Obesity Indicators and Female Asthma Risk: The Mediating Role of White Blood Cell Count Based on NHANES Data Analysis

Obesity Indicators and Female Asthma Risk: The Mediating Role of White Blood Cell Count Based on NHANES Data Analysis | 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 Obesity Indicators and Female Asthma Risk: The Mediating Role of White Blood Cell Count Based on NHANES Data Analysis Xu Qianqian, Zhang Qingmei, Jia Jin, Chen Yalin, He Chenyu, Zhang Huaiwen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6503201/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Aug, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted 20 You are reading this latest preprint version Abstract Objective This study utilizes data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2009 and 2018 to investigate the associations between obesity indicators (BMI and BRI), the inflammatory marker (WBC), and asthma in women. Additionally, the study explores the potential mediating role of WBC in the relationship between BMI, BRI, and asthma. Methods This A total of 3,792 eligible female participants were included in this cross-sectional study. We applied various statistical methods, including logistic regression, restricted cubic spline (RCS), generalized additive model (GAM), and mediation analysis, to assess the relationships between BMI, BRI, WBC, and asthma in women. Results The weighted analysis revealed that asthma patients had significantly higher levels of BMI, BRI, and WBC compared to non-asthmatic individuals (all P < 0.001). In unadjusted logistic regression models, these indicators were significantly associated with asthma, and the associations remained significant after adjusting for potential confounders. Quartile-based analysis showed that participants in the highest quartile (Q4) for BMI, BRI, and WBC exhibited a significantly increased risk of asthma. RCS analysis further supported the nonlinear relationships between BMI, BRI, WBC, and asthma, while mediation analysis demonstrated that WBC acted as a significant mediator in the relationship between BMI/BRI and asthma. Conclusion This study identifies significant and nonlinear associations between BMI, BRI, and WBC with the risk of asthma in women. Moreover, WBC was found to partially mediate the effects of BMI and BRI on asthma, offering new insights into the inflammatory mechanisms underlying obesity-related asthma in females. BMI BRI Mediating effect asthma association Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Respiratory diseases, particularly chronic obstructive pulmonary disease (COPD) and asthma, are major causes of morbidity and mortality worldwide. Asthma is a common chronic inflammatory disease of the airways, characterized by chronic inflammation, airway hyperresponsiveness, and reversible airflow limitation. It affects approximately 334 million people globally, and its prevalence continues to rise [ 1 ][ 2 ]. With the increasing prevalence of obesity, the relationship between obesity and asthma has become an area of growing research interest in recent years. Obesity is considered an independent risk factor for both the onset and exacerbation of asthma. Epidemiological studies have confirmed a positive correlation between increased body mass index (BMI) and asthma risk[ 3 ][ 4 ]. The relationship between obesity and asthma is multifaceted, involving complex interactions across several systems. On one hand, obesity mechanically restricts lung function, and visceral fat accumulation may reduce lung volume and ventilatory function, thereby contributing to asthma-like symptoms[ 5 ]. On the other hand, obesity exacerbates asthma’s pathological process through systemic inflammatory responses, with white blood cells (WBC) and their subsets playing a key role in this process[ 6 ]. Specifically, obesity intensifies chronic inflammatory responses in the body, leading to airway restriction and obstruction, which increases the frequency of asthma symptoms[ 7 ]. Additionally, pro-inflammatory factors secreted by adipose tissue (e.g., leptin, IL-6) can activate a systemic low-grade inflammatory state, while a reduction in the anti-inflammatory factor adiponectin further exacerbates this process[ 8 ]. This state of inflammation is often reflected by elevated WBC counts in peripheral blood, particularly an increase in neutrophil proportions, which is closely associated with the non-eosinophilic asthma phenotype[ 9 ]. Genetic studies suggest that a genetic predisposition to high BMI may also increase the risk of early lower respiratory tract infections in children, independent of current BMI levels[ 10 ]. In addition to BMI, the Body Roundness Index (BRI) has emerged as a promising metric for assessing obesity. Recent research has highlighted the significant association between BRI and the risk of asthma and COPD, particularly when abdominal fat accumulation is more pronounced, as respiratory symptoms may be more severe in such cases[ 11 ]. Furthermore, studies have shown that during asthma acute exacerbations, peripheral blood WBC count and the neutrophil/lymphocyte ratio (NLR) are often significantly elevated, suggesting a distinct inflammatory phenotype[ 12 ]. This inflammatory characteristic may contribute to poor responses to conventional inhaled corticosteroid therapy, with approximately 40% of obese asthma patients exhibiting steroid resistance[ 13 ]. High WBC levels play a critical role in chronic respiratory diseases, especially in the pathogenesis of asthma. An increase in WBC typically reflects an intensification of chronic inflammation in the body, which can lead to airway damage and potentially worsen asthma over time[ 14 ][ 15 ]. Therefore, analyzing WBC and its subsets may provide valuable diagnostic insights for obesity-related asthma[ 16 ]. Prospective cohort studies have shown that elevated baseline WBC levels can predict the risk of subsequent asthma acute exacerbations, with this association being more pronounced in obese populations[ 17 ]. In recent years, some studies using data from the National Health and Nutrition Examination Survey (NHANES) have further investigated the relationship between BMI and asthma[ 18 ]. However, most studies have focused on single variables, and there is a lack of systematic analysis of the relationship between BMI, BRI, inflammatory markers (such as WBC), and asthma. Therefore, this study utilizes the NHANES (2009–2018) database to explore the relationship between BMI, BRI, WBC, and the risk of asthma in women. 2 Methods 2.1 Study Population The study initially included 49,693 participants from the NHANES database covering the period from 2009 to 2018. NHANES conducted comprehensive physical measurements and complete blood count (CBC) tests for individuals aged 20 and above, and participants self-reported whether they had been diagnosed with asthma by a doctor or other health professionals. The primary aim of the study was to assess the nutrition and health status of the U.S. female population and to further explore the epidemiological characteristics of asthma and its related factors. Inclusion criteria included: ① complete baseline data on waist circumference, height, BMI, smoking and drinking habits, and history of hypertension and diabetes; ② complete five-part blood count data; ③ U.S. adult women aged ≥ 20 years; ④ complete data on age at menarche, age at first and last live birth, and hormone use history; ⑤ complete self-reported asthma diagnosis data. A total of 3,729 female participants who met these criteria were included in the analysis (Fig. 1 ). 2.2 BMI, BRI, and Inflammatory Index BMI (kg/m²), waist circumference (WC, cm), and standing height (cm) were extracted from the physical examination data provided by NHANES (file name: "BMX.Doc"). The Body Roundness Index (BRI)[ 19 ] was calculated using waist circumference (in meters) and height (in meters) with the following formula: Using NHANES laboratory data (file name: "CBC.Doc"), the neutrophil-to-lymphocyte ratio (NLR)[ 20 ] and platelet-to-lymphocyte ratio (PLR) [ 21 ]were calculated. The relevant indicators included white blood cell count (WBC), lymphocyte count, neutrophil count, monocyte count, and platelet count, with units expressed as thousands of cells per microliter (×10³/µL)[ 22 ][ 23 ][ 24 ]. The calculation formulas are as follows: NLR = Neutrophil count / Lymphocyte count PLR = Platelet count / Lymphocyte count 2.3 Asthma Asthma information was obtained from the medical condition questionnaire module (file name: "MCQ.Doc") using question MCQ010: "Have you ever been told by a doctor or other health professional that you have asthma?" Women who answered "yes" were classified as having a history of asthma, while non-respondents were excluded. Previous studies have shown that this self-reported method has high accuracy. 2.4 Data Covariates To control for potential confounding factors, several covariates were included in the study. Demographic variables included age, race, education level, marital status, and poverty-income ratio (PIR). Lifestyle variables included smoking status (defined as having smoked ≥ 100 cigarettes in a lifetime) and drinking status (defined as having consumed any form of alcohol or having drunk ≥ 12 alcoholic beverages in a lifetime). Health status variables, including hypertension and diabetes, were self-reported through the questionnaire, with the question "Have you ever been diagnosed by a doctor or health professional (yes/no)?" Female reproductive-related variables included age at menarche, use of female hormones, and age at first and last childbirth, with data from the reproductive health module, specifically variables RHQ010, RHQ540 (yes/no), RHD180, and RHD190. 2.5 Statistical Analysis All statistical analyses were performed using the DecisionLinnc 1.0 software platform[ 25 ], which integrates multiple programming language environments and supports data processing, statistical modeling, and visual analysis. Given NHANES's complex sampling design, sample weighting was applied during the analysis. The weighted variables used the two-year cycle examination sample weight (WTMEC2YR) for each period, multiplied by 1/5 to obtain the weight for the 10-year combined sample[ 26 ][ 27 ]. The analysis was limited to female participants with complete exposure and outcome variable data. The study first described the characteristics of the asthma and non-asthma groups: categorical variables were presented as percentages; continuous variables were tested for normality, and if normally distributed, presented as mean ± standard deviation (Mean ± SD). If not normally distributed, the median and interquartile range (IQR) were used to represent central tendency and dispersion. Three multivariate logistic regression models were then constructed to assess the independent associations between BMI, BRI, and WBC with asthma risk. Model 1 was the unadjusted model; Model 2 adjusted for age, race, education level, marital status, and PIR based on Model 1; Model 3 further included age at menarche, age at first and last childbirth, hormone use, smoking and drinking behaviors, hypertension, and diabetes status based on Model 2. Simultaneously, BMI, BRI, and WBC were grouped by quartiles, and trend tests (P for trend) were calculated for sensitivity analysis to verify the stability of the analysis results. To improve the robustness of the model, multicollinearity tests were performed on all covariates before modeling, using the variance inflation factor (VIF) to assess correlations between variables. If a variable's VIF > 10, it was considered to have severe multicollinearity and was excluded from modeling[ 28 ]. To explore the nonlinear relationship between BMI, BRI, and WBC and asthma risk, a weighted restricted cubic spline (RCS) regression model was constructed, and smoothing curve fitting was applied using a generalized additive model based on the fully adjusted model (Model 3). Threshold effect analysis was performed to identify potential inflection points, with model differences before and after the inflection point assessed by likelihood ratio tests. Based on this, subgroup analysis and interaction tests were conducted to explore the heterogeneous effects of variables on asthma risk in different populations. Mediation analysis was then used to assess the indirect effect of BMI and BRI on asthma through WBC, revealing the potential mediating mechanism of WBC between obesity and asthma. Finally, to assess the discriminatory ability of each indicator in predicting asthma risk, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to compare the predictive performance of BMI, BRI, and WBC. Additionally, to validate the robustness of the study results, sensitivity analysis was performed by excluding current smokers, and demographic characteristic analysis and multivariate logistic regression were repeated under weighted conditions, with model settings consistent with the main analysis. In statistical tests, a P-value < 0.05 was considered statistically significant. 3 Results 3.1 General Characteristics of the Study Population A total of 3,729 eligible female participants from the NHANES database were included in this study. The participants were categorized into asthma and non-asthma groups based on asthma status, and inter-group comparisons of covariates were conducted. The results (Table 1 ) revealed significant differences between the two groups in several variables, including race, marital status, waist circumference, BMI, BRI, smoking status, alcohol consumption, hypertension history, age at first and last live birth, WBC, monocytes, and neutrophils (P < 0.05). However, no significant differences were observed between the groups in terms of age, education level, poverty-income ratio (PIR), height, diabetes, age at menarche, hormone use, lymphocytes, platelets, NLR, and PLR (P > 0.05). 