Association between a body shape index and the chronic obstructive pulmonary disease among middle-aged and elderly individuals in China: insights from CHARLS

Association between a body shape index and the chronic obstructive pulmonary disease among middle-aged and elderly individuals in China: insights from CHARLS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between a body shape index and the chronic obstructive pulmonary disease among middle-aged and elderly individuals in China: insights from CHARLS Quankun Lv, Zihao Huang, Jiahao Liu, Lisha Hu, Jiaxian Huo, Yi Ye, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9196992/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Although A Body Shape Index (ABSI) has been linked to various health outcomes, its relationship with chronic obstructive pulmonary disease (COPD) remains unclear. We therefore aimed to determine whether ABSI is independently associated with COPD prevalence and incidence. Methods At baseline, 9,611 participants were included. Cox models assessed the association between ABSI and COPD risk, with subgroup analyses for confounding. Cumulative incidence across ABSI levels was compared using log-rank tests. Restricted cubic splines evaluated dose-response relationships. An XGBoost- and logistic regression-based nomogram was developed for COPD risk prediction. Results Cox regression identified ABSI as an independent COPD risk factor: each ABSI unit increase raised COPD risk by 72.4% (Hazard Ratio (HR)=1.724, 95% Confidence Interval (CI):1.423–2.089) in the fully adjusted model. Cumulative incidence curve showed significantly increase COPD cumulative incidence in high-ABSI groups. Subgroup analyses found consistent ABSI effects mostly. RCS shown the positive linear dose-response connection between ABSI and COPD risk, and the ABSI was the second most significant variable in predicting COPD. The nomogram based on ABSI and other important covariates showed excellent performance, which had good clinical prediction ability. Conclusion In middle-aged and older Chinese adults, ABSI is an independent risk factor for COPD, showing a positive linear dose-response relationship with COPD risk. The ABSI-based nomogram performs well, suggesting ABSI is a useful marker for early identification of high-risk individuals and for guiding public health interventions. A Body Shape Index Chronic obstructive pulmonary disease CHARLS Nomogram Prospective cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Chronic obstructive pulmonary disease (COPD) ranks among the leading causes of death globally, imposing a heavy social and economic burden[1–3]. Despite progress in primary prevention measures such as tobacco control, air quality improvement, and enhanced occupational protection, the prevalence and mortality rates of COPD continue to rise. Its onset is significantly associated with multiple factors including smoking, air pollution, occupational dust exposure, genetic factor, and respiratory infections[4, 5]. However, these known risk factors cannot fully explain its high prevalence. Therefore, developing low-cost, reproducible, and easily scalable indicators for early identification and prediction of high-risk populations for COPD holds urgent practical significance for slowing disease progression and reducing societal and healthcare burdens. Obesity is a major contributing factor to numerous chronic diseases and is significantly associated with cardiovascular disease, diabetes, and respiratory disorders[6–8]. According to the World Obesity Atlas 2025, the global obesity problem is becoming increasingly severe.By 2030, it is estimated that more than 29 billion adults will be overweight, with 11 billion reaching the level of obesity : 4.87 billion men and 6.43 billion women[9]. Central obesity is more strongly associated with visceral fat accumulation[10, 11]. Adiposity exert not only triggers systemic inflammatory responses[12] and oxidative stress[13] but also causes changes in respiratory mechanics, increasing the risk of respiratory diseases[14, 15]. Among traditional assessment metrics, while Body Mass Index (BMI) and waist circumference reflect overall obesity levels, they struggle to accurately evaluate fat distribution characteristics. To address this, researchers have proposed the Body Shape Index (ABSI) as a metric for assessing health risks. Combining waist circumference (WC), BMI, and height measurements, ABSI focuses on quantifying the relationship between waist size and body shape to more accurately reflect abdominal fat distribution. Its formula is calculated as WC divided by (BMI²/3) × (height¹/²)[16]. Previous studies have shown that compared with traditional indicators, ABSI and its derived indicators have obvious advantages in predicting adverse outcomes of cardiovascular diseases and other conditions[17, 18]. However, whether elevated ABSI levels increase the risk of COPD remains inconclusive. Based on this, this study utilized nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2015 to conduct a longitudinal analysis of middle-aged and older adults aged 45 and above. It assessed the association between ABSI and the risk of developing COPD and performed subgroup analyses across different populations. Furthermore, by integrating machine learning and logistic regression methods to identify significant variables, we constructed an ABSI-based COPD risk prediction nomogram. This aims to provide a simple and reliable tool for early identification of high-risk individuals and the development of targeted public health interventions, while also offering crucial insights into how obesity and body fat distribution impact respiratory health. 2 Materials and methodology 2.1 Data source and participants This cohort study relied on data obtained from CHARLS database. CHARLS is a prospective cohort study that included individuals aged 45 and above from 28 regions in China[19], with the aims of analyzing population aging and facilitating multidisciplinary research about aging in China. To ensure sample representativeness, CHARLS baseline survey, conducted in 2011, encompassed 150 county-level units and 450 village-level units. involving 17,708 individuals from 10,257 households. Following baseline survey, participants were followed up every 2–3 years. For the present study, data from three survey waves (2011, 2013, and 2015) were included, with the 2011 wave designated as the baseline and subsequent waves used to determine the timing of outcome onset and follow-up duration. The Biomedical Ethics Committee of Peking University (Ethics Approval Number: IRB00001052-11015) gave their stamp of approval to this study, and all subjects gave written informed permission. This study enrolled 35,516 participants aged over 45 years from three survey waves conducted in 2011, 2013, and 2015. Participants were excluded based on the following criteria: missing data on the A Body Shape Index (ABSI, n = 204), smoking (n = 49), sleep time (n = 144), hypertension (n = 43), or diabetes (n = 67); presence of chronic obstructive pulmonary disease (COPD) at baseline (n = 33); and with no follow-up data available (n = 1,761). After these exclusions, a total of 9,611 participants with baseline data and at least one subsequent wave of follow-up information were involved in final study (Fig. 1 ). 2.2 Calculation of ABSI On physical examination, a vertical stadiometer was used to measure height, and a calibrated scale was employed for weight measurement. Waist circumference (WC) was assessed with a flexible measuring tape, applied horizontally around waist at the level of umbilicus while participants stood in an upright, relaxed posture with feet shoulder-width apart. Then used the following formula to determine body mass index (BMI): \(\:\text{BMI=weight(kg)/}{\text{[height}\left(\text{m}\right)\text{]}}^{\text{2}}\) . The calculation of the ABSI necessitates the use of body mass index (BMI), as BMI serves as an integral component of the ABSI formula. \(\:\text{ABSI=WC(cm)×}{\text{[}{\text{BMI}}^{\frac{\text{2}}{\text{3}}}\text{×}{\text{height(m)}}^{\frac{\text{1}}{\text{2}}}\text{]}}^{\text{-1}}\) [16]. 2.3 Outcome ascertainment The outcome of this study was COPD. According to the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) report[20], airway abnormalities (bronchitis, bronchiolitis) and/or alveolar abnormalities (emphysema) cause persistent and progressive airflow limitation in COPD, a diverse lung disorder characterized by chronic respiratory symptoms (e.g., dyspnea, cough, and sputum production). In CHARLS, information on chronic lung disease was derived from participants’ self-reports. Participants who responded to the question "Have you ever been diagnosed with chronic lung disease (excluding tumor or cancer) by a doctor?" Patients who answered "yes" were defined as having chronic lung disease. Pulmonary function was assessed using a peak flow meter, with Peak Expiratory Flow (PEF) serving as the indicator to evaluate lung function. Those individuals who have both chronic lung disease and PEF less than 60 L/min are classified as COPD patients[21, 22]. 2.4 Covariates To account for the influence of potential confounders, a set of key covariates were incorporated into this study, including: age, gender, educational attainment (categorized as: incomplete primary school, primary school, middle school, and high school or above), place of residence (rural vs. urban), marital status (categorized as: unmarried or separated, marriage or cohabitation ), smoking status, drinking status, sleep duration, hypertension, and diabetes. 2.5 Statistical analysis To summarize the individuals' characteristics, descriptive statistics were utilized. Continuous data were first tested for normality. Data adhering to a normal distribution were presented as mean ± standard deviation (mean ± SD), and categorical variables were described using frequencies and percentages (%). When comparing data across groups, normally distributed continuous variables were analyzed via one-way analysis of variance (ANOVA), and categorical variables were tested using the chi-square test. To evaluate the association between ABSI and COPD risk, Cox proportional hazards regression models were used to estimate hazard ratios (HRs) along with their respective 95% confidence intervals (CIs). Three hierarchical regression models were established: Model 1 is unadjusted; Model 2 is a minimally adjusted version of Model 1 , adjusted further for age, gender, educational background, place of residence, and marital status; and Model 3 is a fully adjusted model that builds on Model 2 by adding adjustments for smoking, drinking, sleep duration, hypertension, and diabetes. Furthermore, to verify the consistency of conclusions across different populations, subgroup analyses were performed based on Model 3 to assess the modifying effects of potential confounders in this study. Participants were stratified into four groups according to ABSI quartiles. Cumulative incidence curves were generated using the survminer package (v0.5.0)[23] to visualize the cumulative incidence probabilities of COPD across these groups, with intergroup differences assessed via the log-rank test. To explore potential nonlinear associations between variables, a restricted cubic spline (RCS) model was constructed using rms package (v8.0-0)[24]. Stratified Cox regression analyses were performed using the jstable package (v1.3.13)[25], with subgroup analyses conducted based on Model 3 to evaluate the potential modifying effects of different population characteristics on the association of interest. The xgboost package (v1.7.11)[26] was used to build an XGBoost model, which quantified the relative importance of selected variables for COPD risk prediction. Additionally, a nomogram was developed using the regplot package (v1.1)[27] to assess intrinsic capacity-related risk, with relevant predictors incorporated into the model. R software (v4.4.3) was used to carry out all statistical analyses, with a two-sided P -value < 0.05 considered to indicate statistical significance. 3 Results 3.1 Baseline information statistics The baseline analysis included 9,611 participants, whose mean age was 59.25 ± 8.82 years. For educational attainment, 8,654 individuals in the less than junior high school education group, accounting for approximately 90.04%. Moreover, the participants living in rural (64.89%) areas were nearly twice as many as those in urban areas (35.11%). Additionally, the majority of the participants did not drink alcohol (67.35%) or smoke (68.83%) (Table 1 ). As presented in the table, all covariates exhibited significant differences across the ABSI categories ( P < 0.001). Notably, as ABSI increased, the prevalence of hypertension or diabetes among participants also rose. Furthermore, as the ABSI increased from the first quartile (Q1) to the fourth quartile (Q4), the PEF exhibited a significant downward trend, whereas Waist showed a significant upward trend. Table 1 Baseline statistics table n level Overall Q1 Q2 Q3 Q4 p 9611 2,403 2,403 2,402 2,403 PEF(L/min, mean (SD)) 296.593 (123.940) 317.581 (123.958) 314.922 (125.886) 297.358 (122.967) 256.511 (112.895) < 0.001 Waist (cm, mean (SD)) 85.592 (10.014) 79.025 (8.421) 84.761 (8.707) 87.806 (9.258) 90.777 (9.653) < 0.001 Height (m, mean (SD)) 1.582 (0.085) 1.587 (0.078) 1.592 (0.082) 1.589 (0.086) 1.561 (0.089) < 0.001 BMI (mean (SD)) 23.580 (3.899) 23.708 (4.574) 23.835 (3.568) 23.806 (3.643) 22.973 (3.660) < 0.001 Age (mean (SD)) 59.253 (8.819) 56.255 (7.820) 57.623 (8.113) 59.522 (8.403) 63.613 (9.102) < 0.001 ABSI (mean (SD)) 8.316 (0.585) 7.653 (0.392) 8.134 (0.090) 8.442 (0.095) 9.034 (0.438) < 0.001 Gender (%) female 5077 (52.825) 1188 (49.438) 1119 (46.567) 1205 (50.167) 1565 (65.127) < 0.001 male 4534 (47.175) 1215 (50.562) 1284 (53.433) 1197 (49.833) 838 (34.873) Educational (%) less than junior high school education 8654 (90.043) 2091 (87.016) 2147 (89.347) 2157 (89.800) 2259 (94.007) < 0.001 high school and vocational training 844 (8.782) 270 ( 11.236) 231 (9.613) 216 (8.993) 127 (5.285) higher education 113 (1.176) 42 (1.748) 25 (1.040) 29 (1.207) 17 (0.707) Urban-rural (%) urban 3374 (35.106) 778 (32.376) 823 (34.249) 907 (37.760) 866 (36.038) 0.001 rural 6237 (64.894) 1625 (67.624) 1580 (65.751) 1495 (62.240) 1537 (63.962) Married (%) unmarried or separated 1517 (15.784) 348 (14.482) 330 (13.733) 360 (14.988) 479 (19.933) < 0.001 marriage or cohabitation 8094 (84.216) 2055 (85.518) 2073 (86.267) 2042 (85.012) 1924 (80.067) Smoking (%) no 6615 (68.827) 1610 (67.000) 1584 (65.918) 1628 (67.777) 1793 (74.615) < 0.001 yes 2996 (31.173) 793 (33.000) 819 (34.082) 774 (32.223) 610 (25.385) Drinking (%) no 6473 (67.350) 1588 (66.084) 1523 (63.379) 1610 (67.027) 1752 (72.909) < 0.001 yes 3138 (32.650) 815 (33.916) 880 (36.621) 792 (32.973) 651 (27.091) Sleep night (hours, mean (SD)) 6.349 (1.879) 6.403 (1.790) 6.434 (1.854) 6.355 (1.849) 6.204 (2.009) < 0.001 Hypertension (%) no 7139 (74.279) 1865 (77.611) 1842 (76.654) 1774 (73.855) 1658 (68.997) < 0.001 yes 2472 (25.721) 538 (22.389) 561 (23.346) 628 (26.145) 745 (31.003) Diabetes (%) no 9025 (93.903) 2299 (95.672) 2292 (95.381) 2233 (92.964) 2201 (91.594) < 0.001 yes 586 (6.097) 104 (4.328) 111 (4.619) 169 (7.036) 202 (8.406) 3.2 ABSI was a risk factor for COPD To quantify the link between ABSI and the risk of COPD, Cox proportional hazards regression models were employed (Table 2 ). Compared with T1 (Referenc), T3 had a P -value < 0.05 across all three models, indicating that risk association between ABSI and COPD remained stable in T3 subgroup. The HR with 95% CI was 5.815 (2.445–13.832) for Model 1 , 3.460 (1.361–8.793) for the adjusted Model 2 , and 3.491 (1.368–8.913) for the fully adjusted Model 3 , which revealed that ABSI was an independent risk factor for incident COPD: for each unit increase in ABSI, the risk of developing COPD increased by 72.4% (fully adjusted Model 3 ). Subsequently, cumulative incidence curves for COPD were plotted to compare individuals with different ABSI levels, where participants were stratified into high- and low-ABSI groups using either the median or tertiles as cutoffs (Fig. 2 A-B). These curves further demonstrated that a higher ABSI value was associated with a significantly higher cumulative incidence of COPD (log-rank test, P < 0.0001). Interestingly, in the marital status indicator, a significant interaction effect was detected through the overall analysis ( P < 0.05), however, in the subgroup of unmarried or separated individuals, ABSI does not act as a risk factor for COPD in this group. The dose-response relationship between ABSI and COPD risk across the aforementioned models was examined using RCS analysis. As illustrated in Fig. 2 C, the overall association between ABSI and COPD risk exhibited a linear pattern, and it was a positive correlation. Notably, a significant linear increase in COPD risk associated with ABSI emerged when ABSI exceeded 7.69. Table 2 Survival model Variable Model 1 P value Model 2 P value Model 3 P value ABSI HR(95% CI) 2.292(1.869–2.81) 1.586E-15 1.673(1.243–2.253) 6.971E-04 1.724(1.278–2.326) 3.589E-04 ABSI_Tertiles T1 Reference T2 HR(95% CI) 1.765(0.653–4.773) 2.631E-01 1.47(0.546–3.959) 4.461E-01 1.465(0.537–3.999) 4.558E-01 T3 HR(95% CI) 5.815(2.445–13.832) 6.829E-05 3.46(1.361–8.793) 9.101E-03 3.491(1.368–8.913) 8.933E-03 ABSI: Although A Body Shape Index; HR: Hazard Ratio; CI: Confidence Interval. 3.3 Subgroup analysis Subgroup analyses were conducted based on the covariates included in the models described above to evaluate whether the link between ABSI and COPD risk altered among prespecified subgroups (Fig. 3 A). The findings indicated that there was a positive relationship between ABSI and COPD risk remained consistent with primary analysis in most subgroups, including participants aged ≥ 60 years, those with education below junior high school, rural residents, individuals with married or cohabiting status, non-smokers, and those without diabetes. Notably, the interaction effect of ABSI on COPD risk disappeared among unmarried individuals. 3.4 Machine learning and construction of nomogram An XGBoost model was constructed to assess the relative importance of the selected variables—including ABSI, gender, age, educational attainment, place of residence, smoking status, marital status, drinking status, sleep duration, hypertension, and diabetes—with respect to COPD risk. This model generated a ranking of variable importance, and notably, ABSI ranked among the top 2 (Fig. 3 B). Building on the machine learning results, we incorporated the top 5 most important covariates—ABSI, age, sleep hours, married, and smoking—to develop a nomogram (Fig. 4 A), which was used to predict the 2-year and 4-year cumulative incidence of COPD. Notably, a higher total score from the nomogram corresponded to a higher cumulative incidence of COPD. We also generated a calibration curve for the constructed nomogram (Fig. 4 B). The slope of this curve was close to 1, indicating that the predicted probabilities of COPD incidence from the nomogram were highly consistent with the actual observed incidences, confirming the model’s good predictive accuracy. Then we generated receiver operating characteristic (ROC) curves to further validate nomogram, ROC curves of these different models for the 2-year and 4-year endpoints are presented in Figs. 4 C–D. Notably, area under the curve (AUC) values of the nomogram model for the 2-year and 4-year predictions were all greater than 0.8, demonstrating that the nomogram has excellent performance in predicting an individual’s disease risk. 4 Discussion This study conducted a prospective analysis of 9,611 middle-aged and older adults aged 45 years and above using nationally representative cohort data from the China Health and Retirement Longitudinal Study (CHARLS). It systematically confirmed for the first time that ABSI is a significant independent risk factor for COPD development, with a stable positive linear correlation between the two. Multivariate Cox regression results showed that after fully adjusting for confounding factors including age, gender, education level, lifestyle, and metabolic diseases, each additional unit of ABSI increased the risk of COPD incidence by approximately 72.4% (HR = 1.724, 95% CI: 1.278–2.326). After stratifying ABSI scores, the highest quartile group exhibited a 3.49-fold higher COPD risk compared to the lowest quartile group (HR = 3.491, 95% CI: 1.368–8.913). Restricted cubic splines (RCS) and cumulative incidence curves jointly indicated a continuous, linear dose-response relationship between ABSI and COPD risk. This finding highlights the potential advantage of ABSI in identifying respiratory disease risk. BMI is widely used to assess obesity and disease risk due to its simplicity of calculation[28, 29]. However, a rapidly growing body of research has revealed that individuals classified as obese based on BMI actually exhibit better outcomes in multiple diseases—a phenomenon known as the “obesity paradox”[30, 31]. In a large cohort study involving 110,585 individuals aged 40–79 years with 19.1 years of follow-up, each one-standard deviation (SD) increase in BMI was associated with a 52% reduction in the risk of COPD mortality (HR = 0.48, 95% CI: 0.41–0.57)[32]. This phenomenon primarily stems from BMI's inability to distinguish fat from muscle mass and its limited capacity to reflect central obesity, thereby constraining the accuracy of disease risk prediction[33]. The Adiposity Body Size Index (ABSI), which adjusts waist circumference for height and weight, more precisely characterizes body fat distribution and visceral fat burden[34]. A prospective Italian study demonstrated that ABSI does not exhibit the obesity paradox and outperforms waist circumference in predicting mortality risks associated with central obesity[35]. Recent research also recommends incorporating ABSI into the definition of metabolic syndrome[36, 37]. This study also yielded similar findings: elevated ABSI was associated with increased waist circumference (79.0 cm to 90.8 cm, P < 0.001) and decreased lung function PEF (317.6 L/min to 256.5 L/min, P < 0.001), suggesting that abdominal fat accumulation is significantly associated with impaired lung function.The high-ABSI group exhibited a decrease in BMI, further highlighting the limitations of BMI. Overall, ABSI better reflects central obesity characteristics than BMI or waist circumference, demonstrates greater predictive efficacy for metabolic and respiratory disease risks, and avoids the obesity paradox phenomenon. From a pathophysiological perspective, the accumulation of visceral fat reflected by ABSI may contribute to the onset and progression of COPD through multiple pathways. First,visceral fat, acting as an active endocrine organ, continuously secretes pro-inflammatory cytokines (such as TNF-αand IL-6) and disrupts adipokine balance (leptin/adiponectin), inducing systemic low-grade inflammation and oxidative stress[2, 12, 38].This exacerbates chronic airway inflammation and lung tissue remodeling. Second, abdominal fat accumulation mechanically restricts diaphragmatic movement and thoracic expansion, reducing lung capacity and increasing airway resistance, thereby promoting long-term respiratory mechanics abnormalities and functional decline[39, 40]. Furthermore, this study observed significantly elevated prevalence of hypertension and diabetes in the high ABSI group ( P < 0.001), suggesting insulin resistance and metabolic disorders may bridge the association between ABSI and COPD risk. Collectively, these biological mechanisms provide a plausible explanation for the linear positive correlation between ABSI and COPD risk. Notably, the dose-response curve in this study revealed a significant increase in COPD risk when ABSI exceeded 7.69, suggesting this threshold serves as a critical reference point for clinical screening to identify high-risk individuals early and implement interventions. Subgroup analysis results indicate that the positive association between ABSI and COPD risk remains consistent across most populations, including those aged ≥ 60 years, non-smokers, individuals with lower education levels, and those without diabetes, demonstrating good generalizability and stability. However, marital status exhibited a significant