{"paper_id":"24ccd9d0-c56a-4239-b86d-9d1e9092fe4d","body_text":"Association between a body shape index and stroke: a cross- sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between a body shape index and stroke: a cross- sectional study Jun You, Yiwen He, Min Xu, Zhenjie Fan, Zhiyong Wang, Min Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4261745/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Globally, stroke remains a top cause of mortality and morbidity, highlighting the critical need for new predictive biomarkers to assess risk. A body shape index (ABSI) is increasingly recognized as a possible predictor of cardiovascular risk, though its connection with stroke incidence remains unclear. Methods This research utilizes data from the National Health and Nutrition Examination Survey (NHANES), covering a representative sample of the US population from 2005 to 2018. A weighted multivariable logistic regression method was used to investigate the relationship between ABSI and stroke incidence, including subgroup analyses to investigate potential interactions involving coronary heart disease (CHD). Results Following covariate adjustment, the incidence of stroke and ABSI were found to correlate significantly positively (OR = 1.47, 95% CI: 1.18, 1.81). This association remained consistent when ABSI was categorized into quartiles. Subgroup analysis indicated an interaction effect among patients with CHD ( P for interaction = 0.002). Conclusion A significant association between ABSI and stroke incidence was demonstrated in our study. however, the relationship between ABSI and stroke may be attenuated or masked in patients with CHD. A Body Shape Index Stroke Obesity NHANES Cross-Sectional Study Figures Figure 1 Figure 2 Figure 3 Background Stroke remains a major public health issue globally, positioned as the second most common cause of death and a leading contributor to mortality and disability worldwide ( 1 ). As the aging population grows, the incidence of stroke in the elderly is on the rise( 2 ). The most prevalent stroke type was ischemic stroke, with less than 5% of patients receiving effective treatment within the eligible treatment time window( 3 ). The extensive burden of stroke extends beyond healthcare costs, encompassing productivity loss and long-term disability. Epidemiological data reveal the profound impact of stroke on global health, particularly in low and middle-income nations ( 4 , 5 ). Consequently, identifying and modifying modifiable stroke risk factors holds critical public health importance. Obesity represents a global health crisis, intricately linked to a range of diseases including cardiovascular diseases, stroke, non-alcoholic fatty liver disease, diabetes, sleep disorders, and depression( 6 – 8 ). Traditionally, Body Mass Index (BMI) has long served as the fundamental measure for evaluating obesity and associated health risks ( 9 ). However, BMI lacks specificity in differentiating fat from muscle mass and does not accurately indicate the proportion of body fat in total body weight, nor does it account for fat distribution. Previous studies have highlighted these limitations of BMI ( 10 – 12 ). Krakauer and Krakauer's 2012 study introduced the 'A Body Shape Index' (ABSI), aimed at overcoming the limitations of the BMI index by focusing on abdominal/visceral obesity, and demonstrated that ABSI is a more accurate indicator of cardiovascular disease risk and mortality than BMI ( 13 ). Subsequent studies have further demonstrated ABSI's close association with diabetes, cardiovascular diseases, and mortality risk( 14 – 17 ). The etiology of stroke is complex, and obesity is recognized as a significant risk factor( 18 ). However, to date, the potential relationship between ABSI and stroke has not been clarified. this research undertook a cross-sectional analysis to investigate this association, using data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2005 to 2018. Methods Study design and population NHANES is a research project aimed at assessing the health and nutritional statuses of people of all ages across the United States, utilizing stratified multistage probability sampling to generate a representative sample, conducting household interviews, physical examinations, and a series of laboratory tests for all participants. Our selection criteria for the study population encompassed a multistage exclusion process. Initially, individuals lacking data pertinent to the ABSI—inclusive of waist circumference, and BMI—were excluded, resulting in the removal of 11,360 participants from an original cohort of 70,190. Subsequently, an additional 23,069 individuals without stroke status information were also excluded from the study. Consequently, the final sample size comprised 35,761 participants for analysis (Fig. 1 ). As all data were sourced from the NHANES database, the First Clinical Medical College of China Three Gorges University's Institutional Review Board waived the need for ethical approval and written informed consent. A body shape index The ABSI is calculated by the following formula: ABSI (m 11/6 /kg − 2/3 ) = WC (m)/(BMI [kg/m 2 ] 2/3 ×height [m] 1/2 )( 13 ). ABSI data were obtained from the NHANES database \"Body Measures\", and the examinees were instructed by professional staff and measured on professional equipment in trailer 1 of the mobile examination center to ensure the reliability of the data. Stroke The definition of stroke was \"Has a doctor or other health professional ever told you that you have had a stroke?\" in the NHANES Medical Conditions questionnaire( 19 , 20 ). Only responses of 'Yes' or 'No' were included in the survey population. Additionally, the study lacked data for individuals under 20 years of age due to age restrictions on the question. Covariates In the present investigation, a comprehensive range of potential covariates was incorporated. Covariates in this study included demographic factors (sex, age, race, poverty-income ratio [PIR < 1 indicating poverty], marital status, education level) and health behaviors (smoking, drinking, physical activity). Medical conditions (hypertension, diabetes, coronary heart disease [CHD], sleep disorders, depression, cancer) and the following biochemical parameters: hemoglobin A1c, cholesterol totals, LDL cholesterol levels, and measurements of systolic blood pressure were also assessed. the ratio of triglycerides to HDL cholesterol (TG/HDL-C) was also included due to its known correlation with cardiovascular events( 21 , 22 ). Statistical analysis This study adhered to NHANES reporting guidelines and used the appropriate NHANES sample weights for statistical analysis, given the complex sampling design of NHANES. The participants' characteristics were assessed based on the ABSI quartiles. The quartiles were compared using t-tests or chi-square analysis. Continuous variables are presented using weighted means (95% confidence intervals (CI)), whereas categorical variables are detailed using unweighted sample sizes and weighted proportions. Weighted univariate logistic regression analyses were initially conducted to identify the relationship between each covariate and the dependent variable. Subsequently, all covariates were included in weighted multivariate regression analyses. The study examined the relationship between ABSI and stroke using multivariate logit modeling. Three models were used: Model 1, which was unadjusted for covariates, Model 2, which was adjusted for age, sex, and race, and Model 3, which was adjusted for significant covariates from weighted multivariate analyses. ABSI was converted to categorical variables (quartiles) and the linear trend between ABSI and stroke was investigated using a trend test. To look at possible nonlinear relationships between stroke and ABSI, the study used the restricted cubic spline (RCS). Subgroup analyses of ABSI and stroke were performed based on age, gender, race, poverty level, obesity (BMI > 30), smoking, drinking, hypertension, CHD, diabetes, sleep disorders, and depression. To assess the consistency of associations between different subgroups, interaction tests were performed. To avoid selection bias caused by the exclusion of samples with missing data, the 'multiple filling method' was used to fill in missing covariates. We applied R software (version 4.3.3) for all statistical analyses, and Statistical significance was ascribed to two-tailed P values falling below the 0.05 threshold. Results Baseline characteristics The study included 35,761 participants with an average age of 49.1 ± 17.7 years, comprising 48.6% males and 51.4% females. The study revealed a stroke prevalence of 3.53%, with an increment noted in association with higher A Body Shape Index (ABSI) values. The overall mean ABSI of the participants was 0.08170 ± 0.00494 (m 11/6 /kg − 2/3 ). The weighted quartiles of ABSI were delineated as follows: Q1: <0.0781; Q2: 0.07817 to 0.08135; Q3: 0.08135 to 0.08459; Q4: >0.08459. In contrast to the lowest ABSI quartile group, the highest quartile was distinguished by a higher percentage of non-Hispanic whites, the elderly, and males. In addition to being less physically active, smoking was more common in this group. Furthermore, there was a significant increase in the incidence rates of hypertension, CHD, diabetes, sleep disorders, and stroke among these individuals (Table 1). Table 1 Using a body shape index quartile, the participants' fundamental characteristics. For continuous variables, mean ± SD was utilized, with P values derived from the weighted linear regression model; for categorical variables, percentages were used, with P values obtained through the weighted chi-square test. Abbreviations: Q Quartile, BMI Body mass index, CHD Coronary heart disease, TC Total cholesterol, TG Triglycerides, HDL-C High-density lipoprotein cholesterol, SBP Systolic blood pressure. Association between the ABSI and stroke The weighted univariate regression analysis for each covariate demonstrated an association with stroke ( P <0.05). Upon inclusion in the multivariate regression analysis, the heightened risk associated with age over 65 persisted (OR: 9.58, 95% CI: 6.35-14.46). Smoking (OR:1.77, 95% CI: 1.47-2.12), hypertension (OR: 2.73, 95% CI: 2.24-3.31), coronary heart disease (CHD) (OR: 2.56, 95% CI: 1.99-3.29), diabetes (OR: 1.57, 95% CI: 1.25-1.95), sleep disorders (OR: 1.41, 95% CI: 1.1-1.81), and depression (OR: 1.76, 95% CI: 1.3-2.38) maintained a significant association with the likelihood of stroke. The probability of stroke with each 0.01 unit increase in ABSI remained significant though slightly diminished (OR: 1.44, 95% CI: 1.16-1.8). Furthermore, marital status, educational level, BMI, alcohol consumption, physical activity, glycosylated proteins, the TG/HDL-C ratio, and systolic blood pressure (SBP) became non-significant in the weighted multivariate regression analysis (Figure 2). Table 2 results indicate that