3.2 Multicollinearity Analysis A total of 16 core covariates were included in this study, and the associated variance inflation factors (VIF) are listed in Table 2 . The results showed that the VIF for all variables was less than 10, indicating no significant multicollinearity issues in the model, allowing all covariates to be included in the final regression model. Table 1 Sample characteristics and comparison between participants with and without asthma(weighted) Characteristic Total Yes asthma No asthma p-value c N = 28,740,316 a N = 4,995,275 b N = 23,745,041 b Age 53.36 ± (16.33) 51.56 ± (16.98) 53.73 ± (16.17) 0.056 Race 0.001 Mexican American 671(10.03%) 69 (6.12%) 602 (10.85%) Other Hispanic 446 (6.71%) 70(6.04%) 376 (6.85%) Non-Hispanic White 1,268 (61.58%) 248 (66.19%) 1,020 (60.60%) Non-Hispanic Black 809 (12.45%) 155 (14.09%) 654 (12.10%) Other Race-Including Multi-Racial 535 (9.24%) 61(7.56%) 474 (9.59%) Education level 0.068 Less than 9th grade 497 (6.94%) 60(5.17%) 437 (7.31%) 9-11th grade 534 (11.14%) 83(10.88%) 451 (11.19%) High school graduate/GED 872(26.50%) 151 (26.15%) 721 (26.58%) High school graduate/GED 1,138 (31.18%) 206 (36.74%) 932 (30.01%) College graduate or above 688 (24.24%) 103 (21.07%) 585 (24.90%) Marital status 0.036 Married/Living with partner 2,223 (64.98%) 332 (59.96%) 1,891 (66.03%) Widowed/Divorced/Separated 1,204 (28.29%) 205 (30.10%) 999 (27.90%) Never married 302 (6.74%) 66 (9.94%) 236 (6.06%) PIR 0.075 < 1 952 (18.23%) 171 (23.10%) 781 (17.21%) 1 ≤ PIR < 2 1,130 (24.80%) 189 (24.71%) 941 (24.82%) 2 ≤ PIR < 4 936 (27.30%) 138 (25.43%) 798 (27.70%) ≥ 4 711 (29.66%) 105 (26.76%) 606 (30.27%) Waist (cm) 100.03 ± (16.54) 105.10 ± (18.98) 98.97 ± (15.78) < 0.001 Height (cm) 160.20 ± (6.85) 160.99 ± (6.96) 160.04 ± (6.82) 0.070 BMI (kg/m 2 ) 30.32 ± (7.45) 32.58 ± (9.14) 29.84 ± (6.95) < 0.001 BRI 6.22 ± (2.55) 6.98 ± (3.08) 6.06 ± (2.39) < 0.001 Smoking 0.002 Yes 978 (28.77%) 214 (36.79%) 764 (27.08%) No 2,751 (71.23%) 389 (63.21%) 2,362 (72.92%) Alcohol consumption < 0.001 Yes 2,398 (71.87%) 431 (79.50%) 1,967 (70.26%) No 1,331 (28.13%) 172 (20.50%) 1,159 (29.74%) Hypertension 0.002 Yes 1,625 (39.50%) 313 (46.45%) 1,312 (38.04%) No 2,104 (60.50%) 290 (53.55%) 1,814 (61.96%) Diabetes 0.148 Yes 654 (14.51%) 134 (16.87%) 520 (14.02%) No 3,075 (85.49%) 469 (83.13%) 2,606 (85.98%) Menophania 12.70 ± (1.75) 12.59 ± (1.80) 12.72 ±(1.74) 0.416 First live birth time 23.35 ± (20.74) 22.19 ± (4.92) 23.59 ± (22.69) 0.017 Last live birth time 29.12 ± (5.83) 27.87 ± (5.82) 29.38 ± (5.80) 0.005 Female hormones use 0.590 Yes 698 (22.85%) 131 (23.98%) 567 (22.61%) No 3,031 (77.15%) 472 (76.02%) 2,559 (77.39%) WBC (1000 cell/uL) 7.33 ± (2.25) 7.79 ± (2.25) 7.23 ± (2.24) < 0.001 Lymphocyte (1000 cell/uL) 2.21 ± (0.95) 2.29 ± (0.82) 2.20 ± (0.97) 0.082 Monocyte (1000 cell/uL) 0.55 ± (0.19) 0.57 ± (0.18) 0.54 ± (0.19) 0.003 Neutrophile (1000 cell/uL) 4.33 ± (1.68) 4.64 ± (1.75) 4.26 ± (1.66) < 0.001 Blood platelet (1000 cell/uL) 253.40 ± 64.85 257.41 ± 62.55 252.55 ± 65.31 0.180 NLR 2.13 ± (1.10) 2.21 ± (1.22) 2.11 ± (1.07) 0.055 PLR 125.33 ± (47.59) 123.46 ± (48.65) 125.73 ± (47.36) 0.337 a N not missing unweighted b N (unweighted); (%) (weighted); Mean ± SD (weighted) c chi-squared test with Rao & Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples NLR: Neutrophil count/Lymphocyte count, PLR:Platelet count/Lymphocyte count, PIR: Poverty to Income Ratio, BMI: Body mass index, BRI: Body Roundness Index. Table 2 Variance inflation factors(VIF) Variable Name GVIF Age 2.08 Race 1.74 Education level 1.87 Marital status 1.47 PIR 1.54 Smoking 1.20 Alcohol consumption 1.18 Hypertension 1.41 Diabetes 1.16 Menophania 1.07 First live birth time 1.64 Last live birth time 1.46 Female hormones use 1.22 BMI 7.64 BRI 7.74 WBC 1.10 3.3 Association between BMI, BRI, and WBC with Asthma As shown in Table 3 , in the unadjusted model (Model 1), BMI, BRI, and WBC were significantly positively correlated with asthma, with ORs of BMI: 1.05 (95% CI: 1.03–1.06, P < 0.001), BRI: 1.15 (95% CI: 1.08–1.20, P < 0.001), and WBC: 1.11 (95% CI: 1.06–1.17, P < 0.001). After adjusting for demographic factors (Model 2), all three remained significantly associated with asthma, with ORs of BMI: 1.04 (95% CI: 1.03–1.06), BRI: 1.14 (95% CI: 1.09–1.20), and WBC: 1.10 (95% CI: 1.04–1.17), with P-values all < 0.001. Further adjustment for all covariates in Model 3 confirmed that the associations for BMI, BRI, and WBC remained significant, with ORs of BMI: 1.04 (95% CI: 1.02–1.06, P < 0.001), BRI: 1.13 (95% CI: 1.07–1.19, P < 0.001), and WBC: 1.08 (95% CI: 1.03–1.14, P = 0.004), indicating that for each unit increase in WBC, asthma risk increased by approximately 8%. After grouping BMI into quartiles, analysis showed that compared with the Q1 group, the Q2 and Q3 groups did not show a significant association with asthma risk in Models 1–3 (P > 0.05). However, the Q4 group showed a significantly increased asthma risk in all three models, with ORs of Model 1: 1.97 (95% CI: 1.37–2.82, P < 0.001), Model 2: 1.94 (95% CI: 1.34–2.79, P < 0.001), and Model 3: 1.73 (95% CI: 1.19–2.52, P = 0.002). The P for trend for the BMI quartile groups was < 0.001 in all models, indicating a clear dose-response trend. Results from the BRI quartile analysis showed that there were mostly no significant differences between the Q2 and Q3 groups, with Q3 in Model 2 showing a weak association with asthma (OR = 1.47, 95% CI: 1.01–2.13, P = 0.046). The Q4 group was significantly associated with increased asthma risk in Models 1–3, with ORs of Model 1: 2.07 (95% CI: 1.40–3.06), Model 2: 2.16 (95% CI: 1.47–3.18), and Model 3: 1.96 (95% CI: 1.32–2.93), with P-values all < 0.001. The quartile trend test for BRI was statistically significant in all models (P for trend ≤ 0.001). After performing quartile analysis for WBC, it was found that the Q2 and Q3 groups were not significantly associated with asthma in all three models. However, the Q4 group showed a significant increase in asthma risk in Models 1 and 2 (Model 1: OR = 1.77, 95% CI: 1.23–2.25, P = 0.002; Model 2: OR = 1.70, 95% CI: 1.15–2.51, P = 0.008), but was no longer significant in Model 3 (OR = 1.49, 95% CI: 1.00–2.21, P = 0.483). Nevertheless, the P for trend for the WBC quartile groups was ≤ 0.024 in Models 1–3, indicating a statistical trend between WBC and asthma risk. Table 3 Multivariate logistic regression analysis of BMI, BRI, and WBC Exposure Model1 Model2 Model3 OR(95%CI) P-vale OR(95%CI) P-vale OR(95%CI) P-vale BMI 1.05(1.03,1.06) <0.001 1.04(1.03,1.06) < 0.001 1.04(1.02,1.06) < 0.001 BMI Quartiles Q1 Reference Reference Reference Q2 0.87(0.59,1.30) 0.502 0.91(0.62,1.33) 0.613 0.90(0.60,1.35) 0.596 Q3 1.19(0.76,1.86) 0.447 1.23(0.78,1.93) 0.358 1.17(0.73,1.87) 0.516 Q4 1.97(1.37,2.82) <0.001 1.94(1.34,2.79) < 0.001 1.73(1.19,2.52) 0.005 P for trend <0.001 < 0.001 0.002 BRI 1.14(1.08,1.20) <0.001 1.14(1.09,1.20) < 0.001 1.13(1.07,1.19) < 0.001 BRI Quartiles Q1 Reference Reference Reference Q2 0.99(0.66,1.49) 0.974 1.09(0.73,1.64) 0.666 1.09(0.72,1.67) 0.677 Q3 1.32(0.91,1.93) 0.141 1.47(1.01,2.13) 0.046 1.37(0.91,2.07) 0.132 Q4 2.07(1.40,3.06) <0.001 2.16(1.47,3.18) <0.001 1.96(1.32,2.93) 0.001 P for trend <0.001 <0.001 0.001 WBC 1.11(1.06,1.17) <0.001 1.10(1.04,1.17) <0.001 1.08(1.03,1.14) 0.004 WBC Quartiles Q1 Reference Reference Reference Q2 1.13(0.75,1.71) 0.558 1.10(0.72,,1.66) 0.659 1.04(0.68,1.58) 0.870 Q3 1.37(0.95,1.98) 0.093 1.33(0.89,1.98) 0.162 1.22(0.81,1.84) 0.325 Q4 1.77(1.23,2.55) 0.002 1.70(1.15,2.51) 0.008 1.49(1.00,2.21) 0.483 P for trend <0.001 0.003 0.024 Model 1: No covariates were adjusted. Model 2: Age, race, education level, marital status, PIR. Model 3: Model 2 + smoking, alcohol consumption, hypertension, diabetes, menophania, age at first live birth, age at last live birth, female hormones use. 3.4 RCS Analysis Figure 2 (a–i) illustrates the fitted results of the relationship between BMI, BRI, and WBC and female asthma using the restricted cubic spline (RCS) model in the weighted sample, and compares the trends across different covariate adjustment levels (Models 1–3). The results indicate that BMI, BRI, and WBC were nonlinearly associated with asthma in most models, with WBC displaying a linear trend in Model 3. The overall association of these three indicators with asthma was statistically significant in all models (P for overall = 0). To further visualize the relationship between BMI, BRI, and WBC and asthma risk, smoothing curve fitting was conducted based on the fully adjusted model (Model 3). The results, shown in Fig. 3 (a-c), reveal that all three indicators exhibit a nonlinear dose-response relationship with asthma. Threshold effect analysis was performed based on Model 3, and the results are presented in Table 4 . The inflection point for BMI was 25.30. Before this inflection point, BMI was significantly associated with asthma risk (OR = 0.94, 95% CI: 0.89–0.99, P = 0.022), indicating that for each unit increase in BMI, asthma risk decreased by approximately 6%. After the inflection point, each unit increase in BMI was associated with a 6% increase in asthma risk (OR = 1.06, 95% CI: 1.06–1.08, P < 0.001). The likelihood ratio test (P < 0.001) further supported the advantage of the threshold model. BRI showed a similar trend, with an inflection point at 3.84 (P < 0.001), indicating a differential effect on asthma risk before and after the inflection point. The inflection point for WBC was 4.80×10⁹/L (P < 0.001). Before the inflection point, each unit increase in WBC was associated with a 2.93-fold increase in asthma risk; after the inflection point, each unit increase in WBC was associated with an approximately 8% increase in asthma risk. Table 4 Threshold effect analysis of BMI, BRI, and WBCI on asthma using a logistic regression model. Exposure OR(95%CI) P-value BMI Inflection point 25.30 25.30 1.06(1.05,1.08) < 0.001 Log likelihood ratio test < 0.001 BRI Inflection point 3.84 3.84 1.18(1.14,1.22) < 0.001 Log likelihood ratio test < 0.001 WBC Inflection point 4.80 < 4.80 3.93(1.91,8.10) 4.80 1.08(1.04,1.12) < 0.001 Log likelihood ratio test < 0.001 Notes: Age, race, education level, marital status, PIR, smoking, alcohol consumption, hypertension, diabetes, menophania, age at first live birth, age at last live birth, female hormones use were adjusted. 3.5 Subgroup Analysis To assess the robustness of the associations between BMI, BRI, and WBC with female asthma and to identify potential population heterogeneity, subgroup analysis and interaction tests were conducted based on multiple covariates (Fig. 4 ). The results consistently showed a positive correlation between the three indicators and asthma in most subgroups, with stable associations. Specifically, for each unit increase in BMI, asthma risk increased by approximately 4% (OR = 1.04, 95% CI: 1.03–1.06, P < 0.001). This association was more significant in individuals aged < 65, of various races, with less than a university education, with low income, smokers, drinkers, those with hypertension, diabetes, and those who had never used female hormones. For each unit increase in BRI, asthma risk increased by approximately 13% (OR = 1.13, 95% CI: 1.08–1.19, P < 0.001). This trend was particularly evident across different age groups, among African Americans, individuals with varying marital statuses, low-income people, smokers, drinkers, and those who had used female hormones. For each unit increase in WBC, asthma risk increased by approximately 10% (OR = 1.10, 95% CI: 1.05–1.15, P < 0.001). The association between WBC and asthma risk was more pronounced in younger populations, African Americans, married individuals, low-income people, non-smokers, drinkers, those with or without hypertension, non-diabetic individuals, and those who had used female hormones. Furthermore, no significant interaction was observed between the subgroup stratifications of the indicators (P > 0.05). 3.6 Mediation Analysis The results in Table 5 demonstrate that both BMI and BRI have a significant mediating effect on the risk of female asthma through WBC. Specifically, the total effect of BMI on asthma was 0.001888 (0.001583, 0.002093), P < 0.001; the indirect effect was 0.000167 (0.000041, 0.000338), P = 0.014; the direct effect was 0.001721 (0.001464, 0.001948), P < 0.001; and the proportion of the mediating effect was 8.6% (0.022980, 0.170784), P = 0.014. The total effect of BRI was 0.009231 (0.007705, 0.010425), P < 0.001; the indirect effect was 0.000998 (0.000247, 0.001864), P = 0.016; the direct effect was 0.008233 (0.006306, 0.009623), P < 0.001; and the proportion of the mediating effect was 10.5% (0.026169, 0.214565), P = 0.016. These results indicate that both BMI and BRI are positively correlated with asthma risk, and that they exert a significant mediating effect on asthma risk through white blood cell count (WBC), an inflammatory marker. This suggests that obesity may promote the development of female asthma by activating systemic inflammatory pathways. Table 5 The mediating effect of WBC on the relationship between BMI, BRI, and asthma. BMI Name Estimate 95%CI(lower) 95%CI(upper) P-vale Total effect 0.001888 0.001583 0.002093 0 Indirect effect 0.000167 0.000041 0.000338 0.014 Direct effect 0.001721 0.001464 0.001948 0 Proportion Mediated 0.086140 0.022980 0.170784 0.014 BRI Name Estimate 95%CI(lower) 95%CI(upper) P-vale Total effect 0.009231 0.007705 0.010425 0 Indirect effect 0.000998 0.000247 0.001864 0.016 Direct effect 0.008233 0.006306 0.009623 0 Proportion Mediated 0.105034 0.026169 0.214565 0.016 BMI: The mediating effect of WBC on the relationship between BMI and asthma. BRI: The mediating effect of WBC on the relationship between BRI and asthma. Notes: Age, race, education level, marital status, PIR, smoking, alcohol consumption, hypertension, diabetes, menophania, age at first live birth, age at last live birth, female hormones use were adjusted. 3.7 ROC Prediction ROC curve analysis was conducted to further evaluate the predictive ability of BMI, BRI, and WBC for asthma (Table 6 ). The results showed that the area under the curve (AUC) for BMI, BRI, and WBC (Fig. 5 ) were 0.593, 0.590, and 0.564, respectively, indicating that BMI and BRI had better predictive performance than WBC. In terms of sensitivity, BMI performed better than BRI and WBC in predicting the occurrence of female asthma, although it slightly lagged behind the other two in terms of specificity. Table 6 The sensitivity, specificity, and AUC of ROC curves for predicting asthma using BMI, BRI, and WBC. Exposure Sensitivity(95%CI) Specificity(95%CI) AUC(95%CI) BMI 0.82(0.49,0.87) 0.33(0.27,0.67) 0.59(0.57,0.61) BRI 0.72(0.53,0.86) 0.43(0.28,0.62) 0.59(0.56,0.62) WBC 0.63(0.22,0.67) 0.48(0.43,0.87) 0.56(0.54,0.59) AUC: Area under the curve; CI: confidence interval. 