Early Smoking and its impact on Cardio-cerebrovascular Diseases in patients with Chronic Kidney Disease: A Nationwide Population-Based Study

Early Smoking and its impact on Cardio-cerebrovascular Diseases in patients with Chronic Kidney Disease: A Nationwide Population-Based Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Smoking and its impact on Cardio-cerebrovascular Diseases in patients with Chronic Kidney Disease: A Nationwide Population-Based Study Sehyun Jung, Kyungdo Han, Seong Geun Kim, Semin Cho, Hyuk Huh, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5175217/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jun, 2025 Read the published version in BMC Public Health → Version 1 posted 8 You are reading this latest preprint version Abstract Objective Smoking is a leading preventable cause of disease and death worldwide, with severe implications for individuals with chronic kidney disease (CKD). Although smoking at a younger age is linked to higher mortality risk, the specific effects of early smoking on all-cause and cardio-cerebrovascular diseases (CCVDs)-specific mortality in CKD patients are not well established. This study aims to examine the association between early smoking, smoking intensity, and mortality in patients with CKD. Methods This nationwide, population-based cohort study utilized data from the National Health Insurance Database (NHID) of South Korea, provided by the National Health Insurance Service (NHIS). The study included 549,739 adults with CKD who underwent national health examinations in 2009. The primary exposures were the age at smoking initiation and smoking intensity, measured in pack-years. Cox proportional hazards models were used to analyze the association between these exposures and mortality outcomes. Results Earlier smoking initiation and higher smoking intensity were significantly associated with increased risks of all-cause and CCVDs-specific mortality among patients with CKD. Specifically, individuals who began smoking at a younger age and those with higher pack-years had a notably higher risk of mortality. Conclusions The findings highlight the significant health risks associated with early smoking and smoking intensity in CKD patients. Preventive measures targeting early smoking initiation may help improve the long-term outcomes in high-risk population. Smoking Chronic Kidney disease cardio-cerebrovascular disease Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Smoking is a major, preventable cause of numerous diseases and death worldwide[ 1 – 4 ]. Smoking adversely affects multiple organs, and is known to cause cardiovascular complications and various cancers[ 3 , 5 , 6 ]. Previous studies have highlighted the association between age at smoking initiation and mortality rates, and revealed that individuals who started smoking at a younger age showed an elevated risk of mortality[ 7 – 10 ]. Chronic kidney disease (CKD) is associated with an increased risk of cardiovascular and cerebrovascular diseases[ 11 , 12 ]. Cardiovascular events are the leading cause of death in patients with CKD[ 13 ]. Smoking is an important risk factor that accelerates CKD progression[ 14 ], increases the risk of cardiovascular complications and mortality in patients with CKD[ 15 ]. Moreover, several studies have demonstrated that current smokers with CKD have an increased risk of adverse events, such as vascular events, cancer, and all-cause mortality, compared to never smokers[ 15 , 16 ]. Thus, smoking cessation and minimizing smoking exposure are crucial for mitigating further adverse events in patients with CKD. To reduce the morbidity and mortality rates, comprehensive public health measures focusing on reducing smoking demand are essential, particularly through preventive efforts targeting early smoking initiation and clinical interventions aimed at promoting smoking cessation among smokers[ 17 ]. The aim of the present study was to investigate the adverse effects of early smoking initiation in patients with CKD. We investigated the all-cause and cardio-cerebrovascular disease (CCVDs)-specific mortality rates according to smoking initiation age and smoking intensity in patients with CKD aged 20 years or older who underwent the national health examination in 2012. MATERIALS AND METHODS Ethical consideration This study obtained approval from the Institutional Review Board of Seoul National University Hospital (IRB No. E-2001-112-1096). The use of the National Health Insurance Database (NHID) was approved by a government organization. The study was conducted in accordance with the Declaration of Helsinki. Data Source We conducted a nationwide population-based cohort study by reviewing the National Health Insurance Database (NHID) provided by the National Health Insurance Service (NHIS) in South Korea[ 18 ]. All citizens of the Republic of Korea are covered by the National Health Insurance. The complimentary general health check-up provided by the NHIS includes measurements of serum creatinine levels and urine stick albuminuria. The NHIS provides complimentary general health checkups annually for nonoffice workers and biannually for office workers or nonworkplace subscribers. Dependent members over the age of 40 years also receive a checkup every two years. Since 2009, the general health checkup rate has been approximately 70% in approximately 15 million eligible people. The NHID provided by the NHIS is an insurance claims database that encompasses information related to national general health checkups, sociodemographic variables, and mortality rates[ 19 ]. Study Population A total of 11,419,350 adults aged 20 years and above who received the national health screening in 2012 were considered. Among these, 679,882 individuals were diagnosed with CKD during the 2012 national health screening. CKD was defined as eGFR of less than 60 mL/min/1.73 m 2 or a positive result on the dipstick albuminuria test in the national health examination. Individuals receiving dialysis or those who received kidney transplantation were excluded from the study. To eliminate confounding variables, we excluded 12,841 participants with missing data, 33,564 patients previously diagnosed with myocardial infarction, 78,177 with stroke at baseline, and 90,562 former smokers. Finally, 464,738 participants were included in the study (Fig. 1 ). We included participants who underwent health examinations in 2012, and the follow-up period continued until December 31, 2021. The average follow-up duration was 8.99 years. The first, second (median), and third quartiles (Q1/Q2/Q3) of follow-up duration were 9.09/9.35/9.65 years. Study Exposure The study exposures were the estimated glomerular filtration rate (eGFR) and dipstick albuminuria measured during national health examinations. Additionally, data, including smoking status, age at smoking initiation, and pack-years smoked, were extracted from the National Health Screening Questionnaire. Data Collection The NHID provided the baseline characteristics, including age; sex; low-income status; history of diabetes mellitus, hypertension, and dyslipidemia; alcohol consumption; regular exercise; body mass index; waist circumference; blood pressure; and baseline laboratory parameters, including fasting glucose values, lipid profiles, estimated glomerular filtration rate, and proteinuria from urine tests. Low-income status was defined as an income below the 25th percentile of the country’s income distribution. Baseline comorbidities, including diabetes mellitus, hypertension, and hyperlipidemia, were inferred from the tenth edition of the International Classification of Diseases (ICD-10) diagnostic codes and prescription records of related medications. The coders used the ICD-10 for myocardial infarction (MI), stroke, and death.. The eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) equation. Heavy alcohol consumption was defined as consumption of more than 30 grams of alcohol per day, and mild alcohol consumption was defined as consumption of 0–30 grams of alcohol per day. Regular physical activity was defined as moderate-intensity physical activity for ≥ 5 days or vigorous-intensity physical activity ≥ 3 days per week. Metabolic syndrome was defined when three or more of the following criteria were present in the collected data: triglyceride elevation (≥ 150 mg/dL) or use of related medication; decrease in high-density lipoprotein cholesterol (men: <40 mg/dL, women: <50 mg/dL) or use of related medication; blood pressure elevation (systolic ≥ 130 mmHg and/or diastolic ≥ 80 mmHg) or use of antihypertensive medications; fasting glucose elevation (≥ 100 mg/dL) or use of antidiabetic medications; and increased waist circumference (≥ 90 cm for Asian men, ≥ 80 cm for Asian women). Study Outcomes The primary outcome was the occurrence of MI and stroke. We investigated the association between age at smoking initiation, pack-years, and the risk of CCVDs. To minimize the cumulative effect of cigarette smoking due to an early age at smoking initiation, we analyzed the CCVDs risk and incidence rate based on the ratio of pack-years to the smoking initiation age. Statistical Analysis We utilized SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA) to perform all statistical analyses. In the baseline characteristics, categorical variables were presented as numbers (percentages), and continuous variables were expressed as means (± standard deviation). We conducted Cox regression analysis to explore the potential association between the age at which individuals initiated smoking, smoking intensity, and the risk of CCVDs and mortality. Cox regression analysis was conducted to explore the independence of the associations after adjusting for potential confounding factors, including age, sex, income, alcohol consumption, physical activity, BMI, eGFR, metabolic syndrome, proteinuria, and pack-years. We validated the proportional hazards assumption using the log-log cumulative survival plot. The p-values were two-tailed, and the results were considered significant when the p-value was less than 0.05. RESULTS Baseline characteristics In total, 464,738 participants were included in this study. The baseline characteristics were compared according to smoking status, smoking initiation age, and pack-years (Table 1 ). The study groups were divided into four categories based on smoking initiation age and pack-years. The group with less than 20 pack-years and a smoking initiation age less than 20 years was defined as the smoking group 1. Smoking group 2 was designated as those with less than 20 pack-years and a smoking initiation age of 20 years or older, while smoking group 3 was assigned to individuals with 20 or more pack-years and a smoking initiation age of less than 20. Finally, the group with 20 or more pack-years and a smoking initiation age of 20 years or older was named the smoking group 4. The non-smoker, smoking group 1, smoking group 2, smoking group 3, and smoking group 4 comprised 364,513, 10,210, 42,601, 11,658, and 35,756 individuals, respective lt. Table 1 Baseline characteristics of the study groups according to pack-year and smoking age. . Non smoker (N = 364,513) Smoking group 1 (N = 10,210) Smoking group 2 (N = 42,601) Smoking group 3 (N = 11,658) Smoking group 4 (N = 35,756) P-value Age (yr) 58.5 ± 14.69 35.03 ± 10.22 49.87 ± 13.01 53.14 ± 11.32 57.99 ± 10.41 < 0.001 Sex (male) 85918(23.57) 9419(92.25) 35186(82.59) 11534(98.94) 34265(95.83) < 0.001 Body shape measures Body mass index (kg/m 2 ) 24.19 ± 3.51 24.88 ± 4.33 24.54 ± 3.64 24.75 ± 3.52 24.33 ± 3.31 < 0.001 Waist circumference (cm) 80.7 ± 9.66 84.02 ± 10.63 84.07 ± 9.35 86.26 ± 8.72 85.54 ± 8.44 < 0.001 Social and lifestyle factors Drinker a < 0.001 Nondrinker 282552(77.51) 2022(19.8) 13888(32.6) 3421(29.34) 12167(34.03) Mild drinker 75362(20.67) 6084(59.59) 24071(56.5) 4901(42.04) 16787(46.95) Heavy drinker 6599(1.81) 2104(20.61) 4642(10.9) 3336(28.62) 6802(19.02) Regular physical activity b 69402(19.04) 1696(16.61) 8384(19.68) 1883(16.15) 6285(17.58) < 0.001 Low income c 92685(25.43) 1780(17.43) 9662(22.68) 2781(23.85) 9173(25.65) < 0.001 Baseline comorbidities Diabetes mellitus 74045(20.31) 1120(10.97) 9682(22.73) 3787(32.48) 12179(34.06) < 0.001 Hypertension 190948(52.38) 2580(25.27) 19164(44.98) 6000(51.47) 20567(57.52) < 0.001 Dyslipidemia 129979(35.66) 2032(19.9) 13286(31.19) 4215(36.16) 13444(37.6) < 0.001 Metabolic syndrome 161823(44.39) 2430(23.8) 16859(39.57) 5573(47.8) 18055(50.5) < 0.001 Laboratory measurements Systolic blood pressure (mmHg) 125.96 ± 16.91 124.27 ± 15.66 126.45 ± 16.55 127.77 ± 16.52 128.45 ± 16.7 < 0.001 Diastolic blood pressure (mmHg) 77.17 ± 10.64 78.57 ± 11.31 79.19 ± 11.39 79.72 ± 11.26 79.48 ± 11.1 < 0.001 Impaired fasting glucose 104.28 ± 31.86 100.97 ± 33.56 109.6 ± 40.36 117.38 ± 46.95 116.93 ± 44.51 < 0.001 Total cholesterol 198.93 ± 40.42 195.8 ± 40.68 201.05 ± 41.58 200.51 ± 42.97 199.6 ± 42.18 < 0.001 HDL 55.5 ± 18.31 52.9 ± 17.73 52.3 ± 17.9 50.17 ± 17.98 50.19 ± 16.94 < 0.001 LDL 117.21 ± 36.76 111.31 ± 37.18 115.48 ± 39.52 113.51 ± 39.75 114.02 ± 39.25 < 0.001 TG 113.89(113.69-114.09) 136.96(135.27-138.66) 146.3(145.48-147.11) 163.67(161.97-165.39) 156.65(155.74-157.56) < 0.001 eGFR (mL/min/1.73 m 2 ) 64.53 ± 27.24 84.94 ± 37.34 73.09 ± 34.58 74.12 ± 34.57 70.5 ± 32.73 < 0.001 Proteinuria 1+ 42278(11.6) 2236(21.9) 7721(18.12) 