ≥ 65 years participants, the aHR for cancer in those who quitting smoking and experienced BMI gain was 2.06 (95% CI, 1.29–3.29), indicating a particularly significant association. Among men participants, the aHR for cancer was 1.53 (95% CI, 1.05–2.22), demonstrating a significant increase in cancer risk associated with BMI gain after smoking cessation (Fig. 2 ). Figure 3 depicts the results of the stratified analysis based on comorbidities. No significant interactions were observed for cancer risk in patients with hypertension or diabetes mellitus. However, in patients with a Charlson Comorbidity Index (CCI) of ≥ 2, the aHR for cancer was 1.52 (95% CI, 1.03–2.23), indicating a significant increase in cancer risk associated with BMI gain after smoking cessation. The analysis of cancer risk stratified by liver and lung cancers is presented in Fig. 4 . For liver cancer, the analysis demonstrated that non-smokers with an increase in an increase in BMI had a significantly lower risk of developing liver cancer than non-smokers with a stable BMI (aHR, 0.71; 95% CI, 0.51–0.99). Conversely, smoking relapsers who experienced a BMI gain showed a significantly higher risk of liver cancer (aHR, 1.77; 95% CI, 1.08–2.89). Smoking cessation followed by BMI gain (aHR, 2.28; 95% CI, 1.21–4.28) and sustained smoking, regardless of BMI changes (stable BMI: aHR, 1.86; 95% CI, 1.33–2.61 / BMI gain: aHR, 1.88; 95% CI, 1.16–3.04), were significantly associated with an increased risk of lung cancer. The results for other cancer types, which were not significant, are presented in the Supplementary table S1 . Discussion In this large-scale Korean cohort of patients with allergic diseases, individuals with allergic diseases and a history of smoking had an increased risk of cancer than nonsmokers, owing to the residual effects of smoking despite cessation. Moreover, weight gain following smoking cessation further increased this risk. In the general population, the impact of weight gain after smoking cessation on cancer incidence has been varied. Few studies reported a significant association, whereas others did not 25–27 . This inconsistency highlights the complexity of the relationship among smoking cessation, weight gain, and cancer risk. In our study, this association remained significant across various sensitivity analyses, underscoring the need for personalized weight management strategies to maximize the benefits of smoking cessation in high-risk groups. Smoking alters the immune system, leading to chronic inflammation and immunosuppression states, which can increase the long-term risk of cancer 28 . Furthermore, the residual effects of smoking may persist even after cessation, continuously altering immune responses at the cellular level, enhancing chronic inflammation, and creating a carcinogenic microenvironment 18,28 . Changes in metabolic and hormonal pathways can lead to weight gain after smoking cessation 28,29 . Specifically, the cessation discontinuation of nicotine use, which normally suppresses appetite and increases energy expenditure, results in increased appetite and a reduced metabolic rate, thus contributing to weight gain 29 . Weight gain can cause metabolic disturbances such as insulin resistance, hyperinsulinemia, and increased adipokine levels, creating a pro-inflammatory environment conducive to tumor growth 30 . Moreover, the accumulation of adipose tissue can elevate estrogen levels through increased aromatase activity, transitioning the body from a nicotine-suppressed anti-estrogenic state to a state that fosters carcinogenesis, especially in hormone-sensitive tissues 31 . Elevated insulin and insulin-like growth factor 1 levels promote cellular proliferation and inhibit apoptosis 31,32 . Furthermore, increased oxidative stress and chronic inflammation can damage DNA, collectively contributing to a higher risk of cancer after smoking cessation, particularly in individuals with significant weight gain 33–35 . In this study, the residual effects of smoking and post-cessation weight gain in individuals with allergic diseases significantly increased the cancer risk, particularly among high-risk groups. These high-risk groups included men, individuals aged > 65 years, and patients with a CCI of ≥ 2. First, in men with allergic diseases, weight gain after smoking cessation worsened metabolic changes that increase the risk of cancer. Men with allergic diseases typically accumulate visceral fat in the abdomen, which is strongly associated with insulin resistance and metabolic syndrome 34 . Visceral fat is metabolically active and prone to induce inflammation, thereby increasing the release of inflammatory cytokines and adipokines, which promote carcinogenesis 36,37 . Second, older adults with allergic diseases are more vulnerable to metabolic changes associated with weight gain, which can trigger chronic low-grade inflammation, a well-known risk factor for cancer 38 . This chronic inflammation may lead to increased secretion of pro-inflammatory cytokines that promote the growth and metastasis of cancer cells and reduce immune surveillance 39,40 . Lastly, among patients with a CCI of ≥ 2, the presence of multiple comorbidities can further amplify the negative impact of weight gain following smoking cessation. Although the differences in individual comorbidities such as diabetes and hypertension were not statistically significant, having multiple comorbidities generally indicates a more vulnerable health status 41 . This heightened vulnerability increases susceptibility to metabolic disturbances caused by the residual effects of smoking and weight gain, potentially elevating the overall risk of cancer in this subgroup 42–44 . Additionally, we conducted subgroup analyses according to the cancer type to further explore the relationships between smoking cessation, weight gain, and cancer risk. Contrary to previous studies that have reported a significant increase in the risk of obesity-related cancers (colorectal, breast, and endometrial cancers) associated with weight gain, we did not find a significant association between post-cessation weight gain and these types of cancers in this specific population 25 . However, smoking-related cancers (e.g., lung cancer) were significantly more common among those who gained weight after cessation than among those who continued smoking 45 . Furthermore, the analysis revealed a significantly higher risk of liver cancer among those who resumed smoking and had an increased BMI 46,47 . Thus, the impact of post-cessation weight gain on cancer risk may vary according to the cancer type and population characteristics. This observation underscores the importance of considering the cancer type and population-specific factors when evaluating the impact of weight gain on cancer risk after smoking cessation 48 . To the best of our knowledge, this study is among the first to use the NHIS-HEALS database to investigate the impact of lifestyle changes such as smoking cessation and subsequent weight gain on cancer risk in patients with allergic diseases. This study elucidates the effects of weight gain on long-term health outcomes and underscores the need for clinical interventions to prevent adverse changes and promote weight control. Nevertheless, this study had several limitations. First, the reliance on International Classification of Diseases 10th revision (ICD-10) codes for disease diagnosis may lead to potential misclassification or underreporting if