3.8 Sensitivity Analysis To further verify the robustness of the study results, sensitivity analysis was performed after excluding current smokers. The demographic characteristics analysis after excluding smokers (Table 7 ) was consistent with the main analysis. Based on this, the same three regression models (Models1-3) were used to evaluate the relationship between BMI, BRI, and WBC with asthma (Table 8 ). The results showed that all three indicators remained significantly positively correlated with asthma risk in all models. These results were highly consistent with the analysis of the complete sample, further validating the reliability of the analysis. Table 7 Sample characteristics and comparison between participants with and without asthma(weighted) in the sensitivity analysis Characteristic Overall Yes asthma No asthma p value b N = 24,999,002 a N = 4,186,833 a N = 20,812,169 a Age 54.45 ± 16.26 53.40 ± 16.94 54.66 ± 16.11 0.321 Race 0.005 Mexican American 626 (10.72%) 66 (7.05%) 560 (11.46%) Other Hispanic 418(7.29%) 63(6.12%) 355 (7.52%) Non-Hispanic White 1,035 (60.00%) 193(64.73%) 842 (59.05%) Non-Hispanic Black 702 (12.53%) 131 (14.43%) 571(12.15%) Other Race-Including Multi-Racial 505(9.47%) 55(7.67%) 450 (9.83%) Education level 0.180 Less than 9th grade 470 (7.41%) 55 (5.40%) 415 (7.81%) 9-11th grade 429 (10.00%) 59 (9.02%) 370 (10.20%) High school graduate/GED 730 (24.15%) 128 (25.64%) 602 (23.85%) High school graduate/GED 992 (31.40%) 167(35.92%) 825 (30.49%) College graduate or above 665(27.04%) 99 (24.01%) 566 (27.65%) Marital status 0.008 Married/Living with partner 2,004 (65.89%) 285(59.35%) 1,719(67.20%) Widowed/Divorced/Separated 1,054 (28.35%) 176(31.34%) 878(27.75%) Never married 228 (5.76%) 47(9.32%) 181 (5.05%) PIR 0.347 < 1 768 (15.70%) 124 (19.16%) 644 (15.01%) 1 ≤ PIR < 2 984(23.90%) 160(23.97%) 824(23.88%) 2 ≤ PIR < 4 849 (27.60%) 125 (26.26%) 724 (27.88%) ≥ 4 685(32.79%) 99 (30.61%) 586 (33.23%) Waist (cm) 100.20 ± 16.39 105.93 ± 19.38 99.05 ± 15.48 < 0.001 Height (cm) 159.87 ± 6.84 160.69 ± 7.15 159.71 ± 6.77 0.115 BMI (kg/m 2 ) 30.47 ± 7.41 33.00 ± 9.34 29.97 ± 6.85 < 0.001 BRI 6.27 ± 2.54 7.15 ± 3.16 6.09 ± 2.36 < 0.001 Alcohol consumption < 0.001 Yes 2,003 (68.89%) 348 (77.73%) 1,655(67.11%) No 1,283 (31.11%) 160 (22.27%) 1,123 (32.89%) Hypertension 0.001 Yes 1,449(40.42%) 272(48.31%) 1,177 (38.84%) No 1,837 (59.58%) 236 (51.69%) 1,601 (61.16%) Diabetes 0.201 Yes 592 (14.99%) 114 (16.97%) 478 (14.59%) No 2,694(85.01%) 394 (83.03%) 2,300 (85.41%) Menophania 12.71 ± 1.72 12.61 ± 1.77 12.73 ± 1.71 0.487 First live birth time 23.79 ± 22.15 22.60 ± 5.00 24.03 ± 24.17 0.060 Last live birth time 29.45 ± 5.78 28.39 ± 5.72 29.66 ± 5.77 0.021 Female hormones use 0.869 Yes 632 (24.09%) 111 (24.44%) 521 (24.02%) No 2,654 (75.91%) 397 (75.56%) 2,257 (75.98%) WBC (1000 cell/uL) 7.16 ± 2.20 7.63 ± 2.15 7.06 ± 2.19 < 0.001 Lymphocyte (1000 cell/uL) 2.16 ± 0.96 2.23 ± 0.80 2.15 ± 0.98 0.173 Monocyte (1000 cell/uL) 0.54 ± 0.19 0.57 ± 0.18 0.54 ± 0.19 0.002 Neutrophile (1000 cell/uL) 4.22 ± 1.63 4.54 ± 1.66 4.15 ± 1.62 < 0.001 Blood platelet (1000 cell/uL) 250.76 ± 63.42 254.83 ± 58.65 249.94 ± 64.32 0.135 NLR 2.13 ± 1.11 2.22 ± 1.23 2.11 ± 1.09 0.048 PLR 126.56 ± 46.70 124.53 ± 45.76 126.97 ± 46.89 0.505 a N not missing unweighted b N (unweighted); (%) (weighted); Mean ± SD (weighted) c chi-squared test with Rao & Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples NLR: Neutrophil count/Lymphocyte count, PLR:Platelet count/Lymphocyte count, PIR: Poverty to Income Ratio, BMI: Body mass index, BRI: Body Roundness Index. Table 8 Multivariate logistic regression analysis of BMI, BRI, and WBC with asthma in the sensitivity analysis Exposure Model1 Model2 Model3 OR(95%CI) P-vale OR(95%CI) P-vale OR(95%CI) P-vale BMI 1.05(1.03,1.07) <0.001 1.05(1.03,1.07) < 0.001 1.04(1.02,1.06) < 0.001 BRI 1.16(1.10,1.23) <0.001 1.16(1.10,1.23) < 0.001 1.14(1.08,1.21) < 0.001 WBC 1.12(1.05,1.19) <0.001 1.12(1.05,1.20) 0.002 1.10(1.03,1.18) 0.006 4 Discussion This study, based on the 2009–2018 NHANES database, used a cross-sectional design to assess the relationship between body mass index (BMI), body roundness index (BRI), and white blood cell count (WBC) with the risk of asthma in U.S. adult women. The results revealed significant positive correlations between BMI, BRI, and WBC levels and asthma prevalence, with these associations remaining consistent across various statistical models and analytical methods. RCS analysis further showed a nonlinear dose-response relationship between these three indicators and asthma, with good curve fitting and a clear threshold effect. Subgroup analysis also confirmed that the associations between these indicators and asthma risk were consistently positive, underscoring their broad applicability and stability. Mediation analysis indicated that BMI and BRI may indirectly influence asthma risk through WBC, suggesting that obesity may contribute to asthma development by inducing inflammatory processes. Finally, ROC curve analysis demonstrated that BMI was a better predictor than BRI and WBC for identifying high-risk individuals for asthma in women. To verify the robustness of the results, sensitivity analysis was conducted after excluding current smokers, and the conclusions remained consistent. In conclusion, BMI and BRI as obesity indicators, and WBC as a systemic inflammation marker, are potential independent risk factors for female asthma. This study also suggests that WBC may mediate the relationship between BMI, BRI, and asthma. Previous studies have widely shown that obesity, particularly abdominal obesity, is a significant risk factor for asthma. A prospective cohort study by Wang et al. demonstrated a close association between weight gain in women and the onset and progression of asthma[ 29 ]. Liu et al. further found that women with a higher waist circumference had a significantly increased risk of asthma[ 30 ], which is consistent with the findings of this study. Women tend to have lower lung capacity and poorer airway ventilation[ 31 ], and their immune systems are more active and responsive[ 32 ]. As women age and undergo physiological processes such as pregnancy and childbirth, particularly in multiparous women, fat distribution tends to shift toward abdominal and visceral areas[ 33 ]. This abdominal fat accumulation, coupled with skeletal muscle relaxation, may further impair lung function[ 34 ]. Moreover, estrogen in women influences immune function and airway health. Studies have shown that estrogen may enhance airway reactivity, making women more susceptible to asthma symptoms when exposed to environmental pollutants, allergens, and other triggers[ 35 ]. This study found that BMI and BRI were nonlinearly associated with female asthma prevalence, aligning with Xu et al.'s study, which indicated a nonlinear relationship between BRI, WWI indices, and asthma[ 36 ]. BMI and BRI are closely related to central obesity and metabolic syndrome and may increase the risk of asthma in women through various mechanisms, particularly by activating chronic low-grade inflammation. In obese individuals, low-grade chronic inflammation persists, and its negative impact on airway immune function may be the underlying cause of asthma symptom exacerbation[ 37 ]. As an indicator of abdominal fat distribution, the higher the BRI, the more pronounced the accumulation of visceral fat[ 38 ]. After pregnancy and childbirth, women may experience abdominal muscle separation, making it easier for abdominal fat to accumulate. Over time, this fat accumulation can lead to abdominal obesity. Visceral fat not only serves as a marker of metabolic disease but also secretes pro-inflammatory factors such as TNF-α, IL-6, adiponectin, leptin, and lipid transport proteins into the bloodstream. These factors affect the systemic immune system and indirectly or directly influence airway inflammation[ 39 ][ 40 ][ 41 ]. These pro-inflammatory factors stimulate immune cells, particularly neutrophils, monocytes, and eosinophils, activating and increasing their numbers, which intensifies ongoing airway inflammation and immune responses[ 42 ]. Stimulated immune cells also secrete cytokines such as IL-8 and TNF-α, further exacerbating chronic low-grade inflammation[ 43 ][ 44 ], enhancing airway inflammation, leading to airway hyperreactivity and remodeling[ 45 ], and promoting asthma onset and worsening[ 46 ]. Furthermore, obesity alters the immune system's response patterns by affecting the function of T cells and B cells. Specifically, obesity is closely associated with Th2 cell dysfunction, a key player in asthma’s immune response. Obesity may exacerbate inflammation by altering T cell differentiation and enhancing Th2 cell responses[ 47 ]. Additionally, obesity influences airway remodeling through abnormal fatty acid metabolism, increasing airway smooth muscle proliferation, basement membrane thickening, and collagen deposition, thereby contributing to airway functional limitation and airflow obstruction[ 48 ]. Studies have also found that the accumulation of visceral fat is closely related to increased oxidative stress. The accumulation of abdominal fat produces reactive oxygen species and free radicals, and the increased oxidative stress exacerbates airway inflammation, leading to reduced airway function[ 49 ]. Obesity and increased abdominal fat not only significantly elevate oxidative stress levels, affecting the immune system, but may also directly impair lung function[ 50 ][ 51 ]. Thus, oxidative stress may also be an important mechanism through which abdominal fat influences asthma. This study found that BMI and BRI were significantly associated with female asthma risk. Both BMI and BRI accurately reflect abdominal fat accumulation and show potential for predicting asthma risk, making them valuable for early asthma screening. White blood cell count (WBC) is an important marker of systemic inflammation. An increase in WBC typically indicates the activation of the inflammatory response, accompanied by the aggregation of immune cells, particularly eosinophils, neutrophils, and monocytes. These cells release reactive oxygen species (ROS), proteases (such as neutrophil elastase), and pro-inflammatory factors (such as IL-8 and TNF-α) in asthma airways, causing airway epithelial damage, increased mucus secretion, and airway hyperreactivity[ 52 ]. These pathological responses are particularly prominent in asthma patients[ 53 ]. Simultaneously, increased WBC levels release cytokines like IL-6 and TNF-α, which activate airway epithelial cells, upregulate the expression of adhesion molecules (e.g., ICAM-1), and promote the migration of more immune cells to the airways, creating a vicious cycle[ 54 ]. An increase in WBC may indicate ongoing immune responses in the airways, exacerbating asthma symptoms and driving disease progression[ 55 ]. Moreover, WBC levels are closely associated with airway remodeling, suggesting that elevated WBC not only serves as a marker of inflammation but may also directly participate in asthma’s pathophysiological process. Studies have shown that, particularly in obese individuals, elevated pro-inflammatory factors lead to increased WBC release, which further exacerbates airway inflammation through the bloodstream[ 56 ]. This study’s results confirm this, with a significant positive correlation between WBC and asthma. For each unit increase in WBC, the risk of asthma increased by approximately 10% (OR = 1.10, 95% CI: 1.05–1.15, P < 0.001). In conclusion, WBC, as a marker of inflammation, can effectively reflect airway inflammation activity and predict asthma risk, serving as an independent risk factor for asthma. This study further determined through mediation analysis that BMI and BRI significantly influence asthma risk via the inflammatory marker WBC, offering new insights into the interaction between obesity and immune responses. This study has several strengths. First, based on the large female sample from the NHANES database, it found significant associations between BMI, BRI, WBC, and asthma risk in women. Second, it primarily focused on adult women and adjusted for several key potential confounding factors, such as age, lifestyle, age at menarche, hormone use, and reproductive history. Third, subgroup and sensitivity analyses verified the robustness and reliability of the study results. Additionally, mediation analysis revealed the mediating role of WBC between obesity and asthma development. Finally, the study assessed the predictive performance of these three indicators in relation to asthma risk. Overall, this study provides valuable new insights into the role of BMI, BRI, and WBC in asthma risk and offers important references for clinical management and future research. However, this study also has limitations. First, the data from NHANES is based on self-reporting by participants, which may introduce recall bias. Second, although several potential confounding factors were adjusted for, there may still be unknown factors (such as genetic background and environmental pollution) that could influence the results. Third, this study relied on one-time height and weight measurements to calculate BRI and laboratory-determined WBC counts, which may introduce bias. 5 Conclusion This study confirmed a positive correlation between BMI, BRI, and WBC with asthma in U.S. adult women and highlighted the significant mediating role of WBC in the relationship between BMI, BRI, and asthma risk. Additionally, the study demonstrated that BMI, BRI, and WBC hold potential for predicting asthma prevalence, with the discovery of threshold effects carrying important clinical implications. Future studies should further validate these findings in larger prospective cohorts. Moreover, experimental research is needed to explore the mechanisms underlying the interactions between these biomarkers, immune responses, and cytokines, offering new strategies for early asthma screening and personalized treatment. Declarations Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/. Clinical trial registration Not applicable Ethics statement The studies involving human participants were reviewed and approved by the Institutional Review Board (IRB) of the National Center for Health Statistics, in accordance with the ethical standards set forth by the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study. Consent to participate Not applicable Consent to publish Not applicable Author contributions XQQ contributed to data curation, formal analysis, writing and software; ZQM contributed to visualization and validation; JJ contributed to study conception, design and review; CYL contributed to data collection and data review; HCY and ZHW contributed to literature search; CJ contributed to review. All authors reviewed the manuscript. Funding The authors declare that financial support has been received for the research, author identity, and/or publication of this article. This study was funded by the National Administration of Traditional Chinese Medicine's High-Level Traditional Chinese Medicine Key Discipline Construction Project, Integrative Traditional Chinese and Western Medicine Clinical Project (Project No.: zyyzdxk-2023104). Acknowledgments The authors would like to express their gratitude to Fujian University of Traditional Chinese Medicine for their valuable support and contributions. We also thank all the staff for their assistance throughout the study. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Smith, R., Jones, D., & Wang, L. (2021). Global burden of asthma: Trends in prevalence and control. 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Chronic Inflammation in Asthma: Looking Beyond the Th2 Cell. Immunological Reviews, 330(1), e70010. https://doi.org/10.1111/imr.70010. Garcia-Rio F, Alvarez-Puebla MJ, Esteban-Gorgojo I, Barranco P, Olaguibel JM. Obesity and Asthma: Key Clinical Questions. J Investig Allergol Clin Immunol. 2019;29(4):262-271. doi: 10.18176/jiaci.0316. Epub 2018 Sep 17. PMID: 30222113. Liu, S., Zhang, J., & Li, X. (2018). A review of inflammatory mechanism in airway diseases. Journal of Molecular Medicine, 96(10), 1011–1019. https://doi.org/10.1007/s00011-018-1191-2. Peters U, Dixon AE, Forno E. Obesity and asthma. J Allergy Clin Immunol. 2018 Apr;141(4):1169-1179. doi: 10.1016/j.jaci.2018.02.004. PMID: 29627041; PMCID: PMC5973542. Furukawa S, Fujita T, Shimabukuro M, Iwaki M, Yamada Y, Nakajima Y, Nakayama O, Makishima M, Matsuda M, Shimomura I. Increased oxidative stress in obesity and its impact on metabolic syndrome. J Clin Invest. 2004 Dec;114(12):1752-61. doi: 10.1172/JCI21625. PMID: 15599400; PMCID: PMC535065. Grasemann H, Holguin F. Oxidative stress and obesity-related asthma. Paediatr Respir Rev. 2021 Mar;37:18-21. doi: 10.1016/j.prrv.2020.05.004. Epub 2020 May 30. PMID: 32660723. Holguin F, Fitzpatrick A. Obesity, asthma, and oxidative stress. J Appl Physiol (1985). 2010 Mar;108(3):754-9. doi: 10.1152/japplphysiol.00702.2009. Epub 2009 Nov 19. PMID: 19926826. Barnes PJ. The cytokine network in asthma and chronic obstructive pulmonary disease. J Clin Invest. 2008 Nov;118(11):3546-56. doi: 10.1172/JCI36130. PMID: 18982161; PMCID: PMC2575722. Fahy JV. Type 2 inflammation in asthma--present in most, absent in many. Nat Rev Immunol. 2015 Jan;15(1):57-65. doi: 10.1038/nri3786. PMID: 25534623; PMCID: PMC4390063. Tosi MF, Stark JM, Smith CW, Hamedani A, Gruenert DC, Infeld MD. Induction of ICAM-1 expression on human airway epithelial cells by inflammatory cytokines: effects on neutrophil-epithelial cell adhesion. Am J Respir Cell Mol Biol. 1992 Aug;7(2):214-21. doi: 10.1165/ajrcmb/7.2.214. PMID: 1353976. Pelaia, G., Vatrella, A., Busceti, M. T., Gallelli, L., Calabrese, C., & Terracciano, R. (2015). Cellular mechanisms underlying eosinophilic and neutrophilic airway inflammation in asthma. Mediators of Inflammation, 2015, 879783. https://doi.org/10.1155/2015/879783 Al Khathlan N. Association of inflammatory cytokines with obesity and pulmonary function testing. PLoS One. 2023 Nov 22;18(11):e0294592. doi: 10.1371/journal.pone.0294592. PMID: 37992066; PMCID: PMC10664933. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6503201","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456761161,"identity":"35aec9d2-1f72-4f85-8fd4-9863489e819d","order_by":0,"name":"Xu Qianqian","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Qianqian","suffix":""},{"id":456761162,"identity":"1842f078-ff7a-4e0b-b7c1-1896e523f1b1","order_by":1,"name":"Zhang Qingmei","email":"","orcid":"","institution":"Affiliated Provincial People's Hospital of Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Qingmei","suffix":""},{"id":456761163,"identity":"4f9996bd-fb1d-4697-9a8d-b9416fe2e4a1","order_by":2,"name":"Jia Jin","email":"","orcid":"","institution":"Affiliated Provincial People's Hospital of Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Jin","suffix":""},{"id":456761164,"identity":"7a72bbd8-243e-4fdd-8fca-3349265171a2","order_by":3,"name":"Chen Yalin","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Yalin","suffix":""},{"id":456761165,"identity":"5a36af5f-1da8-450b-9d14-4f9ffb4d68fc","order_by":4,"name":"He Chenyu","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Chenyu","suffix":""},{"id":456761166,"identity":"49a801c7-9dcb-4c0c-b3c2-072ae16fb579","order_by":5,"name":"Zhang Huaiwen","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Huaiwen","suffix":""},{"id":456761167,"identity":"6a8936e6-b8ec-4a01-b100-72ddffb9e9fb","order_by":6,"name":"Chen Jie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDACCRA2ALGYDxz4UEGaFrbEgzPOEKsFAniMD/O2EKGDf3bzsQcWBTb25tPOfDjA28Agzy92gIAld46lG0gYpCXOuZ274YDkDgbDmbMT8GsxkMgxk5AwOJwgIQ3UYniGIcHgNkEt+d+AWv7bS0jnPDiQ2EaUlhw2oJYDjDOkcxgOHCRGi8SNNJDDkhNnSKcZHGw4I0HYL/wzkp9JS/yxAzos+fHnPxU28vzSBLSAADMibpCiCS9g/ECculEwCkbBKBipAACSUkGyHwhgPgAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Provincial People's Hospital of Fujian University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Jie","suffix":""}],"badges":[],"createdAt":"2025-04-22 10:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6503201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6503201/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-025-03885-6","type":"published","date":"2025-08-21T16:29:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82885402,"identity":"765a7ff7-2460-493d-97fa-874e3ba69f1a","added_by":"auto","created_at":"2025-05-16 11:46:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":305310,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram illustrating the study population´s inclusion and exclusion. NHANES, National Health and Nutrition Examination Survey\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6503201/v1/627f0c313d4c63a664a629db.png"},{"id":82885406,"identity":"a08e3e27-e7ab-4c21-b244-190cba46295d","added_by":"auto","created_at":"2025-05-16 11:46:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":315773,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analysis of the association between BMI, BRI, WBC, and the risk of asthma in model 1 (unadjusted) (a)(d)(g), in model 2 (adjusted for age, race, education levels, marital status, and PIR) (b)(e)(h), and in model 3 (adjusted for Model 2+ smoking, alcohol consumption, hypertension, diabetes, menophania, age at first live birth, age at last live birth, female hormones use) (c)(f)(i).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6503201/v1/7987211557446c40858c2cc3.png"},{"id":82886882,"identity":"ccf8b373-183b-4d0e-8e20-be8d16881c09","added_by":"auto","created_at":"2025-05-16 11:54:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123086,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear relationship between BMI, BRI, WBC, and asthma. The solid blue line represents the smooth curve fit between the variables. The dashed area represents the 95% confidence interval of the fit.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6503201/v1/c39ca92e01c3289ffe31a9da.png"},{"id":82885405,"identity":"cf44d2ab-dbcc-4d75-8bd5-31bc161aef7d","added_by":"auto","created_at":"2025-05-16 11:46:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":391894,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between BMI, BRI, WBC and the risk of asthma. OR, odds ratio; CI, confidence interval, p-value, p for interaction.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6503201/v1/353c44a8b3187c9a042f80e5.png"},{"id":82886885,"identity":"f549a2d3-8a93-4fb8-aa4b-a8e59843991f","added_by":"auto","created_at":"2025-05-16 11:54:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127431,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for different indices to predict asthma.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6503201/v1/2c8e3452cc704fb068d7230e.png"},{"id":89847371,"identity":"2f80b877-9d24-465f-aa83-df0547dee607","added_by":"auto","created_at":"2025-08-25 16:43:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2583607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6503201/v1/0ac4e323-11ee-4851-ab29-26c2d05ee0ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Obesity Indicators and Female Asthma Risk: The Mediating Role of White Blood Cell Count Based on NHANES Data Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRespiratory diseases, particularly chronic obstructive pulmonary disease (COPD) and asthma, are major causes of morbidity and mortality worldwide. Asthma is a common chronic inflammatory disease of the airways, characterized by chronic inflammation, airway hyperresponsiveness, and reversible airflow limitation. It affects approximately 334\u0026nbsp;million people globally, and its prevalence continues to rise [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the increasing prevalence of obesity, the relationship between obesity and asthma has become an area of growing research interest in recent years.\u003c/p\u003e \u003cp\u003eObesity is considered an independent risk factor for both the onset and exacerbation of asthma. Epidemiological studies have confirmed a positive correlation between increased body mass index (BMI) and asthma risk[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The relationship between obesity and asthma is multifaceted, involving complex interactions across several systems. On one hand, obesity mechanically restricts lung function, and visceral fat accumulation may reduce lung volume and ventilatory function, thereby contributing to asthma-like symptoms[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. On the other hand, obesity exacerbates asthma\u0026rsquo;s pathological process through systemic inflammatory responses, with white blood cells (WBC) and their subsets playing a key role in this process[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecifically, obesity intensifies chronic inflammatory responses in the body, leading to airway restriction and obstruction, which increases the frequency of asthma symptoms[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, pro-inflammatory factors secreted by adipose tissue (e.g., leptin, IL-6) can activate a systemic low-grade inflammatory state, while a reduction in the anti-inflammatory factor adiponectin further exacerbates this process[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This state of inflammation is often reflected by elevated WBC counts in peripheral blood, particularly an increase in neutrophil proportions, which is closely associated with the non-eosinophilic asthma phenotype[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic studies suggest that a genetic predisposition to high BMI may also increase the risk of early lower respiratory tract infections in children, independent of current BMI levels[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition to BMI, the Body Roundness Index (BRI) has emerged as a promising metric for assessing obesity. Recent research has highlighted the significant association between BRI and the risk of asthma and COPD, particularly when abdominal fat accumulation is more pronounced, as respiratory symptoms may be more severe in such cases[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, studies have shown that during asthma acute exacerbations, peripheral blood WBC count and the neutrophil/lymphocyte ratio (NLR) are often significantly elevated, suggesting a distinct inflammatory phenotype[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis inflammatory characteristic may contribute to poor responses to conventional inhaled corticosteroid therapy, with approximately 40% of obese asthma patients exhibiting steroid resistance[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. High WBC levels play a critical role in chronic respiratory diseases, especially in the pathogenesis of asthma. An increase in WBC typically reflects an intensification of chronic inflammation in the body, which can lead to airway damage and potentially worsen asthma over time[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, analyzing WBC and its subsets may provide valuable diagnostic insights for obesity-related asthma[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProspective cohort studies have shown that elevated baseline WBC levels can predict the risk of subsequent asthma acute exacerbations, with this association being more pronounced in obese populations[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In recent years, some studies using data from the National Health and Nutrition Examination Survey (NHANES) have further investigated the relationship between BMI and asthma[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, most studies have focused on single variables, and there is a lack of systematic analysis of the relationship between BMI, BRI, inflammatory markers (such as WBC), and asthma. Therefore, this study utilizes the NHANES (2009\u0026ndash;2018) database to explore the relationship between BMI, BRI, WBC, and the risk of asthma in women.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Population\u003c/h2\u003e\n \u003cp\u003eThe study initially included 49,693 participants from the NHANES database covering the period from 2009 to 2018. NHANES conducted comprehensive physical measurements and complete blood count (CBC) tests for individuals aged 20 and above, and participants self-reported whether they had been diagnosed with asthma by a doctor or other health professionals. The primary aim of the study was to assess the nutrition and health status of the U.S. female population and to further explore the epidemiological characteristics of asthma and its related factors. Inclusion criteria included: ① complete baseline data on waist circumference, height, BMI, smoking and drinking habits, and history of hypertension and diabetes; ② complete five-part blood count data; ③ U.S. adult women aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years; ④ complete data on age at menarche, age at first and last live birth, and hormone use history; ⑤ complete self-reported asthma diagnosis data. A total of 3,729 female participants who met these criteria were included in the analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 BMI, BRI, and Inflammatory Index\u003c/h2\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;), waist circumference (WC, cm), and standing height (cm) were extracted from the physical examination data provided by NHANES (file name: \u0026quot;BMX.Doc\u0026quot;). The Body Roundness Index (BRI)[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] was calculated using waist circumference (in meters) and height (in meters) with the following formula:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eUsing NHANES laboratory data (file name: \u0026quot;CBC.Doc\u0026quot;), the neutrophil-to-lymphocyte ratio (NLR)[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] and platelet-to-lymphocyte ratio (PLR) [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]were calculated. The relevant indicators included white blood cell count (WBC), lymphocyte count, neutrophil count, monocyte count, and platelet count, with units expressed as thousands of cells per microliter (\u0026times;10\u0026sup3;/\u0026micro;L)[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The calculation formulas are as follows:\u003c/p\u003e\n \u003cp\u003eNLR\u0026thinsp;=\u0026thinsp;Neutrophil count / Lymphocyte count\u003c/p\u003e\n \u003cp\u003ePLR\u0026thinsp;=\u0026thinsp;Platelet count / Lymphocyte count\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Asthma\u003c/h2\u003e\n \u003cp\u003eAsthma information was obtained from the medical condition questionnaire module (file name: \u0026quot;MCQ.Doc\u0026quot;) using question MCQ010: \u0026quot;Have you ever been told by a doctor or other health professional that you have asthma?\u0026quot; Women who answered \u0026quot;yes\u0026quot; were classified as having a history of asthma, while non-respondents were excluded. Previous studies have shown that this self-reported method has high accuracy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Data Covariates\u003c/h2\u003e\n \u003cp\u003eTo control for potential confounding factors, several covariates were included in the study. Demographic variables included age, race, education level, marital status, and poverty-income ratio (PIR). Lifestyle variables included smoking status (defined as having smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes in a lifetime) and drinking status (defined as having consumed any form of alcohol or having drunk\u0026thinsp;\u0026ge;\u0026thinsp;12 alcoholic beverages in a lifetime). Health status variables, including hypertension and diabetes, were self-reported through the questionnaire, with the question \u0026quot;Have you ever been diagnosed by a doctor or health professional (yes/no)?\u0026quot; Female reproductive-related variables included age at menarche, use of female hormones, and age at first and last childbirth, with data from the reproductive health module, specifically variables RHQ010, RHQ540 (yes/no), RHD180, and RHD190.