2381(20.42) 6691(18.71) Data are presented as the mean (1 standard deviation) for continuous variables or number (%) for categorical variables. HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; eGFR, estimated glomerular filtration rate a There are three types of drinkers: nondrinker (0 g/day), mild drinker (0–30 g/day), and heavy drinker (≥ 30 g/day). b Regular physical activity was defined as moderate-intensity physical activity ≥ 5 days or vigorous-intensity physical activity ≥ 3 days per week. c Individuals included in the lowest quartile (regarding required insurance fees or receiving free insurance) were categorized as the low-income group. Among the smoking groups, the largest number of participants (42,601 individuals) was in smoking group 2. This group started smoking after the age of 20 years and smoked for less than 20 pack-years. The age of participants in the smoking group varied across the subgroups. The smoking group 1 had the youngest average age at 35.03 ± 10.22, while smoking group 4 had the oldest average age at 57.99 ± 10.41. In non-smokers, the proportion of men was 23.57%, whereas in the smoking group, most groups had a higher prevalence of men. The proportion of heavy drinkers was higher in groups with a lower smoking initiation age. The rate of regular physical activity was lower in groups with a lower smoking initiation age. Regarding comorbid conditions such as diabetes, hypertension, hyperlipidemia, and metabolic syndrome, the smoking group showed an increasing prevalence of comorbidities with the age of the smokers. Group 4 had the highest incidence of comorbidities among patients with concurrent conditions. Risk of CCVDs and all-cause death according to the pack-year and smoking initiation age The risk of CCVDs and all-cause death according to smoking initiation age and pack-years was also examined. Elevated risks of CCVDs and all-cause deaths were exhibited in smoking participants irrespective of smoking initiation age and pack-years, compared to the non-smoker group, even after multivariate analysis (Table 2 ). Table 2 Risk of cardio-cerebrovascular and all-cause deaths according to pack-year and smoking age N CCVDs Duration Incidence rate (/ 1000PY) Univariate model Multivariate model 1 a Multivariate model 2 b Death Duration Incidence rate (/ 1000PY) Univariate model Multivariate model 1 a Multivariate model 2 b Non smoker 364513 31421 3499724.46 8.98 1(Ref.) 1(Ref.) 1(Ref.) 38495 3288132 11.7073 1(Ref.) 1(Ref.) 1(Ref.) Pack-year < 20 & smoking age < 20 10210 259 93268.15 2.78 0.32 (0.28,0.36) 1.26 (1.11,1.42) 1.27 (1.12,1.44) 329 94073.66 3.4973 0.3 (0.27,0.33) 2.12 (1.9,2.37) 1.93 (1.73,2.16) Pack-year < 20 & smoking age ≥ 20 42601 3164 371281.05 8.52 0.98 (0.95,1.02) 1.48 (1.42,1.54) 1.49 (1.43,1.55) 4212 381958.98 11.0274 0.95 (0.92,0.98) 1.66 (1.61,1.72) 1.6 (1.55,1.66) Pack-year ≥ 20 & smoking age < 20 11658 1274 97780.62 13.03 1.51 (1.43,1.6) 1.91 (1.8,2.03) 1.84 (1.73,1.95) 1721 102177.48 16.8432 1.45 (1.39,1.53) 2.08 (1.98,2.18) 1.9 (1.8,2) Pack-year ≥ 20 & smoking age ≥ 20 35756 4407 295153.84 14.93 1.73 (1.68,1.79) 1.68 (1.62,1.74) 1.66 (1.6,1.72) 6216 310687.91 20.0072 1.73 (1.68,1.77) 1.73 (1.68,1.78) 1.63 (1.58,1.68) Pack-year < 30 & smoking age < 20 14363 568 129478.5 4.387 0.5 (0.46,0.55) 1.53 (1.4,1.66) 1.53 (1.4,1.66) 721 131302.42 5.4911 0.47 (0.44,0.51) 2.26 (2.09,2.43) 2.08 (1.92,2.24) Pack-year < 30 & smoking age ≥ 20 59462 4927 513484.11 9.6 1.11 (1.07,1.14) 1.54 (1.48,1.59) 1.54 (1.49,1.59) 6578 530438.03 12.4011 1.07 (1.04,1.1) 1.67 (1.63,1.72) 1.61 (1.57,1.66) Pack-year ≥ 30 & smoking age < 20 7505 965 61570.27 15.67 1.82 (1.7,1.94) 1.92 (1.8,2.05) 1.83 (1.71,1.96) 1329 64948.72 20.4623 1.77 (1.67,1.87) 2 (1.89,2.12) 1.82 (1.72,1.93) Pack-year ≥ 30 & smoking age ≥ 20 18895 2644 152950.78 17.29 2.01 (1.93,2.09) 1.7 (1.63,1.78) 1.66 (1.59,1.74) 3850 162208.87 23.7348 2.05 (1.98,2.12) 1.75 (1.69,1.81) 1.62 (1.57,1.69) Pack-year < 40 & smoking age < 20 17448 862 155637.52 5.54 0.64 (0.6,0.68) 1.6 (1.49,1.72) 1.59 (1.48,1.71) 1125 158471.23 7.0991 0.61 (0.58,0.65) 2.16 (2.03,2.3) 2 (1.88,2.13) Pack-year < 40 & smoking age ≥ 20 70012 6191 601367.73 10.3 1.19 (1.16,1.22) 1.56 (1.51,1.61) 1.56 (1.51,1.61) 8227 622853.38 13.2086 1.14 (1.11,1.16) 1.69 (1.64,1.73) 1.62 (1.57,1.66) Pack-year ≥ 40 & smoking age < 20 4420 671 35411.26 18.95 2.2 (2.04,2.38) 1.99 (1.84,2.16) 1.88 (1.74,2.04) 925 37779.92 24.4839 2.12 (1.98,2.26) 2 (1.87,2.13) 1.79 (1.68,1.92) Pack-year ≥ 40 & smoking age ≥ 20 8345 1380 65067.16 21.21 2.47 (2.34,2.61) 1.72 (1.62,1.82) 1.68 (1.59,1.78) 2201 69793.52 31.5359 2.73 (2.62,2.85) 1.76 (1.68,1.84) 1.62 (1.55,1.7) Pack-year < 50 & smoking age < 20 19334 1090 171420.02 6.36 0.73 (0.69,0.78) 1.66 (1.56,1.77) 1.63 (1.53,1.74) 1387 175114.31 7.9205 0.68 (0.65,0.72) 2.13 (2.02,2.25) 1.96 (1.85,2.08) Pack-year < 50 & smoking age ≥ 20 75300 7009 643718.96 10.89 1.26 (1.23,1.29) 1.58 (1.53,1.63) 1.57 (1.52,1.62) 9377 668161 14.034 1.21 (1.18,1.24) 1.69 (1.64,1.73) 1.61 (1.57,1.65) Pack-year ≥ 50 & smoking age < 20 2534 443 19628.76 22.57 2.63 (2.4,2.89) 2.02 (1.83,2.22) 1.91 (1.74,2.11) 663 21136.84 31.367 2.72 (2.52,2.94) 1.99 (1.84,2.15) 1.79 (1.65,1.94) Pack-year ≥ 50 & smoking age ≥ 20 3057 562 22715.93 24.74 2.9 (2.67,3.15) 1.72 (1.57,1.87) 1.68 (1.54,1.83) 1051 24485.9 42.9227 3.75 (3.53,3.99) 1.83 (1.72,1.95) 1.68 (1.58,1.79) a Multivariate model 1 was adjusted for age, sex. b Multivariate model 2 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria and metabolic syndrome. CCVDs, cardio-cerebrovascular diseases To compare the risk of CCVDs and all-cause death based on smoking intensity and initiation age, we categorized participants into four groups based on smoking intensity and initiation age and assessed the risk of adverse events. In the participants with pack years < 20, all-cause death was increased when the smoking initiation age was below 20 years (hazards ratio (HR) 1.93; 95% confidence interval (CI) 1.73–2.16). Since a lower smoking initiation age may have led to the increased pack-years, we conducted examinations in the high-risk groups, which had 30, 40, and 50 pack-years of smoking history. Participants who began smoking before the age of 20 showed an increased risk of all-cause mortality, regardless of smoking intensity. In this group, a higher pack-years was significantly associated with increased risks of CCVDs, MI, and stroke (Table 2 and Supplementary Table 1). Smoking group 3 (pack-years ≥ 20, smoking age < 20) demonstrated the highest risks of CCVDs, MI, and stroke, while the highest risk of all-cause death was observed in smoking group 1 (Fig. 2 ). To mitigate the confounding bias related to the potential for higher smoking intensity with a younger smoking initiation age, we categorized the pack-year/smoking age ratio into quartiles (Table 3 and Supplementary Table 2). CCVDs and all-cause death were elevated in the group with a high pack-year/smoking age ratio (Q4), as depicted in Fig. 3 , which illustrates the probability of CCVDs, MI, stroke, and all-cause death in a multivariate model based on pack-year/smoking age ratio quartiles. Table 3 Risk by quartile of pack year/age of smoking start Pack-years / Smoking age N CCVDs Duration Incidence rate (/ 1000PY) Univariate model Multivariate model 1 a Multivariate model2 b Death Duration Incidence rate (/ 1000PY) Univariate model Multivariate model 1 a Multivariate model2 b Non smoker 364513 27831 3196927.96 8.71 1(Ref.) 1(Ref.) 1(Ref.) 38495 3288132 11.71 1(Ref.) 1(Ref.) 1(Ref.) Q1 24885 1752 217581.72 8.05 0.93 (0.88,0.97) 1.48 (1.41,1.56) 1.49 (1.42,1.57) 2411 223446.58 10.79 0.93 (0.89,0.97) 1.69 (1.62,1.76) 1.63 (1.56,1.7) Q2 24933 1895 216729.71 8.74 1.01 (0.96,1.06) 1.5 (1.42,1.57) 1.5 (1.43,1.58) 2567 223045.25 11.51 0.99 (0.95,1.03) 1.67 (1.6,1.74) 1.6 (1.53,1.67) Q3 25327 2248 217324.8 10.34 1.19 (1.14,1.25) 1.62 (1.55,1.7) 1.61 (1.53,1.68) 2926 225460.18 12.98 1.12 (1.08,1.16) 1.72 (1.65,1.79) 1.63 (1.57,1.7) Q4 25080 3209 205847.44 15.59 1.81 (1.74,1.88) 1.8 (1.72,1.87) 1.74 (1.67,1.82) 4574 216946.04 21.08 1.82 (1.77,1.88) 1.86 (1.8,1.93) 1.72 (1.66,1.78) P value < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 a Multivariate model 1 was adjusted for age, sex. b Multivariate model 2 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria and metabolic syndrome. CCVDs, cardio-cerebrovascular diseases Furthermore, to explore whether the risk of CCVDs, MI, stroke, and all-cause deaths increases with younger smoking initiation age, participants were reclassified based on their smoking initiation age. The risk factors according to age at smoking initiation were reanalyzed in multivariate model 3, which included pack-years as an adjustment factor, following multivariate model 2 (Table 4 ). As the age at smoking initiation decreased, the risks of CCVDs, MI, and stroke did not consistently increase. The highest risks of CCVDs, stroke and all-cause death were observed in the group that began smoking before the age of 15. Notably, the risk of all-cause deaths increased significantly with earlier smoking initiation, with the highest risk observed in those who started smoking before age 15 (HR 1.32; 95% CI 1.2–1.46). Table 4 Comparison of the risk of deaths according to the age of smoking initiation Smoking age N CCVDs Duration Incidence rate (/1000PY) Multivariate model2 a Multivariate model3 b MI Duration Incidence rate (/1000PY) Multivariate model2 a Multivariate model3 b < 15 2631 307 21601.62 14.21 1.3 (1.16,1.46) 1.1 (0.97,1.25) 131 22203.02 5.9 1.21 (1.01,1.446) 1 (0.83,1.21) < 17 3694 297 31949.15 9.3 1.12 (1,1.27) 0.98 (0.86,1.11) 152 32473.03 4.68 1.22 (1.031,1.445) 1.05 (0.88,1.25) < 20 15543 929 137498.01 6.76 1.03 (0.96,1.12) 0.92 (0.85,1) 502 139037.35 3.61 1.15 (1.032,1.276) 1.01 (0.9,1.13) < 25 31358 2523 271892.67 9.28 1.05 (0.99,1.1) 0.96 (0.9,1.01) 1260 276682.48 4.55 1.1 (1.018,1.19) 1 (0.92,1.08) < 30 16630 1518 142038.8 10.69 1.02 (0.96,1.08) 0.96 (0.9,1.02) 715 145005.36 4.93 1.02 (0.936,1.121) 0.96 (0.87,1.05) ≥ 30 30369 3530 252503.42 13.98 1(Ref.) 1(Ref.) 1604 259319.16 6.19 1(Ref.) 1(Ref.) P for trend 0.0005 0.0649 0.0122 0.8924 Smoking age N Stroke Duration Incidence rate (/1000PY) Multivariate model2 a MultivariateModel3 b Death Duration Incidence rate (/1000PY) Multivariate model2 a Multivariate model3 b < 15 2631 206 21971.75 9.38 1.4 (1.21,1.62) 1.2 (1.03,1.4) 491 22634.39 21.69 1.37 (1.25,1.5) 1.32 (1.2,1.46) < 17 3694 178 32347.72 5.5 1.11 (0.95,1.3) 0.97 (0.83,1.14) 423 32940.22 12.84 1.17 (1.06,1.3) 1.14 (1.03,1.27) < 20 15543 500 139004.2 3.6 0.97 (0.87,1.07) 0.87 (0.78,0.97) 1136 140676.54 8.08 1.12 (1.05,1.19) 1.09 (1.02,1.17) < 25 31358 1446 275620.89 5.25 1.01 (0.94,1.08) 0.93 (0.86,1.0) 3197 280798.85 11.39 1.03 (0.99,1.08) 1.01 (0.96,1.06) < 30 16630 913 144222.05 6.33 1.02 (0.94,1.1) 0.96 (0.88,1.04) 2062 147468.19 13.98 1.036 (0.98,1.09) 1.02 (0.97,1.08) ≥ 30 30369 2198 257037.7 8.55 1(Ref.) 1(Ref.) 5169 264379.85 19.55 1(Ref.) 1(Ref.) P for trend 0.0002 0.0017 < 0.001 < 0.001 a Multivariate model 2 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria and metabolic syndrome. b Multivariate model 3 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria, metabolic syndrome and pack-year. CCVDs, cardio-cerebrovascular diseases; MI, myocardial infarction. Association of pack-year and risk of CCVDs, death, MI and stroke according to smoking age The risks of CCVDs and mortality according to pack-years categorized according to smoking age were examined (Supplementary Table 3). When participants were stratified according to smoking initiation age, higher pack-years demonstrated a significant increase in the risk of CCVDs, MI, stroke, and all-cause death. Among the patients with CKD who started smoking before the age of 20, those with over 20 pack-years had an increased risk of CCVDs compared to those with less than 10 pack-years (HR 1.79; 95% CI 1.43–2.26). The risk of all-cause mortality according to pack-years was not statistically significant according to the age at which smoking began. Impact of smoking initiation age and intensity on CCVDs, MI, stroke, and mortality across demographics and clinical factors The smoking intensity and smoking initiation age significantly impact the risk of CCVDs, MI, stroke, and all-cause mortality (Supplementary Table 4). These associations remained consistent across gender, age, alcohol consumption, physical activity, kidney function, and comorbidities (diabetes, hypertension, metabolic syndrome). Additionally, we presented stratified analyses in Supplementary Table 5, which demonstrated an increased risk in the smoking groups compared to non-smokers. These findings are consistent with our results of the risks of CCVDs, MI, stroke, and all-cause mortality according to pack-years and age at smoking initiation. DISCUSSION In the present nationwide retrospective cohort study of patients with CKD, we investigated the associations between smoking initiation age, smoking intensity (pack-years), and the risks of CCVDs and all-cause mortality. The results demonstrated that both early smoking initiation and higher cumulative smoking exposure were independently associated with worse health outcomes in CKD patients. This study adds novel evidence detrimental effects of early smoking in individuals with CKD. The use of a comprehensive, population-based dataset with