diagnostic standards differ among healthcare providers. Moreover, the absence of specific clinical data, such as genetic information and inflammatory markers, limits the comprehensive understanding of the biological mechanisms linking allergic diseases to cancer. Second, the NHIS-HEALS data did not provide sufficiently detailed lifestyle information, which is crucial for understanding the interactions among smoking cessation, weight gain, and cancer risk. Although data on smoking and alcohol consumption are included, granular details on dietary habits, physical activity levels, and medication adherence are lacking. Future studies should use datasets with more precise lifestyle data to explore these relationships better. Third, the generalizability of the findings is limited to the Korean population and the results may not be directly applicable to populations with different genetic backgrounds or healthcare systems. To overcome this limitation, future studies should include multiethnic cohorts involving different ethnicities and geographic regions to enhance the universality of the findings. Finally, most studies using NHIS-HEALS data have a retrospective design, which limits their ability to establish causality. Although associations can be identified, prospective studies are needed to determine definitive cause-and-effect relationships. However, the limitations of retrospective studies can be partially mitigated using large datasets, adjusting for various confounding variables, and conducting sensitivity analyses to assess the robustness of the results. In conclusion, weight gain after smoking cessation may be associated with an increased risk of cancer in patients with allergic diseases. To reduce the risk of cancer, these patients may need to prevent weight gain after quitting smoking and maintain appropriate weight. Further research is needed to elucidate the relationship between weight gain and cancer risk in patients with allergic diseases and to explore the effects of various exercises and lifestyle interventions on health outcomes. Methods Data source This study used claims data from the NHIS-HEALS database from January 2005 to December 2013. The Republic of Korea has a universal single-payer national health system that covers approximately 98% of the Korean population 49 . All clinics and hospitals in Korea submit data for inpatient hospitalization and outpatient visits to the NHIS to claim reimbursement for patient care. The NHIS comprises four databases: insurance eligibility, medical treatment, medical care institutions, and the general health exams database. The NHIS data include diagnoses and prescriptions based on the ICD-10 and Korean Drug and Anatomical Therapeutic Chemical codes. This study was approved by the institutional review board of Korea University Guro Hospital (No. 2024GR0390). The requirement for obtaining informed consent was waived since the NHIS database is de-identified in compliance with strict confidentiality protocols 49 . Study population The study included patients with stable or increased BMI, aged ≥ 40 years, and newly diagnosed with allergic diseases, such as asthma (ICD-10 codes J45-J46), allergic rhinitis (ICD-10 codes J30.1, J30.2, J30.8, J30.9), and atopic dermatitis (ICD-10 code L20) between January 2005 and December 2006. We enrolled 36,186 participants with newly diagnosed allergic diseases who underwent at least one health screening between 2005 and 2006 and between 2007 and 2008, respectively. Next, participants whose BMI decreased during the second examination compared with that during the first examination were included (n = 5,573). Patients with missing information for other key variables in the adjusted analyses (n = 194), cancer diagnosis before the follow-up investigation (n = 2,051), and death before the index date (n = 1,754) were excluded. Therefore, 26,614 patients with newly diagnosed allergic diseases were enrolled and followed up until December 31, 2013 (Fig. 1 ). Smoking status and BMI classification Participants were classified into four distinct groups based on their smoking status during the two health examinations between 2005 and 2006 and between 2007 and 2008. These groups were as follows: nonsmokers who did not smoke during either of the health examinations; smoking relapsers who were nonsmokers during the first health examination but resumed smoking by the time of the second health examination; those who quit smoking during the first health examination and continued to be non-smokers during the second health examination; and sustained smokers who were current smokers during both the first and second health examinations. A change in BMI exceeding + 1.0 kg/m² in the second health examination (2007–2008), relative to the first health examination (2005–2006), was classified as increased BMI. Conversely, a BMI change between − 1.0 kg/m² and + 1.0 kg/m² during the same period was classified as stable BMI 47 . Diagnosis and follow-up of cancer Patients with newly diagnosed cancer were defined using ICD-10 codes C00-C96, classified according to cancer organ and subtype 50 . Patients diagnosed with cancer before the index date were excluded; and the others with newly developed cancer were followed up from the start of the health examination period until cancer event, death, or December 31, 2013, whichever occurred first. Key variables The following key variables were selected for the adjusted analyses: age (age < 65 years or ≥ 65 years), sex (men or women), household income (upper half or lower half), BMI (continuous; kg/m 2 ), hypertension (yes or no), diabetes mellitus (yes or no), alcohol consumption (yes or no), exercise (none, 1–2 times/week, 3–4 times/week, or ≥ 5 times/week), and CCI (CCI < 2 or CCI ≥ 2), and total cholesterol levels (mg/dL). Statistical analysis Continuous variables, including age, BMI, and total cholesterol, are presented as means (standard deviations), whereas categorical variables are presented as n (%). We calculated the crude rate of the total number of events per 1,000 person-years (PY) for each group. Hazard ratios and 95% CIs for cancer events were calculated using multivariable Cox proportional hazards models according to smoking status and changes in BMI. Individuals were censored when a cancer event or death occurred. If no events occurred, the patients were followed up until December 31, 2013. Next, adjusted HR (aHR) and 95% CI were calculated using age and sex as minimally adjusted models and all key variables including age, sex, household income, body mass index, hypertension, diabetes mellitus, smoking, alcohol consumption, exercise, CCI, and total cholesterol levels. Sensitivity was analyzed using the criteria for cancer, which was defined based on ICD-10 codes and required at least 2 days of hospitalization, and by excluding 1-year latent periods to account for potential bias from other causes before the follow-up investigation. Subgroup analyses were conducted by stratifying the patients by age (< 65 years and ≥ 65 years), sex (men and women), and comorbidities (hypertension, diabetes mellitus, and CCI). Additionally, the analysis was stratified by cancer type to explore the specific associations within these subgroups. Statistical significance was set at P < 0.05. All statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). Declarations Competing interests The authors declare no competing interests. Ethical statement This study received approval from the