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed using the DecisionLinnc 1.0 software platform[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], which integrates multiple programming language environments and supports data processing, statistical modeling, and visual analysis. Given NHANES\u0026apos;s complex sampling design, sample weighting was applied during the analysis. The weighted variables used the two-year cycle examination sample weight (WTMEC2YR) for each period, multiplied by 1/5 to obtain the weight for the 10-year combined sample[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. The analysis was limited to female participants with complete exposure and outcome variable data.\u003c/p\u003e\n \u003cp\u003eThe study first described the characteristics of the asthma and non-asthma groups: categorical variables were presented as percentages; continuous variables were tested for normality, and if normally distributed, presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). If not normally distributed, the median and interquartile range (IQR) were used to represent central tendency and dispersion.\u003c/p\u003e\n \u003cp\u003eThree multivariate logistic regression models were then constructed to assess the independent associations between BMI, BRI, and WBC with asthma risk. Model 1 was the unadjusted model; Model 2 adjusted for age, race, education level, marital status, and PIR based on Model 1; Model 3 further included age at menarche, age at first and last childbirth, hormone use, smoking and drinking behaviors, hypertension, and diabetes status based on Model 2. Simultaneously, BMI, BRI, and WBC were grouped by quartiles, and trend tests (P for trend) were calculated for sensitivity analysis to verify the stability of the analysis results.\u003c/p\u003e\n \u003cp\u003eTo improve the robustness of the model, multicollinearity tests were performed on all covariates before modeling, using the variance inflation factor (VIF) to assess correlations between variables. If a variable\u0026apos;s VIF\u0026thinsp;\u0026gt;\u0026thinsp;10, it was considered to have severe multicollinearity and was excluded from modeling[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTo explore the nonlinear relationship between BMI, BRI, and WBC and asthma risk, a weighted restricted cubic spline (RCS) regression model was constructed, and smoothing curve fitting was applied using a generalized additive model based on the fully adjusted model (Model 3). Threshold effect analysis was performed to identify potential inflection points, with model differences before and after the inflection point assessed by likelihood ratio tests. Based on this, subgroup analysis and interaction tests were conducted to explore the heterogeneous effects of variables on asthma risk in different populations. Mediation analysis was then used to assess the indirect effect of BMI and BRI on asthma through WBC, revealing the potential mediating mechanism of WBC between obesity and asthma.\u003c/p\u003e\n \u003cp\u003eFinally, to assess the discriminatory ability of each indicator in predicting asthma risk, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to compare the predictive performance of BMI, BRI, and WBC.\u003c/p\u003e\n \u003cp\u003eAdditionally, to validate the robustness of the study results, sensitivity analysis was performed by excluding current smokers, and demographic characteristic analysis and multivariate logistic regression were repeated under weighted conditions, with model settings consistent with the main analysis. In statistical tests, a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eA total of 3,729 eligible female participants from the NHANES database were included in this study. The participants were categorized into asthma and non-asthma groups based on asthma status, and inter-group comparisons of covariates were conducted. The results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed significant differences between the two groups in several variables, including race, marital status, waist circumference, BMI, BRI, smoking status, alcohol consumption, hypertension history, age at first and last live birth, WBC, monocytes, and neutrophils (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant differences were observed between the groups in terms of age, education level, poverty-income ratio (PIR), height, diabetes, age at menarche, hormone use, lymphocytes, platelets, NLR, and PLR (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multicollinearity Analysis\u003c/h2\u003e \u003cp\u003eA total of 16 core covariates were included in this study, and the associated variance inflation factors (VIF) are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results showed that the VIF for all variables was less than 10, indicating no significant multicollinearity issues in the model, allowing all covariates to be included in the final regression model.\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\u003eSample characteristics and comparison between participants with and without asthma(weighted)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes asthma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo asthma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;28,740,316\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,995,275\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;23,745,041\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.36 \u0026plusmn; (16.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.56 \u0026plusmn; (16.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.73 \u0026plusmn; (16.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e671(10.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (6.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602 (10.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446 (6.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(6.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e376 (6.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,268 (61.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (66.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,020 (60.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e809 (12.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (14.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e654 (12.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race-Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e535 (9.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(7.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e474 (9.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e497 (6.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(5.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437 (7.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e534 (11.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(10.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e451 (11.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e872(26.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (26.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e721 (26.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,138 (31.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (36.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e932 (30.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688 (24.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (21.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e585 (24.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,223 (64.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332 (59.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,891 (66.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,204 (28.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (30.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e999 (27.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302 (6.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (9.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236 (6.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e952 (18.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (23.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e781 (17.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;\u0026le;\u0026thinsp;PIR\u0026thinsp;\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,130 (24.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (24.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e941 (24.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026thinsp;\u0026le;\u0026thinsp;PIR\u0026thinsp;\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e936 (27.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (25.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e798 (27.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e711 (29.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (26.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e606 (30.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.03 \u0026plusmn; (16.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.10 \u0026plusmn; (18.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.97 \u0026plusmn; (15.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160.20 \u0026plusmn; (6.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160.99 \u0026plusmn; (6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.04 \u0026plusmn; (6.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.32 \u0026plusmn; (7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.58 \u0026plusmn; (9.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.84 \u0026plusmn; (6.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.22 \u0026plusmn; (2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.98 \u0026plusmn; (3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.06 \u0026plusmn; (2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e978 (28.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (36.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e764 (27.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,751 (71.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389 (63.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,362 (72.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,398 (71.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e431 (79.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,967 (70.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,331 (28.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (20.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,159 (29.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,625 (39.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (46.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,312 (38.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,104 (60.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290 (53.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,814 (61.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e654 (14.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (16.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520 (14.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,075 (85.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469 (83.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,606 (85.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenophania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.70 \u0026plusmn; (1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.59 \u0026plusmn; (1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.72 \u0026plusmn;(1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst live birth time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.35 \u0026plusmn; (20.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.19 \u0026plusmn; (4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.59 \u0026plusmn; (22.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast live birth time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.12 \u0026plusmn; (5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.87 \u0026plusmn; (5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.38 \u0026plusmn; (5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale hormones use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e698 (22.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (23.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e567 (22.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,031 (77.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e472 (76.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,559 (77.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.33 \u0026plusmn; (2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.79 \u0026plusmn; (2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.23 \u0026plusmn; (2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLymphocyte (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21 \u0026plusmn; (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29 \u0026plusmn; (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20 \u0026plusmn; (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55 \u0026plusmn; (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 \u0026plusmn; (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 \u0026plusmn; (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.33 \u0026plusmn; (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.64 \u0026plusmn; (1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.26 \u0026plusmn; (1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBlood platelet (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253.40\u0026thinsp;\u0026plusmn;\u0026thinsp;64.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257.41\u0026thinsp;\u0026plusmn;\u0026thinsp;62.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252.55\u0026thinsp;\u0026plusmn;\u0026thinsp;65.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.13 \u0026plusmn; (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21 \u0026plusmn; (1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11 \u0026plusmn; (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.33 \u0026plusmn; (47.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.46 \u0026plusmn; (48.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125.73 \u0026plusmn; (47.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003eN not missing unweighted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003eN (unweighted); (%) (weighted); Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (weighted)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ec\u003c/sup\u003echi-squared test with Rao \u0026amp; Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNLR: Neutrophil count/Lymphocyte count, PLR:Platelet count/Lymphocyte count, PIR: Poverty to Income Ratio, BMI: Body mass index, BRI: Body Roundness Index.