The Role of Six Anthropometric Indicators in Predicting the Prevalence and Mortality of Obstructive Sleep Apnoea, Asthma, and Chronic Obstructive Pulmonary Disease: A Population- Based Study from the US National Health and Nutrition Examination Survey (NHANES)

The Role of Six Anthropometric Indicators in Predicting the Prevalence and Mortality of Obstructive Sleep Apnoea, Asthma, and Chronic Obstructive Pulmonary Disease: A Population- Based Study from the US National Health and Nutrition Examination Survey (NHANES) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Role of Six Anthropometric Indicators in Predicting the Prevalence and Mortality of Obstructive Sleep Apnoea, Asthma, and Chronic Obstructive Pulmonary Disease: A Population- Based Study from the US National Health and Nutrition Examination Survey (NHANES) Jingdi Hu, Songwen Tang, Qijiang Zhu, Huai Liao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5834187/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Obesity is linked to a greaterrisk of respiratory diseases. Due to limitations in body mass index (BMI), alternative anthropometric indicators have been developed to reflect body fat distribution. This study compares six anthropometric measures—BMI, waist circumference(WC), the waist-to-height ratio(WHtR), the body roundness index (BRI), the body shape index(ABSI), and the weight-adjusted waist index (WWI)—and their relationships withthe prevalence and mortality of obstructive sleep apnoea (OSA), asthma, and chronic obstructive pulmonary disease (COPD) in the US population. Methods : Data from four NHANES cycles were analysed. Multivariable logistic regression assessedthe cross-sectional associations between the six anthropometric measures and disease prevalence. Mortality associations were analysed via Cox proportional hazards models, and time‒dependent ROC curve was utilised to evaluate the predictive performance of the significant marker for mortality. Results : BMI, WC, WWI, BRI, ABSI, and WHtR were positively correlated with the prevalence of OSA, and COPD. For asthma, BMI, WC, BRI, and WHtR were positively associated with prevalence, while ABSI and WWI were negatively associated. Concerningmortality, higher WC and BMI were associated with better survival in the OSA and COPD groups, whereas elevated WWI and ABSI were linked to greater mortality risk in the participants with OSA symptoms. An increase of one standard deviation (SD) in the ABSI resulted in an 18% increase in mortality (95% CI: 1.09-1.27) for the OSA population. The area under the curve (AUC) for ABSI was 0.752 for 3-year, 0.755 for 5-year, and 0.744 for 10-year mortality. Conclusions : Novel anthropometric indicators, including WWI, BRI, ABSI and WHtR, show positive associations with the prevalence of OSA, and COPD, alongside traditional measures likeBMI and WC. However, WWI and ABSI were more limited in their association with asthma prevalence. Longitudinal analyses revealed that traditional anthropometric indicators such as BMI and WC were negatively associated with mortality risks in the OSA and COPD, supporting the "obesity paradox." ABSI, however, emerged as a significant mortality predictor for OSA, providing a more nuanced view of central obesity’s impact on mortality. However, in COPD patients, routine anthropometric measurements may not fully capture the effects of obesity. Health sciences/Diseases Health sciences/Diseases/Metabolic disorders Health sciences/Diseases/Respiratory tract diseases the US National Health and Nutrition Examination Survey Obesity Obstructive sleep apnoea Asthma Chronic obstructive pulmonary disease Mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Obesity has become a global health crisis. In 2022, obesity affected approximately 16% of the global adult population. Between 1990 and 2022, the worldwide prevalence of obesity increased by more than twofold [1]. Increased risk of several chronic diseases associated with the prevalence of obesity [2]. Furthermore, obesity has been linked to airway inflammation, reduced lung function, and the occurrence and worsening of respiratory diseases, including obstructive sleep apnoea (OSA) [3], asthma [4], and chronic obstructive pulmonary disease (COPD) [5]. These effects can be attributed to the fact that obesity leads to metabolic dysregulation and chronic inflammation, which influence overall body health, as well as fat accumulation in the abdominal cavity, which negatively impacts lung function [6,7]. Body mass index (BMI) remains the most widely used measure to assess obesity. The World Health Organization (WHO) classifies adults with a BMI of 30 or higher as obese [1]. However, BMI has been shown to be poor at distinguishing between fat mass and lean mass. Recent studies have increasingly focused on body fat distribution rather than merely excess weight [8]. Dual-energy X-ray absorptiometry (DXA) provides highly accurate body composition assessments, but its complexity and high cost hinder widespread use, but its complexity and high cost limit widespread use [9]. Waist circumference (WC) serves as a simpler, more accessible proxy for abdominal fat. It has been advocated for inclusion as a routine clinical measurement [10]. However, WC is highly correlated with BMI, which may also give rise to the obesity paradox, wherein obesity appears to have protective effects in some conditions [11]. To address this issue, the body shape index (ABSI) and waist-to-height ratio (WHtR) have been developed to capture the added value of WC; these indices correspond to visceral fat and can predict mortality [12,13]. The body roundness index (BRI), assuming the body is elliptical and calculates its eccentricity, serves as another promising measure of abdominal adiposity. It has been studied for its value in predicting cardiovascular events [14], mortality [15], and the deterioration of lung function [16,17]. The weight-adjusted waist index (WWI) also undergoes evaluation alongside WHtR, BMI, and WC, demonstrating the strongest association with sarcopenic obesity [18]. Several surveys have examined and compared the role of various anthropometric measures of lung function, which are related to the onset and prognosis of respiratory diseases. For example, a longitudinal study in Taiwan found that elevated levels of BRI, WHtR, and BMI were linked to a decline in lung function [16]. However, the impact of various anthropometric measures on the prevalence and mortality of respiratory diseases remains underexplored. This study sought to elucidate the impacts of diverse anthropometric measures on the prevalence and mortality of diseases such as OSA, asthma, and COPD. Furthermore, it aimed to identify the most accurate metric for assessing and addressing adiposity in individuals with these chronic respiratory diseases. Methods Study population Four cycles of data (2005–2006, 2007‒2008, 2015‒2016, and 2017‒2020) came from the US National Health and Nutrition Examination Survey (NHANES) database. Initially, the four-cycle NHANES data included 46028 participants. Individuals with missing data on key anthropometric measures—such as weight (n = 3030), height (n = 3295), and waist circumference (n = 1698)—were excluded. The remaining participants were grouped based on available data for OSA, asthma, and COPD, resulting in 26077 with OSA-related data, 37972 with asthma-related data, and 22989 with COPD-related data. For the longitudinal analysis, additional exclusions were made to remove non-OSA, non-asthma, and non-COPD individuals, as well as those with missing valid mortality data. Ultimately, the analysis focused on 7702 participants with OSA, 2232 with asthma, and 1083 with COPD, all having complete body measurement and mortality data. Figure 1 presents a flow chart detailing the selection procedure of participants for this research. Figure 1. Flow chart of the selection procedure. Assessment of OSA, asthma, and COPD OSA symptoms were defined on the basis of individuals reporting snoring, gasping for air, experiencing pauses in breathing three or more times a week, or experiencing excessive daytime sleepiness on at least 16 days per month [19]. People were diagnosed with asthma if they had ever been told by doctors that they had asthma. People were diagnosed with COPD if they had ever been told by doctors that they had COPD, chronic bronchitis, or emphysema. Measurement of six body measurement indicators Trained health professionals collected participants’ height, weight, and waist circumference (WC) data. From these measurements, six body measurement indicators—BMI, WWI, BRI, ABSI, and WHtR—were calculated via the following equations: $$\:BMI=\:weight/{height}^{2}$$ $$\:WWI=WC/\sqrt{weight}$$ $$\:BRI=364.2-365.5\times\:\sqrt{1-{(WC/2\pi\:)}^{2}/{\left(0.5\:height\right)}^{2}}$$ $$\:ABSI=WC/\left({BMI}^{2/3}\:{height}^{1/3}\right)$$ $$\:WHtR=WC/height$$ Mortality Mortality data were obtained from the National Death Index (NDI), which is linked with NHANES records by the National Center for Health Statistics. The analysis focused on all-cause mortality, and monitoring starting from the participant's enrolment date and continuing until death or December 31, 2019. Covariables The analysis considered multiple covariables, including demographic factors like sex, age, race, the poverty-income ratio (PIR) and education level, along with medical history variables including diabetes, cardiovascular disease (CVD), hypertension, chronic kidney disease (CKD), smoking status, and alcohol consumption. Smoking status classified individuals as current smokers, quitters and never smokers. Alcohol consumption was classified into four distinct levels: daily binge drinking, heavy drinking, moderate drinking, and mild drinking, as well as individuals who do not consume alcohol. Statistical analysis Missing covariable values were imputed via the random forest algorithm implemented via missRanger in R (version 4.3.3). This method uses chained random forest models to iteratively impute missing values, leveraging the observed data from other variables to predict missing values. This study took into account the complex sampling design and sampling weights of the NHANES database by employing a full sample exam weight. Continuous variable outcomes are reported as survey-weighted means (standard errors), with differences between groups calculated using survey-weighted linear regression. Categorical variables are displayed as unweighted counts alongside their corresponding weighted percentages, and group differences were assessed using the survey-weighted Chi-square test. The anthropometric indicators were assessed both as continuous variables, with each standard deviation increment represented, and as categorical variables, with the quartiles delineated. The tests for linear and p-values for trend were calculated to assess whether there was a significant linear association between the quartiles of the anthropometric indicators and the outcomes, with the median values of each quartile assigned to the variables. Multivariable logistic regression models investigated the associations of six anthropometric indicators with the prevalence of OSA, asthma, and COPD. The findings are reported as odds ratios (ORs) and their 95% confidence intervals (CIs). Multivariable Cox proportional hazards models assessed the relationships between the examined factors and mortality in the overall population, as well as in the OSA, asthma, and COPD subgroups. The results are shown as the hazard ratios (HRs) alongside 95% CIs. A time-dependent ROC curve evaluated the predictive performance of ABSI for mortality at different time points among the OSA population. Furthermore, Kaplan-Meier survival curves for all-cause mortality were generated, and the differences were assessed using the log-rank test. A stratified analysis further investigated the relationship between ABSI and mortality, considering factors such as sex, age, race, PIR, diabetes, hypertension, CVD, CKD, smoking status, and alcohol consumption. All data analyses were conducted via R programming (version 4.3.3) and EmpowerStats software (X&Y Solutions, Inc.), with statistical significance set at a p -value below 0.05. Results Characteristics of the population included in the cross-sectional analysis Table 1 outlines the basic characteristics of the participants, comparing those with OSA, asthma, and COPD to healthy individuals. OSA participants were older (47.47 years) and predominantly male (54.81%) compared to healthy participants (43.10 years, 42.76% male). Asthma participants were younger (36.06 years) with a slightly higher proportion of females (54.14%) compared to healthy individuals (38.77 years, 50.42% female). COPD participants were older (55.38 years) and more likely to be female (60.89%) compared to healthy participants (46.52 years, 51.01% female). OSA, asthma, and COPD participants exhibited lower education levels and PIR compared to their healthy counterparts. Smoking and alcohol consumption were more prevalent in all three disease groups. Regarding anthropometric measurements, BMI, WC, BRI, and WHtR, were elevated in all disease groups, while ABSI was lower in asthma participants and WWI showed no significant difference between asthma and healthy individuals. Comorbidities such as hypertension, diabetes, CVD, and CKD were more common in participants with OSA and COPD compared to healthy individuals, with the highest prevalence observed in the COPD group. Associations between anthropometric indicators and OSA, asthma and COPD Table 2 illustrates the association between the six body measurement indicators and the prevalence of OSA, asthma, and COPD, adjusted for potential confounding variables, including sex, age, race, education level, PIR, diabetes, hypertension, CVD, CKD, smoking status, and alcohol consumption. All six indicators were significantly associated with an increased prevalence of OSA and COPD, with stronger associations observed for OSA. For each standard deviation (SD) increase, the prevalence of OSA rose by 64% for BMI (95% CI for