Lifestyle behaviors and site-specific cancer risk after kidney transplantation: age- and comorbidity-related differences in a nationwide cohort

Lifestyle behaviors and site-specific cancer risk after kidney transplantation: age- and comorbidity-related differences in a nationwide cohort | 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 Lifestyle behaviors and site-specific cancer risk after kidney transplantation: age- and comorbidity-related differences in a nationwide cohort Hojin Jeon, Yebin Park, Seung Min Song, Kyungho Lee, Junseok Jeon, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8685285/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 Kidney transplant recipients (KTRs) have an increased cancer risk, but the influence of lifestyle factors remains unclear. This study investigated how smoking, alcohol, and physical activity affect post-transplant malignancy. We assembled a nationwide cohort of KTRs using health screening data. The primary outcome was incidence of malignancy 1 year after transplantation. The secondary outcomes were all-cause mortality and graft failure. Overall cancer incidence was not significantly associated with smoking, alcohol consumption, or physical activity in the fully adjusted models. Site-specific analyses revealed that smoking was associated with lung cancer (current vs. never: hazard ratio [HR], 5.94; 95% confidence interval [CI], 2.06–17.09). Alcohol consumption was associated with esophageal cancer (yes vs. no: HR, 7.41; 95% CI, 1.20–45.67). Age strengthens these associations. Among KTRs with diabetes, current smoking was linked to higher risks of colorectal (HR 3.09) and pancreatic (5.81) cancer. Although physical activity was not associated with cancer incidence, it was associated with lower mortality rates (HR, 0.72; 95% CI, 0.61–0.85). Lifestyle factors had a limited impact on the overall cancer incidence; however, significant site-specific or subgroup associations were evident. These results support smoking cessation, alcohol consumption reduction, and routine exercise in improving the survival of KTRs. Biological sciences/Cancer Health sciences/Diseases Health sciences/Oncology Health sciences/Risk factors cancer risk comorbidities kidney transplant recipient lifestyle factors survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Kidney transplantation (KT) significantly improves the survival and quality of life in patients with end-stage kidney disease (ESKD). However, long-term complications such as malignancy have emerged as major threats to the health outcomes of KT recipients (KTRs). Previous research has reported that KTRs have a 2- to 4-fold increased risk of developing cancer after transplantation compared with the general population 1,2 . Despite this elevated risk, current clinical recommendations for cancer prevention in KTRs are largely extrapolated from guidelines developed for the general population, particularly regarding modifiable lifestyle factors, such as smoking, alcohol consumption, and physical activity 3 . Although the benefits of smoking cessation in reducing lung cancer incidence and mortality are well documented in KTRs 4 , evidence of allograft or patient survival in relation to alcohol intake and physical activity remains unclear 5,6 . Several studies have suggested that moderate alcohol consumption is associated with reduced mortality or a lower incidence of post-transplant diabetes mellitus (DM) 7 and that regular exercise improves cardiovascular fitness and metabolic outcomes in patients with chronic kidney disease 8 . However, few studies have investigated the association between lifestyle factors and cancer incidence in KTRs. This study aimed to investigate the association among smoking, alcohol consumption, physical activity, and the risk of solid cancers in KTRs using a nationwide database. We hypothesized that these lifestyle factors would demonstrate differential associations with cancer risk according to age and metabolic comorbidities. To address this hypothesis, we further evaluated the potential interactions between age and comorbid conditions to better understand the differential vulnerabilities and elucidate risk-stratified cancer prevention strategies in this high-risk population. Results Baseline characteristics The primary cohort for cancer incidence comprised 67,327 individuals (8,074 KTRs and 59,253 age- and sex-matched members of the general population) with a mean follow-up of 6.2 ± 4.23 years. KTRs had a mean age of 47.5 ± 9.96 years at transplantation, and 59.7% were male. Baseline characteristics differed between KTRs and controls across several domains, including age, income, comorbidities, and lifestyle behaviors. Compared with controls, KTRs had a higher prevalence of DM, HTN, and dyslipidemia but a lower prevalence of current smoking and alcohol consumption. The distributions of residential areas and physical activity were similar between the groups. The cumulative overall incidence of cancer was significantly higher in KTRs than in the general population (7.3% vs. 4.2%) (Table 1 ). Table 1 Baseline characteristics Matched general population (n = 59,253) KTRs (n = 8,074) Age 48.2 ± 10.29 47.5 ± 9.96 20–39 years 11,287 (19.1) 1,570 (19.4) 40–55 years 31,149 (52.6) 4,491 (55.6) ≥ 55 years 16,817 (28.4) 2,013 (24.9) Male sex 35,097 (59.2) 4,820 (59.7) Low income, < 20% 12,174 (20.6) 2,255 (27.9) Seoul or city place 27,758 (46.9) 3,756 (46.5) Smoking Never 32,164 (54.3) 4,895 (60.6) Ex 10,574 (17.9) 2,187 (27.1) Current 16,515 (27.9) 992 (12.3) Alcohol consumption Non 28,085 (47.4) 6,271 (77.7) Mild 25,342 (42.8) 1,648 (20.4) Heavy 5,826 (9.8) 155 (1.9) Regular exercise, yes or none 11,323 (19.1) 1,677 (20.8) Exercise, yes or none 31,973 (54.0) 4,240 (52.5) BMI, kg/m 2 24.0 ± 3.29 23.12 ± 3.36 Diabetes mellitus 5,986 (10.1) 2,416 (29.9) Hypertension 15,762 (26.6) 5,683 (70.4) Dyslipidemia 12,873 (21.7) 4,148 (51.4) Estimated GFR, mL/min/1.73m 2 92.2 ± 42.91 49.4 ± 39.96 Cancer incidence 2,474 (4.2) 586 (7.3) Follow-up duration, years 6.2 ± 2.40 6.3 ± 2.66 BMI, body mass index; GFR, glomerular filtration rate; KTRs, kidney transplant recipients Categorical variables are presented as numbers (percentages), and continuous variables are presented as means ± standard deviation. Overall cancer risk according to lifestyle factors In fully adjusted analyses (model 3), former and current smoking were not associated with overall cancer (former vs. never: HR, 1.12; 95% CI, 0.96–1.50; current vs. never: HR, 1.16; 95% CI, 0.84–1.60). In site-specific analyses, both former and current smoking were associated with a higher risk of lung cancer (former vs. never: HR, 3.08; 95% CI, 1.22–7.75; current vs. never: HR, 5.94; 95% CI, 2.06–17.09), whereas no consistent pattern was observed for other cancers (Table 2 ). Table 2 Incidence rate of cancer in KTRs according to smoking status Smoking n Event IR † Adjusted HR (95% CI) Model 1 Model 2 Model 3 Any cancer Never 4,895 354 11.38 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 175 12.48 1.09 (0.91–1.31) 1.19 (0.95–1.48) 1.20 (0.96–1.50) Current 992 57 10.14 0.90 (0.68–1.19) 1.14 (0.83–1.55) 1.16 (0.84–1.60) Oral cavity, pharyngeal, and laryngeal Never 4,895 5 0.16 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 7 0.48 3.13 (0.99–9.87) 2.03 (0.54–7.59) 2.04 (0.54–7.65) Current 992 3 0.52 3.33 (0.79–13.94) 2.94 (0.59–14.57) 2.86 (0.55–14.75) Esophagus Never 4,895 4 0.12 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 2 0.14 1.12 (0.21–6.12) 0.94 (0.13–6.58) 0.76 (0.10–5.63) Current 992 0 0.00 Stomach Never 4,895 40 1.25 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 24 1.66 1.32 (0.80–2.19) 0.99 (0.556–1.77) 0.94 (0.53–1.69) Current 992 4 0.69 0.57 (0.20–1.60) 0.57 (0.19–1.65) 0.50 (0.17–1.49) Colorectal Never 4,895 31 0.96 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 17 1.17 1.19 (0.66–2.16) 1.60 (0.73–3.47) 1.59 (0.73–3.48) Current 992 6 1.04 1.11 (0.46–2.66) 1.84 (0.67–5.10) 1.81 (0.64–5.13) Lung Never 4,895 19 0.59 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 17 1.17 1.99 (1.03–3.83) 3.02 (1.21–7.56) 3.08 (1.22–7.75) Current 992 9 1.56 2.66 (1.20–5.88) 5.84 (2.09–16.36) 5.94 (2.06–17.09) Liver Never 4,895 14 0.43 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 14 0.97 2.18 (1.04–4.59) 2.66 (0.98–7.26) 2.46 (0.90–6.74) Current 992 5 0.87 1.98 (0.71–5.52) 3.30 (0.96–11.34) 2.87 (0.81–10.15) Pancreatic Never 4,895 16 0.50 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 4 0.28 0.55 (0.19–1.65) 1.03 (0.26–4.12) 1.05 (0.26–4.21) Current 992 3 0.52 1.05 (0.30–3.60) 2.74 (0.61–12.30) 2.99 (0.65–13.85) Thyroid Never 4,895 60 1.88 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 17 1.18 0.63 (0.37–1.08) 1.48 (0.72–3.02) 1.58 (0.76–3.27) Current 992 4 0.69 0.36 (0.13–0.99) 0.81 (0.26–2.48) 0.96 (0.31–3.01) Biliary Never 4,895 14 0.43 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 4 0.28 0.62 (0.21–1.90) 0.59 (0.17–2.07) 0.57 (0.16–2.01) Current 992 2 0.35 0.80 (0.18–3.53) 1.11 (0.22–5.56) 1.11 (0.21–5.80) Renal or bladder Never 4,895 60 1.87 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,187 29 2.01 1.07 (0.69–1.66) 0.77 (0.47–1.27) 0.80 (0.48–1.32) Current 992 11 1.92 1.05 (0.55–1.99) 0.81 (0.41–1.62) 0.91 (0.45–1.86) Prostate ‡ Never 1,759 18 1.57 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,112 17 1.21 0.77 (0.40–1.49) 0.87 (0.45–1.70) 0.86 (0.44–1.68) Current 949 4 0.72 0.48 (0.16–1.43) 0.81 (0.27–2.42) 0.78 (0.25–2.41) Breast § Never 3,136 32 1.56 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 75 2 4.02 2.67 (0.64–11.17) 2.24 (0.53–9.48) 2.73 (0.63–11.92) Current 43 0 0.00 Ovarian § Never 3,136 6 0.29 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 75 0 0.00 Current 43 0 0.00 Uterine § Never 3,136 10 0.48 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 75 0 0.00 Current 43 0 0.00 CI, confidence interval; diabetes mellitus; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipient. Model 1 was not adjusted for any variable. Model 2 was adjusted for age and sex. Model 3 was adjusted for age, sex, low income, diabetes mellitus, hypertension, and dyslipidemia. † Incidence rate is presented as per 1,000 person-years. ‡ Data of male participants were analyzed. § Data of female participants were analyzed. Regarding alcohol consumption, there was no association with overall cancer risk in model 3 (yes vs. none: HR, 0.96; 95% CI, 0.76–1.20). In site-specific cancer analyses, esophageal cancer risk increased with alcohol exposure (yes vs. none: HR, 7.41; 95% CI, 1.20–45.67) (Table 3 ). Table 3 Incidence rate of cancer in KTRs according to alcohol consumption Drink n Event IR † Adjusted HR (95% CI) Model 1 Model 2 Model 3 Any cancer No 6,271 480 11.98 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 106 9.93 0.83 (0.68–1.03) 0.96 (0.77–1.20) 0.96 (0.76–1.20) Oral cavity, pharyngeal, and laryngeal No 6,271 11 0.26 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 4 0.36 1.37 (0.44–4.31) 1.22 (0.38–3.93) 1.12 (0.33–3.73) Esophagus No 6,271 3 0.07 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 3 0.27 3.71 (0.75–18.45) 6.31 (1.03–38.56) 7.41 (1.20–45.67) Stomach No 6,271 52 1.26 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 16 1.46 1.19 (0.68–2.08) 1.28 (0.71–2.29) 1.38 (0.76–2.48) Colorectal No 6,271 43 1.04 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 11 1.00 0.99 (0.51–1.91) 1.25 (0.62–2.52) 1.21 (0.59–2.46) Lung No 6,271 35 0.84 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 10 0.91 1.08 (0.54–2.19) 1.30 (0.62–2.72) 1.07 (0.50–2.30) Liver No 6,271 24 0.58 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 9 0.82 1.42 (0.66–3.06) 1.63 (0.73–3.67) 1.51 (0.66–3.46) Pancreatic No 6,271 21 0.51 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 2 0.18 0.36 (0.08–1.53) 0.58 (0.13–2.62) 0.51 (0.11–2.36) Thyroid No 6,271 72 1.74 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 9 0.82 0.46 (0.23–0.93) 0.62 (0.30–1.28) 0.65 (0.31–1.35) Biliary No 6,271 16 0.39 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 4 0.36 0.94 (0.32–2.83) 1.30 (0.41–4.15) 1.21 (0.37–3.94) Renal or bladder No 6,271 82 1.99 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,803 