\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\u003eVariance inflation factors(VIF)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenophania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst live birth time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast live birth time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale hormones use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association between BMI, BRI, and WBC with Asthma\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in the unadjusted model (Model 1), BMI, BRI, and WBC were significantly positively correlated with asthma, with ORs of BMI: 1.05 (95% CI: 1.03\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BRI: 1.15 (95% CI: 1.08\u0026ndash;1.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and WBC: 1.11 (95% CI: 1.06\u0026ndash;1.17, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for demographic factors (Model 2), all three remained significantly associated with asthma, with ORs of BMI: 1.04 (95% CI: 1.03\u0026ndash;1.06), BRI: 1.14 (95% CI: 1.09\u0026ndash;1.20), and WBC: 1.10 (95% CI: 1.04\u0026ndash;1.17), with P-values all \u0026lt;\u0026thinsp;0.001. Further adjustment for all covariates in Model 3 confirmed that the associations for BMI, BRI, and WBC remained significant, with ORs of BMI: 1.04 (95% CI: 1.02\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BRI: 1.13 (95% CI: 1.07\u0026ndash;1.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and WBC: 1.08 (95% CI: 1.03\u0026ndash;1.14, P\u0026thinsp;=\u0026thinsp;0.004), indicating that for each unit increase in WBC, asthma risk increased by approximately 8%.\u003c/p\u003e \u003cp\u003eAfter grouping BMI into quartiles, analysis showed that compared with the Q1 group, the Q2 and Q3 groups did not show a significant association with asthma risk in Models 1\u0026ndash;3 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the Q4 group showed a significantly increased asthma risk in all three models, with ORs of Model 1: 1.97 (95% CI: 1.37\u0026ndash;2.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Model 2: 1.94 (95% CI: 1.34\u0026ndash;2.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Model 3: 1.73 (95% CI: 1.19\u0026ndash;2.52, P\u0026thinsp;=\u0026thinsp;0.002). The P for trend for the BMI quartile groups was \u0026lt;\u0026thinsp;0.001 in all models, indicating a clear dose-response trend.\u003c/p\u003e \u003cp\u003eResults from the BRI quartile analysis showed that there were mostly no significant differences between the Q2 and Q3 groups, with Q3 in Model 2 showing a weak association with asthma (OR\u0026thinsp;=\u0026thinsp;1.47, 95% CI: 1.01\u0026ndash;2.13, P\u0026thinsp;=\u0026thinsp;0.046). The Q4 group was significantly associated with increased asthma risk in Models 1\u0026ndash;3, with ORs of Model 1: 2.07 (95% CI: 1.40\u0026ndash;3.06), Model 2: 2.16 (95% CI: 1.47\u0026ndash;3.18), and Model 3: 1.96 (95% CI: 1.32\u0026ndash;2.93), with P-values all \u0026lt;\u0026thinsp;0.001. The quartile trend test for BRI was statistically significant in all models (P for trend\u0026thinsp;\u0026le;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAfter performing quartile analysis for WBC, it was found that the Q2 and Q3 groups were not significantly associated with asthma in all three models. However, the Q4 group showed a significant increase in asthma risk in Models 1 and 2 (Model 1: OR\u0026thinsp;=\u0026thinsp;1.77, 95% CI: 1.23\u0026ndash;2.25, P\u0026thinsp;=\u0026thinsp;0.002; Model 2: OR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.15\u0026ndash;2.51, P\u0026thinsp;=\u0026thinsp;0.008), but was no longer significant in Model 3 (OR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.00\u0026ndash;2.21, P\u0026thinsp;=\u0026thinsp;0.483). Nevertheless, the P for trend for the WBC quartile groups was \u0026le;\u0026thinsp;0.024 in Models 1\u0026ndash;3, indicating a statistical trend between WBC and asthma risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of BMI, BRI, and WBC\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-vale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-vale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-vale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05(1.03,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04(1.03,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04(1.02,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eBMI Quartiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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 \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.59,1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91(0.62,1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90(0.60,1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19(0.76,1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23(0.78,1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17(0.73,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97(1.37,2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.94(1.34,2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.73(1.19,2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14(1.08,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14(1.09,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13(1.07,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eBRI Quartiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\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 \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99(0.66,1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09(0.73,1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09(0.72,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32(0.91,1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47(1.01,2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.37(0.91,2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07(1.40,3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16(1.47,3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.96(1.32,2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11(1.06,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10(1.04,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08(1.03,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC Quartiles\u003c/p\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13(0.75,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10(0.72,,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04(0.68,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37(0.95,1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33(0.89,1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22(0.81,1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77(1.23,2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70(1.15,2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49(1.00,2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: No covariates were adjusted.\u003c/p\u003e \u003cp\u003eModel 2: Age, race, education level, marital status, PIR.\u003c/p\u003e \u003cp\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;smoking, alcohol consumption, hypertension, diabetes, menophania, age at first live birth, age at last live birth, female hormones use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 RCS Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a\u0026ndash;i) illustrates the fitted results of the relationship between BMI, BRI, and WBC and female asthma using the restricted cubic spline (RCS) model in the weighted sample, and compares the trends across different covariate adjustment levels (Models 1\u0026ndash;3). The results indicate that BMI, BRI, and WBC were nonlinearly associated with asthma in most models, with WBC displaying a linear trend in Model 3. The overall association of these three indicators with asthma was statistically significant in all models (P for overall\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003eTo further visualize the relationship between BMI, BRI, and WBC and asthma risk, smoothing curve fitting was conducted based on the fully adjusted model (Model 3). The results, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a-c), reveal that all three indicators exhibit a nonlinear dose-response relationship with asthma.\u003c/p\u003e \u003cp\u003eThreshold effect analysis was performed based on Model 3, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The inflection point for BMI was 25.30. Before this inflection point, BMI was significantly associated with asthma risk (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.89\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.022), indicating that for each unit increase in BMI, asthma risk decreased by approximately 6%. After the inflection point, each unit increase in BMI was associated with a 6% increase in asthma risk (OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.06\u0026ndash;1.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The likelihood ratio test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) further supported the advantage of the threshold model. BRI showed a similar trend, with an inflection point at 3.84 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a differential effect on asthma risk before and after the inflection point. The inflection point for WBC was 4.80\u0026times;10⁹/L (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Before the inflection point, each unit increase in WBC was associated with a 2.93-fold increase in asthma risk; after the inflection point, each unit increase in WBC was associated with an approximately 8% increase in asthma risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of BMI, BRI, and WBCI on asthma using a logistic regression model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94(0.89,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06(1.05,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eLog likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68(0.53,0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18(1.14,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eLog likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.93(1.91,8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u0026gt;\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08(1.04,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eLog likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Age, race, education level, marital status, PIR, smoking, alcohol consumption, hypertension, diabetes, menophania, age at first live birth, age at last live birth, female hormones use were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup Analysis\u003c/h2\u003e \u003cp\u003eTo assess the robustness of the associations between BMI, BRI, and WBC with female asthma and to identify potential population heterogeneity, subgroup analysis and interaction tests were conducted based on multiple covariates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results consistently showed a positive correlation between the three indicators and asthma in most subgroups, with stable associations. Specifically, for each unit increase in BMI, asthma risk increased by approximately 4% (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 1.03\u0026ndash;1.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association was more significant in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;65, of various races, with less than a university education, with low income, smokers, drinkers, those with hypertension, diabetes, and those who had never used female hormones. For each unit increase in BRI, asthma risk increased by approximately 13% (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI: 1.08\u0026ndash;1.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This trend was particularly evident across different age groups, among African Americans, individuals with varying marital statuses, low-income people, smokers, drinkers, and those who had used female hormones. For each unit increase in WBC, asthma risk increased by approximately 10% (OR\u0026thinsp;=\u0026thinsp;1.10, 95% CI: 1.05\u0026ndash;1.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The association between WBC and asthma risk was more pronounced in younger populations, African Americans, married individuals, low-income people, non-smokers, drinkers, those with or without hypertension, non-diabetic individuals, and those who had used female hormones. Furthermore, no significant interaction was observed between the subgroup stratifications of the indicators (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Mediation Analysis\u003c/h2\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrate that both BMI and BRI have a significant mediating effect on the risk of female asthma through WBC. Specifically, the total effect of BMI on asthma was 0.001888 (0.001583, 0.002093), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; the indirect effect was 0.000167 (0.000041, 0.000338), P\u0026thinsp;=\u0026thinsp;0.014; the direct effect was 0.001721 (0.001464, 0.001948), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and the proportion of the mediating effect was 8.6% (0.022980, 0.170784), P\u0026thinsp;=\u0026thinsp;0.014. The total effect of BRI was 0.009231 (0.007705, 0.010425), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; the indirect effect was 0.000998 (0.000247, 0.001864), P\u0026thinsp;=\u0026thinsp;0.016; the direct effect was 0.008233 (0.006306, 0.009623), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and the proportion of the mediating effect was 10.5% (0.026169, 0.214565), P\u0026thinsp;=\u0026thinsp;0.016. These results indicate that both BMI and BRI are positively correlated with asthma risk, and that they exert a significant mediating effect on asthma risk through white blood cell count (WBC), an inflammatory marker. This suggests that obesity may promote the development of female asthma by activating systemic inflammatory pathways.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mediating effect of WBC on the relationship between BMI, BRI, and asthma.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI(lower)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI(upper)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-vale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\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\u003eIndirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\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\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\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\u003eProportion Mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eName\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95%CI(lower)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e95%CI(upper)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP-vale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\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\u003eIndirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\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\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\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\u003eProportion Mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.105034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.214565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBMI: The mediating effect of WBC on the relationship between BMI and asthma.\u003c/p\u003e \u003cp\u003eBRI: The mediating effect of WBC on the relationship between BRI and asthma.