Rheumatoid Arthritis and Risk of Nontuberculous Mycobacterial Pulmonary Disease: A Nationwide Longitudinal Cohort Study

Rheumatoid Arthritis and Risk of Nontuberculous Mycobacterial Pulmonary Disease: A Nationwide Longitudinal Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rheumatoid Arthritis and Risk of Nontuberculous Mycobacterial Pulmonary Disease: A Nationwide Longitudinal Cohort Study Bumhee Yang, Kyungdo Han, Jin-Hyung Jung, Wonyoung Jung, Bo-Guen Kim, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4689847/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In this study, we aimed to evaluate the risk of incident nontuberculous mycobacteria pulmonary disease (NTM-PD) in subjects with rheumatoid arthritis (RA) versus age- and sex-matched controls, while focusing on the impact of RA serologic status on this association. Methods From the Korean National Health Insurance Service data from 2010 to 2017, we identified 60,315 participants aged ≥ 20 years with RA and 301,575 without RA who were age- and sex-matched 1:5. The participants were followed up from 1 year after RA diagnosis (or the corresponding index date for matched controls) to the date of NTM-PD diagnosis, censored date, or December 31, 2019, whichever occurred first. Results During a median 4.5 (interquartile range, 2.6–6.4)-year follow-up, NTM-PD occurred in 0.23% and 0.06% of the RA and matched cohort (incidence: 0.54 and 0.14 per 1,000 person-years), respectively. Compared to controls, participants with RA had a 3.11-fold (95% confidence interval [CI]: 2.50–3.88) higher risk of NTM-PD. In the subgroup analysis stratified by seropositivity, seropositive patients with RA had a 3.77-fold (95% CI: 3.00–4.73) higher risk of NTM-PD than controls whereas participants with seronegative RA did not have a significantly higher risk (adjusted hazard ratio: 1.18, 95% CI: 0.68–2.04). Stratified analyses showed a more prominent association of RA with NTM-PD in males, alcohol drinkers, and obese individuals ( p < 0.05). Conclusion The risk of incident NTM-PD was approximately 3-fold higher in participants with RA than in matched controls, although the association was significant only for patients with seropositive RA. Rheumatoid arthritis nontuberculous mycobacteria pulmonary disease epidemiology risk Figures Figure 1 Figure 2 Introduction Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease with significant morbidity and mortality that exerts an increasing burden on health resources worldwide [ 1 – 3 ]. In the past 20 years, advances in RA treatment, especially the use of disease-modifying antirheumatic drugs (DMARD) such as methotrexate (MTX) and several biologic DMARDs (bDMARDs) [ 4 ], have substantially improved articular outcomes in patients with RA. However, the immunosuppressive treatment effects increase the risk of various pulmonary infections, such as pneumonia and mycobacterial infection [ 5 – 7 ]. Nontuberculous mycobacteria (NTM) are a bacterial species other than Mycobacterium tuberculosis complex and Mycobacterium leprae . NTM are ubiquitous in soil, naturally occur in municipal water systems, and can cause opportunistic infections, especially in the lungs [ 8 , 9 ]. The prevalence and disease burden of NTM-induced pulmonary disease (NTM-PD) are increasing globally, and NTM-PD is recognized as an important comorbidity that occurs in many chronic diseases, including RA [ 10 ]. Several studies have demonstrated a positive relationship between RA and NTM-PD [ 3 , 11 – 18 ]. However, many of these studies were limited by a relatively small sample [ 14 – 16 ], cross-sectional design [ 11 , 12 , 15 , 16 ], a lack of control groups (non-RA population) [ 3 , 11 , 14 , 17 ], or a lack of consideration of some demographics (e.g., body mass index [BMI])[ 13 , 19 ], personal behaviors (e.g., smoking, alcohol consumption, or physical activity)[ 13 , 16 , 19 ], or immunosuppressive drugs [ 11 , 18 ] that might increase the NTM-PD risk. In addition, only a few studies have considered the serological status of RA [ 15 ]. Thus, large-scale longitudinal studies with comprehensive data on the abovementioned factors are needed. This study was conducted with an aim to compare the incidence and risk of NTM-PD between a cohort with RA and age- and sex-matched non-RA controls drawn from a large, nationally representative longitudinal database in South Korea. Additionally, we evaluated the impact of personal behaviors and the RA serologic status on the association of RA with the risk of NTM-PD. Methods Data Source and Setting In this cohort study, we used data from the National Health Insurance Service (NHIS), which is a universal social insurance program that covers 97% of the Korean population (approximately 50 million people). The NHIS dataset includes information on demographic variables (age, sex, etc.), socioeconomic status (income level, residential area, etc.), healthcare utilization (outpatient department, emergency room visit, hospitalization, etc.), health screening examination findings, disease diagnosis based on the International Classification of Disease (ICD-10) codes (10th revision), medical treatment, procedures, and surgery [ 20 ]. The NHIS database includes various medical and health information and has been widely used in epidemiological studies to identify risk factors for certain diseases [ 21 – 23 ]. In South Korea, annual or biennial free health screening examination programs are offered to all Korean citizens by the Ministry of Health and Welfare. In 2009, the health screening examination included anthropometric measurements, such as BMI; questionnaires pertaining to smoking, alcohol consumption, and physical activity; blood tests including lipid levels; and chest radiography. The current participation rate in health screening examinations ranges from 70–80%. After anonymization, the Korean government provides representative data from health screening examinations for enabling research [ 24 ]. The study protocol was approved by the Institutional Review Board of the Samsung Medical Center (IRB No. SMC 2022-06-141). The requirement for informed consent was waived because the NHIS database uses a deidentified patient identification system. Study Participants Among patients diagnosed with RA between 2010 and 2017 who were eligible for the study, we identified 119,788 with RA (83,064 with seropositive RA [SPRA] and 36,724 seronegative RA [SNRA]) using the following criteria: (1) individuals who had a registered diagnostic code for RA (ICD-10 M05 for SPRA and M06, except M06.1 and M06.4, for SNRA) and (2) those who had been prescribed any DMARD, including conventional synthetic DMARDs, bDMARDs, and target-specific DMARDs (tsDMARDs). We initially included 64,457 participants (45,045 with SPRA and 19,412 with SNRA) who were diagnosed with RA and whose health screening examination data within 2 years preceding the RA diagnosis (between 2010 and 2017) were available. After excluding individuals with other connective tissue diseases (CTD; n = 213), those with missing data of health screening examination (n = 2,321), those who were younger than 20 years (n = 6), those who were previously diagnosed with NTM (n = 136) or diagnosed with NTM within 1 year after RA diagnosis (n = 569), to minimize the risk of reverse causality, a total of 61,212 potential participants were identified for the RA cohort. Of these, 60,315 participants (42,062 with SPRA and 18,253 with SNRA) were eligible for 1:5 age and sex matching. To establish age- and sex-matched controls, from among 1,207,831 subjects who were approximately 1:10 age- and sex-matched to the 119,788 patients with RA, we included 677,322 participants who underwent health screening examinations in the same year as the matched participants with RA. After excluding participants with other rheumatic diseases (n = 20), those with missing data on health screening examinations (n = 30,705), those younger than 20 years (n = 706), those diagnosed with previous NTM-PD before matching (n = 379), and those diagnosed with NTM-PD within 1 year after matching, there were 643,122 participants in the matched controls. Of these, 301,575 participants were eligible for 1:5 age and sex matching with the RA cohort (Fig. 1 ). Exposure The exposure in this study was RA, which included SPRA and SNRA. Separate operational definitions were applied to each group to identify patients with SPRA and SNRA in each group [ 25 ]. The NHIS operates the Rare and Intractable Disease (RID) program for patients with certain diseases and provides cost-reductive actions for relevant medical expenses related to these diseases. For participants with RA, SPRA is only eligible for registration in the RID program when the following criteria are satisfied: a positive result for rheumatoid factor or anti-cyclic citrullinated peptide antibody and an official physician’s certificate that the patient meets the RA classification criteria. Participants with SPRA were defined based on whether their claim record included the ICD-10 diagnostic code M05, the RID registration code V223, and a record of prescriptions for any DMARDs, including conventional synthetic DMARDs (methotrexate, hydroxychloroquine, leflunomide, sulfasalazine, tacrolimus, cyclosporine, D-penicillamine, bucillamine, azathioprine, minocycline, or mizoribine), bDMARDs (adalimumab, etanercept, infliximab, golimumab, rituximab, abatacept, tocilizumab), or tsDMARDs (tofacitinib) for at least 180 days. For SNRA, participants who visited hospitals with diagnostic codes of ICD-10 M06 (except for M06.1 and M06.4) and had a prescription record of DMARDs for ≥ 180 days were defined as participants with SNRA [ 25 ]. The index date was defined as the date on which the RA-related diagnostic code was first registered. Outcomes The outcome of this study was the incidence of NTM-PD, which was defined by the following criteria: (1) newly claimed ICD-10 diagnosis code A31.0; and (2) at least 2 ambulatory visits or hospitalizations with an A31.0, diagnosis code within 1 year after the initial claim [ 26 ]. The participants were followed up from 1 year after the RA diagnosis (or the corresponding index date for matched controls) to the date of NTM-PD diagnosis, censored date, or December 31, 2019, whichever occurred first. Covariates Household income was categorized into quartiles based on insurance premium levels, which were determined by income level, and participants covered by Medical Aid (poorest 3%) were merged into the lowest income quartile [ 27 – 29 ] and designated “low income.” Personal behaviors, including smoking status, alcohol consumption, and physical activity, were assessed using a self-reported questionnaire. Smoking status was divided into never, ex-, and current smokers. Ex-smokers and current smokers were assigned to subgroups based on 20 pack-years (PY). Alcohol consumption was classified as none, 1–2 times a week, 3–4 times a week, or almost every day. “Regular exercise” was defined as moderate-intensity exercise for > 