18 1.65 0.84 (0.51–1.40) 0.77 (0.46–1.31) 0.74 (0.43–1.26) Prostate ‡ No 3,303 29 1.33 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,517 10 1.08 0.85 (0.412–1.74) 1.08 (0.52–2.21) 1.06 (0.51–2.20) Breast § No 2,968 33 1.69 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 286 1 0.58 0.36 (0.05–2.60) 0.28 (0.04–2.04) 0.29 (0.04–2.13) Ovarian § No 2,968 5 0.25 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 286 1 0.58 2.47 (0.29–21.29) 3.14 (0.33–29.97) 3.24 (0.34–30.74) Uterine § No 2,968 8 0.41 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 286 2 1.17 2.94 (0.62–13.87) 2.46 (0.49–12.34) 2.58 (0.51–12.95) CI, confidence interval; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipient. Mild and heavy consumption of alcohol are classified in the “Yes” group. Model 1 was not adjusted for any variable. Model 2 was adjusted for age and sex. Model 3 was adjusted for age, sex, low income, diabetes mellitus, hypertension, and dyslipidemia. † Incidence rate is presented as per 1,000 person-years. ‡ Data of male participants were analyzed. § Data of female participants were analyzed. Regarding physical activity, neither exercise (yes/no) nor regular exercise showed significant differences in the overall cancer or site-specific cancer risks (Table 4 ). Table 4 Incidence rate of cancer in KTRs according to physical activity Exercise n Event IR † Adjusted HR (95% CI) Model 1 Model 2 Model 3 Any cancer No 3,834 276 11.66 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 310 11.45 0.98 (0.83–1.15) 0.99 (0.85–1.17) 1.00 (0.85–1.18) Oral cavity, pharyngeal, and laryngeal No 3,834 7 0.29 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 8 0.29 1.00 (0.36–2.76) 0.93 (0.34–2.57) 0.95 (0.34–2.64) Esophagus No 3,834 3 0.12 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 3 0.11 0.88 (0.18–4.37) 0.93 (0.19–4.68) 0.85 (0.16–4.55) Stomach No 3,834 26 1.07 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 42 1.50 1.40 (0.86–2.28) 1.39 (0.85–2.27) 1.37 (0.83–2.24) Colorectal No 3,834 23 0.94 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 31 1.11 1.17 (0.68–2.00) 1.21 (0.70–2.09) 1.25 (0.72–2.16) Lung No 3,834 23 0.94 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 22 0.79 0.83 (0.47–1.50) 0.85 (0.47–1.53) 0.86 (0.48–1.56) Liver No 3,834 15 0.61 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 18 0.64 1.05 (0.53–2.07) 1.04 (0.52–2.08) 1.01 (0.51–2.02) Pancreatic No 3,834 10 0.41 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 13 0.46 1.13 (0.50–2.58) 1.28 (0.56–2.94) 1.36 (0.59–3.12) Thyroid No 3,834 45 1.85 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 36 1.29 0.70 (0.45–1.09) 0.78 (0.50–1.21) 0.80 (0.51–1.24) Biliary No 3,834 7 0.29 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 13 0.46 1.61 (0.64–4.03) 1.71 (0.68–4.33) 1.74 (0.68–4.40) Renal or bladder No 3,834 42 1.73 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,240 58 2.08 1.20 (0.81–1.78) 1.16 (0.78–1.72) 1.14 (0.76–1.70) Prostate ‡ No 2,082 16 1.22 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 2,738 23 1.29 1.05 (0.55–1.98) 1.09 (0.57–2.06) 1.05 (0.55–1.99) Breast § No 1,752 18 1.61 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,502 16 1.59 0.98 (0.50–1.92) 0.98 (0.50–1.93) 1.06 (0.54–2.07) Ovarian § No 1,752 5 0.44 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,502 1 0.10 0.21 (0.02–1.77) 0.21 (0.02–1.77) 0.21 (0.02–1.78) Uterine § No 1,752 6 0.53 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,502 4 0.40 0.75 (0.21–2.65) 0.75 (0.21–2.65) 0.70 (0.20–2.48) CI, confidence interval; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipient. Model 1 was not adjusted for any variable. Model 2 is adjusted for age and sex. Model 3 was adjusted for age, sex, low income, diabetes mellitus, hypertension, and dyslipidemia. † Incidence rate is presented as per 1,000 person-years. ‡ Data of male participants were analyzed. § Data of female participants were analyzed. Subgroup analyses for cancer risk by age and comorbidities In the prespecified subgroup and interaction analyses restricted to KTRs aged ≥ 40 years, we evaluated the associations of lifestyle factors with overall solid cancer across strata of age and baseline comorbidities. Smoking The association between smoking and overall cancer increased with age (model 3, p for interaction = 0.006) (Table S1 ). In site-specific analyses, smoking was associated with higher risk of colorectal and lung cancer in KTRs aged ≥ 50 years (current vs. never: colorectal: HR, 3.47; 95% CI, 1.12–10.24; lung: HR, 9.48; 95% CI, 3.15–28.49). The overall cancer risk associated with smoking was significantly higher among KTRs with DM (p = 0.02). In KTRs with DM, current smoking was linked to increased risks of colorectal and pancreatic cancers (current vs. never, colorectal: HR, 3.09; 95% CI, 1.03–14.83; pancreatic: 5.81; 95% CI, 1.13–30.02) (Table S2). No significant interaction was observed among HTN, dyslipidemia, and obesity (all p for interaction > 0.05) (Tables S3–S5). Alcohol The impact of alcohol consumption on overall cancer increased with age (model 3, p = 0.018) (Table S6). In site-specific analyses, esophageal cancer risk was higher with alcohol exposure among those aged ≥ 50 years (yes vs. none: HR, 8.11; 95% CI, 1.24–53.07) (Table S6). For comorbidity strata, there was a higher risk of esophageal cancer with alcohol consumption among KTRs with DM or dyslipidemia (yes or no: DM: HR, 7.41; 95% CI, 1.20–45.67; dyslipidemia: HR, 18.97; 95% CI, 1.43–251.55). No significant interaction was observed between HTN and obesity (all p for interaction > 0.05) (Tables S7–S10). Physical activity No exercise (yes/no) showed age-dependent differences or comorbidity-specific differences in overall or site-specific cancer risk (all p for interaction > 0.05) (Tables S11–S15). Patient and graft survival in KTRs according to lifestyle factors Kaplan–Meier curves according to lifestyle factors are presented in Figs. 2 – 4 (A: all-cause mortality; B: death-censored graft failure). In multivariable Cox models (Table 5 ), current smoking was associated with a higher risk of mortality (HR, 1.65; 95% CI, 1.24–2.20) and graft failure (HR, 1.57; 95% CI, 1.21–2.05). Former smoking showed an intermediate risk profile (mortality: HR, 1.12; 95% CI, 0.89–1.40; graft failure: HR, 1.14; 95% CI, 0.92–1.42). No significant association was observed between alcohol consumption and mortality or graft failure. Exercise was associated with lower mortality (regular vs. none: HR, 0.74; 95% CI, 0.63–0.95; yes vs. none: HR, 0.72; 95% CI, 0.61–0.85), whereas graft survival did not differ by exercise status (yes vs. none: HR, 0.96; 95% CI, 0.82–1.13). Table 5 Patient and graft survival in KTRs according to lifestyle factors n Event IR † Adjusted HR (95% CI) Model 1 Model 2 Model 3 Smoking Patient survival Never 5,047 300 9.05 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,238 167 11.26 1.23 (1.02–1.49) 1.13 (0.90–1.42) 1.12 (0.89–1.40) Current 1,020 78 13.15 1.51 (1.17–1.93) 1.79 (1.35–2.37) 1.65 (1.24–2.20) Graft survival Never 5,047 346 10.83 1 (Ref.) 1 (Ref.) 1 (Ref.) Ex 2,238 185 13.06 1.20 (1.00–1.44) 1.154 (0.93–1.43) 1.14 (0.92–1.42) Current 1,020 100 17.91 1.72 (1.37–2.15) 1.61 (1.25–2.08) 1.57 (1.21–2.05) Alcohol consumption Patient survival No 6,446 436 10.22 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,859 109 9.68 0.97 (0.79–1.20) 1.08 (0.87–1.34) 1.04 (0.83–1.30) Graft survival No 6,446 487 11.91 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,859 144 13.35 1.15 (0.95–1.38) 1.05 (0.86–1.27) 0.99 (0.81–1.22) Regular exercise Patient survival No 6,545 425 10.08 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,760 120 10.21 1.00 (0.82–1.23) 0.79 (0.64–0.97) 0.77 (0.63–0.95) Graft survival No 6,545 512 12.68 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 1,760 119 10.54 0.82 (0.67–1.01) 0.84 (0.69–1.03) 0.85 (0.69–1.04) Exercise Patient survival No 3,918 297 11.89 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,387 248 8.57 0.72 (0.60–0.85) 0.70 (0.59–0.83) 0.72 (0.61–0.85) Graft survival No 3,918 298 12.42 1 (Ref.) 1 (Ref.) 1 (Ref.) Yes 4,387 333 12.02 0.96 (0.82–1.12) 0.94 (0.80–1.10) 0.96 (0.82–1.13) CI, confidence interval; Ex, former smoker; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipients. † Incidence rate is presented as per 1,000 person-years. Discussion In this nationwide cohort linked to standardized national health screening data, most lifestyle factors were not associated with the overall cancer incidence after multivariable adjustment. However, site- and subgroup-specific patterns were observed. Smoking was associated with lung cancer, and alcohol consumption was associated with esophageal cancer. The association among smoking, alcohol consumption, and cancer was stronger in older recipients. Among the KTRs with DM, current smoking was associated with an increased risk of colorectal and pancreatic cancer. Physical activity was associated with lower all-cause mortality, but it was not related to the overall or site-specific cancer incidence. These findings support the importance of lifestyle modifications in old KTRs with metabolically vulnerable characteristics, such as DM. The increased risk of cancer among transplant recipients has been well-documented in previous population-based analyses that showed a doubling of the overall cancer risk after solid organ transplantation and highlighted the prominence of lung and other solid tumors in KTRs 2,9,10 . Smoking adversely affects many transplant outcomes including infection, wound complications, rejection, and survival. Both recipient and donor smoking have been linked to poor graft and patient outcomes 11,12 . Tobacco smoke contains polycyclic aromatic hydrocarbons and nitrosamines that induce DNA damage, promote field cancerization, and drive oncogenesis in the airways and gastrointestinal epithelia 13 . In organ transplant recipients, chronic immunosuppression reduces immune surveillance against transformed cells and oncogenic viruses, thus lowering the threshold at which these carcinogens exert a clinical impact 14,15 . In this context, smoking cessation is imperative in post-transplant care. Beyond cancer prevention, our findings of higher mortality and risk of graft failure among current smokers support the implementation of a comprehensive antismoking strategy that combines behavioral counseling with pharmacotherapy during routine follow-up. The International Agency for Research on Cancer classifies alcoholic beverages as carcinogenic to humans (group 1), with sufficient evidence for cancers of the oral cavity and esophagus and additional evidence implicating other sites in a contemporary review 16,17 . Ethanol is metabolized to acetaldehyde, a genotoxic and mutagenic metabolite that directly forms DNA adducts and impairs DNA repair 16 . Chronic alcohol exposure also disturbs the gut microbiota, induces epithelial barrier dysfunction, and increases intestinal permeability. These changes promote microbial translocation and endotoxemia, followed by oxidative stress, cytokine dysregulation, and impaired antitumor immunity, which may increase the risk 16,18,19 . In KTRs, immunosuppression, gastroesophageal reflux, nutritional deficits, such as folate deficiency, and frequent co-exposure to smoking may further lower the dose threshold for alcohol-related esophageal carcinogenesis. This appears to be most relevant in older recipients in whom tissue repair and immune surveillance are diminished. Although we did not detect an association between alcohol consumption and overall cancer, the site-specific signal for esophageal cancer, which was strongest in KTRs aged 50 years or older, supported age-aware counseling for abstinence. These data also support a more aggressive early endoscopic evaluation in KTRs with persistent alcohol use, reflux symptoms, or other risk factors for esophageal disease. Observational studies in KTRs consistently showed that higher physical activity was associated with lower all-cause and cardiovascular mortality and that pretransplant activity predicted post-transplant survival 20,21 . A recent review