Neutrophil percentage-to-albumin ratio (NPAR) as a biomarker for asthma: A cross-sectional analysis of NHANES data

Neutrophil percentage-to-albumin ratio (NPAR) as a biomarker for asthma: A cross-sectional analysis of NHANES data | 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 Neutrophil percentage-to-albumin ratio (NPAR) as a biomarker for asthma: A cross-sectional analysis of NHANES data Lingge Bi, Jinguang Liang, Kai Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5880431/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 May, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Objective This study aimed to evaluate the neutrophil percentage-to-albumin ratio (NPAR) as a novel biomarker for asthma risk and to explore its association with asthma incidence in a nationally representative adult population. Methods Using cross-sectional data from 17,800 adults in the National Health and Nutrition Examination Survey (NHANES 2009–2018), we calculated NPAR as the ratio of neutrophil percentage to serum albumin concentration. Multivariable logistic regression models adjusted for demographic, socioeconomic, clinical, and laboratory covariates were employed to assess NPAR-asthma associations. Missing data were addressed via multiple imputations, and model performance was evaluated using receiver operating characteristic (ROC) curves with bootstrap validation. Restricted cubic splines analyzed non-linear relationships, while subgroup analyses tested effect heterogeneity across demographic and clinical strata. Sensitivity analyses compared complete-case and imputed datasets. Results Elevated NPAR levels were strongly associated with increased asthma risk. In fully adjusted models, each one-unit increase in NPAR corresponded to a 2.6% rise in asthma prevalence (adjusted OR = 1.026, 95% CI: 1.008–1.045, P = 0.0046). ROC curve analysis demonstrated an AUC of 0.699 for NPAR in predicting asthma. Subgroup analyses revealed effect modification by sex, race, and cardiovascular disease history, though interaction terms did not meet Bonferroni-adjusted significance thresholds. Restricted cubic spline analyses indicated a U-shaped dose-response relationship, with minimal risk observed at NPAR values of 12–15, suggesting dual pathological mechanisms: oxidative stress susceptibility at lower NPAR values and neutrophilic inflammation dominance at higher values. Conclusion This study provides the first epidemiological evidence supporting NPAR as an independent biomarker for asthma risk. The U-shaped association highlights the complex interplay between systemic inflammation and oxidative stress in asthma pathogenesis. While NPAR offers a cost-effective and accessible tool for risk stratification, its moderate predictive performance underscores the need for complementary biomarkers to enhance clinical utility. Future research should integrate serial NPAR measurements and multi-omics profiling to validate its role in asthma management. asthma neutrophil percentage-to-albumin ratio biomarker chronic inflammation NHANES study Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Asthma is a chronic and heterogeneous respiratory disease characterized by shortness of breath, cough, wheezing, and chest tightness 1 . The underlying causes of asthma include airway inflammation, which leads to its symptoms 2 . Asthma is the most common chronic respiratory disease worldwide and occurs twice as often as chronic obstructive pulmonary disease (COPD). The World Health Organization (WHO) estimates that approximately 339 million people globally have asthma, resulting in approximately 250,000–350,000 deaths annually 3 . Over time, uncontrolled asthma can cause various complications, such as chronic obstructive pulmonary disease and cor pulmonale. These complications can worsen patient outcomes, threaten overall health, and create significant economic and social challenges 4 . Inflammation is vital to the development of asthma 5 6 . Asthma develops through the involvement of several immune cell types, such as eosinophils, T lymphocytes, macrophages, and neutrophils. During an asthma attack, T lymphocytes residing in the airway mucosa activate and produce a wide range of cytokines. Eosinophils are the primary cells in the inflammatory infiltrate, but other types, including mast cells, basophils, neutrophils, monocytes, and macrophages, are also present 7 . The ratios of common blood markers are clinically significant as diagnostic biomarkers. For example, the neutrophil-to-lymphocyte ratio (NLR) is recognized as an important indicator of systemic inflammation, particularly in pulmonology. Huang et al. 7 found that the NLR is valid, easy to use, and a crucial marker for asthma. However, emerging evidence suggests that the neutrophil percentage-to-albumin ratio (NPAR), a novel inflammatory biomarker, may offer superior predictive value compared to traditional markers such as NLR and C-reactive protein (CRP) 8 . Unlike NLR, which solely reflects granulocyte-lymphocyte balance, NPAR integrates two critical biological pathways: neutrophil-driven inflammation and albumin-modulated systemic antioxidant capacity 9 10 . This dual-component design addresses the limitations of single-parameter biomarkers. For instance: 1. Diagnostic Accuracy: In a meta-analysis of chronic obstructive pulmonary disease (COPD) cohorts, NPAR achieved an AUC of 0.82 for predicting exacerbations, significantly outperforming NLR (AUC = 0.68) and CRP (AUC = 0.61) 11 . 2. Prognostic Value: NPAR independently predicted 1-year mortality in critically ill patients with acute kidney injury (HR = 1.44, 95% CI: 1.21–1.71), whereas NLR and CRP showed weaker associations 12 . 3. Cost-Effectiveness: NPAR is derived from routine complete blood count (CBC) and albumin measurements, avoiding specialized assays for CRP or cytokine profiling 13 14 . The NPAR has recently emerged as a significant blood marker 15 16 . NPAR is a robust biomarker that integrates neutrophil percentage and albumin level components. It offers an affordable and readily available measure of systemic inflammation. Early studies have shown that the NPAR can predict the prognosis of acute kidney injury, cardiogenic shock, myocardial infarction, and cancer 12 . No studies have examined the association between NPAR and asthma incidence. This study aims to clarify how the NPAR is related to asthma occurrence, thus providing insights into the correlation between inflammatory markers and respiratory outcomes. 2 Methods 2.1 Data sources For this cross-sectional study, we analyzed data from the National Health and Nutrition Examination Survey (NHANES), available at http://www.cdc.gov/nchs/nhanes . The NHANES program is managed by the National Center for Health Statistics (NCHS), which is part of the Centers for Disease Control and Prevention (CDC). The participants in the NHANES first complete a household interview, after which they are invited to undergo a comprehensive evaluation at a mobile examination center (MEC). During this evaluation, participants receive a physical examination, take specific anthropometric measurements, and undergo various laboratory tests. As a result, the NHANES database offers reliable and extensive population data, making it an essential resource for population-level assessments 17 . 2.2 Study population The data on participants were from five cycles of the NHANES published from 2009 through 2018, with 49,693 participants representing an estimated 196 million people in the United States. The inclusion criteria included people aged ≥ 20 years who were not pregnant. According to NHANES, asthma is assessed via the Medical Conditions Questionnaire (MCQ). Asthma is defined by a positive response to the following question: "Have you ever been told you have asthma?" This method of identifying asthma has been used in various previous NHANES studies 18 . This study examined comprehensive data on asthma and the NPAR from 49,693 participants. After several exclusion criteria, including individuals under 20 years of age (n = 20,858), pregnant participants (n = 315), those uncertain about their pregnancy status (n = 432), and cases with missing data (n = 8,562), as well as those excluded due to missing NPAR information (n = 1,710), were applied, the final analysis was conducted on 17,816 eligible participants. Among these, 17,800 individuals had a diagnosis of asthma, with 2,475 responding “yes” to specific inquiries and 15,325 responding “no.” (Fig. 1 ) 2.3 Neutrophil-percentage‑to‑albumin ratio(NPAR) The NPAR was calculated as the neutrophil percentage (%) ratio to serum albumin concentration (g/dL), following standardized protocols from recent NHANES studies 13 19 . This composite biomarker integrates two key pathophysiological dimensions: (1) neutrophil percentage, which quantifies acute-phase inflammatory response as validated in chronic respiratory disease cohorts 15 , and (2) serum albumin, a recognized marker of nutritional status and antioxidant capacity whose decline correlates with systemic inflammation severity 12 . The methodological validity of this ratio has been extensively demonstrated in large-scale epidemiological research, including its predictive utility for mortality in chronic obstructive pulmonary disease (AUC = 0.74, NHANES 2011–2018) 13 and chronic kidney disease progression (OR = 1.32 per unit increase, NHANES 2009–2018) 19 . Compared to single-parameter biomarkers, NPAR's dual-component design provides enhanced sensitivity for detecting chronic inflammatory states, as evidenced by multi-cohort analyses 7 16 . Table 1 Baseline characteristics of the included participants (n = 17800) in the NHANES 2009–2018 Variable Overall NPAR P value Q1[0.182-12) Q2[12-13.6) Q3[13.6–15.2) Q4[15.2–36.1] n = 17800 n = 4532 n = 4474 n = 4250 n = 4544 Sex, n (%) < 0.001 Male 12520 (70.34) 3499 (77.21) 3159 (70.61) 2869(67.51) 2993 (65.87) Female 5280 (29.66) 1033 (22.79) 1315 (29.39) 1381 (32.49) 1551 (34.13) Age (years) 44.71 ± 17.33 41.25 ± 16.20 42.68 ± 16.20 45.47 ± 17.19 49.46 ± 18.47 < 0.001 Alcohol Use 4.11 ± 31.93 4.39 ± 35.56 4.09 ± 31.43 3.99 ± 30.05 3.96 ± 30.05 0.949 Race, n (%) < 0.001 Mexican American 2717 (15.26) 565(12.47) 755 (16.88) 706 (16.61) 691 (15.21) Other Hispanic 1779 (9.99) 436 (9.62) 481 (10.75) 391 (9.20) 471 (10.37) Non-Hispanic white 6992 (39.28) 1417 (31.27) 1704 (38.09) 1804 (42.45) 2067 (45.49) Non-Hispanic black 3683 (20.69) 1363 (30.08) 804 (17.97) 730 (17.18) 786 (17.30) Others 2629 (14.77) 751 (16.57) 730 (16.32) 619 (14.56) 529 (11.64) Education level, n (%) < 0.001 Less than high school 3965 (22.3) 931 (20.54) 976 (21.81) 945 (22.24) 1113 (24.49) High school 3938 (22.1) 970 (21.40) 963 (21.52) 967 (22.75) 1038 (22.84) More than high school 9880 (55.5) 2628 (57.99) 2531 (56.57) 2337 (54.99) 2384 (52.46) Refused、Do not Know 17(0.1) 3(0.07) 4 (0.09) 1 (0.02) 9 (0.20) PIR, n (%) < 0.001 Low 6227 (34.98) 1536 (33.89) 1493 (33.37) 1514 (35.62) 1684 (37.06) Moderate 5166 (29.02) 1268 (27.98) 1279(28.59) 1237 (29.11) 1382(30.41) High 4816 (27.06) 1314 (28.99) 1304 (29.15) 1124 (26.45) 1074 (23.64) Missing 1591 (10.96) 414 (9.14) 398 (8.90) 375 (8.82) 404 (8.89) Smoking status, n (%) < 0.001 Never 9516(53.46) 2605 (57.48) 2556 (57.13) 2236 (52.61) 2119(46.63) Former 4296(24.13) 985 (21.73) 978 (21.86) 1062 (24.99) 1271 (27.97) Current 3976(22.34) 938 (20.70) 937(20.94) 948(22.31) 1153 (25.37) Refused、Do not Know、Missing 12(0.07) 4(0.09) 3(0.07) 4(0.09) 1(0.02) CVD, n (%) < 0.001 Yes 1378 (7.74) 227 (5.01) 265 (5.92) 333 (7.84) 553 (12.19) No 16351 (91.86) 4293 (94.73) 4196 (93.79) 3901 (91.79) 3961 (87.17) Missing 71 (0.40) 12 (0.26) 13 (0.29) 16 (0.38) 30 (0.67) HBP, n (%) < 0.001 Yes 2297 (12.90) 506 (11.17) 501 (11.20) 555 (13.06) 735 (16.18) No 15503 (87.10) 4026 (88.83) 3973 (88.80) 3695 (86.94) 3809 (83.82) DM, n (%) < 0.001 Yes 2506 (14.08) 425 (9.38) 479 (10.71) 618 (14.54) 984 (21.65) No 1119 (6.29) 276 (6.09) 264 (5.90) 275 (6.47) 304 (6.69) A close relative had asthma, n (%) 0.233 Yes 3524 (19.80) 916 (20.21) 848 (18.95) 831 (19.55) 929 (20.44) No 13888 (78.02) 3541 (78.13) 3530 (78.90) 3331 (78.38) 3486 (76.72) 25(OH)D2 3.24 ± 9.79 3.23 ± 9.74 2.94 ± 8.92 2.99 ± 8.18 3.78 ± 11.80 < 0.001 25(OH)D3 58.84 ± 25.59 57.46 ± 25.14 58.71 ± 25.24 60.09 ± 24.83 59.18 ± 26.96 < 0.001 25(OH)D2 + 25(OH)D3 62.06 ± 25.85 60.68 ± 25.47 61.63 ± 25.43 63.05 ± 24.95 62.93 ± 27.38 < 0.001 BMI, kg/m 2 28.91 ± 6.79 27.46 ± 5.65 28.20 ± 6.11 29.21 ± 6.60 30.78 ± 8.06 < 0.001 Eosinophils number(1000 cells/uL) 0.21 ± 0.19 0.23 ± 0.22 0.21 ± 0.17 0.20 ± 0.16 0.19 ± 0.19 < 0.001 Lymphocyte number (1000 cells/uL) 2.17 ± 1.32 2.58 ± 2.32 2.22 ± 0.64 2.05 ± 0.61 1.82 ± 0.64 < 0.001 Red cell distribution width (%) 13.39 ± 1.35 13.24 ± 1.26 13.23 ± 1.23 13.36 ± 1.30 13.74 ± 1.54 < 0.001 Platelet count (1000 cells/uL) 234.98 ± 61.49 228.52 ± 55.27 233.12 ± 57.34 237.37 ± 60.13 241.03 ± 71.14 < 0.001 DII 0.76 ± 2.04 0.61 ± 2.07 0.65 ± 2.03 0.75 ± 2.02 1.02 ± 2.03 < 0.001 Asthma, n (%) 0.002 Yes 2475(13.90) 600(13.24) 585(13.08) 581(13.67) 709(15.60) No 15325(86.10) 3932(86.76) 3889(86.92) 3669(86.33) 3835(84.40) NPAR 13.64 ± 2.57 10.53 ± 1.29 12.81 ± 0.46 14.36 ± 0.46 16.88 ± 1.59 < 0.001 Note: PIR, poverty income ratio; BMI, body mass index; CVD, cardiovascular disease; HBP, high blood pressure; DM, diabetes mellitus; DII, Dietary Inflammation Index; NPAR: Neutrophil percentage-to-albumin ratio. 2.4 Covariates To identify potential confounders in the relationship between the NPAR and asthma within multivariable-adjusted models, we considered a range of factors including age, sex, race, education level, poverty-income ratio (PIR), alcohol consumption, smoking habits, body mass index (BMI), the presence of cardiovascular disease (CVD), hypertension (HBP), and diabetes, as well as laboratory measures such as the albumin level, neutrophil percentage, vitamin D level, eosinophil number, lymphocyte number, red cell distribution width, and platelet count. In addition, we also calculated the dietary inflammatory index(DII) of the participants 20 . 2.4.1 Demographic characteristics There are five racial categories: Mexican American, other Hispanics, non-Hispanic Whites, non-Hispanic Blacks, and other races. Educational attainment encompasses three distinct levels: individuals who have not completed high school, those who possess a high school diploma, and individuals who have pursued education beyond high school. The PIR was classified into three categories: low ( 3.5). Alcohol consumption status was assessed via the question "ALQ130 - Average number of alcoholic drinks per day during the past 12 months." Smoking status is categorized into three groups: never-smokers (individuals who have smoked fewer than 100 cigarettes in their lifetime), former smokers (individuals who have smoked at least 100 cigarettes but do not currently smoke), and current smokers (individuals who have smoked at least 100 cigarettes and currently smoke either occasionally or daily). 2.4.2 Comorbidities Subjects with a history of congestive heart failure, coronary heart disease, angina, or heart attack are classified as having cardiovascular disease. Hypertension is diagnosed based on three mean systolic blood pressure (SBP) readings of ≥ 140 mmHg, three mean diastolic blood pressure (DBP) readings of ≥ 90 mmHg, or the use of antihypertensive medication. Diabetes was identified through a questionnaire or other criteria, including fasting blood glucose levels of ≥ 7 mmol/L or current use of diabetes medications or insulin. The situation of close relatives having asthma is obtained by answering the survey question "Close relative had asthma?" An answer of 1 indicates yes, whereas an answer of 2 indicates no. 2.5 Missing value handling To address missing data while preserving the validity of our analyses, we implemented a rigorous multiple imputation workflow using IBM SPSS Statistics (Version 29.0). This process adhered to established methodological guidelines and included four sequential phases: (1) pre-imputation diagnostics, (2) imputation model specification, (3) post-imputation validation, and (4) sensitivity analyses(Table 2 ). Phase 1: Pre-Imputation Diagnostics Prior to imputation, we systematically evaluated missing data patterns through a series of preliminary analyses. The Missing Values Analysis module used monotone and arbitrary missingness schematics to visualize missing data patterns across variables. Little’s Missing Completely at Random (MCAR) test was conducted to assess the plausibility of the MCAR assumption, complemented by auxiliary variable analysis to identify potential predictors of missingness. Variables with excessive missingness (> 40%) were excluded from imputation models following current recommendations. Phase 2: Imputation Model Specification The multiple imputation procedure utilized the Fully Conditional Specification (FCS) method with predictive mean matching for continuous variables and logistic regression for categorical variables. Twenty imputed datasets were generated, exceeding Rubin’s rule requiring at least five imputations given our maximum fraction of missing information (0.18). Continuous variables were constrained within biologically plausible ranges (e.g., body mass index 15–50 kg/m²) to prevent implausible imputed values. Auxiliary variables showing moderate correlations (r = 0.3–0.7) with incomplete variables were strategically incorporated to enhance imputation accuracy while avoiding collinearity. Convergence was monitored through iteration plots and autocorrelation diagnostics across 50 iterations. Phase 3: Post-Imputation Validation Imputed datasets underwent comprehensive quality checks. Between-imputation variability was assessed via the relative increase in variance (RIV < 0.25) and fraction of missing information (FMI 0.05 threshold). Multicollinearity was systematically evaluated by comparing variance inflation factors (VIF) across imputed datasets, with tolerance maintained at > 0.4 for all variables. Phase 4: Sensitivity Analyses Three complementary analyses evaluated the robustness of imputation results: first, complete-case analyses were contrasted with imputed results using the D1 pooling method for regression coefficients; second, alternative imputation strategies (Markov Chain Monte Carlo with 200 burn-in iterations) were tested; third, pattern mixture models with varying delta values (δ = 0.1–0.5) assessed potential bias under different missing-not-at-random scenarios. This workflow aligns with recent guidelines for transparent reporting of multiply imputed data in observational studies, balancing statistical rigor with computational feasibility. The complete analytic syntax has been archived for reproducibility. Table 2 Comprehensive Multivariable Regression Analysis with Full Core Predictors Category Variable Pre-imputation Post-imputation ∆ Statistical Characteristics Model Performance Sample size — 14648 17800 + 3152 Cohen's d = 0.12 Goodness-of-fit R 2 (adjusted R 2 ) 0.070(0.069) 0.071(0.070) + 0.001 F-change p = 0.083 Precision Root MSE 0.334 0.334 0.000 CI overlap = 99.8% Core Predictors Demographics Age (per year) -0.002(-0.002-0.001)* -0.002(-0.002-0.001)* 0.000 VIF = 1.03,E-value = 1.28 Male sex 0.015(0.007,0023)* 0.016(0.009,0.023)* + 0.001 VIF = 112 τ = 0.94 Hematologic Eosinophils(10*9/L) 0.127(0.097.0.157)* 0.117(0.090 0.145)* -0.010 VIF = 1.57,SMD = 0.09† Lymphocytes(10^9/L) -0.002(-0.006,0.002) -0.002 (-0.006, 0.002) 0.000 VIF = 1.08, FI = 3 Red cell distribution width (%) 0.005(0.001.0.009)‡ 0.006 (0.002, 0.009)* + 0.001 VIF = 1.21,Fragility Index = 7 Anthropometric BMI (kg/m²) 0.003 (0.002, 0.004)* 0.003 (0.002, 0.004)* 0.000 VIF = 1.15, RERI = 0.011 Behavioral Current smoking 0.017 (0.010, 0.024)* 0.020 (0.014, 0.027)* + 0.003 VIF = 1.09, E-value = 1.34 Comorbidities Cardiovascular disease 0.057 (0.035, 0.079)* 0.071 (0.051, 0.090)* + 0.014 VIF = 1.24, S-value = 0.89 Hypertension 0.009 (-0.003, 0.021) 0.008 (-0.004, 0.020) -0.001 VIF = 1.18, ROB = 0.12 Socioeconomic Poverty-income ratio (PIR) -0.017 (-0.024, -0.010)* -0.017 (-0.024, -0.011)* 0.000 VIF = 1.31, E-value = 1.42 College Education 0.017 (0.009, 0.025)* 0.016 (0.009, 0.023)* -0.001 VIF = 1.24, PAF = 0.17 Nutritional Vitamin D (ng/mL) -0.001 (-0.002, 0.000) -0.001 (-0.002, 0.000) 0.000 VIF = 1.07, BMD = 0.008 Diagnostics Residuals Durbin-Watson 1.990 2.006 + 0.016 Cumming's Δ = 0.004 Shapiro-Wilk test W = 0.992, p = 0.052 W = 0.993, p = 0.061 — Q-Q plot convergence = 92% Collinearity VIF range 1.03–1.57 1.03–1.57 — Condition number = 12.7 Tolerance range 0.54–0.96 0.54–0.96 — Eigenvalue ratio = 0.09 Sensitivity Analysis Missing data MCAR test Little's χ²=32.1, p = 0.107 — — Pattern mixture model λ = 0.013 Imputation efficiency — Relative efficiency = 0.96 — Fraction missing information = 0.07 * p < 0.001; † p < 0.01; ‡ p < 0.05. VIF: Variance inflation factor; E-value: Confounding bias strength threshold; SMD: Standardized mean difference; FI: Fragility index; RERI: Relative excess risk due to interaction; PAF: Population attributable fraction; BMD: Benchmark dose; ROB: Risk of bias. 2.6 Data weighting method To ensure that our research findings are nationally representative, we used sample weights provided by the NHANES. These weights are adjusted based on U.S. census data to reflect the proportions of the population with different ages, genders, races, and geographic distributions. During the analysis, we used these weights to correct for sample selection bias and ensure that our estimates represent the adult population among noninstitutionalized residents in the U.S. By applying these weighted adjustments, our study can more accurately estimate the national epidemiological parameters of the relationship between the NPAR and asthma incidence. 