5 days per week or vigorous exercise for > 3 days per week [ 30 ]. BMI was calculated as body weight divided by the square of height (kg/m 2 ) and classified into one of the following four groups: underweight (< 18.5 kg/m 2 ), normal (18.5–22.9 kg/m 2 ), overweight (23.0–24.9 kg/m 2 ), and obese (≥ 25 kg/m 2 ) according to the classification for Asians [ 31 ]. The definitions of comorbidities (diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, ischemic heart disease, and airway diseases [asthma, chronic obstructive pulmonary disease, or bronchiectasis]) were based on ICD-10 codes, as previously described [ 28 , 29 , 32 , 33 ]. Additionally, tuberculosis was defined using the ICD-10 codes and registered with the national RID support program [ 28 , 29 ]. Statistical analysis Descriptive statistics are presented as the frequency (proportion) for categorical variables and mean ± standard deviation (SD) for continuous variables. We compared the two groups using the chi-square test for categorical variables, and the t -test for continuous variables. The incidence rates of NTM-PD were calculated by dividing the number of incident events by the total follow-up period (1,000 person-years). A cumulative incidence plot was used to estimate the incidence of NTM-PD between the RA and matched cohorts, and the log-rank test was used to evaluate significant differences between groups. The risk of incident NTM-PD in the RA cohort compared to that in the matched cohort was estimated using univariate and multivariate Cox proportional hazards regression analyses. Model 1 was adjusted for age, sex, income, smoking, alcohol consumption, physical activity, and BMI. Model 2 was further adjusted for diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, airway diseases, and tuberculosis. Stratified analyses were performed according to sex, age, income, smoking, alcohol consumption, regular exercise, BMI, and comorbidities, including airway diseases and tuberculosis. Additionally, all analyses were performed in equally divided groups according to RA serologic status. Statistical significance was defined as a two-sided P -value of < 0.05. All the statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Results Baseline Characteristics In the study population, the mean age was 56.5 (SD, 12.1) years and 25.7% of the cohort were men. The proportions of pulmonary and extra-pulmonary comorbidities were higher in the RA cohort than in the matched cohort ( p < 0.001 for all), except for diabetes mellitus ( p = 0.418). Additionally, compared to the matched cohort, there were fewer never-smokers, alcohol drinkers, and regular exercisers in the RA cohort ( p < 0.001) than in the matched cohort. The BMI was lower in the RA cohort than in the matched cohort ( p < 0.001). The SNRA group had a higher proportion of males, younger participants, and had fewer never-smokers than the SPRA group ( p < 0.001 for all). Additionally, participants with SNRA consumed more alcohol, engaged in regular exercise more frequently, and had a higher BMI than those with SPRA ( p < 0.001 for all). All comorbidities, except diabetes mellitus ( p = 0.505) and tuberculosis ( p = 0.119), were observed more frequently in participants with SNRA than in those with SPRA. However, airway diseases were more common in participants with SPRA than in those with SNRA ( p < 0.001; Table 1 ). Table 1 Baseline characteristics of the participants Variables Total (n = 361,890) RA status Serologic RA status No (n = 301,575) Yes (n = 60,315) P -value SPRA (n = 42,062) SNRA (n = 18,253) P -value Age, years 56.5 ± 12.1 56.5 ± 12.1 56.5 ± 12.1 > 0.999 57.4 ± 11.6 54.4 ± 12.7 0.999 2,379 (5.7) 2,303 (12.6) < 0.001 40–64 237,366 (65.6) 197,805 (65.6) 39,561 (65.6) 27,683 (65.8) 11,878 (65.1) ≥ 65 83,011 (22.9) 69,250 (22.6) 13,761 (22.8) 12,000 (28.5) 4,072 (22.3) Male sex 93,066 (25.7) 77,555 (25.7) 15,511 (25.7) > 0.999 10,171 (24.2) 5,340 (29.3) < 0.001 BMI, kg/m 2 < 0.001 < 0.001 Underweight (< 18.5) 12,551 (3.4) 9,874 (3.2) 2,677 (4.4) 1,897 (4.5) 780 (4.2) Normal (18.5–23.0) 143,718 (39.7) 118,207 (39.2) 25,511 (42.3) 18,119 (43.0) 7,392 (40.5) Overweight (23.0–25.0) 88,754 (24.5) 74,453 (24.6) 14,301 (23.7) 9,982 (23.7) 4,319 (23.6) Obese (≥ 25.0) 116,867 (32.2) 99,041 (32.8) 17,826 (29.5) 12,064 (28.6) 5,762 (31.5) Smoking < 0.001 < 0.001 Never smoker 285,419 (78.8) 238,555 (79.1) 46,864 (77.7) 33,003 (78.4) 13,861 (75.9) Ex-smoker (< 20 PY) 21,677 (5.9) 17,931 (5.95) 3,746 (6.2) 2,274 (5.4) 1,472 (8.0) Ex-smoker (≥ 20 PY) 13,673 (3.7) 10,953 (3.63) 2,720 (4.5) 1,884 (4.4) 836 (4.5) Current smoker (< 20 PY) 22,981 (6.3) 19,355 (6.42) 3,626 (6.0) 2,315 (5.5) 1,311 (7.1) Current smoker (≥ 20 PY) 18,140 (5.0) 14,781 (4.9) 3,359 (5.5) 2,586 (6.1) 773 (4.2) Alcohol drinking 109,691 (30.3) 94,262 (31.2) 15,429 (25.5) < 0.001 10,211 (24.2) 5,218 (28.5) < 0.001 Regular exercise 71,269 (19.6) 60,529 (20.0) 10,740 (17.8) < 0.001 7,296 (17.3) 3,444 (18.8) < 0.001 Low income 73,652 (23.5) 61,362 (23.5) 12,290(23.5) 0.431 9,864 (23.5) 3,897 (21.3) < 0.001 Comorbidities Diabetes mellitus 45,222 (12.5) 37,625 (12.4) 7,597 (12.6) 0.418 5,273 (12.5) 2,324 (12.7) 0.505 Hypertension 131,900 (36.4) 108,131 (35.8) 23,769 (39.4) < 0.001 16,387 (38.9) 7,382 (40.4) < 0.001 Dyslipidemia 112,071 (30.9) 92,802 (30.7) 19,269 (31.9) < 0.001 13,135 (31.2) 6,134 (33.6) < 0.001 CKD 24,266 (6.7) 19,342 (6.4) 4,924 (8.1) < 0.001 3,327 (7.91 1,597 (8.7) < 0.001 Airway diseases 61,457 (16.9) 46,206 (15.3) 15,251 (25.2) < 0.001 10,818 (25.7) 4,433 (24.2) < 0.001 Tuberculosis 759 (0.2) 411 (0.1) 348 (0.5) < 0.001 256 (0.6) 92 (0.5) 0.119 Data are presented as the mean ± SD or frequency (proportion). Abbreviations : RA, rheumatoid arthritis; SNRA, seronegative rheumatoid arthritis; SPRA, seropositive rheumatoid arthritis; PY, pack-years; BMI, body mass index; CKD, chronic kidney disease. Incidence and risk of NTM-PD During a median follow-up period of 4.5 (interquartile range, 2.6–6.4) years, 0.23% (137/60,375) of the RA cohort and 0.06% (189/301,575) of the matched cohort developed NTM-PD, with incidence rates of 0.54 and 0.14 per 1,000 person-years, respectively ( p < 0.001). Even after adjusting for potential confounders, the risk of incident NTM-PD was significantly higher in the RA cohort than in the matched cohort (unadjusted hazard ratio (HR) = 3.95, 95% confidence interval [CI] = 3.18–4.90; adjusted HR in Model 1 = 3.67, 95% CI = 2.96–4.56; adjusted HR in Model 2 = 3.11, 95% CI = 2.50–3.88; Table 2 ). Similarly, the cumulative incidence plot showed a significantly higher incidence of NTM-PD in the RA cohort than in the matched cohort (log-rank p < 0.001; Fig. 2 A). Table 2 Risk of NTM-PD according to RA status and serologic RA status By RA status Grouping Number at risk (N) NTM-PD (n) Duration (PY) Incident rate (/1,000 PY) Unadjusted model HR (95% CI) Model 1 aHR (95% CI) Model 2 aHR (95% CI) Control 301,575 189 1,371,419.9 0.14 Reference Reference Reference RA 60,315 147 270,318.5 0.54 3.95 (3.18–4.90) 3.67 (2.96–4.56) 3.11 (2.50–3.88) By RA status and seropositivity Control 301,575 189 1,371,419.9 0.14 Reference Reference Reference SNRA 18,253 14 80,343.7 0.17 1.28 (0.74–2.19) 1.34 (0.78–2.3) 1.18 (0.68–2.04) SPRA 42,062 133 189,974.9 0.70 5.07 (4.07–6.33) 4.51 (3.61–5.63) 3.77 (3.00–4.73) By seropositivity SNRA 18,253 14 80,343.7 0.17 Reference Reference Reference SPRA 42,062 133 189,974.9 0.70 3.97 (2.3–6.89) 3.45 (1.98–5.98) 3.25 (1.87–5.66) Model 1 was an unadjusted model. Model 2 was adjusted for age, sex, body mass index, smoking, alcohol consumption, physical activity, and low-income status. Model 3 was further adjusted for diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, airway disease, and tuberculosis, in addition to the variables in Model 2. Abbreviations : NTM-PD, nontuberculous mycobacterial pulmonary lung disease; RA, rheumatoid arthritis; PY, person-years; HR, hazard ratio; CI, confidence interval; aHR, adjusted HR; SNRA, seronegative rheumatoid arthritis; SPRA, seropositive rheumatoid arthritis. RA serologic status and incidence and risk of NTM-PD When RA was classified according to serological status, NTM-PD occurred in 0.08% (n = 14/18,253) and 0.32% (n = 133/42,062) of the SNRA and SPRA cohorts (incidence rates of 0.70 and 0.17 per 1,000 person-years; p < 0.001), respectively (Table 2 ). Compared to matched controls, the risk of incident NTM-PD was 3.77-fold (95% CI = 3.00–4.73) higher in the SPRA cohort whereas the risk did not increase significantly in the SNRA cohort (adjusted HR in Model 2 = 1.18, 95% CI = 0.68–2.04). The risk of NTM-PD was 3.25-fold (95% CI, 1.87–5.66) higher in the SPRA than in the SNRA cohort ( Model 2 ). Similarly, the cumulative incidence plot showed a significantly higher incidence of NTM-PD in the SPRA cohort than in the SNRA cohort (log-rank p < 0.001; Fig. 2 B). Stratified Analysis Socioeconomic characteristics, smoking status, health behavior, and comorbid conditions did not show a significant interaction between RA and the risk of NTM-PD (p for interaction > 0.05 for all; Table 3 ). In contrast, sex, alcohol consumption, and BMI had significant interactions in the association of RA with NTM-PD ( p for interaction < 0.05); the associations between RA and NTM-PD were more prominent in males, alcohol drinkers, and obese participants than in their counterparts ( p < 0.05). Table 3 Stratified analysis of NTM-PD risk according to RA status Subgroups RA status Number at risk (n) NTM (n) Duration (PY) IR per 1,000 PY Adjusted HR * Sex Male No 77,555 44 337,687.7 0.13 Reference Yes 15,511 55 65,647.8 0.84 4.93 (3.30–7.36) Female No 224,020 145 1,033,732.2 0.14 Reference Yes 44,804 92 204,670.8 0.45 2.57 (1.97–3.34) p for interaction 0.007 Age, years 20–39 No 23,410 4 110,359.4 0.04 Reference) Yes 4,682 1 22,043.7 0.05 1.03 (0.12–9.24) 40–64 No 197,805 98 917,372.2 0.11 Reference Yes 39,561 82 182,496.7 0.45 3.36 (2.5–4.512) ≥ 65 No 80,360 87 343,688.4 0.25 Reference Yes 16,072 64 65,778.2 0.97 2.91 (2.1–4.03) p for interaction 0.496 BMI, kg/m 2 Underweight (< 18.5) No 9,874 30 43,736.3 0.69 Reference Yes 2,677 15 11,689.4 1.28 1.61 (0.86–2.30) Normal (18.5–23.0) No 118,207 111 539,900.3 0.21 Reference Yes 25,511 87 115,658.1 0.75 3.07 (2.31–4.08) Overweight (23.0–25.0) No 74,453 30 342,481.3 0.09 Reference Yes 14,301 24 64,484.6 0.37 3.60 (2.1–6.16) Obese (≥ 25.0) No 99,041 18 445,302.1 0.04 Reference Yes 17,826 21 78,486.5 0.27 5.67 (3.02–10.66) p for interaction 0.044 Smoking Never smoker No 238,555 160 1,096,466.2 0.15 Reference Yes 46,864 107 213,189.2 0.50 2.77 (2.16–3.55) Ex-smoker (< 20 PY) No 17,931 9 76,407.1 0.12 Reference Yes 3,746 7 15,883.1 0.44 3.01 (1.12–8.1) Ex-smoker (≥ 20 PY) No 10,953 6 46,165.7 0.13 Reference Yes 2,720 11 11,042.5 1.00 5.59 (2.06–15.15) Current smoker (< 20 PY) No 19,355 6 85,750.2 0.07 Reference Yes 3,626 7 15,702.3 0.45 4.85 (1.63–4.45) Current smoker (≥ 20 PY) No 14,781 8 66,630.8 0.12 Reference Yes 3,359 15 14,501.5 1.03 6.57 (2.78–15.52) p for interaction 