of solid-organ transplantation encouraged moderate-to-vigorous training when feasible 22 . Exercise exerts systemic anti-inflammatory, metabolic, and cardiorespiratory effects These include improved insulin sensitivity, reduced visceral adiposity, enhanced endothelial function, attenuation of sarcopenia, and reduced mortality in KTRs 23,24 . Therefore, despite the absence of a detectable reduction in cancer incidence in our cohort, physical activity should be routinely recommended as a core survivorship intervention, with primary benefits focused on survival and quality of life. Our subgroup analyses revealed subgroup-specific patterns of cancer incidence in KTRs. Stronger associations with older age may reflect the consequences of immunosenescence, in which aging diminishes antitumor immune surveillance and coincides with longer cumulative exposure to carcinogens 25 . DM-specific amplification, where current smoking in recipients with DM was linked to higher risks of colorectal and pancreatic cancers, was consistent with previous studies reporting that DM and insulin-like growth factor 1 signaling were associated with carcinogenesis at these sites 26,27 . These findings support risk-stratified prevention, with priority given to smoking cessation and alcohol risk counseling in older recipients and those with metabolic vulnerability. This study has several limitations. First, rare site-specific cancers produce sparse events and wide confidence intervals, limiting precision and increasing the risk of type II errors. Second, lifestyle factors were self-reported during a single screening and were not updated regularly. Misclassification from resumption of smoking or changes in drinking or activity tends to bias the estimates toward the null. Third, although we adjusted for several covariates, residual confounding factors could not be excluded. Important factors, such as pack-years of smoking, alcohol dose, diet, infection history, and intensity or changes in immunosuppression, have not been fully captured. Fourth, subgroup analyses were prespecified to align with our focus on solid tumors and with prior evidence that hematologic malignancies are more common in younger recipients. This restriction may limit the generalizability of our results to KTRs younger than 40 years. We addressed this by presenting primary analyses in the fully eligible cohort and by clearly labeling the subgroup and interaction analyses as prespecified and grounded in biological plausibility. In this large real-world cohort of KTRs, lifestyle factors showed little association with overall cancer incidence after adjustment; however, clinically relevant site-specific risks were evident. Smoking was associated with lung cancer, and its impact was stronger in older KTRs and in those with DM, specifically in colorectal and pancreatic cancers. Alcohol consumption was associated with esophageal cancer, particularly in older recipients. Physical activity did not reduce cancer incidence but was clearly associated with lower mortality, which supports its central role in survivorship. These findings highlight smoking cessation, reduction in alcohol-related risk, and the promotion of exercise as practical strategies for improving long-term outcomes. Ideally, interventional prospective studies are warranted to confirm causality, define dose responses, and refine personalized recommendations for this high-risk population. Methods Data source and cohort population This nationwide, population-based, retrospective cohort study was conducted using the Korean National Health Insurance Service (NHIS) database from January 1, 2004, to December 31, 2017. Adults aged ≥ 20 years who underwent KT during this period were identified (n = 19,018). The Korean NHIS is a mandatory, single-payer system that covers approximately 97% of the South Korean population; the remaining 3% are insured through medical aid beneficiaries. The NHIS database encompasses nearly the entire Korean population and has been extensively used in large-scale epidemiological research 28–30 . It includes an eligibility database that provides demographic and lifestyle information such as age, sex, income level, smoking status, alcohol consumption, and physical activity, as well as a healthcare utilization database that includes claims data submitted by medical institutions. Eligibility and cohort assembly for cancer incidence Eligible participants were KTRs with a transplantation date within the study period and a linked national health screening record that provided baseline information. A 1-year post-transplant lag period (first post-transplant year) was applied to mitigate reverse causation and early surveillance bias. The workflow for the analysis of the cancer incidence is shown in Fig. 1 . To minimize reverse causation, surveillance, and detection bias (n = 147), we excluded individuals who (1) lacked a screening record or had missing baseline variables (n = 10,364), (2) had a documented malignancy before KT (n = 433), or (3) died or developed cancer within the first year after KT. The final analytical cohort comprised 8,074 KTRs. Eligibility and cohort assembly for patient and graft survival Starting from the same eligibility frame (n = 19,018), we excluded KTRs with ESKD due to perioperative graft dysfunction and early graft failure (n = 216). Subsequently, we excluded individuals who lacked a screening record, had missing baseline variables (n = 10,373), or died or developed ESKD within the first year of KT (n = 124). The final analytical sample for patient and graft survival analyses comprised 8,305 KTRs (Fig. 1 ). Control group selection and matching For each KTR, we used incidence-density sampling to select five comparators from the general population who were alive and at risk on the recipient’s index date (date of KT) and were matched for age, sex, and calendar year. Eligible controls had no history of KT, ESKD, or malignancy before the index date. We adopted a 1:5 matching ratio to enhance statistical efficiency and precision while maintaining analytic feasibility; increasing the control-to-case ratio up to approximately 4–5 per case captures most of the potential gains in power with minimal additional benefits 31,32 . Covariates and exposures Socioeconomic position was approximated by the NHIS premium level and dichotomized into the lowest quintile (including medical aid beneficiaries). Baseline comorbidities were defined at the index date using health screening measurements, prescribed medication, and International Classification of Diseases, 10th Revision (ICD-10) codes: DM (fasting plasma glucose ≥ 126 mg/dL, taking any glucose-lowering agent/insulin, or ICD-10 E11–E14); hypertension (HTN) (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, taking ≥ 1 antihypertensive medication, or ICD-10 I10–I13 or I15); and dyslipidemia (total cholesterol ≥ 240 mg/dL, taking ≥ 1 lipid-lowering agent, or ICD-10 E78). Obesity was defined as a body mass index (BMI) ≥ 25 kg/m 2 . Lifestyle exposures were obtained from the NHIS health screening questionnaire and included smoking status (never, former, or current), alcohol consumption (none; mild: <30 g/day for men or < 20 g/day for women; heavy: ≥30 g/day for men or ≥ 20 g/day for women), and physical activity. Physical activity was assessed in two ways: (1) any vs. none (physical activity yes/no; moderate or vigorous exercise ≥ 1 d per week) and (2) regular activity, defined as moderate exercise ≥ 5 days per week or vigorous exercise ≥ 3 days per week, according to NHIS categorization. Outcomes and follow-up The primary outcome was the incidence of newly diagnosed malignancy occurring after 1-year post-transplant. Secondary outcomes were patient and graft survival. Cancer incidence was identified using ICD-10 codes for solid malignant neoplasm (C0-75). Graft failure was defined as return to chronic dialysis occurring after 3-month post-transplant and a cumulative total of ≥ 25 dialysis sessions thereafter. Follow-up started 1-year post-transplant and continued until the earliest occurrence of cancer diagnosis, death, graft failure, or study endpoint. Subgroup analyses for cancer risk Subgroup and interaction analyses were prespecified and restricted to KTRs aged ≥ 40 years. Our previous work showed that KTRs aged < 40 years were disproportionately affected by hematologic malignancies (Hodgkin’s lymphoma, non-Hodgkin’s lymphoma, or multiple myeloma) rather than solid tumors. Restricting subgroup analyses to participants ≥ 40 years reduced heterogeneity driven by hematologic cancers and improved statistical precision for solid cancer effect estimates. Therefore, age-stratified subgroup/interaction analyses were limited to participants aged ≥ 40 years to align with the study’s solid cancer focus. Ethical statement This study was approved by the Institutional Review Board of Samsung Medical Center in compliance with the Declaration of Helsinki (IRB no. 2023-01-006). The requirement for informed consent was waived because of the anonymized and de-identified data collection. Statistical analyses Baseline characteristics are summarized as means with standard deviations for continuous variables and counts with percentages for categorical variables. Cancer incidence rates were expressed per 1,000 person-years with 95% confidence intervals (CIs), using the Poisson method. Time zero for all time-to-event analyses was the 1-year post-transplant lag period. We fitted Cox proportional hazards models to estimate the hazard ratios (HRs) and 95% CIs after adjusting for age, sex, income, smoking status, alcohol consumption, physical activity, DM, HTN, and dyslipidemia. Cox models were stratified using matched risk sets with robust standard errors. Stratified subgroup analyses for cancer incidence were conducted among participants aged ≥40 years according to age groups (40 and ≥50 years) and underlying diseases (DM, HTN, dyslipidemia, or obesity), with p-values for interaction. All tests were two-sided, with a significance threshold of p < 0.05. Analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA). Declarations Authors‘ contributions: HRJ conceptualized and designed the study. HJ and HRJ drafted the manuscript. HJ, YP, and KH analyzed the data. KL, SMS, JJ, JEL, WH, KH, and HRJ interpreted the data. HJ, and HRJ revised the manuscript for intellectual content. DWS, JEL and WH supervised the study. All the authors have read and approved the final version of this manuscript. Data availability statement: The datasets generated and/or analyzed in the current study are available from the South Korean National Health Insurance Sharing Service database repository (https://nhiss.nhis.or.kr). The datasets used and/or analyzed in the current study are available from the NHIS upon reasonable request. Disclosures: The authors of this manuscript have no conflicts of interest and no relevant financial or non-financial disclosures, as required by Scientific Reports . Funding: KL was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (RS-2025-00519288). JJ was supported by a National Research Foundation of Korea grant funded by the Korean government (NRF-2022R1F1A1068198) and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (RS-2023-00222838). HRJ was supported by grants from the National Research Foundation (grant number: RS-2025-00554916) and Korean Health Technology Research and Development Project (grant number: RS-2024-00340973) through the Korean Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea. References Vajdic, C. M. et al. Cancer incidence before and after kidney transplantation. JAMA 296 , 2823–2831 (2006). https://doi.org/10.1001/jama.296.23.2823 Engels, E. A. et al. Spectrum of cancer risk among US solid organ transplant recipients. JAMA 306 , 1891–1901 (2011). https://doi.org/10.1001/jama.2011.1592 Kidney Disease: Improving Global Outcomes Transplant Work, G. KDIGO clinical practice guideline for the care of kidney transplant recipients. Am J Transplant 9 Suppl 3 , S1-155 (2009). https://doi.org/10.1111/j.1600-6143.2009.02834.x Opelz, G. & Dohler, B. Influence of Current and Previous Smoking on Cancer and Mortality After Kidney Transplantation. 