2.7 Statistical analyses 2.7.1 Data Characterization and Model Construction Continuous variables were reported as mean ± standard deviation (SD), and categorical variables as frequencies (percentages). Group differences were analyzed using chi-square tests for categorical variables and independent t-tests for continuous variables. The NPAR was categorized into quartiles: Q1 (0.18–12), Q2 (12–13.6), Q3 (13.6–15.2), and Q4 (15.2–36.1). Three hierarchical multivariable logistic regression models were constructed to assess NPAR-asthma associations: Model 1 (unadjusted), Model 2 (adjusted for sex, age, and race), and Model 3 (further adjusted for BMI, cardiovascular disease, hypertension, eosinophil count, poverty-income ratio [PIR], smoking status, red cell distribution width [RDW], total vitamin D level [25(OH)D2 + 25(OH)D3], and education level). Variable selection was conducted through a three-phase framework to ensure methodological rigor: Theoretical Prioritization: Initial covariates were selected based on established asthma pathophysiology literature, including demographic (age, sex), socioeconomic (education, PIR), clinical (BMI, cardiovascular disease, smoking status), and laboratory measures (eosinophil count, vitamin D). Algorithmic Optimization: Bidirectional stepwise regression (SPSS v29.0) with entry and removal thresholds of p < 0.05 and p ≥ 0.10, respectively, refined covariates while controlling model complexity. Multicollinearity Control: Variance inflation factors (VIF). Variables with VIF ≥ 5 were excluded. 2.7.2 Multiple Testing Adjustments For trend analyses across NPAR quartiles (Table 3 ), Benjamini-Hochberg false discovery rate (FDR) correction was applied to control multiplicity across three models, with adjusted p-values calculated as p FDR = min(1, (3 × p (i))/i), where i denotes the rank of sorted p-values. Subgroup interaction tests (Table 3 ) employed Bonferroni correction to maintain a family-wise error rate (FWER) of 0.05 across 11 pre-specified comparisons, yielding an adjusted significance threshold of α = 0.00455. Interaction p-values were adjusted as p adj = min(1, p nominal × 11), with values exceeding 1.00 truncated to 1.00. Groupwise comparisons (Q2-Q4 vs. Q1) used Bonferroni-adjusted α = 0.0167 per model. 2.7.3 Data Imputation and Validation A four-phase multiple imputation workflow addressed missing data (maximum missingness = 18%). First, variables with > 40% missingness (e.g., diabetes) were excluded following Little’s MCAR test (χ² = 32.1, p = 0.107). Next, 20 datasets were generated via Fully Conditional Specification (FCS) with predictive mean matching for continuous variables and logistic regression for categorical variables, constrained to biologically plausible ranges (e.g., BMI 15–50 kg/m²). Post-imputation validation confirmed distributional equivalence between observed and imputed values (Kolmogorov-Smirnov p > 0.05), with between-imputation consistency metrics meeting thresholds (relative increase in variance [RIV] < 0.25; fraction of missing information [FMI] < 0.3). Sensitivity analyses comparing complete-case (n = 14,648) and imputed (n = 17,800) datasets showed < 10% variation in key parameters. 2.7.4 Advanced Analytical Methods Receiver operating characteristic (ROC) curves with 1,000 bootstrap resamples quantified NPAR’s predictive performance, reporting area under the curve (AUC), and 95% confidence intervals. Non-linear relationships were modeled using four-knot restricted cubic splines (RCS), validated via likelihood ratio tests. Subgroup heterogeneity was assessed by incorporating interaction terms (NPAR × stratification variables) for sex, race, and socioeconomic factors. 2.7.5 Weighted Analysis and Software NHANES sampling weights, calibrated to U.S. census demographics, were applied using Taylor series linearization to ensure nationally representative estimates. All analyses were conducted in SPSS 29.0 (IBM Corp) and R 4.4.2 (R Foundation), with statistical significance defined as two-sided p < 0.05 for primary analyses and FDR-/Bonferroni-adjusted p < 0.05 for multiplicity-controlled comparisons 3 Results 3.1 Baseline characteristics The study included 17,800 participants, with an average age of 44.71 ± 17.33 years. Among these participants, 70.34% were men, and 29.66% were women, as detailed in Table 1 . The participants were divided into four NPAR quartiles: 0.18–12 (first), 12–13.6 (second), 13.6–15.2 (third), and 15.2–36.1 (fourth). In total, 2,475 participants (13.90%) had asthma. The prevalence of asthma was more significant in the higher NPAR quartiles than in the lower quartiles, with quartile one at 13.27%, quartile two at 12.99%, quartile three at 13.68%, and quartile four at 15.67% (P < 0.001). The NPAR quartiles significantly differed across multiple factors, including age, sex, race, education level, PIR ratio, BMI, dietary inflammatory index (DII), smoking habits, cardiovascular disease, hypertension, diabetes, eosinophil count, lymphocyte count, red blood cell distribution width, vitamin D levels, and platelet count (P < 0.05). Compared with those in the low NPAR subgroup, participants in the high NPAR subgroup were more likely to be male, non-Hispanic white, nonsmokers, and free of cardiovascular disease. They generally have a higher level of education. No statistically significant differences were found between the two groups regarding alcohol consumption or family history of asthma (P > 0.05). 3.2 Variable selection After applying the three-phase selection framework (theoretical prioritization, stepwise regression, and multicollinearity control), the final model included NPAR, age, sex, race, BMI, cardiovascular disease, eosinophil count, PIR, smoking status, red cell distribution width, vitamin D level, and education level. All variables exhibited VIF < 5, confirming minimal multicollinearity. Subsequent multivariate logistic regression analysis demonstrated that NPAR remained independently associated with asthma risk (OR = 1.026, 95% CI: 1.008–1.045, p = 0.0046), even after adjusting for these covariates. 3.3 The relationship between NPAR and asthma Table 3 presents the logistic regression model findings, highlighting the association between NPAR and asthma. Initially, treating the NPAR as a continuous variable revealed a significant association with asthma. After controlling for factors such as sex, age, and race, the association between the NPAR and asthma persisted, highlighting the potential role of the NPAR as a significant predictor of asthma. Furthermore, even after accounting for variables such as BMI, CVD, eosinophil count, PIR, smoking status, red cell distribution width (RDW), total vitamin D level, and education level, the associations between NPAR and asthma remained significant. These findings indicate that the NPAR may have an independent effect on the development or exacerbation of asthma. Model 1: The odds ratio for the continuous variable of the NPAR is 1.029, with a 95% CI of 1.012–1.046 and a p-value of less than 0.001, indicating a significant positive correlation. In the quartile analysis, the odds ratio for the Q4 group was 1.08, which approached significance (p = 0.072), and the overall trend was significant (p = 0.001, FDR-adjusted p = 0.003). Model 2: The odds ratio for the continuous variable of the NPAR is 1.049, with a 95% CI of 1.031–1.067 and a p-value of less than 0.001, indicating a stronger positive correlation. In the quartile analysis, the odds ratio for the Q4 group was 1.09, reaching significance (p = 0.008), and the overall trend was very significant (p < 0.001, FDR-adjusted p < 0.001). Model 3: The odds ratio for the continuous variable of the NPAR is 1.026, with a 95% CI of 1.008–1.045 and a p-value of 0.0046, indicating a positive correlation. In the quartile analysis, the odds ratio for the Q4 group was 1.04, which did not reach significance (p = 0.382), but the overall trend remained significant (p = 0.006, FDR-adjusted p = 0.018). Table 3 Associations between the NPAR and asthma incidence Models NPAR (Continuous) NPAR (As Quartiles) OR (95% CI) Q1 (Reference) Q2 Group OR (95% CI) Q3 Group OR (95% CI) Q4 Group OR (95% CI) p for trend FDR-Adjusted p for trend Model1 1.029(1.012–1.046) < 0.001 Reference 0.926(0.739–1.08) 0.235 1.16(0.925–1.36) 0.247 1.08 (0.995–1.10) 0.072 0.001 0.003 Model2 1.049 (1.031–1.067) < 0.001 Reference 0.917(0.742–1.08) 0.257 1.18(0.955–1.40) 0.136 1.09 (1.02–1.12) 0.008 < 0.001 < 0.001 Model3 1.026 (1.008–1.045) 0.0046 Reference 0.914 (0.742–1.10) 0.308 1.14 (0.926–1.38) 0.228 1.04 (0.969–1.08) 0.382 0.006 0.018 Note: Model 1: no excoriates were adjusted; Model 2: adjusted for sex, age, and race; Model 3: Adjusted for age, sex, race, body mass index (BMI), cardiovascular disease (CVD), eosinophil count, poverty income ratio (PIR), smoking status (categorized as never/former/current ), red cell distribution width (RDW), total vitamin D level (25(OH)D2 + 25(OH)D3), and education level. Variables were selected via stepwise regression ( p < 0.05) and validated for multicollinearity (variance inflation factor, VIF < 5). Abbreviation: Q, quartile; NPAR, neutrophil-percentage-to-albumin ratio; CVD: cardiovascular disease; BMI: body mass index; PIR, poverty income ratio. Trend p-values were adjusted via Benjamini-Hochberg FDR (q = 0.05) for 3 model comparisons. Quartile group p-values are presented as raw values due to their exploratory nature; Bonferroni-adjusted thresholds: α = 0.0167 per model. 3.4 ROC curve analysis of asthma prediction models We evaluated the classification performance of NPAR for asthma prediction using receiver operating characteristic (ROC) curves across three nested models (Fig. 2 ): Model 1 (Unadjusted, Fig. 2 a): With no covariate adjustments, the baseline AUC was 0.520 (95% CI: 0.498–0.542), indicating limited discriminative power of NPAR alone. Model 2 (Demographic-Adjusted, Fig. 2 b): After adjusting for sex, age, and race, the AUC improved to 0.583 (95% CI: 0.560–0.606). This incremental gain highlights the role of demographic heterogeneity in asthma risk stratification. Model 3 (Fully Adjusted, Fig. 2 c): Further incorporating clinical, socioeconomic, and lifestyle covariates—BMI, cardiovascular disease, eosinophil count, poverty income ratio (PIR), smoking status (Never/Former/Current), red cell distribution width (RDW), total vitamin D level [25(OH)D2 + 25(OH)D3], and education level—the AUC reached 0.699 (95% CI: 0.675–0.723). This substantial improvement underscores the multifactorial nature of asthma, where inflammatory, metabolic, and socioeconomic determinants collectively enhance risk prediction. At the optimal threshold of 0.135, Model 3 achieved a sensitivity of 0.721 and a specificity of 0.587. Clinical relevance: High sensitivity ensures effective identification of true asthma cases, while moderate specificity reduces unnecessary interventions in non-asthmatic populations. This balance is critical for deploying NPAR as a cost-effective screening tool in primary care, particularly for high-risk subgroups (e.g., individuals with low PIR or smoking history). 3.5 Asthma risk assessment This study employed the restricted cubic spline (RCS) method to examine the non-linear relationship between NPAR and asthma risk (Fig. 3). We illustrated the relationship between NPAR and asthma risk with three subplots (a, b, c), each reflecting different variable adjustments. In each subplot, the blue curve shows the trend of asthma risk. The purple-shaded area indicates the confidence interval, showing the uncertainty in the estimates. In subplot a, we observed an apparent U-shaped curve between NPAR and asthma risk without adjusting for variables. These findings indicate that asthma risk is more significant at low and high NPAR values but lower in the middle range. The confidence interval in the middle range is narrower, suggesting greater precision of the estimates. This pattern suggests a complex interaction between NPAR and asthma risk. Plot b illustrates the relationship between NPAR and asthma risk, adjusting for age, sex, and race. Compared with plot a, the shape of the curve remains consistent. The confidence interval narrows in the lower NPAR value region, indicating improved accuracy after adjustment. Additionally, Plot b shows that the increase in asthma probability slows down when NPAR values exceed 25, suggesting a reduced impact of NPAR on asthma risk in these populations. Plot c illustrates the relationship between NPAR and asthma probability after adjusting for all relevant variables. The curve indicates that asthma probability is low at low NPAR values, rises with higher NPAR values, and decreases as NPAR approaches 30. After adjustment, the confidence interval across the entire range of NPAR values is relatively wide, particularly at the extremes, suggesting increased uncertainty in the estimates at these values. Figure 3 RCS analysis of the relationship between NPAR and asthma risk. The solid blue line in the figure represents the trend in the probability of asthma, whereas the purple-shaded area represents the 95% confidence interval. 3.6 Subgroup analyses Subgroup analyses assessed whether the association between NPAR and asthma risk varies across different demographic and clinical subgroups. The results are presented in Fig. 4 and Table 4 . Figure 4 visually illustrates the associations between NPAR and asthma across various subgroups. It shows that males have a higher odds ratio for asthma than females, which is statistically significant in at least one model (P = 0.008). Similarly, non-Hispanic white individuals exhibit a higher odds ratio for asthma in at least one model (P = 0.008). Participants with a history of cardiovascular disease (CVD) also show a significant odds ratio (P = 0.019), as do former smokers (P = 0.039). Additionally, individuals with a 25(OH)D level between 50 and 250 have a significant odds ratio in at least one model (P = 0.049). Table 3 provides a detailed statistical analysis of the subgroup interactions. The table lists the nominal p-values for each subgroup interaction, along with the Bonferroni-adjusted significance. The Bonferroni correction was applied to control the family-wise error rate for the 11 pre-specified subgroup interaction tests, with an adjusted significance threshold of 0.00455. The results indicate that the interaction between sex and NPAR shows a nominal p-value of 0.029, which does not meet the Bonferroni-adjusted significance threshold. Similarly, the interaction between race and NPAR has a nominal p-value of 0.086, which is also not significant after adjustment. The poverty income ratio (PIR) interaction has a nominal p-value of 0.034, again not meeting the adjusted significance threshold. Other subgroup interactions, including age, hypertension, CVD, smoking status, education level, a close relative with asthma, BMI, and 25(OH)D levels, all have nominal p-values greater than 0.05, indicating no statistically significant interactions after adjustment. In summary, the subgroup analyses suggest that while there are some differences in the odds ratios across subgroups, these differences are not statistically significant after applying the Bonferroni correction. This implies that the association between NPAR and asthma risk is consistent across the different demographic and clinical subgroups considered in this study. Table 4 Subgroup Interaction Analysis (Bonferroni-Adjusted) Subgroup Variable Nominal p-Value Bonferroni Adjustment Significance (α = 0.00455) Age 0.046 0.506 No Gender 0.029 0.319 No Race 0.086 0.946 No Hypertension 0.945 1.00 No CVD 0.508 1.00 No Smoking status 0.388 1.00 No PIR 0.034 0.374 No Education level 0.141 1.00 No A close relative had asthma 0.334 1.00 No BMI 0.185 1.00 No 25(OH)D 0.833 1.00 No Multiplicity Adjustment: Bonferroni correction was applied to control the family-wise error rate for 11 pre-specified subgroup interaction tests. The adjusted significance threshold was calculated as 0.05 divided by 11 (approximately 0.00455). Statistical Significance: A subgroup interaction was considered statistically significant if the nominal (unadjusted) p-value was less than the adjusted threshold of 0.00455. Equivalently, adjusted p-values (calculated by multiplying the nominal p-value by 11) were compared to the original alpha level 0.05. Data Truncation: Adjusted p-values exceeding 1.00 were capped at 1.00 to maintain valid probability bounds. Interpretation: All nominal p-values greater than 0.00455 (adjusted p-values greater than 0.05) indicate no statistically significant effect modification, supporting consistent NPAR-asthma associations across demographic and clinical subgroups. 4 Discussion This large-scale, nationally representative study of 17,800 U.S. adults from NHANES (2009–2018) provides the first epidemiological evidence that NPAR is a novel, independent biomarker for asthma risk, exhibiting a characteristic U-shaped dose-response relationship. In fully adjusted models accounting for 12 demographic, clinical, and socioeconomic confounders, each unit increase in NPAR was associated with a 2.6% elevation in asthma prevalence (adjusted OR = 1.026, 95% CI:1.008–1.045, P = 0.0046). The robustness of this association was confirmed through sensitivity analyses, which showed minimal difference (< 1.5%) between complete-case and multiple imputation analyses and alternative NPAR operationalization strategies, with Benjamini-Hochberg FDR correction yielding significant results for trend tests (P < 0.05). Neutrophils drive multifaceted pathological processes in asthma through coordinated effector mechanisms 21 22 . Neutrophil elastase (NE) disrupts airway epithelial barrier function through proteolytic cleavage of tight junction proteins, including occludin and E-cadherin, as demonstrated by in vitro polarized epithelial monolayers and murine asthma models 23 . This protease specifically targets the extracellular domains of claudin-18 (CLDN18) at Ser68 and Tyr69 residues, leading to significantly increased paracellular permeability 24 . Concurrently, neutrophil elastase (NE)-mediated proteolytic degradation of zonula occludens-1 (ZO-1) triggers MAPK/ERK signaling pathway activation, thereby promoting IL-33 release and enhancing the paracellular penetration of aeroallergens such as house dust mite-derived proteins 25 . Concurrently, neutrophil-derived reactive oxygen species (ROS) amplify oxidative stress, activating the NLRP3 inflammasome to enhance IL-1β/IL-18 secretion, which synergizes with Th2 cytokines (IL-4/IL-13) to sustain eosinophilic inflammation 26 27 . Emerging evidence further implicates neutrophil extracellular traps (NETs) in promoting Th17-mediated neutrophilic asthma endotypes through IL-17A/IL-23 axis activation 28 29 . Furthermore, environmental risk factors such as smoking and air pollution exacerbate airway inflammation by enhancing neutrophil recruitment and activation 30 . Hypoalbuminemia, defined as serum albumin < 3.5 g/dL, is independently associated with asthma severity due to its role in reducing antioxidant capacity and increasing vascular permeability 31 . The NPAR synergistically combines these two pathophysiological dimensions, providing a more robust predictor of asthma risk than isolated neutrophil or albumin measurements 32 . Derived from routine blood tests, NPAR offers a cost-effective and accessible biomarker for clinical practice. In our analysis, each unit increase in NPAR was associated with a 2.6% rise in asthma risk (OR = 1.026, 95% CI:1.008–1.045), even after adjusting for socioeconomic and clinical confounders. This is consistent with previous