interaction effect ( P = 0.014): the association between ABSI and COPD risk was most pronounced among married or cohabiting individuals (HR = 3.10, 95% CI: 2.17–4.44), whereas this association failed to reach statistical significance among unmarried or separated individuals. We hypothesize that married individuals, benefiting from spousal support, exhibit advantages in dietary management, health behaviors, and disease monitoring, thereby amplifying the biological risk reflected by ABSI. Conversely, unmarried or widowed individuals may experience diminished social support, heightened psychological stress, elevated inflammation levels, and inadequate health behaviors, partially masking the independent effect of ABSI. These findings suggest that psychosocial factors may modulate the relationship between ABSI and COPD through complex behavioral and inflammatory pathways, warranting further investigation in future studies. Overall, the strengths of this study lie in its systematic evaluation and confirmation of the linear relationship between ABSI as an independent risk factor and COPD incidence based on CHARLS data. Furthermore, machine learning methods were employed to construct an ABSI-based predictive nomogram, which demonstrated good predictive performance.This provides strong evidence for the application of ABSI in clinical practice and public health screening, and offers a feasible approach for identifying high-risk populations for COPD through physical measurement indicators and developing personalized prevention strategies.However, this study also has certain limitations. First, although CHARLS is a prospective cohort, this analysis is retrospective, and COPD diagnosis relies on self-reporting and peak flow measurement, which may still introduce misclassification bias. Second, potential confounding factors such as environmental pollution, occupational exposure, and dietary patterns were not included, making it difficult to completely rule out residual confounding. Third, our findings are currently based solely on middle-aged and older Chinese populations. External validation in other ethnic groups and younger cohorts is needed to assess generalizability. Finally, while statistical analysis indicates a significant association between ABSI and COPD, the underlying biological mechanisms require further elucidation through basic experimental studies and longitudinal intervention research. Future integration of imaging assessments of visceral fat, inflammatory biomarker testing, and multi-omics studies may help elucidate the causal relationship between ABSI and COPD risk. 5 Conclusion In summary, this study is the first to confirm that ABSI is an independent and linear risk factor for COPD development in a large Chinese middle-aged and elderly population, and it established an efficient COPD prediction model. ABSI holds promise as a simple, low-cost, and reproducible physical measurement indicator for early identification of high-risk individuals and guiding public health interventions. This finding not only enriches the evidence base on the relationship between obesity and respiratory diseases but also provides new strategies and directions for COPD prevention and precision management. Statements and Declarations Conflict of interest statement The authors declares that there is no conflict of interest. Funding This work was supported by the Key Clinical Specialty Program of Guangdong Province (Emergency Medicine; Grant No. Yueweibanyihan [2024] No. 10); the Guangdong Yiyang Health Charity Foundation (Grant No. JZ2024087); the Foshan High-level Medical Key Specialty Program during the 14th Five-Year Plan (Grant No. FSGSP145075); the Nanhai District (Foshan) High-level Medical Key Specialty Program during the 14th Five-Year Plan (QGSP002JZK); and the Foshan Municipal Medical Research Program (Grant No. 20260287). Data availability statement The data for this study were sourced from CHARLS (https://charls.pku.edu.cn/). Ethics statement The study was approved by the Biomedical Ethics Committee of Peking University (Ethics Approval Number: IRB00001052-11015). All procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication Not applicable. Acknowledgments We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Author contributions Q.L. Conceptualization; Methodology; Investigation; Formal analysis; Data curation; Visualization; Writing - original draft; Writing - review & editing. Z.H. Conceptualization; Methodology; Investigation; Formal analysis; Data curation; Visualization; Writing - original draft; Writing - review & editing. J.L. Methodology; Software; Formal analysis; Data curation; Visualization; Writing - review & editing. L.H. Investigation; Validation; Resources. J. H. Investigation; Data curation; Validation. Y.Y. Investigation; Data curation; Validation. Y.C. Investigation; Data curation; Validation. B.Y. Investigation; Resources; Validation. S.C. Investigation; Resources; Validation. X.H. Investigation; Resources. Z.G. Conceptualization; Methodology; Supervision; Project administration; Resources; Writing - review & editing; Funding acquisition. G.W. Conceptualization; Supervision; Project administration; Resources; Writing - review & editing; Funding acquisition. References Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017 . Lancet (London, England) 2018, 392 (10159):1736-1788. Rabe KF, Watz H: Chronic obstructive pulmonary disease . The Lancet 2017, 389 (10082):1931-1940. Shah T, Press VG, Huisingh-Scheetz M, White SR: COPD Readmissions: Addressing COPD in the Era of Value-based Health Care . Chest 2016, 150 (4):916-926. Yang IA, Jenkins CR, Salvi SS: Chronic obstructive pulmonary disease in never-smokers: risk factors, pathogenesis, and implications for prevention and treatment . The Lancet Respiratory medicine 2022, 10 (5):497-511. Linden D, Guo-Parke H, Coyle PV, Fairley D, McAuley DF, Taggart CC, Kidney J: Respiratory viral infection: a potential "missing link" in the pathogenesis of COPD . European respiratory review : an official journal of the European Respiratory Society 2019, 28 (151). Després JP: Body fat distribution and risk of cardiovascular disease: an update . Circulation 2012, 126 (10):1301-1313. Piché ME, Tchernof A, Després JP: Obesity Phenotypes, Diabetes, and Cardiovascular Diseases . Circulation research 2020, 126 (11):1477-1500. Higbee DH, Granell R, Davey Smith G, Dodd JW: Prevalence, risk factors, and clinical implications of preserved ratio impaired spirometry: a UK Biobank cohort analysis . The Lancet Respiratory medicine 2022, 10 (2):149-157. World Obesity Atlas 2025 [https://data.worldobesity.org/publications/?cat=23] Després JP: Cardiovascular disease under the influence of excess visceral fat . Critical pathways in cardiology 2007, 6 (2):51-59. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA et al : Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study . Circulation 2007, 116 (1):39-48. Ellulu MS, Patimah I, Khaza'ai H, Rahmat A, Abed Y: Obesity and inflammation: the linking mechanism and the complications . Archives of medical science : AMS 2017, 13 (4):851-863. Marseglia L, Manti S, D'Angelo G, Nicotera A, Parisi E, Di Rosa G, Gitto E, Arrigo T: Oxidative stress in obesity: a critical component in human diseases . International journal of molecular sciences 2014, 16 (1):378-400. Hibbert K, Rice M, Malhotra A: Obesity and ARDS . Chest 2012, 142 (3):785-790. Lin CK, Lin CC: Work of breathing and respiratory drive in obesity . Respirology (Carlton, Vic) 2012, 17 (3):402-411. Zhang R, Hong J, Wu Y, Lin L, Chen S, Xiao Y: Joint association of triglyceride glucose index (TyG) and a body shape index (ABSI) with stroke incidence: a nationwide prospective cohort study . Cardiovascular diabetology 2025, 24 (1):7. Zheng X, Zhang W, Yang F, Wang L, Yu B, Liang B: Evaluative performance of TyG-ABSI versus traditional indices in relation to cardiovascular disease and mortality: evidence from the U.S. NHANES . Cardiovascular diabetology 2025, 24 (1):344. Ji M, Zhang S, An R: Effectiveness of A Body Shape Index (ABSI) in predicting chronic diseases and mortality: a systematic review and meta-analysis . Obesity reviews : an official journal of the International Association for the Study of Obesity 2018, 19 (5):737-759. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G: Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS) . International journal of epidemiology 2014, 43 (1):61-68. Agustí A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M et al : Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary . The European respiratory journal 2023, 61 (4). Ni J, Huang JX, Wang P, Huang YX, Yin KJ, Tian T, Cen H, Sui C, Pan HF: Arthritis and incident pulmonary diseases in middle-aged and elderly Chinese: a longitudinal population-based study . Clinical rheumatology 2023, 42 (3):687-693. Jin C, Zhang T, Li Y, Shi W: Early-Life Exposure to Malnutrition From the Chinese Famine on Risk of Asthma and Chronic Obstructive Pulmonary Disease in Adulthood . Frontiers in nutrition 2022, 9 :848108. Lei J, Qu T, Cha L, Tian L, Qiu F, Guo W, Cao J, Sun C, Zhou B: Clinicopathological characteristics of pheochromocytoma/paraganglioma and screening of prognostic markers . Journal of surgical oncology 2023, 128 (4):510-518. Zhang JA, Zhou XY, Huang D, Luan C, Gu H, Ju M, Chen K: Development of an Immune-Related Gene Signature for Prognosis in Melanoma . Frontiers in oncology 2020, 10 :602555. Kim J JY, Shon J, Myung H, Jo H, Choi S, Heo J, Jee M: jstable: Create Tables from Different Types of Regression [CP/OL]. R package, v1.3.13, 2025. In . ; 2025. Inoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone WD, Endo T, Nizuma K, Tominaga T: XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury . Neurotrauma reports 2020, 1 (1):8-16. Zhu H, Hu H, Hao B, Zhan W, Yan T, Zhang J, Wang S, Hu H, Zhang T: Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients . Technology in cancer research & treatment 2024, 23 :15330338241263434. Candelli M, Pignataro G, Saviano A, Ojetti V, Gabrielli M, Piccioni A, Gullì A, Antonelli M, Gasbarrini A, Franceschi F: Is BMI Associated with COVID-19 Severity? A Retrospective Observational Study . Current medicinal chemistry 2023, 30 (39):4466-4478. Bhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L: Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK . The lancet Diabetes & endocrinology 2018, 6 (12):944-953. Cho GJ, Yoo HJ, Hwang SY, Choi J, Lee KM, Choi KM, Baik SH, Han SW, Kim T: Differential relationship between waist circumference and mortality according to age, sex, and body mass index in Korean with age of 30-90 years; a nationwide health insurance database study . BMC medicine 2018, 16 (1):131. Krakauer NY, Krakauer JC: A new body shape index predicts mortality hazard independently of body mass index . PloS one 2012, 7 (7):e39504. Wada H, Ikeda A, Maruyama K, Yamagishi K, Barnes PJ, Tanigawa T, Tamakoshi A, Iso H: Low BMI and weight loss aggravate COPD mortality in men, findings from a large prospective cohort: the JACC study . Scientific reports 2021, 11 (1):1531. Claudel SE, Verma A: Association between adipose deposition and mortality among adults without major cardiovascular risk factors . Diabetes & metabolism 2025, 51 (1):101595. Shafran I, Krakauer NY, Krakauer JC, Goshen A, Gerber Y: The predictive ability of ABSI compared to BMI for mortality and frailty among older adults . Frontiers in nutrition 2024, 11 :1305330. Orsi E, Solini A, Penno G, Bonora E, Fondelli C, Trevisan R, Vedovato M, Cavalot F, Lamacchia O, Haxhi J et al : Body mass index versus surrogate measures of central adiposity as independent predictors of mortality in type 2 diabetes . Cardiovascular diabetology 2022, 21 (1):266. Shirai K: Should the Definition of Metabolic Syndrome be Reconsidered from the Aspect of Arterial Stiffness? Journal of atherosclerosis and thrombosis 2022, 29 (12):1701-1703. Nagayama D, Watanabe Y, Yamaguchi T, Suzuki K, Saiki A, Fujishiro K, Shirai K: Issue of Waist Circumference for the Diagnosis of Metabolic Syndrome Regarding Arterial Stiffness: Possible Utility of a Body Shape Index in Middle-Aged Nonobese Japanese Urban Residents Receiving Health Screening . Obesity facts 2022, 15 (2):160-169. Iyengar NM, Gucalp A, Dannenberg AJ, Hudis CA: Obesity and Cancer Mechanisms: Tumor Microenvironment and Inflammation . Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2016, 34 (35):4270-4276. Rabec C, Janssens JP, Murphy PB: Ventilation in the obese: physiological insights and management . European respiratory review : an official journal of the European Respiratory Society 2025, 34 (176). Huang L, Wang ST, Kuo HP, Delclaux C, Jensen ME, Wood LG, Costa D, Nowakowski D, Wronka I, Oliveira PD et al : Effects of obesity on pulmonary function considering the transition from obstructive to restrictive pattern from childhood to young adulthood . Obesity reviews : an official journal of the International Association for the Study of Obesity 2021, 22 (12):e13327. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 26 Apr, 2026 Editor invited by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 07 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9196992","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634823151,"identity":"f09265ef-d171-4c51-8c5c-623e0149a817","order_by":0,"name":"Quankun Lv","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Quankun","middleName":"","lastName":"Lv","suffix":""},{"id":634823152,"identity":"d2759c49-d568-4768-90cf-5cbc30d6822d","order_by":1,"name":"Zihao Huang","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Huang","suffix":""},{"id":634823153,"identity":"0d82b0fe-42a9-47f8-827d-894f76eeaa57","order_by":2,"name":"Jiahao Liu","email":"","orcid":"","institution":"Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Liu","suffix":""},{"id":634823154,"identity":"4a86318e-ede9-4569-a51b-526d269fdf08","order_by":3,"name":"Lisha Hu","email":"","orcid":"","institution":"Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lisha","middleName":"","lastName":"Hu","suffix":""},{"id":634823155,"identity":"053cf567-ce38-40ff-ba51-de898c4ddae0","order_by":4,"name":"Jiaxian Huo","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiaxian","middleName":"","lastName":"Huo","suffix":""},{"id":634823156,"identity":"220ccbf2-f9b0-4f7e-8058-ec9d91749d61","order_by":5,"name":"Yi Ye","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Ye","suffix":""},{"id":634823157,"identity":"d5c0ca5e-d764-432a-a026-7c0e2a2aa975","order_by":6,"name":"Yanglin Cai","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yanglin","middleName":"","lastName":"Cai","suffix":""},{"id":634823158,"identity":"8df6da4e-95ec-4ee7-984b-2759a0494d00","order_by":7,"name":"Bin Yang","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yang","suffix":""},{"id":634823159,"identity":"383ce869-b148-42d8-9ce4-fc158385dd38","order_by":8,"name":"Shengan Chen","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shengan","middleName":"","lastName":"Chen","suffix":""},{"id":634823160,"identity":"918b3201-6ed1-4b07-9309-3a0a19cf2b3b","order_by":9,"name":"Xinming Huang","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinming","middleName":"","lastName":"Huang","suffix":""},{"id":634823161,"identity":"b30f0aa6-4ca4-4a37-944f-f87a40066838","order_by":10,"name":"Ziyun Guan","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ziyun","middleName":"","lastName":"Guan","suffix":""},{"id":634823162,"identity":"21ee3895-f4a2-417f-8d13-93980b4f70fa","order_by":11,"name":"Guoxin Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPghlA+WyEaEFqiYNiJlJ03KYFC0SOWaSX9vO2/PPyD/A8KHsMAP/7AbCWqRlztxOnHEjmYFxxrnDDBJ3DhDSkrtNWqLidoKBRDIDM2/bYQYDiQRitBicswdr+UusFskPFQcYN4C0MBKlhef9Z2uGM8mJM848NjjYcy6dR+IGAS387GmJN3+22dnztyc+fPCjzFqOfwYBLQwCCQzMPFD2ASDmwaMWZs0BBsYfhJWNglEwCkbBSAYAHRs79bKgEyAAAAAASUVORK5CYII=","orcid":"","institution":"South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Guoxin","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-23 07:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9196992/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9196992/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108942630,"identity":"6d7b54d3-b53c-4458-a982-8f14d3ee48ae","added_by":"auto","created_at":"2026-05-11 05:42:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111091,"visible":true,"origin":"","legend":"\u003cp\u003eThe inclusion and exclusion flowchart of this work\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9196992/v1/90f89d2fdd9de0c28f41ab28.png"},{"id":108942634,"identity":"41dc4856-4ee4-4793-b510-2e9ba39212ff","added_by":"auto","created_at":"2026-05-11 05:42:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136930,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between a body shape index (ABSI) and the risk of chronic obstructive pulmonary disease (COPD) shows a positive linear pattern. Cumulative incidence curves of COPD for participants with varying ABSI levels, grouped according to the median (A) and tertiles (B). (C) Restricted cubic spline (RCS) curve (dose-response relationship).\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9196992/v1/9e5b4ea7b29a920838696d7f.png"},{"id":108942633,"identity":"afbb4ee5-c3be-42b1-b5f1-f7798b2759e5","added_by":"auto","created_at":"2026-05-11 05:42:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103686,"visible":true,"origin":"","legend":"\u003cp\u003eMachine learning. (A) Subgroup analysis. (B) XGBoost was used to determine the significance of each covariate.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9196992/v1/39975fb0b3d49f91532bdfbf.png"},{"id":108942596,"identity":"a01e8bd2-b0f3-4643-b8cb-4cb57221b9d9","added_by":"auto","created_at":"2026-05-11 05:41:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215403,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nomogram. (A) nomogram. (B) calibration curve. (C) 2-year receiver operating characteristic (ROC) curve. (D) 4-year curve. ROC,receiver operating characteristic.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9196992/v1/af8e561113d921dd68789f11.png"},{"id":108942836,"identity":"2c4587cd-6b7b-4334-abf6-2818e949e996","added_by":"auto","created_at":"2026-05-11 05:42:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1310239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9196992/v1/bc305c91-e574-44bc-87a9-0a0dbf662309.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between a body shape index and the chronic obstructive pulmonary disease among middle-aged and elderly individuals in China: insights from CHARLS","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) ranks among the leading causes of death globally, imposing a heavy social and economic burden[1\u0026ndash;3]. Despite progress in primary prevention measures such as tobacco control, air quality improvement, and enhanced occupational protection, the prevalence and mortality rates of COPD continue to rise. Its onset is significantly associated with multiple factors including smoking, air pollution, occupational dust exposure, genetic factor, and respiratory infections[4, 5]. However, these known risk factors cannot fully explain its high prevalence. Therefore, developing low-cost, reproducible, and easily scalable indicators for early identification and prediction of high-risk populations for COPD holds urgent practical significance for slowing disease progression and reducing societal and healthcare burdens.\u003c/p\u003e \u003cp\u003eObesity is a major contributing factor to numerous chronic diseases and is significantly associated with cardiovascular disease, diabetes, and respiratory disorders[6\u0026ndash;8]. According to the World Obesity Atlas 2025, the global obesity problem is becoming increasingly severe.By 2030, it is estimated that more than 29\u0026nbsp;billion adults will be overweight, with 11\u0026nbsp;billion reaching the level of obesity : 4.87\u0026nbsp;billion men and 6.43\u0026nbsp;billion women[9]. Central obesity is more strongly associated with visceral fat accumulation[10, 11]. Adiposity exert not only triggers systemic inflammatory responses[12] and oxidative stress[13] but also causes changes in respiratory mechanics, increasing the risk of respiratory diseases[14, 15]. Among traditional assessment metrics, while Body Mass Index (BMI) and waist circumference reflect overall obesity levels, they struggle to accurately evaluate fat distribution characteristics. To address this, researchers have proposed the Body Shape Index (ABSI) as a metric for assessing health risks. Combining waist circumference (WC), BMI, and height measurements, ABSI focuses on quantifying the relationship between waist size and body shape to more accurately reflect abdominal fat distribution. Its formula is calculated as WC divided by (BMI\u0026sup2;/3) \u0026times; (height\u0026sup1;/\u0026sup2;)[16]. Previous studies have shown that compared with traditional indicators, ABSI and its derived indicators have obvious advantages in predicting adverse outcomes of cardiovascular diseases and other conditions[17, 18]. However, whether elevated ABSI levels increase the risk of COPD remains inconclusive.\u003c/p\u003e \u003cp\u003eBased on this, this study utilized nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2015 to conduct a longitudinal analysis of middle-aged and older adults aged 45 and above. It assessed the association between ABSI and the risk of developing COPD and performed subgroup analyses across different populations. Furthermore, by integrating machine learning and logistic regression methods to identify significant variables, we constructed an ABSI-based COPD risk prediction nomogram. This aims to provide a simple and reliable tool for early identification of high-risk individuals and the development of targeted public health interventions, while also offering crucial insights into how obesity and body fat distribution impact respiratory health.\u003c/p\u003e"},{"header":"2 Materials and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and participants\u003c/h2\u003e \u003cp\u003eThis cohort study relied on data obtained from CHARLS database. CHARLS is a prospective cohort study that included individuals aged 45 and above from 28 regions in China[19], with the aims of analyzing population aging and facilitating multidisciplinary research about aging in China. To ensure sample representativeness, CHARLS baseline survey, conducted in 2011, encompassed 150 county-level units and 450 village-level units. involving 17,708 individuals from 10,257 households. Following baseline survey, participants were followed up every 2\u0026ndash;3 years. For the present study, data from three survey waves (2011, 2013, and 2015) were included, with the 2011 wave designated as the baseline and subsequent waves used to determine the timing of outcome onset and follow-up duration. The Biomedical Ethics Committee of Peking University (Ethics Approval Number: IRB00001052-11015) gave their stamp of approval to this study, and all subjects gave written informed permission. This study enrolled 35,516 participants aged over 45 years from three survey waves conducted in 2011, 2013, and 2015. Participants were excluded based on the following criteria: missing data on the A Body Shape Index (ABSI, n\u0026thinsp;=\u0026thinsp;204), smoking (n\u0026thinsp;=\u0026thinsp;49), sleep time (n\u0026thinsp;=\u0026thinsp;144), hypertension (n\u0026thinsp;=\u0026thinsp;43), or diabetes (n\u0026thinsp;=\u0026thinsp;67); presence of chronic obstructive pulmonary disease (COPD) at baseline (n\u0026thinsp;=\u0026thinsp;33); and with no follow-up data available (n\u0026thinsp;=\u0026thinsp;1,761). After these exclusions, a total of 9,611 participants with baseline data and at least one subsequent wave of follow-up information were involved in final study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Calculation of ABSI\u003c/h2\u003e \u003cp\u003eOn physical examination, a vertical stadiometer was used to measure height, and a calibrated scale was employed for weight measurement. Waist circumference (WC) was assessed with a flexible measuring tape, applied horizontally around waist at the level of umbilicus while participants stood in an upright, relaxed posture with feet shoulder-width apart. Then used the following formula to determine body mass index (BMI): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{BMI=weight(kg)/}{\\text{[height}\\left(\\text{m}\\right)\\text{]}}^{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e. The calculation of the ABSI necessitates the use of body mass index (BMI), as BMI serves as an integral component of the ABSI formula. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ABSI=WC(cm)\u0026times;}{\\text{[}{\\text{BMI}}^{\\frac{\\text{2}}{\\text{3}}}\\text{\u0026times;}{\\text{height(m)}}^{\\frac{\\text{1}}{\\text{2}}}\\text{]}}^{\\text{-1}}\\)\u003c/span\u003e\u003c/span\u003e[16].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcome ascertainment\u003c/h2\u003e \u003cp\u003eThe outcome of this study was COPD. According to the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) report[20], airway abnormalities (bronchitis, bronchiolitis) and/or alveolar abnormalities (emphysema) cause persistent and progressive airflow limitation in COPD, a diverse lung disorder characterized by chronic respiratory symptoms (e.g., dyspnea, cough, and sputum production). In CHARLS, information on chronic lung disease was derived from participants\u0026rsquo; self-reports. Participants who responded to the question \"Have you ever been diagnosed with chronic lung disease (excluding tumor or cancer) by a doctor?\" Patients who answered \"yes\" were defined as having chronic lung disease. Pulmonary function was assessed using a peak flow meter, with Peak Expiratory Flow (PEF) serving as the indicator to evaluate lung function. Those individuals who have both chronic lung disease and PEF less than 60 L/min are classified as COPD patients[21, 22].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eTo account for the influence of potential confounders, a set of key covariates were incorporated into this study, including: age, gender, educational attainment (categorized as: incomplete primary school, primary school, middle school, and high school or above), place of residence (rural vs. urban), marital status (categorized as: unmarried or separated, marriage or cohabitation ), smoking status, drinking status, sleep duration, hypertension, and diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo summarize the individuals' characteristics, descriptive statistics were utilized. Continuous data were first tested for normality. Data adhering to a normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and categorical variables were described using frequencies and percentages (%). When comparing data across groups, normally distributed continuous variables were analyzed via one-way analysis of variance (ANOVA), and categorical variables were tested using the chi-square test.\u003c/p\u003e \u003cp\u003eTo evaluate the association between ABSI and COPD risk, Cox proportional hazards regression models were used to estimate hazard ratios (HRs) along with their respective 95% confidence intervals (CIs). Three hierarchical regression models were established: \u003cb\u003eModel 1\u003c/b\u003e is unadjusted; \u003cb\u003eModel 2\u003c/b\u003e is a minimally adjusted version of \u003cb\u003eModel 1\u003c/b\u003e, adjusted further for age, gender, educational background, place of residence, and marital status; and \u003cb\u003eModel 3\u003c/b\u003e is a fully adjusted model that builds on \u003cb\u003eModel 2\u003c/b\u003e by adding adjustments for smoking, drinking, sleep duration, hypertension, and diabetes. Furthermore, to verify the consistency of conclusions across different populations, subgroup analyses were performed based on \u003cb\u003eModel 3\u003c/b\u003e to assess the modifying effects of potential confounders in this study.\u003c/p\u003e \u003cp\u003eParticipants were stratified into four groups according to ABSI quartiles. Cumulative incidence curves were generated using the survminer package (v0.5.0)[23] to visualize the cumulative incidence probabilities of COPD across these groups, with intergroup differences assessed via the log-rank test. To explore potential nonlinear associations between variables, a restricted cubic spline (RCS) model was constructed using rms package (v8.0-0)[24]. Stratified Cox regression analyses were performed using the jstable package (v1.3.13)[25], with subgroup analyses conducted based on \u003cb\u003eModel 3\u003c/b\u003e to evaluate the potential modifying effects of different population characteristics on the association of interest. The xgboost package (v1.7.11)[26] was used to build an XGBoost model, which quantified the relative importance of selected variables for COPD risk prediction. Additionally, a nomogram was developed using the regplot package (v1.1)[27] to assess intrinsic capacity-related risk, with relevant predictors incorporated into the model. R software (v4.4.3) was used to carry out all statistical analyses, with a two-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline information statistics\u003c/h2\u003e \u003cp\u003eThe baseline analysis included 9,611 participants, whose mean age was 59.25\u0026thinsp;\u0026plusmn;\u0026thinsp;8.82 years. For educational attainment, 8,654 individuals in the less than junior high school education group, accounting for approximately 90.04%. Moreover, the participants living in rural (64.89%) areas were nearly twice as many as those in urban areas (35.11%). Additionally, the majority of the participants did not drink alcohol (67.35%) or smoke (68.83%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As presented in the table, all covariates exhibited significant differences across the ABSI categories (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, as ABSI increased, the prevalence of hypertension or diabetes among participants also rose. Furthermore, as the ABSI increased from the first quartile (Q1) to the fourth quartile (Q4), the PEF exhibited a significant downward trend, whereas Waist showed a significant upward trend.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline statistics table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9611\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,403\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,403\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,402\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,403\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePEF(L/min, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296.593 (123.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e317.581 (123.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e314.922 (125.886)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e297.358 (122.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e256.511 (112.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist (cm, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.592 (10.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.025 (8.421)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.761 (8.707)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.806 (9.258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.777 (9.653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight (m, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.582 (0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.587 (0.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.592 (0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.589 (0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.561 (0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.580 (3.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.708 (4.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.835 (3.568)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.806 (3.643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.973 (3.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.253 (8.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.255 (7.820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.623 (8.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.522 (8.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.613 (9.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eABSI (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.316 (0.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.653 (0.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.134 (0.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.442 (0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.034 (0.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5077 (52.825)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1188 (49.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1119 (46.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1205 (50.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1565 (65.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4534 (47.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1215 (50.562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1284 (53.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1197 (49.833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e838 (34.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eless than junior high school education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8654 (90.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2091 (87.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2147 (89.347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2157 (89.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2259 (94.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh school and vocational training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e844 (8.782)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270 ( 11.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e231 (9.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e216 (8.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e127 (5.285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113 (1.176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (1.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25 (1.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (1.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17 (0.707)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban-rural (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eurban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3374 (35.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e778 (32.376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e823 (34.249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e907 (37.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e866 (36.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003erural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6237 (64.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1625 (67.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1580 (65.751)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1495 (62.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1537 (63.962)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunmarried or separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1517 (15.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348 (14.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e330 (13.733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e360 (14.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e479 (19.933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarriage or cohabitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8094 (84.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2055 (85.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2073 (86.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2042 (85.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1924 (80.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6615 (68.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1610 (67.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1584 (65.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1628 (67.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1793 (74.615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2996 (31.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e793 (33.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e819 (34.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e774 (32.