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J., Gilbertson, D. T. & Wang, C. Cancer after kidney transplantation in the United States. Am J Transplant 4 , 905–913 (2004). https://doi.org/10.1111/j.1600-6143.2004.00450.x Al-Adra, D., Al-Qaoud, T., Fowler, K. & Wong, G. De Novo Malignancies after Kidney Transplantation. Clin J Am Soc Nephrol 17 , 434–443 (2022). https://doi.org/10.2215/CJN.14570920 Khalil, M. A. M. et al. Cigarette Smoking and Its Hazards in Kidney Transplantation. Adv Med 2017 , 6213814 (2017). https://doi.org/10.1155/2017/6213814 Anis, K. H., Weinrauch, L. A. & D'Elia, J. A. Effects of Smoking on Solid Organ Transplantation Outcomes. Am J Med 132 , 413–419 (2019). https://doi.org/10.1016/j.amjmed.2018.11.005 Xue, J., Yang, S. & Seng, S. Mechanisms of Cancer Induction by Tobacco-Specific NNK and NNN. Cancers (Basel) 6 , 1138–1156 (2014). https://doi.org/10.3390/cancers6021138 Tie, Y., Tang, F., Wei, Y. Q. & Wei, X. W. Immunosuppressive cells in cancer: mechanisms and potential therapeutic targets. J Hematol Oncol 15 , 61 (2022). https://doi.org/10.1186/s13045-022-01282-8 Iizuka, N. et al. Immunosuppressants/Immunomodulators and Malignancy. J Clin Med 14 (2025). https://doi.org/10.3390/jcm14145160 Gapstur, S. M. et al. The IARC Perspective on Alcohol Reduction or Cessation and Cancer Risk. N Engl J Med 389 , 2486–2494 (2023). https://doi.org/10.1056/NEJMsr2306723 Humans, I. W. G. o. t. E. o. C. R. t. Alcohol consumption and ethyl carbamate. IARC Monogr Eval Carcinog Risks Hum 96 , 3-1383 (2010). Testino, G. The burden of cancer attributable to alcohol consumption. Maedica (Bucur) 6 , 313–320 (2011). Nieminen, M. T. & Salaspuro, M. Local Acetaldehyde-An Essential Role in Alcohol-Related Upper Gastrointestinal Tract Carcinogenesis. Cancers (Basel) 10 (2018). https://doi.org/10.3390/cancers10010011 Zelle, D. M. et al. Low physical activity and risk of cardiovascular and all-cause mortality in renal transplant recipients. Clin J Am Soc Nephrol 6 , 898–905 (2011). https://doi.org/10.2215/CJN.03340410 Rosas, S. E. et al. Pretransplant physical activity predicts all-cause mortality in kidney transplant recipients. Am J Nephrol 35 , 17–23 (2012). https://doi.org/10.1159/000334732 Stylemans, D. et al. Physical Exercise After Solid Organ Transplantation: A Cautionary Tale. Transpl Int 37 , 12448 (2024). https://doi.org/10.3389/ti.2024.12448 Takahashi, A., Hu, S. L. & Bostom, A. Physical Activity in Kidney Transplant Recipients: A Review. Am J Kidney Dis 72 , 433–443 (2018). https://doi.org/10.1053/j.ajkd.2017.12.005 Kanbay, M. et al. Physical exercise in kidney disease: A commonly undervalued treatment modality. Eur J Clin Invest 54 , e14105 (2024). https://doi.org/10.1111/eci.14105 Lian, J., Yue, Y., Yu, W. & Zhang, Y. Immunosenescence: a key player in cancer development. J Hematol Oncol 13 , 151 (2020). https://doi.org/10.1186/s13045-020-00986-z Giovannucci, E. Insulin, insulin-like growth factors and colon cancer: a review of the evidence. J Nutr 131 , 3109S-3120S (2001). https://doi.org/10.1093/jn/131.11.3109S Ben, Q. et al. Diabetes mellitus and risk of pancreatic cancer: A meta-analysis of cohort studies. Eur J Cancer 47 , 1928–1937 (2011). https://doi.org/10.1016/j.ejca.2011.03.003 Lee, Y. H. et al. Data Analytic Process of a Nationwide Population-Based Study Using National Health Information Database Established by National Health Insurance Service. Diabetes Metab J 40 , 79–82 (2016). https://doi.org/10.4093/dmj.2016.40.1.79 Kim, M. K. et al. Associations of Variability in Blood Pressure, Glucose and Cholesterol Concentrations, and Body Mass Index With Mortality and Cardiovascular Outcomes in the General Population. Circulation 138 , 2627–2637 (2018). https://doi.org/10.1161/CIRCULATIONAHA.118.034978 Hong, J. Y., Han, K., Jung, J. H. & Kim, J. S. Association of Exposure to Diagnostic Low-Dose Ionizing Radiation With Risk of Cancer Among Youths in South Korea. JAMA Netw Open 2 , e1910584 (2019). https://doi.org/10.1001/jamanetworkopen.2019.10584 Iwagami, M. & Shinozaki, T. Introduction to Matching in Case-Control and Cohort Studies. Ann Clin Epidemiol 4 , 33–40 (2022). https://doi.org/10.37737/ace.22005 Hong, S., Kim, K. S., Han, K. & Park, C. Y. A cohort study found a high risk of end-stage kidney disease associated with acromegaly. Kidney Int 104 , 820–827 (2023). https://doi.org/10.1016/j.kint.2023.06.037 Additional Declarations No competing interests reported. Supplementary Files KTcalifestylesupplementarytables.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8685285","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":582718983,"identity":"b129639a-13e0-46cc-9ac5-4bdaa4c6c5bb","order_by":0,"name":"Hojin Jeon","email":"","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Hojin","middleName":"","lastName":"Jeon","suffix":""},{"id":582718984,"identity":"a687f3f5-2d1e-44f6-a69d-9b61244be9db","order_by":1,"name":"Yebin Park","email":"","orcid":"","institution":"Soongsil University","correspondingAuthor":false,"prefix":"","firstName":"Yebin","middleName":"","lastName":"Park","suffix":""},{"id":582718985,"identity":"29dd8549-09f7-4092-92ad-4f80e366a48b","order_by":2,"name":"Seung Min Song","email":"","orcid":"","institution":"Korea University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Seung","middleName":"Min","lastName":"Song","suffix":""},{"id":582718986,"identity":"3697c3a6-14dd-45b8-bb42-602443a9ee27","order_by":3,"name":"Kyungho Lee","email":"","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Kyungho","middleName":"","lastName":"Lee","suffix":""},{"id":582718987,"identity":"772c7e24-c24c-4eae-80f6-3776ec5d180c","order_by":4,"name":"Junseok Jeon","email":"","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Junseok","middleName":"","lastName":"Jeon","suffix":""},{"id":582718988,"identity":"99ef6392-3f1c-4a27-b9d7-e99a5dd30dc2","order_by":5,"name":"Dong Wook Shin","email":"","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Wook","lastName":"Shin","suffix":""},{"id":582718989,"identity":"0e0ba361-d62b-4f83-af94-48df1bddf508","order_by":6,"name":"Jung Eun Lee","email":"","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Eun","lastName":"Lee","suffix":""},{"id":582718990,"identity":"254dcd20-b113-4a20-9415-f82001018c33","order_by":7,"name":"Wooseong Huh","email":"","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Wooseong","middleName":"","lastName":"Huh","suffix":""},{"id":582718991,"identity":"df9d39b1-679f-41f4-b979-785d5da2960a","order_by":8,"name":"Kyungdo Han","email":"","orcid":"","institution":"Soongsil University","correspondingAuthor":false,"prefix":"","firstName":"Kyungdo","middleName":"","lastName":"Han","suffix":""},{"id":582718992,"identity":"a197ec3c-977b-4682-aa46-55db6b4ead59","order_by":9,"name":"Hye Ryoun Jang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACCcYGBiCSY+BhA3GZiddiTIoWIAZqSWwgWov87Oa2Bx933Euf33MsTYKhwjqxgZAWgzsH2w1nninO3XC27ZgEw5l0IrRIJLZJ87Yl5G7gZ2+TYGw7TFiL/Ayglr9tCeny/SAt/4jQwnADqIWxLSGBAeQwxgYitBgAtUj2tiUYbjhzLNki4Vi6MREOS38m8bMtQV6+J83wxocaa1nCDkMBCaQpHwWjYBSMglGACwAAVGg+3BQqWWgAAAAASUVORK5CYII=","orcid":"","institution":"Samsung Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Hye","middleName":"Ryoun","lastName":"Jang","suffix":""}],"badges":[],"createdAt":"2026-01-24 09:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8685285/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8685285/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101518450,"identity":"8f2720b4-a56b-44a4-b62b-70b13abafe67","added_by":"auto","created_at":"2026-01-30 16:32:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of study population selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo analytical cohorts were assembled from 19,018 KTRs in the NHIS database. For the primary cancer incidence cohort, 10,944 patients were excluded (no screening record or missing baseline data, n = 10,364; prior malignancy, n = 433; death or cancer within 1-year post-transplant, n = 147), yielding 8,074 KTRs, which were matched to 59,253 age- and sex-matched controls from the general population (1:5). For the secondary patient and graft survival cohorts, 10,713 were excluded (no screening record or missing baseline data, n = 10,373; perioperative graft failure, n = 216; death or ESKD within 1-year post-transplant, n = 124), yielding 8,305 KTRs. ESKD, end-stage kidney disease; KTRs, kidney transplant recipients; NHIS, National Health Insurance Service.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8685285/v1/0c1498db6d1698332bd13a45.png"},{"id":101518454,"identity":"5d90106d-c71c-4bbd-833f-d8b3e3907b27","added_by":"auto","created_at":"2026-01-30 16:32:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative incidence of death and graft failure by smoking status after kidney transplantation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier curves showing the cumulative incidence probability of (A) death and (B) death-censored graft failure in the survival cohort of KTRs (n = 8,305). Curves were stratified according to baseline smoking status using the National Health Screening Questionnaire as follows: never(black), former (green), and current (red). Graft failure was defined as the return to maintenance dialysis, confirmed by a cumulative total of at least 25 sessions of dialysis starting 3 months post-transplant. Current smoking was associated with a higher risk of death and graft failure than non-smoking (both p \u0026lt; 0.005). KTR, kidney transplant recipient.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8685285/v1/d535e69ff3ae294332e357eb.png"},{"id":101518451,"identity":"59d15966-f8d4-46a7-9f32-3764779aa7f8","added_by":"auto","created_at":"2026-01-30 16:32:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative incidence of death and graft failure by alcohol consumption after kidney transplantation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier curves showing the cumulative incidence probability of (A) death and (B) death-censored graft failure in the survival cohort of KTRs (n = 8,305). Curves were stratified according to baseline alcohol consumption status using the National Health Screening Questionnaire as follows: no drinking (black) or drinking (red). Alcohol consumption was not associated with the risk of death or graft failure (both, p \u0026gt; 0.10). KTR, kidney transplant recipient.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8685285/v1/b577846724bf4700bbbe14ba.png"},{"id":101518452,"identity":"8c84433e-499c-4ff2-9843-99773d607c5b","added_by":"auto","created_at":"2026-01-30 16:32:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative incidence of death and graft failure by physical activity after kidney transplantation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier curves showing the cumulative incidence probability of (A) death and (B) death-censored graft failure in the survival cohort of KTRs (n = 8,305). Curves were stratified according to baseline physical activity status using the National Health Screening Questionnaire as follows: no activity (black) or any activity (red). Physical activity was associated with a lower risk of death (p \u0026lt; 0.001) but was not associated with graft failure (p = 0.61). KTR, kidney transplant recipient.