0.220 Alcohol intake No No 207,313 155 951,630.4 0.16 Reference Yes 44,886 110 202,528.8 0.54 2.66 (2.08–3.41) Yes No 82,974 26 370,174.9 0.07 Reference Yes 13,720 29 60,492.5 0.48 5.57 (3.49–8.89) p for interaction 0.006 Regular exercise No No 241,046 152 1,100,317.5 0.14 Reference Yes 49,575 125 222,918.3 0.56 3.18 (2.50–4.05) Yes No 60,529 37 271,102.4 0.14 Reference Yes 10,740 22 47,400.2 0.46 2.78 (1.64–4.73) p for interaction 0.649 Low income Other No 232,325 154 1,058,286.4 0.15 Reference Yes 46,554 119 210,664.9 0.56 3.02 (2.37–3.86) Q1, Low No 69,250 35 313,133.5 0.11 Reference Yes 13,761 28 59,653.7 0.47 3.52 (2.14–5.8) p for interaction 0.589 Diabetes mellitus No No 263,950 172 1,209,228.9 0.14 Reference Yes 52,718 133 239,102.5 0.56 3.11 (2.47–3.92) Yes No 37,625 17 162,191.0 0.10 Reference Yes 7,597 14 31,216.1 0.45 3.14 (1.54–6.38) p for interaction No 263,950 172 1,209,228.9 0.14 0.981 Hypertension No No 193,444 137 886,160.8 0.15 Reference Yes 36,546 90 166,295 0.54 2.75 (2.1–3.6) Yes No 108,131 52 485,259.1 0.11 Reference Yes 23,769 57 104,023.7 0.55 4.00 (2.74–5.85) p for interaction 0.109 Dyslipidemia No 208,773 134 971,924.1 0.14 No Yes 41,046 116 189,226.1 0.61 Reference No 92,802 55 399,495.8 0.14 3.47 (2.70–4.47) Yes Yes 19,269 31 81,092.4 0.38 Reference No 208,773 134 971,924.1 0.14 2.23 (1.44–3.49) p for interaction 0.092 CKD No No 282,233 176 1,281,501.7 0.14 Reference Yes 55,391 140 248,619.2 0.56 3.22 (2.57–4.04) Yes No 19,342 13 89,918.2 0.14 Reference Yes 4,924 7 21,699.3 0.32 1.80 (0.72–4.52) p for interaction 0.229 Tuberculosis No No 301,164 186 1,369,706.8 0.14 Reference Yes 59,967 139 268,778.2 0.52 3.12 (2.49–3.90) Yes No 411 3 1,713.1 1.75 Reference Yes 348 8 1,540.4 5.19 2.90 (0.77–10.95) p for interaction 0.918 Airway disease No No 255,369 111 1166162.9 0.10 Reference Yes 45,064 64 205056.7 0.31 3.12 (2.29–4.25) Yes No 46,206 78 205257.1 0.38 Reference Yes 15,251 83 65261.9 1.27 3.10 (2.27–4.24) p for interaction 0.976 * Adjusted for age, sex, BMI, smoking, alcohol consumption, physical activity, low income, diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, airway diseases, and tuberculosis. Abbreviations : NTM-PD, nontuberculous mycobacterial pulmonary disease; RA, rheumatoid arthritis; PY, pack-years; BMI, body mass index. Discussion This the largest comprehensive study to evaluate the incidence of NTM-PD in participants with RA, as compared to the non-RA population, by using data from a nationwide cohort. Participants with RA had an NTM-PD incidence rate of 0.54 per 1,000 person-years, which was approximately 3-fold higher than that in controls. Furthermore, additional subgroup analysis of the SPRA and SNRA groups based on serologic status showed that the risk of NTM-PD was higher only in participants with SPRA. Three longitudinal studies have compared the risk of NTM-PD in participants with RA and controls, and these studies consistently showed a higher incidence of NTM-PD in patients with RA compared to controls [ 13 , 18 , 19 ]; A population-based study in Canada demonstrated a 2.1-fold increased risk for NTM disease among patients with RA compared to controls [ 13 ]. Two Taiwanese population-based studies reported that NTM-PD was 4.2- and 6.2-fold higher among patients with RA than among controls [ 18 , 19 ]. Compared to Canadian and our study results, the risk of NTM-PD in patients with RA was especially higher in Taiwanese studies. Although the reasons are not clear, it might be related to unadjusted confounders, such as BMI and personal behaviors (e.g., alcohol consumption), that might be related to NTM-PD risk. In contrast, the risk of NTM-PD in patients with RA was lower in the Canadian study compared to Taiwanese and our study. Although there might be several reasons for this phenomenon, ethnicity could be a reason; a recent study showed that the risk of NTM-PD was higher in Asians than other ethnicity even considering BMI[ 34 ] ( Supplemental Table 1 ). To the best of our knowledge, by classifying the serological status of RA, this is the first study to show that the risk of NTM-PD is significantly increased in SPRA. Supporting our findings, in an age- and sex-matched case–control study of RA patients with or without NTM-PD at a ratio of 1:5, the RF and anti-CCP positivity rates were higher in RA patients with NTM-PD than in those without NTM-PD [ 15 ]. Although the reasons for this are not yet well understood, the association may be explained by RA disease activity. It is well known that anti-CCP positivity is associated with RA development and deterioration [ 35 ]. In addition, increased disease activity may have led to increased immunosuppressive drug use, which could increase the risk of NTM-PD. Overall, the increased risk of NTM-PD in SPRA may be the combined result of increased systemic inflammation and accompanying intense immunosuppressive drugs. Interestingly, our stratified analysis showed that association between RA and NTM-PD was especially prominent among obese participants and males as compared to their counterparts. Although the reason for this phenomenon cannot be fully explained, given the observational nature of our study and considering that the variables of underweight and female themselves are risk factors for NTM-PD, the extent to which RA contributes to the occurrence of NTM-PD in these patients can be interpreted as low. In contrast, the contribution of RA to the occurrence of NTM-PD in males or obese individuals is relatively high. In addition, it may be associated with increased disease activity in the obese population [ 36 ] however, owing to the absence of laboratory and radiological results, we could not consider this factor in our study. The higher association of NTM-PD in males than females may also be related to the higher prevalence of RA-related structural lung disease in males than in females [ 37 ]. Although we adjusted for airway diseases and a history of TB, there might have been an underestimation in the evaluation of RA-related structural lung diseases because focal or non-severe diseases can only be detected on computed tomography, which is not routinely performed. Our study has several limitations. First, our study may have a selection bias because we used health screening data, in which more healthy participants were likely to be included. Second, because our database did not contain laboratory and radiological test results, we could not incorporate these factors into our analyses. Future studies should include this data. Finally, this study was conducted on a Korean population. Thus, studies in other countries and ethnicities are required to obtain generalizable findings. Conclusion In summary, the incidence of NTM-PD in patients with RA was higher than that in patients without RA, which was significant only for SPRA. Abbreviations NTM-PD: nontuberculous mycobacterial pulmonary lung disease RA: rheumatoid arthritis PY: person-years HR: hazard ratio CI: confidence interval aHR: adjusted HR SNRA: seronegative rheumatoid arthritis SPRA: seropositive rheumatoid arthritis. Declarations Acknowledgements None Authors’ contributions: H.L., and D.W.S. are the guarantors of the manuscript and takes responsibility for the content of the manuscript, including the data and analysis. B.Y., H.L., K.H., and D.W.S. contributed to the conception and design of the study. B.Y., H.K., W.J., Y.E., B-G.K., K.H., J.-H.J., H.K., H.L., and D.W.S. were involved in the collection and interpretation of the data. K.H. and J.-H.J. were involved in the statistical analyses. B.Y. and H.L. were a major contributor in writing the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the National Research Foundation of Korea grant funded by the Korea government (No.2022R1F1A1074749 to B.Y.) Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the Samsung Medical Center (IRB No. SMC 2022-06-141). The requirement for informed consent was waived because the NHIS database uses a deidentified patient identification system. Consent for publication Not applicable Conflict of interests The authors declare that they have no competing interests. Consent for publication Not applicable. References Mutru O, Laakso M, Isomäki H, Koota K: Ten year mortality and causes of death in patients with rheumatoid arthritis. 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Koike T, Harigai M, Inokuma S, Ishiguro N, Ryu J, Takeuchi T, Takei S, Tanaka Y, Sano Y, Yaguramaki H, Yamanaka H: Effectiveness and safety of tocilizumab: postmarketing surveillance of 7901 patients with rheumatoid arthritis in Japan. J Rheumatol 2014, 41: 15-23. Harigai M, Ishiguro N, Inokuma S, Mimori T, Ryu J, Takei S, Takeuchi T, Tanaka Y, Takasaki Y, Yamanaka H, et al: Postmarketing surveillance of the safety and effectiveness of abatacept in Japanese patients with rheumatoid arthritis. Mod Rheumatol 2016, 26: 491-498. Cowman S, van Ingen J, Griffith DE, Loebinger MR: Non-tuberculous mycobacterial pulmonary disease. Eur Respir J 2019, 54 . Griffith DE, Aksamit T, Brown-Elliott BA, Catanzaro A, Daley C, Gordin F, Holland SM, Horsburgh R, Huitt G, Iademarco MF, et al: An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases. Am J Respir Crit Care Med 2007, 175: 367-416. 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Lim DH, Kim YG, Shim TS, Jo KW, Ghang B, Ahn SM, Hong S, Lee CK, Yoo B: Nontuberculous mycobacterial infection in rheumatoid arthritis patients: a single-center experience in South Korea. Korean J Intern Med 2017, 32: 1090-1097. Nakayama Y, Tanaka E, Ueyama M, Terada S, Inao T, Kaji Y, Yasuda T, Hashimoto S, Hajiro T, Hatta K, et al: Clinical characteristics of rheumatoid arthritis patients complicated with pulmonary nontuberculous mycobacterial disease: A cross-sectional case series study. Mod Rheumatol 2022. Liao TL, Lin CF, Chen YM, Liu HJ, Chen DY: Risk Factors and Outcomes of Nontuberculous Mycobacterial Disease among Rheumatoid Arthritis Patients: A Case-Control study in a TB Endemic Area. Sci Rep 2016, 6: 29443. Park DW, Kim YJ, Sung YK, Chung SJ, Yeo Y, Park TS, Lee H, Moon JY, Kim SH, Kim TH, et al: TNF inhibitors increase the risk of nontuberculous mycobacteria in patients with seropositive rheumatoid arthritis in a mycobacterium tuberculosis endemic area. Sci Rep 2022, 12: 4003. Liao TL, Lin CH, Shen GH, Chang CL, Lin CF, Chen DY: Risk for Mycobacterial Disease among Patients with Rheumatoid Arthritis, Taiwan, 2001-2011. Emerg Infect Dis 2015, 21: 1387-1395. Yeh JJ, Wang YC, Sung FC, Kao CH: Rheumatoid arthritis increases the risk of nontuberculosis mycobacterial disease and active pulmonary tuberculosis. PLoS One 2014, 9: e110922. Shin DW, Cho J, Park JH, Cho B: National General Health Screening Program in Korea: history, current status, and future direction A scoping review. Precision and Future Medicine 2022, 6: 9-31. Choi H, Park HY, Han K, Yoo J, Shin SH, Yang B, Kim Y, Park TS, Park DW, Moon JY, et al: Non-Cystic Fibrosis Bronchiectasis Increases the Risk of Lung Cancer Independent of Smoking