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e610 (25.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6473 (67.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1588 (66.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1523 (63.379)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1610 (67.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1752 (72.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3138 (32.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e815 (33.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e880 (36.621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e792 (32.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e651 (27.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep night (hours, mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.349 (1.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.403 (1.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.434 (1.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.355 (1.849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.204 (2.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7139 (74.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1865 (77.611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1842 (76.654)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1774 (73.855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1658 (68.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2472 (25.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e538 (22.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e561 (23.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e628 (26.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e745 (31.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9025 (93.903)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2299 (95.672)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2292 (95.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2233 (92.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2201 (91.594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e586 (6.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104 (4.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111 (4.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169 (7.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e202 (8.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ABSI was a risk factor for COPD\u003c/h2\u003e \u003cp\u003eTo quantify the link between ABSI and the risk of COPD, Cox proportional hazards regression models were employed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with T1 (Referenc), T3 had a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 across all three models, indicating that risk association between ABSI and COPD remained stable in T3 subgroup. The HR with 95% CI was 5.815 (2.445\u0026ndash;13.832) for \u003cb\u003eModel 1\u003c/b\u003e, 3.460 (1.361\u0026ndash;8.793) for the adjusted \u003cb\u003eModel 2\u003c/b\u003e, and 3.491 (1.368\u0026ndash;8.913) for the fully adjusted \u003cb\u003eModel 3\u003c/b\u003e, which revealed that ABSI was an independent risk factor for incident COPD: for each unit increase in ABSI, the risk of developing COPD increased by 72.4% (fully adjusted \u003cb\u003eModel 3\u003c/b\u003e). Subsequently, cumulative incidence curves for COPD were plotted to compare individuals with different ABSI levels, where participants were stratified into high- and low-ABSI groups using either the median or tertiles as cutoffs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). These curves further demonstrated that a higher ABSI value was associated with a significantly higher cumulative incidence of COPD (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Interestingly, in the marital status indicator, a significant interaction effect was detected through the overall analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), however, in the subgroup of unmarried or separated individuals, ABSI does not act as a risk factor for COPD in this group. The dose-response relationship between ABSI and COPD risk across the aforementioned models was examined using RCS analysis. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, the overall association between ABSI and COPD risk exhibited a linear pattern, and it was a positive correlation. Notably, a significant linear increase in COPD risk associated with ABSI emerged when ABSI exceeded 7.69.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvival model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.292(1.869\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.586E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.673(1.243\u0026ndash;2.253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.971E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.724(1.278\u0026ndash;2.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.589E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI_Tertiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.765(0.653\u0026ndash;4.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.631E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.47(0.546\u0026ndash;3.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.461E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.465(0.537\u0026ndash;3.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.558E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.815(2.445\u0026ndash;13.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.829E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.46(1.361\u0026ndash;8.793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.101E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.491(1.368\u0026ndash;8.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.933E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eABSI: Although A Body Shape Index; HR: Hazard Ratio; CI: Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted based on the covariates included in the models described above to evaluate whether the link between ABSI and COPD risk altered among prespecified subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The findings indicated that there was a positive relationship between ABSI and COPD risk remained consistent with primary analysis in most subgroups, including participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, those with education below junior high school, rural residents, individuals with married or cohabiting status, non-smokers, and those without diabetes. Notably, the interaction effect of ABSI on COPD risk disappeared among unmarried individuals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Machine learning and construction of nomogram\u003c/h2\u003e \u003cp\u003eAn XGBoost model was constructed to assess the relative importance of the selected variables\u0026mdash;including ABSI, gender, age, educational attainment, place of residence, smoking status, marital status, drinking status, sleep duration, hypertension, and diabetes\u0026mdash;with respect to COPD risk. This model generated a ranking of variable importance, and notably, ABSI ranked among the top 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Building on the machine learning results, we incorporated the top 5 most important covariates\u0026mdash;ABSI, age, sleep hours, married, and smoking\u0026mdash;to develop a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), which was used to predict the 2-year and 4-year cumulative incidence of COPD. Notably, a higher total score from the nomogram corresponded to a higher cumulative incidence of COPD. We also generated a calibration curve for the constructed nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The slope of this curve was close to 1, indicating that the predicted probabilities of COPD incidence from the nomogram were highly consistent with the actual observed incidences, confirming the model\u0026rsquo;s good predictive accuracy. Then we generated receiver operating characteristic (ROC) curves to further validate nomogram, ROC curves of these different models for the 2-year and 4-year endpoints are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;D. Notably, area under the curve (AUC) values of the nomogram model for the 2-year and 4-year predictions were all greater than 0.8, demonstrating that the nomogram has excellent performance in predicting an individual\u0026rsquo;s disease risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study conducted a prospective analysis of 9,611 middle-aged and older adults aged 45 years and above using nationally representative cohort data from the China Health and Retirement Longitudinal Study (CHARLS). It systematically confirmed for the first time that ABSI is a significant independent risk factor for COPD development, with a stable positive linear correlation between the two. Multivariate Cox regression results showed that after fully adjusting for confounding factors including age, gender, education level, lifestyle, and metabolic diseases, each additional unit of ABSI increased the risk of COPD incidence by approximately 72.4% (HR\u0026thinsp;=\u0026thinsp;1.724, 95% CI: 1.278\u0026ndash;2.326). After stratifying ABSI scores, the highest quartile group exhibited a 3.49-fold higher COPD risk compared to the lowest quartile group (HR\u0026thinsp;=\u0026thinsp;3.491, 95% CI: 1.368\u0026ndash;8.913). Restricted cubic splines (RCS) and cumulative incidence curves jointly indicated a continuous, linear dose-response relationship between ABSI and COPD risk. This finding highlights the potential advantage of ABSI in identifying respiratory disease risk.\u003c/p\u003e \u003cp\u003eBMI is widely used to assess obesity and disease risk due to its simplicity of calculation[28, 29]. However, a rapidly growing body of research has revealed that individuals classified as obese based on BMI actually exhibit better outcomes in multiple diseases\u0026mdash;a phenomenon known as the \u0026ldquo;obesity paradox\u0026rdquo;[30, 31]. In a large cohort study involving 110,585 individuals aged 40\u0026ndash;79 years with 19.1 years of follow-up, each one-standard deviation (SD) increase in BMI was associated with a 52% reduction in the risk of COPD mortality (HR\u0026thinsp;=\u0026thinsp;0.48, 95% CI: 0.41\u0026ndash;0.57)[32]. This phenomenon primarily stems from BMI's inability to distinguish fat from muscle mass and its limited capacity to reflect central obesity, thereby constraining the accuracy of disease risk prediction[33]. The Adiposity Body Size Index (ABSI), which adjusts waist circumference for height and weight, more precisely characterizes body fat distribution and visceral fat burden[34]. A prospective Italian study demonstrated that ABSI does not exhibit the obesity paradox and outperforms waist circumference in predicting mortality risks associated with central obesity[35]. Recent research also recommends incorporating ABSI into the definition of metabolic syndrome[36, 37]. This study also yielded similar findings: elevated ABSI was associated with increased waist circumference (79.0 cm to 90.8 cm, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and decreased lung function PEF (317.6 L/min to 256.5 L/min, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that abdominal fat accumulation is significantly associated with impaired lung function.The high-ABSI group exhibited a decrease in BMI, further highlighting the limitations of BMI. Overall, ABSI better reflects central obesity characteristics than BMI or waist circumference, demonstrates greater predictive efficacy for metabolic and respiratory disease risks, and avoids the obesity paradox phenomenon.