Institutional Review Board of Korea University Guro Hospital (IRB No. 2024GR0390), with a waiver for informed consent as the NHIS database is anonymized in compliance with the Personal Data Protection Act. All procedures were conducted according to relevant guidelines and regulations. Supplementary Information Table S1 . Subgroup analyses of cancer risks associated with changes in smoking status and subsequent weight change compared to no weight change following a non-smoker status by cancer type. CONSENT STATEMENT Written informed consent was waived as this study was based on de-identified administrative data. Author Contribution H.C. and S.J. conceptualized and designed the study. H.H., S.H., S.J., and H.L. were responsible for data acquisition. Data analysis and interpretation were carried out by H.C., S.J., and H.L. H.C. and S.H. drafted the manuscript, while all authors contributed to critical revision for important intellectual content. Statistical analysis was performed by H.C. and S.J. Administrative, technical, or material support was provided by S.H. and H.L. H.L. supervised the study. All authors reviewed and approved the final manuscript. Acknowledgement This work was supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)(IITP-2024-RS-2022-00156439) grant funded by the Korea government(Ministry of Science and ICT) and the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT)(RS-2024-00438263). Data Availability The data supporting the findings of this study are available from the Korean National Health Insurance Service (NHIS). Access to these data is restricted due to participant privacy and licensing agreements; thus, they are not publicly accessible. 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British journal of cancer 91 , 1525–1531 (2004). Komiyama, M. et al. The effects of weight gain after smoking cessation on atherogenic α1-antitrypsin–low-density lipoprotein. Heart and Vessels 30 , 734–739 (2015). Ohmori, K. et al. The relationship between body mass index and a plasma lipid peroxidation biomarker in an older, healthy Asian community. Annals of Epidemiology 15 , 80–84 (2005). Crudele, L., Piccinin, E. & Moschetta, A. Visceral adiposity and cancer: role in pathogenesis and prognosis. Nutrients 13 , 2101 (2021). Lysaght, J. et al. Pro-inflammatory and tumour proliferative properties of excess visceral adipose tissue. Cancer letters 312 , 62–72 (2011). Ellis, A., Crowe, K. & Lawrence, J. Obesity-related inflammation: implications for older adults. Journal of nutrition in gerontology and geriatrics 32 , 263–290 (2013). Lutz, C. T. & Quinn, L. S. Sarcopenia, obesity, and natural killer cell immune senescence in aging: altered cytokine levels as a common mechanism. Aging (Albany NY) 4 , 535 (2012). Millar, S. R., Harrington, J. M., Perry, I. J. & Phillips, C. M. Associations between a protective lifestyle behaviour score and biomarkers of chronic low-grade inflammation: a cross-sectional analysis in middle-to-older aged adults. International Journal of Obesity 46 , 476–485 (2022). Roubenoff, R. Physical activity, inflammation, and muscle loss. Nutrition reviews 65 , S208-S212 (2007). Goren, A., Annunziata, K., Schnoll, R. A. & Suaya, J. A. Smoking cessation and attempted cessation among adults in the United States. PloS one 9 , e93014 (2014). Chen, Y., Rennie, D., Cormier, Y. & Dosman, J. Association between obesity and atopy in adults. International archives of allergy and immunology 153 , 372–377 (2010). Murthy, N., Mukherjee, S., Ray, G. & Ray, A. Dietary factors and cancer chemoprevention: an overview of obesity-related malignancies. Journal of postgraduate medicine 55 , 45–54 (2009). Samet, J. M. Vol. 110 795–796 (Oxford University Press, 2018). Yang, W. et al. Body Mass Index Trajectories, Weight Gain, and Risks of Liver and Biliary Tract Cancers. JNCI Cancer Spectrum 6 , pkac056 (2022). Jeong, S. et al. Association of change in smoking status and subsequent weight change with risk of Nonalcoholic fatty liver disease. Gut and Liver 17 , 150 (2023). Bea, J. W., De Heer, H. D. & Schwartz, A. L. Symptom management: weight gain. Supportive Cancer Care , 241–269 (2016). Shin, D. W., Cho, B. & Guallar, E. Korean national health insurance database. JAMA internal medicine 176 , 138–138 (2016). Lee, H. H. et al. Post-diagnosis smoking habit change and incident dementia in cancer survivors. Alzheimer's & Dementia (2024). Additional Declarations No competing interests reported. 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Ha","email":"","orcid":"","institution":"Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea","correspondingAuthor":false,"prefix":"","firstName":"Soonho","middleName":"","lastName":"Ha","suffix":""},{"id":377229576,"identity":"d7ade5d4-1b4c-43ec-86a6-8cb34eda5b6f","order_by":2,"name":"Seogsong Jeong","email":"","orcid":"","institution":"Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea","correspondingAuthor":false,"prefix":"","firstName":"Seogsong","middleName":"","lastName":"Jeong","suffix":""},{"id":377229577,"identity":"abb2f4dc-b3cc-4fc2-8e9b-d6d4345f84f0","order_by":3,"name":"Hwamin Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACAxCRwGBjABeRIFJLGqlaGBgOk6DFXCL56YaHO84b80sfPviBocaOQXL2AfxaLGekmd1IPHPbTLIvLVmC4VgygzRfAgGH3U4Aamm7bWNwhsdAgoHtAIMcDwGHGdxO/wbUcg6ohf/zD4Z/RGnJAdlywAxoC5sEY9sBBmmCWu6/KQNqSTaW7GEzs0jsS+aR7CGk5czxbTd/ttkZ9vMwP77x4ZudnMQZAlpQQQIDAyFnjYJRMApGwSggBgAAuKU/wgdnd08AAAAASUVORK5CYII=","orcid":"","institution":"Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea","correspondingAuthor":true,"prefix":"","firstName":"Hwamin","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-10-15 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2","display":"","copyAsset":false,"role":"figure","size":1836050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSee image above for figure legend\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.Subgroupanalysesofcancerrisksassociatedwithchangesinsmokingstatusandsubsequentweightchangecomparedwithnoweightchangefollowinganonsmokerstatus.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5265057/v1/ef567df8956c4946f115d1ae.jpg"},{"id":69462159,"identity":"570f17b5-61ec-4647-9d25-23874aae8890","added_by":"auto","created_at":"2024-11-20 15:00:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1842437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSee image above for figure legend\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.Subgroupanalysesofcancerrisksassociatedwithchangesinsmokingstatusandsubsequentweightchangecomparedwithnoweightchangefollowinganonsmokerstatus.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5265057/v1/d68a6becef4e962cd280f286.jpg"},{"id":69462160,"identity":"5314db82-d979-4875-b2f3-74882d61b0fd","added_by":"auto","created_at":"2024-11-20 15:00:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1054922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSee image above for figure legend\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.Subgroupanalysesofcancerrisksassociatedwithchangesinsmokingstatusandsubsequentweightchangecomparedwithnoweightchangefollowinganonsmokerstatusbyca.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5265057/v1/dd3b9e515f156b85910a7ffb.jpg"},{"id":72873633,"identity":"d6a89f2d-135e-4230-9fcc-14a0e148c582","added_by":"auto","created_at":"2025-01-03 07:47:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5876816,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5265057/v1/6a16447c-2fff-41d1-8dda-e89bb4ce51ea.pdf"},{"id":69462163,"identity":"1d8e2b2e-23e0-456b-8e18-be8bb3f02943","added_by":"auto","created_at":"2024-11-20 15:00:52","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":43614,"visible":true,"origin":"","legend":"","description":"","filename":"AllergicdiseasesCancerSupplementaryInformationScientificReportsV1.1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5265057/v1/1e8dd9ef7ece144fd4dd977e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of weight change after smoking cessation on the risk of cancer in patients with allergic diseases: a nationally representative cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAllergic diseases, such as asthma, allergic rhinitis, and atopic dermatitis, along with cancer, are major contributors to the global social burden\u003csup\u003e1,2\u003c/sup\u003e. Allergic diseases, characterized by chronic inflammation and immune dysregulation, are increasingly prevalent worldwide, leading to substantial social and economic burdens\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. This rising prevalence, combined with new discoveries in cancer immunology, has heightened the interest in understanding the relationship between allergic diseases and cancer\u003csup\u003e3\u003c/sup\u003e. Concurrently, advancements in cancer immunology have uncovered novel insights into the role of immune cells, which play crucial roles in allergic responses, in tumorigenesis\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe dual roles of immune cells in allergic diseases and cancer has been reported\u003csup\u003e6\u003c/sup\u003e. M1 macrophages, cytotoxic CD8\u0026thinsp;+\u0026thinsp;immune and natural killer cells suppress tumors, whereas M2 macrophages are associated with tumor promotion\u003csup\u003e7,8\u003c/sup\u003e. Mast cells and eosinophils, which are involved in allergic responses, influence cancer development in complex ways\u003csup\u003e9,10\u003c/sup\u003e. Similarly, immunoglobulin E antibodies may exert anti-cancer effects, whereas immunoglobulin G4 antibodies can promote immune tolerance, potentially aiding tumor growth\u003csup\u003e10,11\u003c/sup\u003e. Regulatory cytokines and epigenetic changes further add to this complexity, as they can either suppress or promote tumors depending on the conditions\u003csup\u003e12,13\u003c/sup\u003e. Understanding these interactions is crucial for elucidating the relationship between allergic diseases and cancer\u003csup\u003e14,15\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough smoking is a well-known risk factor for both cancer and the worsening of allergic diseases, many individuals suffering from allergic conditions continue to smoke\u003csup\u003e16\u003c/sup\u003e. Smoking can exacerbate inflammatory processes and induce long-term changes in innate and adaptive immune responses, leading to a persistent inflammatory state that aggravates disease outcomes and contributes to an increased cancer risk\u003csup\u003e17\u003c/sup\u003e. The negative effects of smoking persist despite smoking cessation, with long-term effects on the immune system\u003csup\u003e18\u003c/sup\u003e. Weight gain after smoking cessation may be concerning\u003csup\u003e19\u003c/sup\u003e. Although smoking cessation is beneficial in reducing cancer risk, the associated weight gain can lead to metabolic changes that exacerbate inflammatory conditions\u003csup\u003e20,21\u003c/sup\u003e. This issue is particularly significant for patients with allergic diseases because the combined effects of weight gain and the residual impact of smoking may potentially increase the risk of cancer development\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHerein, this study is conducted to evaluate the effects of changes in smoking status and subsequent weight gain increased the cancer risk in patients with allergic diseases using the 2005\u0026ndash;2013 National Health Insurance Service-Health Screening Cohort (NHIS-HEALS), which is designed to represent the entire South Korean population on research purposes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive characteristics of the study participants. This study included 26,614 participants with newly diagnosed allergic diseases. The mean age of the participants was 58.1 years, and 52.4% were women. Regarding smoking status, the vast majority were non-smokers (82.8%), followed by sustained smokers (10.9%), those who had quit smoking (3.5%), and a smaller proportion of smokers who had relapsed after quitting (2.8%). The median body mass index (BMI) was 24.3, 23.8, 24.2, and 23.9 kg/m\u003csup\u003e2\u003c/sup\u003e in the non-smokers, patients who quit smoking, smoking relapsers, and sustained smokers groups, respectively\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 newly diagnosed allergic diseases.\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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-smokers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22,041)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmoking relapsers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;734)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients who quit smoking\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;929)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSustained smokers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,910)\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\u003e58.4 (\u0026plusmn;\u0026thinsp;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6 (\u0026plusmn;\u0026thinsp;9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.0 (\u0026plusmn;\u0026thinsp;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.1 (\u0026plusmn;\u0026thinsp;9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,654 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income, upper half\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,447 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e506 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e632 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,041 (70.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.3 (\u0026plusmn;\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8 (\u0026plusmn;\u0026thinsp;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2 (\u0026plusmn;\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.9 (\u0026plusmn;\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.0 (\u0026plusmn;\u0026thinsp;36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197.4 (\u0026plusmn;\u0026thinsp;37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199.3 (\u0026plusmn;\u0026thinsp;40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197.1 (\u0026plusmn;\u0026thinsp;35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,704 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e489 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,331 (45.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,632 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,020 (35.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,207 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e328 (11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,498(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e231 (7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, non-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,770 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e546 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e886 (30.