a large sample size strengthened the generalizability and reliability of the study results. Excessive mortality among smokers is primarily caused by tobacco-related cancers, vascular diseases, and respiratory conditions[ 20 , 21 ]. Numerous studies have shown that earlier smoking initiation is associated with higher risks of all-cause and cause-specific mortality in the general population[ 7 – 10 ]. In particular, individuals who began smoking before the age of 20 were found to have significantly increased risks of premature death compared to those who started after age 20[ 7 , 9 ]. A recent nationwide observational study further confirmed that both mortality risk and the benefits of smoking cessation are closely linked to the age of smoking initiation[ 10 ]. While the association between early smoking and mortality has been well established in the general population, limited evidence exists for patients with CKD. Our nationwide population-based cohort study addresses this gap and demonstrates that early smoking initiation is significantly associated with increased risks of CCVDs and all-cause mortality in individuals with CKD. Prior research has consistently indicated that quitting smoking before the age of 30–40 significantly decreases mortality, highlighting the critical need for robust smoking cessation education and policies[ 7 , 10 ]. Since childhood is a critical period for organ development, smoking during this time increases the risk of long-term morbidity and mortality[ 8 ]. Moreover, chronic kidney disease (CKD) independently elevates the risk of cardiovascular and cerebrovascular complications[ 11 , 12 ]. Therefore, early smoking poses a particularly serious threat to individuals with CKD, highlighting the critical importance of early smoking cessation for mitigating health risks in the vulnerable population. In this retrospective, nationwide cohort study from South Korea, we analyzed the impact of smoking intensity and age at smoking initiation on the risks of CCVDs and all-cause mortality in patients with CKD. These results clearly demonstrate that early smoking initiation and higher smoking intensity significantly worsen health outcomes in patients with CKD. Specifically, those who started smoking at a young age and had higher pack-years had an increased risk of CCVDs and all-cause mortality. Subgroup and stratified analyses further confirmed that these associations remained consistent across various demographic and clinical subgroups. These findings reinforce previous evidence linking early smoking to increased mortality and morbidity, and extend this knowledge to the high-risk CKD population Smoking is strongly associated with atherothrombotic vascular disease[ 22 ]. Several mechanisms may explain this association. Smoking induces a systemic inflammatory response, which plays a key role in the development and progression of atherosclerosis. Both male and female smokers have been shown to exhibit elevated levels of inflammatory markers, such as C-reactive protein, tumor necrosis factor-alpha, and interleukin-6[ 23 , 24 ]. Additionally, smoking contributes to insulin resistance and dyslipidemia, characterized by elevated triglycerides and reduced high-density lipoprotein levels, further elevating cardiovascular risk[ 25 ]. Based on previous findings, early exposure to smoking may exacerbate these pathological mechanisms and thereby worsen CKD-related complications. Our findings suggest that early smoking initiation is an independent risk factor for CCVDs and all-cause mortality in CKD patients, even after adjusting for total smoking exposure (pack-years). Given the strong association between early smoking and adverse cardio-cerebrovascular outcomes, traditional CKD risk assessment models, which primarily focus on hypertension, diabetes, and proteinuria, may benefit from the inclusion of detailed smoking history. From a clinical perspective, targeted interventions are warranted to mitigate the risks associated with early smoking in CKD patients. First, smoking history, including initiation age and cumulative exposure, should be routinely assessed during nephrology consultations. Second, personalized interventions should be developed for younger CKD patients with a history of early smoking, including intensified smoking cessation programs, close cardiovascular monitoring, and targeted education about the heightened risks of CCVDs. Future studies are needed to evaluate the effectiveness of integrating smoking history into CKD prediction models and assess whether targeted interventions based on smoking initiation age improve long-term patient outcomes. Enhancing CKD risk stratification through detailed smoking assessments may enable clinicians to deliver more individualized preventive strategies and improve prognosis in this high-risk population. Considering the strong dose-response relationship between early smoking initiation and adverse outcomes observed in this study, targeted public health interventions are essential to reduce the long-term burden of CKD-related complications. School-based prevention programs and public education campaigns could enhance the prevention strategy. Moreover, comprehensive national smoking cessation campaigns could be strengthened for high-risk populations, including CKD patients with a history of early smoking. This study has several strengths. It utilized a large, nationally representative cohort and adjusted for a wide range of potential confounders, including sociodemographic characteristics, lifestyle behaviors, and comorbidities. Moreover, the inclusion of pack-years as an adjustment variable allowed us to distinguish the effect of early smoking initiation from cumulative tobacco exposure. However, some limitations must be acknowledged. First, the study population consisted predominantly of Koreans, which may limits the generalizability of the findings. Second, smoking data were self-reported during health examinations, which may introduce recall bias. Third, reverse causality may have led to underestimation of the risks or benefits of smoking and smoking cessation. Additionally, survival bias could not be ruled out, as individuals who began smoking at a very early age and experienced severe health consequences may have died before inclusion in the cohort. Finally, although we adjusted for multiple covariates, the possibility of residual or unmeasured confounding remains. Future research is needed to explore the long-term effects of smoking cessation at different stages of CKD and evaluate whether early intervention can alter disease progression or cardiovascular outcomes. A better understanding of how smoking cessation impacts CKD-related risks could inform both clinical practice and public health strategies Conclusions This study demonstrates that early smoking initiation is an independent risk factor for both CCVDs and all-cause mortality in patients with CKD. These findings highlight the importance of preventing early smoking initiation and integrating smoking history into CKD risk assessment. Preventing early smoking initiation through strengthened public health policies and youth-targeted tobacco control strategies may contribute to reducing the long-term cardiovascular burden in this high-risk population. Future research should focus on refining risk prediction models and evaluating the effectiveness of tailored interventions for CKD patients with early smoking exposure. Abbreviations CKD chronic kidney disease CCVDs cardio-cerebrovascular diseases NHID National Health Insurance Database NHIS National Health Insurance Service CI confidence interval IRB Institutional Review Board Declarations Ethical approval and consent to participate This study was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. E-2001-112-1096). The requirement for informed consent was waived as the NHIS database utilized in the study consists of deidentified patient data. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available from NHID but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of NHID. Competing interests All the authors declare no conflicts of interest. Funding The present study was supported by grants of the Seoul National University Hospital (30-2021-0020, 30-2016-0030) and Research Program 2019 funded by Seoul National University College of Medicine Research Foundation (800-20190571). Author contribution S. L. and K. H. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. S. L. and K. H. contributed equally as co–corresponding authors. S. J., S. P., Y. K., K. W. J., D. K. K. and S, L. interpreted the results. S. C., H. H., S. G. K., J. C., J. H. K., M. K., M. W. K., E. K., and D. K. K. conceptualized and designed the study with the support of the study team. K. H. performed the statistical analysis and visualization of data. S. J. and S. L. wrote the original draft. All authors reviewed and edited the manuscript and have read and approved the final manuscript. Acknowledgements We would like to thank Editage (www.editage.com) for their assistance with English language editing. Author’s information 1 Department of Internal Medicine, Gyeongsang National University Hospital, Jinju, Republic of Korea, 2 Department of Statistics and Actuarial Science, Soongsil University, Seoul, Republic of Korea, 3 Department of Internal Medicine, Inje University Sanggye Paik Hospital, Seoul, Republic of Korea, 4 Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Republic of Korea, 5 Department of Internal Medicine, Inje University Busan Paik Hospital, Busan, Republic of Korea, 6 Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea, 7 Department of Transplantation center, Seoul National University Hospital, Republic of Korea, 8 Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea, 9 Department of Internal Medicine, Seoul National University College of Medicine, Republic of Korea, 10 Department of Internal Medicine, Uijeongbu Eulji University Medical Center, Seoul, Republic of Korea References Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. 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Contrasting male and female trends in tobacco-attributed mortality in China: evidence from successive nationwide prospective cohort studies. Lancet [Internet]. 2015;386(10002):1447–56. Available from: http://dx.doi.org/10.1016/S0140-6736(15)00340-2 Choi SH, Stommel M. Impact of Age at Smoking Initiation on Smoking-Related Morbidity and All-Cause Mortality. Am J Prev Med. 2017;53(1):33–41. Thomson B, Rojas NA, Lacey B, Burrett JA, Varona-Pérez P, Martínez MC, et al. Association of childhood smoking and adult mortality: prospective study of 120 000 Cuban adults. Lancet Glob Heal. 2020;8(6):e850–7. Liu X, Sun J, Zhao M, Bovet P, Xi B. Cigarette smoking in childhood and risk of all-cause and cause-specific mortality in adulthood. Front Public Heal. 2023;11(July). Miglinas M, Cesniene U, Janusaite MM, Vinikovas A. Cerebrovascular Disease and Cognition in Chronic Kidney Disease Patients. Front Cardiovasc Med. 2020;7(June):1–13. Zoccali C, Mallamaci F, Adamczak M, De Oliveira RB, Massy ZA, Sarafidis P et al. Cardiovascular complications in chronic kidney disease: a review from the European Renal and Cardiovascular Medicine Working Group of the European Renal Association. Cardiovasc Res [Internet]. 2023;119(11):2017–32. Available from: https://doi.org/10.1093/cvr/cvad083 Matsushita K, Ballew SH, Wang AYM, Kalyesubula R, Schaeffner E, Agarwal R. Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease. Nat Rev Nephrol. 2022;18(11):696–707. Lee S, Kang S, Joo YS, Lee C, Nam KH, Yun HR, et al. Smoking, Smoking Cessation, and Progression of Chronic Kidney Disease: Results From KNOW-CKD Study. Nicotine Tob Res Off J Soc Res Nicotine Tob. 2021;23(1):92–8. Staplin N, Haynes R, Herrington WG, Reith C, Cass A, Fellström B, et al. Smoking and Adverse Outcomes in Patients With CKD: The Study of Heart and Renal Protection (SHARP). Am J kidney Dis Off J Natl Kidney Found. 2016;68(3):371–80. Ricardo AC, Anderson CA, Yang W, Zhang X, Fischer MJ, Dember LM, et al. Healthy lifestyle and risk of kidney disease progression, atherosclerotic events, and death in CKD: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J kidney Dis Off J Natl Kidney Found. 2015;65(3):412–24. Jatoi I, Oppeltz RF. Tobacco and the escalating global cancer burden. J Oncol. 2011;2011. Cheol Seong S, Kim YY, Khang YH, Heon Park J, Kang HJ, Lee H, et al. Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017;46(3):799–800. Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, et al. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open. 2017;7(9):e016640. Pirie K, Peto R, Reeves GK, Green J, Beral V. The 21st century hazards of smoking and benefits of stopping: a prospective study of one million women in the UK. Lancet (London England). 2013;381(9861):133–41. Jha P, Ramasundarahettige C, Landsman V, Rostron B, Thun M, Anderson RN, et al. 21st-century hazards of smoking and benefits of cessation in the United States. N Engl J Med. 2013;368(4):341–50. Jöckel KH, Lehmann N, Jaeger BR, Moebus S, Möhlenkamp S, Schmermund A, et al. Smoking cessation and subclinical atherosclerosis–results from the Heinz Nixdorf Recall Study. Atherosclerosis. 2009;203(1):221–7. Smith CJ, Fischer TH. Particulate and vapor phase constituents of cigarette mainstream smoke and risk of myocardial infarction. Atherosclerosis. 