\u003c/p\u003e \u003cp\u003eFrom a pathophysiological perspective, the accumulation of visceral fat reflected by ABSI may contribute to the onset and progression of COPD through multiple pathways. First,visceral fat, acting as an active endocrine organ, continuously secretes pro-inflammatory cytokines (such as TNF-αand IL-6) and disrupts adipokine balance (leptin/adiponectin), inducing systemic low-grade inflammation and oxidative stress[2, 12, 38].This exacerbates chronic airway inflammation and lung tissue remodeling. Second, abdominal fat accumulation mechanically restricts diaphragmatic movement and thoracic expansion, reducing lung capacity and increasing airway resistance, thereby promoting long-term respiratory mechanics abnormalities and functional decline[39, 40]. Furthermore, this study observed significantly elevated prevalence of hypertension and diabetes in the high ABSI group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting insulin resistance and metabolic disorders may bridge the association between ABSI and COPD risk. Collectively, these biological mechanisms provide a plausible explanation for the linear positive correlation between ABSI and COPD risk.\u003c/p\u003e \u003cp\u003eNotably, the dose-response curve in this study revealed a significant increase in COPD risk when ABSI exceeded 7.69, suggesting this threshold serves as a critical reference point for clinical screening to identify high-risk individuals early and implement interventions. Subgroup analysis results indicate that the positive association between ABSI and COPD risk remains consistent across most populations, including those aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, non-smokers, individuals with lower education levels, and those without diabetes, demonstrating good generalizability and stability. However, marital status exhibited a significant interaction effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014): the association between ABSI and COPD risk was most pronounced among married or cohabiting individuals (HR\u0026thinsp;=\u0026thinsp;3.10, 95% CI: 2.17\u0026ndash;4.44), whereas this association failed to reach statistical significance among unmarried or separated individuals. We hypothesize that married individuals, benefiting from spousal support, exhibit advantages in dietary management, health behaviors, and disease monitoring, thereby amplifying the biological risk reflected by ABSI. Conversely, unmarried or widowed individuals may experience diminished social support, heightened psychological stress, elevated inflammation levels, and inadequate health behaviors, partially masking the independent effect of ABSI. These findings suggest that psychosocial factors may modulate the relationship between ABSI and COPD through complex behavioral and inflammatory pathways, warranting further investigation in future studies.\u003c/p\u003e \u003cp\u003eOverall, the strengths of this study lie in its systematic evaluation and confirmation of the linear relationship between ABSI as an independent risk factor and COPD incidence based on CHARLS data. Furthermore, machine learning methods were employed to construct an ABSI-based predictive nomogram, which demonstrated good predictive performance.This provides strong evidence for the application of ABSI in clinical practice and public health screening, and offers a feasible approach for identifying high-risk populations for COPD through physical measurement indicators and developing personalized prevention strategies.However, this study also has certain limitations. First, although CHARLS is a prospective cohort, this analysis is retrospective, and COPD diagnosis relies on self-reporting and peak flow measurement, which may still introduce misclassification bias. Second, potential confounding factors such as environmental pollution, occupational exposure, and dietary patterns were not included, making it difficult to completely rule out residual confounding. Third, our findings are currently based solely on middle-aged and older Chinese populations. External validation in other ethnic groups and younger cohorts is needed to assess generalizability. Finally, while statistical analysis indicates a significant association between ABSI and COPD, the underlying biological mechanisms require further elucidation through basic experimental studies and longitudinal intervention research. Future integration of imaging assessments of visceral fat, inflammatory biomarker testing, and multi-omics studies may help elucidate the causal relationship between ABSI and COPD risk.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, this study is the first to confirm that ABSI is an independent and linear risk factor for COPD development in a large Chinese middle-aged and elderly population, and it established an efficient COPD prediction model. ABSI holds promise as a simple, low-cost, and reproducible physical measurement indicator for early identification of high-risk individuals and guiding public health interventions. This finding not only enriches the evidence base on the relationship between obesity and respiratory diseases but also provides new strategies and directions for COPD prevention and precision management.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Clinical Specialty Program of Guangdong Province (Emergency Medicine; Grant No. Yueweibanyihan [2024] No. 10); the Guangdong Yiyang Health Charity Foundation (Grant No. JZ2024087); the Foshan High-level Medical Key Specialty Program during the 14th Five-Year Plan (Grant No. FSGSP145075); the Nanhai District (Foshan) High-level Medical Key Specialty Program during the 14th Five-Year Plan (QGSP002JZK); and the Foshan Municipal Medical Research Program (Grant No. 20260287).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were sourced from CHARLS (https://charls.pku.edu.cn/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Biomedical Ethics Committee of Peking University (Ethics Approval Number: IRB00001052-11015). All procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ.L. Conceptualization; Methodology; Investigation; Formal analysis; Data curation; Visualization; Writing - original draft; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eZ.H. Conceptualization; Methodology; Investigation; Formal analysis; Data curation; Visualization; Writing - original draft; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eJ.L. Methodology; Software; Formal analysis; Data curation; Visualization; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eL.H. Investigation; Validation; Resources.\u003c/p\u003e\n\u003cp\u003eJ. H. Investigation; Data curation; Validation.\u003c/p\u003e\n\u003cp\u003eY.Y. Investigation; Data curation; Validation.\u003c/p\u003e\n\u003cp\u003eY.C. Investigation; Data curation; Validation.\u003c/p\u003e\n\u003cp\u003eB.Y. Investigation; Resources; Validation.\u003c/p\u003e\n\u003cp\u003eS.C. Investigation; Resources; Validation.\u003c/p\u003e\n\u003cp\u003eX.H. Investigation; Resources.\u003c/p\u003e\n\u003cp\u003eZ.G. Conceptualization; Methodology; Supervision; Project administration; Resources; Writing - review \u0026amp; editing; Funding acquisition.\u003c/p\u003e\n\u003cp\u003eG.W. Conceptualization; Supervision; Project administration; Resources; Writing - review \u0026amp; editing; Funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eGlobal, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017\u003c/strong\u003e. \u003cem\u003eLancet (London, England)\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e392\u003c/strong\u003e(10159):1736-1788.\u003c/li\u003e\n \u003cli\u003eRabe KF, Watz H: \u003cstrong\u003eChronic obstructive pulmonary disease\u003c/strong\u003e. \u003cem\u003eThe Lancet\u0026nbsp;\u003c/em\u003e2017, \u003cstrong\u003e389\u003c/strong\u003e(10082):1931-1940.\u003c/li\u003e\n \u003cli\u003eShah T, Press VG, Huisingh-Scheetz M, White SR: \u003cstrong\u003eCOPD Readmissions: Addressing COPD in the Era of Value-based Health Care\u003c/strong\u003e. \u003cem\u003eChest\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e150\u003c/strong\u003e(4):916-926.\u003c/li\u003e\n \u003cli\u003eYang IA, Jenkins CR, Salvi SS: \u003cstrong\u003eChronic obstructive pulmonary disease in never-smokers: risk factors, pathogenesis, and implications for prevention and treatment\u003c/strong\u003e. \u003cem\u003eThe Lancet Respiratory medicine\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e10\u003c/strong\u003e(5):497-511.\u003c/li\u003e\n \u003cli\u003eLinden D, Guo-Parke H, Coyle PV, Fairley D, McAuley DF, Taggart CC, Kidney J: \u003cstrong\u003eRespiratory viral infection: a potential \u0026quot;missing link\u0026quot; in the pathogenesis of COPD\u003c/strong\u003e. \u003cem\u003eEuropean respiratory review : an official journal of the European Respiratory Society\u0026nbsp;\u003c/em\u003e2019, \u003cstrong\u003e28\u003c/strong\u003e(151).\u003c/li\u003e\n \u003cli\u003eDespr\u0026eacute;s JP: \u003cstrong\u003eBody fat distribution and risk of cardiovascular disease: an update\u003c/strong\u003e. \u003cem\u003eCirculation\u0026nbsp;\u003c/em\u003e2012, \u003cstrong\u003e126\u003c/strong\u003e(10):1301-1313.\u003c/li\u003e\n \u003cli\u003ePich\u0026eacute; ME, Tchernof A, Despr\u0026eacute;s JP: \u003cstrong\u003eObesity Phenotypes, Diabetes, and Cardiovascular Diseases\u003c/strong\u003e. \u003cem\u003eCirculation research\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e126\u003c/strong\u003e(11):1477-1500.\u003c/li\u003e\n \u003cli\u003eHigbee DH, Granell R, Davey Smith G, Dodd JW: \u003cstrong\u003ePrevalence, risk factors, and clinical implications of preserved ratio impaired spirometry: a UK Biobank cohort analysis\u003c/strong\u003e. \u003cem\u003eThe Lancet Respiratory medicine\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e10\u003c/strong\u003e(2):149-157.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWorld Obesity Atlas 2025\u0026nbsp;\u003c/strong\u003e[https://data.worldobesity.org/publications/?cat=23]\u003c/li\u003e\n \u003cli\u003eDespr\u0026eacute;s JP: \u003cstrong\u003eCardiovascular disease under the influence of excess visceral fat\u003c/strong\u003e. \u003cem\u003eCritical pathways in cardiology\u0026nbsp;\u003c/em\u003e2007, \u003cstrong\u003e6\u003c/strong\u003e(2):51-59.\u003c/li\u003e\n \u003cli\u003eFox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eAbdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study\u003c/strong\u003e. \u003cem\u003eCirculation\u0026nbsp;\u003c/em\u003e2007, \u003cstrong\u003e116\u003c/strong\u003e(1):39-48.\u003c/li\u003e\n \u003cli\u003eEllulu MS, Patimah I, Khaza\u0026apos;ai H, Rahmat A, Abed Y: \u003cstrong\u003eObesity and inflammation: the linking mechanism and the complications\u003c/strong\u003e. \u003cem\u003eArchives of medical science : AMS\u0026nbsp;\u003c/em\u003e2017, \u003cstrong\u003e13\u003c/strong\u003e(4):851-863.\u003c/li\u003e\n \u003cli\u003eMarseglia L, Manti S, D\u0026apos;Angelo G, Nicotera A, Parisi E, Di Rosa G, Gitto E, Arrigo T: \u003cstrong\u003eOxidative stress in obesity: a critical component in human diseases\u003c/strong\u003e. \u003cem\u003eInternational journal of molecular sciences\u0026nbsp;\u003c/em\u003e2014, \u003cstrong\u003e16\u003c/strong\u003e(1):378-400.\u003c/li\u003e\n \u003cli\u003eHibbert K, Rice M, Malhotra A: \u003cstrong\u003eObesity and ARDS\u003c/strong\u003e. \u003cem\u003eChest\u0026nbsp;\u003c/em\u003e2012, \u003cstrong\u003e142\u003c/strong\u003e(3):785-790.\u003c/li\u003e\n \u003cli\u003eLin CK, Lin CC: \u003cstrong\u003eWork of breathing and respiratory drive in obesity\u003c/strong\u003e. \u003cem\u003eRespirology (Carlton, Vic)\u0026nbsp;\u003c/em\u003e2012, \u003cstrong\u003e17\u003c/strong\u003e(3):402-411.\u003c/li\u003e\n \u003cli\u003eZhang R, Hong J, Wu Y, Lin L, Chen S, Xiao Y: \u003cstrong\u003eJoint association of triglyceride glucose index (TyG) and a body shape index (ABSI) with stroke incidence: a nationwide prospective cohort study\u003c/strong\u003e. \u003cem\u003eCardiovascular diabetology\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e24\u003c/strong\u003e(1):7.\u003c/li\u003e\n \u003cli\u003eZheng X, Zhang W, Yang F, Wang L, Yu B, Liang B: \u003cstrong\u003eEvaluative performance of TyG-ABSI versus traditional indices in relation to cardiovascular disease and mortality: evidence from the U.S. NHANES\u003c/strong\u003e. \u003cem\u003eCardiovascular diabetology\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e24\u003c/strong\u003e(1):344.\u003c/li\u003e\n \u003cli\u003eJi M, Zhang S, An R: \u003cstrong\u003eEffectiveness of A Body Shape Index (ABSI) in predicting chronic diseases and mortality: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eObesity reviews : an official journal of the International Association for the Study of Obesity\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e19\u003c/strong\u003e(5):737-759.