OR: 1.60-1.69), 64% for WC (95% CI for OR: 1.59-1.68), 42% for WWI (95% CI for OR: 1.37-1.46), 62% for BRI (95% CI for OR: 1.58-1.67), 3% for ABSI (95% CI for OR: 1.00-1.06), and 65% for WHtR (95% CI for OR: 1.60-1.70). Among these, ABSI exhibited the weakest predictive value for OSA. For asthma, BMI, WC, BRI, and WHtR were positively associated with prevalence, while ABSI and WWI were negatively associated. Each SD increase in ABSI and WWI was linked to a 7% decrease in asthma prevalence (95% CI for OR: 0.90-0.96). Characteristics of participants with eligible mortality data Table 3 shows the population characteristics in the mortality analysis. Among the 7002 OSA participants, 920 deaths were recorded during a median follow-up of 84.45 months. In the asthma cohort, 267 deaths occurred among 2,232 participants, with a median follow-up of 71.62 months. Similarly, in the COPD cohort, 314 deaths were documented among 2,232 participants, with a median follow-up of 71.82 months. A comparison of the characteristics of deceased and surviving individuals revealed that those who died tended to be older, male, white, and have a lower PIR. Additionally, the deceased were more probable to have smoked and drunk alcohol. They also presented higher levels of WC, WWI, BRI, ABSI, and WHtR. Furthermore, individuals who died were more frequently affected by comorbidities such as hypertension, diabetes, CVD, and CKD. Relationships between anthropometric indicators and mortality Table 4 shows the results relating anthropometric indicators to mortality in different population groups. In the OSA group, a one-SD increase in ABSI is linked to an 18% rise in mortality (95% CI: 1.09-1.27), while higher BMI and WC are associated with reduced mortality. In the COPD group, increased BMI, WC, and WHtR are linked to lower mortality risks, with BMI showing an HR per SD increment of 0.84 (95% CI: 0.72-0.97), WC showing an HR per SD increment of 0.85 (95% CI: 0.74-0.97) and WHtR showing an HR per SD increment of 0.86 (95% CI: 0.75-0.98). However, in the asthma group, none of the six anthropometric indicators demonstrated significant associations with mortality, suggesting variability in their prognostic value across different respiratory conditions. Prediction performance of the ABSI for 3-year, 5-year and 10-year mortality Figure 2 depicts the time-dependent ROC curves for the prediction of the ABSI for 3-year, 5-year and 10-year mortality in the OSA group. The area under the curve (AUC) for ABSI was 0.752 (95% CI: 0.717-0.786) for 3-year, 0.755 (95% CI: 0.729-0.782) for 5-year, and 0.744 (95% CI: 0.724-0.764) for 10-year mortality. These results underscore the significance of ABSI as a valuable marker for assessing survival prognosis, with superior predictive performance for 5-year mortality. Moreover, Kaplan-Meier survival analyses revealed that individuals in the fourth quartile of ABSI exhibited a notably higher risk of mortality (Figure 3). Stratified analyses Figure 4 provides insights into the impact of the ABSI on mortality among patients with OSA, across various subgroups. ABSI was found to interact with alcohol consumption (p for interaction = 0.0081) and CVD (p for interaction = 0.0477). A stronger association between ABSI and mortality was observed in alcohol consumers (HR: 1.39, 95% CI: 1.20–1.60) compared to non-drinkers (HR: 1.11, 95% CI: 1.02–1.21). Similarly, ABSI was more strongly linked to mortality in individuals with CVD (HR: 1.33, 95% CI: 1.15–1.54) than those without CVD (HR: 1.13, 95% CI: 1.03–1.23). The association between ABSI and mortality was not significant in participants under 40 years old, in those of other races, or in non-smokers. Discussion This study offers a thorough analysis of the predictive performance of various anthropometric indicators in relation to the prevalence of OSA, asthma, COPD, and mortality. All six anthropometric indicators—BMI, WC, WWI, BRI, ABSI, and WHtR—were significantly associated with increased prevalence of OSA and COPD, with particularly strong associations observed for OSA. However, the relationship between asthma and these indicators is more complex: while BMI, WC, BRI, and WHtR are positively correlated with asthma prevalence, ABSI and WWI demonstrate inverse associations. Longitudinal analysis further revealed that traditional anthropometric indicators such as BMI and WC were negatively associated with mortality risks in the OSA and COPD groups, supporting the phenomenon known as the "obesity paradox." However, ABSI emerged as a significant predictor of mortality in OSA, suggesting that the ABSI provides a more nuanced perspective on the impact of central obesity on the risk of death, and may help mitigate the "obesity paradox." Obesity is widely related to various comorbidities. Anthropometric indices, particularly BMI and WC, have long been recognized for their connection with the development of respiratory diseases, including OSA, asthma, and COPD. For instance, a community-based cohort study from South Korea revealed that fat mass index and waist-to-hip ratio showed an inverse relationship with lung function in the overall population [20]. Additionally, a meta-analysis concluded that those classified as overweight or obese face a dose-dependent, increased risk of developing asthma [21]. Another cohort study from China revealed that both higher WC and being underweight were linked to a greater risk of developing COPD [22]. This relationship can be attributed, at least in part, to the phenomenon of insulin resistance and the systemic inflammation that occurs in conjunction with obesity, especially abdominal obesity [23]. This study revealed the association between novel indicators WWI, BRI, ABSI, and WHtR and the prevalence of OSA and COPD. However, the inverse associations observed between ABSI, WWI, and asthma prevalence suggest a limitation in these indicators' ability to accurately reflect the impact of obesity on asthma prevalence. This aligns with previous studies that have demonstrated their limited capacity to identify metabolic syndrome [24]. Interestingly, obesity has been linked to reduced mortality from chronic diseases, known as the “obesity paradox.” For instance, a meta-analysis of 22 studies comprising 21150 COPD patients revealed that overweight and obese individuals experienced better survival rates than normal weight people [25]. Another 12-year prospective cohort of Koreans indicated that individuals with lower BMIs had an elevated risk of death from respiratory causes [26]. The survival advantage in obese individuals is complex and disease-specific. One reason for this paradox could be the inherent limitations of the use of BMI as a measurement tool. This study highlights the value of the ABSI, which offers a more nuanced understanding of how central obesity influences mortality risk in those with OSA. ABSI helps address the weaknesses of conventional measures like BMI and WC, providing deeper insights into how body fat distribution influences mortality. However, in COPD patients, the obesity paradox is even more intricate. This could be attributed to the fact that being underweight, together with loss of muscle mass, represents a principal acute risk element for death in advanced COPD patients [27]. However, recent studies have noted that obese individuals can exhibit low muscle mass, known as sarcopenic obesity, which increases systemic inflammation and worsens quality of life [28]. Therefore, in addition to routine anthropometric measurements, evaluating skeletal muscle function and body composition in COPD patients with an obese phenotype is crucial for understanding the impact of obesity on disease progression [5]. Strengths and limitations This study represents the first comparison of the positive associations of BMI, WC, WWI, BRI and WHtR with OSA, asthma and COPD. The data were sourced from the NHANES database, a nationwide, population-based survey featuring a large sample size and an extended follow-up period. The application of standardized protocols enhanced the reliability of these findings. Nevertheless, this research presents some inherent limitations. On the one hand, the identification of conditions such as OSA, asthma, and COPD relies on self-report questionnaires rather than objective criteria, such as laboratory testing, home sleep apnoea monitoring, or pulmonary function tests, which may have led to inaccuracies in diagnoses and limited our ability to assess disease severity. On the other hand, the cross-sectional study design limits the ability to draw conclusions about causal links between anthropometric measures and disease prevalence. Conclusion This study demonstrated that novel anthropometric indicators, including WWI, BRI, ABSI and WHtR, show positive associations with the prevalence of OSA, asthma, and COPD, alongside traditional measures such as BMI and WC. However, WWI and ABSI were more limited in their association with asthma prevalence. Longitudinal analyses revealed that traditional anthropometric indicators such as BMI and WC were negatively associated with mortality risks in the OSA and COPD groups, supporting the phenomenon known as the "obesity paradox." In contrast, ABSI emerged as a significant predictor of mortality in OSA, suggesting that the ABSI provides a more nuanced perspective on the impact of central obesity on the risk of death, and may help mitigate the "obesity paradox." However, in COPD patients, routine anthropometric measurements may not fully capture the impact of obesity on the progression of the disease. Abbreviations NHANES, the US National Health and Nutrition Examination Survey; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist‒height ratio; DXA, dual-energy X-ray absorptiometry; PIR, poverty‒income ratio; CVD, cardiovascular disease; CKD, chronic kidney disease; SD, standard deviation; OR, odds ratio; 95% CI, 95% confidence interval; HR, hazard ratio; ROC, receiver operating characteristic. AUC, the area under the curve. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the National Center for Health Statistics Research Ethics Review Committee (https://www.cdc.gov/nchs/nhanes/irba98.htm). All adult participants provided written informed consent, and those under the age of 18 were required to obtain consent from a parent or guardian prior to participation. Consent for publication Not applicable. Availability of data and materials The data examined in this study were sourced from a publicly accessible database, NHANES, available for download from the website: http://www.cdc.gov/nchs/nhanes.htm. Competing interests The authors declare that they have no competing interests. Funding None. Acknowledgements We would like to express our gratitude to all the individuals and teams who contributed to the collection and maintenance of the survey data in the NHANES database. Authors' contributions HL was responsible for the study design and manuscript revision. JH and ST collected the data and performed the statistical analyses. JH, ST, and QZ validated the analysis results and drafted the manuscript. All the authors contributed to the article and approved the submitted version. 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OSA Asthma COPD No (n=13870) Yes (n=12207) p -value No (n=32187) Yes (n=5785) p -value No (n=21187) Yes (n=1802) p -value Demographic data Age, year 43.10 (0.32) 47.47 (0.31) <0.0001 38.77 (0.33) 36.06 (0.41) <0.0001 46.52 (0.29) 55.38 (0.55) <0.0001 Gender <0.0001 0.0036 <0.0001 Male 6168 (42.76%) 6578 (54.81%) 15955 (49.58%) 2815 (45.86%) 10337 (48.99%) 817 (39.11%) Female 7702 (57.24%) 5629 (45.19%) 16232 (50.42%) 2970 (54.14%) 10850 (51.01%) 985 (60.89%) Race 0.4047 <0.0001 <0.0001 White 5371 (66.32%) 4732 (66.45%) 11474 (63.47%) 2089 (63.93%) 8179 (66.23%) 1033 (76.55%) Black 3347 (11.50%) 2858 (11.37%) 7641 (11.52%) 1736 (14.35%) 4952 (11.37%) 368 (9.23%) Hispanic 3546 (14.20%) 3359 (14.75%) 9429 (16.63%) 1383 (13.80%) 5662 (14.63%) 267 (7.05%) Others 1606 (7.98%) 1258 (7.43%) 3643 (8.38%) 577 (7.92%) 2394 (7.77%) 134 (7.17%) Education level <0.0001 0.0001 <0.0001 Under high school 1149 (4.64%) 1279 (5.69%) 2822 (4.90%) 311 (3.24%) 2235 (5.44%) 191 (6.31%) High school 4945 (31.29%) 4688 (36.23%) 12150 (34.19%) 2197 (32.61%) 7774 (33.58%) 786 (43.42%) Above high school 7776 (64.07%) 6240 (58.08%) 17215 (60.90%) 3277 (64.15%) 11178 (60.97%) 825 (50.27%) PIR 3.02 (0.04) 3.03 (0.04) 0.6814 2.96 (0.04) 2.81 (0.05) 0.0004 3.10 (0.04) 2.57 (0.06) <0.0001 Smoking status <0.0001 0.0099 <0.0001 Never 9199 (63.14%) 6622 (50.98%) 23552 (66.01%) 4126 (63.51%) 12320 (56.62%) 511 (29.19%) Former 2549 (20.92%) 2985 (25.98%) 4692 (18.94%) 836 (19.17%) 4861 (24.18%) 656 (34.83%) Now 2122 (15.95%) 2600 (23.04%) 3943 (15.05%) 823 (17.32%) 4006 (19.20%) 635 (35.98%) Alcohol consumption <0.0001 0.4940 <0.0001 Never 9591 (62.60%) 7318 (53.87%) 24403 (65.85%) 4380 (65.59%) 13041 (56.45%) 1031 (55.00%) Mild 2289 (21.54%) 2226 (21.77%) 3856 (17.96%) 683 (17.55%) 4083 (23.10%) 229 (14.35%) Moderate 511 (4.65%) 497 (5.02%) 860 (4.09%) 148 (4.17%) 924 (5.23%) 51 (3.26%) Heavy 223 (1.95%) 242 (2.46%) 406 (1.94%) 59 (1.54%) 426 (2.38%) 29 (1.74%) Binge drinking daily 1256 (9.26%) 1924 (16.89%) 2662 (10.16%) 515 (11.15%) 2713 (12.84%) 462 (25.65%) Body measurement data Weight, kg 76.81 (0.35) 87.69 (0.34) <0.0001 73.29 (0.30) 76.50 (0.56) <0.0001 82.52 (0.32) 84.69 (0.65) 0.0009 Height, cm 167.81 (0.12) 169.33 (0.15) <0.0001 161.91 (0.17) 163.00 (0.30) 0.0016 168.63 (0.12) 166.56 (0.31) <0.0001 BMI, kg/m² 27.20 (0.12) 30.51 (0.10) <0.0001 26.86 (0.09) 28.02 (0.18) <0.0001 28.92 (0.10) 30.47 (0.22) <0.0001 WC, cm 93.86 (0.32) 103.04 (0.28) <0.0001 91.89 (0.27) 94.15 (0.46) <0.0001 98.80 (0.28) 103.74 (0.54) <0.0001 WWI 10.76 (0.02) 11.06 (0.01) <0.0001 10.98 (0.01) 10.95 (0.02) 0.0919 10.93 (0.01) 11.35 (0.03) <0.0001 BRI 5.23 (0.04) 6.33 (0.04) <0.0001 5.35 (0.03) 5.65 (0.06) <0.0001 5.81 (0.04) 6.70 (0.08) <0.0001 ABSI 0.81 (0.00) 0.82 (0.00) <0.0001 0.81 (0.00) 0.80 (0.00) 0.0004 0.81 (0.00) 0.83 (0.00) <0.0001 WHtR 0.56 (0.00) 0.61 (0.00) <0.0001 