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8685285/v1/4c4bf4a6f5f2f109f8d2ce3e.png"},{"id":109538056,"identity":"5a871524-c36a-49b3-852e-ee0c9ae8db99","added_by":"auto","created_at":"2026-05-19 09:26:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1023276,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8685285/v1/22e8e934-ef7d-47ef-acea-4af797fe27d3.pdf"},{"id":101518453,"identity":"1939d1a7-4d20-4ab6-9533-94385703e2cc","added_by":"auto","created_at":"2026-01-30 16:32:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":484946,"visible":true,"origin":"","legend":"","description":"","filename":"KTcalifestylesupplementarytables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8685285/v1/417c468ff1274591d3131b8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lifestyle behaviors and site-specific cancer risk after kidney transplantation: age- and comorbidity-related differences in a nationwide cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney transplantation (KT) significantly improves the survival and quality of life in patients with end-stage kidney disease (ESKD). However, long-term complications such as malignancy have emerged as major threats to the health outcomes of KT recipients (KTRs). Previous research has reported that KTRs have a 2- to 4-fold increased risk of developing cancer after transplantation compared with the general population\u003csup\u003e1,2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite this elevated risk, current clinical recommendations for cancer prevention in KTRs are largely extrapolated from guidelines developed for the general population, particularly regarding modifiable lifestyle factors, such as smoking, alcohol consumption, and physical activity\u003csup\u003e3\u003c/sup\u003e. Although the benefits of smoking cessation in reducing lung cancer incidence and mortality are well documented in KTRs\u003csup\u003e4\u003c/sup\u003e, evidence of allograft or patient survival in relation to alcohol intake and physical activity remains unclear\u003csup\u003e5,6\u003c/sup\u003e. Several studies have suggested that moderate alcohol consumption is associated with reduced mortality or a lower incidence of post-transplant diabetes mellitus (DM)\u003csup\u003e7\u003c/sup\u003e and that regular exercise improves cardiovascular fitness and metabolic outcomes in patients with chronic kidney disease\u003csup\u003e8\u003c/sup\u003e. However, few studies have investigated the association between lifestyle factors and cancer incidence in KTRs.\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the association among smoking, alcohol consumption, physical activity, and the risk of solid cancers in KTRs using a nationwide database. We hypothesized that these lifestyle factors would demonstrate differential associations with cancer risk according to age and metabolic comorbidities. To address this hypothesis, we further evaluated the potential interactions between age and comorbid conditions to better understand the differential vulnerabilities and elucidate risk-stratified cancer prevention strategies in this high-risk population.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe primary cohort for cancer incidence comprised 67,327 individuals (8,074 KTRs and 59,253 age- and sex-matched members of the general population) with a mean follow-up of 6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23 years. KTRs had a mean age of 47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96 years at transplantation, and 59.7% were male. Baseline characteristics differed between KTRs and controls across several domains, including age, income, comorbidities, and lifestyle behaviors. Compared with controls, KTRs had a higher prevalence of DM, HTN, and dyslipidemia but a lower prevalence of current smoking and alcohol consumption. The distributions of residential areas and physical activity were similar between the groups. The cumulative overall incidence of cancer was significantly higher in KTRs than in the general population (7.3% vs. 4.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched general population (n\u0026thinsp;=\u0026thinsp;59,253)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKTRs (n\u0026thinsp;=\u0026thinsp;8,074)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,287 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,570 (19.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31,149 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,491 (55.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,817 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,013 (24.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,097 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,820 (59.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income, \u0026lt;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,174 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,255 (27.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeoul or city place\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,758 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,756 (46.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,164 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895 (60.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,574 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187 (27.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,515 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992 (12.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,085 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271 (77.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25,342 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,648 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,826 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise, yes or none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,323 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,677 (20.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise, yes or none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31,973 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240 (52.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\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\u003e5,986 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,416 (29.9)\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\u003e15,762 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,683 (70.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,873 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,148 (51.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated GFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.2\u0026thinsp;\u0026plusmn;\u0026thinsp;42.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4\u0026thinsp;\u0026plusmn;\u0026thinsp;39.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,474 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e586 (7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up duration, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI, body mass index; GFR, glomerular filtration rate; KTRs, kidney transplant recipients\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCategorical variables are presented as numbers (percentages), and continuous variables are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOverall cancer risk according to lifestyle factors\u003c/h3\u003e\n\u003cp\u003eIn fully adjusted analyses (model 3), former and current smoking were not associated with overall cancer (former vs. never: HR, 1.12; 95% CI, 0.96\u0026ndash;1.50; current vs. never: HR, 1.16; 95% CI, 0.84\u0026ndash;1.60). In site-specific analyses, both former and current smoking were associated with a higher risk of lung cancer (former vs. never: HR, 3.08; 95% CI, 1.22\u0026ndash;7.75; current vs. never: HR, 5.94; 95% CI, 2.06\u0026ndash;17.09), whereas no consistent pattern was observed for other cancers (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence rate of cancer in KTRs according to smoking status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAny cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 (0.91\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19 (0.95\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20 (0.96\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.68\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14 (0.83\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16 (0.84\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOral cavity, pharyngeal, and laryngeal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.13 (0.99\u0026ndash;9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.03 (0.54\u0026ndash;7.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.04 (0.54\u0026ndash;7.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.33 (0.79\u0026ndash;13.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.94 (0.59\u0026ndash;14.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.86 (0.55\u0026ndash;14.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12 (0.21\u0026ndash;6.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94 (0.13\u0026ndash;6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76 (0.10\u0026ndash;5.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32 (0.80\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99 (0.556\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94 (0.53\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57 (0.20\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57 (0.19\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50 (0.17\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eColorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19 (0.66\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.60 (0.73\u0026ndash;3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.59 (0.73\u0026ndash;3.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11 (0.46\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.84 (0.67\u0026ndash;5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.81 (0.64\u0026ndash;5.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.99 (1.03\u0026ndash;3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.02 (1.21\u0026ndash;7.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.08 (1.22\u0026ndash;7.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.66 (1.20\u0026ndash;5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.84 (2.09\u0026ndash;16.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.94 (2.06\u0026ndash;17.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.18 (1.04\u0026ndash;4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.66 (0.98\u0026ndash;7.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.46 (0.90\u0026ndash;6.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.98 (0.71\u0026ndash;5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.30 (0.96\u0026ndash;11.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.87 (0.81\u0026ndash;10.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePancreatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55 (0.19\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03 (0.26\u0026ndash;4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05 (0.26\u0026ndash;4.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.30\u0026ndash;3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.74 (0.61\u0026ndash;12.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.99 (0.65\u0026ndash;13.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63 (0.37\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48 (0.72\u0026ndash;3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58 (0.76\u0026ndash;3.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36 (0.13\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.26\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96 (0.31\u0026ndash;3.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62 (0.21\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59 (0.17\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57 (0.16\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80 (0.18\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11 (0.22\u0026ndash;5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11 (0.21\u0026ndash;5.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRenal or bladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (0.69\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.47\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80 (0.48\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.55\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.41\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91 (0.45\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProstate\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77 (0.40\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.45\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86 (0.44\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48 (0.16\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.27\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78 (0.25\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBreast\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.67 (0.64\u0026ndash;11.