the ABSI (multiplied by 100) as a continuous variable had an initial effect size (OR=3.92, 95% CI: 3.30, 4.66) which, after full adjustment for covariates, was reduced (OR=1.47, 95% CI: 1.18, 1.81) yet remained significant. The positive correlation remained consistent and significant across the ABSI quartiles (trend P -value <0.001). Subjects in the highest quartile experienced a 52% increase in stroke incidence compared to those in the lowest quartile, with an odds ratio of 1.52 (95% CI: 1.12, 2.06) in the fully adjusted model. Furthermore, the nonlinear relationship between ABSI and stroke was not significant ( P for nonlinearity = 0.7730) (Figure 3). Table 2 The association between ABSI and stroke. In Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC). Subgroup analyses Subgroup analyses were conducted based on characteristics such as sex, age, PIR, BMI, smoking, alcohol use, presence of hypertension, CHD, diabetes, sleep disorders, and depression (Table 3). The presence of CHD may influence the link between ABSI and stroke, as P for interaction showed that the association of ABSI and the likelihood of stroke differed considerably with the CHD status (P for interaction = 0.002). However, in other subgroups, the association between ABSI and stroke risk did not demonstrate significant interaction effects, implying a relatively consistent relationship between ABSI and stroke across these groups. Table 3 Subgroup Analysis by ABSI on Stroke Incidence Age, sex, race, poverty level, BMI, smoking, drinking, hypertension, CHD, diabetes, sleep disorders, depression, and TC were adjusted. Abbreviations: OR Odds ratio, CI Confidence interval, BMI Body mass index, CHD Coronary heart disease. Sensitive analysis Additionally, we performed sensitivity analyses using unweighted logistic regression to look at the connection between stroke and ABSI (Table 4). These results were consistent with those obtained from the weighted logistic regression analysis. The sensitivity analyses underscore the reliability of our current findings, demonstrating a positive correlation between increased ABSI and the incidence of stroke. Table 4 Sensitive analysis using unweighted logistic regression to examine the relationship between ABI and stroke. In Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC). Discussion After adjustment for potential covariates, this cross-sectional study shows a significant association between ABSI and stroke. The association remained stable and present when ABSI was categorized into quartiles. This aligns with previous studies suggesting ABSI as a predictor of the risk of cardiovascular disease ( 13 ). However, unlike Nam et al.'s study on the relationship between ABSI and small vessel disease, there appeared to be no discernible non-linear trend in the correlation between stroke and ABSI. ( 15 ). In subgroup analysis, the presence of CHD interacted with the association between ABSI and stroke. Studies on the association between ABSI and stroke are sparse. Abete et al. found that ABSI was Significantly in line with the overall incidence of stroke only in males ( 23 ). Further research on the association of ABSI with cardiovascular and cerebrovascular diseases in different populations could corroborate our findings. For instance, in the Turkish population, particularly in men, ABSI alone predicted cardiovascular disease 10-year risk more accurately than BMI or other obesity indices ( 24 ). In a prospective study based on the Rotterdam population, ABSI was also found to be an equivalent predictor of cardiovascular risk for men( 25 ). Similar predictive abilities of ABSI for cardiovascular risk were observed in the Caucasian population( 26 ) and in association with cerebral small vessel disease in the Korean population( 15 ). Additionally, ABSI has been linked to cardiac metabolic risk factors( 27 , 28 ). However, contrary results exist, suggesting no association between ABSI and cardiovascular disease( 29 , 30 ). Our study establishes a significant correlation between ABSI and stroke, persisting even after considering traditional obesity measurements like BMI, whether as a continuous variable or categorized as > 30. The higher Odds Ratio (OR = 1.78, 95% CI:1.39, 2.29) in the male subgroup of our study further emphasizes the unique relevance of ABSI in the male population, aligning with previous research findings. Although BMI has long been used as the primary metric for evaluating obesity and related health risks, it has limitations in differentiating fat from muscle mass, especially regarding the distribution of body fat ( 10 – 12 ). BMI was not significantly associated with stroke in multivariable analysis (P = 0.896). Some studies even suggest a protective role of BMI against cardiovascular diseases, known as the \"obesity paradox\" ( 31 , 32 ). In contrast, ABSI offers an additional perspective, focusing more on the proportion of waist circumference to height and weight, thereby emphasizing the state of abdominal or visceral fat accumulation ( 13 ). A large epidemiological study involving 168,000 individuals as early as 2007 showed a significant correlation between increased Waist circumference and cardiovascular disease risk ( 33 ). Subsequent studies confirmed this abdominal obesity-cardiovascular link ( 34 ), with direct evidence linking visceral obesity to a significant risk of stroke( 35 – 37 ). An ABSI value of 0.083 (m 11/6 /kg − 2/3 ) has been suggested as the optimal threshold for assessing muscle loss and visceral fat deposition( 38 ), which could explain the meaningful OR values in our adjusted model for the Q3 and Q4 quartiles covering this threshold. The link between ABSI and stroke could have several potential mechanisms. Infiltration of macrophages into abdominal fat results in elevated levels of pro-inflammatory cytokines, causing oxidative stress and endothelial dysfunction ( 39 ). Increased leptin release from visceral adipose tissue contributes to atherosclerosis, and a reduction in anti-inflammatory adipokines like adiponectin induces oxidative stress and endothelial damage, increasing stroke risk ( 40 ). As an endocrine organ, visceral fat contributes to insulin resistance, potentially leading to arteriosclerosis( 41 ). Additionally, the accumulation of abdominal fat may lead to elevated levels of oxidized LDL-C, which is also involved in the development of arteriosclerosis( 40 ). Moreover, we found that the presence of CHD alters the association between ABSI and stroke. The association is stronger in individuals without CHD and weakened in those with CHD. CHD itself involves several factors related to stroke risk, such as atherosclerosis, blood pressure, and lipid levels( 42 , 43 ), which might interact with the effects of ABSI, resulting in a suppressed or masked association. Therefore, although ABSI generally correlates positively with stroke, it is crucial to consider these interactions when using ABSI for assessment, making personalized evaluations based on individual patient conditions. With an increasing number of new indices based on visceral fat being applied to disease prediction ( 44 – 46 ), we hope our study can provide new perspectives and data support for public health strategies, especially in the early identification and prevention of stroke. Our study has limitations. The cross-sectional study's findings merely demonstrate an association, not a causative relationship, between stroke and ABSI. Despite considering various covariates, potential unknown or unmeasured confounding factors might affect the accuracy of our results. Particularly in the analysis of the CHD subgroup, although we observed a differential association between ABSI and stroke among CHD patients, the specific mechanisms behind this difference were not fully explained in our study. Conclusion This research examines the link between ABSI and stroke occurrence. Upon examining data from the NHANES national database, a positive association was evident between ABSI and stroke rates, noting differences within the CHD subgroup. Nonetheless, the cross-sectional study design necessitates additional research to investigate the underlying causal pathways for these relationships. List of abbreviations ABSI : A Body Shape Index BMI : Body Mass Index NHANES : National Health and Nutrition Examination Survey OR : Odds Ratio CI : Confidence Interval CHD : Coronary Heart Disease PIR : Poverty-Income Ratio TC : Total Cholesterol LDL-C : Low-Density Lipoprotein Cholesterol HDL-C : High-Density Lipoprotein Cholesterol TG : Triglycerides SBP : Systolic Blood Pressure Declarations Ethics approval and consent to participate This research, which included human subjects, biological materials, and personal data, adhered to the Declaration of Helsinki and received approval from the NCHS Ethics Review Board. All participants gave their written informed consent for involvement in the research. Consent for publication Not applicable. Availability of data and materials Public data sets, accessible at https://www.cdc.gov/nchs/nhanes/, provided all of the information used in this investigation. Competing interests No conflicting interests are stated by the authors. Funding No funding. Authors' contributions Jun You conceived and designed the study, led the data analysis, and drafted the manuscript. Yiwen He assisted in the study design and played a significant role in revising the manuscript for critical intellectual content. Min Xu, Zhenjie Fan, and Zhiyong Wang participated in collecting and analyzing data and aided in manuscript preparation. Min Qian oversaw the entire study and provided final approval for the version to be published. Each author contributed significantly to the research and manuscript preparation, ensuring the integrity and accuracy of the work. Acknowledgments We are grateful to all those who supported and participated in this study. References Collaborators GS. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795-820. Sang S, Chu C, Zhang T, Chen H, Yang X. 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Visceral Adiposity and Risk of Stroke: A Mendelian Randomization Study. Front Neurol. 2022;13:804851. Muuronen AT, Taina M, Hedman M, Marttila J, Kuusisto J, Onatsu J, et al. Increased visceral adipose tissue as a potential risk factor in patients with embolic stroke of undetermined source (ESUS). PloS One. 2015;10(3):e0120598. Gomez-Peralta F, Abreu C, Cruz-Bravo M, Alcarria E, Gutierrez-Buey G, Krakauer NY, et al. Relationship between \"a body shape index (ABSI)\" and body composition in obese patients with type 2 diabetes. Diabetol Metab Syndr. 2018;10:21. Zhang Z, Tang J, Cui X, Qin B, Zhang J, Zhang L, et al. New Insights and Novel Therapeutic Potentials for Macrophages in Myocardial Infarction. Inflammation. 2021;44(5):1696-712. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875-80. Patel P, Abate N. Body fat distribution and insulin resistance. Nutrients. 2013;5(6):2019-27. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. The New England Journal of Medicine. 2005;352(16):1685-95. Libby P, Buring JE, Badimon L, Hansson GK, Deanfield J, Bittencourt MS, et al. Atherosclerosis. Nat Rev Dis Primers. 2019;5(1):56. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920-2. Zhang X, Hong F, Liu L, Nie F, Du L, Guan H, et al. Lipid accumulation product is a reliable indicator for identifying metabolic syndrome: the China Multi-Ethnic Cohort (CMEC) Study. QJM. 2022;115(3):140-7. Jin J, Woo H, Jang Y, Lee W-K, Kim J-G, Lee I-K, et al. Novel Asian-Specific Visceral Adiposity Indices Are Associated with Chronic Kidney Disease in Korean Adults. Diabetes Metab J. 2023;47(3):426-36. Tables Table 1 Using a body shape index quartile, the participants' fundamental characteristics. Characteristics A body shape index Q1 (<0.0781) Q2 (0.07817-0.08135) Q3 (0.08135-0.08459) Q4 (>0.08459) P value N=8483 N=8438 N=8963 N=9877 Sex, n (%) <0.001 Female 5043 (60.5%) 4433 (51.6%) 4362 (47.3%) 4548 ((47.1%) Male 3440 (39.5%) 4005 (48.4%) 4601 (52.7%) 5329 (52.9%) Age, n (%) <0.001 age<35years 4094 (49.8%) 2567 ((31.8%) 1735 (20.5%) 901 (9.7%) 35-65years 3876 (45.4%) 4809 (58.4%) 5307 (63.1%) 4316 (51.0%) age>65years 513 (4.8%) 1062 (9.8%) 1921 (16.4%) 4660 (39.3%) Race, n (%) <0.001 Mexican American 1067 (7.7%) 1444 (9.6%) 1645 (10.0%) 1487 (6.8%) Non-Hispanic White 2887 (59.5%) 3252 (64.7%) 3677 (68.1%) 5099 (75.3%) Non-Hispanic Black 2757 (18.3%) 1837 (11.1%) 1664 (8.8%) 1440 (6.6%) Other 1772 (14.4%) 1905 (14.6%) 1977 (13.1%) 1851 (11.3%) Poverty level, n (%) 0.338 Not poverty 6876 (86.4%) 6837 (87.3%) 7260 (87.4%) 7904 (86.6%) Poverty 1607 (13.6%) 1601 (12.7%) 1703 (12.6%) 1973 (13.4%) Married, n (%) <0.001 No 4004 (42.9%) 3196 (34.3%) 3187 (32.2%) 3871 (35.3%) Yes 4479 (57.1%) 5242 (65.7%) 5776 (67.8%) 6006 (64.7%) Education level, n (%) <0.001 High School or above 6930 (87.6%) 6562 (85.7%) 6598 (82.8%) 6819 (80.0%) Below high school 1553 (12.4%) 1876 (14.3%) 2365 (17.2%) 3058 (20.0%) BMI, (kg/m²) 28.43 (7.50) 28.73 (6.68) 29.36 (6.67) 29.22 (6.07) <0.001 Smoking, n (%) 0.017 No 6767 (81.2%) 6655 (79.7%) 7107 (79.2%) 7844 (78.5%) Yes 1716 (18.8%) 1783 (20.3%) 1856 (20.8%) 2033 (21.5%) Drinking, n (%) 0.0013 No 2955 (30.3%) 2868 (28.0%) 3020 (28.3%) 3509 (30.0%) Yes 5528 (69.7%) 5570 (72.0%) 5943 (71.7%) 6368 (70.0%) Physical Activity, n (%) <0.001 Inactive 4213 (45.7%) 4401 (47.8%) 4925 (49.8%) 5845 (53.4%) Moderate 1968 (24.8%) 1989 (25.8%) 2088 (25.8%) 2379 (27.1%) Vigorous 479 (5.4%) 457 (5.6%) 416 (4.3%) 358 (3.8%) Both moderate and vigorous 1823 (24.1%) 1591 (20.7%) 1534 (20.1%) 1295 (15.7%) Stroke, n (%) <0.001 Non-stroke 8368 (98.9%) 8254 (98.5%) 8657 (97.3%) 9219 ((94.7%) Stroke 115 (1.1%) 184 (1.5%) 306 (2.7%) 658 (5.3%) Hypertension, n (%) <0.001 No 6376 (78.5%) 5487 (68.5%) 4927 (59.6%) 4050 (45.1%) Yes 2107 (21.5%) 2951 (31.5%) 4036 (40.4%) 5827 (54.9%) CHD, n (%) <0.001 No 8408 (99.2%) 8299 (98.6%) 8634 (96.8%) 9031 (92.2%) Yes 75 (0.8%) 139 (1.4%) 329 (3.2%) 846 (7.8%) Diabetes, n (%) <0.001 No 7858 (94.6%) 7366 (91.0%) 7215 (85.4%) 6841 (74.8%) Yes 625 (5.4%) 1072 (9.0%) 1748 (14.6%) 3036 (25.2%) Sleep disorders, n (%) <0.001 No 7421 (87.7%) 7305 (86.9%) 7646 (85.8%) 8269 (84.2%) Yes 1062 (12.3%) 1133 (13.1%) 1317 (14.2%) 1608 (15.8%) Depression, n (%) <0.001 No 7892 (93.6%) 7771 (93.1%) 8256 (93.4%) 8965 (91.4%) Yes 591 (6.4%) 667 (6.9%) 707 (6.6%) 912 (8.6%) Cancer, n (%) <0.001 No 8115 (95.0%) 7903 (92.8%) 8152 (90.1%) 8290 (82.8%) Yes 368 (5.0%) 535 (7.2%) 811 (9.9%) 1587 (17.2%) Glycohemoglobin, % 5.39 (0.71) 5.50 (0.81) 5.66 (0.96) 5.89 (1.08) <0.001 TC, mg/dL 186.65 (38.13) 194.96 (40.08) 198.90 (41.36) 195.53 (44.96) <0.001 TG/HDL-C, mg/dL 2.61 (3.38) 3.48 (4.93) 3.91 (4.77) 4.01 (4.40) <0.001 SBP, mmHg 117.49 (14.76) 120.43 (16.13) 123.47 (17.15) 127.37 (19.22) <0.001 For continuous variables, mean ± SD was utilized, with P values derived from the weighted linear regression model; for categorical variables, percentages were used, with P values obtained through the weighted chi-square test. Abbreviations: Q Quartile, BMI Body mass index, CHD Coronary heart disease, TC Total cholesterol, TG Triglycerides, HDL-C High-density lipoprotein cholesterol, SBP Systolic blood pressure. Table 2 The association between ABSI and stroke. Exposure Model1 OR (95%CI) Model2 OR (95%CI) Model3 OR (95%CI) A body shape index (per100) (continuous) 3.92 (3.30, 4.66) 2.07 (1.69, 2.52) 1.47 (1.18, 1.83) A body shape index (quartile) Quartile 1 reference reference reference Quartile 2 1.38 (1.00, 1.90) 1.09 (0.80, 1.49) 1.00 (0.73, 1.36) Quartile 3 2.54 (1.93, 3.33) 1.67 (1.27, 2.20) 1.36 (1.03, 1.78) Quartile 4 5.16 (3.88, 6.86) 2.31 (1.71, 3.12) 1.52 (1.12, 2.06) P for trend <0.001 <0.001 0.001 In Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC). Table 3 Subgroup Analysis by ABSI on Stroke Incidence Characteristic OR (95%CI) P value P for interaction Sex 0.165 Female 1.338(1.069,1.675) 0.011 Male 1.781(1.386,2.290) <0.001 Age 0.204 age<35 0.982(0.371,2.595) 0.970 35-65 1.772(1.304,2.409) <0.001 age>65 1.314(1.060,1.629) 0.013 Poverty level 0.844 Not poor 1.486(1.214,1.818) <0.001 Poor 1.427(1.001,2.034) 0.049 BMI 0.177 BMI<30 1.620(1.279,2.053) <0.001 BMI>30 1.300(1.012,1.670) 0.040 Smoking 0.139 No 1.593(1.298,1.954) <0.001 Yes 1.174(0.853,1.615) 0.324 Drinking 0.486 No 1.361(1.035,1.790) 0.027 Yes 1.546(1.242,1.924) <0.001 Hypertension 0.065 No 1.990(1.375,2.882) <0.001 Yes 1.341(1.102,1.632) 0.003 CHD 0.002 No 1.646(1.357,1.996) <0.001 Yes 0.770(0.505,1.174) 0.225 Diabetes 0.118 No 1.605(1.286,2.005) <0.001 Yes 1.253(0.959,1.637) 0.098 Sleep disorders 0.069 No 1.610(1.321,1.964) <0.001 Yes 1.137(0.814,1.589) 0.451 Depression 0.280 No 1.544(1.273,1.873) <0.001 Yes 1.180(0.793,1.755) 0.414 Age, sex, race, poverty level, BMI, smoking, drinking, hypertension, CHD, diabetes, sleep disorders, depression, and TC were adjusted. Abbreviations: OR Odds ratio, CI Confidence interval, BMI Body mass index, CHD Coronary heart disease. Table 4 Sensitive analysis using unweighted logistic regression to examine the relationship between ABI and stroke. Exposure Model1 OR (95%CI) Model2 OR (95%CI) Model3 OR (95%CI) A body shape index (per100) (continuous) 3.56 (3.18, 3.99) 2.05 (1.80, 2.33) 1.54 (1.35, 1.77) A body shape index (quartile) Quartile 1 reference reference reference Quartile 2 1.62 (1.28, 2.06) 1.28 (1.01, 1.63) 1.17 (0.92, 1.49) Quartile 3 2.57 (2.08, 3.21) 1.70 (1.36, 2.13) 1.38 (1.10, 1.73) Quartile 4 5.19 (4.27, 6.38) 2.46 (1.99, 3.06) 1.69 (1.36, 2.12) P for trend <0.001 <0.001 0.001 In Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4261745\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":292168662,\"identity\":\"f4dca472-17cb-45e9-aa1b-6cf2241cf2aa\",\"order_by\":0,\"name\":\"Jun You\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yichang Central People’s Hospital, China Three Gorges University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jun\",\"middleName\":\"\",\"lastName\":\"You\",\"suffix\":\"\"},{\"id\":292168666,\"identity\":\"543ddbbc-1c36-4ad4-8c1b-270e9cdc23b2\",\"order_by\":1,\"name\":\"Yiwen He\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yichang Central People’s Hospital, China Three Gorges University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yiwen\",\"middleName\":\"\",\"lastName\":\"He\",\"suffix\":\"\"},{\"id\":292168668,\"identity\":\"fb8f3478-becf-42c9-8d3e-21e94250c7a4\",\"order_by\":2,\"name\":\"Min Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yichang Central People’s Hospital, China Three Gorges University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Min\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":292168669,\"identity\":\"e7a01936-03b4-4fba-b17f-27682968b0d1\",\"order_by\":3,\"name\":\"Zhenjie Fan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yichang Central People’s Hospital, China Three Gorges University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhenjie\",\"middleName\":\"\",\"lastName\":\"Fan\",\"suffix\":\"\"},{\"id\":292168670,\"identity\":\"d554cd2b-8f7f-4f34-a6e4-a2b47fdac335\",\"order_by\":4,\"name\":\"Zhiyong Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Yichang Central People’s Hospital, China Three Gorges University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhiyong\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":292168671,\"identity\":\"cfc25890-c16d-4574-8580-4c28c8d538b9\",\"order_by\":5,\"name\":\"Min Qian\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACZhBhwCCHzCVOizEDG9FaoCCxgWgtfMeZnz26UXAnvX9++zUJhgrrxAb2swfwapE8zGZunGPwLHfGMZ4yCYYz6YkNPHkJeLUYHGYwk84xOJy7gY0nTYKx7XBigwSPAQEt7N9AWtINwFr+EaWFB2xLggEb+zEJxgYitEge5ikDaTGccSyH2SLhWLpxG08Ofi18549vk875c1iev/n4wxsfaqxl+9nP4NfCcADOAronAUix4VePooX9AUHFo2AUjIJRMDIBAKXvP+sJEJV1AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Yichang Central People’s Hospital, China Three Gorges University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Min\",\"middleName\":\"\",\"lastName\":\"Qian\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-04-13 12:29:27\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4261745/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4261745/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":55316164,\"identity\":\"aa6c849d-a2d8-417a-9651-dd5b8957fda1\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:45:33\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":134505,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eParticipant Selection Flowchart.\\u003c/p\\u003e\\n\\u003cp\\u003eNHANES National Health and Nutrition Examination Survey, ABSI A body shape index.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4261745/v1/8e30000a2ad88f45a2613dcd.png\"},{\"id\":55317800,\"identity\":\"b341d089-125a-4af3-81a6-9b758c5b29fc\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:53:33\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":222567,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUnivariate and multivariate logistic regression analysis. A comparison of categorical variables is omitted.\\u003c/p\\u003e\\n\\u003cp\\u003eCrude OR and Crude P Value were the results of weighted univariate logistic regression analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eadj OR and adj P Value were the results of weighted multivariate logistic regression analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: OR Odds ratio, CI Confidence interval, BMI Body mass index, ABSI A body shape index, CHD Coronary heart disease, TC Total cholesterol, TG Triglycerides, HDL-C High-density lipoprotein cholesterol, SBP Systolic blood pressure.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4261745/v1/2c1f3cc86624082478193238.png\"},{\"id\":55316165,\"identity\":\"13fc6147-cdbd-4683-81a6-71f38c0f6cd2\",\"added_by\":\"auto\",\"created_at\":\"2024-04-25 15:45:33\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":41420,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRestricted cubic spline analysis of ABSI and stroke.