0.57 (0.00) 0.58 (0.00) <0.0001 0.59 (0.00) 0.62 (0.00) <0.0001 Comorbidity Hypertension 4702 (31.28%) 5962 (45.38%) <0.0001 9072 (30.88%) 1664 (31.84%) 0.3477 9360 (39.02%) 1129 (56.59%) <0.0001 Diabetes 1783 (10.03%) 2618 (16.73%) <0.0001 3740 (10.93%) 746 (11.84%) 0.1863 3790 (13.19%) 553 (24.44%) <0.0001 CVD 981 (5.89%) 1296 (8.76%) <0.0001 1825 (5.71%) 449 (7.40%) 0.0005 1790 (6.57%) 486 (22.65%) <0.0001 CKD 993 (6.05%) 899 (5.93%) 0.7613 1652 (5.04%) 241 (3.86%) 0.0025 1639 (6.06%) 255 (11.05%) <0.0001 Note : Continuous variables are presented as survey-weighted means (SEs), and p- value was calculated by survey-weighted linear regression. Categorical variables are presented as unweighted counts (weighted percentages), and p- value was calculated by survey-weighted Chi-square test. Abbreviations : PIR, poverty-income ratio; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist‒height ratio; CVD, cardiovascular disease; CKD, chronic kidney disease; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease. Table 2 . Associations of six anthropometric indicators with OSA, asthma and COPD. Adjusted odds ratio (95% CI) a Quartile 1 Quartile 2 Quartile 3 Quartile 4 P for trend Per SD increment OSA BMI Ref 1.51 (1.40, 1.62) 2.25 (2.09, 2.43) 3.50 (3.24, 3.79) <0.0001 1.64 (1.60, 1.69) WC Ref 1.68 (1.55, 1.81) 2.50 (2.31, 2.70) 3.60 (3.32, 3.90) <0.0001 1.64 (1.59, 1.68) WWI Ref 1.73 (1.60, 1.87) 2.02 (1.86, 2.19) 2.44 (2.23, 2.67) <0.0001 1.42 (1.37, 1.46) BRI Ref 1.74 (1.61, 1.88) 2.47 (2.28, 2.67) 3.66 (3.37, 3.98) <0.0001 1.62 (1.58, 1.67) ABSI Ref 1.32 (1.23, 1.42) 1.29 (1.19, 1.39) 1.17 (1.07, 1.27) 0.0013 1.03 (1.00, 1.06) WHtR Ref 1.74 (1.61, 1.88) 2.46 (2.27, 2.66) 3.66 (3.37, 3.98) <0.0001 1.65 (1.60, 1.70) Asthma BMI Ref 1.45 (1.33, 1.59) 1.47 (1.34, 1.62) 1.94 (1.77, 2.14) <0.0001 1.27 (1.23, 1.31) WC Ref 1.56 (1.43, 1.71) 1.52 (1.37, 1.68) 1.91 (1.72, 2.12) <0.0001 1.31 (1.26, 1.36) WWI Ref 0.86 (0.79, 0.93) 0.87 (0.80, 0.95) 0.86 (0.79, 0.94) 0.0017 0.93 (0.90, 0.96) BRI Ref 0.92 (0.85, 1.00) 1.02 (0.93, 1.11) 1.40 (1.28, 1.53) <0.0001 1.21 (1.17, 1.25) ABSI Ref 0.83 (0.77, 0.90) 0.83 (0.76, 0.90) 0.80 (0.73, 0.88) <0.0001 0.93 (0.90, 0.96) WHtR Ref 0.92 (0.85, 1.00) 1.02 (0.93, 1.11) 1.40 (1.27, 1.53) <0.0001 1.20 (1.16, 1.24) COPD BMI Ref 0.85 (0.73, 0.99) 0.96 (0.82, 1.11) 1.44 (1.25, 1.67) <0.0001 1.21 (1.15, 1.27) WC Ref 0.87 (0.74, 1.02) 1.08 (0.93, 1.27) 1.48 (1.27, 1.72) <0.0001 1.23 (1.16, 1.29) WWI Ref 1.38 (1.15, 1.65) 1.35 (1.13, 1.62) 1.71 (1.42, 2.05) <0.0001 1.22 (1.15, 1.30) BRI Ref 0.94 (0.80, 1.11) 1.02 (0.87, 1.20) 1.52 (1.30, 1.77) <0.0001 1.24 (1.18, 1.30) ABSI Ref 0.88 (0.74, 1.05) 1.04 (0.88, 1.22) 1.22 (1.03, 1.44) 0.0013 1.12 (1.05, 1.18) WHtR Ref 0.94 (0.80, 1.11) 1.02 (0.87, 1.20) 1.51 (1.30, 1.77) <0.0001 1.24 (1.17, 1.30) Abbreviations : OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist-to-height ratio. a Adjusted for sex, age, race, education level, family poverty-income ratio, diabetes, hypertension, cardiovascular disease, chronic kidney disease, smoking status, and alcohol consumption. Table 3 . Weighted baseline characteristics of participants with eligible mortality data. OSA Asthma COPD Alive (n=6782) Deceased (n=920) p- value No (n=1964) Yes (n=267) p- value No (n=769) Yes (n=314) p- value Follow-up time, month 116.56 (2.19) 84.45 (2.03) <0.0001 112.35 (2.60) 71.62 (4.29) <0.0001 114.90 (3.77) 71.82 (3.34) <0.0001 Demographic data Age, year 45.79 (0.35) 64.93 (0.37) <0.0001 41.46 (0.41) 63.22 (1.06) <0.0001 50.26 (0.64) 67.46 (0.95) <0.0001 Gender <0.0001 0.1120 <0.0001 Male 3631 (55.03%) 566 (58.60%) 818 (41.69%) 142 (47.26%) 281 (32.51%) 190 (53.13%) Female 3151 (44.97%) 354 (41.40%) 1146 (58.31%) 125 (52.74%) 488 (67.49%) 124 (46.87%) Race 0.4047 0.0454 0.0263 White 2626 (66.91%) 550 (78.07%) 869 (70.47%) 154 (76.47%) 428 (75.88%) 215 (83.01%) Black 1462 (11.28%) 200 (11.61%) 497 (12.51%) 67 (13.26%) 145 (9.11%) 56 (9.10%) Hispanic 2161 (14.99%) 142 (5.60%) 446 (10.12%) 34 (4.18%) 149 (7.66%) 32 (3.37%) Others 533 (6.82%) 28 (4.72%) 152 (6.90%) 12 (6.08%) 47 (7.34%) 11 (4.52%) Education level <0.0001 <0.0001 0.0001 Under high school 765 (5.67%) 201 (15.46%) 117 (3.32%) 54 (15.83%) 84 (5.93%) 60 (13.57%) High school 2645 (35.33%) 416 (45.36%) 723 (32.17%) 111 (37.20%) 311 (39.76%) 141 (41.19%) Above high school 3372 (59.00%) 303 (39.18%) 1124 (64.51%) 102 (46.97%) 374 (54.31%) 113 (45.24%) PIR 3.10 (0.06) 2.14 (0.06) <0.0001 2.97 (0.07) 2.21 (0.13) <0.0001 2.71 (0.08) 2.24 (0.11) 0.0008 Smoking status <0.0001 <0.0001 <0.0001 Never 3646 (50.75%) 320 (33.09%) 1103 (53.00%) 83 (31.50%) 265 (34.19%) 53 (18.57%) Former 1573 (24.79%) 364 (36.71%) 421 (23.67%) 100 (34.44%) 238 (30.19%) 149 (46.52%) Now 1563 (24.46%) 236 (30.19%) 440 (23.33%) 84 (34.06%) 266 (35.62%) 112 (34.90%) Alcohol consumption <0.0001 0.0001 0.0169 Never 4034 (53.95%) 580 (59.76%) 1256 (57.74%) 169 (62.17%) 445 (56.25%) 196 (59.19%) Mild 1301 (22.44%) 97 (10.97%) 363 (21.84%) 25 (10.14%) 105 (15.36%) 23 (7.84%) Moderate 273 (4.56%) 21 (3.13%) 68 (4.89%) 2 (2.15%) 20 (2.59%) 6 (4.37%) Heavy 123 (2.35%) 6 (0.70%) 29 (1.63%) 1 (0.31%) 12 (1.56%) 1 (0.27%) Binge drinking daily 1051 (16.70%) 216 (25.45%) 248 (13.89%) 70 (25.23%) 187 (24.24%) 88 (28.33%) Body measurement data Weight, kg 87.79 (0.44) 83.44 (0.86) <0.0001 84.30 (0.72) 82.20 (1.46) 0.2069 85.00 (1.00) 81.32 (1.58) 0.0819 Height, cm 169.79 (0.17) 168.30 (0.37) 0.0001 168.47 (0.33) 166.51 (0.82) 0.0212 166.47 (0.36) 167.82 (1.00) 0.2191 BMI, kg/m² 30.41 (0.14) 29.34 (0.30) 0.0017 29.70 (0.26) 29.65 (0.67) 0.9474 30.65 (0.32) 28.72 (0.48) 0.0031 WC, cm 102.57 (0.36) 104.00 (0.67) 0.0429 99.62 (0.59) 103.15 (1.15) 0.0089 103.15 (0.82) 103.11 (1.36) 0.9850 WWI 11.00 (0.02) 11.46 (0.03) <0.0001 10.90 (0.03) 11.45 (0.06) <0.0001 11.25 (0.04) 11.52 (0.07) 0.0012 BRI 6.23 (0.05) 6.53 (0.10) 0.0058 6.01 (0.09) 6.62 (0.21) 0.0070 6.62 (0.11) 6.49 (0.19) 0.5910 ABSI 0.81 (0.00) 0.85 (0.00) <0.0001 0.81 (0.00) 0.84 (0.00) <0.0001 0.82 (0.00) 0.86 (0.00) <0.0001 WHtR 0.61 (0.00) 0.62 (0.00) 0.0045 0.59 (0.00) 0.62 (0.01) 0.0033 0.62 (0.00) 0.61 (0.01) 0.5494 Comorbidity Hypertension 3045 (42.29%) 683 (73.32%) <0.0001 768 (36.07%) 210 (73.80%) <0.0001 420 (50.31%) 227 (68.89%) <0.0001 Diabetes 1226 (14.36%) 344 (32.52%) <0.0001 325 (12.67%) 92 (31.78%) <0.0001 189 (19.05%) 111 (30.24%) 0.0053 CVD 521 (6.51%) 287 (29.57%) <0.0001 184 (7.66%) 96 (33.65%) <0.0001 155 (16.94%) 128 (35.60%) <0.0001 CKD 317 (4.11%) 267 (26.01%) <0.0001 73 (3.12%) 74 (22.45%) <0.0001 54 (6.00%) 92 (25.18%) <0.0001 Note : Continuous variables are presented as survey-weighted means (SEs), and p values were calculated via survey-weighted linear regression. Categorical variables are presented as unweighted counts (weighted percentages), and p values were calculated via the survey-weighted chi-square test. Abbreviations : PIR, poverty-income ratio; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist‒height ratio; CVD, cardiovascular disease; CKD, chronic kidney disease; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease. Table 4 . Associations between anthropometric indicators and mortality in the total population, OSA population, and COPD population. Adjusted hazard ratio (95% CI) a Quartile 1 Quartile 2 Quartile 3 Quartile 4 P for trend Per SD increment OSA BMI 1.00 0.79 (0.66, 0.94) 0.73 (0.61, 0.88) 0.74 (0.60, 0.91) 0.0034 0.89 (0.81, 0.96) WC 1.00 0.76 (0.62, 0.92) 0.70 (0.57, 0.85) 0.79 (0.65, 0.97) 0.0626 0.94 (0.87, 1.02) WWI 1.00 0.81 (0.63, 1.05) 1.01 (0.80, 1.28) 1.15 (0.91, 1.45) 0.0131 1.08 (1.00, 1.18) BRI 1.00 0.86 (0.71, 1.05) 0.80 (0.65, 0.97) 0.84 (0.68, 1.03) 0.1507 0.96 (0.88, 1.04) ABSI 1.00 1.02 (0.77, 1.35) 1.09 (0.84, 1.43) 1.41 (1.08, 1.83) 0.0002 1.18 (1.09, 1.27) WHtR 1.00 0.86 (0.71, 1.05) 0.80 (0.65, 0.97) 0.84 (0.68, 1.03) 0.1192 0.94 (0.87, 1.02) Asthma BMI 1.00 0.92 (0.65, 1.32) 0.89 (0.62, 1.26) 0.79 (0.54, 1.16) 0.2211 0.89 (0.77, 1.04) WC 1.00 0.75 (0.50, 1.13) 0.86 (0.58, 1.27) 0.75 (0.50, 1.12) 0.3101 0.91 (0.79, 1.05) WWI 1.00 0.85 (0.50, 1.43) 0.84 (0.51, 1.40) 0.93 (0.55, 1.55) 0.8810 1.01 (0.87, 1.19) BRI 1.00 0.98 (0.64, 1.48) 1.02 (0.68, 1.52) 0.85 (0.56, 1.30) 0.3712 0.92 (0.79, 1.08) ABSI 1.00 1.03 (0.60, 1.75) 1.05 (0.63, 1.74) 1.10 (0.66, 1.83) 0.6393 1.06 (0.93, 1.22) WHtR 1.00 0.98 (0.64, 1.48) 1.02 (0.68, 1.52) 0.85 (0.56, 1.30) 0.3963 0.92 (0.79, 1.07) COPD BMI 1.00 0.70 (0.51, 0.97) 0.75 (0.55, 1.02) 0.66 (0.45, 0.94) 0.0352 0.84 (0.72, 0.97) WC 1.00 0.71 (0.51, 0.99) 0.58 (0.42, 0.81) 0.71 (0.50, 1.00) 0.0409 0.85 (0.74, 0.97) WWI 1.00 1.01 (0.71, 1.44) 0.87 (0.61, 1.24) 0.88 (0.61, 1.26) 0.3590 0.93 (0.81, 1.06) BRI 1.00 0.82 (0.59, 1.13) 0.78 (0.57, 1.08) 0.70 (0.50, 0.99) 0.0508 0.86 (0.75, 0.99) ABSI 1.00 0.88 (0.57, 1.37) 1.26 (0.83, 1.89) 1.03 (0.67, 1.57) 0.6717 1.00 (0.87, 1.16) WHtR 1.00 0.82 (0.59, 1.13) 0.78 (0.57, 1.08) 0.70 (0.50, 0.99) 0.0463 0.86 (0.75, 0.98) Abbreviations : BMI, body mass index; WC waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist-to-height ratio; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease. a Adjusted for sex, age, race, education level, family poverty-income ratio, diabetes, hypertension, cardiovascular disease, chronic kidney disease, smoking status, and alcohol consumption. 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08:06:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":439059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-dependent ROC curves of the ABSI for predicting mortality.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5834187/v1/336c3dad9951e849c7a5d018.png"},{"id":74427610,"identity":"6c7c33f0-3685-4291-8738-f81d489c0fcf","added_by":"auto","created_at":"2025-01-22 08:13:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":414301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analysis across ABSI quartiles.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5834187/v1/c6761c0490eb4ad21c34759b.png"},{"id":74426082,"identity":"98483bcc-76a5-4075-b736-d8f5a96a0817","added_by":"auto","created_at":"2025-01-22 08:06:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratified analysis of the associations between ABSI and mortality among participants with OSA symptoms.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-5834187/v1/a9d9befe8d522cf431f34378.png"},{"id":82537550,"identity":"29ab1db7-4fd3-4826-95eb-7473b57dcddc","added_by":"auto","created_at":"2025-05-12 16:08:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2769131,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5834187/v1/9bd78292-9785-4fae-a8d3-d09b13095740.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Six Anthropometric Indicators in Predicting the Prevalence and Mortality of Obstructive Sleep Apnoea, Asthma, and Chronic Obstructive Pulmonary Disease: A Population- Based Study from the US National Health and Nutrition Examination Survey (NHANES)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity has become a global health crisis. In 2022, obesity affected approximately 16% of the global adult population. Between 1990 and 2022, the worldwide prevalence of obesity increased by more than twofold [1]. Increased risk of several chronic diseases associated with the prevalence of obesity [2]. Furthermore, obesity has been linked to airway inflammation, reduced lung function, and the occurrence and worsening of respiratory diseases, including obstructive sleep apnoea (OSA) [3], asthma [4], and chronic obstructive pulmonary disease (COPD) [5]. These effects can be attributed to the fact that obesity leads to metabolic dysregulation and chronic inflammation, which influence overall body health, as well as fat accumulation in the abdominal cavity, which negatively impacts lung function [6,7].\u003c/p\u003e \u003cp\u003eBody mass index (BMI) remains the most widely used measure to assess obesity. The World Health Organization (WHO) classifies adults with a BMI of 30 or higher as obese [1]. However, BMI has been shown to be poor at distinguishing between fat mass and lean mass. Recent studies have increasingly focused on body fat distribution rather than merely excess weight [8]. Dual-energy X-ray absorptiometry (DXA) provides highly accurate body composition assessments, but its complexity and high cost hinder widespread use, but its complexity and high cost limit widespread use [9]. Waist circumference (WC) serves as a simpler, more accessible proxy for abdominal fat. It has been advocated for inclusion as a routine clinical measurement [10]. However, WC is highly correlated with BMI, which may also give rise to the obesity paradox, wherein obesity appears to have protective effects in some conditions [11]. To address this issue, the body shape index (ABSI) and waist-to-height ratio (WHtR) have been developed to capture the added value of WC; these indices correspond to visceral fat and can predict mortality [12,13]. The body roundness index (BRI), assuming the body is elliptical and calculates its eccentricity, serves as another promising measure of abdominal adiposity. It has been studied for its value in predicting cardiovascular events [14], mortality [15], and the deterioration of lung function [16,17]. The weight-adjusted waist index (WWI) also undergoes evaluation alongside WHtR, BMI, and WC, demonstrating the strongest association with sarcopenic obesity [18].\u003c/p\u003e \u003cp\u003eSeveral surveys have examined and compared the role of various anthropometric measures of lung function, which are related to the onset and prognosis of respiratory diseases. For example, a longitudinal study in Taiwan found that elevated levels of BRI, WHtR, and BMI were linked to a decline in lung function [16]. However, the impact of various anthropometric measures on the prevalence and mortality of respiratory diseases remains underexplored.