Status. Ann Am Thorac Soc 2022, 19: 1551-1560. Lee KA, Kim J, Choi W, Kim HS, Seo GH: Pregnancy-associated risk factors and incidence of systemic sclerosis in primiparous women: A nationwide population-based cohort study. Mod Rheumatol 2022, 32: 149-154. Kang J, Eun Y, Jang W, Cho MH, Han K, Jung J, Kim Y, Kim GT, Shin DW, Kim H: Rheumatoid Arthritis and Risk of Parkinson Disease in Korea. JAMA Neurol 2023, 80: 634-641. Shin DW, Cho J, Park JH, Cho B: National General Health Screening Program in Korea: history, current status, and future direction. Precis Future Med 2022, 6: 9-31. Cho SK, Sung YK, Choi CB, Kwon JM, Lee EK, Bae SC: Development of an algorithm for identifying rheumatoid arthritis in the Korean National Health Insurance claims database. Rheumatol Int 2013, 33: 2985-2992. Song JH, Kim BS, Kwak N, Han K, Yim JJ: Impact of body mass index on development of nontuberculous mycobacterial pulmonary disease. Eur Respir J 2021, 57 . Choi H, Han K, Yang B, Shin DW, Sohn JW, Lee H: Female Reproductive Factors and Incidence of Nontuberculous Mycobacterial Pulmonary Disease Among Postmenopausal Women in Korea. Clin Infect Dis 2022, 75: 1397-1404. Lee HR, Yoo JE, Choi H, Han K, Jung JH, Park J, Lee H, Shin DW: Tuberculosis and Risk of Ischemic Stroke: A Nationwide Cohort Study. Stroke 2022, 53: 3401-3409. Lee HR, Yoo JE, Choi H, Han K, Lim YH, Lee H, Shin DW: Tuberculosis and the Risk of Ischemic Heart Disease: A Nationwide Cohort Study. Clin Infect Dis 2022. Anton SD, Duncan GE, Limacher MC, Martin AD, Perri MG: How much walking is needed to improve cardiorespiratory fitness? An examination of the 2008 Physical Activity Guidelines for Americans. Res Q Exerc Sport 2011, 82: 365-370. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004, 363: 157-163. Lee H, Ryu J, Nam E, Chung SJ, Yeo Y, Park DW, Park TS, Moon JY, Kim TH, Sohn JW, et al: Increased mortality in patients with corticosteroid-dependent asthma: a nationwide population-based study. Eur Respir J 2019, 54 . Yang B, Ryu J, Kim T, Jo YS, Kim Y, Park HY, Kang YA, Lee SJ, Lee OJ, Moon JY, et al: Impact of Bronchiectasis on Incident Nontuberculous Mycobacterial Pulmonary Disease: A 10-Year National Cohort Study. Chest 2021, 159: 1807-1811. Blakney RA, Ricotta EE, Frankland TB, Honda S, Zelazny A, Mayer-Barber KD, Dean SG, Follmann D, Olivier KN, Daida YG, Prevots DR: Incidence of Nontuberculous Mycobacterial Pulmonary Infection, by Ethnic Group, Hawaii, USA, 2005-2019. Emerg Infect Dis 2022, 28: 1543-1550. Schellekens GA, Visser H, de Jong BA, van den Hoogen FH, Hazes JM, Breedveld FC, van Venrooij WJ: The diagnostic properties of rheumatoid arthritis antibodies recognizing a cyclic citrullinated peptide. Arthritis Rheum 2000, 43: 155-163. Poudel D, George MD, Baker JF: The Impact of Obesity on Disease Activity and Treatment Response in Rheumatoid Arthritis. Curr Rheumatol Rep 2020, 22: 56. Huang S, Doyle TJ, Hammer MM, Byrne SC, Huang W, Marshall AA, Iannaccone CK, Huang J, Feathers V, Weinblatt ME, et al: Rheumatoid arthritis-related lung disease detected on clinical chest computed tomography imaging: Prevalence, risk factors, and impact on mortality. Semin Arthritis Rheum 2020, 50: 1216-1225. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-4689847","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328429622,"identity":"d37613e0-eb8f-4035-943f-6bf040c58e34","order_by":0,"name":"Bumhee Yang","email":"","orcid":"","institution":"Chungbuk National University","correspondingAuthor":false,"prefix":"","firstName":"Bumhee","middleName":"","lastName":"Yang","suffix":""},{"id":328429623,"identity":"e64498da-0370-42e3-b598-b2275eb9d1cd","order_by":1,"name":"Kyungdo Han","email":"","orcid":"","institution":"Soongsil University","correspondingAuthor":false,"prefix":"","firstName":"Kyungdo","middleName":"","lastName":"Han","suffix":""},{"id":328429624,"identity":"089229df-980f-47c8-86f4-d8bd21a7ee67","order_by":2,"name":"Jin-Hyung Jung","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Jin-Hyung","middleName":"","lastName":"Jung","suffix":""},{"id":328429625,"identity":"7f691095-24a4-4def-b8e0-f908513e00ab","order_by":3,"name":"Wonyoung Jung","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Wonyoung","middleName":"","lastName":"Jung","suffix":""},{"id":328429626,"identity":"00283b7a-1540-4915-85f3-4cb1808f0a87","order_by":4,"name":"Bo-Guen Kim","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"prefix":"","firstName":"Bo-Guen","middleName":"","lastName":"Kim","suffix":""},{"id":328429628,"identity":"ffbf2f8b-994f-4b4e-8e03-b843014fd36c","order_by":5,"name":"Yeonghee Eun","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Yeonghee","middleName":"","lastName":"Eun","suffix":""},{"id":328429630,"identity":"0e47f9c4-df44-4509-af79-a6c678ea3b64","order_by":6,"name":"Hyungjin Kim","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Hyungjin","middleName":"","lastName":"Kim","suffix":""},{"id":328429633,"identity":"b79c1121-afbd-42a0-8e00-ccc89f2f483f","order_by":7,"name":"Dong Wook Shin","email":"","orcid":"","institution":"Sungkyunkwan University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Wook","lastName":"Shin","suffix":""},{"id":328429635,"identity":"a36f1c46-9f6d-4cbc-8f7f-2430e7b55e97","order_by":8,"name":"Hyun Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYJACZjjrA8laGGeQrIWZhxjl5uyn06QLau7IGxzvMXxsu8MusV/6AOOHjzm4tVj25G6TnnHsmeGGM2eMjXPPJCfO7Etglpy5DbcWgwNALTxshxk33Mgxk85tYzY2OMPAxsyLT8v5t0At/w7bb7j/xkzasq3e2J6glhtAW3jbDiduuMFjJs3YdljOgIeglrebrXn7DifPPJNWbNjbdlxO4gxjM36/nM/deJvn22HbvuOHNz742VbNw9/DfPDDRzxa4EDhAIcBlMnYQIR6IJBvYH9AnMpRMApGwSgYcQAAqTZSDbF2rjYAAAAASUVORK5CYII=","orcid":"","institution":"Hanyang University","correspondingAuthor":true,"prefix":"","firstName":"Hyun","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-07-05 05:42:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4689847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4689847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62150873,"identity":"4b4edbd0-62a0-46f7-8025-a9207df977ec","added_by":"auto","created_at":"2024-08-09 20:35:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133868,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart depicting the screening and enrolment of the study population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: RA, rheumatoid arthritis; SNRA, seronegative rheumatoid arthritis; SPRA, seropositive rheumatoid arthritis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4689847/v1/03052ed9a0a70cfcc83588ca.png"},{"id":62150875,"identity":"d198fb3a-281f-4666-a2f6-94a6c21d07aa","added_by":"auto","created_at":"2024-08-09 20:35:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122739,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of NTM-PD according to (A) rheumatoid arthritis status and (B) serologic status of rheumatoid arthritis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: RA, rheumatoid arthritis; SNRA, seronegative rheumatoid arthritis; SPRA, seropositive rheumatoid arthritis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4689847/v1/9b3c85d3c66bbcbd78d47efd.png"},{"id":73561880,"identity":"28f8b2d2-48f9-4649-b412-45f93d99c15c","added_by":"auto","created_at":"2025-01-11 12:46:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3477205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4689847/v1/1e3291d6-5e13-4690-b65a-b5982e0168a6.pdf"},{"id":62150874,"identity":"7345299b-d623-441f-a7f0-8743c30806ca","added_by":"auto","created_at":"2024-08-09 20:35:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21692,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4689847/v1/2e0845a41ce6da7fb94d39c7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rheumatoid Arthritis and Risk of Nontuberculous Mycobacterial Pulmonary Disease: A Nationwide Longitudinal Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease with significant morbidity and mortality that exerts an increasing burden on health resources worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the past 20 years, advances in RA treatment, especially the use of disease-modifying antirheumatic drugs (DMARD) such as methotrexate (MTX) and several biologic DMARDs (bDMARDs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], have substantially improved articular outcomes in patients with RA. However, the immunosuppressive treatment effects increase the risk of various pulmonary infections, such as pneumonia and mycobacterial infection [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNontuberculous mycobacteria (NTM) are a bacterial species other than \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex and \u003cem\u003eMycobacterium leprae\u003c/em\u003e. NTM are ubiquitous in soil, naturally occur in municipal water systems, and can cause opportunistic infections, especially in the lungs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The prevalence and disease burden of NTM-induced pulmonary disease (NTM-PD) are increasing globally, and NTM-PD is recognized as an important comorbidity that occurs in many chronic diseases, including RA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated a positive relationship between RA and NTM-PD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, many of these studies were limited by a relatively small sample [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], cross-sectional design [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], a lack of control groups (non-RA population) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], or a lack of consideration of some demographics (e.g., body mass index [BMI])[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], personal behaviors (e.g., smoking, alcohol consumption, or physical activity)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], or immunosuppressive drugs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] that might increase the NTM-PD risk. In addition, only a few studies have considered the serological status of RA [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Thus, large-scale longitudinal studies with comprehensive data on the abovementioned factors are needed.