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4261745/v1/0776a5efb6e1f3eef044f81d.png\"},{\"id\":67050192,\"identity\":\"b0916190-6bdb-401a-b4e3-a206ac4821e5\",\"added_by\":\"auto\",\"created_at\":\"2024-10-20 13:31:59\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1201857,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4261745/v1/18973a12-3b80-4a21-97c2-f186d894f2e4.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association between a body shape index and stroke: a cross- sectional study\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eStroke remains a major public health issue globally, positioned as the second most common cause of death and a leading contributor to mortality and disability worldwide (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). As the aging population grows, the incidence of stroke in the elderly is on the rise(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). The most prevalent stroke type was ischemic stroke, with less than 5% of patients receiving effective treatment within the eligible treatment time window(\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). The extensive burden of stroke extends beyond healthcare costs, encompassing productivity loss and long-term disability. Epidemiological data reveal the profound impact of stroke on global health, particularly in low and middle-income nations (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). Consequently, identifying and modifying modifiable stroke risk factors holds critical public health importance.\\u003c/p\\u003e \\u003cp\\u003eObesity represents a global health crisis, intricately linked to a range of diseases including cardiovascular diseases, stroke, non-alcoholic fatty liver disease, diabetes, sleep disorders, and depression(\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Traditionally, Body Mass Index (BMI) has long served as the fundamental measure for evaluating obesity and associated health risks (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). However, BMI lacks specificity in differentiating fat from muscle mass and does not accurately indicate the proportion of body fat in total body weight, nor does it account for fat distribution. Previous studies have highlighted these limitations of BMI (\\u003cspan additionalcitationids=\\\"CR11\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Krakauer and Krakauer's 2012 study introduced the 'A Body Shape Index' (ABSI), aimed at overcoming the limitations of the BMI index by focusing on abdominal/visceral obesity, and demonstrated that ABSI is a more accurate indicator of cardiovascular disease risk and mortality than BMI (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). Subsequent studies have further demonstrated ABSI's close association with diabetes, cardiovascular diseases, and mortality risk(\\u003cspan additionalcitationids=\\\"CR15 CR16\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe etiology of stroke is complex, and obesity is recognized as a significant risk factor(\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). However, to date, the potential relationship between ABSI and stroke has not been clarified. this research undertook a cross-sectional analysis to investigate this association, using data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2005 to 2018.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eStudy design and population\\u003c/p\\u003e\\n\\u003cp\\u003eNHANES is a research project aimed at assessing the health and nutritional statuses of people of all ages across the United States, utilizing stratified multistage probability sampling to generate a representative sample, conducting household interviews, physical examinations, and a series of laboratory tests for all participants. Our selection criteria for the study population encompassed a multistage exclusion process. Initially, individuals lacking data pertinent to the ABSI\\u0026mdash;inclusive of waist circumference, and BMI\\u0026mdash;were excluded, resulting in the removal of 11,360 participants from an original cohort of 70,190. Subsequently, an additional 23,069 individuals without stroke status information were also excluded from the study. Consequently, the final sample size comprised 35,761 participants for analysis (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). As all data were sourced from the NHANES database, the First Clinical Medical College of China Three Gorges University\\u0026apos;s Institutional Review Board waived the need for ethical approval and written informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003eA body shape index\\u003c/p\\u003e\\n\\u003cp\\u003eThe ABSI is calculated by the following formula: ABSI (m\\u003csup\\u003e11/6\\u003c/sup\\u003e/kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2/3\\u003c/sup\\u003e)\\u0026thinsp;=\\u0026thinsp;WC (m)/(BMI [kg/m\\u003csup\\u003e2\\u003c/sup\\u003e]\\u003csup\\u003e2/3\\u003c/sup\\u003e\\u0026times;height [m]\\u003csup\\u003e1/2\\u003c/sup\\u003e)(\\u003cspan class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). ABSI data were obtained from the NHANES database \\u0026quot;Body Measures\\u0026quot;, and the examinees were instructed by professional staff and measured on professional equipment in trailer 1 of the mobile examination center to ensure the reliability of the data.\\u003c/p\\u003e\\n\\u003cp\\u003eStroke\\u003c/p\\u003e\\n\\u003cp\\u003eThe definition of stroke was \\u0026quot;Has a doctor or other health professional ever told you that you have had a stroke?\\u0026quot; in the NHANES Medical Conditions questionnaire(\\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). Only responses of \\u0026apos;Yes\\u0026apos; or \\u0026apos;No\\u0026apos; were included in the survey population. Additionally, the study lacked data for individuals under 20 years of age due to age restrictions on the question.\\u003c/p\\u003e\\n\\u003cp\\u003eCovariates\\u003c/p\\u003e\\n\\u003cp\\u003eIn the present investigation, a comprehensive range of potential covariates was incorporated. Covariates in this study included demographic factors (sex, age, race, poverty-income ratio [PIR\\u0026thinsp;\\u0026lt;\\u0026thinsp;1 indicating poverty], marital status, education level) and health behaviors (smoking, drinking, physical activity). Medical conditions (hypertension, diabetes, coronary heart disease [CHD], sleep disorders, depression, cancer) and the following biochemical parameters: hemoglobin A1c, cholesterol totals, LDL cholesterol levels, and measurements of systolic blood pressure were also assessed. the ratio of triglycerides to HDL cholesterol (TG/HDL-C) was also included due to its known correlation with cardiovascular events(\\u003cspan class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eThis study adhered to NHANES reporting guidelines and used the appropriate NHANES sample weights for statistical analysis, given the complex sampling design of NHANES. The participants\\u0026apos; characteristics were assessed based on the ABSI quartiles. The quartiles were compared using t-tests or chi-square analysis. Continuous variables are presented using weighted means (95% confidence intervals (CI)), whereas categorical variables are detailed using unweighted sample sizes and weighted proportions. Weighted univariate logistic regression analyses were initially conducted to identify the relationship between each covariate and the dependent variable. Subsequently, all covariates were included in weighted multivariate regression analyses. The study examined the relationship between ABSI and stroke using multivariate logit modeling. Three models were used: Model 1, which was unadjusted for covariates, Model 2, which was adjusted for age, sex, and race, and Model 3, which was adjusted for significant covariates from weighted multivariate analyses. ABSI was converted to categorical variables (quartiles) and the linear trend between ABSI and stroke was investigated using a trend test. To look at possible nonlinear relationships between stroke and ABSI, the study used the restricted cubic spline (RCS). Subgroup analyses of ABSI and stroke were performed based on age, gender, race, poverty level, obesity (BMI\\u0026thinsp;\\u0026gt;\\u0026thinsp;30), smoking, drinking, hypertension, CHD, diabetes, sleep disorders, and depression. To assess the consistency of associations between different subgroups, interaction tests were performed. To avoid selection bias caused by the exclusion of samples with missing data, the \\u0026apos;multiple filling method\\u0026apos; was used to fill in missing covariates. We applied R software (version 4.3.3) for all statistical analyses, and Statistical significance was ascribed to two-tailed P values falling below the 0.05 threshold.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eBaseline characteristics\\u003c/p\\u003e\\n\\u003cp\\u003eThe study included 35,761 participants with an average age of 49.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.7 years, comprising 48.6% males and 51.4% females. The study revealed a stroke prevalence of 3.53%, with an increment noted in association with higher A Body Shape Index (ABSI) values. The overall mean ABSI of the participants was 0.08170\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.00494 (m\\u003csup\\u003e11/6\\u003c/sup\\u003e/kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2/3\\u003c/sup\\u003e). The weighted quartiles of ABSI were delineated as follows: Q1: \\u0026lt;0.0781; Q2: 0.07817 to 0.08135; Q3: 0.08135 to 0.08459; Q4: \\u0026gt;0.08459. In contrast to the lowest ABSI quartile group, the highest quartile was distinguished by a higher percentage of non-Hispanic whites, the elderly, and males. In addition to being less physically active, smoking was more common in this group. Furthermore, there was a significant increase in the incidence rates of hypertension, CHD, diabetes, sleep disorders, and stroke among these individuals (Table\\u0026nbsp;1).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable\\u0026nbsp;1\\u003c/strong\\u003e Using a body shape index quartile, the participants\\u0026apos; fundamental characteristics.\\u003c/p\\u003e\\n\\u003cp\\u003eFor continuous variables, mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD was utilized, with \\u003cem\\u003eP\\u003c/em\\u003e values derived from the weighted linear regression model; for categorical variables, percentages were used, with \\u003cem\\u003eP\\u003c/em\\u003e values obtained through the weighted chi-square test.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAbbreviations:\\u003c/em\\u003e Q Quartile, BMI Body mass index, CHD Coronary heart disease, TC Total cholesterol, TG Triglycerides, HDL-C High-density lipoprotein cholesterol, SBP Systolic blood pressure.