\u003c/li\u003e\n \u003cli\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G: \u003cstrong\u003eCohort profile: the China Health and Retirement Longitudinal Study (CHARLS)\u003c/strong\u003e. \u003cem\u003eInternational journal of epidemiology\u0026nbsp;\u003c/em\u003e2014, \u003cstrong\u003e43\u003c/strong\u003e(1):61-68.\u003c/li\u003e\n \u003cli\u003eAgust\u0026iacute; A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Bourbeau J, Han MK, Martinez FJ, Montes de Oca M\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eGlobal Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary\u003c/strong\u003e. \u003cem\u003eThe European respiratory journal\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e61\u003c/strong\u003e(4).\u003c/li\u003e\n \u003cli\u003eNi J, Huang JX, Wang P, Huang YX, Yin KJ, Tian T, Cen H, Sui C, Pan HF: \u003cstrong\u003eArthritis and incident pulmonary diseases in middle-aged and elderly Chinese: a longitudinal population-based study\u003c/strong\u003e. \u003cem\u003eClinical rheumatology\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e42\u003c/strong\u003e(3):687-693.\u003c/li\u003e\n \u003cli\u003eJin C, Zhang T, Li Y, Shi W: \u003cstrong\u003eEarly-Life Exposure to Malnutrition From the Chinese Famine on Risk of Asthma and Chronic Obstructive Pulmonary Disease in Adulthood\u003c/strong\u003e. \u003cem\u003eFrontiers in nutrition\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e9\u003c/strong\u003e:848108.\u003c/li\u003e\n \u003cli\u003eLei J, Qu T, Cha L, Tian L, Qiu F, Guo W, Cao J, Sun C, Zhou B: \u003cstrong\u003eClinicopathological characteristics of pheochromocytoma/paraganglioma and screening of prognostic markers\u003c/strong\u003e. \u003cem\u003eJournal of surgical oncology\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e128\u003c/strong\u003e(4):510-518.\u003c/li\u003e\n \u003cli\u003eZhang JA, Zhou XY, Huang D, Luan C, Gu H, Ju M, Chen K: \u003cstrong\u003eDevelopment of an Immune-Related Gene Signature for Prognosis in Melanoma\u003c/strong\u003e. \u003cem\u003eFrontiers in oncology\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e10\u003c/strong\u003e:602555.\u003c/li\u003e\n \u003cli\u003eKim J JY, Shon J, Myung H, Jo H, Choi S, Heo J, Jee M: \u003cstrong\u003ejstable: Create Tables from Different Types of Regression [CP/OL]. R package, v1.3.13, 2025.\u003c/strong\u003e In\u003cem\u003e.\u003c/em\u003e; 2025.\u003c/li\u003e\n \u003cli\u003eInoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone WD, Endo T, Nizuma K, Tominaga T: \u003cstrong\u003eXGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury\u003c/strong\u003e. \u003cem\u003eNeurotrauma reports\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e1\u003c/strong\u003e(1):8-16.\u003c/li\u003e\n \u003cli\u003eZhu H, Hu H, Hao B, Zhan W, Yan T, Zhang J, Wang S, Hu H, Zhang T: \u003cstrong\u003eInsights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients\u003c/strong\u003e. \u003cem\u003eTechnology in cancer research \u0026amp; treatment\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e23\u003c/strong\u003e:15330338241263434.\u003c/li\u003e\n \u003cli\u003eCandelli M, Pignataro G, Saviano A, Ojetti V, Gabrielli M, Piccioni A, Gull\u0026igrave; A, Antonelli M, Gasbarrini A, Franceschi F: \u003cstrong\u003eIs BMI Associated with COVID-19 Severity? A Retrospective Observational Study\u003c/strong\u003e. \u003cem\u003eCurrent medicinal chemistry\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e30\u003c/strong\u003e(39):4466-4478.\u003c/li\u003e\n \u003cli\u003eBhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L: \u003cstrong\u003eAssociation of BMI with overall and cause-specific mortality: a population-based cohort study of 3\u0026middot;6 million adults in the UK\u003c/strong\u003e. \u003cem\u003eThe lancet Diabetes \u0026amp; endocrinology\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e6\u003c/strong\u003e(12):944-953.\u003c/li\u003e\n \u003cli\u003eCho GJ, Yoo HJ, Hwang SY, Choi J, Lee KM, Choi KM, Baik SH, Han SW, Kim T: \u003cstrong\u003eDifferential relationship between waist circumference and mortality according to age, sex, and body mass index in Korean with age of 30-90 years; a nationwide health insurance database study\u003c/strong\u003e. \u003cem\u003eBMC medicine\u0026nbsp;\u003c/em\u003e2018, \u003cstrong\u003e16\u003c/strong\u003e(1):131.\u003c/li\u003e\n \u003cli\u003eKrakauer NY, Krakauer JC: \u003cstrong\u003eA new body shape index predicts mortality hazard independently of body mass index\u003c/strong\u003e. \u003cem\u003ePloS one\u0026nbsp;\u003c/em\u003e2012, \u003cstrong\u003e7\u003c/strong\u003e(7):e39504.\u003c/li\u003e\n \u003cli\u003eWada H, Ikeda A, Maruyama K, Yamagishi K, Barnes PJ, Tanigawa T, Tamakoshi A, Iso H: \u003cstrong\u003eLow BMI and weight loss aggravate COPD mortality in men, findings from a large prospective cohort: the JACC study\u003c/strong\u003e. \u003cem\u003eScientific reports\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(1):1531.\u003c/li\u003e\n \u003cli\u003eClaudel SE, Verma A: \u003cstrong\u003eAssociation between adipose deposition and mortality among adults without major cardiovascular risk factors\u003c/strong\u003e. \u003cem\u003eDiabetes \u0026amp; metabolism\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e51\u003c/strong\u003e(1):101595.\u003c/li\u003e\n \u003cli\u003eShafran I, Krakauer NY, Krakauer JC, Goshen A, Gerber Y: \u003cstrong\u003eThe predictive ability of ABSI compared to BMI for mortality and frailty among older adults\u003c/strong\u003e. \u003cem\u003eFrontiers in nutrition\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e11\u003c/strong\u003e:1305330.\u003c/li\u003e\n \u003cli\u003eOrsi E, Solini A, Penno G, Bonora E, Fondelli C, Trevisan R, Vedovato M, Cavalot F, Lamacchia O, Haxhi J\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eBody mass index versus surrogate measures of central adiposity as independent predictors of mortality in type 2 diabetes\u003c/strong\u003e. \u003cem\u003eCardiovascular diabetology\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e21\u003c/strong\u003e(1):266.\u003c/li\u003e\n \u003cli\u003eShirai K: \u003cstrong\u003eShould the Definition of Metabolic Syndrome be Reconsidered from the Aspect of Arterial Stiffness?\u003c/strong\u003e \u003cem\u003eJournal of atherosclerosis and thrombosis\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e29\u003c/strong\u003e(12):1701-1703.\u003c/li\u003e\n \u003cli\u003eNagayama D, Watanabe Y, Yamaguchi T, Suzuki K, Saiki A, Fujishiro K, Shirai K: \u003cstrong\u003eIssue of Waist Circumference for the Diagnosis of Metabolic Syndrome Regarding Arterial Stiffness: Possible Utility of a Body Shape Index in Middle-Aged Nonobese Japanese Urban Residents Receiving Health Screening\u003c/strong\u003e. \u003cem\u003eObesity facts\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e15\u003c/strong\u003e(2):160-169.\u003c/li\u003e\n \u003cli\u003eIyengar NM, Gucalp A, Dannenberg AJ, Hudis CA: \u003cstrong\u003eObesity and Cancer Mechanisms: Tumor Microenvironment and Inflammation\u003c/strong\u003e. \u003cem\u003eJournal of clinical oncology : official journal of the American Society of Clinical Oncology\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e34\u003c/strong\u003e(35):4270-4276.\u003c/li\u003e\n \u003cli\u003eRabec C, Janssens JP, Murphy PB: \u003cstrong\u003eVentilation in the obese: physiological insights and management\u003c/strong\u003e. \u003cem\u003eEuropean respiratory review : an official journal of the European Respiratory Society\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e34\u003c/strong\u003e(176).\u003c/li\u003e\n \u003cli\u003eHuang L, Wang ST, Kuo HP, Delclaux C, Jensen ME, Wood LG, Costa D, Nowakowski D, Wronka I, Oliveira PD\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eEffects of obesity on pulmonary function considering the transition from obstructive to restrictive pattern from childhood to young adulthood\u003c/strong\u003e. \u003cem\u003eObesity reviews : an official journal of the International Association for the Study of Obesity\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e(12):e13327.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"A Body Shape Index, Chronic obstructive pulmonary disease, CHARLS, Nomogram, Prospective cohort study","lastPublishedDoi":"10.21203/rs.3.rs-9196992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9196992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eAlthough A Body Shape Index (ABSI) has been linked to various health outcomes, its relationship with chronic obstructive pulmonary disease (COPD) remains unclear. We therefore aimed to determine whether ABSI is independently associated with COPD prevalence and incidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eAt baseline, 9,611 participants were included. Cox models assessed the association between ABSI and COPD risk, with subgroup analyses for confounding. Cumulative incidence across ABSI levels was compared using log-rank tests. Restricted cubic splines evaluated dose-response relationships. An XGBoost- and logistic regression-based nomogram was developed for COPD risk prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eCox regression identified ABSI as an independent COPD risk factor: each ABSI unit increase raised COPD risk by 72.4% (Hazard Ratio (HR)=1.724, 95% Confidence Interval (CI):1.423–2.089) in the fully adjusted model. Cumulative incidence curve showed significantly increase COPD cumulative incidence in high-ABSI groups. Subgroup analyses found consistent ABSI effects mostly. RCS shown the positive linear dose-response connection between ABSI and COPD risk, and the ABSI was the second most significant variable in predicting COPD. The nomogram based on ABSI and other important covariates showed excellent performance, which had good clinical prediction ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eIn middle-aged and older Chinese adults, ABSI is an independent risk factor for COPD, showing a positive linear dose-response relationship with COPD risk. The ABSI-based nomogram performs well, suggesting ABSI is a useful marker for early identification of high-risk individuals and for guiding public health interventions.\u003c/p\u003e","manuscriptTitle":"Association between a body shape index and the chronic obstructive pulmonary disease among middle-aged and elderly individuals in China: insights from CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:39:23","doi":"10.21203/rs.3.rs-9196992/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T12:46:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151862321398235491350884897675887130410","date":"2026-05-05T12:42:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T15:51:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68877272204395548671878449666531479787","date":"2026-05-03T15:42:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T11:26:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-26T22:32:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T07:36:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T07:27:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-07T06:50:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e69976e5-dc55-4cce-b599-074a529a100d","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T12:46:48+00:00","index":67,"fulltext":""},{"type":"reviewerAgreed","content":"151862321398235491350884897675887130410","date":"2026-05-05T12:42:45+00:00","index":66,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T15:51:19+00:00","index":50,"fulltext":""},{"type":"reviewerAgreed","content":"68877272204395548671878449666531479787","date":"2026-05-03T15:42:27+00:00","index":49,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:39:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:39:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9196992","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9196992","identity":"rs-9196992","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}