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,542 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e460 (15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,214 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson comorbidity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,363 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e385 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,342 (46.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,678 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e544 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,568 (53.9)\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the significant association between smoking cessation, BMI gain, and increased risk of developing cancer in patients with allergic diseases. Among individuals who quit smoking and subsequently experienced an increase in BMI, the risk of cancer was significantly higher than that among non-smokers with a stable BMI, with an adjusted hazard ratio [aHR] of 1.59 (95% confidence interval [CI] 1.11\u0026ndash;2.27). Sensitivity analyses were performed to confirm the robustness of the associations among smoking cessation, BMI gain, and cancer risk. The first sensitivity analysis, considering at least 2 days of hospitalization, revealed a similar association with an aHR of 1.64 (95% CI, 1.09\u0026ndash;2.47). To further validate these findings and account for potential confounding factors, a second sensitivity analysis was conducted by excluding the 1-year latent period. After this 1-year washout period, the association between smoking cessation\u0026thinsp;+\u0026thinsp;BMI gain and incident cancer risk remained significant, with an aHR of 2.39 (95% CI, 1.40\u0026ndash;4.05).\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\u003eEffects of smoking and BMI change in patients with allergic diseases with incident cancer risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNon-smokers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22,041)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSmoking relapsers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;734)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePatients who quit smoking\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;929)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSustained smokers\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,910)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI stable\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17,399)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI gain\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,519)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI stable\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;581)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBMI gain\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;164)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMI stable\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;715)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBMI gain\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;301)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBMI stable\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,354)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBMI gain\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;553)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence/1,000 PY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e(0.93\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003cp\u003e(0.86\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e(0.68\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e(1.03\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003cp\u003e(1.28\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(1.04\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003cp\u003e(1.28\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaHR (95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.88\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.74\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.55\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e(0.87\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003cp\u003e(1.05\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e(0.95\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003cp\u003e(1.11\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaHR (95% CI)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.90\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.64\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e(0.34\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e(0.74\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003cp\u003e(1.11\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e(0.77\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.64\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaHR (95% CI)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.90\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.64\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e(0.34\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e(0.74\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003cp\u003e(1.11\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e(0.77\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.64\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaHR (95% CI)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.89\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.60\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003cp\u003e(0.32\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.75\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003cp\u003e(1.09\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003cp\u003e(0.72\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaHR (95% CI)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.78\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.60\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003cp\u003e(1.18\u0026ndash;3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.62\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003cp\u003e(1.40\u0026ndash;4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003cp\u003e(1.11\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003cp\u003e(0.90\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eHR calculated using the Cox proportional hazards model.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted for age and sex.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eAdjusted for age, sex, household income, body mass index, hypertension, diabetes mellitus, smoking, alcohol consumption, moderate-to-vigorous physical activity,\u003c/p\u003e \u003cp\u003eand Charlson comorbidity index.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eAdjusted for age, sex, household income, body mass index, hypertension, diabetes mellitus, smoking, alcohol consumption, moderate-to-vigorous physical activity,\u003c/p\u003e \u003cp\u003eCharlson comorbidity index, and total cholesterol.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e1 years of latent period washed out for sensitivity analysis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ee\u003c/sup\u003eMore than two hospital admissions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eAcronyms: BMI, body mass index; PY, person-year; HR, hazard ratio; CI, confidence interval; aHR, adjusted hazard ratio.