2001;158(2):257–67. Bermudez EA, Rifai N, Buring JE, Manson JE, Ridker PM. Relation between markers of systemic vascular inflammation and smoking in women. Am J Cardiol. 2002;89(9):1117–9. Reaven G, Tsao PS. Insulin resistance and compensatory hyperinsulinemia: The key player between cigarette smoking and cardiovascular disease? J Am Coll Cardiol [Internet]. 2003;41(6):1044–7. Available from: http://dx.doi.org/10.1016/S0735-1097(02)02982-0 Additional Declarations No competing interests reported. Supplementary Files supplementarytablefinal.docx Cite Share Download PDF Status: Published Journal Publication published 03 Jun, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 29 Mar, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5175217","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440435120,"identity":"91e61bcf-b5b8-4196-8e48-be7d2148c8a7","order_by":0,"name":"Sehyun Jung","email":"","orcid":"","institution":"Gyeongsang National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sehyun","middleName":"","lastName":"Jung","suffix":""},{"id":440435121,"identity":"9af75bdf-dbf8-488e-95e7-26d04647e85b","order_by":1,"name":"Kyungdo Han","email":"","orcid":"","institution":"Soongsil 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13:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5175217/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5175217/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-23276-0","type":"published","date":"2025-06-03T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80587897,"identity":"4913bd5e-7c9d-4d9c-b916-82096289fdde","added_by":"auto","created_at":"2025-04-15 01:26:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":528794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart. \u003c/strong\u003eFlow chart represents the selection of study participants. CKD, chronic kidney disease.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5175217/v1/761eb851dd06b704e341841a.png"},{"id":80587143,"identity":"67666b77-665a-4576-9557-baf8cb6d9586","added_by":"auto","created_at":"2025-04-15 01:18:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":795404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe risk of cardio-cerebrovascular and all-cause deaths according to smoking intensity and age at smoking initiation. \u003c/strong\u003eIn non-smoker and smoking groups, the highest risk for CCVDs, MI, and stroke was in patients in smoking group 3. Similarly, the 5- and 10-year risks of CCVDs, MI, and stroke were highest in smoking group 3. The risk of all-cause deaths was highest in smoking group 1, followed by smoking group 3. Smoking group 1, Pack-year \u0026lt; 20 \u0026amp;smoking age \u0026lt; 20; Smoking group 2, Pack-year \u0026lt; 20 \u0026amp;smoking age ≥ 20; Smoking group 3, Pack-year ≥ 20 \u0026amp;smoking age \u0026lt; 20; Smoking group 4, Pack-year ≥ 20 \u0026amp;smoking age ≥ 20; CCVDs, cardio-cerebrovascular diseases; MI, myocardial infarction.\u003c/p\u003e","description":"","filename":"figure2mixedtableE.png","url":"https://assets-eu.researchsquare.com/files/rs-5175217/v1/199d06278f4d2c7a07abec57.png"},{"id":80587145,"identity":"71e452a8-7634-41dd-bcc3-9f76124fd7ac","added_by":"auto","created_at":"2025-04-15 01:18:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":675410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe risk of cardio-cerebrovascular and all-cause deaths by quartile of pack-year/age of smoking start.\u003c/strong\u003e The group with the highest pack-year/smoking age ratio (Q4) had the highest rates of cardio-cerebrovascular and all-cause mortality. Similarly, the 5- and 10-year risks of CCVDs, MI, stroke and all-cause mortality were highest in the Q4 group.\u003c/p\u003e","description":"","filename":"figure3mixedtableE.png","url":"https://assets-eu.researchsquare.com/files/rs-5175217/v1/68b5b8166b23ccf6c2e2ce7c.png"},{"id":84242679,"identity":"63b7d6cd-8e08-4552-9502-51051a0c6f6c","added_by":"auto","created_at":"2025-06-09 16:11:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3373462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5175217/v1/81deed1d-9f4b-49ec-8240-8d24c3e47d62.pdf"},{"id":80587146,"identity":"f7703846-8880-4a81-84d0-2d12fb0cde38","added_by":"auto","created_at":"2025-04-15 01:18:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":130511,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytablefinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5175217/v1/7aa3dac02dbc2941c93f57ba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Smoking and its impact on Cardio-cerebrovascular Diseases in patients with Chronic Kidney Disease: A Nationwide Population-Based Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSmoking is a major, preventable cause of numerous diseases and death worldwide[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Smoking adversely affects multiple organs, and is known to cause cardiovascular complications and various cancers[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Previous studies have highlighted the association between age at smoking initiation and mortality rates, and revealed that individuals who started smoking at a younger age showed an elevated risk of mortality[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic kidney disease (CKD) is associated with an increased risk of cardiovascular and cerebrovascular diseases[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Cardiovascular events are the leading cause of death in patients with CKD[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Smoking is an important risk factor that accelerates CKD progression[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], increases the risk of cardiovascular complications and mortality in patients with CKD[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, several studies have demonstrated that current smokers with CKD have an increased risk of adverse events, such as vascular events, cancer, and all-cause mortality, compared to never smokers[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, smoking cessation and minimizing smoking exposure are crucial for mitigating further adverse events in patients with CKD. To reduce the morbidity and mortality rates, comprehensive public health measures focusing on reducing smoking demand are essential, particularly through preventive efforts targeting early smoking initiation and clinical interventions aimed at promoting smoking cessation among smokers[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of the present study was to investigate the adverse effects of early smoking initiation in patients with CKD. We investigated the all-cause and cardio-cerebrovascular disease (CCVDs)-specific mortality rates according to smoking initiation age and smoking intensity in patients with CKD aged 20 years or older who underwent the national health examination in 2012.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003e This study obtained approval from the Institutional Review Board of Seoul National University Hospital (IRB No. E-2001-112-1096). The use of the National Health Insurance Database (NHID) was approved by a government organization. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Source\u003c/h3\u003e\n\u003cp\u003eWe conducted a nationwide population-based cohort study by reviewing the National Health Insurance Database (NHID) provided by the National Health Insurance Service (NHIS) in South Korea[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll citizens of the Republic of Korea are covered by the National Health Insurance. The complimentary general health check-up provided by the NHIS includes measurements of serum creatinine levels and urine stick albuminuria. The NHIS provides complimentary general health checkups annually for nonoffice workers and biannually for office workers or nonworkplace subscribers. Dependent members over the age of 40 years also receive a checkup every two years. Since 2009, the general health checkup rate has been approximately 70% in approximately 15\u0026nbsp;million eligible people. The NHID provided by the NHIS is an insurance claims database that encompasses information related to national general health checkups, sociodemographic variables, and mortality rates[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eA total of 11,419,350 adults aged 20 years and above who received the national health screening in 2012 were considered. Among these, 679,882 individuals were diagnosed with CKD during the 2012 national health screening. CKD was defined as eGFR of less than 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e or a positive result on the dipstick albuminuria test in the national health examination. Individuals receiving dialysis or those who received kidney transplantation were excluded from the study. To eliminate confounding variables, we excluded 12,841 participants with missing data, 33,564 patients previously diagnosed with myocardial infarction, 78,177 with stroke at baseline, and 90,562 former smokers. Finally, 464,738 participants were included in the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe included participants who underwent health examinations in 2012, and the follow-up period continued until December 31, 2021. The average follow-up duration was 8.99 years. The first, second (median), and third quartiles (Q1/Q2/Q3) of follow-up duration were 9.09/9.35/9.65 years.\u003c/p\u003e\n\u003ch3\u003eStudy Exposure\u003c/h3\u003e\n\u003cp\u003eThe study exposures were the estimated glomerular filtration rate (eGFR) and dipstick albuminuria measured during national health examinations. Additionally, data, including smoking status, age at smoking initiation, and pack-years smoked, were extracted from the National Health Screening Questionnaire.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eThe NHID provided the baseline characteristics, including age; sex; low-income status; history of diabetes mellitus, hypertension, and dyslipidemia; alcohol consumption; regular exercise; body mass index; waist circumference; blood pressure; and baseline laboratory parameters, including fasting glucose values, lipid profiles, estimated glomerular filtration rate, and proteinuria from urine tests. Low-income status was defined as an income below the 25th percentile of the country\u0026rsquo;s income distribution. Baseline comorbidities, including diabetes mellitus, hypertension, and hyperlipidemia, were inferred from the tenth edition of the International Classification of Diseases (ICD-10) diagnostic codes and prescription records of related medications. The coders used the ICD-10 for myocardial infarction (MI), stroke, and death.. The eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) equation. Heavy alcohol consumption was defined as consumption of more than 30 grams of alcohol per day, and mild alcohol consumption was defined as consumption of 0\u0026ndash;30 grams of alcohol per day. Regular physical activity was defined as moderate-intensity physical activity for \u0026ge;\u0026thinsp;5 days or vigorous-intensity physical activity\u0026thinsp;\u0026ge;\u0026thinsp;3 days per week. Metabolic syndrome was defined when three or more of the following criteria were present in the collected data: triglyceride elevation (\u0026ge;\u0026thinsp;150 mg/dL) or use of related medication; decrease in high-density lipoprotein cholesterol (men: \u0026lt;40 mg/dL, women: \u0026lt;50 mg/dL) or use of related medication; blood pressure elevation (systolic\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg and/or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg) or use of antihypertensive medications; fasting glucose elevation (\u0026ge;\u0026thinsp;100 mg/dL) or use of antidiabetic medications; and increased waist circumference (\u0026ge;\u0026thinsp;90 cm for Asian men, \u0026ge;\u0026thinsp;80 cm for Asian women).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was the occurrence of MI and stroke. We investigated the association between age at smoking initiation, pack-years, and the risk of CCVDs. To minimize the cumulative effect of cigarette smoking due to an early age at smoking initiation, we analyzed the CCVDs risk and incidence rate based on the ratio of pack-years to the smoking initiation age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe utilized SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA) to perform all statistical analyses. In the baseline characteristics, categorical variables were presented as numbers (percentages), and continuous variables were expressed as means (\u0026plusmn;\u0026thinsp;standard deviation). We conducted Cox regression analysis to explore the potential association between the age at which individuals initiated smoking, smoking intensity, and the risk of CCVDs and mortality. Cox regression analysis was conducted to explore the independence of the associations after adjusting for potential confounding factors, including age, sex, income, alcohol consumption, physical activity, BMI, eGFR, metabolic syndrome, proteinuria, and pack-years. We validated the proportional hazards assumption using the log-log cumulative survival plot. The p-values were two-tailed, and the results were considered significant when the p-value was less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eIn total, 464,738 participants were included in this study. The baseline characteristics were compared according to smoking status, smoking initiation age, and pack-years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study groups were divided into four categories based on smoking initiation age and pack-years. The group with less than 20 pack-years and a smoking initiation age less than 20 years was defined as the smoking group 1. Smoking group 2 was designated as those with less than 20 pack-years and a smoking initiation age of 20 years or older, while smoking group 3 was assigned to individuals with 20 or more pack-years and a smoking initiation age of less than 20. Finally, the group with 20 or more pack-years and a smoking initiation age of 20 years or older was named the smoking group 4. The non-smoker, smoking group 1, smoking group 2, smoking group 3, and smoking group 4 comprised 364,513, 10,210, 42,601, 11,658, and 35,756 individuals, respective lt.