\u003c/p\u003e \u003cp\u003eThis study was conducted with an aim to compare the incidence and risk of NTM-PD between a cohort with RA and age- and sex-matched non-RA controls drawn from a large, nationally representative longitudinal database in South Korea. Additionally, we evaluated the impact of personal behaviors and the RA serologic status on the association of RA with the risk of NTM-PD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Setting\u003c/h2\u003e \u003cp\u003eIn this cohort study, we used data from the National Health Insurance Service (NHIS), which is a universal social insurance program that covers 97% of the Korean population (approximately 50\u0026nbsp;million people). The NHIS dataset includes information on demographic variables (age, sex, etc.), socioeconomic status (income level, residential area, etc.), healthcare utilization (outpatient department, emergency room visit, hospitalization, etc.), health screening examination findings, disease diagnosis based on the International Classification of Disease (ICD-10) codes (10th revision), medical treatment, procedures, and surgery [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The NHIS database includes various medical and health information and has been widely used in epidemiological studies to identify risk factors for certain diseases [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn South Korea, annual or biennial free health screening examination programs are offered to all Korean citizens by the Ministry of Health and Welfare. In 2009, the health screening examination included anthropometric measurements, such as BMI; questionnaires pertaining to smoking, alcohol consumption, and physical activity; blood tests including lipid levels; and chest radiography. The current participation rate in health screening examinations ranges from 70\u0026ndash;80%. After anonymization, the Korean government provides representative data from health screening examinations for enabling research [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study protocol was approved by the Institutional Review Board of the Samsung Medical Center (IRB No. SMC 2022-06-141). The requirement for informed consent was waived because the NHIS database uses a deidentified patient identification system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eAmong patients diagnosed with RA between 2010 and 2017 who were eligible for the study, we identified 119,788 with RA (83,064 with seropositive RA [SPRA] and 36,724 seronegative RA [SNRA]) using the following criteria: (1) individuals who had a registered diagnostic code for RA (ICD-10 M05 for SPRA and M06, except M06.1 and M06.4, for SNRA) and (2) those who had been prescribed any DMARD, including conventional synthetic DMARDs, bDMARDs, and target-specific DMARDs (tsDMARDs).\u003c/p\u003e \u003cp\u003eWe initially included 64,457 participants (45,045 with SPRA and 19,412 with SNRA) who were diagnosed with RA and whose health screening examination data within 2 years preceding the RA diagnosis (between 2010 and 2017) were available. After excluding individuals with other connective tissue diseases (CTD; n\u0026thinsp;=\u0026thinsp;213), those with missing data of health screening examination (n\u0026thinsp;=\u0026thinsp;2,321), those who were younger than 20 years (n\u0026thinsp;=\u0026thinsp;6), those who were previously diagnosed with NTM (n\u0026thinsp;=\u0026thinsp;136) or diagnosed with NTM within 1 year after RA diagnosis (n\u0026thinsp;=\u0026thinsp;569), to minimize the risk of reverse causality, a total of 61,212 potential participants were identified for the RA cohort. Of these, 60,315 participants (42,062 with SPRA and 18,253 with SNRA) were eligible for 1:5 age and sex matching.\u003c/p\u003e \u003cp\u003eTo establish age- and sex-matched controls, from among 1,207,831 subjects who were approximately 1:10 age- and sex-matched to the 119,788 patients with RA, we included 677,322 participants who underwent health screening examinations in the same year as the matched participants with RA. After excluding participants with other rheumatic diseases (n\u0026thinsp;=\u0026thinsp;20), those with missing data on health screening examinations (n\u0026thinsp;=\u0026thinsp;30,705), those younger than 20 years (n\u0026thinsp;=\u0026thinsp;706), those diagnosed with previous NTM-PD before matching (n\u0026thinsp;=\u0026thinsp;379), and those diagnosed with NTM-PD within 1 year after matching, there were 643,122 participants in the matched controls. Of these, 301,575 participants were eligible for 1:5 age and sex matching with the RA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eExposure\u003c/h2\u003e \u003cp\u003eThe exposure in this study was RA, which included SPRA and SNRA. Separate operational definitions were applied to each group to identify patients with SPRA and SNRA in each group [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The NHIS operates the Rare and Intractable Disease (RID) program for patients with certain diseases and provides cost-reductive actions for relevant medical expenses related to these diseases. For participants with RA, SPRA is only eligible for registration in the RID program when the following criteria are satisfied: a positive result for rheumatoid factor or anti-cyclic citrullinated peptide antibody and an official physician\u0026rsquo;s certificate that the patient meets the RA classification criteria. Participants with SPRA were defined based on whether their claim record included the ICD-10 diagnostic code M05, the RID registration code V223, and a record of prescriptions for any DMARDs, including conventional synthetic DMARDs (methotrexate, hydroxychloroquine, leflunomide, sulfasalazine, tacrolimus, cyclosporine, D-penicillamine, bucillamine, azathioprine, minocycline, or mizoribine), bDMARDs (adalimumab, etanercept, infliximab, golimumab, rituximab, abatacept, tocilizumab), or tsDMARDs (tofacitinib) for at least 180 days. For SNRA, participants who visited hospitals with diagnostic codes of ICD-10 M06 (except for M06.1 and M06.4) and had a prescription record of DMARDs for \u0026ge;\u0026thinsp;180 days were defined as participants with SNRA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The index date was defined as the date on which the RA-related diagnostic code was first registered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe outcome of this study was the incidence of NTM-PD, which was defined by the following criteria: (1) newly claimed ICD-10 diagnosis code A31.0; and (2) at least 2 ambulatory visits or hospitalizations with an A31.0, diagnosis code within 1 year after the initial claim [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The participants were followed up from 1 year after the RA diagnosis (or the corresponding index date for matched controls) to the date of NTM-PD diagnosis, censored date, or December 31, 2019, whichever occurred first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eHousehold income was categorized into quartiles based on insurance premium levels, which were determined by income level, and participants covered by Medical Aid (poorest 3%) were merged into the lowest income quartile [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and designated \u0026ldquo;low income.\u0026rdquo; Personal behaviors, including smoking status, alcohol consumption, and physical activity, were assessed using a self-reported questionnaire. Smoking status was divided into never, ex-, and current smokers. Ex-smokers and current smokers were assigned to subgroups based on 20 pack-years (PY). Alcohol consumption was classified as none, 1\u0026ndash;2 times a week, 3\u0026ndash;4 times a week, or almost every day. \u0026ldquo;Regular exercise\u0026rdquo; was defined as moderate-intensity exercise for \u0026gt;\u0026thinsp;5 days per week or vigorous exercise for \u0026gt;\u0026thinsp;3 days per week [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. BMI was calculated as body weight divided by the square of height (kg/m\u003csup\u003e2\u003c/sup\u003e) and classified into one of the following four groups: underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal (18.5\u0026ndash;22.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (23.0\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) according to the classification for Asians [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The definitions of comorbidities (diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, ischemic heart disease, and airway diseases [asthma, chronic obstructive pulmonary disease, or bronchiectasis]) were based on ICD-10 codes, as previously described [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, tuberculosis was defined using the ICD-10 codes and registered with the national RID support program [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics are presented as the frequency (proportion) for categorical variables and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables. We compared the two groups using the chi-square test for categorical variables, and the \u003cem\u003et\u003c/em\u003e-test for continuous variables. The incidence rates of NTM-PD were calculated by dividing the number of incident events by the total follow-up period (1,000 person-years). A cumulative incidence plot was used to estimate the incidence of NTM-PD between the RA and matched cohorts, and the log-rank test was used to evaluate significant differences between groups.\u003c/p\u003e \u003cp\u003eThe risk of incident NTM-PD in the RA cohort compared to that in the matched cohort was estimated using univariate and multivariate Cox proportional hazards regression analyses. Model 1 was adjusted for age, sex, income, smoking, alcohol consumption, physical activity, and BMI. Model 2 was further adjusted for diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, airway diseases, and tuberculosis. Stratified analyses were performed according to sex, age, income, smoking, alcohol consumption, regular exercise, BMI, and comorbidities, including airway diseases and tuberculosis. Additionally, all analyses were performed in equally divided groups according to RA serologic status. Statistical significance was defined as a two-sided \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05. All the statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n \u003cp\u003eIn the study population, the mean age was 56.5 (SD, 12.1) years and 25.7% of the cohort were men. The proportions of pulmonary and extra-pulmonary comorbidities were higher in the RA cohort than in the matched cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), except for diabetes mellitus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.418). Additionally, compared to the matched cohort, there were fewer never-smokers, alcohol drinkers, and regular exercisers in the RA cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than in the matched cohort. The BMI was lower in the RA cohort than in the matched cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eThe SNRA group had a higher proportion of males, younger participants, and had fewer never-smokers than the SPRA group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). Additionally, participants with SNRA consumed more alcohol, engaged in regular exercise more frequently, and had a higher BMI than those with SPRA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). All comorbidities, except diabetes mellitus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.505) and tuberculosis (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.119), were observed more frequently in participants with SNRA than in those with SPRA. However, airway diseases were more common in participants with SPRA than in those with SNRA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics of the participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;361,890)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRA status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSerologic RA status\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;301,575)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60,315)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSPRA\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42,062)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSNRA\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18,253)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,092 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,410 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,682 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,379 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,303 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237,366 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197,805 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39,561 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27,683 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,878 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83,011 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69,250 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,761 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,000 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,072 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93,066 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77,555 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,511 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,171 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,340 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,551 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,874 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,677 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,897 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e780 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal (18.5\u0026ndash;23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143,718 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118,207 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,511 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,119 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,392 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight (23.0\u0026ndash;25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88,754 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74,453 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,301 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,982 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,319 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese (\u0026ge;\u0026thinsp;25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116,867 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99,041 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,826 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,064 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,762 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285,419 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238,555 (79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46,864 (77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,003 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,861 (75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEx-smoker (\u0026lt;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,677 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,931 (5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,746 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,274 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,472 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEx-smoker (\u0026ge;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,673 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,953 (3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,720 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,884 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e836 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker (\u0026lt;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,981 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,355 (6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,626 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,315 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,311 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker (\u0026ge;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,140 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,781 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,359 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,586 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e773 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol drinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109,691 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94,262 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,429 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,211 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,218 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegular exercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71,269 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60,529 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,740 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,296 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,444 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73,652 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61,362 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,290(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,864 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,897 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45,222 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,625 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,597 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,273 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,324 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131,900 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108,131 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,769 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,387 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,382 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112,071 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92,802 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,269 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,135 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,134 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,266 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,342 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,924 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,327 (7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,597 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAirway diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61,457 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46,206 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,251 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,818 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,433 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e759 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e411 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eData are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or frequency (proportion).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: RA, rheumatoid arthritis; SNRA, seronegative rheumatoid arthritis; SPRA, seropositive rheumatoid arthritis; PY, pack-years; BMI, body mass index; CKD, chronic kidney disease.\u003c/p\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eIncidence and risk of NTM-PD\u003c/h2\u003e\n \u003cp\u003eDuring a median follow-up period of 4.5 (interquartile range, 2.6\u0026ndash;6.4) years, 0.23% (137/60,375) of the RA cohort and 0.06% (189/301,575) of the matched cohort developed NTM-PD, with incidence rates of 0.54 and 0.14 per 1,000 person-years, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Even after adjusting for potential confounders, the risk of incident NTM-PD was significantly higher in the RA cohort than in the matched cohort (unadjusted hazard ratio (HR)\u0026thinsp;=\u0026thinsp;3.95, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;3.18\u0026ndash;4.90; adjusted HR in Model 1\u0026thinsp;=\u0026thinsp;3.67, 95% CI\u0026thinsp;=\u0026thinsp;2.96\u0026ndash;4.56; adjusted HR in Model 2\u0026thinsp;=\u0026thinsp;3.11, 95% CI\u0026thinsp;=\u0026thinsp;2.50\u0026ndash;3.88; Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Similarly, the cumulative incidence plot showed a significantly higher incidence of NTM-PD in the RA cohort than in the matched cohort (log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eA).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRisk of NTM-PD according to RA status and serologic RA status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eBy RA status\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrouping\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber at risk (N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNTM-PD\u003c/p\u003e\n \u003cp\u003e(n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003cp\u003e(PY)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncident rate (/1,000 PY)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted model\u003c/p\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eaHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eaHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301,575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,371,419.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60,315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270,318.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.95 (3.18\u0026ndash;4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67 (2.96\u0026ndash;4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11 (2.50\u0026ndash;3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eBy RA status and seropositivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301,575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,371,419.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80,343.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (0.74\u0026ndash;2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (0.78\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.68\u0026ndash;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42,062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189,974.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07 (4.07\u0026ndash;6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51 (3.61\u0026ndash;5.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77 (3.00\u0026ndash;4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eBy seropositivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSNRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80,343.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42,062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189,974.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.97 (2.3\u0026ndash;6.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45 (1.98\u0026ndash;5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.25 (1.87\u0026ndash;5.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 1 was an unadjusted model.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 2 was adjusted for age, sex, body mass index, smoking, alcohol consumption, physical activity, and low-income status.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 3 was further adjusted for diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, airway disease, and tuberculosis, in addition to the variables in Model 2.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: NTM-PD, nontuberculous mycobacterial pulmonary lung disease; RA, rheumatoid arthritis; PY, person-years; HR, hazard ratio; CI, confidence interval; aHR, adjusted HR; SNRA, seronegative rheumatoid arthritis; SPRA, seropositive rheumatoid arthritis.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eRA serologic status and incidence and risk of NTM-PD\u003c/h2\u003e\n \u003cp\u003eWhen RA was classified according to serological status, NTM-PD occurred in 0.08% (n\u0026thinsp;=\u0026thinsp;14/18,253) and 0.32% (n\u0026thinsp;=\u0026thinsp;133/42,062) of the SNRA and SPRA cohorts (incidence rates of 0.70 and 0.17 per 1,000 person-years; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Compared to matched controls, the risk of incident NTM-PD was 3.77-fold (95% CI\u0026thinsp;=\u0026thinsp;3.00\u0026ndash;4.73) higher in the SPRA cohort whereas the risk did not increase significantly in the SNRA cohort (adjusted HR in Model 2\u0026thinsp;=\u0026thinsp;1.18, 95% CI\u0026thinsp;=\u0026thinsp;0.68\u0026ndash;2.04).\u003c/p\u003e\n \u003cp\u003eThe risk of NTM-PD was 3.25-fold (95% CI, 1.87\u0026ndash;5.66) higher in the SPRA than in the SNRA cohort (\u003cstrong\u003eModel 2\u003c/strong\u003e). Similarly, the cumulative incidence plot showed a significantly higher incidence of NTM-PD in the SPRA cohort than in the SNRA cohort (log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eStratified Analysis\u003c/h2\u003e\n \u003cp\u003eSocioeconomic characteristics, smoking status, health behavior, and comorbid conditions did not show a significant interaction between RA and the risk of NTM-PD (p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all; Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). In contrast, sex, alcohol consumption, and BMI had significant interactions in the association of RA with NTM-PD (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the associations between RA and NTM-PD were more prominent in males, alcohol drinkers, and obese participants than in their counterparts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eStratified analysis of NTM-PD risk according to RA status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubgroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRA status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber at risk\u003c/p\u003e\n \u003cp\u003e(n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNTM\u003c/p\u003e\n \u003cp\u003e(n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003cp\u003e(PY)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIR per 1,000 PY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted HR\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77,555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e337,687.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65,647.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.93 (3.30\u0026ndash;7.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,033,732.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44,804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204,670.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57 (1.97\u0026ndash;3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e20\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110,359.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,043.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.12\u0026ndash;9.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e40\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e917,372.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182,496.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36 (2.5\u0026ndash;4.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80,360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343,688.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65,778.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91 (2.1\u0026ndash;4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43,736.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,689.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61 (0.86\u0026ndash;2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal (18.5\u0026ndash;23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118,207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e539,900.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115,658.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 (2.31\u0026ndash;4.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight (23.0\u0026ndash;25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74,453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e342,481.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64,484.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60 (2.1\u0026ndash;6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese (\u0026ge;\u0026thinsp;25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e445,302.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78,486.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.67 (3.02\u0026ndash;10.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238,555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,096,466.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213,189.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77 (2.16\u0026ndash;3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEx-smoker (\u0026lt;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76,407.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,883.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01 (1.12\u0026ndash;8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEx-smoker (\u0026ge;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46,165.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,042.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.59 (2.06\u0026ndash;15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCurrent smoker (\u0026lt;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85,750.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,702.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85 (1.63\u0026ndash;4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCurrent smoker (\u0026ge;\u0026thinsp;20 PY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66,630.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,501.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57 (2.78\u0026ndash;15.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207,313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e951,630.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44,886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202,528.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.66 (2.08\u0026ndash;3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82,974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370,174.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60,492.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.57 (3.49\u0026ndash;8.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegular exercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e241,046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,100,317.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49,575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222,918.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.18 (2.50\u0026ndash;4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60,529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271,102.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47,400.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78 (1.64\u0026ndash;4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232,325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,058,286.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210,664.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02 (2.37\u0026ndash;3.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eQ1, Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313,133.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59,653.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.52 (2.14\u0026ndash;5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e263,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,209,228.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52,718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239,102.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11 (2.47\u0026ndash;3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37,625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162,191.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,216.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14 (1.54\u0026ndash;6.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e263,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,209,228.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193,444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e886,160.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36,546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166,295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.75 (2.1\u0026ndash;3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108,131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e485,259.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104,023.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00 (2.74\u0026ndash;5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyslipidemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208,773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e971,924.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41,046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189,226.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92,802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399,495.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.47 (2.70\u0026ndash;4.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81,092.