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.24 (0.53\u0026ndash;9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.73 (0.63\u0026ndash;11.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOvarian\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUterine\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCI, confidence interval; diabetes mellitus; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipient.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1 was not adjusted for any variable. Model 2 was adjusted for age and sex. Model 3 was adjusted for age, sex, low income, diabetes mellitus, hypertension, and dyslipidemia.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eIncidence rate is presented as per 1,000 person-years.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eData of male participants were analyzed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eData of female participants were analyzed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding alcohol consumption, there was no association with overall cancer risk in model 3 (yes vs. none: HR, 0.96; 95% CI, 0.76\u0026ndash;1.20). In site-specific cancer analyses, esophageal cancer risk increased with alcohol exposure (yes vs. none: HR, 7.41; 95% CI, 1.20\u0026ndash;45.67) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence rate of cancer in KTRs according to alcohol consumption\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAny cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83 (0.68\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96 (0.77\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96 (0.76\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOral cavity, pharyngeal, and laryngeal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37 (0.44\u0026ndash;4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.22 (0.38\u0026ndash;3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.12 (0.33\u0026ndash;3.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.71 (0.75\u0026ndash;18.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.31 (1.03\u0026ndash;38.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.41 (1.20\u0026ndash;45.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19 (0.68\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28 (0.71\u0026ndash;2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.38 (0.76\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eColorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.51\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25 (0.62\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21 (0.59\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08 (0.54\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.30 (0.62\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07 (0.50\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42 (0.66\u0026ndash;3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.63 (0.73\u0026ndash;3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51 (0.66\u0026ndash;3.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePancreatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36 (0.08\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58 (0.13\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51 (0.11\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46 (0.23\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62 (0.30\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65 (0.31\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94 (0.32\u0026ndash;2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.30 (0.41\u0026ndash;4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21 (0.37\u0026ndash;3.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRenal or bladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84 (0.51\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.46\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.74 (0.43\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProstate\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.412\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08 (0.52\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (0.51\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreast\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36 (0.05\u0026ndash;2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28 (0.04\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29 (0.04\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOvarian\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.47 (0.29\u0026ndash;21.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.14 (0.33\u0026ndash;29.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.24 (0.34\u0026ndash;30.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUterine\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.94 (0.62\u0026ndash;13.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.46 (0.49\u0026ndash;12.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.58 (0.51\u0026ndash;12.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCI, confidence interval; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipient.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eMild and heavy consumption of alcohol are classified in the \u0026ldquo;Yes\u0026rdquo; group. Model 1 was not adjusted for any variable. Model 2 was adjusted for age and sex. Model 3 was adjusted for age, sex, low income, diabetes mellitus, hypertension, and dyslipidemia.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eIncidence rate is presented as per 1,000 person-years.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eData of male participants were analyzed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eData of female participants were analyzed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding physical activity, neither exercise (yes/no) nor regular exercise showed significant differences in the overall cancer or site-specific cancer risks (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence rate of cancer in KTRs according to physical activity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAny cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.83\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99 (0.85\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.85\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOral cavity, pharyngeal, and laryngeal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (0.36\u0026ndash;2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93 (0.34\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95 (0.34\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88 (0.18\u0026ndash;4.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93 (0.19\u0026ndash;4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85 (0.16\u0026ndash;4.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40 (0.86\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.39 (0.85\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.37 (0.83\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eColorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (0.68\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21 (0.70\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25 (0.72\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83 (0.47\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.47\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86 (0.48\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.53\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04 (0.52\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.51\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePancreatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (0.50\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28 (0.56\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36 (0.59\u0026ndash;3.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.45\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78 (0.50\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80 (0.51\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61 (0.64\u0026ndash;4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.71 (0.68\u0026ndash;4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.74 (0.68\u0026ndash;4.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRenal or bladder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20 (0.81\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16 (0.78\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14 (0.76\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProstate\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.55\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09 (0.57\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05 (0.55\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreast\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.50\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98 (0.50\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (0.54\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOvarian\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21 (0.02\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21 (0.02\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21 (0.02\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUterine\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75 (0.21\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75 (0.21\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70 (0.20\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCI, confidence interval; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipient.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1 was not adjusted for any variable. Model 2 is adjusted for age and sex. Model 3 was adjusted for age, sex, low income, diabetes mellitus, hypertension, and dyslipidemia.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eIncidence rate is presented as per 1,000 person-years.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eData of male participants were analyzed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eData of female participants were analyzed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSubgroup analyses for cancer risk by age and comorbidities\u003c/h3\u003e\n\u003cp\u003eIn the prespecified subgroup and interaction analyses restricted to KTRs aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years, we evaluated the associations of lifestyle factors with overall solid cancer across strata of age and baseline comorbidities.\u003c/p\u003e\n\u003ch3\u003eSmoking\u003c/h3\u003e\n\u003cp\u003eThe association between smoking and overall cancer increased with age (model 3, p for interaction\u0026thinsp;=\u0026thinsp;0.006) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In site-specific analyses, smoking was associated with higher risk of colorectal and lung cancer in KTRs aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years (current vs. never: colorectal: HR, 3.47; 95% CI, 1.12\u0026ndash;10.24; lung: HR, 9.48; 95% CI, 3.15\u0026ndash;28.49).\u003c/p\u003e \u003cp\u003eThe overall cancer risk associated with smoking was significantly higher among KTRs with DM (p\u0026thinsp;=\u0026thinsp;0.02). In KTRs with DM, current smoking was linked to increased risks of colorectal and pancreatic cancers (current vs. never, colorectal: HR, 3.09; 95% CI, 1.03\u0026ndash;14.83; pancreatic: 5.81; 95% CI, 1.13\u0026ndash;30.02) (Table S2). No significant interaction was observed among HTN, dyslipidemia, and obesity (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Tables S3\u0026ndash;S5).\u003c/p\u003e\n\u003ch3\u003eAlcohol\u003c/h3\u003e\n\u003cp\u003eThe impact of alcohol consumption on overall cancer increased with age (model 3, p\u0026thinsp;=\u0026thinsp;0.018) (Table S6). In site-specific analyses, esophageal cancer risk was higher with alcohol exposure among those aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years (yes vs. none: HR, 8.11; 95% CI, 1.24\u0026ndash;53.07) (Table S6).