studies, indicating the predictive value of NPAR in chronic respiratory diseases, where the AUC for predicting the five-year mortality rate in COPD is 0.808, while the AUC for NLR is 0.799 33 . This study is the first to systematically explore the NPAR-asthma link in a nationally representative adult population. Our regression analyses identified NPAR as an independent predictor post-adjustment for key confounders. In contrast ROC curve analysis highlighted its added predictive value alongside traditional risk factors, with AUC improving from 0.520 to 0.699. The U-shaped dose-response relationship, with minimal risk at NPAR 12–15, implies dual pathological pathways: oxidative stress susceptibility at lower values and neutrophilic inflammation dominance at higher values. This non-linear pattern was corroborated by subgroup analyses (Fig. 4 ), showing consistent NPAR-asthma associations across demographic subgroups despite non-significant interaction effects post-Bonferroni correction. Notably, subgroup analyses identified potential interaction effects by sex (P-interaction = 0.029) and poverty-income ratio (P-interaction = 0.034); however, these associations did not remain significant after Bonferroni correction for multiple comparisons (adjusted significance threshold: P = 0.00455). Sensitivity analyses and alternative NPAR categorizations reinforced NPAR's reliability, though its moderate standalone predictive performance (AUC = 0.699) suggests a need for complementary biomarkers like FeNO for clinical. Our study has several notable strengths. To our knowledge, this is the first large-scale epidemiological investigation to establish the NPAR as an independent biomarker for asthma risk in the general adult population. Second, our multivariable models rigorously adjusted for 12 potential confounders, including demographic, socioeconomic, and clinical variables (e.g., BMI, smoking status, cardiovascular disease), ensuring robust control of confounding effects. Third, using NHANES data (2009–2018, n = 17,800) with nationally representative sampling weights enhances the generalizability of our findings to noninstitutionalized U.S. adults. However, several limitations should be acknowledged. The cross-sectional design precludes causal inference, and longitudinal studies are needed to determine whether NPAR elevation precedes asthma onset or results from chronic inflammation. Additionally, NPAR was calculated from single-timepoint laboratory measurements, which may not capture dynamic inflammatory changes over time. Asthma diagnoses relied on self-reported questionnaires rather than spirometry-confirmed criteria, potentially introducing misclassification bias. Furthermore, we lacked data on airway-specific biomarkers (e.g., fractional exhaled nitric oxide [FeNO], eosinophil cationic protein [ECP]) and pulmonary function tests, which could refine asthma phenotyping and mechanistic interpretations. Future research should integrate serial NPAR measurements, multi-omics profiling (e.g., transcriptomics of neutrophilic asthma subtypes), and randomized interventions targeting NPAR modulation to validate its clinical utility. Declarations Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding No funding. Author Contribution L.B. and H.K. conceived and designed the study. L.B. performed the data analysis and wrote the initial draft of the manuscript. J.L. assisted with data collection and validation. H.K. revised the manuscript and provided critical feedback. L.B. prepared figures 1-4. All authors contributed to the final version of the manuscript and approved the submitted version. Acknowledgments We want to thank the NHANES and GWASs for making summary data publicly available. Data Availability The data analyzed in this study are from the NHANES 2009–2018, which are publicly available and can be downloaded from the NHANES website (http://www.cdc.gov/nchs/nhanes.htm). References Damask C, Franzese C. Incorporating Asthma Evaluation into the Otolaryngic Allergy Practice: Presentation and Diagnosis. Otolaryngol Clin North Am. 2024;57(2):191–99. 10.1016/j.otc.2023.09.002 . Qu J, Li Y, Zhong W, et al. Recent developments in the role of reactive oxygen species in allergic asthma. J Thorac Dis. 2017;9(1):E32–43. 10.21037/jtd.2017.01.05 . Asher MI, Garcia-Marcos L, Pearce NE, et al. Trends in worldwide asthma prevalence. Eur Respir J. 2020;56(6). 10.1183/13993003.02094-2020 . Shin YH, Hwang J, Kwon R et al. Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019. Allergy 2023;78(8):2232-54. 10.1111/all.15807 Holgate ST, Wenzel S, Postma DS, et al. Asthma. Nat Rev Dis Primers. 2015;1(1):15025. 10.1038/nrdp.2015.25 . Alobaidi AH, Alsamarai AM, Alsamarai MA. Inflammation in Asthma Pathogenesis: Role of T Cells, Macrophages, Epithelial Cells and Type 2 Inflammation. Antiinflamm Antiallergy Agents Med Chem. 2021;20(4):317–32. 10.2174/1871523020666210920100707 . Huang WJ, Huang GT, Zhan QM, et al. The neutrophil to lymphocyte ratio as a novel predictor of asthma and its exacerbation: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. 2020;24(22):11719–28. 10.26355/eurrev_202011_23819 . Yang F, Dong R, Wang Y, et al. Prediction of pulmonary infection in patients with severe myelitis by NPAR combined with spinal cord lesion segments. Front Neurol. 2024;15:1364108. 10.3389/fneur.2024.1364108 . Kurkiewicz K, Gasior M, Szygula-Jurkiewicz BE. Markers of malnutrition, inflammation, and tissue remodeling are associated with 1-year outcomes in patients with advanced heart failure. Pol Arch Intern Med. 2023;133(6). 10.20452/pamw.16411 . Sun T, Shen H, Guo Q, et al. Association between Neutrophil Percentage-to-Albumin Ratio and All-Cause Mortality in Critically Ill Patients with Coronary Artery Disease. Biomed Res Int. 2020;2020:8137576. 10.1155/2020/8137576 . Aksoy E, Karakurt Z, Gungor S, et al. Neutrophil to lymphocyte ratio is a better indicator of COPD exacerbation severity in neutrophilic endotypes than eosinophilic endotypes. Int J Chron Obstruct Pulmon Dis. 2018;13:2721–30. 10.2147/COPD.S170353 . Wang B, Li D, Cheng B, et al. The Neutrophil Percentage-to-Albumin Ratio Is Associated with All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury. Biomed Res Int. 2020;2020:5687672. 10.1155/2020/5687672 . Lan CC, Su WL, Yang MC, et al. Predictive role of neutrophil-percentage-to-albumin, neutrophil-to-lymphocyte and eosinophil-to-lymphocyte ratios for mortality in patients with COPD: Evidence from NHANES 2011–2018. Respirology. 2023;28(12):1136–46. 10.1111/resp.14589 . Lv XN, Shen YQ, Li ZQ, et al. Neutrophil percentage to albumin ratio is associated with stroke-associated pneumonia and poor outcome in patients with spontaneous intracerebral hemorrhage. Front Immunol. 2023;14:1173718. 10.3389/fimmu.2023.1173718 . He X, Dai F, Zhang X, et al. The neutrophil percentage-to-albumin ratio is related to the occurrence of diabetic retinopathy. J Clin Lab Anal. 2022;36(4):e24334. 10.1002/jcla.24334 . Cucoranu DC, Pop M, Niculescu R, et al. The Association of Nonalcoholic Fatty Liver Disease With Neutrophil-to-Lymphocyte Ratio and Neutrophil-Percentage-to-Albumin Ratio. Cureus. 2023;15(6):e41197. 10.7759/cureus.41197 . Zipf G, Chiappa M, Porter KS et al. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat 1 2013(56):1–37. Wen J, Wang C, Giri M, et al. Association between serum folate levels and blood eosinophil counts in American adults with asthma: Results from NHANES 2011–2018. Front Immunol. 2023;14:1134621. 10.3389/fimmu.2023.1134621 . Li J, Xiang T, Chen X, et al. Neutrophil-percentage-to-albumin ratio is associated with chronic kidney disease: Evidence from NHANES 2009–2018. PLoS ONE. 2024;19(8):e0307466. 10.1371/journal.pone.0307466 . Shivappa N, Steck SE, Hurley TG, et al. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–96. 10.1017/S1368980013002115 . Kim SH, Uuganbayar U, Trinh HKT, et al. Evaluation of Neutrophil Activation Status According to the Phenotypes of Adult Asthma. Allergy Asthma Immunol Res. 2019;11(3):381–93. 10.4168/aair.2019.11.3.381 . Mao R, Jiang Z, Min Z, et al. Peripheral neutrophils and oxidative stress-associated molecules for predicting the severity of asthma: a cross-sectional study based on multidimensional assessment. Front Med (Lausanne). 2023;10:1240253. 10.3389/fmed.2023.1240253 . Ma Q, Qian Y, Jiang J, et al. IL-33/ST2 axis deficiency exacerbates neutrophil-dominant allergic airway inflammation. Clin Transl Immunol. 2021;10(6):e1300. 10.1002/cti2.1300 . Sweerus K, Lachowicz-Scroggins M, Gordon E, et al. Claudin-18 deficiency is associated with airway epithelial barrier dysfunction and asthma. J Allergy Clin Immunol. 2017;139(1):72–e811. 10.1016/j.jaci.2016.02.035 . Shin SH, Ye MK, Lee DW, et al. Nasal Epithelial Cells Activated with Alternaria and House Dust Mite Induce Not Only Th2 but Also Th1 Immune Responses. Int J Mol Sci. 2020;21(8). 10.3390/ijms21082693 . Magallon M, Castillo-Corullon S, Banuls L, et al. Hypoxia Enhances Oxidative Stress in Neutrophils from ZZ Alpha-1 Antitrypsin Deficiency Patients. Antioxid (Basel). 2023;12(4). 10.3390/antiox12040872 . Liu T, He Y, Liao Y. Esculentoside A ameliorates DNCB-induced atopic dermatitis by suppressing the ROS-NLRP3 axis via activating the Nrf2 pathway. Clin Exp Pharmacol Physiol. 2023;50(11):844–54. 10.1111/1440-1681.13809 . Ntinopoulou M, Cassimos D, Roupakia E, et al. Iotanterleukin-17A-Enriched Neutrophil Extracellular Traps Promote Immunofibrotic Aspects of Childhood Asthma Exacerbation. Biomedicines. 2023;11(8). 10.3390/biomedicines11082104 . Chen YR, Xiang XD, Sun F, et al. Simvastatin Reduces NETosis to Attenuate Severe Asthma by Inhibiting PAD4 Expression. Oxid Med Cell Longev. 2023;2023:1493684. 10.1155/2023/1493684 . Xia M, Xu F, Ni H, et al. Neutrophil activation and NETosis are the predominant drivers of airway inflammation in an OVA/CFA/LPS induced murine model. Respir Res. 2022;23(1):289. 10.1186/s12931-022-02209-0 . Jiang Z, Zhu X, Jiang H, et al. Relationship between prognostic nutritional index and asthma: a cross-sectional analysis. Front Nutr. 2025;12:1467270. 10.3389/fnut.2025.1467270 . Mousa N, Salah M, Elbaz S, et al. Neutrophil percentage-to-albumin ratio is a new diagnostic marker for spontaneous bacterial peritonitis: a prospective multicenter study. Gut Pathog. 2024;16(1):18. 10.1186/s13099-024-00610-2 . Correction to. Predictive role of neutrophil-percentage-to-albumin, neutrophil-to-lymphocyte and eosinophil-to-lymphocyte ratios for mortality in patients with COPD: Evidence from NHANES 2011–2018. Respirology. 2025;30(1):89. 10.1111/resp.14875 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 May, 2025 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 14 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 29 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5880431","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436897359,"identity":"92cd5262-ad12-4bd6-9fa4-e6807a7b2622","order_by":0,"name":"Lingge Bi","email":"","orcid":"","institution":"Huangpu People's Hospital of Zhongshan","correspondingAuthor":false,"prefix":"","firstName":"Lingge","middleName":"","lastName":"Bi","suffix":""},{"id":436897360,"identity":"1c03dde2-2ace-4383-b9ca-3a1615a2efea","order_by":1,"name":"Jinguang Liang","email":"","orcid":"","institution":"Huangpu People's Hospital of Zhongshan","correspondingAuthor":false,"prefix":"","firstName":"Jinguang","middleName":"","lastName":"Liang","suffix":""},{"id":436897361,"identity":"62b2d710-1e8a-4662-9745-e33d5f6af69a","order_by":2,"name":"Kai Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBAC+/nHj3/4YCAhZ9/eQKQWAwmeNMYZFTbGBjwHiNbCYMbMcyYtcYNEApFazKUb0h7zth1m3C75eOMNhhqbaIJaLOccPG44t+0ws+XstGILhmNpuQ0E9RxISJB423aYjeF2jpkEY8NhorQYSAAdxsNw8wyRWgxuJJhJAr0vYXCDh0gtkj1nkg2BgWwg2QP0SwIxfuFnbz/4ABiV9f3shzfe+FBjQ4RfkB1JdNQgaSFVxygYBaNgFIwMAAAmIEPhE9YBhQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-01-22 11:27:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5880431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5880431/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-025-03701-1","type":"published","date":"2025-05-29T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79820647,"identity":"148989e7-cd64-46e2-9cea-d581aa0cb60f","added_by":"auto","created_at":"2025-04-03 08:41:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99682,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection. NHANES, National Health and Nutrition Examination Survey; NPAR, neutrophil percentage-to-albumin ratio.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5880431/v1/968efb08dc261f1566106ced.png"},{"id":79819669,"identity":"a25262b9-cafe-4a13-9603-413895fb0030","added_by":"auto","created_at":"2025-04-03 08:33:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141742,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis evaluated the predictive performance of NPAR under three nested adjustment models.\u003c/p\u003e\n\u003cp\u003e(a) Model 1 (Unadjusted): No covariate adjustments. (b) Model 2 (Demographic-Adjusted): Adjusted for sex, age, and race. (c) Model 3 (Fully Adjusted): Adjusted for sex, age, race, body mass index (BMI), cardiovascular disease (CVD), eosinophil count, poverty income ratio (PIR), smoking status (Never/Former/Current), red cell distribution width (RDW), total vitamin D level [25(OH)D2 + 25(OH)D3], and education level. \u003cem\u003eAbbreviations\u003c/em\u003e: AUC, area under the curve; CI, confidence interval; PIR, poverty income ratio.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5880431/v1/16c184c178be8f448f1c525e.png"},{"id":79819672,"identity":"4b132b82-58b6-4df6-8b1b-72b69be3e0f0","added_by":"auto","created_at":"2025-04-03 08:33:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59709,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the relationship between NPAR and asthma risk. The solid blue line in the figure represents the trend in the probability of asthma, whereas the purple-shaded area represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5880431/v1/7712a5fb082c9078030692fd.png"},{"id":79820648,"identity":"2e5e09f5-d6c9-49bb-b6bb-a34367da1288","added_by":"auto","created_at":"2025-04-03 08:41:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":501040,"visible":true,"origin":"","legend":"\u003cp\u003eVerify the association between the NPAR and asthma via subgroup analyses.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5880431/v1/f0ee18b65ab345c77586ca05.png"},{"id":83783418,"identity":"3119068e-cd76-4c8a-b535-66082ac9dfef","added_by":"auto","created_at":"2025-06-02 16:11:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2150294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5880431/v1/1b2f32ee-c35c-4a16-9cc0-c6025df891d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neutrophil percentage-to-albumin ratio (NPAR) as a biomarker for asthma: A cross-sectional analysis of NHANES data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAsthma is a chronic and heterogeneous respiratory disease characterized by shortness of breath, cough, wheezing, and chest tightness\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The underlying causes of asthma include airway inflammation, which leads to its symptoms\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Asthma is the most common chronic respiratory disease worldwide and occurs twice as often as chronic obstructive pulmonary disease (COPD). The World Health Organization (WHO) estimates that approximately 339\u0026nbsp;million people globally have asthma, resulting in approximately 250,000\u0026ndash;350,000 deaths annually\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Over time, uncontrolled asthma can cause various complications, such as chronic obstructive pulmonary disease and cor pulmonale. These complications can worsen patient outcomes, threaten overall health, and create significant economic and social challenges\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eInflammation is vital to the development of asthma\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Asthma develops through the involvement of several immune cell types, such as eosinophils, T lymphocytes, macrophages, and neutrophils. During an asthma attack, T lymphocytes residing in the airway mucosa activate and produce a wide range of cytokines. Eosinophils are the primary cells in the inflammatory infiltrate, but other types, including mast cells, basophils, neutrophils, monocytes, and macrophages, are also present\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The ratios of common blood markers are clinically significant as diagnostic biomarkers. For example, the neutrophil-to-lymphocyte ratio (NLR) is recognized as an important indicator of systemic inflammation, particularly in pulmonology. Huang et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e found that the NLR is valid, easy to use, and a crucial marker for asthma. However, emerging evidence suggests that the neutrophil percentage-to-albumin ratio (NPAR), a novel inflammatory biomarker, may offer superior predictive value compared to traditional markers such as NLR and C-reactive protein (CRP)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUnlike NLR, which solely reflects granulocyte-lymphocyte balance, NPAR integrates two critical biological pathways: neutrophil-driven inflammation and albumin-modulated systemic antioxidant capacity\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This dual-component design addresses the limitations of single-parameter biomarkers. For instance:\u003c/p\u003e\n\u003cp\u003e1. Diagnostic Accuracy: In a meta-analysis of chronic obstructive pulmonary disease (COPD) cohorts, NPAR achieved an AUC of 0.82 for predicting exacerbations, significantly outperforming NLR (AUC\u0026thinsp;=\u0026thinsp;0.68) and CRP (AUC\u0026thinsp;=\u0026thinsp;0.61)\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2. Prognostic Value: NPAR independently predicted 1-year mortality in critically ill patients with acute kidney injury (HR\u0026thinsp;=\u0026thinsp;1.44, 95% CI: 1.21\u0026ndash;1.71), whereas NLR and CRP showed weaker associations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e3. Cost-Effectiveness: NPAR is derived from routine complete blood count (CBC) and albumin measurements, avoiding specialized assays for CRP or cytokine profiling\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe NPAR has recently emerged as a significant blood marker\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. NPAR is a robust biomarker that integrates neutrophil percentage and albumin level components. It offers an affordable and readily available measure of systemic inflammation. Early studies have shown that the NPAR can predict the prognosis of acute kidney injury, cardiogenic shock, myocardial infarction, and cancer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. No studies have examined the association between NPAR and asthma incidence. This study aims to clarify how the NPAR is related to asthma occurrence, thus providing insights into the correlation between inflammatory markers and respiratory outcomes.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eFor this cross-sectional study, we analyzed data from the National Health and Nutrition Examination Survey (NHANES), available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The NHANES program is managed by the National Center for Health Statistics (NCHS), which is part of the Centers for Disease Control and Prevention (CDC). The participants in the NHANES first complete a household interview, after which they are invited to undergo a comprehensive evaluation at a mobile examination center (MEC). During this evaluation, participants receive a physical examination, take specific anthropometric measurements, and undergo various laboratory tests. As a result, the NHANES database offers reliable and extensive population data, making it an essential resource for population-level assessments\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study population\u003c/h2\u003e \u003cp\u003eThe data on participants were from five cycles of the NHANES published from 2009 through 2018, with 49,693 participants representing an estimated 196\u0026nbsp;million people in the United States. The inclusion criteria included people aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years who were not pregnant. According to NHANES, asthma is assessed via the Medical Conditions Questionnaire (MCQ). Asthma is defined by a positive response to the following question: \"Have you ever been told you have asthma?\" This method of identifying asthma has been used in various previous NHANES studies\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study examined comprehensive data on asthma and the NPAR from 49,693 participants. After several exclusion criteria, including individuals under 20 years of age (n\u0026thinsp;=\u0026thinsp;20,858), pregnant participants (n\u0026thinsp;=\u0026thinsp;315), those uncertain about their pregnancy status (n\u0026thinsp;=\u0026thinsp;432), and cases with missing data (n\u0026thinsp;=\u0026thinsp;8,562), as well as those excluded due to missing NPAR information (n\u0026thinsp;=\u0026thinsp;1,710), were applied, the final analysis was conducted on 17,816 eligible participants. Among these, 17,800 individuals had a diagnosis of asthma, with 2,475 responding \u0026ldquo;yes\u0026rdquo; to specific inquiries and 15,325 responding \u0026ldquo;no.\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Neutrophil-percentage‑to‑albumin ratio(NPAR)\u003c/h2\u003e \u003cp\u003eThe NPAR was calculated as the neutrophil percentage (%) ratio to serum albumin concentration (g/dL), following standardized protocols from recent NHANES studies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This composite biomarker integrates two key pathophysiological dimensions: (1) neutrophil percentage, which quantifies acute-phase inflammatory response as validated in chronic respiratory disease cohorts\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and (2) serum albumin, a recognized marker of nutritional status and antioxidant capacity whose decline correlates with systemic inflammation severity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The methodological validity of this ratio has been extensively demonstrated in large-scale epidemiological research, including its predictive utility for mortality in chronic obstructive pulmonary disease (AUC\u0026thinsp;=\u0026thinsp;0.74, NHANES 2011\u0026ndash;2018)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and chronic kidney disease progression (OR\u0026thinsp;=\u0026thinsp;1.32 per unit increase, NHANES 2009\u0026ndash;2018)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Compared to single-parameter biomarkers, NPAR's dual-component design provides enhanced sensitivity for detecting chronic inflammatory states, as evidenced by multi-cohort analyses\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the included participants (n\u0026thinsp;=\u0026thinsp;17800) in the NHANES 2009\u0026ndash;2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1[0.182-12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2[12-13.6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3[13.6\u0026ndash;15.2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4[15.2\u0026ndash;36.1]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;17800\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4532\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4474\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4250\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4544\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12520 (70.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3499 (77.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3159 (70.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2869(67.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2993 (65.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5280 (29.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1033 (22.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1315 (29.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1381 (32.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1551 (34.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.71\u0026thinsp;\u0026plusmn;\u0026thinsp;17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.25\u0026thinsp;\u0026plusmn;\u0026thinsp;16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.68\u0026thinsp;\u0026plusmn;\u0026thinsp;16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.47\u0026thinsp;\u0026plusmn;\u0026thinsp;17.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.46\u0026thinsp;\u0026plusmn;\u0026thinsp;18.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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 Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;31.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;35.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;31.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;30.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;30.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2717 (15.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e565(12.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e755 (16.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e706 (16.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e691 (15.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1779 (9.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e436 (9.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e481 (10.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e391 (9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e471 (10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6992 (39.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1417 (31.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1704 (38.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1804 (42.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2067 (45.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3683 (20.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1363 (30.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e804 (17.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e730 (17.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e786 (17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2629 (14.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e751 (16.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e730 (16.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e619 (14.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e529 (11.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3965 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e931 (20.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e976 (21.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e945 (22.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1113 (24.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3938 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e970 (21.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e963 (21.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e967 (22.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1038 (22.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9880 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2628 (57.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2531 (56.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2337 (54.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2384 (52.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefused、Do not Know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17(0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6227 (34.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1536 (33.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1493 (33.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1514 (35.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1684 (37.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5166 (29.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1268 (27.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1279(28.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1237 (29.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1382(30.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4816 (27.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1314 (28.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1304 (29.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1124 (26.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1074 (23.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1591 (10.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e414 (9.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e398 (8.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e375 (8.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e404 (8.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9516(53.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2605 (57.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2556 (57.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2236 (52.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2119(46.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4296(24.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e985 (21.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e978 (21.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1062 (24.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1271 (27.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3976(22.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e938 (20.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e937(20.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e948(22.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1153 (25.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefused、Do not Know、Missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1378 (7.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227 (5.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e265 (5.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e333 (7.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e553 (12.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16351 (91.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4293 (94.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4196 (93.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3901 (91.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3961 (87.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBP, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2297 (12.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e506 (11.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e501 (11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e555 (13.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e735 (16.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15503 (87.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4026 (88.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3973 (88.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3695 (86.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3809 (83.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2506 (14.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e425 (9.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e479 (10.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e618 (14.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e984 (21.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1119 (6.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276 (6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264 (5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e275 (6.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e304 (6.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA close relative had asthma, n (%)\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=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.233\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3524 (19.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e916 (20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e848 (18.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e831 (19.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e929 (20.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13888 (78.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3541 (78.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3530 (78.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3331 (78.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3486 (76.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25(OH)D2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;9.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003e25(OH)D3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.84\u0026thinsp;\u0026plusmn;\u0026thinsp;25.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.46\u0026thinsp;\u0026plusmn;\u0026thinsp;25.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.71\u0026thinsp;\u0026plusmn;\u0026thinsp;25.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.09\u0026thinsp;\u0026plusmn;\u0026thinsp;24.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.18\u0026thinsp;\u0026plusmn;\u0026thinsp;26.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003e25(OH)D2\u0026thinsp;+\u0026thinsp;25(OH)D3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.06\u0026thinsp;\u0026plusmn;\u0026thinsp;25.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.68\u0026thinsp;\u0026plusmn;\u0026thinsp;25.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.63\u0026thinsp;\u0026plusmn;\u0026thinsp;25.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.05\u0026thinsp;\u0026plusmn;\u0026thinsp;24.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.93\u0026thinsp;\u0026plusmn;\u0026thinsp;27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.46\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.20\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.78\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eEosinophils number(1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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 number (1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eRed cell distribution width (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003ePlatelet count (1000 cells/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234.98\u0026thinsp;\u0026plusmn;\u0026thinsp;61.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228.52\u0026thinsp;\u0026plusmn;\u0026thinsp;55.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233.12\u0026thinsp;\u0026plusmn;\u0026thinsp;57.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e237.37\u0026thinsp;\u0026plusmn;\u0026thinsp;60.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e241.03\u0026thinsp;\u0026plusmn;\u0026thinsp;71.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eDII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eAsthma, n (%)\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=\"char\" char=\".\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2475(13.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e600(13.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e585(13.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e581(13.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e709(15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15325(86.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3932(86.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3889(86.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3669(86.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3835(84.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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=\"7\"\u003eNote: PIR, poverty income ratio; BMI, body mass index; CVD, cardiovascular disease; HBP, high blood pressure; DM, diabetes mellitus; DII, Dietary Inflammation Index; NPAR: Neutrophil percentage-to-albumin ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eTo identify potential confounders in the relationship between the NPAR and asthma within multivariable-adjusted models, we considered a range of factors including age, sex, race, education level, poverty-income ratio (PIR), alcohol consumption, smoking habits, body mass index (BMI), the presence of cardiovascular disease (CVD), hypertension (HBP), and diabetes, as well as laboratory measures such as the albumin level, neutrophil percentage, vitamin D level, eosinophil number, lymphocyte number, red cell distribution width, and platelet count. In addition, we also calculated the dietary inflammatory index(DII) of the participants\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Demographic characteristics\u003c/h2\u003e \u003cp\u003eThere are five racial categories: Mexican American, other Hispanics, non-Hispanic Whites, non-Hispanic Blacks, and other races. Educational attainment encompasses three distinct levels: individuals who have not completed high school, those who possess a high school diploma, and individuals who have pursued education beyond high school. The PIR was classified into three categories: low (\u0026lt;\u0026thinsp;1.5), medium (1.5\u0026ndash;3.5), and high (\u0026gt;\u0026thinsp;3.5). Alcohol consumption status was assessed via the question \"ALQ130 - Average number of alcoholic drinks per day during the past 12 months.\" Smoking status is categorized into three groups: never-smokers (individuals who have smoked fewer than 100 cigarettes in their lifetime), former smokers (individuals who have smoked at least 100 cigarettes but do not currently smoke), and current smokers (individuals who have smoked at least 100 cigarettes and currently smoke either occasionally or daily).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Comorbidities\u003c/h2\u003e \u003cp\u003eSubjects with a history of congestive heart failure, coronary heart disease, angina, or heart attack are classified as having cardiovascular disease. Hypertension is diagnosed based on three mean systolic blood pressure (SBP) readings of \u0026ge;\u0026thinsp;140 mmHg, three mean diastolic blood pressure (DBP) readings of \u0026ge;\u0026thinsp;90 mmHg, or the use of antihypertensive medication. Diabetes was identified through a questionnaire or other criteria, including fasting blood glucose levels of \u0026ge;\u0026thinsp;7 mmol/L or current use of diabetes medications or insulin. The situation of close relatives having asthma is obtained by answering the survey question \"Close relative had asthma?\" An answer of 1 indicates yes, whereas an answer of 2 indicates no.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Missing value handling\u003c/h2\u003e \u003cp\u003eTo address missing data while preserving the validity of our analyses, we implemented a rigorous multiple imputation workflow using IBM SPSS Statistics (Version 29.0). This process adhered to established methodological guidelines and included four sequential phases: (1) pre-imputation diagnostics, (2) imputation model specification, (3) post-imputation validation, and (4) sensitivity analyses(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhase 1: Pre-Imputation Diagnostics\u003c/p\u003e \u003cp\u003ePrior to imputation, we systematically evaluated missing data patterns through a series of preliminary analyses. The Missing Values Analysis module used monotone and arbitrary missingness schematics to visualize missing data patterns across variables. Little\u0026rsquo;s Missing Completely at Random (MCAR) test was conducted to assess the plausibility of the MCAR assumption, complemented by auxiliary variable analysis to identify potential predictors of missingness. Variables with excessive missingness (\u0026gt;\u0026thinsp;40%) were excluded from imputation models following current recommendations.\u003c/p\u003e \u003cp\u003ePhase 2: Imputation Model Specification\u003c/p\u003e \u003cp\u003eThe multiple imputation procedure utilized the Fully Conditional Specification (FCS) method with predictive mean matching for continuous variables and logistic regression for categorical variables. Twenty imputed datasets were generated, exceeding Rubin\u0026rsquo;s rule requiring at least five imputations given our maximum fraction of missing information (0.18). Continuous variables were constrained within biologically plausible ranges (e.g., body mass index 15\u0026ndash;50 kg/m\u0026sup2;) to prevent implausible imputed values. Auxiliary variables showing moderate correlations (r\u0026thinsp;=\u0026thinsp;0.3\u0026ndash;0.7) with incomplete variables were strategically incorporated to enhance imputation accuracy while avoiding collinearity. Convergence was monitored through iteration plots and autocorrelation diagnostics across 50 iterations.\u003c/p\u003e \u003cp\u003ePhase 3: Post-Imputation Validation\u003c/p\u003e \u003cp\u003eImputed datasets underwent comprehensive quality checks. Between-imputation variability was assessed via the relative increase in variance (RIV\u0026thinsp;\u0026lt;\u0026thinsp;0.25) and fraction of missing information (FMI\u0026thinsp;\u0026lt;\u0026thinsp;0.3). The kernel density plots and Kolmogorov-Smirnov tests verified distributional equivalence between observed and imputed values (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 threshold). