Development and validation of a risk prediction model for abdominal aortic aneurysm: A nationwide population-based cohort study

Development and validation of a risk prediction model for abdominal aortic aneurysm: A nationwide population-based cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and validation of a risk prediction model for abdominal aortic aneurysm: A nationwide population-based cohort study Hyung-jin Cho, Mi-hyeong Kim, Kyung-jai Ko, Kang-woong Jun, Kyung-do Han, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6275213/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Background Abdominal aortic aneurysm (AAA) is characterized by irreversible localized dilatation of the abdominal aorta. It poses a significant health risk. As AAA size tends to increase over time, there is a heightened risk of rupture, resulting in a substantially high mortality rate. Although AAA screening programs targeting specific demographics are available, there is room for improvement in terms of inclusivity and cost-effectiveness. This study aimed to develop a predictive model for AAA occurrence utilizing seven years of data from the Korean National Health Insurance Service database (NHIS). Materials and Methods This study utilized NHIS data from 2009 to 2020. A total of 4,234,415 individuals who underwent health examinations in 2009 were identified. After applying exclusion criteria, a total of 3,937,535 individuals were selected. Of them, 70% were used for model development and 30% were used for validation. Results The mean follow-up duration was 10.11 ± 1.29 years, during which 2,836 cases of AAA were identified among 1,181,131 (2.4%) participants in the validation cohort. The model incorporated a set of 10 variables, encompassing age, sex, obesity, smoking, drinking, diabetes (DM), hypertension (HTN), dyslipidemia, chronic kidney disease (CKD), and cardiovascular disease (CVD). Evaluation of the model's predictive performance revealed an area under the curve (AUC) of 0.807 (95% CI: 0.80–0.81) when it was applied to the development cohort. The AUC remained high at 0.803 (95% CI: 0.79–0.81) when the model was applied to the validation cohort, indicating its effectiveness in forecasting AAA occurrence. Conclusions A multivariable risk model for predicting the onset of AAA was successfully developed, showcasing an excellent performance with an AUC value of 0.807, surpassing traditional screening methods. This model has the potential to selectively identify high-risk patients from a slightly broader pool than current screening approaches. Priority should be given to proactive screening efforts targeting individuals at elevated risk for AAA, with the goal of reducing AAA-related mortality. Health sciences/Cardiology Health sciences/Health care Health sciences/Risk factors aortic aneurysm abdominal nomograms Figures Figure 1 Figure 2 Figure 3 1. Introduction An abdominal aortic aneurysm (AAA) refers to an irreversible localized dilatation of the abdominal aorta. Globally, the prevalence of AAA is reported to be around 2–8%. In Korea, its prevalence is approximately 2.8%. 1,2 In general, the size of AAA tends to gradually increase over time. As the size increases, the risk of rupture also increases. 3 , 4 Once a rupture occurs, the mortality rate has been reported to be as high as 81%. 5 When treating intact AAA, the 30-day mortality rate has been reported to range from 1.16–3.27%. For cases with ruptured AAA, among patients receiving treatment in hospitals, the 30-day mortality rate has been reported to be in the range of 30.2–39.6%. 6 Due to reasons mentioned earlier, several countries including the United States, the United Kingdom, Sweden, Denmark, and others have implemented AAA screening programs. Numerous randomized controlled trials (RCTs) and observational follow-up studies have been conducted in this context. 7 – 10 These screening programs typically target men aged 65 and older with a history of smoking. While such screening programs do not significantly affect the overall all-cause mortality, studies have reported that such screening programs can reduce the rate of ruptured abdominal aortic aneurysms and decrease AAA-related mortality. Additionally, these screenings have been shown to be cost-effective in terms of healthcare resource utilization. However, considering that these approaches have focused on men aged 65 and older with a history of smoking, there is a possibility of missing out on patients. According to Summers et al., there are still significant high-risk groups that fall outside the current guidelines who could greatly benefit from AAA screening. 11 By developing a model that can predict AAA occurrence based on basic screening results, it would be possible to expand screening to a wider population. This could help reduce chances of missing out on individuals who may be at risk and minimize unnecessary screenings, ultimately leading to improved cost-effectiveness. Therefore, this study aimed to develop a predictive model for the presence of AAA using 11 years of data from the Korean National Health Insurance Service (NHIS) database and subsequently conduct validation. To the best of our knowledge, this is a novel approach. 2. Methods This study was a parallel study to “Risk of various cancers in adults with abdominal aortic aneurysm” and “The risk of dementia in adults with abdominal aortic aneurysm” by Cho et al. 12 , 13 It showed similarities in protocol, patient group selection method, and statistical method. 2.1 Data source The healthcare insurance system in Korea has been introduced in the two previous parallel studies. Data utilized in this research spanned from 2009 to 2020. They were gathered from the NHIS database. 2.2 Patients The study initially enrolled 4,234,415 individuals aged 20 and above who underwent health examinations in 2009. Patients who had previously been diagnosed with AAA at the time of the health examination were excluded (n = 2,409). Individuals with missing data in the examination were also excluded (n = 284,471). The AAA patient group was defined using diagnostic codes and procedure codes, similar to previous studies. (Appendix 1) Patients who were lost to follow-up within one year after the health examination were excluded. Likewise, those who developed AAA within one year were also excluded to establish a clear cause-and-effect relationship (n = 10,431). Seventy percent of these patients were assigned into a development cohort for model training and the remaining 30% were allocated to a validation cohort. 2.3 Data collection and definition Demographic data were gathered from the NHIS database, encompassing age, sex, smoking habits, alcohol consumption, physical activity, waist circumference, body mass index (BMI), and income level. Information regarding underlying health conditions, including hypertension, diabetes mellitus (DM), dyslipidemia, chronic kidney disease (CKD), and a history of cardiocerebrovascular disease (CVD), was also collated. Definitions of variables were similar to those described in previous papers. They are summarized in Appendix 1. This study was conducted in accordance with relevant guidelines and regulations. The requirement for informed consent was waived because the study used de-identified data from the National Health Insurance Service (NHIS) database of Korea. This study was approved by the Institutional Review Board (IRB) of The Catholic University of Korea, Eunpyeong St. Mary’s Hospital, Seoul, Korea (IRB approval number: PC23ZASI0143). 2.4 Statistical analysis Continuous variables are presented as mean ± SD or 95% CIs, while categorical variables are expressed as numbers and percentages (%). For comparing characteristics between patient and control groups, Student’s t-tests were employed for continuous variables and Chi-squared test or Fisher’s exact tests were used for categorical variables. Incidence rates of AAA are presented per 1,000 person-years. To investigate hazard ratios (HR) of various variables on the occurrence of AAA, the Cox proportional hazard regression model was employed. Variables included factors associated with AAA based on the literature. These variables were selected from data obtainable through health examination records. Risk scores were allocated according to the HR for each risk factor identified in the final Cox hazard regression model. Each of the 10 variables (age, sex, obesity, smoking status, drinking, fasting glucose level, blood pressure, total cholesterol level, presence of CKD, and previous CVD) was assigned a score ranging from 0 to 100. Each variable was then mapped to a specific point by extending a line vertically along the score axis. To assess the performance of the model, calibration and discrimination were conducted. For calibration, predicted 5-years disease free survival was plotted against observed 5-years disease free survival to visually inspect the alignment. 14 For discrimination, receiver operating characteristic curves were generated and the area under curve was examined. 15 All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and the R Project for Statistical Computing version 3.3 (Vienna, Austria). 3. Results 3.1 Baseline characteristics according to presence of AAA Initially, it was observed that distribution patterns of variables were not significantly different between the development cohort and the validation cohort. (Table 1 ) Average follow-up period was 10.11 ± 1.28 years, with AAA occurring in 6,514 (2.36%) out of 2,755,973 participants in the development cohort. At baseline, the mean age was 47.22 ± 14.01 years. Male patients accounted for 54.56%. The AAA patient group was older (62.88 ± 11.3 years vs. 47.19 ± 14 years, p -value < .001), had a higher proportion of males (67.88% vs. 54.53%, p -value < .001), and higher BMI (24.25 ± 3.1 vs. 23.7 ± 3.22, p -value < .001) than the control group. Additionally, the smoking rate was higher (31.98% vs. 26.01%, p -value < .001), while the proportion of patients who consumed alcohol was comparatively lower (38.52% vs. 48.31%, p -value < .001) in the AAA patient group. Interestingly, the AAA group exhibited higher levels of physical activity (21.08% vs. 17.96%, p -value < .001). In terms of comorbidities, the AAA group had higher prevalances of hypertension (55.63% vs. 25.37%, p -value < .001), hyperlipidemia (32.7% vs. 17.34%, p -value < .001), CKD (15.32% vs. 6.91%, p -value < .001), CVD (7.52% vs. 1.87%, p -value < .001), and DM (12.48% vs. 8.66%, p -value < .001). Table 1 Clinical characteristics of study participants based on the occurrence of abdominal aortic aneurysm (AAA) in development and validation cohorts used for the predictive model Development cohort (n = 2,755,973) Validation cohort (n = 1,181,131) p -value Control (n = 2,749,459) AAA (n = 6,514) P -value Control (n = 1,178,295) AAA (n = 2,836) P -value Age (years) < .001 < .001 1.0 20–39 852,451 (31) 233 (3.58) 365,202 (30.99) 95 (3.35) 40–64 1,540,291 (56.02) 3,033 (46.56) 659,514 (55.97) 1,363 (48.06) 65≤ 356,717 (12.97) 3,248 (49.86) 154,957 (13.12) 1,378 (48.59) Sex (male) 1,499,346 (54.53) 4,422 (67.88) < .001 642,713 (54.55) 1,899 (66.96) 25) 896,723 (32.61) 2,582 (39.64) < .001 384,172 (32.6) 1,090 (38.43) < .001 1.0 Smoking < .001 < .001 1.0 Never 1,640,878 (69.68) 3,105 (47.67) 702,867 (59.65) 1,367 (48.2) Former 393,372 (14.31) 1,326 (20.36) 168,886 (14.33) 558 (19.68) Current 715,209(26.01) 2,083 (31.98) 106,542 (26.02) 911 (32.12) Drinking (yes) 1,328,379 (48.31) 2,509 (38.52) < .001 569,823 (48.36) 1,139 (40.16) < .001 1.0 Exercise (yes) 493,732 (17.96) 1,373 (21.08) < .001 211,776 (17.97) 574 (20.24) .001 1.0 Diabetes mellitus (yes) 238,240 (8.66) 813 (12.48) < .001 102,195 (8.67) 368 (12.98) < .001 1.0 Diabetes mellitus (3 levels) < .001 < .001 1.0 Normal 1,883,391 (68.5) 3,898 (59.84) 806,380 (68.44) 1,699 (59.91) Impaired fasting glucose 627,828 (22.83) 1,803 (27.68) 269,720 (22.89) 769 (27.12) Diabetes mellitus 238,240 (8.66) 813 (12.48) 102,195 (8.67) 368 (12.98) Hypertension (yes) 697,404 (25.37) 3,624 (55.63) < .001 298,660 (25.35) 1,572 (55.43) < .001 1.0 Hypertension (3 levels) < .001 < .001 1.0 Normal 951,112 (34.59) 1,015 (15.58) 408,897 (34.7) 435 (15.34) Pre-Hypertension 1,100,943 (40.04) 1,875 (28.78) 470,738 (39.95) 829 (29.23) Hypertension 697,404 (25.37) 3,624 (55.63) 298,660 (25.35) 1,572 (55.43) Dyslipidemia (yes) 476,635 (17.34) 2,130 (32.7) < .001 203,517 (17.27) 925 (32.62) < .001 1.0 Total cholesterol (3 levels) < .001 < .001 1.0 < 200 1,480,637 (53.85) 2,556 (39.24) 634,808 (53.88) 1,094 (38.58) 200–239 792,187 (28.81) 1,828 (28.06) 339,970 (28.85) 817 (28.81) 240≤ 476,635 (17.34) 2,130 (32.7) 203,517 (17.27) 925 (32.62) Chronic kidney disease (yes) 190,024 (6.91) 998 (15.32) < .001 81,389 (6.91) 488 (17.21) < .001 1.0 Cardiocerebrovascular disease (yes) 51,358 (1.87) 490 (7.52) < .001 21,800 (1.85) 230 (8.11) < .001 1.0 Age (years) 47.19 ± 14 62.88 ± 11.3 < .001 47.2 ± 14.01 62.75 ± 11.27 < .001 1.0 BMI (kg/m 2 ) 23.7 ± 3.22 24.25 ± 3.1 < .001 23.7 ± 3.23 24.2 ± 3.1 < .001 1.0 Waist circumference (cm) 80.22 ± 9.1 84.51 ± 8.57 < .001 80.24 ± 9.1 84.28 ± 8.56 < .001 1.0 Glucose (mmol/L) 97.29 ± 23.81 99.99 ± 23.64 < .001 97.31 ± 23.82 100.46 ± 29.33 < 0.001 1.0 Total cholesterol (mmol/L) 195.05 ± 36.8 200.38 ± 39.56 < .001 195 ± 36.79 200.14 ± 40.13 < .001 1.0 HDL-cholesterol (mmol/L) 56.09 ± 27.87 52.31 ± 31.15 < .001 56.1 ± 27.76 53.23 ± 33.29 < .001 1.0 LDL-cholesterol (mmol/L) 113.59 ± 38.63 120.3 ± 42.97 < .001 113.53 ± 38.59 118.56 ± 39.11 < .001 1.0 Note: Data are number (%) or mean ± SD. Abbreviations: BMI, body mass index. 3.2 Selection of variables Among variables listed in Table 1 that showed significant distribution differences between the AAA group and the control group, a total of 12 variables, excluding those with similar meanings, were selected. (Table 2 , 3 ) The Cox proportional hazard regression model was then utilized to examine the HR for the occurrence of AAA. In multivariate analysis, we ultimately selected 10 variables that were statistically significant, including age and sex, obesity, smoking, drinking, DM, HTN, dyslipidemia, CKD, and CVD. Old age [HR: 30.43 (95% CI: 26.48–34.97)], male sex [HR: 2.01 (95% CI: 1.88–2.16)], obesity [HR: 1.06 (95% CI: 1.01–1.11)], smoking [HR: 2.20 (95% CI: 2.05–2.36)], DM [HR: 0.64 (95% CI: 0.59–0.69)], HTN [HR: 2.04 (95% CI: 1.89–2.20)], dyslipidemia [HR: 1.56 (95% CI: 1.47–1.66)], CKD [HR: 1.41 (95% CI: 1.31–1.51)], CVD [HR: 1.50 (95% CI: 1.67–1.65)] were significant predictive factors for occurrence of AAA after adjusting for all 10 variables. A nomogram for risk scoring developed from the risk prediction model was constructed to estimate the five-year risk of AAA. (Fig. 1 ) Table 2 Hazards ratios (95% CIs) for the occurrence of abdominal aortic aneurysm (Univariate model) N Event Duration Rate Univariate model P -value Age 20–39 852,684 233 8,772,934.9 0.03 1 (Ref.) < .001 40–64 1,543,324 3,033 15,764,115.73 0.19 7.25 (6.34–8.28) 65≤ 359,965 3,248 3,337,159.96 0.97 37.285(32.64–42.58) Sex Male 1,503,768 4,422 15,111,342.75 0.29 1.791(1.7–1.88) < .001 Female 1,252,205 2,092 12,762,867.84 0.16 1 (Ref.) Income level (1st quartile) No 2,218,790 5,208 22,459,933.36 0.23 1 (Ref.) 25) Yes 1,856,668 3,932 18,758,964.92 0.21 1 (Ref.) < .001 No 899,305 2,582 9,115,245.68 0.28 1.351 (1.28–1.41) Smoking Never 1,643,983 3,105 16,686,049.9 0.19 1 (Ref.) < .001 Former 394,698 1,326 3,967,496.03 0.33 1.801 (1.68–1.92) Current 717,292 2,083 7,220,664.67 0.29 1.555 (1.47–1.64) Drinking No 1,425,085 4,005 14,356,278.92 0.28 1 (Ref.) < .001 Yes 1,330,888 2,509 13,517,931.67 0.19 0.665 (0.63–0.69) Exercise No 2,260,868 5,141 22,855,563.58 0.22 1 (Ref.) < .001 Yes 495,105 1,373 5,018,647.02 0.27 1.216 (1.14–1.29) Diabetes mellitus (3 levels) Normal 1,887,289 3,898 19,209,840.47 0.20 1 (Ref.) < .001 Impaired fasting glucose 629,631 1,803 6,347,986.4 0.28 1.402 (1.32–1.48) Diabetes mellitus 239,053 813 2,316,383.73 0.35 1.741 (1.61–1.87) Hypertension (3 levels) Normal 952,127 1,015 9,744,813.14 0.10 1 (Ref.) < .001 Pre-hypertension 1,102,818 1,875 11,215,353.96 0.17 1.607 (1.48–1.73) Hypertension 701,028 3,624 6,914,043.5 0.52 5.056 (4.71–5.42) Total cholesterol < 200 1,483,193 2,556 15,011,409.06 0.17 1 (Ref.) < .001 200–239 794,015 1,828 8,056,784.65 0.23 1.332 (1.25–1.41) 240≤ 478,765 2,130 4,806,016.88 0.44 2.604 (2.45–2.75) Chronic kidney disease No 2,564,951 5,516 26,006,659.17 0.21 1 (Ref.) < .001 Yes 191,022 998 1,867,551.43 0.53 2.529 (2.36–2.70) Cardiocerebrovascular disease No 2,704,125 6,024 27,392,872.21 0.22 1 (Ref.) < .001 Yes 51,848 490 481,338.38 1.02 4.681 (4.26–5.13) Note : Rate: incidence rate per 1 000 person-years. Abbreviations: BMI, body mass index. Table 3 Hazards ratios (95% CIs) for the occurrence of abdominal aortic aneurysm (Multivariate model and final model) N Event Duration Rate Multivariate model P -value Final model P -value Age 20–39 852,684 233 8,772,934.9 0.03 1 (Ref.) < .001 1 (Ref.) < .001 40–64 1,543,324 3,033 15,764,115.73 0.19 6.919 (6.04–7.91) 6.937 (6.06–7.93) 65≤ 359,965 3,248 3,337,159.96 0.97 30.328 (26.38–34.85) 30.428 (26.47–34.96) Sex Male 1,503,768 4,422 15,111,342.75 0.29 2.014 (1.87–2.15) < .001 2.013 (1.87–2.15) < .001 Female 1,252,205 2,092 12,762,867.84 0.16 1 (Ref.) 1 (Ref.) Income level (1st quartile) No 2,218,790 5,208 22,459,933.36 0.23 1 (Ref.) .12 Yes 537,183 1,306 5,414,277.23 0.24 1.048 (0.98–1.11) Obesity (BMI > 25) Yes 1,856,668 3,932 18,758,964.92 0.21 1 (Ref.) .029 1 (Ref.) .03 No 899,305 2,582 9,115,245.68 0.28 1.058 (1.00–1.11) 1.058 (1.00–1.11) Smoking Never 1,643,983 3,105 16,686,049.9 0.19 1 (Ref.) < .001 1 (Ref.) < .001 Former 394,698 1,326 3,967,496.03 0.33 1.442 (1.33–1.55) 1.442 (1.33–1.55) Current 717,292 2,083 7,220,664.67 0.29 2.193 (2.04–2.35) 2.195 (2.04–2.35) Drinking No 1,425,085 4,005 14,356,278.92 0.28 1 (Ref.) < .001 1 (Ref.) < .001 Yes 1,330,888 2,509 13,517,931.67 0.19 0.662 (0.62–0.70) 0.662 (0.62–0.70) Exercise No 2,260,868 5,141 22,855,563.58 0.22 1 (Ref.) .74 Yes 495,105 1,373 5,018,647.02 0.27 1.01 (0.95–1.07) Diabetes mellitus (3 levels) Normal 1,887,289 3,898 19,209,840.47 0.20 1 (Ref.) < .001 1 (Ref.) < .001 Impaired fasting glucose 629,631 1,803 6,347,986.4 0.28 0.919 (0.86–0.97) 0.919 (0.86–0.97) Diabetes mellitus 239,053 813 2,316,383.73 0.35 0.637 (0.58–0.68) 0.637 (0.58–0.68) Hypertension (3 levels) Normal 952,127 1,015 9,744,813.14 0.10 1 (Ref.) < .001 1 (Ref.) < .001 Pre-hypertension 1,102,818 1,875 11,215,353.96 0.17 1.204 (1.11–1.3) 1.204 (1.11–1.30) Hypertension 701,028 3,624 6,914,043.5 0.52 2.036 (1.88–2.19) 2.038 (1.89–2.19) Total cholesterol < 200 1,483,193 2,556 15,011,409.06 0.17 1 (Ref.) < .001 1 (Ref.) < .001 200–239 794,015 1,828 8,056,784.65 0.23 1.136 (1.06–1.20) 1.136 (1.06–1.20) 240≤ 478,765 2,130 4,806,016.88 0.44 1.561 (1.47–1.65) 1.56 (1.46–1.65) Chronic kidney disease No 2,564,951 5,516 26,006,659.17 0.21 1 (Ref.) < .001 1 (Ref.) < .001 Yes 191,022 998 1,867,551.43 0.53 1.408 (1.31–1.50) 1.406 (1.31–1.50) Cardiocerebrovascular disease No 2,704,125 6,024 27,392,872.21 0.22 1 (Ref.) < .001 1 (Ref.) < .001 Yes 51,848 490 481,338.38 1.02 1.505 (1.36–1.65) 1.504 (1.36–1.65) Note : Rate: incidence rate per 1 000 person-years. Abbreviations: BMI, body mass index. 3.3 Validation of the prediction model Average follow-up period was 10.11 ± 1.29 years, with AAA occurring in 2,836 (2.4%) out of 1,181,131 participants in the validation cohort. At baseline, the mean age was 47.24 ± 14.02 years. Male patients accounted for 54.58%. Additionally, after examining the area under curve (AUC) value for AAA occurrence prediction in the prediction model, when applied to the development cohort data, the AUC was found to be 0.807 (95% CI: 0.80–0.81). When applied to the validation cohort data, the AUC value was 0.803 (95% CI: 0.79–0.81). (Fig. 2 ) This suggests that the model is effective in predicting the occurrence of AAA. 3.4 Prediction model The sum of total scores was obtained by combining scores of the 10 variables. It ranged from 0 to a maximum of 226 points. For example, a male (20 points) aged 65 or older (100 points) who smoked (23 points), had a normal weight (0 points), and had diabetes (0 points) with no other underlying conditions (0 points) would have a total score of 143 points. On the other hand, a female (0 points) aged 40 (57 points) who smoked (23 points), had a normal weight (0 points) who did not drink alcohol (12 points), had no DM (13 points) but had HTN (21 points), hyperlipidemia (13 points), and CKD (10 points) would have a total score of 149 points.(Table S1) In this case, it can be inferred that the probability of developing abdominal aortic aneurysm within 5 years is less than 0.5%. In the patient group with the highest score range of 215 points or above in the prediction model, the incidence rate of AAA was confirmed to be 1.194 per 1000 person-years.(Fig. 3 ) Additionally, to assess the potential overfitting of the model, we compared the incidence in the validation cohort. It was observed that similar patterns were present in each interval, confirming the excellence of this predictive model. 4. Discussion Utilizing the NHIS database in Korea, we developed and validated a straightforward yet effective risk prediction model for the occurrence of AAA. To the best of our knowledge, this study developed the first model capable of predicting the likelihood of AAA occurrence after a prolonged period of 5 years using data obtained through long-term follow-up observations. Our model exhibited a strong performance with an AUC of 0.807 (95% CI: 0.80–0.81). Notably, older age, male sex, obesity, current smoking, non-drinking, absence of DM, presence of hypertension, hyperlipidemia, CKD, and CVD were identified as independent predictors of an increased risk of AAA. In opinions of vascular and endovascular surgeons, prioritizing research on methods for predicting AAA growth is considered essential. 16 Therefore, various studies are being conducted to predict the growth of AAA. For example, approaches that include geometric perspectives from CT images, attempts to predict AAA growth using imaging modalities such as MRI to examine metabolism, and exploring associations with biomarkers have been reported. 17 – 19 Of course, while predicting the growth of AAA is important, it is even more crucial to first predict the presence of AAA. AAA screening is most widely conducted based on the U.S. Preventive Services Task Force guidelines. 20 Additionally, there are other guidelines such as the slightly more sensitive Society for Vascular Surgery (SVS) guideline and the SVS expanded criteria. 21 The SVS guideline significantly broadens the scope of patients who could potentially be included in screening programs. Nevertheless, about one-quarter to one-third of patients undergoing treatment for AAA could still potentially go unnoticed by any existing screening guidelines. 22 This aspect can also be verified through the model developed in this study. For instance, scores corresponding to the criteria of traditional guidelines targeting males over 60 who smoke can also be sufficiently obtained from individuals under 60, females, or non-smokers. Through this, one aspect of the model's excellence can be confirmed. Specifically, its ability to include a slightly more comprehensive range of patients while effectively identifying specific individuals within that group is confirmed. The second excellence lies in the fact that variables included in this model are easily recognizable factors such as medical history, smoking history, age, and gender. Due to this simplicity, the general public can easily check their risk of developing AAA at any time. If a cutoff value is determined through subsequent research, strongly recommending screening to individuals with risk levels above this value could help improve the screening rate, addressing one of the issues with the current screening method. Lastly, due to being developed with long-term data from a substantial number of patients, it is likely to have strength in terms of reliability. As the model developed in this study is an initial iteration, further research involving additional easily obtainable variables through subsequent validation could enhance the model's accuracy. This aligns with the initial goal of the model development, which is to expedite identification of AAA patients, enabling timely intervention before complications such as rupture, ultimately improving the survival rate. The current study is subject to several limitation. Firstly, due to the utilization of NHIS data, defining the presence or absence of a patient's diseases based on diagnosis codes and medication intake might have led to various errors and inconsistencies. This is because there may be undiagnosed asymptomatic AAA cases or patients who died from ruptured AAA without diagnosis compared to the group identified through screening. Secondly, in the selection of variables included in this model, efforts were made to incorporate well-known risk factors based on available information. However, due to the nature of the data, only variables that could be confirmed through health check-ups were included, while factors such as family history, transplantation status, or the presence of aneurysms in other peripheral arteries could not be incorporated. Thirdly, since the model was developed for the Korean population, it is necessary to assess its applicability and effectiveness when applied to different racial groups. Further research is needed to establish an appropriate cut-off level for recommending screening. Subsequently, when conducting screening on the identified patient group, assessing improvements in aspects such as all-cause mortality, AAA-related mortality, and cost-effectiveness will be essential. 5. Conclusions We developed a multivariable risk model capable of predicting the onset of AAA. In this study, the model demonstrated excellent performance with an AUC value of 0.807, surpassing traditional screening methods. It is anticipated that this model can selectively identify patients from a slightly more comprehensive pool compared to existing screening approaches. Moreover, efforts should be directed towards proactive screening of high-risk individuals for AAA, aiming to reduce AAA-related mortality. For further optimization of the model's performance, external validation is necessary, along with additional analysis and refinement using larger cohort data. The development of an appropriate surveillance program in response to these findings is also crucial. Declarations Author Disclosures None of the authors have anything to disclose. Declaration of conflicting interests None declared. Author Contribution Hyungjin Cho: Conception and design, analysis and interpretation, Writing the manuscript, Critical revision, Approval of the manuscript, Agreement to be accountable Mi-hyeong Kim: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable Kyung-jai Ko: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable Kang-woong Jun: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable Kyung-do Han: Conception and design, analysis and interpretation, Data collection, Critical revision, Approval of the manuscript, Agreement to be accountable, Statistical Analysis Jeong-kye Hwang: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable Acknowledgements This research was supported by a grant funded by The Catholic University of Korea, Eunpyeong St. Mary’s Hospital, Research Institute of Medical Science in program year 2023. Data Availability The data that support the findings of this study are available from the National Health Insurance Service (NHIS) database of Korea. Restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. However, the data can be accessed through the NHIS upon reasonable request and institutional approval. Researchers interested in accessing the data may contact the corresponding author for guidance on how to obtain it from the NHIS. References Marcaccio CL, Schermerhorn ML. Epidemiology of abdominal aortic aneurysms. Seminars in Vascular Surgery 2021; 34 (1):29–37. Doi: 10.1053/j.semvascsurg.2021.02.004. An SY, Hwang W, Sun BJ, Park J-H. Prevalence and Prediction of Aneurysmal Dilatation of the Abdominal Aorta in Koreans: Results of Screening During Transthoracic Echocardiographic Examination. J Cardiovasc Imaging 2020; 28 (4):257–64. Sakalihasan N, Limet R, Defawe OD. Abdominal aortic aneurysm 2005; 365 . Oliver-Williams C, Sweeting MJ, Turton G, Parkin D, Cooper D, Rodd C, et al. Lessons learned about prevalence and growth rates of abdominal aortic aneurysms from a 25-year ultrasound population screening programme. British Journal of Surgery 2017; 105 (1):68–74. Doi: 10.1002/bjs.10715. Reimerink JJ, Van Der Laan MJ, Koelemay MJ, Balm R, Legemate DA. Systematic review and meta-analysis of population-based mortality from ruptured abdominal aortic aneurysm. British Journal of Surgery 2013; 100 (11):1405–13. Doi: 10.1002/bjs.9235. Schmitz-Rixen T, Böckler D, Vogl TJ, Grundmann RT. Endovascular and Open Repair of Abdominal Aortic Aneurysm. Deutsches Ärzteblatt International 2020. Doi: 10.3238/arztebl.2020.0813. Søgaard R, Lindholt JS. Cost-effectiveness of population-based vascular disease screening and intervention in men from the Viborg Vascular (VIVA) trial. British Journal of Surgery 2018; 105 (10):1283–93. Doi: 10.1002/bjs.10872. Glover MJ, Kim LG, Sweeting MJ, Thompson SG, Buxton MJ. Cost-effectiveness of the National Health Service abdominal aortic aneurysm screening programme in England. British Journal of Surgery 2014; 101 (8):976–82. Doi: 10.1002/bjs.9528. US Preventive Services Task Force, Owens DK, Davidson KW, Krist AH, Barry MJ, Cabana M, et al. Screening for Abdominal Aortic Aneurysm: US Preventive Services Task Force Recommendation Statement. JAMA 2019; 322 (22):2211. Doi: 10.1001/jama.2019.18928. Wanhainen A, Hultgren R, Linné A, Holst J, Gottsäter A, Langenskiöld M, et al. Outcome of the Swedish Nationwide Abdominal Aortic Aneurysm Screening Program. Circulation 2016; 134 (16):1141–8. Doi: 10.1161/CIRCULATIONAHA.116.022305. Summers KL, Kerut EK, Sheahan CM, Sheahan MG. Evaluating the prevalence of abdominal aortic aneurysms in the United States through a national screening database. Journal of Vascular Surgery 2021; 73 (1):61–8. Doi: 10.1016/j.jvs.2020.03.046. Cho H, Yoo J, Kim M, Ko K, Jun K, Han K, et al. The risk of dementia in adults with abdominal aortic aneurysm. Sci Rep 2022; 12 (1):1228. Doi: 10.1038/s41598-022-05191-1. Cho H, Yoo J, Kim M, Ko K, Jun K, Han K, et al. Risk of various cancers in adults with abdominal aortic aneurysms. Journal of Vascular Surgery 2023; 77 (1):80-88.e2. Doi: 10.1016/j.jvs.2022.03.896. Bonnett LJ, Snell KIE, Collins GS, Riley RD. Guide to presenting clinical prediction models for use in clinical settings. BMJ 2019:l737. Doi: 10.1136/bmj.l737. Lee Y, Bang H, Kim DJ. How to Establish Clinical Prediction Models. Endocrinol Metab 2016; 31 (1):38. Doi: 10.3803/EnM.2016.31.1.38. Lee R, Jones A, Cassimjee I, Handa A. International opinion on priorities in research for small abdominal aortic aneurysms and the potential path for research to impact clinical management. International Journal of Cardiology 2017; 245 :253–5. Doi: 10.1016/j.ijcard.2017.06.058. Vermeulen JJM, Meijer M, De Vries FBG, Reijnen MMPJ, Holewijn S, Thijssen DHJ. A systematic review summarizing local vascular characteristics of aneurysm wall to predict for progression and rupture risk of abdominal aortic aneurysms. Journal of Vascular Surgery 2023; 77 (1):288-298.e2. Doi: 10.1016/j.jvs.2022.07.008. Forneris A, Beddoes R, Benovoy M, Faris P, Moore RD, Di Martino ES. AI-powered assessment of biomarkers for growth prediction of abdominal aortic aneurysms. JVS-Vascular Science 2023; 4 :100119. Doi: 10.1016/j.jvssci.2023.100119. Chandrashekar A, Handa A, Lapolla P, Shivakumar N, Ngetich E, Grau V, et al. Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period. Annals of Surgery 2023; 277 (1):e175–83. Doi: 10.1097/SLA.0000000000004711. U.S. Preventive Services Task Force. Screening for abdominal aortic aneurysm: recommendation statement. Ann Intern Med 2005; 142 (3):198–202. Doi: 10.7326/0003-4819-142-3-200502010-00011. Chaikof EL, Dalman RL, Eskandari MK, Jackson BM, Lee WA, Mansour MA, et al. The Society for Vascular Surgery practice guidelines on the care of patients with an abdominal aortic aneurysm. Journal of Vascular Surgery 2018; 67 (1):2-77.e2. Doi: 10.1016/j.jvs.2017.10.044. Carnevale ML, Koleilat I, Lipsitz EC, Friedmann P, Indes JE. Extended screening guidelines for the diagnosis of abdominal aortic aneurysm. Journal of Vascular Surgery 2020; 72 (6):1917–26. Doi: 10.1016/j.jvs.2020.03.047. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 May, 2025 Reviews received at journal 06 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviews received at journal 10 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 30 Mar, 2025 Editor assigned by journal 30 Mar, 2025 Editor invited by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6275213","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444535610,"identity":"deb439a6-1d1a-4435-9a71-9932479331df","order_by":0,"name":"Hyung-jin Cho","email":"","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Hyung-jin","middleName":"","lastName":"Cho","suffix":""},{"id":444535614,"identity":"b38defaf-a318-4bc7-bfec-21826086e687","order_by":1,"name":"Mi-hyeong Kim","email":"","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Mi-hyeong","middleName":"","lastName":"Kim","suffix":""},{"id":444535615,"identity":"619a215a-2af0-4094-b321-9cd4def02d51","order_by":2,"name":"Kyung-jai Ko","email":"","orcid":"","institution":"Kangdong Sacred Heart Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kyung-jai","middleName":"","lastName":"Ko","suffix":""},{"id":444535616,"identity":"105fc631-c791-4088-822b-e8e65b67e27c","order_by":3,"name":"Kang-woong Jun","email":"","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Kang-woong","middleName":"","lastName":"Jun","suffix":""},{"id":444535617,"identity":"fd38e41e-d840-47bf-a6e2-810e8663b185","order_by":4,"name":"Kyung-do Han","email":"","orcid":"","institution":"Soongsil University","correspondingAuthor":false,"prefix":"","firstName":"Kyung-do","middleName":"","lastName":"Han","suffix":""},{"id":444535618,"identity":"0a2c3a01-bb4b-433b-a48f-d23878f6472a","order_by":5,"name":"Jeong-kye Hwang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACxoYDUBZ7A4jHIMMG4SYQoYUHyDjYwMBDUAsCSCRAtDAQ0sLceMbs4dc2mzz5yMcPP3/cYcPDx36A8cMPhrR83A47Y24s25ZWbHg7zVji4Jk0HjaeBGbJHoYcywbcWsykJdsOJ26cncMgcbDtMA+bBAODNANDhQEeW6BaZp5h/gHVwvybkBbJj0At8yWAiqFa2IC25ODRcqxMmuFcWuIGnjQzi7NtIL8ktln2GKTh1GI44/A2yR9lNonz2w8/vlHZZiMn33748I0fFcl4tBxgYOYFRp/BASSbgVxcGhgY5PkbGBh//AEyGnArGgWjYBSMghEOAPfmV81nC/3QAAAAAElFTkSuQmCC","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":true,"prefix":"","firstName":"Jeong-kye","middleName":"","lastName":"Hwang","suffix":""}],"badges":[],"createdAt":"2025-03-21 07:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6275213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6275213/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-11956-1","type":"published","date":"2025-07-21T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81023997,"identity":"ab163903-8b77-41fd-a384-2d457805021e","added_by":"auto","created_at":"2025-04-21 10:10:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA nomogram for predicting the 5-year probability of abdominal aortic aneurysm occurrence.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eThe 10 variables—age, sex, obesity, smoking status, drinking, presence of diabetes mellitus or hypertension, chronic kidney disease, cardiocerebrovascular disease, and total cholesterol level—were each assigned scores ranging from 0 to 100. The corresponding score for each variable can be determined by drawing a straight line to the scoring axis. The total score, calculated as the sum of the scores for all variables, ranges from 0 to 226 and is displayed at the bottom of the nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eDM: Diabetes mellitus, IFG: Impaired fasting glucose, HTN: Hypertension, CKD: Chronic kidney disease, CVD: Cardiocerebrovascular disease\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6275213/v1/ccf7b92fe24817cf9b2eb697.png"},{"id":81024393,"identity":"0cc531eb-0666-4013-a9cb-0d100165e71e","added_by":"auto","created_at":"2025-04-21 10:18:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":318488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe model's receiver operating characteristic curve (ROC) when using development and validation cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eA ROC curve is using development cohort, and B ROC curve is using validation cohort.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6275213/v1/9c896711c14815da84f17c18.jpeg"},{"id":81023994,"identity":"9ce116ea-700d-467b-bf4a-23e88a172521","added_by":"auto","created_at":"2025-04-21 10:10:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted incidence rate (per 1 000 person-years) using development and validation cohorts.\u003cbr\u003e\nNote: \u003c/strong\u003eThe x-axis depicts the spectrum of scores from the nomogram corresponding to each decile. The y-axis illustrates the predicted incidence rate of abdominal aortic aneurysm.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6275213/v1/bde4585bd4ef17d38290222f.jpg"},{"id":87757743,"identity":"244649b9-d511-4ccc-98a7-f368e0589239","added_by":"auto","created_at":"2025-07-28 16:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1846786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6275213/v1/0a8a9542-b495-46a0-88e1-b683c0ad1b9c.pdf"},{"id":81023993,"identity":"43c4fe49-6673-4d7c-a386-475cc19008d7","added_by":"auto","created_at":"2025-04-21 10:10:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17105,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6275213/v1/db60f85fa79f47eb1069bf48.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a risk prediction model for abdominal aortic aneurysm: A nationwide population-based cohort study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAn abdominal aortic aneurysm (AAA) refers to an irreversible localized dilatation of the abdominal aorta. Globally, the prevalence of AAA is reported to be around 2\u0026ndash;8%. In Korea, its prevalence is approximately 2.8%.\u003csup\u003e1,2\u003c/sup\u003e In general, the size of AAA tends to gradually increase over time. As the size increases, the risk of rupture also increases.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Once a rupture occurs, the mortality rate has been reported to be as high as 81%.\u003csup\u003e5\u003c/sup\u003e When treating intact AAA, the 30-day mortality rate has been reported to range from 1.16\u0026ndash;3.27%. For cases with ruptured AAA, among patients receiving treatment in hospitals, the 30-day mortality rate has been reported to be in the range of 30.2\u0026ndash;39.6%.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDue to reasons mentioned earlier, several countries including the United States, the United Kingdom, Sweden, Denmark, and others have implemented AAA screening programs. Numerous randomized controlled trials (RCTs) and observational follow-up studies have been conducted in this context.\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e These screening programs typically target men aged 65 and older with a history of smoking. While such screening programs do not significantly affect the overall all-cause mortality, studies have reported that such screening programs can reduce the rate of ruptured abdominal aortic aneurysms and decrease AAA-related mortality. Additionally, these screenings have been shown to be cost-effective in terms of healthcare resource utilization.\u003c/p\u003e \u003cp\u003eHowever, considering that these approaches have focused on men aged 65 and older with a history of smoking, there is a possibility of missing out on patients. According to Summers et al., there are still significant high-risk groups that fall outside the current guidelines who could greatly benefit from AAA screening.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e By developing a model that can predict AAA occurrence based on basic screening results, it would be possible to expand screening to a wider population. This could help reduce chances of missing out on individuals who may be at risk and minimize unnecessary screenings, ultimately leading to improved cost-effectiveness.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to develop a predictive model for the presence of AAA using 11 years of data from the Korean National Health Insurance Service (NHIS) database and subsequently conduct validation. To the best of our knowledge, this is a novel approach.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study was a parallel study to \u0026ldquo;Risk of various cancers in adults with abdominal aortic aneurysm\u0026rdquo; and \u0026ldquo;The risk of dementia in adults with abdominal aortic aneurysm\u0026rdquo; by Cho et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e It showed similarities in protocol, patient group selection method, and statistical method.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.1 Data source\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe healthcare insurance system in Korea has been introduced in the two previous parallel studies. Data utilized in this research spanned from 2009 to 2020. They were gathered from the NHIS database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Patients\u003c/h2\u003e \u003cp\u003eThe study initially enrolled 4,234,415 individuals aged 20 and above who underwent health examinations in 2009. Patients who had previously been diagnosed with AAA at the time of the health examination were excluded (n\u0026thinsp;=\u0026thinsp;2,409). Individuals with missing data in the examination were also excluded (n\u0026thinsp;=\u0026thinsp;284,471). The AAA patient group was defined using diagnostic codes and procedure codes, similar to previous studies. (Appendix 1) Patients who were lost to follow-up within one year after the health examination were excluded. Likewise, those who developed AAA within one year were also excluded to establish a clear cause-and-effect relationship (n\u0026thinsp;=\u0026thinsp;10,431). Seventy percent of these patients were assigned into a development cohort for model training and the remaining 30% were allocated to a validation cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.3 Data collection and definition\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eDemographic data were gathered from the NHIS database, encompassing age, sex, smoking habits, alcohol consumption, physical activity, waist circumference, body mass index (BMI), and income level. Information regarding underlying health conditions, including hypertension, diabetes mellitus (DM), dyslipidemia, chronic kidney disease (CKD), and a history of cardiocerebrovascular disease (CVD), was also collated. Definitions of variables were similar to those described in previous papers. They are summarized in Appendix 1.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with relevant guidelines and regulations. The requirement for informed consent was waived because the study used de-identified data from the National Health Insurance Service (NHIS) database of Korea. This study was approved by the Institutional Review Board (IRB) of The Catholic University of Korea, Eunpyeong St. Mary\u0026rsquo;s Hospital, Seoul, Korea (IRB approval number: PC23ZASI0143).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or 95% CIs, while categorical variables are expressed as numbers and percentages (%). For comparing characteristics between patient and control groups, Student\u0026rsquo;s t-tests were employed for continuous variables and Chi-squared test or Fisher\u0026rsquo;s exact tests were used for categorical variables. Incidence rates of AAA are presented per 1,000 person-years. To investigate hazard ratios (HR) of various variables on the occurrence of AAA, the Cox proportional hazard regression model was employed. Variables included factors associated with AAA based on the literature. These variables were selected from data obtainable through health examination records.\u003c/p\u003e \u003cp\u003eRisk scores were allocated according to the HR for each risk factor identified in the final Cox hazard regression model. Each of the 10 variables (age, sex, obesity, smoking status, drinking, fasting glucose level, blood pressure, total cholesterol level, presence of CKD, and previous CVD) was assigned a score ranging from 0 to 100. Each variable was then mapped to a specific point by extending a line vertically along the score axis. To assess the performance of the model, calibration and discrimination were conducted. For calibration, predicted 5-years disease free survival was plotted against observed 5-years disease free survival to visually inspect the alignment.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e For discrimination, receiver operating characteristic curves were generated and the area under curve was examined.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and the R Project for Statistical Computing version 3.3 (Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e3.1 Baseline characteristics according to presence of AAA\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eInitially, it was observed that distribution patterns of variables were not significantly different between the development cohort and the validation cohort. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) Average follow-up period was 10.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28 years, with AAA occurring in 6,514 (2.36%) out of 2,755,973 participants in the development cohort. At baseline, the mean age was 47.22\u0026thinsp;\u0026plusmn;\u0026thinsp;14.01 years. Male patients accounted for 54.56%. The AAA patient group was older (62.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 years vs. 47.19\u0026thinsp;\u0026plusmn;\u0026thinsp;14 years, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), had a higher proportion of males (67.88% vs. 54.53%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), and higher BMI (24.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 vs. 23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001) than the control group. Additionally, the smoking rate was higher (31.98% vs. 26.01%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), while the proportion of patients who consumed alcohol was comparatively lower (38.52% vs. 48.31%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001) in the AAA patient group. Interestingly, the AAA group exhibited higher levels of physical activity (21.08% vs. 17.96%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001). In terms of comorbidities, the AAA group had higher prevalances of hypertension (55.63% vs. 25.37%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), hyperlipidemia (32.7% vs. 17.34%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), CKD (15.32% vs. 6.91%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), CVD (7.52% vs. 1.87%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001), and DM (12.48% vs. 8.66%, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of study participants based on the occurrence of abdominal aortic aneurysm (AAA) in development and validation cohorts used for the predictive model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDevelopment cohort (n\u0026thinsp;=\u0026thinsp;2,755,973)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eValidation cohort (n\u0026thinsp;=\u0026thinsp;1,181,131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;2,749,459)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAA (n\u0026thinsp;=\u0026thinsp;6,514)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;1,178,295)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAAA (n\u0026thinsp;=\u0026thinsp;2,836)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e852,451 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e365,202 (30.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95 (3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,540,291 (56.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,033 (46.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e659,514 (55.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,363 (48.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356,717 (12.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,248 (49.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e154,957 (13.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,378 (48.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,499,346 (54.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,422 (67.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e642,713 (54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,899 (66.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome level (1st quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e535,877 (19.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,306 (20.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e229,790 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e553 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e896,723 (32.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,582 (39.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e384,172 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,090 (38.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,640,878 (69.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,105 (47.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e702,867 (59.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,367 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e393,372 (14.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,326 (20.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e168,886 (14.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e558 (19.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e715,209(26.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,083 (31.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e106,542 (26.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e911 (32.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,328,379 (48.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,509 (38.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e569,823 (48.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,139 (40.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e493,732 (17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,373 (21.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e211,776 (17.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e574 (20.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238,240 (8.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e813 (12.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e102,195 (8.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e368 (12.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (3 levels)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,883,391 (68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,898 (59.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e806,380 (68.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,699 (59.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpaired fasting glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e627,828 (22.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,803 (27.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e269,720 (22.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e769 (27.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238,240 (8.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e813 (12.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e102,195 (8.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e368 (12.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e697,404 (25.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,624 (55.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e298,660 (25.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,572 (55.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (3 levels)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e951,112 (34.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,015 (15.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e408,897 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e435 (15.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,100,943 (40.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,875 (28.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e470,738 (39.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e829 (29.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e697,404 (25.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,624 (55.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e298,660 (25.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,572 (55.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e476,635 (17.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,130 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e203,517 (17.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e925 (32.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (3 levels)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,480,637 (53.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,556 (39.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e634,808 (53.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,094 (38.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e792,187 (28.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,828 (28.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e339,970 (28.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e817 (28.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e240\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e476,635 (17.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,130 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e203,517 (17.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e925 (32.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190,024 (6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e998 (15.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e81,389 (6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e488 (17.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiocerebrovascular disease (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51,358 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490 (7.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e21,800 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e230 (8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.19\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e80.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.29\u0026thinsp;\u0026plusmn;\u0026thinsp;23.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.99\u0026thinsp;\u0026plusmn;\u0026thinsp;23.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e97.31\u0026thinsp;\u0026plusmn;\u0026thinsp;23.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.46\u0026thinsp;\u0026plusmn;\u0026thinsp;29.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.05\u0026thinsp;\u0026plusmn;\u0026thinsp;36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200.38\u0026thinsp;\u0026plusmn;\u0026thinsp;39.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e195\u0026thinsp;\u0026plusmn;\u0026thinsp;36.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200.14\u0026thinsp;\u0026plusmn;\u0026thinsp;40.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.09\u0026thinsp;\u0026plusmn;\u0026thinsp;27.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.31\u0026thinsp;\u0026plusmn;\u0026thinsp;31.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e56.1\u0026thinsp;\u0026plusmn;\u0026thinsp;27.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.23\u0026thinsp;\u0026plusmn;\u0026thinsp;33.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.59\u0026thinsp;\u0026plusmn;\u0026thinsp;38.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.3\u0026thinsp;\u0026plusmn;\u0026thinsp;42.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e113.53\u0026thinsp;\u0026plusmn;\u0026thinsp;38.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e118.56\u0026thinsp;\u0026plusmn;\u0026thinsp;39.