\u003c/p\u003e \u003cp\u003eFor comorbidity strata, there was a higher risk of esophageal cancer with alcohol consumption among KTRs with DM or dyslipidemia (yes or no: DM: HR, 7.41; 95% CI, 1.20\u0026ndash;45.67; dyslipidemia: HR, 18.97; 95% CI, 1.43\u0026ndash;251.55). No significant interaction was observed between HTN and obesity (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Tables S7\u0026ndash;S10).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhysical activity\u003c/h2\u003e \u003cp\u003eNo exercise (yes/no) showed age-dependent differences or comorbidity-specific differences in overall or site-specific cancer risk (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Tables S11\u0026ndash;S15).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient and graft survival in KTRs according to lifestyle factors\u003c/h3\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier curves according to lifestyle factors are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e (A: all-cause mortality; B: death-censored graft failure). In multivariable Cox models (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), current smoking was associated with a higher risk of mortality (HR, 1.65; 95% CI, 1.24\u0026ndash;2.20) and graft failure (HR, 1.57; 95% CI, 1.21\u0026ndash;2.05). Former smoking showed an intermediate risk profile (mortality: HR, 1.12; 95% CI, 0.89\u0026ndash;1.40; graft failure: HR, 1.14; 95% CI, 0.92\u0026ndash;1.42). No significant association was observed between alcohol consumption and mortality or graft failure. Exercise was associated with lower mortality (regular vs. none: HR, 0.74; 95% CI, 0.63\u0026ndash;0.95; yes vs. none: HR, 0.72; 95% CI, 0.61\u0026ndash;0.85), whereas graft survival did not differ by exercise status (yes vs. none: HR, 0.96; 95% CI, 0.82\u0026ndash;1.13).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient and graft survival in KTRs according to lifestyle factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIR\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePatient survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23 (1.02\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13 (0.90\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.12 (0.89\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51 (1.17\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.79 (1.35\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65 (1.24\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGraft survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20 (1.00\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.154 (0.93\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14 (0.92\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72 (1.37\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61 (1.25\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.57 (1.21\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatient survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.79\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08 (0.87\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04 (0.83\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGraft survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15 (0.95\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05 (0.86\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.81\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRegular exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatient survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (0.82\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79 (0.64\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77 (0.63\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGraft survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82 (0.67\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84 (0.69\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85 (0.69\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExercise\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatient survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72 (0.60\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.59\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.72 (0.61\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGraft survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.82\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94 (0.80\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96 (0.82\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCI, confidence interval; Ex, former smoker; HR, hazard ratio; IR, incidence rate; KTRs, kidney transplant recipients.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eIncidence rate is presented as per 1,000 person-years.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide cohort linked to standardized national health screening data, most lifestyle factors were not associated with the overall cancer incidence after multivariable adjustment. However, site- and subgroup-specific patterns were observed. Smoking was associated with lung cancer, and alcohol consumption was associated with esophageal cancer. The association among smoking, alcohol consumption, and cancer was stronger in older recipients. Among the KTRs with DM, current smoking was associated with an increased risk of colorectal and pancreatic cancer. Physical activity was associated with lower all-cause mortality, but it was not related to the overall or site-specific cancer incidence. These findings support the importance of lifestyle modifications in old KTRs with metabolically vulnerable characteristics, such as DM.\u003c/p\u003e \u003cp\u003eThe increased risk of cancer among transplant recipients has been well-documented in previous population-based analyses that showed a doubling of the overall cancer risk after solid organ transplantation and highlighted the prominence of lung and other solid tumors in KTRs\u003csup\u003e2,9,10\u003c/sup\u003e. Smoking adversely affects many transplant outcomes including infection, wound complications, rejection, and survival. Both recipient and donor smoking have been linked to poor graft and patient outcomes\u003csup\u003e11,12\u003c/sup\u003e. Tobacco smoke contains polycyclic aromatic hydrocarbons and nitrosamines that induce DNA damage, promote field cancerization, and drive oncogenesis in the airways and gastrointestinal epithelia\u003csup\u003e13\u003c/sup\u003e. In organ transplant recipients, chronic immunosuppression reduces immune surveillance against transformed cells and oncogenic viruses, thus lowering the threshold at which these carcinogens exert a clinical impact\u003csup\u003e14,15\u003c/sup\u003e. In this context, smoking cessation is imperative in post-transplant care. Beyond cancer prevention, our findings of higher mortality and risk of graft failure among current smokers support the implementation of a comprehensive antismoking strategy that combines behavioral counseling with pharmacotherapy during routine follow-up.\u003c/p\u003e \u003cp\u003eThe International Agency for Research on Cancer classifies alcoholic beverages as carcinogenic to humans (group 1), with sufficient evidence for cancers of the oral cavity and esophagus and additional evidence implicating other sites in a contemporary review\u003csup\u003e16,17\u003c/sup\u003e. Ethanol is metabolized to acetaldehyde, a genotoxic and mutagenic metabolite that directly forms DNA adducts and impairs DNA repair\u003csup\u003e16\u003c/sup\u003e. Chronic alcohol exposure also disturbs the gut microbiota, induces epithelial barrier dysfunction, and increases intestinal permeability. These changes promote microbial translocation and endotoxemia, followed by oxidative stress, cytokine dysregulation, and impaired antitumor immunity, which may increase the risk\u003csup\u003e16,18,19\u003c/sup\u003e. In KTRs, immunosuppression, gastroesophageal reflux, nutritional deficits, such as folate deficiency, and frequent co-exposure to smoking may further lower the dose threshold for alcohol-related esophageal carcinogenesis. This appears to be most relevant in older recipients in whom tissue repair and immune surveillance are diminished. Although we did not detect an association between alcohol consumption and overall cancer, the site-specific signal for esophageal cancer, which was strongest in KTRs aged 50 years or older, supported age-aware counseling for abstinence. These data also support a more aggressive early endoscopic evaluation in KTRs with persistent alcohol use, reflux symptoms, or other risk factors for esophageal disease.\u003c/p\u003e \u003cp\u003eObservational studies in KTRs consistently showed that higher physical activity was associated with lower all-cause and cardiovascular mortality and that pretransplant activity predicted post-transplant survival\u003csup\u003e20,21\u003c/sup\u003e. A recent review of solid-organ transplantation encouraged moderate-to-vigorous training when feasible\u003csup\u003e22\u003c/sup\u003e. Exercise exerts systemic anti-inflammatory, metabolic, and cardiorespiratory effects These include improved insulin sensitivity, reduced visceral adiposity, enhanced endothelial function, attenuation of sarcopenia, and reduced mortality in KTRs\u003csup\u003e23,24\u003c/sup\u003e. Therefore, despite the absence of a detectable reduction in cancer incidence in our cohort, physical activity should be routinely recommended as a core survivorship intervention, with primary benefits focused on survival and quality of life.\u003c/p\u003e \u003cp\u003eOur subgroup analyses revealed subgroup-specific patterns of cancer incidence in KTRs. Stronger associations with older age may reflect the consequences of immunosenescence, in which aging diminishes antitumor immune surveillance and coincides with longer cumulative exposure to carcinogens\u003csup\u003e25\u003c/sup\u003e. DM-specific amplification, where current smoking in recipients with DM was linked to higher risks of colorectal and pancreatic cancers, was consistent with previous studies reporting that DM and insulin-like growth factor 1 signaling were associated with carcinogenesis at these sites\u003csup\u003e26,27\u003c/sup\u003e. These findings support risk-stratified prevention, with priority given to smoking cessation and alcohol risk counseling in older recipients and those with metabolic vulnerability.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, rare site-specific cancers produce sparse events and wide confidence intervals, limiting precision and increasing the risk of type II errors. Second, lifestyle factors were self-reported during a single screening and were not updated regularly. Misclassification from resumption of smoking or changes in drinking or activity tends to bias the estimates toward the null. Third, although we adjusted for several covariates, residual confounding factors could not be excluded. Important factors, such as pack-years of smoking, alcohol dose, diet, infection history, and intensity or changes in immunosuppression, have not been fully captured. Fourth, subgroup analyses were prespecified to align with our focus on solid tumors and with prior evidence that hematologic malignancies are more common in younger recipients. This restriction may limit the generalizability of our results to KTRs younger than 40 years. We addressed this by presenting primary analyses in the fully eligible cohort and by clearly labeling the subgroup and interaction analyses as prespecified and grounded in biological plausibility.\u003c/p\u003e \u003cp\u003eIn this large real-world cohort of KTRs, lifestyle factors showed little association with overall cancer incidence after adjustment; however, clinically relevant site-specific risks were evident. Smoking was associated with lung cancer, and its impact was stronger in older KTRs and in those with DM, specifically in colorectal and pancreatic cancers. Alcohol consumption was associated with esophageal cancer, particularly in older recipients. Physical activity did not reduce cancer incidence but was clearly associated with lower mortality, which supports its central role in survivorship. These findings highlight smoking cessation, reduction in alcohol-related risk, and the promotion of exercise as practical strategies for improving long-term outcomes. Ideally, interventional prospective studies are warranted to confirm causality, define dose responses, and refine personalized recommendations for this high-risk population.