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study groups according to pack-year and smoking age.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon smoker\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;364,513)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmoking group 1\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;10,210)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmoking group 2\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;42,601)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking group 3\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;11,658)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmoking group 4\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;35,756)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.87\u0026thinsp;\u0026plusmn;\u0026thinsp;13.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.99\u0026thinsp;\u0026plusmn;\u0026thinsp;10.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85918(23.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9419(92.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35186(82.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11534(98.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34265(95.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody shape measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.07\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial and lifestyle factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinker\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNondrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282552(77.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022(19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13888(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3421(29.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12167(34.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75362(20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6084(59.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24071(56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4901(42.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16787(46.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6599(1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2104(20.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4642(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3336(28.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6802(19.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegular physical activity\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69402(19.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1696(16.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8384(19.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1883(16.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6285(17.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow income\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92685(25.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1780(17.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9662(22.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2781(23.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9173(25.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes mellitus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74045(20.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1120(10.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9682(22.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3787(32.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12179(34.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190948(52.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2580(25.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19164(44.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6000(51.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20567(57.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129979(35.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2032(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13286(31.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4215(36.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13444(37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic syndrome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161823(44.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2430(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16859(39.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5573(47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18055(50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory measurements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.96\u0026thinsp;\u0026plusmn;\u0026thinsp;16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.27\u0026thinsp;\u0026plusmn;\u0026thinsp;15.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.45\u0026thinsp;\u0026plusmn;\u0026thinsp;16.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127.77\u0026thinsp;\u0026plusmn;\u0026thinsp;16.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128.45\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.57\u0026thinsp;\u0026plusmn;\u0026thinsp;11.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.19\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.48\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImpaired fasting glucose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.28\u0026thinsp;\u0026plusmn;\u0026thinsp;31.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.97\u0026thinsp;\u0026plusmn;\u0026thinsp;33.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.6\u0026thinsp;\u0026plusmn;\u0026thinsp;40.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117.38\u0026thinsp;\u0026plusmn;\u0026thinsp;46.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.93\u0026thinsp;\u0026plusmn;\u0026thinsp;44.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198.93\u0026thinsp;\u0026plusmn;\u0026thinsp;40.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195.8\u0026thinsp;\u0026plusmn;\u0026thinsp;40.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201.05\u0026thinsp;\u0026plusmn;\u0026thinsp;41.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200.51\u0026thinsp;\u0026plusmn;\u0026thinsp;42.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199.6\u0026thinsp;\u0026plusmn;\u0026thinsp;42.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.17\u0026thinsp;\u0026plusmn;\u0026thinsp;17.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.19\u0026thinsp;\u0026plusmn;\u0026thinsp;16.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.21\u0026thinsp;\u0026plusmn;\u0026thinsp;36.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.31\u0026thinsp;\u0026plusmn;\u0026thinsp;37.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.48\u0026thinsp;\u0026plusmn;\u0026thinsp;39.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113.51\u0026thinsp;\u0026plusmn;\u0026thinsp;39.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114.02\u0026thinsp;\u0026plusmn;\u0026thinsp;39.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.89(113.69-114.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.96(135.27-138.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.3(145.48-147.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163.67(161.97-165.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156.65(155.74-157.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR (mL/min/1.73 m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.53\u0026thinsp;\u0026plusmn;\u0026thinsp;27.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.94\u0026thinsp;\u0026plusmn;\u0026thinsp;37.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.09\u0026thinsp;\u0026plusmn;\u0026thinsp;34.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.12\u0026thinsp;\u0026plusmn;\u0026thinsp;34.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;32.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProteinuria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230105(63.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2139(20.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17924(42.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4522(38.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16677(46.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92130(25.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5835(57.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16956(39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4755(40.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12388(34.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42278(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2236(21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7721(18.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2381(20.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6691(18.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are presented as the mean (1 standard deviation) for continuous variables or number (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; eGFR, estimated glomerular filtration rate\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eThere are three types of drinkers: nondrinker (0 g/day), mild drinker (0\u0026ndash;30 g/day), and heavy drinker (\u0026ge;\u0026thinsp;30 g/day).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eRegular physical activity was defined as moderate-intensity physical activity\u0026thinsp;\u0026ge;\u0026thinsp;5 days or vigorous-intensity physical activity\u0026thinsp;\u0026ge;\u0026thinsp;3 days per week.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003eIndividuals included in the lowest quartile (regarding required insurance fees or receiving free insurance) were categorized as the low-income group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the smoking groups, the largest number of participants (42,601 individuals) was in smoking group 2. This group started smoking after the age of 20 years and smoked for less than 20 pack-years. The age of participants in the smoking group varied across the subgroups. The smoking group 1 had the youngest average age at 35.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22, while smoking group 4 had the oldest average age at 57.99\u0026thinsp;\u0026plusmn;\u0026thinsp;10.41. In non-smokers, the proportion of men was 23.57%, whereas in the smoking group, most groups had a higher prevalence of men. The proportion of heavy drinkers was higher in groups with a lower smoking initiation age. The rate of regular physical activity was lower in groups with a lower smoking initiation age. Regarding comorbid conditions such as diabetes, hypertension, hyperlipidemia, and metabolic syndrome, the smoking group showed an increasing prevalence of comorbidities with the age of the smokers. Group 4 had the highest incidence of comorbidities among patients with concurrent conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk of CCVDs and all-cause death according to the pack-year and smoking initiation age\u003c/h2\u003e \u003cp\u003eThe risk of CCVDs and all-cause death according to smoking initiation age and pack-years was also examined. Elevated risks of CCVDs and all-cause deaths were exhibited in smoking participants irrespective of smoking initiation age and pack-years, compared to the non-smoker group, even after multivariate analysis (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\u003eRisk of cardio-cerebrovascular and all-cause deaths according to pack-year and smoking age\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\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\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCVDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncidence rate (/ 1000PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIncidence rate (/ 1000PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3499724.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.98\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 \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3288132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.7073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;20 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93268.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003cp\u003e(0.28,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003cp\u003e(1.11,1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003cp\u003e(1.12,1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94073.