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eData are number (%) or mean \u0026plusmn; SD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eBMI, body mass index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Selection of variables\u003c/h2\u003e \u003cp\u003eAmong variables listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e that showed significant distribution differences between the AAA group and the control group, a total of 12 variables, excluding those with similar meanings, were selected. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) The Cox proportional hazard regression model was then utilized to examine the HR for the occurrence of AAA. In multivariate analysis, we ultimately selected 10 variables that were statistically significant, including age and sex, obesity, smoking, drinking, DM, HTN, dyslipidemia, CKD, and CVD. Old age [HR: 30.43 (95% CI: 26.48\u0026ndash;34.97)], male sex [HR: 2.01 (95% CI: 1.88\u0026ndash;2.16)], obesity [HR: 1.06 (95% CI: 1.01\u0026ndash;1.11)], smoking [HR: 2.20 (95% CI: 2.05\u0026ndash;2.36)], DM [HR: 0.64 (95% CI: 0.59\u0026ndash;0.69)], HTN [HR: 2.04 (95% CI: 1.89\u0026ndash;2.20)], dyslipidemia [HR: 1.56 (95% CI: 1.47\u0026ndash;1.66)], CKD [HR: 1.41 (95% CI: 1.31\u0026ndash;1.51)], CVD [HR: 1.50 (95% CI: 1.67\u0026ndash;1.65)] were significant predictive factors for occurrence of AAA after adjusting for all 10 variables. A nomogram for risk scoring developed from the risk prediction model was constructed to estimate the five-year risk of AAA. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazards ratios (95% CIs) for the occurrence of abdominal aortic aneurysm (Univariate model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnivariate model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e852,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,772,934.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,543,324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,764,115.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.25 (6.34\u0026ndash;8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359,965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,337,159.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.285(32.64\u0026ndash;42.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,503,768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,111,342.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.791(1.7\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,252,205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,762,867.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome level (1st quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,218,790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22,459,933.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e537,183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,414,277.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.042 (0.98\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,856,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,758,964.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e899,305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,115,245.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.351 (1.28\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,643,983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,686,049.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394,698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,967,496.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.801 (1.68\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e717,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,220,664.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.555 (1.47\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,425,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,356,278.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,330,888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,517,931.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.665 (0.63\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,260,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22,855,563.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e495,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,018,647.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.216 (1.14\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (3 levels)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,887,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,209,840.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpaired fasting glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e629,631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,347,986.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.402 (1.32\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,316,383.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.741 (1.61\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (3 levels)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e952,127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,744,813.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,102,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,215,353.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.607 (1.48\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e701,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,914,043.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.056 (4.71\u0026ndash;5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,483,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,011,409.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e794,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,056,784.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.332 (1.25\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478,765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,806,016.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.604 (2.45\u0026ndash;2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,564,951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26,006,659.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,867,551.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.529 (2.36\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiocerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,704,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,392,872.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e481,338.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.681 (4.26\u0026ndash;5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: Rate: incidence rate per 1 000 person-years.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eBMI, body mass index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazards ratios (95% CIs) for the occurrence of abdominal aortic aneurysm (Multivariate model and final model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultivariate model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFinal model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e852,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,772,934.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,543,324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,764,115.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.919 (6.04\u0026ndash;7.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.937 (6.06\u0026ndash;7.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359,965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,337,159.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.328 (26.38\u0026ndash;34.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.428 (26.47\u0026ndash;34.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,503,768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,111,342.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.014 (1.87\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.013 (1.87\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,252,205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12,762,867.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome level (1st quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,218,790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22,459,933.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e537,183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,414,277.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.048 (0.98\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,856,668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,758,964.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e899,305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,115,245.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.058 (1.00\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.058 (1.00\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,643,983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,686,049.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394,698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,967,496.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.442 (1.33\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.442 (1.33\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e717,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,220,664.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.193 (2.04\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.195 (2.04\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,425,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,356,278.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,330,888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,517,931.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.662 (0.62\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.662 (0.62\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,260,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22,855,563.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e495,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,018,647.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01 (0.95\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (3 levels)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,887,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,209,840.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpaired fasting glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e629,631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,347,986.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.919 (0.86\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.919 (0.86\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,316,383.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.637 (0.58\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.637 (0.58\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (3 levels)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e952,127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,744,813.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,102,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,215,353.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.204 (1.11\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.204 (1.11\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e701,028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,914,043.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.036 (1.88\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.038 (1.89\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,483,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,011,409.