\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\u003eSubgroup analyses were conducted for cancer events by stratifying the patients by\u0026rsquo; age and, sex. For participants aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years participants, the aHR for cancer in those who quitting smoking and experienced BMI gain was 2.06 (95% CI, 1.29\u0026ndash;3.29), indicating a particularly significant association. Among men participants, the aHR for cancer was 1.53 (95% CI, 1.05\u0026ndash;2.22), demonstrating a significant increase in cancer risk associated with BMI gain after smoking cessation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the results of the stratified analysis based on comorbidities. No significant interactions were observed for cancer risk in patients with hypertension or diabetes mellitus. However, in patients with a Charlson Comorbidity Index (CCI) of \u0026ge;\u0026thinsp;2, the aHR for cancer was 1.52 (95% CI, 1.03\u0026ndash;2.23), indicating a significant increase in cancer risk associated with BMI gain after smoking cessation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of cancer risk stratified by liver and lung cancers is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For liver cancer, the analysis demonstrated that non-smokers with an increase in an increase in BMI had a significantly lower risk of developing liver cancer than non-smokers with a stable BMI (aHR, 0.71; 95% CI, 0.51\u0026ndash;0.99). Conversely, smoking relapsers who experienced a BMI gain showed a significantly higher risk of liver cancer (aHR, 1.77; 95% CI, 1.08\u0026ndash;2.89). Smoking cessation followed by BMI gain (aHR, 2.28; 95% CI, 1.21\u0026ndash;4.28) and sustained smoking, regardless of BMI changes (stable BMI: aHR, 1.86; 95% CI, 1.33\u0026ndash;2.61 / BMI gain: aHR, 1.88; 95% CI, 1.16\u0026ndash;3.04), were significantly associated with an increased risk of lung cancer. The results for other cancer types, which were not significant, are presented in the Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale Korean cohort of patients with allergic diseases, individuals with allergic diseases and a history of smoking had an increased risk of cancer than nonsmokers, owing to the residual effects of smoking despite cessation. Moreover, weight gain following smoking cessation further increased this risk. In the general population, the impact of weight gain after smoking cessation on cancer incidence has been varied. Few studies reported a significant association, whereas others did not\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e. This inconsistency highlights the complexity of the relationship among smoking cessation, weight gain, and cancer risk. In our study, this association remained significant across various sensitivity analyses, underscoring the need for personalized weight management strategies to maximize the benefits of smoking cessation in high-risk groups.\u003c/p\u003e \u003cp\u003eSmoking alters the immune system, leading to chronic inflammation and immunosuppression states, which can increase the long-term risk of cancer\u003csup\u003e28\u003c/sup\u003e. Furthermore, the residual effects of smoking may persist even after cessation, continuously altering immune responses at the cellular level, enhancing chronic inflammation, and creating a carcinogenic microenvironment\u003csup\u003e18,28\u003c/sup\u003e. Changes in metabolic and hormonal pathways can lead to weight gain after smoking cessation\u003csup\u003e28,29\u003c/sup\u003e. Specifically, the cessation discontinuation of nicotine use, which normally suppresses appetite and increases energy expenditure, results in increased appetite and a reduced metabolic rate, thus contributing to weight gain\u003csup\u003e29\u003c/sup\u003e. Weight gain can cause metabolic disturbances such as insulin resistance, hyperinsulinemia, and increased adipokine levels, creating a pro-inflammatory environment conducive to tumor growth\u003csup\u003e30\u003c/sup\u003e. Moreover, the accumulation of adipose tissue can elevate estrogen levels through increased aromatase activity, transitioning the body from a nicotine-suppressed anti-estrogenic state to a state that fosters carcinogenesis, especially in hormone-sensitive tissues\u003csup\u003e31\u003c/sup\u003e. Elevated insulin and insulin-like growth factor 1 levels promote cellular proliferation and inhibit apoptosis\u003csup\u003e31,32\u003c/sup\u003e. Furthermore, increased oxidative stress and chronic inflammation can damage DNA, collectively contributing to a higher risk of cancer after smoking cessation, particularly in individuals with significant weight gain\u003csup\u003e33\u0026ndash;35\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, the residual effects of smoking and post-cessation weight gain in individuals with allergic diseases significantly increased the cancer risk, particularly among high-risk groups. These high-risk groups included men, individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years, and patients with a CCI of \u0026ge;\u0026thinsp;2. First, in men with allergic diseases, weight gain after smoking cessation worsened metabolic changes that increase the risk of cancer. Men with allergic diseases typically accumulate visceral fat in the abdomen, which is strongly associated with insulin resistance and metabolic syndrome\u003csup\u003e34\u003c/sup\u003e. Visceral fat is metabolically active and prone to induce inflammation, thereby increasing the release of inflammatory cytokines and adipokines, which promote carcinogenesis\u003csup\u003e36,37\u003c/sup\u003e. Second, older adults with allergic diseases are more vulnerable to metabolic changes associated with weight gain, which can trigger chronic low-grade inflammation, a well-known risk factor for cancer\u003csup\u003e38\u003c/sup\u003e. This chronic inflammation may lead to increased secretion of pro-inflammatory cytokines that promote the growth and metastasis of cancer cells and reduce immune surveillance\u003csup\u003e39,40\u003c/sup\u003e. Lastly, among patients with a CCI of \u0026ge;\u0026thinsp;2, the presence of multiple comorbidities can further amplify the negative impact of weight gain following smoking cessation. Although the differences in individual comorbidities such as diabetes and hypertension were not statistically significant, having multiple comorbidities generally indicates a more vulnerable health status\u003csup\u003e41\u003c/sup\u003e. This heightened vulnerability increases susceptibility to metabolic disturbances caused by the residual effects of smoking and weight gain, potentially elevating the overall risk of cancer in this subgroup\u003csup\u003e42\u0026ndash;44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, we conducted subgroup analyses according to the cancer type to further explore the relationships between smoking cessation, weight gain, and cancer risk. Contrary to previous studies that have reported a significant increase in the risk of obesity-related cancers (colorectal, breast, and endometrial cancers) associated with weight gain, we did not find a significant association between post-cessation weight gain and these types of cancers in this specific population\u003csup\u003e25\u003c/sup\u003e. However, smoking-related cancers (e.g., lung cancer) were significantly more common among those who gained weight after cessation than among those who continued