Association between weight-adjusted waist index and chronic obstructive pulmonary disease from National Health and Nutrition Examination Survey: 2013-2018

Association between weight-adjusted waist index and chronic obstructive pulmonary disease from National Health and Nutrition Examination Survey: 2013-2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between weight-adjusted waist index and chronic obstructive pulmonary disease from National Health and Nutrition Examination Survey: 2013-2018 Hongjin Wang, Weiming Chen, Feilong Guo, Zengkai Xu, Xin Luo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4495983/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. The weight-adjusted waist index (WWI) is a recently developed index for measuring adiposity, but the relationship between the WWI and COPD has not been well studied. This study aimed to investigate the potential correlation between these two health variables. Methods: This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018. The WWI was calculated as waist circumference divided by the square root of weight. Multiple linear regression was performed to estimate the association between the WWI and COPD incidence, and subgroup analysis was performed to identify sensitive cohorts. Smoothing curve fitting and threshold effect analysis were conducted to assess possible nonlinear relationships. Results: A total of 15,278 participants were included. Participants in the higher WWI tertile tended to have a greater incidence of COPD. Compared to body mass index and waist circumference, the correlation between the WWI and COPD incidence has better discrimination and accuracy. In the final adjusted model, a positive association between the WWI and COPD incidence was observed (OR = 1.70, 95% CI: 1.48–1.95). Participants in the highest WWI quartile had a 290% greater risk of COPD than did those in the lowest WWI tertile (OR = 3.90, 95% CI: 2.60–5.86). A nonlinear correlation was found between the WWI and COPD incidence, with an inflection point of 12.54. Subgroup analysis revealed that the relationship was significant in all subgroups. Conclusion: Compared with BMI and WC, WWI was related to an increased likelihood of COPD and had a stronger relationship with COPD incidence. Our findings indicated that the WWI could be a cost-effective and simple way to detect COPD. Weight-adjusted-waist index Chronic obstructive pulmonary disease Obesity NHANES Cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death and disability worldwide and became the third leading cause of death globally in 2019. ( 1 ) COPD is a lung disease characterized by persistent airflow limitation, which is usually progressive and not fully reversible, and its etiology may involve interactions between genetic and environmental factors. ( 2 ) Smoking is traditionally an environmental risk factor. However, research has suggested that non-tobacco-related factors are responsible for approximately half of COPD cases worldwide. Recent studies have identified the following factors: air pollution( 3 ), occupational exposure( 4 ), asthma, lung function, low socioeconomic status, and impaired lung development. Early onset of COPD may lead to a worse prognosis, and its worsening course is inevitable; identifying more possible risk factors and earlier diagnosis can help prevent and slow its progression. ( 5 , 6 ) In recent years, the impact of obesity on COPD has received increasing attention from researchers. Some studies have shown that overweight or obese patients with chronic diseases have a better prognosis, while obesity may be protective for patients with severe COPD, and weight loss may increase the risk of death in patients with COPD ( 7 ). However, this may be an "obesity paradox" because most studies have used a single assessment, such as the body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR). These indicators mainly reflect nutritional status and have been questioned as measures of obesity because they do not consider the distribution of fat, muscle, or other tissues and cannot effectively reflect the ratio of muscle to fat in patients. ( 8 ) Currently, researchers are gradually paying attention to this point, and the emergence of many new indexes is also improving this situation. The weight-adjusted waist index (WWI) can be summarized as standardizing waist circumference for weight, and it is a new obesity index used as a predictor of disease morbidity and mortality in recent years by Park et al. The results show that the WWI can predict cardiometabolic and cardiovascular diseases, with superior predictive ability combined with BMI. ( 9 ) Recently, research on the correlation between the WWI and disease development has been increasing, but the specific correlation between the WWI and cardiovascular diseaseis still controversial. However, to the best of our knowledge, no studies have investigated whether there is a correlation between the WWI and COPD incidence. Therefore, our study analysed the correlation between the WWI and COPD incidence using National Health and Nutrition Examination Survey (NHANES) data from 2013 to 2018. 2 METHODS 2.1 Study population This study used data collected from the NHANES, a cross-sectional survey conducted by the National Center for Health Statistics (NCHS), to evaluate the health and nutritional status of the U.S. population. The NHANES gathers information through interviews and physical examinations. The NHANES study design employed a stratified multistage probability sampling method, contributing to the high representativeness of the included samples. All data used in the analysis are publicly available at https://www.cdc.gov/nchs/nhanes/ . The survey was conducted over three cycles over six years (2013–2014, 2015–2016, and 2017–2018). Among 29,400 participants, 12,363 with no COPD data and 1,759 with missing waist circumference or weight data were excluded. Ultimately, the study included 15,278 participants (Fig. 1). The NCHS Ethical Review Board approved the conduct and study protocol of the NHANES in human subjects. Written informed consent was obtained from all participants. Figure 1. Flow chart of participant selection. Abbreviation: COPD, chronic obstructive pulmonary disease. 2.2 Weight-adjusted waist index Physical waist circumference and weight measurements were performed under controlled conditions at a mobile testing center (MEC) by trained health technicians. The decimal rounding of the WWI results for each participant was kept to two decimal places. In our analysis, the WWI was considered a continuous variable, and participants were categorized into groups based on their WWI data for additional study. The following method was used to calculate the WWI: $$\mathbf{W}\mathbf{W}\mathbf{I}(\mathbf{c}\mathbf{m}/\surd \mathbf{k}\mathbf{g})=\mathbf{W}\mathbf{C}/\surd \mathbf{W}\mathbf{e}\mathbf{i}\mathbf{g}\mathbf{h}\mathbf{t}$$ 2.3 The definition of COPD Whether a participant had COPD was included in this study as the exposure variable. The values of this variable were extracted from NHANES questionnaire data. In the Medical Conditions section (MCQ160o), investigators asked participants whether a doctor had informed them that they had COPD. Participants who answered “yes” were included in the COPD group, while those who answered “no” were included in the control group. Participants who had missing data for this question were excluded from this study. 2.4 Covariables Demographic variables. The NHANES demographic files contain information on individuals' age, sex, race, family income-to-poverty ratio, education level, and marital status at the interview. Body mass index. Data on the BMI of the study participants were gathered from NHANES body measurements collected by trained health technicians. Smoking status. Smoking status was constructed from responses to the questionnaire section: “Have you smoked at least 100 cigarettes in your entire life?”. Smokers were defined as those who had smoked 100 or more cigarettes, whereas never smokers were defined as those who had never smoked 100 cigarettes. Alcohol consumption. Alcohol consumption was constructed from responses to the questionnaire section: “Have you drunk at least 12 drinks in your entire life?”. Drinkers were those who consumed more than 12 drinks in their lifetime, and never drinkers were those who consumed fewer than 12 drinks in their lifetime. Hypertension. Hypertension in the NHANES was defined as self-reported hypertension (answer “yes” to the question “Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure?”) or elevated blood pressure during physical examination (mean systolic blood pressure ≥ 140 mm Hg or mean diastolic blood pressure ≥ 90 mm Hg). Diabetes. A subject meeting any of the following criteria will be diagnosed with diabetes: 1) Doctor told you that you have diabetes; 2) glycohemoglobin HbA1c (%) > 6.5; 3) fasting glucose (mmol/L) ≥ 7.0; 4) random blood glucose (mmol/L) ≥ 11.1; 5) 2-hour oral glucose tolerance test (OGTT) blood glucose (mmol/L) ≥ 11.1; and 6) use of antidiabetic agents. Cardiovascular disease. Participants who had previously been diagnosed with congestive heart failure, coronary heart disease, angina, or heart attack were referred to the CVD group. Laboratory variables. Triglyceride (TG), high-density lipoprotein (HDL-C), and total cholesterol levels were obtained from the NHANES laboratory data files. 2.5 Statistical analysis The mean ± standard deviation (SD) was used for continuous variables, and categorical variables are expressed as proportions. Continuous variables were analysed using t tests, and categorical variables were analysed using chi-square tests to determine significant differences. Linear associations between the WWI, waist circumference, BMI, and COPD incidence were analysed using weighted multiple linear regression and logistic regression. A trend test was conducted to assess the nonlinear association trend between the WWI and COPD incidence by transforming the WWI from a continuous variable into quartiles. Additionally, smoothed curve fitting and threshold effects were employed to evaluate the potential nonlinear associations between the WWI, WC, BMI, and COPD incidence. The area under the receiver operating characteristic curve (ROC) was adopted to measure the ability of the WWI, WC, and BMI to predict COPD. In this study, three models were employed for analysis. Model 1 did not include any adjustment variables. Model 2 was adjusted for gender, age, and race. Finally, Model 3 was adjusted for gender, age, race, education level, marital status, PIR, alcohol consumption, hypertension, CVD, triglycerides, HDL-C, diabetes status, smoking status, and total cholesterol. Subgroup analyses were conducted to explore the associations between the WWI and COPD among individuals of different genders, ages, races, smoking statuses, diabetes statuses, and hypertension statuses. Statistical analyses were carried out using R (version 4.2) and Empower software (version 4.2), which are statistical computing and plotting software. A two-sided p value of less than 0.05 was considered to indicate statistical significance in this study. 3 RESULTS 3.1 Participant characteristics This study identified 15,278 individuals in the NHANES 2013–2018, of which 567 individuals suffered from COPD. Compared to non-COPD participants, COPD participants were more prone to be older, male, non-Hispanic White, less than high school, and living alone and were more likely to smoke and have hypertension, CVD, and diabetes. Individuals with COPD had significantly greater BMIs and TG levels than individuals without COPD. In addition, significant differences in PIR, alcohol consumption, HDL-C, total cholesterol, and WWI were detected between the two groups (P < 0.05). The detailed characteristics of the included participants with COPD are shown in Table 1 . Table 1 Characteristics of the study participants: NHANES 2013–2018 Overall Non-COPD COPD P value n = 15278 n = 14711 n = 567 Gender 0.014 Male 7390 (48.37%) 7087 (48.17%) 303 (53.44%) Female 7888 (51.63%) 7624 (51.83%) 264 (46.56%) Race < 0.001 Mexican American 2248 (14.71%) 2223 (15.11%) 25 (4.41%) Other Hispanic 1619 (10.60%) 1594 (10.84%) 25 (4.41%) Non-Hispanic White 5633 (36.87%) 5261 (35.76%) 372 (65.61%) Non-Hispanic black 3287 (21.51%) 3193 (21.70%) 94 (16.58%) Other race 2491 (16.30%) 2440 (16.59%) 51 (8.99%) Education level < 0.001 Less than high school 3246 (21.25%) 3082 (20.95%) 164 (28.92%) High school or GED 3460 (22.65%) 3292 (22.38%) 168 (29.63%) Above high school 8561 (56.03%) 8326 (56.60%) 235 (41.45%) Unclear 11 (0.07%) 11 (0.07%) 0 (0.00%) Marital status < 0.001 Married or living with partner 9175 (60.05%) 8904 (60.53%) 271 (47.80%) Living alone 6096 (39.90%) 5801 (39.43%) 295 (52.03%) Unclear 7 (0.05%) 6 (0.04%) 1 (0.18%) Smoking status 12 6834 (44.73%) 6577 (44.71%) 257 (45.33%) <12 7429 (48.63%) 7141 (48.54%) 288 (50.79%) Unclear 1015 (6.64%) 993 (6.75%) 22 (3.88%) Hypertension < 0.001 Yes 6611 (43.27%) 6212 (42.23%) 399 (70.37%) No 8667 (56.73%) 8499 (57.77%) 168 (29.63%) CVD < 0.001 Yes 1588 (10.39%) 1340 (9.11%) 248 (43.74%) No 13688 (89.59%) 13369 (90.88%) 319 (56.26%) Unclear 2 (0.01%) 2 (0.01%) 0 (0.00%) Diabetes < 0.001 Yes 3064 (20.05%) 2860 (19.44%) 204 (35.98%) No 12214 (79.95%) 11851 (80.56%) 363 (64.02%) Age (years) 49.52 ± 17.44 48.96 ± 17.38 64.12 ± 11.78 < 0.001 PIR 2.51 ± 1.54 2.54 ± 1.54 1.85 ± 1.29 < 0.001 BMI (kg/m2) 29.38 ± 7.01 29.35 ± 6.95 30.30 ± 8.38 0.045 TG (mg/dL) 114.12 ± 74.44 113.90 ± 74.96 119.92 ± 59.02 0.002 HDL-C (mg/dL) 53.40 ± 16.10 53.45 ± 16.00 52.28 ± 18.50 0.006 Total cholesterol (mg/dL) 189.53 ± 40.67 189.84 ± 40.63 181.38 ± 40.89 < 0.001 WWI (cm/√kg) 11.12 ± 0.86 11.10 ± 0.85 11.71 ± 0.77 < 0.001 Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model; (%) for categorical variables: the P value was calculated by the weighted chi-square test. Abbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; BMI, body mass index. TG, triglyceride; HDL-C, high-density lipoprotein; WWI, weight-adjusted waist index; COPD, chronic obstructive pulmonary disease. 3.2 Association between weight-adjusted waist index and COPD incidence In this study, the unadjusted (model 1) linear association analysis demonstrated a positive correlation between the WWI and COPD incidence, yielding an odds ratio (OR) of 2.34 (95% CI: 2.11, 2.59). This association was also consistently observed in models 2 and 3. According to the fully adjusted model (Model 3), which accounted for all covariates, the association between the WWI and COPD incidence remained consistent, with an OR of 1.70 (95% CI: 1.48, 1.95). This suggests that each unit increase in the WWI is associated with a 70% increase in the likelihood of developing COPD (Table 2 ). The WWI was more strongly associated with COPD than BMI (OR = 1.03, 95% CI: 1.02–1.05) and waist circumference (OR = 1.02, 95% CI: 1.01–1.03) (Table 3 ). In addition, we obtained reliable findings even after dividing the continuous variables into quartiles. The WWI was 290% greater in Q4 than in Q1 (OR = 3.90, 95% CI: 2.60, 5.86) (Table 2 ). Furthermore, exploration through the GAM and smoothing curves revealed a nonlinear positive association between the WWI or WC and COPD (Fig. 2 A, 2 B, and 2 C). In addition, a nonlinear association was found between BMI and COPD (Fig. 2 D). Further exploration of the association between the WWI and COPD revealed that the cut-off point was 12.5. A significant correlation was found before the turning point (OR = 1.88, 95% CI: 1.60–2.22) (Table 4 ). The AUCs of the WWI, WC, and BMI for discriminating COPD patients are shown in Fig. 3 . The WWI had the highest AUC of 0.707, followed by WC (AUC = 0.609) and BMI (AUC = 0.525). Table 2 Association of the WWI with COPD Exposure OR (95% CI) Model 1 (n = 15278) Model 2 (n = 15278) Model 3 (n = 15278) WWI (cm/√kg) 2.34 (2.11, 2.59) 1.93 (1.70, 2.18) 1.70 (1.48, 1.95) WWI quartile Quartile 1 Reference Reference Reference Quartile 2 2.62 (1.78, 3.86) 1.91 (1.29, 2.84) 1.94 (1.29, 2.92) Quartile 3 3.88 (2.68, 5.62) 2.33 (1.58, 3.44) 2.06 (1.38, 3.10) Quartile 4 9.02 (6.37, 12.79) 4.67 (3.20, 6.83) 3.90 (2.60, 5.86) P for trend < 0.001 < 0.001 < 0.001 Model 1: No covariates were adjusted. Model 2: Gender, age, and race were adjusted. Model 3: Gender, age, race, education level, marital status, PIR, hypertension, alcohol consumption, CVD, TG, HDL-C, diabetes, smoking status, and total cholesterol were adjusted. Abbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; TG, triglyceride; HDL-C, high-density lipoprotein; WWI, weight-adjusted waist index; COPD, chronic obstructive pulmonary disease. Table 3 Associations of BMI and WC with COPD Exposure OR (95% CI) Model 1 (n = 15278) Model 2 (n = 15278) Model 3 (n = 15278) BMI (kg/m2) 1.02 (1.01, 1.03) 1.03 (1.02, 1.04) 1.03 (1.02, 1.05) WC (cm) 1.02 (1.02, 1.03) 1.02 (1.01, 1.02) 1.02 (1.01, 1.03) Model 1: No covariates were adjusted. Model 2: Gender, age, and race were adjusted. Model 3: Gender, age, race, education level, marital status, PIR, hypertension, alcohol consumption, CVD, TG, HDL-C, diabetes, smoking status, and total cholesterol were adjusted. Abbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; TG, triglyceride; HDL-C, high-density lipoprotein; BMI, body mass index; WC, waist circumference; COPD, chronic obstructive pulmonary disease. Table 4 Threshold effect analysis of the WWI on COPD using a two-piecewise linear regression model WWI COPD Adjusted β (95%CI) P value Fitting by the standard linear model 1.70 (1.48, 1.95) <0.0001 Fitting by the two-piecewise linear model Inflection point 12.54 <K segment effect 1.88 (1.60, 2.22) K segment effect 0.83 (0.44, 1.56) 0.5608 Log likelihood ratio 0.017 Gender, age, race, education level, marital status, PIR, hypertension, alcohol consumption, CVD, TG, HDL-C, diabetes, smoking status, and total cholesterol were adjusted. Abbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; TG, triglyceride; HDL-C, high-density lipoprotein; WWI, weight-adjusted waist index; COPD, chronic obstructive pulmonary disease. 3.3 Subgroup analysis To assess the reliability of the association between the WWI and COPD incidence, we conducted subgroup analyses based on gender, age, race, hypertension status, diabetes status, and smoking status. The results showed that all subgroups exhibited a positive association between the WWI and the risk of COPD (Figure 4). WWI was more positively associated with COPD in males (OR =2.29, 95% CI: 1.85–2.84, P <0.001), those aged 20–39 years (OR =3.07, 95% CI: 1.47–6.42, P = 0.003), Mexican Americans (OR =3.24, 95% CI: 1.73–6.07, P <0.001), those in the non-hypertensive group (OR =1.70, 95% CI: 1.34–2.17, P <0.001), those in the diabetic group (OR =1.81, 95% CI: 1.41–2.31, P <0.001), and those in the smoking group (OR =1.76, 95% CI: 1.42–2.19, P <0.001). Discussion This cross-sectional study utilized NHANES data from 2013 to 2018 involving 15,278 participants to explore the potential association between the WWI and COPD incidence. To our knowledge, this is the first investigation to assess the association between the WWI and COPD. The findings revealed a positive association between the WWI and COPD among US adults, which remained significant after adjusting for various confounders. Specifically, for every one-unit increase in the WWI, there was a 70% increase in the incidence of COPD. Notably, this association was stronger than those observed for traditional metrics such as BMI and WC. Subgroup analyses demonstrated the stability of the association between the WWI and COPD across different demographic and health-related variables. This association was not affected by age, gender, race, hypertension status, diabetes status, or smoking status. COPD has a low rate of early diagnosis and irreversible pathogenesis, so exploring the risk factors for its onset is essential for effective prevention and treatment strategies. (10) Our findings suggest that the WWI has significant clinical value in identifying COPD and facilitating early disease recognition. Previous research has highlighted the significant role of obesity in COPD development, with BMI and WC commonly used as indicators. However, the association between BMI and COPD has been the subject of inconsistent studies by various scholars, some of whom have even produced conflicting results. Some researchers have found that low BMI may be a risk factor for the development of COPD and that a decrease in BMI may be strongly associated with worsening COPD and increased mortality. (11, 12) Additionally, large-scale prospective studies have indicated that low BMI and weight loss may independently predict mortality in COPD patients, particularly in individuals over 20 years old with substantial weight loss or a BMI 25 kg/m2) may be a predictor of long-term survival in COPD patients. (14) Although many studies suggest a protective effect of high BMI on COPD, this notion may represent an "obesity paradox." Notably, increased WC was positively linked to COPD risk in a decade-long cohort study involving both genders. (15) This result was confirmed in another study, which concluded that abdominal obesity and underweight may be risk factors for COPD in adults. (16) Subgroup analysis revealed that the risk of developing COPD was lowest in women with BMIs ranging from 23-24 kg/m2, and BMI and COPD exhibited a U-shaped relationship, consistent with our findings. It has been reported that under extreme conditions, such as a BMI >40 kg/m2, obesity leading to low mortality no longer occurs, and we can treat weight loss as a deleterious factor rather than obesity as a protective factor. (7, 16, 17) However, the focus of most studies on diseases has been limited to BMI, WC, and WHR. These indicators primarily reflect nutritional status, and their utility as obesity indicators is questioned because they overlook the distribution of fat, muscle, and other tissues, thus failing to adequately represent the proportion of muscle to fat. An investigation into obesity and COPD compared the correlation between various measures, including BMI, WC, and WHR, revealing that reliance on a single measure overlooks crucial factors such as body composition distribution, resulting in an underestimation of disease risk. (8) In recent years, researchers have increasingly focused on parameters related to fat and muscle, such as the fat mass index (FMI), fat-free mass index (FFMI), and other parameters. Both the FMI and FFMI have been implicated in the severity of emphysema in COPD patients, with the FFMI specifically impacting quality of life and potentially influencing mortality risk. (18) (19) In a prospective study, muscle loss and malnutrition during hospitalization in patients with COPD were analysed. During the 6-month follow-up, patients who experienced muscle loss due to malnutrition, along with sarcopenia, had a lower one-year survival rate. (20) According to a related report, patients with COPD may exhibit intramuscular and intermuscular fat infiltration in the abdomen and intercostal space. This fat infiltration can lead to impaired muscle function and atrophy, potentially exacerbating the severity of COPD. (21, 22) However, less muscle may also be associated with carbonylation of proteins, which can be caused by ROS and other related oxidative substances, and carbonylation leads to greater structural and functional negative effects. (23) Weight loss and muscle atrophy are prevalent among COPD patients and are linked to systemic inflammatory responses. A study revealed elevated serum levels of growth hormone-releasing peptide and lipocalin in COPD patients with low body weight, while BMI and forced expiratory volume in 1 second (FEV1) were negatively correlated with serum growth hormone-releasing peptide levels. (24) As more studies highlight the role of muscle and weight loss in disease progression, indicators of muscle measurements are becoming increasingly important. However, there have been no major breakthroughs in muscle measurement techniques, and to date, there is no universally recognized method for measuring muscle. The WWI is an index that evaluates both fat and muscle, and it has been reported that the WWI increases linearly with age and that abdominal fat measurements increase, but muscle measurements decrease. (25, 26) As research on WWI has progressed, an increasing body of evidence has demonstrated its reliability as an indicator capable of predicting various diseases. In the field of cardiovascular diseases, the WWI has emerged as a distinctive predictor of hypertension, stroke, and abdominal aortic calcification. (27-29) An elevated WWI may also be a risk factor for the development of renal stones and gallstones. (30, 31) Furthermore, research has demonstrated that the WWI is the most accurate predictor of chronic kidney disease and albuminuria among various obesity indicators. Regarding respiratory diseases, ongoing studies have concentrated on the relationship between asthma and the WWI. Cross-sectional investigations have revealed a positive correlation between the WWI and both the prevalence and timing of initial asthma episodes. Additionally, a prolonged duration of asthma may coincide with weight loss and abdominal obesity. (32, 33) While research examining the correlation between the WWI and various diseases is expanding, there remains a notable absence of studies addressing COPD, warranting increased attention. First, our findings revealed a robust correlation between elevated WWI and COPD onset, which was particularly notable among individuals aged 20-39 years. Second, in comparison to the cumbersome process of measuring visceral fat, which demands expensive equipment such as MRI scanners and substantial social resources, WWI has emerged as a more accessible and cost-effective obesity index that is especially beneficial for less developed regions. Moreover, Nam's(26) findings suggest that the WWI is positively correlated with obesity and negatively correlated with human muscle mass. Additionally, these correlations are not significantly influenced by factors such as changes in BMI. This implies that the WWI could be a valuable metric in clinical practice for assessing the risk of COPD, as it appears to be less influenced by BMI and other factors and may offer a better reflection of body visceral fat and muscle mass. This could provide clinicians with a more accurate assessment of health risks associated with COPD. (26, 34). Third, our findings indicate a threshold effect in the relationship between the WWI and COPD, meaning that this association reaches its peak when the WWI exceeds 12.54 cm/√kg. Beyond this threshold, further increases in the WWI do not lead to a corresponding increase in COPD incidence. This evidence underscores the potential benefit of controlling lower levels of WWI as a preventive measure to mitigate the risk of COPD. Such insights may aid clinicians in devising tailored prevention and treatment protocols for patients as well as in identifying high-risk cohorts for targeted screening initiatives. Our study has several strengths. First, we pioneered the examination of the correlation between the WWI and COPD incidence, utilizing a highly representative sample from the NHANES. Second, our investigation serves as another validation of the impact of muscle and adiposity indexes on COPD. This highlights that an elevated WWI may contribute to increased COPD risk, offering insights into potential strategies for COPD prevention and early diagnosis. Last, large-scale cross-sectional studies such as ours employ random sampling methods, rendering their findings potentially representative of the broader population. Nevertheless, our study is not without limitations. Cross-sectional survey results can only imply correlation rather than causation. Additionally, there may be gaps in the NHANES data, such as the omission of factors such as environmental conditions and genetic predispositions, which could bias the outcomes, particularly regarding the increased incidence of COPD. Conclusions This study revealed that the WWI is associated with a greater incidence of COPD, and the correlation between the WWI and COPD incidence is stronger than that between the WWI and other obesity biomarkers, suggesting that obesity management evaluated by the WWI may be beneficial for mental health. Based on these findings, clinicians can consider the WWI to be an important indicator when evaluating COPD. Declarations Ethics statement The human studies involved in this research received approval from the Ethics Review Committee of the National Health Statistics Research Centre in the United States. These studies were conducted in accordance with local legislation and institutional requirements. Participants provided written informed consent to participate in this research. Funding Not applicable. Acknowledgements We extend our gratitude to all researchers who have made NHANES data publicly available. Additionally, we wish to thank all the colleagues who dedicated their time and effort to contributing to this study. Conflict of interest The authors declare no conflicts of interest. Author Contribution HW: Data curation, Formal analysis, Writing original draft, writing review, and editing. WC: Data curation, methodology, writing-original draft, writing-review & editing. ZX: Conceptualization, writing review, and editing. XL: Formal analysis, writing review, and editing. JW: Writing-review and editing. YZ: Funding acquisition, writing review, and editing. ZW: Funding acquisition, writing review, and editing. Data availability statement All the data utilized in this study were obtained and analysed through the NHANES database. The codes employed in the analysis are available upon reasonable request by contacting the authors. References Lopez-Campos, J. L., Tan, W. & Soriano, J. B. Global burden of COPD. Respirology 21, 14-23 (2016). https://doi.org:10.1111/resp.12660 Christenson, S. A., Smith, B. M., Bafadhel, M. & Putcha, N. Chronic obstructive pulmonary disease. Lancet 399, 2227-2242 (2022). https://doi.org:10.1016/S0140-6736(22)00470-6 Sin, D. D. et al. Air pollution and COPD: GOLD 2023 committee report. Eur Respir J 61 (2023). https://doi.org:10.1183/13993003.02469-2022 Murgia, N. & Gambelunghe, A. Occupational COPD-The most under-recognized occupational lung disease? Respirology 27, 399-410 (2022). https://doi.org:10.1111/resp.14272 Soriano, J. B., Polverino, F. & Cosio, B. G. What is early COPD and why is it important? Eur Respir J 52 (2018). https://doi.org:10.1183/13993003.01448-2018 Ferrera, M. C., Labaki, W. W. & Han, M. K. Advances in Chronic Obstructive Pulmonary Disease. Annu Rev Med 72, 119-134 (2021). https://doi.org:10.1146/annurev-med-080919-112707 Spelta, F., Fratta Pasini, A. M., Cazzoletti, L. & Ferrari, M. Body weight and mortality in COPD: focus on the obesity paradox. Eat Weight Disord 23, 15-22 (2018). https://doi.org:10.1007/s40519-017-0456-z Wang, X., Liang, Q., Li, Z. & Li, F. Body Composition and COPD: A New Perspective. Int J Chron Obstruct Pulmon Dis 18, 79-97 (2023). https://doi.org:10.2147/COPD.S394907 Park, Y., Kim, N. H., Kwon, T. Y. & Kim, S. G. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep 8, 16753 (2018). https://doi.org:10.1038/s41598-018-35073-4 Laucho-Contreras, M. E. & Cohen-Todd, M. Early diagnosis of COPD: myth or a true perspective. Eur Respir Rev 29 (2020). https://doi.org:10.1183/16000617.0131-2020 Holtjer, J. C. S. et al. Identifying risk factors for COPD and adult-onset asthma: an umbrella review. Eur Respir Rev 32 (2023). https://doi.org:10.1183/16000617.0009-2023 Kim, E. K. et al. Impact of Body Mass Index Change on the Prognosis of Chronic Obstructive Pulmonary Disease. Respiration 99, 943-953 (2020). https://doi.org:10.1159/000511022 Wada, H. et al. Low BMI and weight loss aggravate COPD mortality in men, findings from a large prospective cohort: the JACC study. Sci Rep 11, 1531 (2021). https://doi.org:10.1038/s41598-020-79860-4 Stoll, P., Foerster, S., Virchow, J. C. & Lommatzsch, M. Overweight is a predictor of long-term survival in hospitalised patients with exacerbations of COPD. Respir Med 116, 59-62 (2016). https://doi.org:10.1016/j.rmed.2016.05.016 Ma, Y. L., Zhao, H. J. & Su, Y. H. Association between waist circumference change and incident chronic obstructive pulmonary disease among Chinese adults: a 10-year cohort study. Sci Rep 12, 18402 (2022). https://doi.org:10.1038/s41598-022-23248-z Li, J. et al. Association between adiposity measures and COPD risk in Chinese adults. Eur Respir J 55 (2020). https://doi.org:10.1183/13993003.01899-2019 Brigham, E. P. et al. Challenging the obesity paradox: extreme obesity and COPD mortality in the SUMMIT trial. ERJ Open Res 7 (2021). https://doi.org:10.1183/23120541.00902-2020 Shimada, T. et al. Differential impacts between fat mass index and fat-free mass index on patients with COPD. Respir Med 217, 107346 (2023). https://doi.org:10.1016/j.rmed.2023.107346 McDonald, M. N. et al. Chest computed tomography-derived low fat-free mass index and mortality in COPD. Eur Respir J 50 (2017). https://doi.org:10.1183/13993003.01134-2017 Perrot, L. et al. Prevalence of sarcopenia and malnutrition during acute exacerbation of COPD and after 6 months recovery. Eur J Clin Nutr 74, 1556-1564 (2020). https://doi.org:10.1038/s41430-020-0623-6 Park, M. J. et al. Mass and fat infiltration of intercostal muscles measured by CT histogram analysis and their correlations with COPD severity. Acad Radiol 21, 711-717 (2014). https://doi.org:10.1016/j.acra.2014.02.003 Jeon, Y. J. et al. Intramuscular and Intermuscular Abdominal Fat Infiltration in COPD: A Propensity Score Matched Study. Int J Chron Obstruct Pulmon Dis 16, 1989-1999 (2021). https://doi.org:10.2147/COPD.S312888 Barreiro, E. Protein carbonylation and muscle function in COPD and other conditions. Mass Spectrom Rev 33, 219-236 (2014). https://doi.org:10.1002/mas.21394 Uzum, A. K. et al. Serum ghrelin and adiponectin levels are increased but serum leptin level is unchanged in low weight Chronic Obstructive Pulmonary Disease patients. Eur J Intern Med 25, 364-369 (2014). https://doi.org:10.1016/j.ejim.2013.02.012 Kim, J. Y. et al. Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis. Diabetes Metab J 46, 747-755 (2022). https://doi.org:10.4093/dmj.2021.0294 Kim, N. H., Park, Y., Kim, N. H. & Kim, S. G. Weight-adjusted waist index reflects fat and muscle mass in the opposite direction in older adults. Age Ageing 50, 780-786 (2021). https://doi.org:10.1093/ageing/afaa208 Wang, J. et al. The relationship between obesity associated weight-adjusted waist index and the prevalence of hypertension in US adults aged >/=60 years: a brief report. Front Public Health 11, 1210669 (2023). https://doi.org:10.3389/fpubh.2023.1210669 Qin, Z. et al. The association between weight-adjusted-waist index and abdominal aortic calcification in adults aged ≥ 40 years: results from NHANES 2013-2014. Sci Rep 12, 20354 (2022). https://doi.org:10.1038/s41598-022-24756-8 Ye, J. et al. Association between the weight-adjusted waist index and stroke: a cross-sectional study. BMC Public Health 23, 1689 (2023). https://doi.org:10.1186/s12889-023-16621-8 Liu, H., Ma, Y. & Shi, L. Higher weight-adjusted waist index is associated with increased likelihood of kidney stones. Front Endocrinol (Lausanne) 14, 1234440 (2023). https://doi.org:10.3389/fendo.2023.1234440 Ke, B., Sun, Y., Dai, X., Gui, Y. & Chen, S. Relationship between weight-adjusted waist circumference index and prevalence of gallstones in U.S. adults: a study based on the NHANES 2017-2020. Front Endocrinol (Lausanne) 14, 1276465 (2023). https://doi.org:10.3389/fendo.2023.1276465 Yu, L. et al. Association of weight-adjusted-waist index with asthma prevalence and the age of first asthma onset in United States adults. Front Endocrinol (Lausanne) 14, 1116621 (2023). https://doi.org:10.3389/fendo.2023.1116621 Han, X. et al. The association of asthma duration with body mass index and Weight-Adjusted-Waist index in a nationwide study of the U.S. adults. Eur J Med Res 28, 122 (2023). https://doi.org:10.1186/s40001-023-01089-4 Nuttall, F. Q. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today 50, 117-128 (2015). https://doi.org:10.1097/nt.0000000000000092 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4495983","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312990930,"identity":"fb104489-72b4-434a-a23d-d12574aaf1a1","order_by":0,"name":"Hongjin Wang","email":"","orcid":"","institution":"Longyan First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongjin","middleName":"","lastName":"Wang","suffix":""},{"id":312990931,"identity":"d01b62ca-0814-4986-a34d-fd3430f8680e","order_by":1,"name":"Weiming Chen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weiming","middleName":"","lastName":"Chen","suffix":""},{"id":312990933,"identity":"2b0eb096-383f-498b-ba2a-0103c36bd0ee","order_by":2,"name":"Feilong Guo","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feilong","middleName":"","lastName":"Guo","suffix":""},{"id":312990934,"identity":"7a8851b3-f291-4949-9413-64717b245c0d","order_by":3,"name":"Zengkai Xu","email":"","orcid":"","institution":"Longyan First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zengkai","middleName":"","lastName":"Xu","suffix":""},{"id":312990936,"identity":"3bbd6733-f746-46cb-a875-c621ec740ea2","order_by":4,"name":"Xin Luo","email":"","orcid":"","institution":"Longyan First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Luo","suffix":""},{"id":312990939,"identity":"0bd4f0c6-7f47-4de2-9bba-ccc35cf7d84e","order_by":5,"name":"Jiahuang Wu","email":"","orcid":"","institution":"Longyan First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiahuang","middleName":"","lastName":"Wu","suffix":""},{"id":312990943,"identity":"5626c72e-1170-4edb-b4c9-d0e1533ad1da","order_by":6,"name":"Yong Zhu","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zhu","suffix":""},{"id":312990946,"identity":"6ec51bd8-ea0e-40d3-8e6b-519fd47d9cda","order_by":7,"name":"Zhisheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDCCAzAGe2Pjww9EamFsADN4DjcbS5CmRSK9TYCHGB18x5ufP/i457C9/MyHbQwSDHZyug0EtEieOWbYOOPZYWaD24ltDwoYko3NDhDQYnAjh7GZ58BtNgPpxHYDCYYDiduI1cIjP/NgmwQPKVokGG4wEqkF5JeZMw78NzA4kwgMZAMi/AIMsQcfPhxIs5dvP/7w4YcKOzmCWtDdSZryUTAKRsEoGAU4AADNsUe+WZJy2AAAAABJRU5ErkJggg==","orcid":"","institution":"Longyan First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhisheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-29 09:37:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4495983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4495983/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58386314,"identity":"05554d31-a7da-479a-9511-7b2a37d58d48","added_by":"auto","created_at":"2024-06-14 18:44:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28998,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participant selection. Abbreviation: COPD, chronic obstructive pulmonary disease.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4495983/v1/393417f875b41b87b762fe32.png"},{"id":58386316,"identity":"bb6f533e-ac23-471e-8604-9bbe73ad597b","added_by":"auto","created_at":"2024-06-14 18:44:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":719969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eAssociation between the weight-adjusted waist index and COPD incidence. \u003cstrong\u003eB\u003c/strong\u003e Association between weight-adjusted waist index quartiles and COPD incidence. \u003cstrong\u003eC\u003c/strong\u003e Association between waist circumference and COPD incidence. \u003cstrong\u003eD\u003c/strong\u003e Association between body mass index and COPD incidence. Abbreviation: COPD, chronic obstructive pulmonary disease.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4495983/v1/75bbf0e5baab033a82bcd843.png"},{"id":58386317,"identity":"c0de04de-baa1-417e-9ea4-36dce650bc5b","added_by":"auto","created_at":"2024-06-14 18:44:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":498921,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the ability of the WWI, BMI, and WC to predict COPD. Abbreviations: WWI, weight-adjusted waist index; BMI, body mass index; WC, waist circumference; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4495983/v1/5fa3bbf420abc4672a55c80f.png"},{"id":58386315,"identity":"5693249a-e79a-45a9-84f3-d9aefaf6d608","added_by":"auto","created_at":"2024-06-14 18:44:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165898,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses. In the subgroup analysis stratified by gender, age, race, hypertension status, diabetes status, and smoking status, the model was not adjusted for gender, age, race, hypertension status, diabetes status, or smoking status.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4495983/v1/8040f38b0333866cd4210c0d.png"},{"id":59549268,"identity":"b0edef7b-287b-4927-a28c-3b8cd74c6f8d","added_by":"auto","created_at":"2024-07-03 05:59:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1890944,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4495983/v1/6fe60156-9e69-4e0e-b034-b12a7e0ae59b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between weight-adjusted waist index and chronic obstructive pulmonary disease from National Health and Nutrition Examination Survey: 2013-2018","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is one of the leading causes of death and disability worldwide and became the third leading cause of death globally in 2019. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) COPD is a lung disease characterized by persistent airflow limitation, which is usually progressive and not fully reversible, and its etiology may involve interactions between genetic and environmental factors. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Smoking is traditionally an environmental risk factor. However, research has suggested that non-tobacco-related factors are responsible for approximately half of COPD cases worldwide. Recent studies have identified the following factors: air pollution(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), occupational exposure(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), asthma, lung function, low socioeconomic status, and impaired lung development. Early onset of COPD may lead to a worse prognosis, and its worsening course is inevitable; identifying more possible risk factors and earlier diagnosis can help prevent and slow its progression. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn recent years, the impact of obesity on COPD has received increasing attention from researchers. Some studies have shown that overweight or obese patients with chronic diseases have a better prognosis, while obesity may be protective for patients with severe COPD, and weight loss may increase the risk of death in patients with COPD (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, this may be an \"obesity paradox\" because most studies have used a single assessment, such as the body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR). These indicators mainly reflect nutritional status and have been questioned as measures of obesity because they do not consider the distribution of fat, muscle, or other tissues and cannot effectively reflect the ratio of muscle to fat in patients. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCurrently, researchers are gradually paying attention to this point, and the emergence of many new indexes is also improving this situation. The weight-adjusted waist index (WWI) can be summarized as standardizing waist circumference for weight, and it is a new obesity index used as a predictor of disease morbidity and mortality in recent years by Park et al. The results show that the WWI can predict cardiometabolic and cardiovascular diseases, with superior predictive ability combined with BMI. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Recently, research on the correlation between the WWI and disease development has been increasing, but the specific correlation between the WWI and cardiovascular diseaseis still controversial.\u003c/p\u003e \u003cp\u003eHowever, to the best of our knowledge, no studies have investigated whether there is a correlation between the WWI and COPD incidence. Therefore, our study analysed the correlation between the WWI and COPD incidence using National Health and Nutrition Examination Survey (NHANES) data from 2013 to 2018.\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThis study used data collected from the NHANES, a cross-sectional survey conducted by the National Center for Health Statistics (NCHS), to evaluate the health and nutritional status of the U.S. population. The NHANES gathers information through interviews and physical examinations. The NHANES study design employed a stratified multistage probability sampling method, contributing to the high representativeness of the included samples. All data used in the analysis are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe survey was conducted over three cycles over six years (2013\u0026ndash;2014, 2015\u0026ndash;2016, and 2017\u0026ndash;2018). Among 29,400 participants, 12,363 with no COPD data and 1,759 with missing waist circumference or weight data were excluded. Ultimately, the study included 15,278 participants (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e The NCHS Ethical Review Board approved the conduct and study protocol of the NHANES in human subjects. Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1.\u003c/b\u003e Flow chart of participant selection. Abbreviation: COPD, chronic obstructive pulmonary disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Weight-adjusted waist index\u003c/h2\u003e \u003cp\u003ePhysical waist circumference and weight measurements were performed under controlled conditions at a mobile testing center (MEC) by trained health technicians. The decimal rounding of the WWI results for each participant was kept to two decimal places. In our analysis, the WWI was considered a continuous variable, and participants were categorized into groups based on their WWI data for additional study. The following method was used to calculate the WWI:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\mathbf{W}\\mathbf{W}\\mathbf{I}(\\mathbf{c}\\mathbf{m}/\\surd \\mathbf{k}\\mathbf{g})=\\mathbf{W}\\mathbf{C}/\\surd \\mathbf{W}\\mathbf{e}\\mathbf{i}\\mathbf{g}\\mathbf{h}\\mathbf{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The definition of COPD\u003c/h2\u003e \u003cp\u003eWhether a participant had COPD was included in this study as the exposure variable. The values of this variable were extracted from NHANES questionnaire data. In the Medical Conditions section (MCQ160o), investigators asked participants whether a doctor had informed them that they had COPD. Participants who answered \u0026ldquo;yes\u0026rdquo; were included in the COPD group, while those who answered \u0026ldquo;no\u0026rdquo; were included in the control group. Participants who had missing data for this question were excluded from this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariables\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDemographic variables.\u003c/b\u003e The NHANES demographic files contain information on individuals' age, sex, race, family income-to-poverty ratio, education level, and marital status at the interview.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBody mass index.\u003c/b\u003e Data on the BMI of the study participants were gathered from NHANES body measurements collected by trained health technicians.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSmoking status.\u003c/b\u003e Smoking status was constructed from responses to the questionnaire section: \u0026ldquo;Have you smoked at least 100 cigarettes in your entire life?\u0026rdquo;. Smokers were defined as those who had smoked 100 or more cigarettes, whereas never smokers were defined as those who had never smoked 100 cigarettes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAlcohol consumption.\u003c/b\u003e Alcohol consumption was constructed from responses to the questionnaire section: \u0026ldquo;Have you drunk at least 12 drinks in your entire life?\u0026rdquo;. Drinkers were those who consumed more than 12 drinks in their lifetime, and never drinkers were those who consumed fewer than 12 drinks in their lifetime.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHypertension.\u003c/b\u003e Hypertension in the NHANES was defined as self-reported hypertension (answer \u0026ldquo;yes\u0026rdquo; to the question \u0026ldquo;Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure?\u0026rdquo;) or elevated blood pressure during physical examination (mean systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg or mean diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiabetes.\u003c/b\u003e A subject meeting any of the following criteria will be diagnosed with diabetes: 1) Doctor told you that you have diabetes; 2) glycohemoglobin HbA1c (%)\u0026thinsp;\u0026gt;\u0026thinsp;6.5; 3) fasting glucose (mmol/L)\u0026thinsp;\u0026ge;\u0026thinsp;7.0; 4) random blood glucose (mmol/L)\u0026thinsp;\u0026ge;\u0026thinsp;11.1; 5) 2-hour oral glucose tolerance test (OGTT) blood glucose (mmol/L)\u0026thinsp;\u0026ge;\u0026thinsp;11.1; and 6) use of antidiabetic agents.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCardiovascular disease.\u003c/b\u003e Participants who had previously been diagnosed with congestive heart failure, coronary heart disease, angina, or heart attack were referred to the CVD group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLaboratory variables.\u003c/b\u003e Triglyceride (TG), high-density lipoprotein (HDL-C), and total cholesterol levels were obtained from the NHANES laboratory data files.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) was used for continuous variables, and categorical variables are expressed as proportions. Continuous variables were analysed using t tests, and categorical variables were analysed using chi-square tests to determine significant differences. Linear associations between the WWI, waist circumference, BMI, and COPD incidence were analysed using weighted multiple linear regression and logistic regression. A trend test was conducted to assess the nonlinear association trend between the WWI and COPD incidence by transforming the WWI from a continuous variable into quartiles. Additionally, smoothed curve fitting and threshold effects were employed to evaluate the potential nonlinear associations between the WWI, WC, BMI, and COPD incidence. The area under the receiver operating characteristic curve (ROC) was adopted to measure the ability of the WWI, WC, and BMI to predict COPD. In this study, three models were employed for analysis. Model 1 did not include any adjustment variables. Model 2 was adjusted for gender, age, and race. Finally, Model 3 was adjusted for gender, age, race, education level, marital status, PIR, alcohol consumption, hypertension, CVD, triglycerides, HDL-C, diabetes status, smoking status, and total cholesterol. Subgroup analyses were conducted to explore the associations between the WWI and COPD among individuals of different genders, ages, races, smoking statuses, diabetes statuses, and hypertension statuses. Statistical analyses were carried out using R (version 4.2) and Empower software (version 4.2), which are statistical computing and plotting software. A two-sided p value of less than 0.05 was considered to indicate statistical significance in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.1 Participant characteristics\u003c/h2\u003e\n \u003cp\u003eThis study identified 15,278 individuals in the NHANES 2013\u0026ndash;2018, of which 567 individuals suffered from COPD. Compared to non-COPD participants, COPD participants were more prone to be older, male, non-Hispanic White, less than high school, and living alone and were more likely to smoke and have hypertension, CVD, and diabetes. Individuals with COPD had significantly greater BMIs and TG levels than individuals without COPD. In addition, significant differences in PIR, alcohol consumption, HDL-C, total cholesterol, and WWI were detected between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The detailed characteristics of the included participants with COPD are shown in Table \u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of the study participants: NHANES 2013\u0026ndash;2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-COPD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;15278\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;14711\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;567\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7390 (48.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7087 (48.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e303 (53.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7888 (51.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7624 (51.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264 (46.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2248 (14.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2223 (15.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (4.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1619 (10.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1594 (10.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (4.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5633 (36.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5261 (35.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372 (65.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3287 (21.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3193 (21.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94 (16.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2491 (16.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2440 (16.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51 (8.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3246 (21.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3082 (20.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (28.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3460 (22.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3292 (22.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168 (29.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8561 (56.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8326 (56.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235 (41.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (0.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (0.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried or living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9175 (60.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8904 (60.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e271 (47.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6096 (39.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5801 (39.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e295 (52.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (0.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (0.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2954 (19.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2694 (18.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260 (45.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12324 (80.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12017 (81.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e307 (54.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6834 (44.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6577 (44.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e257 (45.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7429 (48.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7141 (48.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288 (50.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1015 (6.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e993 (6.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (3.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6611 (43.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6212 (42.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e399 (70.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8667 (56.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8499 (57.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168 (29.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1588 (10.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1340 (9.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e248 (43.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13688 (89.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13369 (90.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319 (56.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3064 (20.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2860 (19.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204 (35.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12214 (79.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11851 (80.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e363 (64.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.52\u0026thinsp;\u0026plusmn;\u0026thinsp;17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.96\u0026thinsp;\u0026plusmn;\u0026thinsp;17.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.38\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.30\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114.12\u0026thinsp;\u0026plusmn;\u0026thinsp;74.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113.90\u0026thinsp;\u0026plusmn;\u0026thinsp;74.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.92\u0026thinsp;\u0026plusmn;\u0026thinsp;59.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.40\u0026thinsp;\u0026plusmn;\u0026thinsp;16.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.45\u0026thinsp;\u0026plusmn;\u0026thinsp;16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.28\u0026thinsp;\u0026plusmn;\u0026thinsp;18.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189.53\u0026thinsp;\u0026plusmn;\u0026thinsp;40.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189.84\u0026thinsp;\u0026plusmn;\u0026thinsp;40.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181.38\u0026thinsp;\u0026plusmn;\u0026thinsp;40.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWWI (cm/\u0026radic;kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the P value was calculated by the weighted linear regression model; (%) for categorical variables: the P value was calculated by the weighted chi-square test. Abbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; BMI, body mass index. TG, triglyceride; HDL-C, high-density lipoprotein; WWI, weight-adjusted waist index; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.2 Association between weight-adjusted waist index and COPD incidence\u003c/h2\u003e\n \u003cp\u003eIn this study, the unadjusted (model 1) linear association analysis demonstrated a positive correlation between the WWI and COPD incidence, yielding an odds ratio (OR) of 2.34 (95% CI: 2.11, 2.59). This association was also consistently observed in models 2 and 3. According to the fully adjusted model (Model 3), which accounted for all covariates, the association between the WWI and COPD incidence remained consistent, with an OR of 1.70 (95% CI: 1.48, 1.95). This suggests that each unit increase in the WWI is associated with a 70% increase in the likelihood of developing COPD (Table \u003cspan\u003e2\u003c/span\u003e). The WWI was more strongly associated with COPD than BMI (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.02\u0026ndash;1.05) and waist circumference (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.03) (Table \u003cspan\u003e3\u003c/span\u003e). In addition, we obtained reliable findings even after dividing the continuous variables into quartiles. The WWI was 290% greater in Q4 than in Q1 (OR\u0026thinsp;=\u0026thinsp;3.90, 95% CI: 2.60, 5.86) (Table \u003cspan\u003e2\u003c/span\u003e). Furthermore, exploration through the GAM and smoothing curves revealed a nonlinear positive association between the WWI or WC and COPD (Fig. \u003cspan\u003e2\u003c/span\u003eA, \u003cspan\u003e2\u003c/span\u003eB, and \u003cspan\u003e2\u003c/span\u003eC). In addition, a nonlinear association was found between BMI and COPD (Fig. \u003cspan\u003e2\u003c/span\u003eD). Further exploration of the association between the WWI and COPD revealed that the cut-off point was 12.5. A significant correlation was found before the turning point (OR\u0026thinsp;=\u0026thinsp;1.88, 95% CI: 1.60\u0026ndash;2.22) (Table \u003cspan\u003e4\u003c/span\u003e). The AUCs of the WWI, WC, and BMI for discriminating COPD patients are shown in Fig. \u003cspan\u003e3\u003c/span\u003e. The WWI had the highest AUC of 0.707, followed by WC (AUC\u0026thinsp;=\u0026thinsp;0.609) and BMI (AUC\u0026thinsp;=\u0026thinsp;0.525).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation of the WWI with COPD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15278)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15278)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15278)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWWI (cm/\u0026radic;kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34 (2.11, 2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93 (1.70, 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70 (1.48, 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWWI quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62 (1.78, 3.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91 (1.29, 2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (1.29, 2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.88 (2.68, 5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33 (1.58, 3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06 (1.38, 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.02 (6.37, 12.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67 (3.20, 6.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.90 (2.60, 5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eModel 1: No covariates were adjusted.\u003c/p\u003e\n \u003cp\u003eModel 2: Gender, age, and race were adjusted.\u003c/p\u003e\n \u003cp\u003eModel 3: Gender, age, race, education level, marital status, PIR, hypertension, alcohol consumption, CVD, TG, HDL-C, diabetes, smoking status, and total cholesterol were adjusted.\u003c/p\u003e\n \u003cp\u003eAbbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; TG, triglyceride; HDL-C, high-density lipoprotein; WWI, weight-adjusted waist index; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociations of BMI and WC with COPD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15278)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15278)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15278)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.01, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.02, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.02, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.02, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.01, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.01, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eModel 1: No covariates were adjusted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eModel 2: Gender, age, and race were adjusted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eModel 3: Gender, age, race, education level, marital status, PIR, hypertension, alcohol consumption, CVD, TG, HDL-C, diabetes, smoking status, and total cholesterol were adjusted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; TG, triglyceride; HDL-C, high-density lipoprotein; BMI, body mass index; WC, waist circumference; COPD, chronic obstructive pulmonary disease.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThreshold effect analysis of the WWI on COPD using a two-piecewise linear regression model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted \u0026beta; (95%CI) P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitting by the standard linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70 (1.48, 1.95) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFitting by the two-piecewise linear model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;K segment effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88 (1.60, 2.22) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;K segment effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.44, 1.56) 0.5608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eGender, age, race, education level, marital status, PIR, hypertension, alcohol consumption, CVD, TG, HDL-C, diabetes, smoking status, and total cholesterol were adjusted. Abbreviations: CVD, cardiovascular disease; PIR, the ratio of income to poverty; TG, triglyceride; HDL-C, high-density lipoprotein; WWI, weight-adjusted waist index; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e\n \u003ch2\u003e3.3 Subgroup analysis\u003c/h2\u003e\n \u003cp\u003eTo assess the reliability of the association between the WWI and COPD incidence, we conducted subgroup analyses based on gender, age, race, hypertension status, diabetes status, and smoking status. The results showed that all subgroups exhibited a positive association between the WWI and the risk of COPD (Figure 4). WWI was more positively associated with COPD in males (OR =2.29, 95% CI: 1.85\u0026ndash;2.84, P \u0026lt;0.001), those aged 20\u0026ndash;39 years (OR =3.07, 95% CI: 1.47\u0026ndash;6.42, P = 0.003), Mexican Americans (OR =3.24, 95% CI: 1.73\u0026ndash;6.07, P \u0026lt;0.001), those in the non-hypertensive group (OR =1.70, 95% CI: 1.34\u0026ndash;2.17, P \u0026lt;0.001), those in the diabetic group (OR =1.81, 95% CI: 1.41\u0026ndash;2.31, P \u0026lt;0.001), and those in the smoking group (OR =1.76, 95% CI: 1.42\u0026ndash;2.19, P \u0026lt;0.001).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study utilized NHANES data from 2013 to 2018 involving 15,278 participants to explore the potential association between the WWI and COPD incidence. To our knowledge, this is the first investigation to assess the association between the WWI and COPD. The findings revealed a positive association between the WWI and COPD among US adults, which remained significant after adjusting for various confounders. Specifically, for every one-unit increase in the WWI, there was a 70% increase in the incidence of COPD. Notably, this association was stronger than those observed for traditional metrics such as BMI and WC. Subgroup analyses demonstrated the stability of the association between the WWI and COPD across different demographic and health-related variables. This association was not affected by age, gender, race, hypertension status, diabetes status, or smoking status. COPD has a low rate of early diagnosis and irreversible pathogenesis, so exploring the risk factors for its onset is essential for effective prevention and treatment strategies. (10) Our findings suggest that the WWI has significant clinical value in identifying COPD and facilitating early disease recognition.\u003c/p\u003e\n\u003cp\u003ePrevious research has highlighted the significant role of obesity in COPD development, with BMI and WC commonly used as indicators. However, the association between BMI and COPD has been the subject of inconsistent studies by various scholars, some of whom have even produced conflicting results. Some researchers have found that low BMI may be a risk factor for the development of COPD and that a decrease in BMI may be strongly associated with worsening COPD and increased mortality. (11, 12) Additionally, large-scale prospective studies have indicated that low BMI and weight loss may independently predict mortality in COPD patients, particularly in individuals over 20 years old with substantial weight loss or a BMI \u0026lt;18.5 kg/m2. (13) Poul et al. reported that being overweight (BMI \u0026gt;25 kg/m2) may be a predictor of long-term survival in COPD patients. (14) Although many studies suggest a protective effect of high BMI on COPD, this notion may represent an \u0026quot;obesity paradox.\u0026quot; Notably, increased WC was positively linked to COPD risk in a decade-long cohort study involving both genders. (15) This result was confirmed in another study, which concluded that abdominal obesity and underweight may be risk factors for COPD in adults. (16) Subgroup analysis revealed that the risk of developing COPD was lowest in women with BMIs ranging from 23-24 kg/m2, and BMI and COPD exhibited a U-shaped relationship, consistent with our findings. It has been reported that under extreme conditions, such as a BMI \u0026gt;40 kg/m2, obesity leading to low mortality no longer occurs, and we can treat weight loss as a deleterious factor rather than obesity as a protective factor. (7, 16, 17)\u003c/p\u003e\n\u003cp\u003eHowever, the focus of most studies on diseases has been limited to BMI, WC, and WHR. These indicators primarily reflect nutritional status, and their utility as obesity indicators is questioned because they overlook the distribution of fat, muscle, and other tissues, thus failing to adequately represent the proportion of muscle to fat. An investigation into obesity and COPD compared the correlation between various measures, including BMI, WC, and WHR, revealing that reliance on a single measure overlooks crucial factors such as body composition distribution, resulting in an underestimation of disease risk. (8)\u003c/p\u003e\n\u003cp\u003eIn recent years, researchers have increasingly focused on parameters related to fat and muscle, such as the fat mass index (FMI), fat-free mass index (FFMI), and other parameters. Both the FMI and FFMI have been implicated in the severity of emphysema in COPD patients, with the FFMI specifically impacting quality of life and potentially influencing mortality risk. (18) (19) In a prospective study, muscle loss and malnutrition during hospitalization in patients with COPD were analysed. During the 6-month follow-up, patients who experienced muscle loss due to malnutrition, along with sarcopenia, had a lower one-year survival rate. (20) According to a related report, patients with COPD may exhibit intramuscular and intermuscular fat infiltration in the abdomen and intercostal space. This fat infiltration can lead to impaired muscle function and atrophy, potentially exacerbating the severity of COPD. (21, 22) However, less muscle may also be associated with carbonylation of proteins, which can be caused by ROS and other related oxidative substances, and carbonylation leads to greater structural and functional negative effects. (23) Weight loss and muscle atrophy are prevalent among COPD patients and are linked to systemic inflammatory responses. A study revealed elevated serum levels of growth hormone-releasing peptide and lipocalin in COPD patients with low body weight, while BMI and forced expiratory volume in 1 second (FEV1) were negatively correlated with serum growth hormone-releasing peptide levels. (24) As more studies highlight the role of muscle and weight loss in disease progression, indicators of muscle measurements are becoming increasingly important. However, there have been no major breakthroughs in muscle measurement techniques, and to date, there is no universally recognized method for measuring muscle.\u003c/p\u003e\n\u003cp\u003eThe WWI is an index that evaluates both fat and muscle, and it has been reported that the WWI increases linearly with age and that abdominal fat measurements increase, but muscle measurements decrease. (25, 26) As research on WWI has progressed, an increasing body of evidence has demonstrated its reliability as an indicator capable of predicting various diseases. In the field of cardiovascular diseases, the WWI has emerged as a distinctive predictor of hypertension, stroke, and abdominal aortic calcification. (27-29) An elevated WWI may also be a risk factor for the development of renal stones and gallstones. (30, 31) Furthermore, research has demonstrated that the WWI is the most accurate predictor of chronic kidney disease and albuminuria among various obesity indicators. Regarding respiratory diseases, ongoing studies have concentrated on the relationship between asthma and the WWI. Cross-sectional investigations have revealed a positive correlation between the WWI and both the prevalence and timing of initial asthma episodes. Additionally, a prolonged duration of asthma may coincide with weight loss and abdominal obesity. (32, 33) While research examining the correlation between the WWI and various diseases is expanding, there remains a notable absence of studies addressing COPD, warranting increased attention. First, our findings revealed a robust correlation between elevated WWI and COPD onset, which was particularly notable among individuals aged 20-39 years. Second, in comparison to the cumbersome process of measuring visceral fat, which demands expensive equipment such as MRI scanners and substantial social resources, WWI has emerged as a more accessible and cost-effective obesity index that is especially beneficial for less developed regions. Moreover, Nam\u0026apos;s(26) findings suggest that the WWI is positively correlated with obesity and negatively correlated with human muscle mass. Additionally, these correlations are not significantly influenced by factors such as changes in BMI. This implies that the WWI could be a valuable metric in clinical practice for assessing the risk of COPD, as it appears to be less influenced by BMI and other factors and may offer a better reflection of body visceral fat and muscle mass. This could provide clinicians with a more accurate assessment of health risks associated with COPD. (26, 34). Third, our findings indicate a threshold effect in the relationship between the WWI and COPD, meaning that this association reaches its peak when the WWI exceeds 12.54 cm/\u0026radic;kg. Beyond this threshold, further increases in the WWI do not lead to a corresponding increase in COPD incidence. This evidence underscores the potential benefit of controlling lower levels of WWI as a preventive measure to mitigate the risk of COPD. Such insights may aid clinicians in devising tailored prevention and treatment protocols for patients as well as in identifying high-risk cohorts for targeted screening initiatives.\u003c/p\u003e\n\u003cp\u003eOur study has several strengths. First, we pioneered the examination of the correlation between the WWI and COPD incidence, utilizing a highly representative sample from the NHANES. Second, our investigation serves as another validation of the impact of muscle and adiposity indexes on COPD. This highlights that an elevated WWI may contribute to increased COPD risk, offering insights into potential strategies for COPD prevention and early diagnosis. Last, large-scale cross-sectional studies such as ours employ random sampling methods, rendering their findings potentially representative of the broader population. Nevertheless, our study is not without limitations. Cross-sectional survey results can only imply correlation rather than causation. Additionally, there may be gaps in the NHANES data, such as the omission of factors such as environmental conditions and genetic predispositions, which could bias the outcomes, particularly regarding the increased incidence of COPD.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study revealed that the WWI is associated with a greater incidence of COPD, and the correlation between the WWI and COPD incidence is stronger than that between the WWI and other obesity biomarkers, suggesting that obesity management evaluated by the WWI may be beneficial for mental health. Based on these findings, clinicians can consider the WWI to be an important indicator when evaluating COPD.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eThe human studies involved in this research received approval from the Ethics Review Committee of the National Health Statistics Research Centre\u0026nbsp;in the United States. These studies were conducted in accordance with local legislation and institutional requirements. Participants provided written informed consent to participate in this research.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to all researchers who have made NHANES data publicly available. Additionally, we wish to thank all\u0026nbsp;the colleagues who dedicated their time and effort to\u0026nbsp;contributing\u0026nbsp;to this study.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eHW: Data curation, Formal analysis, Writing original draft, writing review, and editing. WC: Data curation, methodology, writing-original draft, writing-review \u0026amp; editing. ZX: Conceptualization, writing review, and editing. XL: Formal analysis, writing review, and editing. JW: Writing-review and editing. YZ: Funding acquisition, writing review, and editing. ZW: Funding acquisition, writing review, and editing.\u003c/p\u003e\u003ch1\u003eData availability statement\u003c/h1\u003e\n\u003cp\u003eAll the data utilized in this study were obtained and analysed through the NHANES database. The codes employed in the analysis are available upon reasonable request by contacting the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLopez-Campos, J. L., Tan, W. \u0026amp; Soriano, J. B. Global burden of COPD. Respirology 21, 14-23 (2016). https://doi.org:10.1111/resp.12660\u003c/li\u003e\n\u003cli\u003eChristenson, S. A., Smith, B. M., Bafadhel, M. \u0026amp; Putcha, N. Chronic obstructive pulmonary disease. Lancet 399, 2227-2242 (2022). https://doi.org:10.1016/S0140-6736(22)00470-6\u003c/li\u003e\n\u003cli\u003eSin, D. D. et al. Air pollution and COPD: GOLD 2023 committee report. Eur Respir J 61 (2023). https://doi.org:10.1183/13993003.02469-2022\u003c/li\u003e\n\u003cli\u003eMurgia, N. \u0026amp; Gambelunghe, A. Occupational COPD-The most under-recognized occupational lung disease? Respirology 27, 399-410 (2022). https://doi.org:10.1111/resp.14272\u003c/li\u003e\n\u003cli\u003eSoriano, J. B., Polverino, F. \u0026amp; Cosio, B. G. What is early COPD and why is it important? Eur Respir J 52 (2018). https://doi.org:10.1183/13993003.01448-2018\u003c/li\u003e\n\u003cli\u003eFerrera, M. C., Labaki, W. W. \u0026amp; Han, M. K. Advances in Chronic Obstructive Pulmonary Disease. Annu Rev Med 72, 119-134 (2021). https://doi.org:10.1146/annurev-med-080919-112707\u003c/li\u003e\n\u003cli\u003eSpelta, F., Fratta Pasini, A. M., Cazzoletti, L. \u0026amp; Ferrari, M. Body weight and mortality in COPD: focus on the obesity paradox. Eat Weight Disord 23, 15-22 (2018). https://doi.org:10.1007/s40519-017-0456-z\u003c/li\u003e\n\u003cli\u003eWang, X., Liang, Q., Li, Z. \u0026amp; Li, F. Body Composition and COPD: A New Perspective. Int J Chron Obstruct Pulmon Dis 18, 79-97 (2023). https://doi.org:10.2147/COPD.S394907\u003c/li\u003e\n\u003cli\u003ePark, Y., Kim, N. H., Kwon, T. Y. \u0026amp; Kim, S. G. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep 8, 16753 (2018). https://doi.org:10.1038/s41598-018-35073-4\u003c/li\u003e\n\u003cli\u003eLaucho-Contreras, M. E. \u0026amp; Cohen-Todd, M. Early diagnosis of COPD: myth or a true perspective. Eur Respir Rev 29 (2020). https://doi.org:10.1183/16000617.0131-2020\u003c/li\u003e\n\u003cli\u003eHoltjer, J. C. S. et al. Identifying risk factors for COPD and adult-onset asthma: an umbrella review. Eur Respir Rev 32 (2023). https://doi.org:10.1183/16000617.0009-2023\u003c/li\u003e\n\u003cli\u003eKim, E. K. et al. Impact of Body Mass Index Change on the Prognosis of Chronic Obstructive Pulmonary Disease. Respiration 99, 943-953 (2020). https://doi.org:10.1159/000511022\u003c/li\u003e\n\u003cli\u003eWada, H. et al. Low BMI and weight loss aggravate COPD mortality in men, findings from a large prospective cohort: the JACC study. Sci Rep 11, 1531 (2021). https://doi.org:10.1038/s41598-020-79860-4\u003c/li\u003e\n\u003cli\u003eStoll, P., Foerster, S., Virchow, J. C. \u0026amp; Lommatzsch, M. Overweight is a predictor of long-term survival in hospitalised patients with exacerbations of COPD. Respir Med 116, 59-62 (2016). https://doi.org:10.1016/j.rmed.2016.05.016\u003c/li\u003e\n\u003cli\u003eMa, Y. L., Zhao, H. J. \u0026amp; Su, Y. H. Association between waist circumference change and incident chronic obstructive pulmonary disease among Chinese adults: a 10-year cohort study. Sci Rep 12, 18402 (2022). https://doi.org:10.1038/s41598-022-23248-z\u003c/li\u003e\n\u003cli\u003eLi, J. et al. Association between adiposity measures and COPD risk in Chinese adults. Eur Respir J 55 (2020). https://doi.org:10.1183/13993003.01899-2019\u003c/li\u003e\n\u003cli\u003eBrigham, E. P. et al. Challenging the obesity paradox: extreme obesity and COPD mortality in the SUMMIT trial. ERJ Open Res 7 (2021). https://doi.org:10.1183/23120541.00902-2020\u003c/li\u003e\n\u003cli\u003eShimada, T. et al. Differential impacts between fat mass index and fat-free mass index on patients with COPD. Respir Med 217, 107346 (2023). https://doi.org:10.1016/j.rmed.2023.107346\u003c/li\u003e\n\u003cli\u003eMcDonald, M. N. et al. Chest computed tomography-derived low fat-free mass index and mortality in COPD. Eur Respir J 50 (2017). https://doi.org:10.1183/13993003.01134-2017\u003c/li\u003e\n\u003cli\u003ePerrot, L. et al. Prevalence of sarcopenia and malnutrition during acute exacerbation of COPD and after 6 months recovery. Eur J Clin Nutr 74, 1556-1564 (2020). https://doi.org:10.1038/s41430-020-0623-6\u003c/li\u003e\n\u003cli\u003ePark, M. J. et al. Mass and fat infiltration of intercostal muscles measured by CT histogram analysis and their correlations with COPD severity. Acad Radiol 21, 711-717 (2014). https://doi.org:10.1016/j.acra.2014.02.003\u003c/li\u003e\n\u003cli\u003eJeon, Y. J. et al. Intramuscular and Intermuscular Abdominal Fat Infiltration in COPD: A Propensity Score Matched Study. Int J Chron Obstruct Pulmon Dis 16, 1989-1999 (2021). https://doi.org:10.2147/COPD.S312888\u003c/li\u003e\n\u003cli\u003eBarreiro, E. Protein carbonylation and muscle function in COPD and other conditions. Mass Spectrom Rev 33, 219-236 (2014). https://doi.org:10.1002/mas.21394\u003c/li\u003e\n\u003cli\u003eUzum, A. K. et al. Serum ghrelin and adiponectin levels are increased but serum leptin level is unchanged in low weight Chronic Obstructive Pulmonary Disease patients. Eur J Intern Med 25, 364-369 (2014). https://doi.org:10.1016/j.ejim.2013.02.012\u003c/li\u003e\n\u003cli\u003eKim, J. Y. et al. Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis. Diabetes Metab J 46, 747-755 (2022). https://doi.org:10.4093/dmj.2021.0294\u003c/li\u003e\n\u003cli\u003eKim, N. H., Park, Y., Kim, N. H. \u0026amp; Kim, S. G. Weight-adjusted waist index reflects fat and muscle mass in the opposite direction in older adults. Age Ageing 50, 780-786 (2021). https://doi.org:10.1093/ageing/afaa208\u003c/li\u003e\n\u003cli\u003eWang, J. et al. The relationship between obesity associated weight-adjusted waist index and the prevalence of hypertension in US adults aged \u0026gt;/=60 years: a brief report. Front Public Health 11, 1210669 (2023). https://doi.org:10.3389/fpubh.2023.1210669\u003c/li\u003e\n\u003cli\u003eQin, Z. et al. The association between weight-adjusted-waist index and abdominal aortic calcification in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years: results from NHANES 2013-2014. Sci Rep 12, 20354 (2022). https://doi.org:10.1038/s41598-022-24756-8\u003c/li\u003e\n\u003cli\u003eYe, J. et al. Association between the weight-adjusted waist index and stroke: a cross-sectional study. BMC Public Health 23, 1689 (2023). https://doi.org:10.1186/s12889-023-16621-8\u003c/li\u003e\n\u003cli\u003eLiu, H., Ma, Y. \u0026amp; Shi, L. Higher weight-adjusted waist index is associated with increased likelihood of kidney stones. Front Endocrinol (Lausanne) 14, 1234440 (2023). https://doi.org:10.3389/fendo.2023.1234440\u003c/li\u003e\n\u003cli\u003eKe, B., Sun, Y., Dai, X., Gui, Y. \u0026amp; Chen, S. Relationship between weight-adjusted waist circumference index and prevalence of gallstones in U.S. adults: a study based on the NHANES 2017-2020. Front Endocrinol (Lausanne) 14, 1276465 (2023). https://doi.org:10.3389/fendo.2023.1276465\u003c/li\u003e\n\u003cli\u003eYu, L. et al. Association of weight-adjusted-waist index with asthma prevalence and the age of first asthma onset in United States adults. Front Endocrinol (Lausanne) 14, 1116621 (2023). https://doi.org:10.3389/fendo.2023.1116621\u003c/li\u003e\n\u003cli\u003eHan, X. et al. The association of asthma duration with body mass index and Weight-Adjusted-Waist index in a nationwide study of the U.S. adults. Eur J Med Res 28, 122 (2023). https://doi.org:10.1186/s40001-023-01089-4\u003c/li\u003e\n\u003cli\u003eNuttall, F. Q. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today 50, 117-128 (2015). https://doi.org:10.1097/nt.0000000000000092\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Weight-adjusted-waist index, Chronic obstructive pulmonary disease, Obesity, NHANES, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-4495983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4495983/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\u003eChronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. The weight-adjusted waist index (WWI) is a recently developed index for measuring adiposity, but the relationship between the WWI and COPD has not been well studied. This study aimed to investigate the potential correlation between these two health variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018. The WWI was calculated as waist circumference divided by the square root of weight. Multiple linear regression was performed to estimate the association between the WWI and COPD incidence, and subgroup analysis was performed to identify sensitive cohorts. Smoothing curve fitting and threshold effect analysis were conducted to assess possible nonlinear relationships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: A total of 15,278 participants were included. Participants in the higher WWI tertile tended to have a greater incidence of COPD. Compared to body mass index and waist circumference, the correlation between the WWI and COPD incidence has better discrimination and accuracy. In the final adjusted model, a positive association between the WWI and COPD incidence was observed (OR = 1.70, 95% CI: 1.48–1.95). Participants in the highest WWI quartile had a 290% greater risk of COPD than did those in the lowest WWI tertile (OR = 3.90, 95% CI: 2.60–5.86). A nonlinear correlation was found between the WWI and COPD incidence, with an inflection point of 12.54. Subgroup analysis revealed that the relationship was significant in all subgroups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Compared with BMI and WC, WWI was related to an increased likelihood of COPD and had a stronger relationship with COPD incidence. Our findings indicated that the WWI could be a cost-effective and simple way to detect COPD.\u003c/p\u003e","manuscriptTitle":"Association between weight-adjusted waist index and chronic obstructive pulmonary disease from National Health and Nutrition Examination Survey: 2013-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 18:43:57","doi":"10.21203/rs.3.rs-4495983/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23050da2-3027-4b0a-b9de-69a7ae9d1a99","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-03T05:51:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-14 18:43:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4495983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4495983","identity":"rs-4495983","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}