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.4973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003cp\u003e(0.27,0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003cp\u003e(1.9,2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003cp\u003e(1.73,2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;20 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e371281.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.95,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003cp\u003e(1.42,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003cp\u003e(1.43,1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e381958.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.0274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e(0.92,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003cp\u003e(1.61,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003cp\u003e(1.55,1.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;20 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97780.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003cp\u003e(1.43,1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003cp\u003e(1.8,2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003cp\u003e(1.73,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e102177.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.8432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003cp\u003e(1.39,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003cp\u003e(1.98,2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003cp\u003e(1.8,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;20 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295153.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003cp\u003e(1.68,1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003cp\u003e(1.62,1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003cp\u003e(1.6,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e310687.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003cp\u003e(1.68,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003cp\u003e(1.68,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003cp\u003e(1.58,1.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;30 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129478.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003cp\u003e(0.46,0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003cp\u003e(1.4,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003cp\u003e(1.4,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e131302.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.4911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003cp\u003e(0.44,0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003cp\u003e(2.09,2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003cp\u003e(1.92,2.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;30 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e513484.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e(1.07,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003cp\u003e(1.48,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003cp\u003e(1.49,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e530438.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12.4011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003cp\u003e(1.04,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003cp\u003e(1.63,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003cp\u003e(1.57,1.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;30 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61570.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003cp\u003e(1.7,1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003cp\u003e(1.8,2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003cp\u003e(1.71,1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e64948.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20.4623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003cp\u003e(1.67,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(1.89,2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003cp\u003e(1.72,1.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;30 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152950.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003cp\u003e(1.93,2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003cp\u003e(1.63,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003cp\u003e(1.59,1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e162208.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23.7348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003cp\u003e(1.98,2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003cp\u003e(1.69,1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003cp\u003e(1.57,1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;40 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155637.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003cp\u003e(0.6,0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003cp\u003e(1.49,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003cp\u003e(1.48,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e158471.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.0991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003cp\u003e(0.58,0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003cp\u003e(2.03,2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(1.88,2.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;40 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e601367.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(1.16,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003cp\u003e(1.51,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003cp\u003e(1.51,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e622853.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13.2086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e(1.11,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003cp\u003e(1.64,1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003cp\u003e(1.57,1.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;40 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35411.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003cp\u003e(2.04,2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003cp\u003e(1.84,2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003cp\u003e(1.74,2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37779.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24.4839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003cp\u003e(1.98,2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(1.87,2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003cp\u003e(1.68,1.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;40 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65067.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003cp\u003e(2.34,2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003cp\u003e(1.62,1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003cp\u003e(1.59,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e69793.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31.5359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003cp\u003e(2.62,2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003cp\u003e(1.68,1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003cp\u003e(1.55,1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;50 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171420.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.69,0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003cp\u003e(1.56,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003cp\u003e(1.53,1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e175114.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003cp\u003e(0.65,0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003cp\u003e(2.02,2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003cp\u003e(1.85,2.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026lt;\u0026thinsp;50 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e643718.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003cp\u003e(1.23,1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003cp\u003e(1.53,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003cp\u003e(1.52,1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e668161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003cp\u003e(1.18,1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003cp\u003e(1.64,1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003cp\u003e(1.57,1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;50 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19628.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003cp\u003e(2.4,2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003cp\u003e(1.83,2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003cp\u003e(1.74,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21136.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003cp\u003e(2.52,2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003cp\u003e(1.84,2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003cp\u003e(1.65,1.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-year\u0026thinsp;\u0026ge;\u0026thinsp;50 \u0026amp;\u003c/p\u003e \u003cp\u003esmoking age\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22715.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003cp\u003e(2.67,3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003cp\u003e(1.57,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003cp\u003e(1.54,1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24485.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e42.9227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003cp\u003e(3.53,3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003cp\u003e(1.72,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003cp\u003e(1.58,1.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003ea\u003c/sup\u003e Multivariate model 1 was adjusted for age, sex.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eb\u003c/sup\u003e Multivariate model 2 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria and metabolic syndrome.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eCCVDs, cardio-cerebrovascular diseases\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo compare the risk of CCVDs and all-cause death based on smoking intensity and initiation age, we categorized participants into four groups based on smoking intensity and initiation age and assessed the risk of adverse events. In the participants with pack years\u0026thinsp;\u0026lt;\u0026thinsp;20, all-cause death was increased when the smoking initiation age was below 20 years (hazards ratio (HR) 1.93; 95% confidence interval (CI) 1.73\u0026ndash;2.16). Since a lower smoking initiation age may have led to the increased pack-years, we conducted examinations in the high-risk groups, which had 30, 40, and 50 pack-years of smoking history. Participants who began smoking before the age of 20 showed an increased risk of all-cause mortality, regardless of smoking intensity. In this group, a higher pack-years was significantly associated with increased risks of CCVDs, MI, and stroke (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;1). Smoking group 3 (pack-years\u0026thinsp;\u0026ge;\u0026thinsp;20, smoking age\u0026thinsp;\u0026lt;\u0026thinsp;20) demonstrated the highest risks of CCVDs, MI, and stroke, while the highest risk of all-cause death was observed in smoking group 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo mitigate the confounding bias related to the potential for higher smoking intensity with a younger smoking initiation age, we categorized the pack-year/smoking age ratio into quartiles (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;2). CCVDs and all-cause death were elevated in the group with a high pack-year/smoking age ratio (Q4), as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which illustrates the probability of CCVDs, MI, stroke, and all-cause death in a multivariate model based on pack-year/smoking age ratio quartiles.\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\u003eRisk by quartile of pack year/age of smoking start\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePack-years / Smoking age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCVDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncidence rate (/ 1000PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIncidence rate (/ 1000PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3196927.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.71\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 \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3288132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217581.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003cp\u003e(0.88,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003cp\u003e(1.41,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003cp\u003e(1.42,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e223446.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003cp\u003e(0.89,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003cp\u003e(1.62,1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003cp\u003e(1.56,1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216729.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.96,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003cp\u003e(1.42,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003cp\u003e(1.43,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e223045.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.95,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003cp\u003e(1.6,1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003cp\u003e(1.53,1.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217324.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003cp\u003e(1.14,1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003cp\u003e(1.55,1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003cp\u003e(1.53,1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e225460.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003cp\u003e(1.08,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003cp\u003e(1.65,1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003cp\u003e(1.57,1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e205847.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003cp\u003e(1.74,1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003cp\u003e(1.72,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003cp\u003e(1.67,1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e216946.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e21.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003cp\u003e(1.77,1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003cp\u003e(1.8,1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003cp\u003e(1.66,1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003ea\u003c/sup\u003e Multivariate model 1 was adjusted for age, sex.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eb\u003c/sup\u003e Multivariate model 2 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria and metabolic syndrome.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eCCVDs, cardio-cerebrovascular diseases\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, to explore whether the risk of CCVDs, MI, stroke, and all-cause deaths increases with younger smoking initiation age, participants were reclassified based on their smoking initiation age. The risk factors according to age at smoking initiation were reanalyzed in multivariate model 3, which included pack-years as an adjustment factor, following multivariate model 2 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As the age at smoking initiation decreased, the risks of CCVDs, MI, and stroke did not consistently increase. The highest risks of CCVDs, stroke and all-cause death were observed in the group that began smoking before the age of 15. Notably, the risk of all-cause deaths increased significantly with earlier smoking initiation, with the highest risk observed in those who started smoking before age 15 (HR 1.32; 95% CI 1.2\u0026ndash;1.46).\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\u003eComparison of the risk of deaths according to the age of smoking initiation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCVDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncidence rate\u003c/p\u003e \u003cp\u003e(/1000PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultivariate model2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultivariate model3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIncidence rate\u003c/p\u003e \u003cp\u003e(/1000PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMultivariate model2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMultivariate model3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21601.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003cp\u003e(1.16,1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003cp\u003e(0.97,1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22203.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003cp\u003e(1.01,1.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.83,1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31949.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003cp\u003e(1,1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.86,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32473.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003cp\u003e(1.031,1.445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e(0.88,1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137498.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(0.96,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.85,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e139037.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003cp\u003e(1.032,1.276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.9,1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e271892.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e(0.99,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.9,1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e276682.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003cp\u003e(1.018,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(0.92,1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142038.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.96,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.9,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e145005.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.936,1.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.87,1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252503.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.98\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\u003e1604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e259319.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.8924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncidence rate\u003c/p\u003e \u003cp\u003e(/1000PY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultivariateModel3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIncidence rate\u003c/p\u003e \u003cp\u003e(/1000PY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003cp\u003emodel3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21971.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003cp\u003e(1.21,1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003cp\u003e(1.03,1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22634.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003cp\u003e(1.25,1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003cp\u003e(1.2,1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32347.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e(0.95,1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.83,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32940.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e(1.06,1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003cp\u003e(1.03,1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139004.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.87,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003cp\u003e(0.78,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e140676.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003cp\u003e(1.05,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003cp\u003e(1.02,1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275620.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.94,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003cp\u003e(0.86,1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e280798.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(0.99,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.96,1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144222.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.94,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.88,1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e147468.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003cp\u003e(0.98,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e(0.97,1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257037.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.55\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\u003e5169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e264379.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1(Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003e Multivariate model 2 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria and metabolic syndrome.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003eb\u003c/sup\u003e Multivariate model 3 was adjusted for age, sex, alcohol consumption, regular physical activity, BMI, eGFR, proteinuria, metabolic syndrome and pack-year.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eCCVDs, cardio-cerebrovascular diseases; MI, myocardial infarction.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of pack-year and risk of CCVDs, death, MI and stroke according to smoking age\u003c/h2\u003e \u003cp\u003eThe risks of CCVDs and mortality according to pack-years categorized according to smoking age were examined (Supplementary Table\u0026nbsp;3). When participants were stratified according to smoking initiation age, higher pack-years demonstrated a significant increase in the risk of CCVDs, MI, stroke, and all-cause death. Among the patients with CKD who started smoking before the age of 20, those with over 20 pack-years had an increased risk of CCVDs compared to those with less than 10 pack-years (HR 1.79; 95% CI 1.43\u0026ndash;2.26). The risk of all-cause mortality according to pack-years was not statistically significant according to the age at which smoking began.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImpact of smoking initiation age and intensity on CCVDs, MI, stroke, and mortality across demographics and clinical factors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe smoking intensity and smoking initiation age significantly impact the risk of CCVDs, MI, stroke, and all-cause mortality (Supplementary Table\u0026nbsp;4). These associations remained consistent across gender, age, alcohol consumption, physical activity, kidney function, and comorbidities (diabetes, hypertension, metabolic syndrome). Additionally, we presented stratified analyses in Supplementary Table\u0026nbsp;5, which demonstrated an increased risk in the smoking groups compared to non-smokers. These findings are consistent with our results of the risks of CCVDs, MI, stroke, and all-cause mortality according to pack-years and age at smoking initiation.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the present nationwide retrospective cohort study of patients with CKD, we investigated the associations between smoking initiation age, smoking intensity (pack-years), and the risks of CCVDs and all-cause mortality. The results demonstrated that both early smoking initiation and higher cumulative smoking exposure were independently associated with worse health outcomes in CKD patients. This study adds novel evidence detrimental effects of early smoking in individuals with CKD. The use of a comprehensive, population-based dataset with a large sample size strengthened the generalizability and reliability of the study results.\u003c/p\u003e \u003cp\u003eExcessive mortality among smokers is primarily caused by tobacco-related cancers, vascular diseases, and respiratory conditions[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Numerous studies have shown that earlier smoking initiation is associated with higher risks of all-cause and cause-specific mortality in the general population[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In particular, individuals who began smoking before the age of 20 were found to have significantly increased risks of premature death compared to those who started after age 20[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A recent nationwide observational study further confirmed that both mortality risk and the benefits of smoking cessation are closely linked to the age of smoking initiation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While the association between early smoking and mortality has been well established in the general population, limited evidence exists for patients with CKD. Our nationwide population-based cohort study addresses this gap and demonstrates that early smoking initiation is significantly associated with increased risks of CCVDs and all-cause mortality in individuals with CKD.\u003c/p\u003e \u003cp\u003ePrior research has consistently indicated that quitting smoking before the age of 30\u0026ndash;40 significantly decreases mortality, highlighting the critical need for robust smoking cessation education and policies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Since childhood is a critical period for organ development, smoking during this time increases the risk of long-term morbidity and mortality[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, chronic kidney disease (CKD) independently elevates the risk of cardiovascular and cerebrovascular complications[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, early smoking poses a particularly serious threat to individuals with CKD, highlighting the critical importance of early smoking cessation for mitigating health risks in the vulnerable population.