\u003c/p\u003e \u003cp\u003eThis study sought to elucidate the impacts of diverse anthropometric measures on the prevalence and mortality of diseases such as OSA, asthma, and COPD. Furthermore, it aimed to identify the most accurate metric for assessing and addressing adiposity in individuals with these chronic respiratory diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eFour cycles of data (2005\u0026ndash;2006, 2007‒2008, 2015‒2016, and 2017‒2020) came from the US National Health and Nutrition Examination Survey (NHANES) database. Initially, the four-cycle NHANES data included 46028 participants. Individuals with missing data on key anthropometric measures\u0026mdash;such as weight (n\u0026thinsp;=\u0026thinsp;3030), height (n\u0026thinsp;=\u0026thinsp;3295), and waist circumference (n\u0026thinsp;=\u0026thinsp;1698)\u0026mdash;were excluded. The remaining participants were grouped based on available data for OSA, asthma, and COPD, resulting in 26077 with OSA-related data, 37972 with asthma-related data, and 22989 with COPD-related data. For the longitudinal analysis, additional exclusions were made to remove non-OSA, non-asthma, and non-COPD individuals, as well as those with missing valid mortality data. Ultimately, the analysis focused on 7702 participants with OSA, 2232 with asthma, and 1083 with COPD, all having complete body measurement and mortality data. Figure\u0026nbsp;1 presents a flow chart detailing the selection procedure of participants for this research.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Flow chart of the selection procedure.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of OSA, asthma, and COPD\u003c/h3\u003e\n\u003cp\u003eOSA symptoms were defined on the basis of individuals reporting snoring, gasping for air, experiencing pauses in breathing three or more times a week, or experiencing excessive daytime sleepiness on at least 16 days per month [19]. People were diagnosed with asthma if they had ever been told by doctors that they had asthma. People were diagnosed with COPD if they had ever been told by doctors that they had COPD, chronic bronchitis, or emphysema.\u003c/p\u003e\n\u003ch3\u003eMeasurement of six body measurement indicators\u003c/h3\u003e\n\u003cp\u003eTrained health professionals collected participants\u0026rsquo; height, weight, and waist circumference (WC) data. From these measurements, six body measurement indicators\u0026mdash;BMI, WWI, BRI, ABSI, and WHtR\u0026mdash;were calculated via the following equations:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:BMI=\\:weight/{height}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:WWI=WC/\\sqrt{weight}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:BRI=364.2-365.5\\times\\:\\sqrt{1-{(WC/2\\pi\\:)}^{2}/{\\left(0.5\\:height\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:ABSI=WC/\\left({BMI}^{2/3}\\:{height}^{1/3}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:WHtR=WC/height$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMortality\u003c/h3\u003e\n\u003cp\u003eMortality data were obtained from the National Death Index (NDI), which is linked with NHANES records by the National Center for Health Statistics. The analysis focused on all-cause mortality, and monitoring starting from the participant's enrolment date and continuing until death or December 31, 2019.\u003c/p\u003e\n\u003ch3\u003eCovariables\u003c/h3\u003e\n\u003cp\u003eThe analysis considered multiple covariables, including demographic factors like sex, age, race, the poverty-income ratio (PIR) and education level, along with medical history variables including diabetes, cardiovascular disease (CVD), hypertension, chronic kidney disease (CKD), smoking status, and alcohol consumption. Smoking status classified individuals as current smokers, quitters and never smokers. Alcohol consumption was classified into four distinct levels: daily binge drinking, heavy drinking, moderate drinking, and mild drinking, as well as individuals who do not consume alcohol.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMissing covariable values were imputed via the random forest algorithm implemented via missRanger in R (version 4.3.3). This method uses chained random forest models to iteratively impute missing values, leveraging the observed data from other variables to predict missing values. This study took into account the complex sampling design and sampling weights of the NHANES database by employing a full sample exam weight. Continuous variable outcomes are reported as survey-weighted means (standard errors), with differences between groups calculated using survey-weighted linear regression. Categorical variables are displayed as unweighted counts alongside their corresponding weighted percentages, and group differences were assessed using the survey-weighted Chi-square test.\u003c/p\u003e \u003cp\u003eThe anthropometric indicators were assessed both as continuous variables, with each standard deviation increment represented, and as categorical variables, with the quartiles delineated. The tests for linear and p-values for trend were calculated to assess whether there was a significant linear association between the quartiles of the anthropometric indicators and the outcomes, with the median values of each quartile assigned to the variables. Multivariable logistic regression models investigated the associations of six anthropometric indicators with the prevalence of OSA, asthma, and COPD. The findings are reported as odds ratios (ORs) and their 95% confidence intervals (CIs). Multivariable Cox proportional hazards models assessed the relationships between the examined factors and mortality in the overall population, as well as in the OSA, asthma, and COPD subgroups. The results are shown as the hazard ratios (HRs) alongside 95% CIs. A time-dependent ROC curve evaluated the predictive performance of ABSI for mortality at different time points among the OSA population. Furthermore, Kaplan-Meier survival curves for all-cause mortality were generated, and the differences were assessed using the log-rank test. A stratified analysis further investigated the relationship between ABSI and mortality, considering factors such as sex, age, race, PIR, diabetes, hypertension, CVD, CKD, smoking status, and alcohol consumption.\u003c/p\u003e \u003cp\u003eAll data analyses were conducted via R programming (version 4.3.3) and EmpowerStats software (X\u0026amp;Y Solutions, Inc.), with statistical significance set at a \u003cem\u003ep\u003c/em\u003e-value below 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch3\u003eCharacteristics of the population included in the cross-sectional analysis\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e outlines the basic characteristics of the participants, comparing those with OSA, asthma, and COPD to healthy individuals. OSA participants were older (47.47 years) and predominantly male (54.81%) compared to healthy participants (43.10 years, 42.76% male). Asthma participants were younger (36.06 years) with a slightly higher proportion of females (54.14%) compared to healthy individuals (38.77 years, 50.42% female). COPD participants were older (55.38 years) and more likely to be female (60.89%) compared to healthy participants (46.52 years, 51.01% female).\u003c/p\u003e\n\u003cp\u003eOSA, asthma, and COPD participants exhibited lower education levels and PIR compared to their healthy counterparts. Smoking and alcohol consumption were more prevalent in all three disease groups. Regarding anthropometric measurements, BMI, WC, BRI, and WHtR, were elevated in all disease groups, while ABSI was lower in asthma participants and WWI showed no significant difference between asthma and healthy individuals. Comorbidities such as hypertension, diabetes, CVD, and CKD were more common in participants with OSA and COPD compared to healthy individuals, with the highest prevalence observed in the COPD group.\u003c/p\u003e\n\u003ch3\u003eAssociations between anthropometric indicators and OSA, asthma and COPD\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e illustrates the association between the six body measurement indicators and the prevalence of OSA, asthma, and COPD, adjusted for potential confounding variables, including sex, age, race, education level, PIR, diabetes, hypertension, CVD, CKD, smoking status, and alcohol consumption. All six indicators were significantly associated with an increased prevalence of OSA and COPD, with stronger associations observed for OSA. For each standard deviation (SD) increase, the prevalence of OSA rose by 64% for BMI (95% CI for OR: 1.60-1.69), 64% for WC (95% CI for OR: 1.59-1.68), 42% for WWI (95% CI for OR: 1.37-1.46), 62% for BRI (95% CI for OR: 1.58-1.67), 3% for ABSI (95% CI for OR: 1.00-1.06), and 65% for WHtR (95% CI for OR: 1.60-1.70). Among these, ABSI exhibited the weakest predictive value for OSA. For asthma, BMI, WC, BRI, and WHtR were positively associated with prevalence, while ABSI and WWI were negatively associated. Each SD increase in ABSI and WWI was linked to a 7% decrease in asthma prevalence (95% CI for OR: 0.90-0.96).\u003c/p\u003e\n\u003ch3\u003eCharacteristics of participants with eligible mortality data\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e shows the population characteristics in the mortality analysis. Among the 7002 OSA participants, 920 deaths were recorded during a median follow-up of 84.45 months. In the asthma cohort, 267 deaths occurred among 2,232 participants, with a median follow-up of 71.62 months. Similarly, in the COPD cohort, 314 deaths were documented among 2,232 participants, with a median follow-up of 71.82 months. A comparison of the characteristics of deceased and surviving individuals revealed that those who died tended to be older, male, white, and have a lower PIR. Additionally, the deceased were more probable to have smoked and drunk alcohol. They also presented higher levels of WC, WWI, BRI, ABSI, and WHtR. Furthermore, individuals who died were more frequently affected by comorbidities such as hypertension, diabetes, CVD, and CKD.\u003c/p\u003e\n\u003ch3\u003eRelationships between anthropometric indicators and mortality\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e shows the results relating anthropometric indicators to mortality in different population groups. In the OSA group, a one-SD increase in ABSI is linked to an 18% rise in mortality (95% CI: 1.09-1.27), while higher BMI and WC are associated with reduced mortality. In the COPD group, increased BMI, WC, and WHtR are linked to lower mortality risks, with BMI showing an HR per SD increment of 0.84 (95% CI: 0.72-0.97), WC showing an HR per SD increment of 0.85 (95% CI: 0.74-0.97) and WHtR showing an HR per SD increment of 0.86 (95% CI: 0.75-0.98). However, in the asthma group, none of the six anthropometric indicators demonstrated significant associations with mortality, suggesting variability in their prognostic value across different respiratory conditions.\u003c/p\u003e\n\u003ch3\u003ePrediction performance of the ABSI for 3-year, 5-year and 10-year mortality\u003c/h3\u003e\n\u003cp\u003eFigure 2 depicts the time-dependent ROC curves for the prediction of the ABSI for 3-year, 5-year and 10-year mortality in the OSA group. The area under the curve (AUC) for ABSI was 0.752 (95% CI: 0.717-0.786) for 3-year, 0.755 (95% CI: 0.729-0.782) for 5-year, and 0.744 (95% CI: 0.724-0.764) for 10-year mortality. These results underscore the significance of ABSI as a valuable marker for assessing survival prognosis, with superior predictive performance for 5-year mortality. Moreover, Kaplan-Meier survival analyses revealed that individuals in the fourth quartile of ABSI exhibited a notably higher risk of mortality (Figure 3).\u003c/p\u003e\n\u003ch3\u003eStratified analyses\u003c/h3\u003e\n\u003cp\u003eFigure 4 provides insights into the impact of the ABSI on mortality among patients with OSA, across various subgroups. ABSI was found to interact with alcohol consumption (p for interaction = 0.0081) and CVD (p for interaction = 0.0477). A stronger association between ABSI and mortality was observed in alcohol consumers (HR: 1.39, 95% CI: 1.20\u0026ndash;1.60) compared to non-drinkers (HR: 1.11, 95% CI: 1.02\u0026ndash;1.21). Similarly, ABSI was more strongly linked to mortality in individuals with CVD (HR: 1.33, 95% CI: 1.15\u0026ndash;1.54) than those without CVD (HR: 1.13, 95% CI: 1.03\u0026ndash;1.23). The association between ABSI and mortality was not significant in participants under 40 years old, in those of other races, or in non-smokers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers a thorough analysis of the predictive performance of various anthropometric indicators in relation to the prevalence of OSA, asthma, COPD, and mortality. All six anthropometric indicators\u0026mdash;BMI, WC, WWI, BRI, ABSI, and WHtR\u0026mdash;were significantly associated with increased prevalence of OSA and COPD, with particularly strong associations observed for OSA. However, the relationship between asthma and these indicators is more complex: while BMI, WC, BRI, and WHtR are positively correlated with asthma prevalence, ABSI and WWI demonstrate inverse associations. Longitudinal analysis further revealed that traditional anthropometric indicators such as BMI and WC were negatively associated with mortality risks in the OSA and COPD groups, supporting the phenomenon known as the \"obesity paradox.\" However, ABSI emerged as a significant predictor of mortality in OSA, suggesting that the ABSI provides a more nuanced perspective on the impact of central obesity on the risk of death, and may help mitigate the \"obesity paradox.