smoking\u003csup\u003e45\u003c/sup\u003e. Furthermore, the analysis revealed a significantly higher risk of liver cancer among those who resumed smoking and had an increased BMI\u003csup\u003e46,47\u003c/sup\u003e. Thus, the impact of post-cessation weight gain on cancer risk may vary according to the cancer type and population characteristics. This observation underscores the importance of considering the cancer type and population-specific factors when evaluating the impact of weight gain on cancer risk after smoking cessation\u003csup\u003e48\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study is among the first to use the NHIS-HEALS database to investigate the impact of lifestyle changes such as smoking cessation and subsequent weight gain on cancer risk in patients with allergic diseases. This study elucidates the effects of weight gain on long-term health outcomes and underscores the need for clinical interventions to prevent adverse changes and promote weight control. Nevertheless, this study had several limitations. First, the reliance on International Classification of Diseases 10th revision (ICD-10) codes for disease diagnosis may lead to potential misclassification or underreporting if diagnostic standards differ among healthcare providers. Moreover, the absence of specific clinical data, such as genetic information and inflammatory markers, limits the comprehensive understanding of the biological mechanisms linking allergic diseases to cancer. Second, the NHIS-HEALS data did not provide sufficiently detailed lifestyle information, which is crucial for understanding the interactions among smoking cessation, weight gain, and cancer risk. Although data on smoking and alcohol consumption are included, granular details on dietary habits, physical activity levels, and medication adherence are lacking. Future studies should use datasets with more precise lifestyle data to explore these relationships better. Third, the generalizability of the findings is limited to the Korean population and the results may not be directly applicable to populations with different genetic backgrounds or healthcare systems. To overcome this limitation, future studies should include multiethnic cohorts involving different ethnicities and geographic regions to enhance the universality of the findings. Finally, most studies using NHIS-HEALS data have a retrospective design, which limits their ability to establish causality. Although associations can be identified, prospective studies are needed to determine definitive cause-and-effect relationships. However, the limitations of retrospective studies can be partially mitigated using large datasets, adjusting for various confounding variables, and conducting sensitivity analyses to assess the robustness of the results.\u003c/p\u003e \u003cp\u003eIn conclusion, weight gain after smoking cessation may be associated with an increased risk of cancer in patients with allergic diseases. To reduce the risk of cancer, these patients may need to prevent weight gain after quitting smoking and maintain appropriate weight. Further research is needed to elucidate the relationship between weight gain and cancer risk in patients with allergic diseases and to explore the effects of various exercises and lifestyle interventions on health outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThis study used claims data from the NHIS-HEALS database from January 2005 to December 2013. The Republic of Korea has a universal single-payer national health system that covers approximately 98% of the Korean population\u003csup\u003e49\u003c/sup\u003e. All clinics and hospitals in Korea submit data for inpatient hospitalization and outpatient visits to the NHIS to claim reimbursement for patient care. The NHIS comprises four databases: insurance eligibility, medical treatment, medical care institutions, and the general health exams database. The NHIS data include diagnoses and prescriptions based on the ICD-10 and Korean Drug and Anatomical Therapeutic Chemical codes. This study was approved by the institutional review board of Korea University Guro Hospital (No. 2024GR0390). The requirement for obtaining informed consent was waived since the NHIS database is de-identified in compliance with strict confidentiality protocols\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study included patients with stable or increased BMI, aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years, and newly diagnosed with allergic diseases, such as asthma (ICD-10 codes J45-J46), allergic rhinitis (ICD-10 codes J30.1, J30.2, J30.8, J30.9), and atopic dermatitis (ICD-10 code L20) between January 2005 and December 2006. We enrolled 36,186 participants with newly diagnosed allergic diseases who underwent at least one health screening between 2005 and 2006 and between 2007 and 2008, respectively. Next, participants whose BMI decreased during the second examination compared with that during the first examination were included (n\u0026thinsp;=\u0026thinsp;5,573). Patients with missing information for other key variables in the adjusted analyses (n\u0026thinsp;=\u0026thinsp;194), cancer diagnosis before the follow-up investigation (n\u0026thinsp;=\u0026thinsp;2,051), and death before the index date (n\u0026thinsp;=\u0026thinsp;1,754) were excluded. Therefore, 26,614 patients with newly diagnosed allergic diseases were enrolled and followed up until December 31, 2013 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSmoking status and BMI classification\u003c/h3\u003e\n\u003cp\u003eParticipants were classified into four distinct groups based on their smoking status during the two health examinations between 2005 and 2006 and between 2007 and 2008. These groups were as follows: nonsmokers who did not smoke during either of the health examinations; smoking relapsers who were nonsmokers during the first health examination but resumed smoking by the time of the second health examination; those who quit smoking during the first health examination and continued to be non-smokers during the second health examination; and sustained smokers who were current smokers during both the first and second health examinations.\u003c/p\u003e \u003cp\u003eA change in BMI exceeding\u0026thinsp;+\u0026thinsp;1.0 kg/m\u0026sup2; in the second health examination (2007\u0026ndash;2008), relative to the first health examination (2005\u0026ndash;2006), was classified as increased BMI. Conversely, a BMI change between \u0026minus;\u0026thinsp;1.0 kg/m\u0026sup2; and +\u0026thinsp;1.0 kg/m\u0026sup2; during the same period was classified as stable BMI\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDiagnosis and follow-up of cancer\u003c/h2\u003e \u003cp\u003ePatients with newly diagnosed cancer were defined using ICD-10 codes C00-C96, classified according to cancer organ and subtype\u003csup\u003e50\u003c/sup\u003e. Patients diagnosed with cancer before the index date were excluded; and the others with newly developed cancer were followed up from the start of the health examination period until cancer event, death, or December 31, 2013, whichever occurred first.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eKey variables\u003c/h3\u003e\n\u003cp\u003eThe following key variables were selected for the adjusted analyses: age (age\u0026thinsp;\u0026lt;\u0026thinsp;65 years or \u0026ge;\u0026thinsp;65 years), sex (men or women), household income (upper half or lower half), BMI (continuous; kg/m\u003csup\u003e2\u003c/sup\u003e), hypertension (yes or no), diabetes mellitus (yes or no), alcohol consumption (yes or no), exercise (none, 1\u0026ndash;2 times/week, 3\u0026ndash;4 times/week, or \u0026ge;\u0026thinsp;5 times/week), and CCI (CCI\u0026thinsp;\u0026lt;\u0026thinsp;2 or CCI\u0026thinsp;\u0026ge;\u0026thinsp;2), and total cholesterol levels (mg/dL).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables, including age, BMI, and total cholesterol, are presented as means (standard deviations), whereas categorical variables are presented as n (%). We calculated the crude rate of the total number of events per 1,000 person-years (PY) for each group. Hazard ratios and 95% CIs for cancer events were calculated using multivariable Cox proportional hazards models according to smoking status and changes in BMI. Individuals were censored when a cancer event or death occurred. If no events occurred, the patients were followed up until December 31, 2013. Next, adjusted HR (aHR) and 95% CI were calculated using age and sex as minimally adjusted models and all key variables including age, sex, household income, body mass index, hypertension, diabetes mellitus, smoking, alcohol consumption, exercise, CCI, and total cholesterol levels. Sensitivity was analyzed using the criteria for cancer, which was defined based on ICD-10 codes and required at least 2 days of hospitalization, and by excluding 1-year latent periods to account for potential bias from other causes before the follow-up investigation. Subgroup analyses were conducted by stratifying the patients by age (\u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years), sex (men and women), and comorbidities (hypertension, diabetes mellitus, and CCI). Additionally, the analysis was stratified by cancer type to explore the specific associations within these subgroups. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical statement\u003c/h2\u003e \u003cp\u003e This study received approval from the Institutional Review Board of Korea University Guro Hospital (IRB No. 2024GR0390), with a waiver for informed consent as the NHIS database is anonymized in compliance with the Personal Data Protection Act. All procedures were conducted according to relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSupplementary Information\u003c/strong\u003e \u003cp\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Subgroup analyses of cancer risks associated with changes in smoking status and subsequent weight change compared to no weight change following a non-smoker status by cancer type.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCONSENT STATEMENT\u003c/strong\u003e \u003cp\u003eWritten informed consent was waived as this study was based on de-identified administrative data.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.C. and S.J. conceptualized and designed the study. H.H., S.H., S.J., and H.L. were responsible for data acquisition. Data analysis and interpretation were carried out by H.C., S.J., and H.L. H.C. and S.H. drafted the manuscript, while all authors contributed to critical revision for important intellectual content. Statistical analysis was performed by H.C. and S.J. Administrative, technical, or material support was provided by S.H. and H.L. H.L. supervised the study. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the IITP(Institute of Information \u0026amp; Communications Technology Planning \u0026amp; Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)(IITP-2024-RS-2022-00156439) grant funded by the Korea government(Ministry of Science and ICT) and the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT)(RS-2024-00438263).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the Korean National Health Insurance Service (NHIS). Access to these data is restricted due to participant privacy and licensing agreements; thus, they are not publicly accessible. Researchers may request access by contacting the Customized Data Access Department of NHIS (https://nhiss.nhis.or.kr, +82-033-736-3481, +82-033-736-3477) with a research proposal and obtaining necessary permissions under reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Johnson, K. The relation of cancer to allergy. \u003cem\u003eThe Journal-Lancet\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, 5\u0026ndash;11 (1966).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Shapiro, S. \u0026amp; Fedullo, A. Allergy and cancer. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cb\u003e301\u003c/b\u003e, 1055\u0026ndash;1056 (1973).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Krishna, M. 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H. \u003cem\u003eet al.\u003c/em\u003e Post-diagnosis smoking habit change and incident dementia in cancer survivors. \u003cem\u003eAlzheimer's \u0026amp; Dementia\u003c/em\u003e (2024).\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":"Allergic diseases, Cancer, Smoking habit change, Weight change after smoking cessation, cancer immunology, Smoking-related cancer","lastPublishedDoi":"10.21203/rs.3.rs-5265057/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5265057/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRelationships between allergies and cancer are complex and depend on various factors. However, little is known regarding the effects of weight change after smoking cessation on the risk of cancer in patients with allergic diseases. This study investigated 26,614 patients with asthma, allergic rhinitis, and atopic dermatitis from the NHIS-HEALS cohort, who had biennial health screenings between 2005 and 2008. All patients were followed up from 2009 until the date of cancer diagnosis, death, or 2013, whichever earliest. Allergic diseases patients who quit smoking and gained weight have a significantly higher cancer risk than non-smokers with stable weight (adjusted hazard ratio [aHR], 1.59; 95% confidence interval [CI], 1.11\u0026ndash;2.27). This risk is particularly elevated in older adults (\u0026ge;\u0026thinsp;65 years, aHR, 2.06; 95% CI, 1.29\u0026ndash;3.29), men (aHR, 1.53; 95% CI, 1.05\u0026ndash;2.22), and those with multiple comorbidities (aHR, 1.77; 95% CI, 1.24\u0026ndash;2.52). Moreover, varying effects of weight gain on lung cancer (aHR, 2.28; 95% CI, 1.21\u0026ndash;4.28). These findings highlight the importance of personalized weight management strategies to maximize the benefits of smoking cessation in patients with allergic diseases. Comprehensive public health strategies that address both smoking cessation and weight control may be essential to reduce cancer risk among patients with allergic diseases.\u003c/p\u003e","manuscriptTitle":"Association of weight change after smoking cessation on the risk of cancer in patients with allergic diseases: a nationally representative cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 15:00:47","doi":"10.21203/rs.3.rs-5265057/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":"9591a8c8-e8f1-45e5-96b5-0aa9b4d0dbb9","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40157439,"name":"Health sciences/Health care"},{"id":40157440,"name":"Health sciences/Risk factors"},{"id":40157441,"name":"Health sciences/Diseases/Cancer"}],"tags":[],"updatedAt":"2025-01-03T07:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 15:00:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5265057","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5265057","identity":"rs-5265057","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}