\u003c/p\u003e \u003cp\u003eNotes: Age, race, education level, marital status, PIR, smoking, alcohol consumption, hypertension, diabetes, menophania, age at first live birth, age at last live birth, female hormones use were adjusted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7 ROC Prediction\u003c/h2\u003e \u003cp\u003eROC curve analysis was conducted to further evaluate the predictive ability of BMI, BRI, and WBC for asthma (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results showed that the area under the curve (AUC) for BMI, BRI, and WBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) were 0.593, 0.590, and 0.564, respectively, indicating that BMI and BRI had better predictive performance than WBC. In terms of sensitivity, BMI performed better than BRI and WBC in predicting the occurrence of female asthma, although it slightly lagged behind the other two in terms of specificity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe sensitivity, specificity, and AUC of ROC curves for predicting asthma using BMI, BRI, and WBC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82(0.49,0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33(0.27,0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59(0.57,0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72(0.53,0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43(0.28,0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59(0.56,0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63(0.22,0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48(0.43,0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56(0.54,0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAUC: Area under the curve; CI: confidence interval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eTo further verify the robustness of the study results, sensitivity analysis was performed after excluding current smokers. The demographic characteristics analysis after excluding smokers (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) was consistent with the main analysis. Based on this, the same three regression models (Models1-3) were used to evaluate the relationship between BMI, BRI, and WBC with asthma (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The results showed that all three indicators remained significantly positively correlated with asthma risk in all models. These results were highly consistent with the analysis of the complete sample, further validating the reliability of the analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics and comparison between participants with and without asthma(weighted) in the sensitivity analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes asthma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo asthma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;24,999,002\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,186,833\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;20,812,169\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.45\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.40\u0026thinsp;\u0026plusmn;\u0026thinsp;16.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.66\u0026thinsp;\u0026plusmn;\u0026thinsp;16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e626 (10.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (7.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560 (11.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e418(7.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(6.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e355 (7.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,035 (60.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193(64.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e842 (59.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e702 (12.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (14.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e571(12.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race-Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505(9.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(7.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450 (9.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e470 (7.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (5.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415 (7.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e429 (10.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (9.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e370 (10.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e730 (24.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (25.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e602 (23.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e992 (31.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167(35.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e825 (30.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e665(27.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (24.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e566 (27.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,004 (65.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285(59.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,719(67.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,054 (28.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176(31.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e878(27.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (5.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(9.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181 (5.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e768 (15.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (19.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e644 (15.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;\u0026le;\u0026thinsp;PIR\u0026thinsp;\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984(23.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160(23.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e824(23.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026thinsp;\u0026le;\u0026thinsp;PIR\u0026thinsp;\u0026lt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e849 (27.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (26.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e724 (27.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e685(32.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (30.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e586 (33.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.93\u0026thinsp;\u0026plusmn;\u0026thinsp;19.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.05\u0026thinsp;\u0026plusmn;\u0026thinsp;15.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160.69\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.97\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,003 (68.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e348 (77.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,655(67.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,283 (31.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (22.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,123 (32.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,449(40.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272(48.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,177 (38.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,837 (59.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236 (51.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,601 (61.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e592 (14.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (16.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478 (14.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,694(85.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394 (83.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,300 (85.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenophania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst live birth time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.79\u0026thinsp;\u0026plusmn;\u0026thinsp;22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.03\u0026thinsp;\u0026plusmn;\u0026thinsp;24.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast live birth time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale hormones use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e632 (24.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (24.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e521 (24.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,654 (75.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397 (75.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,257 (75.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLymphocyte (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eBlood platelet (1000 cell/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250.76\u0026thinsp;\u0026plusmn;\u0026thinsp;63.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254.83\u0026thinsp;\u0026plusmn;\u0026thinsp;58.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249.94\u0026thinsp;\u0026plusmn;\u0026thinsp;64.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.56\u0026thinsp;\u0026plusmn;\u0026thinsp;46.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.53\u0026thinsp;\u0026plusmn;\u0026thinsp;45.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.97\u0026thinsp;\u0026plusmn;\u0026thinsp;46.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003eN not missing unweighted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003eN (unweighted); (%) (weighted); Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (weighted)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ec\u003c/sup\u003echi-squared test with Rao \u0026amp; Scott's second-order correction; Wilcoxon rank-sum test for complex survey samples\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNLR: Neutrophil count/Lymphocyte count, PLR:Platelet count/Lymphocyte count, PIR: Poverty to Income Ratio, BMI: Body mass index, BRI: Body Roundness Index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of BMI, BRI, and WBC with asthma in the sensitivity analysis\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=\"char\" char=\".\" 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=\"left\" 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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-vale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-vale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-vale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05(1.03,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05(1.03,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04(1.02,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16(1.10,1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16(1.10,1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.14(1.08,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12(1.05,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12(1.05,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.10(1.03,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study, based on the 2009\u0026ndash;2018 NHANES database, used a cross-sectional design to assess the relationship between body mass index (BMI), body roundness index (BRI), and white blood cell count (WBC) with the risk of asthma in U.S. adult women. The results revealed significant positive correlations between BMI, BRI, and WBC levels and asthma prevalence, with these associations remaining consistent across various statistical models and analytical methods. RCS analysis further showed a nonlinear dose-response relationship between these three indicators and asthma, with good curve fitting and a clear threshold effect. Subgroup analysis also confirmed that the associations between these indicators and asthma risk were consistently positive, underscoring their broad applicability and stability. Mediation analysis indicated that BMI and BRI may indirectly influence asthma risk through WBC, suggesting that obesity may contribute to asthma development by inducing inflammatory processes. Finally, ROC curve analysis demonstrated that BMI was a better predictor than BRI and WBC for identifying high-risk individuals for asthma in women. To verify the robustness of the results, sensitivity analysis was conducted after excluding current smokers, and the conclusions remained consistent. In conclusion, BMI and BRI as obesity indicators, and WBC as a systemic inflammation marker, are potential independent risk factors for female asthma. This study also suggests that WBC may mediate the relationship between BMI, BRI, and asthma.\u003c/p\u003e \u003cp\u003ePrevious studies have widely shown that obesity, particularly abdominal obesity, is a significant risk factor for asthma. A prospective cohort study by Wang et al. demonstrated a close association between weight gain in women and the onset and progression of asthma[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Liu et al. further found that women with a higher waist circumference had a significantly increased risk of asthma[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which is consistent with the findings of this study. Women tend to have lower lung capacity and poorer airway ventilation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and their immune systems are more active and responsive[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As women age and undergo physiological processes such as pregnancy and childbirth, particularly in multiparous women, fat distribution tends to shift toward abdominal and visceral areas[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This abdominal fat accumulation, coupled with skeletal muscle relaxation, may further impair lung function[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, estrogen in women influences immune function and airway health. Studies have shown that estrogen may enhance airway reactivity, making women more susceptible to asthma symptoms when exposed to environmental pollutants, allergens, and other triggers[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study found that BMI and BRI were nonlinearly associated with female asthma prevalence, aligning with Xu et al.'s study, which indicated a nonlinear relationship between BRI, WWI indices, and asthma[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. BMI and BRI are closely related to central obesity and metabolic syndrome and may increase the risk of asthma in women through various mechanisms, particularly by activating chronic low-grade inflammation. In obese individuals, low-grade chronic inflammation persists, and its negative impact on airway immune function may be the underlying cause of asthma symptom exacerbation[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As an indicator of abdominal fat distribution, the higher the BRI, the more pronounced the accumulation of visceral fat[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. After pregnancy and childbirth, women may experience abdominal muscle separation, making it easier for abdominal fat to accumulate. Over time, this fat accumulation can lead to abdominal obesity. Visceral fat not only serves as a marker of metabolic disease but also secretes pro-inflammatory factors such as TNF-α, IL-6, adiponectin, leptin, and lipid transport proteins into the bloodstream. These factors affect the systemic immune system and indirectly or directly influence airway inflammation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e][\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These pro-inflammatory factors stimulate immune cells, particularly neutrophils, monocytes, and eosinophils, activating and increasing their numbers, which intensifies ongoing airway inflammation and immune responses[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Stimulated immune cells also secrete cytokines such as IL-8 and TNF-α, further exacerbating chronic low-grade inflammation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e][\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], enhancing airway inflammation, leading to airway hyperreactivity and remodeling[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and promoting asthma onset and worsening[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Furthermore, obesity alters the immune system's response patterns by affecting the function of T cells and B cells. Specifically, obesity is closely associated with Th2 cell dysfunction, a key player in asthma\u0026rsquo;s immune response. Obesity may exacerbate inflammation by altering T cell differentiation and enhancing Th2 cell responses[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, obesity influences airway remodeling through abnormal fatty acid metabolism, increasing airway smooth muscle proliferation, basement membrane thickening, and collagen deposition, thereby contributing to airway functional limitation and airflow obstruction[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Studies have also found that the accumulation of visceral fat is closely related to increased oxidative stress. The accumulation of abdominal fat produces reactive oxygen species and free radicals, and the increased oxidative stress exacerbates airway inflammation, leading to reduced airway function[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Obesity and increased abdominal fat not only significantly elevate oxidative stress levels, affecting the immune system, but may also directly impair lung function[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e][\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Thus, oxidative stress may also be an important mechanism through which abdominal fat influences asthma. This study found that BMI and BRI were significantly associated with female asthma risk. Both BMI and BRI accurately reflect abdominal fat accumulation and show potential for predicting asthma risk, making them valuable for early asthma screening.\u003c/p\u003e \u003cp\u003eWhite blood cell count (WBC) is an important marker of systemic inflammation. An increase in WBC typically indicates the activation of the inflammatory response, accompanied by the aggregation of immune cells, particularly eosinophils, neutrophils, and monocytes. These cells release reactive oxygen species (ROS), proteases (such as neutrophil elastase), and pro-inflammatory factors (such as IL-8 and TNF-α) in asthma airways, causing airway epithelial damage, increased mucus secretion, and airway hyperreactivity[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These pathological responses are particularly prominent in asthma patients[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Simultaneously, increased WBC levels release cytokines like IL-6 and TNF-α, which activate airway epithelial cells, upregulate the expression of adhesion molecules (e.g., ICAM-1), and promote the migration of more immune cells to the airways, creating a vicious cycle[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. An increase in WBC may indicate ongoing immune responses in the airways, exacerbating asthma symptoms and driving disease progression[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Moreover, WBC levels are closely associated with airway remodeling, suggesting that elevated WBC not only serves as a marker of inflammation but may also directly participate in asthma\u0026rsquo;s pathophysiological process. Studies have shown that, particularly in obese individuals, elevated pro-inflammatory factors lead to increased WBC release, which further exacerbates airway inflammation through the bloodstream[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This study\u0026rsquo;s results confirm this, with a significant positive correlation between WBC and asthma. For each unit increase in WBC, the risk of asthma increased by approximately 10% (OR\u0026thinsp;=\u0026thinsp;1.10, 95% CI: 1.05\u0026ndash;1.15, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In conclusion, WBC, as a marker of inflammation, can effectively reflect airway inflammation activity and predict asthma risk, serving as an independent risk factor for asthma.\u003c/p\u003e \u003cp\u003eThis study further determined through mediation analysis that BMI and BRI significantly influence asthma risk via the inflammatory marker WBC, offering new insights into the interaction between obesity and immune responses. This study has several strengths. First, based on the large female sample from the NHANES database, it found significant associations between BMI, BRI, WBC, and asthma risk in women. Second, it primarily focused on adult women and adjusted for several key potential confounding factors, such as age, lifestyle, age at menarche, hormone use, and reproductive history. Third, subgroup and sensitivity analyses verified the robustness and reliability of the study results. Additionally, mediation analysis revealed the mediating role of WBC between obesity and asthma development. Finally, the study assessed the predictive performance of these three indicators in relation to asthma risk. Overall, this study provides valuable new insights into the role of BMI, BRI, and WBC in asthma risk and offers important references for clinical management and future research.\u003c/p\u003e \u003cp\u003eHowever, this study also has limitations. First, the data from NHANES is based on self-reporting by participants, which may introduce recall bias. Second, although several potential confounding factors were adjusted for, there may still be unknown factors (such as genetic background and environmental pollution) that could influence the results. Third, this study relied on one-time height and weight measurements to calculate BRI and laboratory-determined WBC counts, which may introduce bias.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study confirmed a positive correlation between BMI, BRI, and WBC with asthma in U.S. adult women and highlighted the significant mediating role of WBC in the relationship between BMI, BRI, and asthma risk. Additionally, the study demonstrated that BMI, BRI, and WBC hold potential for predicting asthma prevalence, with the discovery of threshold effects carrying important clinical implications. Future studies should further validate these findings in larger prospective cohorts. Moreover, experimental research is needed to explore the mechanisms underlying the interactions between these biomarkers, immune responses, and cytokines, offering new strategies for early asthma screening and personalized treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial registration\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;Not applicable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Institutional Review Board (IRB) of the National Center for Health Statistics, in accordance with the ethical standards set forth by the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;Not applicable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to publish\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;Not applicable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXQQ contributed to data curation, formal analysis, writing and software; ZQM contributed to visualization and validation; JJ contributed to study conception, design and review; CYL contributed to data collection and data review; HCY and ZHW contributed to literature search; CJ contributed to review. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that financial support has been received for the research, author identity, and/or publication of this article. This study was funded by the National Administration of Traditional Chinese Medicine's High-Level Traditional Chinese Medicine Key Discipline Construction Project, Integrative Traditional Chinese and Western Medicine Clinical Project (Project No.: zyyzdxk-2023104).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to Fujian University of Traditional Chinese Medicine for their valuable support and contributions. We also thank all the staff for their assistance throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher’s note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmith, R., Jones, D., \u0026amp; Wang, L. (2021). 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PMID: 18982161; PMCID: PMC2575722.\u003c/li\u003e\n\u003cli\u003eFahy JV. Type 2 inflammation in asthma--present in most, absent in many. Nat Rev Immunol. 2015 Jan;15(1):57-65. doi: 10.1038/nri3786. PMID: 25534623; PMCID: PMC4390063.\u003c/li\u003e\n\u003cli\u003eTosi MF, Stark JM, Smith CW, Hamedani A, Gruenert DC, Infeld MD. Induction of ICAM-1 expression on human airway epithelial cells by inflammatory cytokines: effects on neutrophil-epithelial cell adhesion. Am J Respir Cell Mol Biol. 1992 Aug;7(2):214-21. doi: 10.1165/ajrcmb/7.2.214. PMID: 1353976.\u003c/li\u003e\n\u003cli\u003ePelaia, G., Vatrella, A., Busceti, M. T., Gallelli, L., Calabrese, C., \u0026amp; Terracciano, R. (2015). Cellular mechanisms underlying eosinophilic and neutrophilic airway inflammation in asthma. Mediators of Inflammation, 2015, 879783. https://doi.org/10.1155/2015/879783\u003c/li\u003e\n\u003cli\u003eAl Khathlan N. Association of inflammatory cytokines with obesity and pulmonary function testing. PLoS One. 2023 Nov 22;18(11):e0294592. doi: 10.1371/journal.pone.0294592. PMID: 37992066; PMCID: PMC10664933.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BMI, BRI, Mediating effect, asthma, association","lastPublishedDoi":"10.21203/rs.3.rs-6503201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6503201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective This study utilizes data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2009 and 2018 to investigate the associations between obesity indicators (BMI and BRI), the inflammatory marker (WBC), and asthma in women. Additionally, the study explores the potential mediating role of WBC in the relationship between BMI, BRI, and asthma.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods This A total of 3,792 eligible female participants were included in this cross-sectional study. We applied various statistical methods, including logistic regression, restricted cubic spline (RCS), generalized additive model (GAM), and mediation analysis, to assess the relationships between BMI, BRI, WBC, and asthma in women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults The weighted analysis revealed that asthma patients had significantly higher levels of BMI, BRI, and WBC compared to non-asthmatic individuals (all P \u0026lt; 0.001). In unadjusted logistic regression models, these indicators were significantly associated with asthma, and the associations remained significant after adjusting for potential confounders. Quartile-based analysis showed that participants in the highest quartile (Q4) for BMI, BRI, and WBC exhibited a significantly increased risk of asthma. RCS analysis further supported the nonlinear relationships between BMI, BRI, WBC, and asthma, while mediation analysis demonstrated that WBC acted as a significant mediator in the relationship between BMI/BRI and asthma.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion This study identifies significant and nonlinear associations between BMI, BRI, and WBC with the risk of asthma in women. Moreover, WBC was found to partially mediate the effects of BMI and BRI on asthma, offering new insights into the inflammatory mechanisms underlying obesity-related asthma in females.\u003c/p\u003e","manuscriptTitle":"Obesity Indicators and Female Asthma Risk: The Mediating Role of White Blood Cell Count Based on NHANES Data Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 11:46:42","doi":"10.21203/rs.3.rs-6503201/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-26T11:12:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T07:29:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T06:28:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158300606766429026791502485584165762644","date":"2025-06-10T16:01:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299222172954108518501110564532952387412","date":"2025-06-05T05:45:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T18:18:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71781375870419994371604753755851356395","date":"2025-06-02T17:30:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86536283755562456985562820972881240891","date":"2025-06-02T17:02:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252771275523975046735564933014394856420","date":"2025-06-01T23:10:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-30T17:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205680999991679006060729499640462160784","date":"2025-05-28T10:05:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55338015482787146373143171760447837692","date":"2025-05-27T21:53:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304574269477610064812164953337454864824","date":"2025-05-14T23:18:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67798391641439107985542769533747312060","date":"2025-05-14T14:25:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235507832831717491357907748639067295004","date":"2025-05-14T14:10:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-12T21:06:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-25T13:42:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-25T07:49:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T07:45:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-04-22T10:14:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8d327ad-26d8-4939-ac9f-e1401afc6d8b","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:36:48+00:00","versionOfRecord":{"articleIdentity":"rs-6503201","link":"https://doi.org/10.1186/s12890-025-03885-6","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2025-08-21 16:29:37","publishedOnDateReadable":"August 21st, 2025"},"versionCreatedAt":"2025-05-16 11:46:42","video":"","vorDoi":"10.1186/s12890-025-03885-6","vorDoiUrl":"https://doi.org/10.1186/s12890-025-03885-6","workflowStages":[]},"version":"v1","identity":"rs-6503201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6503201","identity":"rs-6503201","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}