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eData source and cohort population\u003c/h2\u003e\u003cp\u003eThis nationwide, population-based, retrospective cohort study was conducted using the Korean National Health Insurance Service (NHIS) database from January 1, 2004, to December 31, 2017. Adults aged ≥ 20 years who underwent KT during this period were identified (n = 19,018). The Korean NHIS is a mandatory, single-payer system that covers approximately 97% of the South Korean population; the remaining 3% are insured through medical aid beneficiaries. The NHIS database encompasses nearly the entire Korean population and has been extensively used in large-scale epidemiological research\u003csup\u003e28–30\u003c/sup\u003e. It includes an eligibility database that provides demographic and lifestyle information such as age, sex, income level, smoking status, alcohol consumption, and physical activity, as well as a healthcare utilization database that includes claims data submitted by medical institutions.\u003c/p\u003e\u003ch2\u003eEligibility and cohort assembly for cancer incidence\u003c/h2\u003e\u003cp\u003eEligible participants were KTRs with a transplantation date within the study period and a linked national health screening record that provided baseline information. A 1-year post-transplant lag period (first post-transplant year) was applied to mitigate reverse causation and early surveillance bias. The workflow for the analysis of the cancer incidence is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To minimize reverse causation, surveillance, and detection bias (n = 147), we excluded individuals who (1) lacked a screening record or had missing baseline variables (n = 10,364), (2) had a documented malignancy before KT (n = 433), or (3) died or developed cancer within the first year after KT. The final analytical cohort comprised 8,074 KTRs.\u003c/p\u003e\u003ch2\u003eEligibility and cohort assembly for patient and graft survival\u003c/h2\u003e\u003cp\u003eStarting from the same eligibility frame (n = 19,018), we excluded KTRs with ESKD due to perioperative graft dysfunction and early graft failure (n = 216). Subsequently, we excluded individuals who lacked a screening record, had missing baseline variables (n = 10,373), or died or developed ESKD within the first year of KT (n = 124). The final analytical sample for patient and graft survival analyses comprised 8,305 KTRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eControl group selection and matching\u003c/h2\u003e\u003cp\u003eFor each KTR, we used incidence-density sampling to select five comparators from the general population who were alive and at risk on the recipient’s index date (date of KT) and were matched for age, sex, and calendar year. Eligible controls had no history of KT, ESKD, or malignancy before the index date. We adopted a 1:5 matching ratio to enhance statistical efficiency and precision while maintaining analytic feasibility; increasing the control-to-case ratio up to approximately 4–5 per case captures most of the potential gains in power with minimal additional benefits\u003csup\u003e31,32\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eCovariates and exposures\u003c/h2\u003e\u003cp\u003eSocioeconomic position was approximated by the NHIS premium level and dichotomized into the lowest quintile (including medical aid beneficiaries). Baseline comorbidities were defined at the index date using health screening measurements, prescribed medication, and International Classification of Diseases, 10th Revision (ICD-10) codes: DM (fasting plasma glucose ≥ 126 mg/dL, taking any glucose-lowering agent/insulin, or ICD-10 E11–E14); hypertension (HTN) (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, taking ≥ 1 antihypertensive medication, or ICD-10 I10–I13 or I15); and dyslipidemia (total cholesterol ≥ 240 mg/dL, taking ≥ 1 lipid-lowering agent, or ICD-10 E78). Obesity was defined as a body mass index (BMI) ≥ 25 kg/m\u003csup\u003e2\u003c/sup\u003e. Lifestyle exposures were obtained from the NHIS health screening questionnaire and included smoking status (never, former, or current), alcohol consumption (none; mild: \u0026lt;30 g/day for men or \u0026lt; 20 g/day for women; heavy: ≥30 g/day for men or ≥ 20 g/day for women), and physical activity. Physical activity was assessed in two ways: (1) any vs. none (physical activity yes/no; moderate or vigorous exercise ≥ 1 d per week) and (2) regular activity, defined as moderate exercise ≥ 5 days per week or vigorous exercise ≥ 3 days per week, according to NHIS categorization.\u003c/p\u003e\u003ch2\u003eOutcomes and follow-up\u003c/h2\u003e\u003cp\u003eThe primary outcome was the incidence of newly diagnosed malignancy occurring after 1-year post-transplant. Secondary outcomes were patient and graft survival. Cancer incidence was identified using ICD-10 codes for solid malignant neoplasm (C0-75). Graft failure was defined as return to chronic dialysis occurring after 3-month post-transplant and a cumulative total of ≥ 25 dialysis sessions thereafter. Follow-up started 1-year post-transplant and continued until the earliest occurrence of cancer diagnosis, death, graft failure, or study endpoint.\u003c/p\u003e\u003ch2\u003eSubgroup analyses for cancer risk\u003c/h2\u003e\u003cp\u003eSubgroup and interaction analyses were prespecified and restricted to KTRs aged ≥ 40 years. Our previous work showed that KTRs aged \u0026lt; 40 years were disproportionately affected by hematologic malignancies (Hodgkin’s lymphoma, non-Hodgkin’s lymphoma, or multiple myeloma) rather than solid tumors. Restricting subgroup analyses to participants ≥ 40 years reduced heterogeneity driven by hematologic cancers and improved statistical precision for solid cancer effect estimates. Therefore, age-stratified subgroup/interaction analyses were limited to participants aged ≥ 40 years to align with the study’s solid cancer focus.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Samsung Medical Center in compliance with the Declaration of Helsinki (IRB no. 2023-01-006). The requirement for informed consent was waived because of the anonymized and de-identified data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics are summarized as means with standard deviations for continuous variables and counts with percentages for categorical variables. Cancer incidence rates were expressed per 1,000 person-years with 95% confidence intervals (CIs), using the Poisson method. Time zero for all time-to-event analyses was the 1-year post-transplant lag period. We fitted Cox proportional hazards models to estimate the hazard ratios (HRs) and 95% CIs after adjusting for age, sex, income, smoking status, alcohol consumption, physical activity, DM, HTN, and dyslipidemia. Cox models were stratified using matched risk sets with robust standard errors. Stratified subgroup analyses for cancer incidence were conducted among participants aged \u0026ge;40 years according to age groups (40 and \u0026ge;50 years) and underlying diseases (DM, HTN, dyslipidemia, or obesity), with p-values for interaction. All tests were two-sided, with a significance threshold of p \u0026lt; 0.05. Analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026lsquo; contributions:\u0026nbsp;\u003c/strong\u003eHRJ conceptualized and designed the study. HJ and HRJ drafted the manuscript. HJ, YP, and KH analyzed the data. KL, SMS, JJ, JEL, WH, KH, and HRJ interpreted the data. HJ, and HRJ revised the manuscript for intellectual content. DWS, JEL and WH supervised the study. All the authors have read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed in the current study are available from the South Korean National Health Insurance Sharing Service database repository (https://nhiss.nhis.or.kr). The datasets used and/or analyzed in the current study are available from the NHIS upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors of this manuscript have no conflicts of interest and no relevant financial or non-financial disclosures, as required by \u003cem\u003eScientific Reports\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eKL was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (RS-2025-00519288). JJ was supported by a National Research Foundation of Korea grant funded by the Korean government (NRF-2022R1F1A1068198) and the Bio \u0026amp; Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (RS-2023-00222838). HRJ was supported by grants from the National Research Foundation (grant number: RS-2025-00554916) and Korean Health Technology Research and Development Project (grant number: RS-2024-00340973) through the Korean Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVajdic, C. M. \u003cem\u003eet al.\u003c/em\u003e Cancer incidence before and after kidney transplantation. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e296\u003c/b\u003e, 2823\u0026ndash;2831 (2006). https://doi.org/10.1001/jama.296.23.2823\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngels, E. 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A cohort study found a high risk of end-stage kidney disease associated with acromegaly. \u003cem\u003eKidney Int\u003c/em\u003e \u003cb\u003e104\u003c/b\u003e, 820\u0026ndash;827 (2023). https://doi.org/10.1016/j.kint.2023.06.037\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":"cancer risk, comorbidities, kidney transplant recipient, lifestyle factors, survival","lastPublishedDoi":"10.21203/rs.3.rs-8685285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8685285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKidney transplant recipients (KTRs) have an increased cancer risk, but the influence of lifestyle factors remains unclear. This study investigated how smoking, alcohol, and physical activity affect post-transplant malignancy. We assembled a nationwide cohort of KTRs using health screening data. The primary outcome was incidence of malignancy 1 year after transplantation. The secondary outcomes were all-cause mortality and graft failure. Overall cancer incidence was not significantly associated with smoking, alcohol consumption, or physical activity in the fully adjusted models. Site-specific analyses revealed that smoking was associated with lung cancer (current vs. never: hazard ratio [HR], 5.94; 95% confidence interval [CI], 2.06–17.09). Alcohol consumption was associated with esophageal cancer (yes vs. no: HR, 7.41; 95% CI, 1.20–45.67). Age strengthens these associations. Among KTRs with diabetes, current smoking was linked to higher risks of colorectal (HR 3.09) and pancreatic (5.81) cancer. Although physical activity was not associated with cancer incidence, it was associated with lower mortality rates (HR, 0.72; 95% CI, 0.61–0.85). Lifestyle factors had a limited impact on the overall cancer incidence; however, significant site-specific or subgroup associations were evident. These results support smoking cessation, alcohol consumption reduction, and routine exercise in improving the survival of KTRs.\u003c/p\u003e","manuscriptTitle":"Lifestyle behaviors and site-specific cancer risk after kidney transplantation: age- and comorbidity-related differences in a nationwide cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 16:32:50","doi":"10.21203/rs.3.rs-8685285/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":"f218b2c6-b99a-4b10-be59-7c4f1bb2a1b9","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61997846,"name":"Biological sciences/Cancer"},{"id":61997847,"name":"Health sciences/Diseases"},{"id":61997848,"name":"Health sciences/Oncology"},{"id":61997849,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-19T09:25:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 16:32:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8685285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8685285","identity":"rs-8685285","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}