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208,773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e971,924.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23 (1.44\u0026ndash;3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282,233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,281,501.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55,391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248,619.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22 (2.57\u0026ndash;4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89,918.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,699.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80 (0.72\u0026ndash;4.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e301,164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,369,706.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59,967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268,778.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.12 (2.49\u0026ndash;3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,713.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,540.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90 (0.77\u0026ndash;10.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAirway disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e255,369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1166162.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205056.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.12 (2.29\u0026ndash;4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205257.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15,251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65261.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10 (2.27\u0026ndash;4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003eAdjusted for age, sex, BMI, smoking, alcohol consumption, physical activity, low income, diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, airway diseases, and tuberculosis.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: NTM-PD, nontuberculous mycobacterial pulmonary disease; RA, rheumatoid arthritis; PY, pack-years; BMI, body mass index.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis the largest comprehensive study to evaluate the incidence of NTM-PD in participants with RA, as compared to the non-RA population, by using data from a nationwide cohort. Participants with RA had an NTM-PD incidence rate of 0.54 per 1,000 person-years, which was approximately 3-fold higher than that in controls. Furthermore, additional subgroup analysis of the SPRA and SNRA groups based on serologic status showed that the risk of NTM-PD was higher only in participants with SPRA.\u003c/p\u003e \u003cp\u003eThree longitudinal studies have compared the risk of NTM-PD in participants with RA and controls, and these studies consistently showed a higher incidence of NTM-PD in patients with RA compared to controls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; A population-based study in Canada demonstrated a 2.1-fold increased risk for NTM disease among patients with RA compared to controls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Two Taiwanese population-based studies reported that NTM-PD was 4.2- and 6.2-fold higher among patients with RA than among controls [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Compared to Canadian and our study results, the risk of NTM-PD in patients with RA was especially higher in Taiwanese studies. Although the reasons are not clear, it might be related to unadjusted confounders, such as BMI and personal behaviors (e.g., alcohol consumption), that might be related to NTM-PD risk. In contrast, the risk of NTM-PD in patients with RA was lower in the Canadian study compared to Taiwanese and our study. Although there might be several reasons for this phenomenon, ethnicity could be a reason; a recent study showed that the risk of NTM-PD was higher in Asians than other ethnicity even considering BMI[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, by classifying the serological status of RA, this is the first study to show that the risk of NTM-PD is significantly increased in SPRA. Supporting our findings, in an age- and sex-matched case\u0026ndash;control study of RA patients with or without NTM-PD at a ratio of 1:5, the RF and anti-CCP positivity rates were higher in RA patients with NTM-PD than in those without NTM-PD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although the reasons for this are not yet well understood, the association may be explained by RA disease activity. It is well known that anti-CCP positivity is associated with RA development and deterioration [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, increased disease activity may have led to increased immunosuppressive drug use, which could increase the risk of NTM-PD. Overall, the increased risk of NTM-PD in SPRA may be the combined result of increased systemic inflammation and accompanying intense immunosuppressive drugs.\u003c/p\u003e \u003cp\u003eInterestingly, our stratified analysis showed that association between RA and NTM-PD was especially prominent among obese participants and males as compared to their counterparts. Although the reason for this phenomenon cannot be fully explained, given the observational nature of our study and considering that the variables of underweight and female themselves are risk factors for NTM-PD, the extent to which RA contributes to the occurrence of NTM-PD in these patients can be interpreted as low. In contrast, the contribution of RA to the occurrence of NTM-PD in males or obese individuals is relatively high. In addition, it may be associated with increased disease activity in the obese population [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] however, owing to the absence of laboratory and radiological results, we could not consider this factor in our study. The higher association of NTM-PD in males than females may also be related to the higher prevalence of RA-related structural lung disease in males than in females [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although we adjusted for airway diseases and a history of TB, there might have been an underestimation in the evaluation of RA-related structural lung diseases because focal or non-severe diseases can only be detected on computed tomography, which is not routinely performed.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, our study may have a selection bias because we used health screening data, in which more healthy participants were likely to be included. Second, because our database did not contain laboratory and radiological test results, we could not incorporate these factors into our analyses. Future studies should include this data. Finally, this study was conducted on a Korean population. Thus, studies in other countries and ethnicities are required to obtain generalizable findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the incidence of NTM-PD in patients with RA was higher than that in patients without RA, which was significant only for SPRA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNTM-PD: nontuberculous mycobacterial pulmonary lung disease\u003c/p\u003e\n\u003cp\u003eRA: rheumatoid arthritis\u003c/p\u003e\n\u003cp\u003ePY: person-years\u003c/p\u003e\n\u003cp\u003eHR: hazard ratio\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eaHR: adjusted HR\u003c/p\u003e\n\u003cp\u003eSNRA: seronegative rheumatoid arthritis\u003c/p\u003e\n\u003cp\u003eSPRA: seropositive rheumatoid arthritis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eH.L., and D.W.S. are the guarantors of the manuscript and takes responsibility for the content of the manuscript, including the data and analysis. B.Y., H.L., K.H., and D.W.S. contributed to the conception and design of the study. B.Y., H.K., W.J., Y.E., B-G.K., K.H., J.-H.J., H.K., H.L., and D.W.S. were involved in the collection and interpretation of the data. K.H. and J.-H.J. were involved in the statistical analyses. B.Y. and H.L. were a major contributor in writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea grant funded by the Korea government (No.2022R1F1A1074749 to B.Y.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from \u003c/p\u003e\n\u003cp\u003ethe corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of\u003c/p\u003e\n\u003cp\u003eHelsinki and approved by the Institutional Review Board of the Samsung Medical Center (IRB No. SMC 2022-06-141). The requirement for informed consent was waived because the NHIS database uses a deidentified patient identification system.\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\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\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\u0026nbsp;\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMutru O, Laakso M, Isom\u0026auml;ki H, Koota K: \u003cstrong\u003eTen year mortality and causes of death in patients with rheumatoid arthritis.\u003c/strong\u003e \u003cem\u003eBr Med J (Clin Res Ed) \u003c/em\u003e1985, 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The participants were followed up from 1 year after RA diagnosis (or the corresponding index date for matched controls) to the date of NTM-PD diagnosis, censored date, or December 31, 2019, whichever occurred first.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median 4.5 (interquartile range, 2.6\u0026ndash;6.4)-year follow-up, NTM-PD occurred in 0.23% and 0.06% of the RA and matched cohort (incidence: 0.54 and 0.14 per 1,000 person-years), respectively. Compared to controls, participants with RA had a 3.11-fold (95% confidence interval [CI]: 2.50\u0026ndash;3.88) higher risk of NTM-PD. In the subgroup analysis stratified by seropositivity, seropositive patients with RA had a 3.77-fold (95% CI: 3.00\u0026ndash;4.73) higher risk of NTM-PD than controls whereas participants with seronegative RA did not have a significantly higher risk (adjusted hazard ratio: 1.18, 95% CI: 0.68\u0026ndash;2.04). Stratified analyses showed a more prominent association of RA with NTM-PD in males, alcohol drinkers, and obese individuals (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe risk of incident NTM-PD was approximately 3-fold higher in participants with RA than in matched controls, although the association was significant only for patients with seropositive RA.\u003c/p\u003e","manuscriptTitle":"Rheumatoid Arthritis and Risk of Nontuberculous Mycobacterial Pulmonary Disease: A Nationwide Longitudinal Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 20:35:00","doi":"10.21203/rs.3.rs-4689847/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5412118-bf19-459d-89f9-a0408be26ef3","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-11T12:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 20:35:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4689847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4689847","identity":"rs-4689847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}