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e794,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,056,784.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.136 (1.06\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.136 (1.06\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u0026le;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478,765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,806,016.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.561 (1.47\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.56 (1.46\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,564,951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26,006,659.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,867,551.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.408 (1.31\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.406 (1.31\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiocerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,704,125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,392,872.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e481,338.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.505 (1.36\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.504 (1.36\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNote\u003c/b\u003e: Rate: incidence rate per 1 000 person-years.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eBMI, body mass index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation of the prediction model\u003c/h2\u003e \u003cp\u003eAverage follow-up period was 10.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 years, with AAA occurring in 2,836 (2.4%) out of 1,181,131 participants in the validation cohort. At baseline, the mean age was 47.24\u0026thinsp;\u0026plusmn;\u0026thinsp;14.02 years. Male patients accounted for 54.58%. Additionally, after examining the area under curve (AUC) value for AAA occurrence prediction in the prediction model, when applied to the development cohort data, the AUC was found to be 0.807 (95% CI: 0.80\u0026ndash;0.81). When applied to the validation cohort data, the AUC value was 0.803 (95% CI: 0.79\u0026ndash;0.81). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) This suggests that the model is effective in predicting the occurrence of AAA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Prediction model\u003c/h2\u003e \u003cp\u003eThe sum of total scores was obtained by combining scores of the 10 variables. It ranged from 0 to a maximum of 226 points. For example, a male (20 points) aged 65 or older (100 points) who smoked (23 points), had a normal weight (0 points), and had diabetes (0 points) with no other underlying conditions (0 points) would have a total score of 143 points. On the other hand, a female (0 points) aged 40 (57 points) who smoked (23 points), had a normal weight (0 points) who did not drink alcohol (12 points), had no DM (13 points) but had HTN (21 points), hyperlipidemia (13 points), and CKD (10 points) would have a total score of 149 points.(Table S1) In this case, it can be inferred that the probability of developing abdominal aortic aneurysm within 5 years is less than 0.5%.\u003c/p\u003e \u003cp\u003eIn the patient group with the highest score range of 215 points or above in the prediction model, the incidence rate of AAA was confirmed to be 1.194 per 1000 person-years.(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) Additionally, to assess the potential overfitting of the model, we compared the incidence in the validation cohort. It was observed that similar patterns were present in each interval, confirming the excellence of this predictive model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUtilizing the NHIS database in Korea, we developed and validated a straightforward yet effective risk prediction model for the occurrence of AAA. To the best of our knowledge, this study developed the first model capable of predicting the likelihood of AAA occurrence after a prolonged period of 5 years using data obtained through long-term follow-up observations. Our model exhibited a strong performance with an AUC of 0.807 (95% CI: 0.80\u0026ndash;0.81). Notably, older age, male sex, obesity, current smoking, non-drinking, absence of DM, presence of hypertension, hyperlipidemia, CKD, and CVD were identified as independent predictors of an increased risk of AAA.\u003c/p\u003e \u003cp\u003eIn opinions of vascular and endovascular surgeons, prioritizing research on methods for predicting AAA growth is considered essential.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Therefore, various studies are being conducted to predict the growth of AAA. For example, approaches that include geometric perspectives from CT images, attempts to predict AAA growth using imaging modalities such as MRI to examine metabolism, and exploring associations with biomarkers have been reported.\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Of course, while predicting the growth of AAA is important, it is even more crucial to first predict the presence of AAA.\u003c/p\u003e \u003cp\u003eAAA screening is most widely conducted based on the U.S. Preventive Services Task Force guidelines.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Additionally, there are other guidelines such as the slightly more sensitive Society for Vascular Surgery (SVS) guideline and the SVS expanded criteria.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e The SVS guideline significantly broadens the scope of patients who could potentially be included in screening programs. Nevertheless, about one-quarter to one-third of patients undergoing treatment for AAA could still potentially go unnoticed by any existing screening guidelines.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e This aspect can also be verified through the model developed in this study. For instance, scores corresponding to the criteria of traditional guidelines targeting males over 60 who smoke can also be sufficiently obtained from individuals under 60, females, or non-smokers. Through this, one aspect of the model's excellence can be confirmed. Specifically, its ability to include a slightly more comprehensive range of patients while effectively identifying specific individuals within that group is confirmed.\u003c/p\u003e \u003cp\u003eThe second excellence lies in the fact that variables included in this model are easily recognizable factors such as medical history, smoking history, age, and gender. Due to this simplicity, the general public can easily check their risk of developing AAA at any time. If a cutoff value is determined through subsequent research, strongly recommending screening to individuals with risk levels above this value could help improve the screening rate, addressing one of the issues with the current screening method. Lastly, due to being developed with long-term data from a substantial number of patients, it is likely to have strength in terms of reliability. As the model developed in this study is an initial iteration, further research involving additional easily obtainable variables through subsequent validation could enhance the model's accuracy. This aligns with the initial goal of the model development, which is to expedite identification of AAA patients, enabling timely intervention before complications such as rupture, ultimately improving the survival rate.\u003c/p\u003e \u003cp\u003eThe current study is subject to several limitation. Firstly, due to the utilization of NHIS data, defining the presence or absence of a patient's diseases based on diagnosis codes and medication intake might have led to various errors and inconsistencies. This is because there may be undiagnosed asymptomatic AAA cases or patients who died from ruptured AAA without diagnosis compared to the group identified through screening. Secondly, in the selection of variables included in this model, efforts were made to incorporate well-known risk factors based on available information. However, due to the nature of the data, only variables that could be confirmed through health check-ups were included, while factors such as family history, transplantation status, or the presence of aneurysms in other peripheral arteries could not be incorporated. Thirdly, since the model was developed for the Korean population, it is necessary to assess its applicability and effectiveness when applied to different racial groups.\u003c/p\u003e \u003cp\u003eFurther research is needed to establish an appropriate cut-off level for recommending screening. Subsequently, when conducting screening on the identified patient group, assessing improvements in aspects such as all-cause mortality, AAA-related mortality, and cost-effectiveness will be essential.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe developed a multivariable risk model capable of predicting the onset of AAA. In this study, the model demonstrated excellent performance with an AUC value of 0.807, surpassing traditional screening methods. It is anticipated that this model can selectively identify patients from a slightly more comprehensive pool compared to existing screening approaches. Moreover, efforts should be directed towards proactive screening of high-risk individuals for AAA, aiming to reduce AAA-related mortality. For further optimization of the model's performance, external validation is necessary, along with additional analysis and refinement using larger cohort data. The development of an appropriate surveillance program in response to these findings is also crucial.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eAuthor Disclosures\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNone of the authors have anything to disclose.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeclaration of conflicting interests\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNone declared.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHyungjin Cho: Conception and design, analysis and interpretation, Writing the manuscript, Critical revision, Approval of the manuscript, Agreement to be accountable Mi-hyeong Kim: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable Kyung-jai Ko: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable Kang-woong Jun: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable Kyung-do Han: Conception and design, analysis and interpretation, Data collection, Critical revision, Approval of the manuscript, Agreement to be accountable, Statistical Analysis Jeong-kye Hwang: Conception and design, Critical revision, Approval of the manuscript, Agreement to be accountable\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research was supported by a grant funded by The Catholic University of Korea, Eunpyeong St. Mary\u0026rsquo;s Hospital, Research Institute of Medical Science in program year 2023.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the National Health Insurance Service (NHIS) database of Korea. Restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. However, the data can be accessed through the NHIS upon reasonable request and institutional approval. Researchers interested in accessing the data may contact the corresponding author for guidance on how to obtain it from the NHIS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarcaccio CL, Schermerhorn ML. Epidemiology of abdominal aortic aneurysms. \u003cem\u003eSeminars in Vascular Surgery\u003c/em\u003e 2021;\u003cstrong\u003e34\u003c/strong\u003e(1):29\u0026ndash;37. Doi: 10.1053/j.semvascsurg.2021.02.004.\u003c/li\u003e\n\u003cli\u003eAn SY, Hwang W, Sun BJ, Park J-H. Prevalence and Prediction of Aneurysmal Dilatation of the Abdominal Aorta in Koreans: Results of Screening During Transthoracic Echocardiographic Examination. \u003cem\u003eJ Cardiovasc Imaging\u003c/em\u003e 2020;\u003cstrong\u003e28\u003c/strong\u003e(4):257\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eSakalihasan N, Limet R, Defawe OD. Abdominal aortic aneurysm 2005;\u003cstrong\u003e365\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eOliver-Williams C, Sweeting MJ, Turton G, Parkin D, Cooper D, Rodd C, et al. Lessons learned about prevalence and growth rates of abdominal aortic aneurysms from a 25-year ultrasound population screening programme. \u003cem\u003eBritish Journal of Surgery\u003c/em\u003e 2017;\u003cstrong\u003e105\u003c/strong\u003e(1):68\u0026ndash;74. Doi: 10.1002/bjs.10715.\u003c/li\u003e\n\u003cli\u003eReimerink JJ, Van Der Laan MJ, Koelemay MJ, Balm R, Legemate DA. Systematic review and meta-analysis of population-based mortality from ruptured abdominal aortic aneurysm. \u003cem\u003eBritish Journal of Surgery\u003c/em\u003e 2013;\u003cstrong\u003e100\u003c/strong\u003e(11):1405\u0026ndash;13. Doi: 10.1002/bjs.9235.\u003c/li\u003e\n\u003cli\u003eSchmitz-Rixen T, B\u0026ouml;ckler D, Vogl TJ, Grundmann RT. Endovascular and Open Repair of Abdominal Aortic Aneurysm. \u003cem\u003eDeutsches \u0026Auml;rzteblatt International\u003c/em\u003e 2020. Doi: 10.3238/arztebl.2020.0813.\u003c/li\u003e\n\u003cli\u003eS\u0026oslash;gaard R, Lindholt JS. Cost-effectiveness of population-based vascular disease screening and intervention in men from the Viborg Vascular (VIVA) trial. \u003cem\u003eBritish Journal of Surgery\u003c/em\u003e 2018;\u003cstrong\u003e105\u003c/strong\u003e(10):1283\u0026ndash;93. Doi: 10.1002/bjs.10872.\u003c/li\u003e\n\u003cli\u003eGlover MJ, Kim LG, Sweeting MJ, Thompson SG, Buxton MJ. Cost-effectiveness of the National Health Service abdominal aortic aneurysm screening programme in England. \u003cem\u003eBritish Journal of Surgery\u003c/em\u003e 2014;\u003cstrong\u003e101\u003c/strong\u003e(8):976\u0026ndash;82. Doi: 10.1002/bjs.9528.\u003c/li\u003e\n\u003cli\u003eUS Preventive Services Task Force, Owens DK, Davidson KW, Krist AH, Barry MJ, Cabana M, et al. Screening for Abdominal Aortic Aneurysm: US Preventive Services Task Force Recommendation Statement. \u003cem\u003eJAMA\u003c/em\u003e 2019;\u003cstrong\u003e322\u003c/strong\u003e(22):2211. Doi: 10.1001/jama.2019.18928.\u003c/li\u003e\n\u003cli\u003eWanhainen A, Hultgren R, Linn\u0026eacute; A, Holst J, Gotts\u0026auml;ter A, Langenski\u0026ouml;ld M, et al. Outcome of the Swedish Nationwide Abdominal Aortic Aneurysm Screening Program. \u003cem\u003eCirculation\u003c/em\u003e 2016;\u003cstrong\u003e134\u003c/strong\u003e(16):1141\u0026ndash;8. Doi: 10.1161/CIRCULATIONAHA.116.022305.\u003c/li\u003e\n\u003cli\u003eSummers KL, Kerut EK, Sheahan CM, Sheahan MG. Evaluating the prevalence of abdominal aortic aneurysms in the United States through a national screening database. \u003cem\u003eJournal of Vascular Surgery\u003c/em\u003e 2021;\u003cstrong\u003e73\u003c/strong\u003e(1):61\u0026ndash;8. Doi: 10.1016/j.jvs.2020.03.046.\u003c/li\u003e\n\u003cli\u003eCho H, Yoo J, Kim M, Ko K, Jun K, Han K, et al. The risk of dementia in adults with abdominal aortic aneurysm. \u003cem\u003eSci Rep\u003c/em\u003e 2022;\u003cstrong\u003e12\u003c/strong\u003e(1):1228. Doi: 10.1038/s41598-022-05191-1.\u003c/li\u003e\n\u003cli\u003eCho H, Yoo J, Kim M, Ko K, Jun K, Han K, et al. Risk of various cancers in adults with abdominal aortic aneurysms. \u003cem\u003eJournal of Vascular Surgery\u003c/em\u003e 2023;\u003cstrong\u003e77\u003c/strong\u003e(1):80-88.e2. Doi: 10.1016/j.jvs.2022.03.896.\u003c/li\u003e\n\u003cli\u003eBonnett LJ, Snell KIE, Collins GS, Riley RD. Guide to presenting clinical prediction models for use in clinical settings. \u003cem\u003eBMJ\u003c/em\u003e 2019:l737. Doi: 10.1136/bmj.l737.\u003c/li\u003e\n\u003cli\u003eLee Y, Bang H, Kim DJ. How to Establish Clinical Prediction Models. \u003cem\u003eEndocrinol Metab\u003c/em\u003e 2016;\u003cstrong\u003e31\u003c/strong\u003e(1):38. Doi: 10.3803/EnM.2016.31.1.38.\u003c/li\u003e\n\u003cli\u003eLee R, Jones A, Cassimjee I, Handa A. International opinion on priorities in research for small abdominal aortic aneurysms and the potential path for research to impact clinical management. \u003cem\u003eInternational Journal of Cardiology\u003c/em\u003e 2017;\u003cstrong\u003e245\u003c/strong\u003e:253\u0026ndash;5. Doi: 10.1016/j.ijcard.2017.06.058.\u003c/li\u003e\n\u003cli\u003eVermeulen JJM, Meijer M, De Vries FBG, Reijnen MMPJ, Holewijn S, Thijssen DHJ. A systematic review summarizing local vascular characteristics of aneurysm wall to predict for progression and rupture risk of abdominal aortic aneurysms. \u003cem\u003eJournal of Vascular Surgery\u003c/em\u003e 2023;\u003cstrong\u003e77\u003c/strong\u003e(1):288-298.e2. Doi: 10.1016/j.jvs.2022.07.008.\u003c/li\u003e\n\u003cli\u003eForneris A, Beddoes R, Benovoy M, Faris P, Moore RD, Di Martino ES. AI-powered assessment of biomarkers for growth prediction of abdominal aortic aneurysms. \u003cem\u003eJVS-Vascular Science\u003c/em\u003e 2023;\u003cstrong\u003e4\u003c/strong\u003e:100119. Doi: 10.1016/j.jvssci.2023.100119.\u003c/li\u003e\n\u003cli\u003eChandrashekar A, Handa A, Lapolla P, Shivakumar N, Ngetich E, Grau V, et al. Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period. \u003cem\u003eAnnals of Surgery\u003c/em\u003e 2023;\u003cstrong\u003e277\u003c/strong\u003e(1):e175\u0026ndash;83. Doi: 10.1097/SLA.0000000000004711.\u003c/li\u003e\n\u003cli\u003eU.S. Preventive Services Task Force. Screening for abdominal aortic aneurysm: recommendation statement. \u003cem\u003eAnn Intern Med\u003c/em\u003e 2005;\u003cstrong\u003e142\u003c/strong\u003e(3):198\u0026ndash;202. Doi: 10.7326/0003-4819-142-3-200502010-00011.\u003c/li\u003e\n\u003cli\u003eChaikof EL, Dalman RL, Eskandari MK, Jackson BM, Lee WA, Mansour MA, et al. The Society for Vascular Surgery practice guidelines on the care of patients with an abdominal aortic aneurysm. \u003cem\u003eJournal of Vascular Surgery\u003c/em\u003e 2018;\u003cstrong\u003e67\u003c/strong\u003e(1):2-77.e2. Doi: 10.1016/j.jvs.2017.10.044.\u003c/li\u003e\n\u003cli\u003eCarnevale ML, Koleilat I, Lipsitz EC, Friedmann P, Indes JE. Extended screening guidelines for the diagnosis of abdominal aortic aneurysm. \u003cem\u003eJournal of Vascular Surgery\u003c/em\u003e 2020;\u003cstrong\u003e72\u003c/strong\u003e(6):1917\u0026ndash;26. Doi: 10.1016/j.jvs.2020.03.047.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"aortic aneurysm, abdominal, nomograms","lastPublishedDoi":"10.21203/rs.3.rs-6275213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6275213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAbdominal aortic aneurysm (AAA) is characterized by irreversible localized dilatation of the abdominal aorta. It poses a significant health risk. As AAA size tends to increase over time, there is a heightened risk of rupture, resulting in a substantially high mortality rate. Although AAA screening programs targeting specific demographics are available, there is room for improvement in terms of inclusivity and cost-effectiveness. This study aimed to develop a predictive model for AAA occurrence utilizing seven years of data from the Korean National Health Insurance Service database (NHIS).\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThis study utilized NHIS data from 2009 to 2020. A total of 4,234,415 individuals who underwent health examinations in 2009 were identified. After applying exclusion criteria, a total of 3,937,535 individuals were selected. Of them, 70% were used for model development and 30% were used for validation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean follow-up duration was 10.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 years, during which 2,836 cases of AAA were identified among 1,181,131 (2.4%) participants in the validation cohort. The model incorporated a set of 10 variables, encompassing age, sex, obesity, smoking, drinking, diabetes (DM), hypertension (HTN), dyslipidemia, chronic kidney disease (CKD), and cardiovascular disease (CVD). Evaluation of the model's predictive performance revealed an area under the curve (AUC) of 0.807 (95% CI: 0.80\u0026ndash;0.81) when it was applied to the development cohort. The AUC remained high at 0.803 (95% CI: 0.79\u0026ndash;0.81) when the model was applied to the validation cohort, indicating its effectiveness in forecasting AAA occurrence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA multivariable risk model for predicting the onset of AAA was successfully developed, showcasing an excellent performance with an AUC value of 0.807, surpassing traditional screening methods. This model has the potential to selectively identify high-risk patients from a slightly broader pool than current screening approaches. Priority should be given to proactive screening efforts targeting individuals at elevated risk for AAA, with the goal of reducing AAA-related mortality.\u003c/p\u003e","manuscriptTitle":"Development and validation of a risk prediction model for abdominal aortic aneurysm: A nationwide population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 10:10:13","doi":"10.21203/rs.3.rs-6275213/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T05:58:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-06T09:11:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208805537579246887326592366008953240710","date":"2025-05-06T08:47:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T14:11:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T01:52:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143592614866268708267980677729702470607","date":"2025-04-10T01:20:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240499698219260282243983360037561537817","date":"2025-04-02T13:35:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-30T13:50:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-30T13:48:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-27T11:49:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T05:49:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-21T07:35:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d13813f-cf25-4edb-ab77-868f6896552a","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47329478,"name":"Health sciences/Cardiology"},{"id":47329479,"name":"Health sciences/Health care"},{"id":47329480,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-07-28T16:10:45+00:00","versionOfRecord":{"articleIdentity":"rs-6275213","link":"https://doi.org/10.1038/s41598-025-11956-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-21 15:57:48","publishedOnDateReadable":"July 21st, 2025"},"versionCreatedAt":"2025-04-21 10:10:13","video":"","vorDoi":"10.1038/s41598-025-11956-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-11956-1","workflowStages":[]},"version":"v1","identity":"rs-6275213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6275213","identity":"rs-6275213","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}