\"\u003c/p\u003e \u003cp\u003eObesity is widely related to various comorbidities. Anthropometric indices, particularly BMI and WC, have long been recognized for their connection with the development of respiratory diseases, including OSA, asthma, and COPD. For instance, a community-based cohort study from South Korea revealed that fat mass index and waist-to-hip ratio showed an inverse relationship with lung function in the overall population [20]. Additionally, a meta-analysis concluded that those classified as overweight or obese face a dose-dependent, increased risk of developing asthma [21]. Another cohort study from China revealed that both higher WC and being underweight were linked to a greater risk of developing COPD [22]. This relationship can be attributed, at least in part, to the phenomenon of insulin resistance and the systemic inflammation that occurs in conjunction with obesity, especially abdominal obesity [23]. This study revealed the association between novel indicators WWI, BRI, ABSI, and WHtR and the prevalence of OSA and COPD. However, the inverse associations observed between ABSI, WWI, and asthma prevalence suggest a limitation in these indicators' ability to accurately reflect the impact of obesity on asthma prevalence. This aligns with previous studies that have demonstrated their limited capacity to identify metabolic syndrome [24].\u003c/p\u003e \u003cp\u003eInterestingly, obesity has been linked to reduced mortality from chronic diseases, known as the \u0026ldquo;obesity paradox.\u0026rdquo; For instance, a meta-analysis of 22 studies comprising 21150 COPD patients revealed that overweight and obese individuals experienced better survival rates than normal weight people [25]. Another 12-year prospective cohort of Koreans indicated that individuals with lower BMIs had an elevated risk of death from respiratory causes [26]. The survival advantage in obese individuals is complex and disease-specific. One reason for this paradox could be the inherent limitations of the use of BMI as a measurement tool. This study highlights the value of the ABSI, which offers a more nuanced understanding of how central obesity influences mortality risk in those with OSA. ABSI helps address the weaknesses of conventional measures like BMI and WC, providing deeper insights into how body fat distribution influences mortality. However, in COPD patients, the obesity paradox is even more intricate. This could be attributed to the fact that being underweight, together with loss of muscle mass, represents a principal acute risk element for death in advanced COPD patients [27]. However, recent studies have noted that obese individuals can exhibit low muscle mass, known as sarcopenic obesity, which increases systemic inflammation and worsens quality of life [28]. Therefore, in addition to routine anthropometric measurements, evaluating skeletal muscle function and body composition in COPD patients with an obese phenotype is crucial for understanding the impact of obesity on disease progression [5].\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study represents the first comparison of the positive associations of BMI, WC, WWI, BRI and WHtR with OSA, asthma and COPD. The data were sourced from the NHANES database, a nationwide, population-based survey featuring a large sample size and an extended follow-up period. The application of standardized protocols enhanced the reliability of these findings. Nevertheless, this research presents some inherent limitations. On the one hand, the identification of conditions such as OSA, asthma, and COPD relies on self-report questionnaires rather than objective criteria, such as laboratory testing, home sleep apnoea monitoring, or pulmonary function tests, which may have led to inaccuracies in diagnoses and limited our ability to assess disease severity. On the other hand, the cross-sectional study design limits the ability to draw conclusions about causal links between anthropometric measures and disease prevalence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that novel anthropometric indicators, including WWI, BRI, ABSI and WHtR, show positive associations with the prevalence of OSA, asthma, and COPD, alongside traditional measures such as BMI and WC. However, WWI and ABSI were more limited in their association with asthma prevalence. Longitudinal analyses revealed that traditional anthropometric indicators such as BMI and WC were negatively associated with mortality risks in the OSA and COPD groups, supporting the phenomenon known as the \"obesity paradox.\" In contrast, ABSI emerged as a significant predictor of mortality in OSA, suggesting that the ABSI provides a more nuanced perspective on the impact of central obesity on the risk of death, and may help mitigate the \"obesity paradox.\" However, in COPD patients, routine anthropometric measurements may not fully capture the impact of obesity on the progression of the disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHANES, the US National Health and Nutrition Examination Survey; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist‒height ratio; DXA, dual-energy X-ray absorptiometry; PIR, poverty‒income ratio; CVD, cardiovascular disease; CKD, chronic kidney disease; SD, standard deviation; OR, odds ratio; 95% CI, 95% confidence interval; HR, hazard ratio; ROC, receiver operating characteristic. AUC, the area under the curve.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the National Center for Health Statistics Research Ethics Review Committee (https://www.cdc.gov/nchs/nhanes/irba98.htm). All adult participants provided written informed consent, and those under the age of 18 were required to obtain consent from a parent or guardian prior to participation.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe data examined in this study were sourced from a publicly accessible database, NHANES, available for download from the website: http://www.cdc.gov/nchs/nhanes.htm.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eWe would like to express our gratitude to all the individuals and teams who contributed to the collection and maintenance of the survey data in the NHANES database.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; contributions\u003c/h3\u003e\n\u003cp\u003eHL was responsible for the study design and manuscript revision. JH and ST collected the data and performed the statistical analyses. JH, ST, and QZ validated the analysis results and drafted the manuscript. All the authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eObesity and overweight. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 3 August 2024.\u003c/li\u003e\n\u003cli\u003eKelly T, Yang W, Chen C-S, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes. 2008;32:1431\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eMalhotra A, White DP. Obstructive sleep apnoea. The Lancet. 2002;360:237\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eDixon AE, Holguin F, Sood A, Salome CM, Pratley RE, Beuther DA, et al. An official American Thoracic Society Workshop report: obesity and asthma. Proc Am Thorac Soc. 2010;7:325\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eBeijers RJHCG, Steiner MC, Schols AMWJ. The role of diet and nutrition in the management of COPD. Eur Respir Rev. 2023;32:230003. \u003c/li\u003e\n\u003cli\u003ePeters U, Dixon A, Forno E. Obesity and Asthma. J Allergy Clin Immunol. 2018;141:1169\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eSutherland K, Keenan BT, Bittencourt L, Chen N, Gislason T, Leinwand S, et al. A Global Comparison of Anatomic Risk Factors and Their Relationship to Obstructive Sleep Apnea Severity in Clinical Samples. Journal of Clinical Sleep Medicine. 15:629\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez-Ambrosi J, Silva C, Galofr\u0026eacute; JC, Escalada J, Santos S, Mill\u0026aacute;n D, et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes. 2012;36:286\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eBorga M, West J, Bell JD, Harvey NC, Romu T, Heymsfield SB, et al. Advanced body composition assessment: from body mass index to body composition profiling. J Investig Med. 2018;66:1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eRoss R, Neeland IJ, Yamashita S, Shai I, Seidell J, Magni P, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol. 2020;16:177\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eClark AL, Fonarow GC, Horwich TB. Waist circumference, body mass index, and survival in systolic heart failure: the obesity paradox revisited. J Card Fail. 2011;17:374\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eKrakauer NY, Krakauer JC. A New Body Shape Index Predicts Mortality Hazard Independently of Body Mass Index. PLoS One. 2012;7:e39504. \u003c/li\u003e\n\u003cli\u003ePasdar Y, Moradi S, Moludi J, Saiedi S, Moradinazar M, Hamzeh B, et al. Waist-to-height ratio is a better discriminator of cardiovascular disease than other anthropometric indicators in Kurdish adults. Sci Rep. 2020;10:16228. \u003c/li\u003e\n\u003cli\u003eWu M, Yu X, Xu L, Wu S, Tian Y. Associations of longitudinal trajectories in body roundness index with mortality and cardiovascular outcomes: a cohort study. Am J Clin Nutr. 2022;115:671\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eZhang X, Ma N, Lin Q, Chen K, Zheng F, Wu J, et al. Body Roundness Index and All-Cause Mortality Among US Adults. JAMA Netw Open. 2024;7:e2415051. \u003c/li\u003e\n\u003cli\u003eHsu Y-E, Chen S-C, Geng J-H, Wu D-W, Wu P-Y, Huang J-C. Obesity-Related Indices Are Associated with Longitudinal Changes in Lung Function: A Large Taiwanese Population Follow-Up Study. Nutrients. 2021;13:4055. \u003c/li\u003e\n\u003cli\u003eZhang R-H, Zhou J-B, Cai Y-H, Shu L-P, Yang J, Wei W, et al. Non-linear association of anthropometric measurements and pulmonary function. Sci Rep. 2021;11:14596. \u003c/li\u003e\n\u003cli\u003eKim JE, Choi J, Kim M, Won CW. Assessment of existing anthropometric indices for screening sarcopenic obesity in older adults. Br J Nutr. 129:875\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eZhou T, Chen S, Mao J, Zhu P, Yu X, Lin R. Association between obstructive sleep apnea and visceral adiposity index and lipid accumulation product: NHANES 2015\u0026ndash;2018. Lipids Health Dis. 2024;23:100. \u003c/li\u003e\n\u003cli\u003ePark Y, Kim J, Kim YS, Leem AY, Jo J, Chung K, et al. Longitudinal association between adiposity changes and lung function deterioration. Respir Res. 2023;24:44. \u003c/li\u003e\n\u003cli\u003eBeuther DA, Sutherland ER. Overweight, Obesity, and Incident Asthma. Am J Respir Crit Care Med. 2007;175:661\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eLi J, Zhu L, Wei Y, Lv J, Guo Y, Bian Z, et al. Association between adiposity measures and COPD risk in Chinese adults. Eur Respir J. 2020;55:1901899. \u003c/li\u003e\n\u003cli\u003eThuesen BH, Husemoen LLN, Hersoug L-G, Pisinger C, Linneberg A. Insulin resistance as a predictor of incident asthma-like symptoms in adults. Clinical \u0026amp; Experimental Allergy. 2009;39:700\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eWu L, Zhu W, Qiao Q, Huang L, Li Y, Chen L. Novel and traditional anthropometric indices for identifying metabolic syndrome in non-overweight/obese adults. Nutr Metab (Lond). 2021;18:3. \u003c/li\u003e\n\u003cli\u003eCao C, Wang R, Wang J, Bunjhoo H, Xu Y, Xiong W. Body Mass Index and Mortality in Chronic Obstructive Pulmonary Disease: A Meta-Analysis. PLoS One. 2012;7:e43892. \u003c/li\u003e\n\u003cli\u003eJee SH, Sull JW, Park J, Lee S-Y, Ohrr H, Guallar E, et al. Body-Mass Index and Mortality in Korean Men and Women. New England Journal of Medicine. 2006;355:779\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eFranssen FME, O\u0026rsquo;Donnell DE, Goossens GH, Blaak EE, Schols AMWJ. Obesity and the lung: 5 \u0026middot; Obesity and COPD. Thorax. 2008;63:1110\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eJoppa P, Tkacova R, Franssen FME, Hanson C, Rennard SI, Silverman EK, et al. Sarcopenic Obesity, Functional Outcomes, and Systemic Inflammation in Patients With Chronic Obstructive Pulmonary Disease. J Am Med Dir Assoc. 2016;17:712\u0026ndash;8. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Weighted baseline characteristics of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eparticipants in the cross-sectional study.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=13870)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=12207)\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-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNo (n=32187)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYes (n=5785)\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-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=21187)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes (n=1802)\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-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\u003eDemographic data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\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\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.10 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e47.47 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.77\u0026nbsp;(0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.06\u0026nbsp;(0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.52 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e55.38 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\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\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\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\u003e6168 (42.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e6578 (54.81%)\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\u003e15955\u0026nbsp;(49.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2815\u0026nbsp;(45.86%)\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\u003e10337\u0026nbsp;(48.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e817 (39.11%)\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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7702 (57.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e5629 (45.19%)\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\u003e16232\u0026nbsp;(50.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2970\u0026nbsp;(54.14%)\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\u003e10850 (51.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e985 (60.89%)\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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4047\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;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5371 (66.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e4732 (66.45%)\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\u003e11474\u0026nbsp;(63.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2089\u0026nbsp;(63.93%)\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\u003e8179 (66.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1033 (76.55%)\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\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3347 (11.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2858 (11.37%)\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\u003e7641\u0026nbsp;(11.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1736\u0026nbsp;(14.35%)\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\u003e4952 (11.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e368 (9.23%)\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\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3546 (14.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e3359 (14.75%)\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\u003e9429\u0026nbsp;(16.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1383\u0026nbsp;(13.80%)\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\u003e5662 (14.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e267 (7.05%)\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\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1606 (7.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1258 (7.43%)\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\u003e3643\u0026nbsp;(8.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e577\u0026nbsp;(7.92%)\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\u003e2394 (7.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e134 (7.17%)\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 level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\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\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnder high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1149 (4.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1279 (5.69%)\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\u003e2822\u0026nbsp;(4.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e311\u0026nbsp;(3.24%)\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\u003e2235 (5.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e191 (6.31%)\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\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4945 (31.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e4688 (36.23%)\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\u003e12150\u0026nbsp;(34.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2197\u0026nbsp;(32.61%)\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\u003e7774\u0026nbsp;(33.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e786\u0026nbsp;(43.42%)\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\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7776 (64.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e6240 (58.08%)\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\u003e17215\u0026nbsp;(60.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3277\u0026nbsp;(64.15%)\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\u003e11178\u0026nbsp;(60.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e825 (50.27%)\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\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.02\u0026nbsp;(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e3.03 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.96\u0026nbsp;(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.81\u0026nbsp;(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.10 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.57 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\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\u003e0.0099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9199 (63.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e6622 (50.98%)\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\u003e23552\u0026nbsp;(66.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4126\u0026nbsp;(63.51%)\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\u003e12320\u0026nbsp;(56.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e511 (29.19%)\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\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2549 (20.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2985 (25.98%)\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\u003e4692\u0026nbsp;(18.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e836\u0026nbsp;(19.17%)\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\u003e4861\u0026nbsp;(24.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e656\u0026nbsp;(34.83%)\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\u003eNow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2122 (15.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2600 (23.04%)\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\u003e3943\u0026nbsp;(15.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e823\u0026nbsp;(17.32%)\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\u003e4006\u0026nbsp;(19.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e635 (35.98%)\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\u003eAlcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\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\u003e0.4940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9591 (62.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e7318 (53.87%)\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\u003e24403\u0026nbsp;(65.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4380\u0026nbsp;(65.59%)\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\u003e13041\u0026nbsp;(56.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1031 (55.00%)\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\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2289 (21.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2226 (21.77%)\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\u003e3856\u0026nbsp;(17.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e683\u0026nbsp;(17.55%)\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\u003e4083 (23.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e229 (14.35%)\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\u003e511 (4.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e497 (5.02%)\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\u003e860\u0026nbsp;(4.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e148\u0026nbsp;(4.17%)\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\u003e924 (5.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e51 (3.26%)\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\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e223 (1.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e242 (2.46%)\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\u003e406\u0026nbsp;(1.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u0026nbsp;(1.54%)\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\u003e426 (2.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e29 (1.74%)\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\u003eBinge drinking daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1256 (9.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1924 (16.89%)\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\u003e2662\u0026nbsp;(10.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e515\u0026nbsp;(11.15%)\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\u003e2713 (12.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e462 (25.65%)\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\u003e\u003cstrong\u003eBody measurement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\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\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.81 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e87.69 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.29 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.50 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.52 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e84.69 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeight, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e167.81 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e169.33 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161.91 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e163.00 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e168.63 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e166.56 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\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\u003e27.20 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e30.51 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.86 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.02 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.92 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e30.47 (0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWC, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.86 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e103.04 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91.89 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.15 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.80 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e103.74 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.76 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e11.06 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.98 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.95 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.93 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e11.35 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.23 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e6.33 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.35 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.65 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.81 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e6.70 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.82 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.83 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.56 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.61 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.62 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\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\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4702 (31.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e5962 (45.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9072\u0026nbsp;(30.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1664\u0026nbsp;(31.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9360 (39.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1129 (56.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\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\u003e1783 (10.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2618 (16.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3740\u0026nbsp;(10.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e746\u0026nbsp;(11.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3790 (13.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e553 (24.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e981 (5.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1296 (8.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1825\u0026nbsp;(5.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e449\u0026nbsp;(7.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1790 (6.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e486 (22.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e993 (6.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e899 (5.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1652\u0026nbsp;(5.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e241\u0026nbsp;(3.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1639 (6.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e255 (11.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Continuous variables are presented as survey-weighted means (SEs),\u0026nbsp;and \u003cem\u003ep-\u003c/em\u003evalue was calculated by survey-weighted linear regression. Categorical variables are presented as unweighted counts (weighted\u0026nbsp;percentages),\u0026nbsp;and \u003cem\u003ep-\u003c/em\u003evalue was calculated by survey-weighted Chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e: PIR, poverty-income ratio; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist‒height ratio; CVD, cardiovascular disease; CKD, chronic kidney disease; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. Associations of six anthropometric indicators with OSA, asthma and COPD.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds ratio (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer SD increment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.51 (1.40, 1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.25 (2.09, 2.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.50 (3.24, 3.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.64 (1.60, 1.69)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.68 (1.55, 1.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.50 (2.31, 2.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.60 (3.32, 3.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.64 (1.59, 1.68)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.73 (1.60, 1.87)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.02 (1.86, 2.19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.44 (2.23, 2.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.42 (1.37, 1.46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n 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13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.32 (1.23, 1.42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.29 (1.19, 1.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.17 (1.07, 1.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.03 (1.00, 1.06)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.74 (1.61, 1.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.46 (2.27, 2.66)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.66 (3.37, 3.98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.65 (1.60, 1.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.45 (1.33, 1.59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.47 (1.34, 1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.94 (1.77, 2.14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.27 (1.23, 1.31)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.56 (1.43, 1.71)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.52 (1.37, 1.68)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.91 (1.72, 2.12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.31 (1.26, 1.36)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86 (0.79, 0.93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87 (0.80, 0.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86 (0.79, 0.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.93 (0.90, 0.96)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.92 (0.85, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.02 (0.93, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.40 (1.28, 1.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.21 (1.17, 1.25)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.83 (0.77, 0.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.83 (0.76, 0.90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.80 (0.73, 0.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.93 (0.90, 0.96)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.92 (0.85, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.02 (0.93, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.40 (1.27, 1.53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.20 (1.16, 1.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.85 (0.73, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.96 (0.82, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.44 (1.25, 1.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.21 (1.15, 1.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.87 (0.74, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.08 (0.93, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.48 (1.27, 1.72)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.23 (1.16, 1.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.38 (1.15, 1.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.35 (1.13, 1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.71 (1.42, 2.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.22 (1.15, 1.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.94 (0.80, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.02 (0.87, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.52 (1.30, 1.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.24 (1.18, 1.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.88 (0.74, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.04 (0.88, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.22 (1.03, 1.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.12 (1.05, 1.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.94 (0.80, 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.02 (0.87, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.51 (1.30, 1.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.24 (1.17, 1.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist-to-height ratio.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for sex, age, race, education level, family poverty-income ratio, diabetes, hypertension, cardiovascular disease, chronic kidney disease, smoking status, and alcohol consumption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Weighted baseline characteristics of participants with eligible mortality data.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive (n=6782)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeceased (n=920)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (n=1964)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes (n=267)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=769)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes (n=314)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up time, month\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e116.56 (2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e84.45 (2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e112.35 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e71.62 (4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e114.90 (3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e71.82 (3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eAge, year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e45.79 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e64.93 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e41.46 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e63.22 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e50.26 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e67.46 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.1120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3631\u0026nbsp;(55.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e566\u0026nbsp;(58.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e818 (41.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e142 (47.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e281 (32.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e190 (53.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3151\u0026nbsp;(44.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e354\u0026nbsp;(41.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1146 (58.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e125 (52.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e488 (67.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e124 (46.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.4047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2626\u0026nbsp;(66.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e550\u0026nbsp;(78.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e869 (70.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e154 (76.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e428 (75.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e215 (83.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1462 (11.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e200\u0026nbsp;(11.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e497 (12.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e67 (13.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e145 (9.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e56 (9.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2161\u0026nbsp;(14.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e142\u0026nbsp;(5.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e446 (10.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e34 (4.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e149 (7.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e32 (3.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e533\u0026nbsp;(6.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28\u0026nbsp;(4.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e152 (6.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12 (6.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e47 (7.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e11 (4.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eUnder high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e765\u0026nbsp;(5.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e201\u0026nbsp;(15.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e117 (3.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e54 (15.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e84 (5.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e60 (13.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2645\u0026nbsp;(35.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e416\u0026nbsp;(45.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e723 (32.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e111 (37.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e311 (39.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e141 (41.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3372\u0026nbsp;(59.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e303\u0026nbsp;(39.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1124 (64.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e102 (46.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e374 (54.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e113 (45.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3.10\u0026nbsp;(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.14 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.97 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.21 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.71 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.24 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3646\u0026nbsp;(50.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e320\u0026nbsp;(33.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1103 (53.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e83 (31.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e265 (34.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e53 (18.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1573\u0026nbsp;(24.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e364\u0026nbsp;(36.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e421 (23.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e100 (34.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e238 (30.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e149 (46.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1563\u0026nbsp;(24.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e236\u0026nbsp;(30.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e440 (23.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e84 (34.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e266 (35.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e112 (34.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eAlcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4034\u0026nbsp;(53.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e580\u0026nbsp;(59.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1256 (57.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e169 (62.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e445 (56.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e196 (59.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1301\u0026nbsp;(22.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e97 (10.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e363 (21.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e25 (10.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e105 (15.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e23 (7.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e273 (4.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e21 (3.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e68 (4.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2 (2.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20 (2.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6 (4.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e123 (2.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (0.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29 (1.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1 (0.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12 (1.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1 (0.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eBinge drinking daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1051 (16.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e216 (25.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e248 (13.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e70 (25.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e187 (24.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e88 (28.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody measurement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eWeight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e87.79 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e83.44 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e84.30 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e82.20 (1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.2069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e85.00 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e81.32 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eHeight, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e169.79 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e168.30 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e168.47 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e166.51 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e166.47 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e167.82 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.2191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e30.41 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e29.34 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29.70 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e29.65 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.9474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e30.65 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e28.72 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eWC, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e102.57 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e104.00 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e99.62 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e103.15 (1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e103.15 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e103.11 (1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.9850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e11.00 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11.46 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e10.90 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e11.45 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.25 (0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e11.52 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e6.23 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6.53 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6.01 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.62 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e6.62 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6.49 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.5910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.81 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.85 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.81 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.84 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.82 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.86 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.61 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.62 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.59 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.62 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.62 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.61 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.5494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3045\u0026nbsp;(42.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e683\u0026nbsp;(73.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e768 (36.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e210 (73.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e420 (50.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e227 (68.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1226\u0026nbsp;(14.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e344\u0026nbsp;(32.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e325 (12.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e92 (31.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e189 (19.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e111 (30.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e521\u0026nbsp;(6.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e287\u0026nbsp;(29.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e184 (7.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e96 (33.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e155 (16.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e128 (35.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e317\u0026nbsp;(4.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e267\u0026nbsp;(26.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e73 (3.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e74 (22.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e54 (6.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e92 (25.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Continuous variables are presented as survey-weighted means (SEs), and \u003cem\u003ep\u003c/em\u003e values were calculated via survey-weighted linear regression. Categorical variables are presented as unweighted counts (weighted percentages), and p values were calculated via the survey-weighted chi-square test.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e: PIR, poverty-income ratio; BMI, body mass index; WC, waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist‒height ratio; CVD, cardiovascular disease; CKD, chronic kidney disease; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e. Associations between anthropometric indicators and mortality in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003etotal population, OSA population, and COPD population.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted hazard ratio (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartile 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer SD increment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.79 (0.66, 0.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.73 (0.61, 0.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74 (0.60, 0.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.89 (0.81, 0.96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.76 (0.62, 0.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.70 (0.57, 0.85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.79 (0.65, 0.97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.94 (0.87, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.81 (0.63, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.01 (0.80, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.15 (0.91, 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.08 (1.00, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.86 (0.71, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.80 (0.65, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.84 (0.68, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.1507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.96 (0.88, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.02 (0.77, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.09 (0.84, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.41 (1.08, 1.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.18 (1.09, 1.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.86 (0.71, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.80 (0.65, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.84 (0.68, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.1192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.94 (0.87, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.92 (0.65, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.89 (0.62, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.79 (0.54, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.2211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.89 (0.77, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.75 (0.50, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.86 (0.58, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.75 (0.50, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.3101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.91 (0.79, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.85 (0.50, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.84 (0.51, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.93 (0.55, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.8810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.01 (0.87, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.98 (0.64, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.02 (0.68, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.85 (0.56, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.3712\u003c/p\u003e\n \u003c/td\u003e\n 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14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0352\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.84 (0.72, 0.97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.71 (0.51, 0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.58 (0.42, 0.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.71 (0.50, 1.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n 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\u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.82 (0.59, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.78 (0.57, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.70 (0.50, 0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86 (0.75, 0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.88 (0.57, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.26 (0.83, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.03 (0.67, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.6717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.00 (0.87, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.82 (0.59, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.78 (0.57, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.70 (0.50, 0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0463\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86 (0.75, 0.98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e: BMI, body mass index; WC waist circumference; WWI, weight-adjusted-waist index; BRI, body roundness index; ABSI, body shape index; WHtR, waist-to-height ratio; OSA, obstructive sleep apnoea; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eAdjusted for sex, age, race, education level, family poverty-income ratio, diabetes, hypertension, cardiovascular disease, chronic kidney disease, smoking status, and alcohol consumption.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"the US National Health and Nutrition Examination Survey, Obesity, Obstructive sleep apnoea, Asthma, Chronic obstructive pulmonary disease, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-5834187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5834187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObesity is linked to a greaterrisk of respiratory diseases. Due to limitations in body mass index (BMI), alternative anthropometric indicators have been developed to reflect body fat distribution. This study compares six anthropometric measures—BMI, waist circumference(WC), the waist-to-height ratio(WHtR), the body roundness index (BRI), the body shape index(ABSI), and the weight-adjusted waist index (WWI)—and their relationships withthe prevalence and mortality of obstructive sleep apnoea (OSA), asthma, and chronic obstructive pulmonary disease (COPD) in the US population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eData from four NHANES cycles were analysed. Multivariable logistic regression assessedthe cross-sectional associations between the six anthropometric measures and disease prevalence. Mortality associations were analysed via Cox proportional hazards models, and time‒dependent ROC curve was utilised to evaluate the predictive performance of the significant marker for mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eBMI, WC, WWI, BRI, ABSI, and WHtR were positively correlated with the prevalence of OSA, and COPD. For asthma, BMI, WC, BRI, and WHtR were positively associated with prevalence, while ABSI and WWI were negatively associated. Concerningmortality, higher WC and BMI were associated with better survival in the OSA and COPD groups, whereas elevated WWI and ABSI were linked to greater mortality risk in the participants with OSA symptoms. An increase of one standard deviation (SD) in the ABSI resulted in an 18% increase in mortality (95% CI: 1.09-1.27) for the OSA population. The area under the curve (AUC) for ABSI was 0.752 for 3-year, 0.755 for 5-year, and 0.744 for 10-year mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eNovel anthropometric indicators, including WWI, BRI, ABSI and WHtR, show positive associations with the prevalence of OSA, and COPD, alongside traditional measures likeBMI and WC. However, WWI and ABSI were more limited in their association with asthma prevalence. Longitudinal analyses revealed that traditional anthropometric indicators such as BMI and WC were negatively associated with mortality risks in the OSA and COPD, supporting the \"obesity paradox.\" ABSI, however, emerged as a significant mortality predictor for OSA, providing a more nuanced view of central obesity’s impact on mortality. However, in COPD patients, routine anthropometric measurements may not fully capture the effects of obesity.\u003c/p\u003e","manuscriptTitle":"The Role of Six Anthropometric Indicators in Predicting the Prevalence and Mortality of Obstructive Sleep Apnoea, Asthma, and Chronic Obstructive Pulmonary Disease: A Population- Based Study from the US National Health and Nutrition Examination Survey (NHANES)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 08:05:54","doi":"10.21203/rs.3.rs-5834187/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-03T17:36:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-15T03:40:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-10T08:30:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57829424523777591668440492815765787396","date":"2025-02-05T01:37:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203998724219716379439438033212283094844","date":"2025-02-03T14:35:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-03T02:10:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-03T02:05:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-20T16:13:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-17T11:40:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-15T11:50:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69e18ba3-db59-42bc-931f-cf8eef34c5f6","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43021041,"name":"Health sciences/Diseases"},{"id":43021042,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":43021043,"name":"Health sciences/Diseases/Respiratory tract diseases"}],"tags":[],"updatedAt":"2025-05-12T16:03:44+00:00","versionOfRecord":{"articleIdentity":"rs-5834187","link":"https://doi.org/10.1038/s41598-025-99490-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-09 15:57:50","publishedOnDateReadable":"May 9th, 2025"},"versionCreatedAt":"2025-01-22 08:05:54","video":"","vorDoi":"10.1038/s41598-025-99490-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-99490-y","workflowStages":[]},"version":"v1","identity":"rs-5834187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5834187","identity":"rs-5834187","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}