\u003c/p\u003e \u003cp\u003eIn this retrospective, nationwide cohort study from South Korea, we analyzed the impact of smoking intensity and age at smoking initiation on the risks of CCVDs and all-cause mortality in patients with CKD. These results clearly demonstrate that early smoking initiation and higher smoking intensity significantly worsen health outcomes in patients with CKD. Specifically, those who started smoking at a young age and had higher pack-years had an increased risk of CCVDs and all-cause mortality. Subgroup and stratified analyses further confirmed that these associations remained consistent across various demographic and clinical subgroups. These findings reinforce previous evidence linking early smoking to increased mortality and morbidity, and extend this knowledge to the high-risk CKD population\u003c/p\u003e \u003cp\u003eSmoking is strongly associated with atherothrombotic vascular disease[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Several mechanisms may explain this association. Smoking induces a systemic inflammatory response, which plays a key role in the development and progression of atherosclerosis. Both male and female smokers have been shown to exhibit elevated levels of inflammatory markers, such as C-reactive protein, tumor necrosis factor-alpha, and interleukin-6[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, smoking contributes to insulin resistance and dyslipidemia, characterized by elevated triglycerides and reduced high-density lipoprotein levels, further elevating cardiovascular risk[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Based on previous findings, early exposure to smoking may exacerbate these pathological mechanisms and thereby worsen CKD-related complications.\u003c/p\u003e \u003cp\u003eOur findings suggest that early smoking initiation is an independent risk factor for CCVDs and all-cause mortality in CKD patients, even after adjusting for total smoking exposure (pack-years). Given the strong association between early smoking and adverse cardio-cerebrovascular outcomes, traditional CKD risk assessment models, which primarily focus on hypertension, diabetes, and proteinuria, may benefit from the inclusion of detailed smoking history.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, targeted interventions are warranted to mitigate the risks associated with early smoking in CKD patients. First, smoking history, including initiation age and cumulative exposure, should be routinely assessed during nephrology consultations. Second, personalized interventions should be developed for younger CKD patients with a history of early smoking, including intensified smoking cessation programs, close cardiovascular monitoring, and targeted education about the heightened risks of CCVDs. Future studies are needed to evaluate the effectiveness of integrating smoking history into CKD prediction models and assess whether targeted interventions based on smoking initiation age improve long-term patient outcomes. Enhancing CKD risk stratification through detailed smoking assessments may enable clinicians to deliver more individualized preventive strategies and improve prognosis in this high-risk population.\u003c/p\u003e \u003cp\u003eConsidering the strong dose-response relationship between early smoking initiation and adverse outcomes observed in this study, targeted public health interventions are essential to reduce the long-term burden of CKD-related complications. School-based prevention programs and public education campaigns could enhance the prevention strategy. Moreover, comprehensive national smoking cessation campaigns could be strengthened for high-risk populations, including CKD patients with a history of early smoking.\u003c/p\u003e \u003cp\u003eThis study has several strengths. It utilized a large, nationally representative cohort and adjusted for a wide range of potential confounders, including sociodemographic characteristics, lifestyle behaviors, and comorbidities. Moreover, the inclusion of pack-years as an adjustment variable allowed us to distinguish the effect of early smoking initiation from cumulative tobacco exposure.\u003c/p\u003e \u003cp\u003eHowever, some limitations must be acknowledged. First, the study population consisted predominantly of Koreans, which may limits the generalizability of the findings. Second, smoking data were self-reported during health examinations, which may introduce recall bias. Third, reverse causality may have led to underestimation of the risks or benefits of smoking and smoking cessation. Additionally, survival bias could not be ruled out, as individuals who began smoking at a very early age and experienced severe health consequences may have died before inclusion in the cohort. Finally, although we adjusted for multiple covariates, the possibility of residual or unmeasured confounding remains.\u003c/p\u003e \u003cp\u003eFuture research is needed to explore the long-term effects of smoking cessation at different stages of CKD and evaluate whether early intervention can alter disease progression or cardiovascular outcomes. A better understanding of how smoking cessation impacts CKD-related risks could inform both clinical practice and public health strategies\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that early smoking initiation is an independent risk factor for both CCVDs and all-cause mortality in patients with CKD. These findings highlight the importance of preventing early smoking initiation and integrating smoking history into CKD risk assessment. Preventing early smoking initiation through strengthened public health policies and youth-targeted tobacco control strategies may contribute to reducing the long-term cardiovascular burden in this high-risk population. Future research should focus on refining risk prediction models and evaluating the effectiveness of tailored interventions for CKD patients with early smoking exposure.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCKD chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCCVDs cardio-cerebrovascular diseases\u003c/p\u003e\n\u003cp\u003eNHID National Health Insurance Database\u003c/p\u003e\n\u003cp\u003eNHIS National Health Insurance Service\u003c/p\u003e\n\u003cp\u003eCI confidence interval\u003c/p\u003e\n\u003cp\u003eIRB Institutional Review Board\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. E-2001-112-1096). The requirement for informed consent was waived as the NHIS database utilized in the study consists of deidentified patient data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from NHID but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of NHID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by grants of the Seoul National University Hospital (30-2021-0020, 30-2016-0030) and Research Program 2019 funded by Seoul National University College of Medicine Research Foundation (800-20190571).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS. L. and K. H. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. S. L. and K. H. contributed equally as co\u0026ndash;corresponding authors. S. J., S. P., Y. K., K. W. J., D. K. K. and S, L. interpreted the results. S. C., H. H., S. G. K., J. C., J. H. K., M. K., M. W. K., E. K., and D. K. K. conceptualized and designed the study with the support of the study team. K. H. performed the statistical analysis and visualization of data. S. J. and S. L. wrote the original draft. All authors reviewed and edited the manuscript and have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.com) for their assistance with English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Internal Medicine, Gyeongsang National University Hospital, Jinju, Republic of Korea, \u003csup\u003e2\u003c/sup\u003eDepartment of Statistics and Actuarial Science, Soongsil University, Seoul, Republic of Korea, \u003csup\u003e3\u003c/sup\u003eDepartment of Internal Medicine, Inje University Sanggye Paik Hospital, Seoul, Republic of Korea, \u003csup\u003e4\u003c/sup\u003eDepartment of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Republic of Korea, \u003csup\u003e5\u003c/sup\u003eDepartment of Internal Medicine, Inje University Busan Paik Hospital, Busan, Republic of Korea, \u003csup\u003e6\u003c/sup\u003eDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea, \u003csup\u003e7\u003c/sup\u003eDepartment of Transplantation center, Seoul National University Hospital, Republic of Korea, \u003csup\u003e8\u003c/sup\u003eDepartment of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea, \u0026nbsp;\u003csup\u003e9\u003c/sup\u003eDepartment of Internal Medicine, Seoul National University College of Medicine, Republic of Korea, \u003csup\u003e10\u003c/sup\u003eDepartment of Internal Medicine, Uijeongbu Eulji University Medical Center, Seoul, Republic of Korea\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. 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The 21st century hazards of smoking and benefits of stopping: a prospective study of one million women in the UK. Lancet (London England). 2013;381(9861):133\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha P, Ramasundarahettige C, Landsman V, Rostron B, Thun M, Anderson RN, et al. 21st-century hazards of smoking and benefits of cessation in the United States. N Engl J Med. 2013;368(4):341\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026ouml;ckel KH, Lehmann N, Jaeger BR, Moebus S, M\u0026ouml;hlenkamp S, Schmermund A, et al. Smoking cessation and subclinical atherosclerosis\u0026ndash;results from the Heinz Nixdorf Recall Study. Atherosclerosis. 2009;203(1):221\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith CJ, Fischer TH. Particulate and vapor phase constituents of cigarette mainstream smoke and risk of myocardial infarction. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/S0735-1097(02)02982-0\u003c/span\u003e\u003cspan address=\"10.1016/S0735-1097(02)02982-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Smoking, Chronic Kidney disease, cardio-cerebrovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-5175217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5175217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eSmoking is a leading preventable cause of disease and death worldwide, with severe implications for individuals with chronic kidney disease (CKD). Although smoking at a younger age is linked to higher mortality risk, the specific effects of early smoking on all-cause and cardio-cerebrovascular diseases (CCVDs)-specific mortality in CKD patients are not well established. This study aims to examine the association between early smoking, smoking intensity, and mortality in patients with CKD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis nationwide, population-based cohort study utilized data from the National Health Insurance Database (NHID) of South Korea, provided by the National Health Insurance Service (NHIS). The study included 549,739 adults with CKD who underwent national health examinations in 2009. The primary exposures were the age at smoking initiation and smoking intensity, measured in pack-years. Cox proportional hazards models were used to analyze the association between these exposures and mortality outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEarlier smoking initiation and higher smoking intensity were significantly associated with increased risks of all-cause and CCVDs-specific mortality among patients with CKD. Specifically, individuals who began smoking at a younger age and those with higher pack-years had a notably higher risk of mortality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings highlight the significant health risks associated with early smoking and smoking intensity in CKD patients. Preventive measures targeting early smoking initiation may help improve the long-term outcomes in high-risk population.\u003c/p\u003e","manuscriptTitle":"Early Smoking and its impact on Cardio-cerebrovascular Diseases in patients with Chronic Kidney Disease: A Nationwide Population-Based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 01:18:33","doi":"10.21203/rs.3.rs-5175217/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T18:02:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T14:24:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-15T05:56:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7462775382613183613646080165683006879","date":"2025-04-15T04:58:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79061292782208048659463539519178971517","date":"2025-04-09T06:23:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-09T06:19:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T08:23:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-03-30T03:09:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b25fb04-3cf7-42b0-8bcb-f9e2f06f96c3","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:05:14+00:00","versionOfRecord":{"articleIdentity":"rs-5175217","link":"https://doi.org/10.1186/s12889-025-23276-0","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-06-03 15:57:08","publishedOnDateReadable":"June 3rd, 2025"},"versionCreatedAt":"2025-04-15 01:18:33","video":"","vorDoi":"10.1186/s12889-025-23276-0","vorDoiUrl":"https://doi.org/10.1186/s12889-025-23276-0","workflowStages":[]},"version":"v1","identity":"rs-5175217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5175217","identity":"rs-5175217","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}