\\u003c/p\\u003e\\n\\u003cp\\u003eAssociation between the ABSI and stroke\\u003c/p\\u003e\\n\\u003cp\\u003eThe weighted univariate regression analysis for each covariate demonstrated an association with stroke (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026lt;0.05). Upon inclusion in the multivariate regression analysis, the heightened risk associated with age over 65 persisted (OR: 9.58, 95% CI: 6.35-14.46). Smoking (OR:1.77, 95% CI: 1.47-2.12), hypertension (OR: 2.73, 95% CI: 2.24-3.31), coronary heart disease (CHD) (OR: 2.56, 95% CI: 1.99-3.29), diabetes (OR: 1.57, 95% CI: 1.25-1.95), sleep disorders (OR: 1.41, 95% CI: 1.1-1.81), and depression (OR: 1.76, 95% CI: 1.3-2.38) maintained a significant association with the likelihood of stroke. The probability of stroke with each 0.01 unit increase in ABSI remained significant though slightly diminished (OR: 1.44, 95% CI: 1.16-1.8). Furthermore, marital status, educational level, BMI, alcohol consumption, physical activity, glycosylated proteins, the TG/HDL-C ratio, and systolic blood pressure (SBP) became non-significant in the weighted multivariate regression analysis (Figure 2).\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2 results indicate that the ABSI (multiplied by 100) as a continuous variable had an initial effect size (OR=3.92, 95% CI: 3.30, 4.66) which, after full adjustment for covariates, was reduced (OR=1.47, 95% CI: 1.18, 1.81) yet remained significant. The positive correlation remained consistent and significant across the ABSI quartiles (trend \\u003cem\\u003eP\\u003c/em\\u003e-value \\u0026lt;0.001). Subjects in the highest quartile experienced a 52% increase in stroke incidence compared to those in the lowest quartile, with an odds ratio of 1.52 (95% CI: 1.12, 2.06) in the fully adjusted model. Furthermore, the nonlinear relationship between ABSI and stroke was not significant (\\u003cem\\u003eP\\u003c/em\\u003e for nonlinearity = 0.7730) (Figure 3).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2\\u003c/strong\\u003e The association between ABSI and stroke.\\u003c/p\\u003e\\n\\u003cp\\u003eIn Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC).\\u003c/p\\u003e\\n\\u003cp\\u003eSubgroup analyses\\u003c/p\\u003e\\n\\u003cp\\u003eSubgroup analyses were conducted based on characteristics such as sex, age, PIR, BMI, smoking, alcohol use, presence of hypertension, CHD, diabetes, sleep disorders, and depression (Table 3). The presence of CHD may influence the link between ABSI and stroke, as P for interaction showed that the association of ABSI and the likelihood of stroke differed considerably with the CHD status (P for interaction = 0.002). However, in other subgroups, the association between ABSI and stroke risk did not demonstrate significant interaction effects, implying a relatively consistent relationship between ABSI and stroke across these groups.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e Subgroup Analysis by ABSI on Stroke Incidence\\u003c/p\\u003e\\n\\u003cp\\u003eAge, sex, race, poverty level, BMI, smoking, drinking, hypertension, CHD, diabetes, sleep disorders, depression, and TC were adjusted.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAbbreviations:\\u003c/em\\u003e OR Odds ratio, CI Confidence interval, BMI Body mass index, CHD Coronary heart disease.\\u003c/p\\u003e\\n\\u003cp\\u003eSensitive analysis\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, we performed sensitivity analyses using unweighted logistic regression to look at the connection between stroke and ABSI (Table 4). These results were consistent with those obtained from the weighted logistic regression analysis. The sensitivity analyses underscore the reliability of our current findings, demonstrating a positive correlation between increased ABSI and the incidence of stroke.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4\\u0026nbsp;\\u003c/strong\\u003eSensitive analysis using unweighted logistic regression to examine the relationship between ABI and stroke.\\u003c/p\\u003e\\n\\u003cp\\u003eIn Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eAfter adjustment for potential covariates, this cross-sectional study shows a significant association between ABSI and stroke. The association remained stable and present when ABSI was categorized into quartiles. This aligns with previous studies suggesting ABSI as a predictor of the risk of cardiovascular disease (\\u003cspan class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). However, unlike Nam et al.'s study on the relationship between ABSI and small vessel disease, there appeared to be no discernible non-linear trend in the correlation between stroke and ABSI. (\\u003cspan class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). In subgroup analysis, the presence of CHD interacted with the association between ABSI and stroke.\\u003c/p\\u003e\\n\\u003cp\\u003eStudies on the association between ABSI and stroke are sparse. Abete et al. found that ABSI was Significantly in line with the overall incidence of stroke only in males (\\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Further research on the association of ABSI with cardiovascular and cerebrovascular diseases in different populations could corroborate our findings. For instance, in the Turkish population, particularly in men, ABSI alone predicted cardiovascular disease 10-year risk more accurately than BMI or other obesity indices (\\u003cspan class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). In a prospective study based on the Rotterdam population, ABSI was also found to be an equivalent predictor of cardiovascular risk for men(\\u003cspan class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). Similar predictive abilities of ABSI for cardiovascular risk were observed in the Caucasian population(\\u003cspan class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e) and in association with cerebral small vessel disease in the Korean population(\\u003cspan class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). Additionally, ABSI has been linked to cardiac metabolic risk factors(\\u003cspan class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). However, contrary results exist, suggesting no association between ABSI and cardiovascular disease(\\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eOur study establishes a significant correlation between ABSI and stroke, persisting even after considering traditional obesity measurements like BMI, whether as a continuous variable or categorized as \\u0026gt;\\u0026thinsp;30. The higher Odds Ratio (OR\\u0026thinsp;=\\u0026thinsp;1.78, 95% CI:1.39, 2.29) in the male subgroup of our study further emphasizes the unique relevance of ABSI in the male population, aligning with previous research findings. Although BMI has long been used as the primary metric for evaluating obesity and related health risks, it has limitations in differentiating fat from muscle mass, especially regarding the distribution of body fat (\\u003cspan class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). BMI was not significantly associated with stroke in multivariable analysis (P\\u0026thinsp;=\\u0026thinsp;0.896). Some studies even suggest a protective role of BMI against cardiovascular diseases, known as the \\\"obesity paradox\\\" (\\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). In contrast, ABSI offers an additional perspective, focusing more on the proportion of waist circumference to height and weight, thereby emphasizing the state of abdominal or visceral fat accumulation (\\u003cspan class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). A large epidemiological study involving 168,000 individuals as early as 2007 showed a significant correlation between increased Waist circumference and cardiovascular disease risk (\\u003cspan class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). Subsequent studies confirmed this abdominal obesity-cardiovascular link (\\u003cspan class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e), with direct evidence linking visceral obesity to a significant risk of stroke(\\u003cspan class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e). An ABSI value of 0.083 (m\\u003csup\\u003e11/6\\u003c/sup\\u003e/kg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2/3\\u003c/sup\\u003e) has been suggested as the optimal threshold for assessing muscle loss and visceral fat deposition(\\u003cspan class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e), which could explain the meaningful OR values in our adjusted model for the Q3 and Q4 quartiles covering this threshold.\\u003c/p\\u003e\\n\\u003cp\\u003eThe link between ABSI and stroke could have several potential mechanisms. Infiltration of macrophages into abdominal fat results in elevated levels of pro-inflammatory cytokines, causing oxidative stress and endothelial dysfunction (\\u003cspan class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e). Increased leptin release from visceral adipose tissue contributes to atherosclerosis, and a reduction in anti-inflammatory adipokines like adiponectin induces oxidative stress and endothelial damage, increasing stroke risk (\\u003cspan class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e). As an endocrine organ, visceral fat contributes to insulin resistance, potentially leading to arteriosclerosis(\\u003cspan class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e). Additionally, the accumulation of abdominal fat may lead to elevated levels of oxidized LDL-C, which is also involved in the development of arteriosclerosis(\\u003cspan class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, we found that the presence of CHD alters the association between ABSI and stroke. The association is stronger in individuals without CHD and weakened in those with CHD. CHD itself involves several factors related to stroke risk, such as atherosclerosis, blood pressure, and lipid levels(\\u003cspan class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e), which might interact with the effects of ABSI, resulting in a suppressed or masked association. Therefore, although ABSI generally correlates positively with stroke, it is crucial to consider these interactions when using ABSI for assessment, making personalized evaluations based on individual patient conditions. With an increasing number of new indices based on visceral fat being applied to disease prediction (\\u003cspan class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e), we hope our study can provide new perspectives and data support for public health strategies, especially in the early identification and prevention of stroke.\\u003c/p\\u003e\\n\\u003cp\\u003eOur study has limitations. The cross-sectional study's findings merely demonstrate an association, not a causative relationship, between stroke and ABSI. Despite considering various covariates, potential unknown or unmeasured confounding factors might affect the accuracy of our results. Particularly in the analysis of the CHD subgroup, although we observed a differential association between ABSI and stroke among CHD patients, the specific mechanisms behind this difference were not fully explained in our study.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis research examines the link between ABSI and stroke occurrence. Upon examining data from the NHANES national database, a positive association was evident between ABSI and stroke rates, noting differences within the CHD subgroup. Nonetheless, the cross-sectional study design necessitates additional research to investigate the underlying causal pathways for these relationships.\\u003c/p\\u003e\"},{\"header\":\"List of abbreviations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eABSI\\u003c/strong\\u003e: A Body Shape Index\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBMI\\u003c/strong\\u003e: Body Mass Index\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNHANES\\u003c/strong\\u003e: National Health and Nutrition Examination Survey\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOR\\u003c/strong\\u003e: Odds Ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCI\\u003c/strong\\u003e: Confidence Interval\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCHD\\u003c/strong\\u003e: Coronary Heart Disease\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePIR\\u003c/strong\\u003e: Poverty-Income Ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTC\\u003c/strong\\u003e: Total Cholesterol\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLDL-C\\u003c/strong\\u003e: Low-Density Lipoprotein Cholesterol\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHDL-C\\u003c/strong\\u003e: High-Density Lipoprotein Cholesterol\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTG\\u003c/strong\\u003e: Triglycerides\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSBP\\u003c/strong\\u003e: Systolic Blood Pressure\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research, which included human subjects, biological materials, and personal data, adhered to the Declaration of Helsinki and received approval from the NCHS Ethics Review Board. All participants gave their written informed consent for involvement in the research.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePublic data sets, accessible at https://www.cdc.gov/nchs/nhanes/, provided all of the information used in this investigation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo conflicting interests are stated by the authors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo funding.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJun You conceived and designed the study, led the data analysis, and drafted the manuscript. Yiwen He assisted in the study design and played a significant role in revising the manuscript for critical intellectual content. Min Xu, Zhenjie Fan, and Zhiyong Wang participated in collecting and analyzing data and aided in manuscript preparation. Min Qian oversaw the entire study and provided final approval for the version to be published. Each author contributed significantly to the research and manuscript preparation, ensuring the integrity and accuracy of the work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe are grateful to all those who supported and participated in this study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eCollaborators GS. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. 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Sci Rep. 2020;10(1):14541.\\u003c/li\\u003e\\n\\u003cli\\u003eJi M, Zhang S, An R. Effectiveness of A Body Shape Index (ABSI) in predicting chronic diseases and mortality: a systematic review and meta-analysis. Obes Rev. 2018;19(5):737-59.\\u003c/li\\u003e\\n\\u003cli\\u003eChen Z, Iona A, Parish S, Chen Y, Guo Y, Bragg F, et al. Adiposity and risk of ischaemic and haemorrhagic stroke in 0\\u0026middot;5 million Chinese men and women: a prospective cohort study. Lancet Glob Health. 2018;6(6):e630-e40.\\u003c/li\\u003e\\n\\u003cli\\u003eTeng T-Q, Liu J, Hu F-F, Li Q-Q, Hu Z-Z, Shi Y. Association of composite dietary antioxidant index with prevalence of stroke: insights from NHANES 1999-2018. Front Immunol. 2024;15:1306059.\\u003c/li\\u003e\\n\\u003cli\\u003eYe J, Hu Y, Chen X, Yin Z, Yuan X, Huang L, et al. Association between the weight-adjusted waist index and stroke: a cross-sectional study. BMC Public Health. 2023;23(1):1689.\\u003c/li\\u003e\\n\\u003cli\\u003eChe B, Zhong C, Zhang R, Pu L, Zhao T, Zhang Y, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22(1):34.\\u003c/li\\u003e\\n\\u003cli\\u003eTang M, Zhao Q, Yi K, Wu Y, Xiang Y, Cui S, et al. Association between four nontraditional lipids and ischemic stroke: a cohort study in Shanghai, China. Lipids Health Dis. 2022;21(1):72.\\u003c/li\\u003e\\n\\u003cli\\u003eAbete I, Arriola L, Etxezarreta N, Mozo I, Moreno-Iribas C, Amiano P, et al. Association between different obesity measures and the risk of stroke in the EPIC Spanish cohort. Eur J Nutr. 2015;54(3):365-75.\\u003c/li\\u003e\\n\\u003cli\\u003eS\\u0026ouml;zmen K, Belgin \\u0026Uuml;, Sakarya S, G\\u0026ouml;n\\u0026uuml;l D, YARDIM N, KESKİNKILI\\u0026Ccedil; B, et al. 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PloS One. 2015;10(3):e0120598.\\u003c/li\\u003e\\n\\u003cli\\u003eGomez-Peralta F, Abreu C, Cruz-Bravo M, Alcarria E, Gutierrez-Buey G, Krakauer NY, et al. Relationship between \\u0026quot;a body shape index (ABSI)\\u0026quot; and body composition in obese patients with type 2 diabetes. Diabetol Metab Syndr. 2018;10:21.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang Z, Tang J, Cui X, Qin B, Zhang J, Zhang L, et al. New Insights and Novel Therapeutic Potentials for Macrophages in Myocardial Infarction. Inflammation. 2021;44(5):1696-712.\\u003c/li\\u003e\\n\\u003cli\\u003eVan Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875-80.\\u003c/li\\u003e\\n\\u003cli\\u003ePatel P, Abate N. Body fat distribution and insulin resistance. Nutrients. 2013;5(6):2019-27.\\u003c/li\\u003e\\n\\u003cli\\u003eHansson GK. Inflammation, atherosclerosis, and coronary artery disease. The New England Journal of Medicine. 2005;352(16):1685-95.\\u003c/li\\u003e\\n\\u003cli\\u003eLibby P, Buring JE, Badimon L, Hansson GK, Deanfield J, Bittencourt MS, et al. Atherosclerosis. Nat Rev Dis Primers. 2019;5(1):56.\\u003c/li\\u003e\\n\\u003cli\\u003eAmato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920-2.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang X, Hong F, Liu L, Nie F, Du L, Guan H, et al. Lipid accumulation product is a reliable indicator for identifying metabolic syndrome: the China Multi-Ethnic Cohort (CMEC) Study. QJM. 2022;115(3):140-7.\\u003c/li\\u003e\\n\\u003cli\\u003eJin J, Woo H, Jang Y, Lee W-K, Kim J-G, Lee I-K, et al. Novel Asian-Specific Visceral Adiposity Indices Are Associated with Chronic Kidney Disease in Korean Adults. Diabetes Metab J. 2023;47(3):426-36.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e Using a body shape index quartile, the participants\\u0026apos; fundamental characteristics.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCharacteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eA body shape index\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(\\u0026lt;0.0781)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ2\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e(0.07817-0.08135)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ3\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(0.08135-0.08459)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ4\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(\\u0026gt;0.08459)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eN=8483\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eN=8438\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eN=8963\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eN=9877\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSex,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5043 (60.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4433 (51.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4362 (47.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4548 ((47.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3440 (39.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4005 (48.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4601 (52.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5329 (52.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAge,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eage\\u0026lt;35years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4094 (49.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2567 ((31.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1735 (20.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e901\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e(9.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e35-65years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3876 (45.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4809 (58.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5307 (63.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4316 (51.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eage\\u0026gt;65years\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e513\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(4.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1062\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(9.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1921 (16.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4660 (39.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRace,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMexican American\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1067 (7.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1444\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e(9.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1645 (10.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1487 (6.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNon-Hispanic White\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2887 (59.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3252 (64.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3677 (68.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5099 (75.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNon-Hispanic Black\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2757 (18.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1837 (11.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1664 (8.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1440 (6.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1772 (14.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1905 (14.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1977 (13.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1851 (11.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePoverty level,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.338\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNot\\u0026nbsp;poverty\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6876 (86.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6837 (87.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7260 (87.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7904 (86.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePoverty\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1607 (13.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1601 (12.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1703 (12.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1973 (13.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMarried,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4004 (42.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3196 (34.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3187 (32.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3871 (35.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4479 (57.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5242 (65.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5776 (67.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6006 (64.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eEducation\\u0026nbsp;level,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigh School or above\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6930 (87.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6562 (85.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6598 (82.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6819 (80.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBelow high school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1553 (12.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1876 (14.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2365 (17.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3058 (20.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBMI, (kg/m\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e28.43 (7.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e28.73 (6.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e29.36 (6.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e29.22 (6.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSmoking,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.017\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6767 (81.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6655 (79.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7107 (79.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7844 (78.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1716 (18.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1783 (20.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1856 (20.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2033 (21.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDrinking,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.0013\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2955 (30.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2868 (28.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3020 (28.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3509 (30.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5528 (69.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5570 (72.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5943 (71.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6368 (70.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePhysical\\u0026nbsp;Activity,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eInactive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4213 (45.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4401 (47.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4925 (49.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5845 (53.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eModerate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1968 (24.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1989 (25.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2088 (25.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2379 (27.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eVigorous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e479 (5.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e457 (5.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e416 (4.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e358 (3.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBoth moderate and vigorous\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1823 (24.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1591 (20.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1534 (20.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1295 (15.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eStroke,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNon-stroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8368 (98.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8254 (98.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8657 (97.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e9219 ((94.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eStroke\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e115 (1.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e184 (1.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e306 (2.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e658 (5.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHypertension,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6376 (78.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5487 (68.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4927 (59.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4050 (45.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2107 (21.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2951 (31.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4036 (40.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5827 (54.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCHD,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8408 (99.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8299 (98.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8634 (96.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e9031 (92.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e75 (0.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e139 (1.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e329 (3.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e846 (7.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDiabetes,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7858 (94.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7366 (91.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7215 (85.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e6841 (74.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e625 (5.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1072 (9.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1748 (14.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3036 (25.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSleep disorders,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7421 (87.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7305 (86.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7646 (85.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8269 (84.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1062 (12.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1133 (13.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1317 (14.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1608 (15.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDepression,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7892 (93.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7771 (93.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8256 (93.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8965 (91.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e591 (6.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e667 (6.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e707 (6.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e912 (8.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCancer,\\u0026nbsp;n\\u0026nbsp;(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8115 (95.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7903 (92.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8152 (90.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e8290 (82.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e368 (5.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e535 (7.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e811 (9.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1587 (17.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGlycohemoglobin,\\u0026nbsp;%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5.39 (0.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5.50 (0.81)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5.66 (0.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e5.89 (1.08)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTC,\\u0026nbsp;mg/dL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e186.65 (38.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e194.96 (40.08)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e198.90 (41.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e195.53 (44.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTG/HDL-C,\\u0026nbsp;mg/dL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.61 (3.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3.48 (4.93)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3.91 (4.77)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e4.01 (4.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSBP,\\u0026nbsp;mmHg\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e117.49 (14.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e120.43 (16.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e123.47 (17.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e127.37 (19.22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eFor continuous variables, mean \\u0026plusmn; SD was utilized, with \\u003cem\\u003eP\\u003c/em\\u003e values derived from the weighted linear regression model; for categorical variables, percentages were used, with \\u003cem\\u003eP\\u003c/em\\u003e values obtained through the weighted chi-square test.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAbbreviations:\\u003c/em\\u003e Q Quartile, BMI Body mass index, CHD Coronary heart disease, TC Total cholesterol, TG Triglycerides, HDL-C High-density lipoprotein cholesterol, SBP Systolic blood pressure.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2\\u003c/strong\\u003e The association between ABSI and stroke.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eExposure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eModel1\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eOR\\u0026nbsp;(95%CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eModel2\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eOR\\u0026nbsp;(95%CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eModel3\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eOR\\u0026nbsp;(95%CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eA body shape index\\u0026nbsp;(per100)\\u0026nbsp;(continuous)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e3.92 (3.30, 4.66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e2.07 (1.69, 2.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.47 (1.18, 1.83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eA body shape index (quartile)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.38 (1.00, 1.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.09 (0.80, 1.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.00 (0.73, 1.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e2.54 (1.93, 3.33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.67 (1.27, 2.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.36 (1.03, 1.78)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e5.16 (3.88, 6.86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e2.31 (1.71, 3.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.52 (1.12, 2.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e for trend\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eIn Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e Subgroup Analysis by ABSI on Stroke Incidence\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"465\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCharacteristic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eOR (95%CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003evalue\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e for interaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.165\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.338(1.069,1.675)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.011\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.781(1.386,2.290)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.204\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eage\\u0026lt;35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.982(0.371,2.595)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.970\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e35-65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.772(1.304,2.409)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eage\\u0026gt;65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.314(1.060,1.629)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.013\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePoverty level\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.844\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNot poor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.486(1.214,1.818)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePoor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.427(1.001,2.034)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.049\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBMI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.177\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBMI\\u0026lt;30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.620(1.279,2.053)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBMI\\u0026gt;30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.300(1.012,1.670)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.040\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSmoking\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.139\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.593(1.298,1.954)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.174(0.853,1.615)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDrinking\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.486\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.361(1.035,1.790)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.027\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.546(1.242,1.924)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.065\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.990(1.375,2.882)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.341(1.102,1.632)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCHD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.646(1.357,1.996)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.770(0.505,1.174)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.225\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDiabetes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.118\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.605(1.286,2.005)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.253(0.959,1.637)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.098\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eSleep disorders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.069\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.610(1.321,1.964)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.137(0.814,1.589)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.451\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eDepression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.280\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.544(1.273,1.873)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1.180(0.793,1.755)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.414\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAge, sex, race, poverty level, BMI, smoking, drinking, hypertension, CHD, diabetes, sleep disorders, depression, and TC were adjusted.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAbbreviations:\\u003c/em\\u003e OR Odds ratio, CI Confidence interval, BMI Body mass index, CHD Coronary heart disease.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4\\u0026nbsp;\\u003c/strong\\u003eSensitive analysis using unweighted logistic regression to examine the relationship between ABI and stroke.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eExposure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eModel1\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eOR\\u0026nbsp;(95%CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eModel2\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eOR\\u0026nbsp;(95%CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eModel3\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eOR\\u0026nbsp;(95%CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eA body shape index\\u003c/p\\u003e\\n \\u003cp\\u003e(per100)\\u0026nbsp;(continuous)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e3.56 (3.18, 3.99)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e2.05 (1.80, 2.33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.54 (1.35, 1.77)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eA body shape index (quartile)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.62 (1.28, 2.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.28 (1.01, 1.63)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.17 (0.92, 1.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e2.57 (2.08, 3.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.70 (1.36, 2.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.38 (1.10, 1.73)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003eQuartile 4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e5.19 (4.27, 6.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e2.46 (1.99, 3.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e1.69 (1.36, 2.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e for trend\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"25%\\\"\\u003e\\n \\u003cp\\u003e0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eIn Model 1, no adjustments were made for covariates. In contrast, Model 2 incorporated adjustments for age, sex, and race. Finally, Model 3 was refined further to account for age, sex, race, poverty status, body mass index (BMI), tobacco and alcohol use, hypertension, coronary heart disease (CHD), diabetes, sleep disturbances, depressive states, and total cholesterol (TC).\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"A Body Shape Index, Stroke, Obesity, NHANES, Cross-Sectional Study\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4261745/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4261745/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eGlobally, stroke remains a top cause of mortality and morbidity, highlighting the critical need for new predictive biomarkers to assess risk. A body shape index (ABSI) is increasingly recognized as a possible predictor of cardiovascular risk, though its connection with stroke incidence remains unclear.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThis research utilizes data from the National Health and Nutrition Examination Survey (NHANES), covering a representative sample of the US population from 2005 to 2018. A weighted multivariable logistic regression method was used to investigate the relationship between ABSI and stroke incidence, including subgroup analyses to investigate potential interactions involving coronary heart disease (CHD).\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eFollowing covariate adjustment, the incidence of stroke and ABSI were found to correlate significantly positively (OR\\u0026thinsp;=\\u0026thinsp;1.47, 95% CI: 1.18, 1.81). This association remained consistent when ABSI was categorized into quartiles. Subgroup analysis indicated an interaction effect among patients with CHD (\\u003cem\\u003eP\\u003c/em\\u003e for interaction\\u0026thinsp;=\\u0026thinsp;0.002).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eA significant association between ABSI and stroke incidence was demonstrated in our study. however, the relationship between ABSI and stroke may be attenuated or masked in patients with CHD.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Association between a body shape index and stroke: a cross- sectional study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-04-25 15:45:29\",\"doi\":\"10.21203/rs.3.rs-4261745/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c7c8be91-cf20-4770-a82d-1626a272317b\",\"owner\":[],\"postedDate\":\"April 25th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-10-20T13:23:45+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-04-25 15:45:29\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4261745\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4261745\",\"identity\":\"rs-4261745\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}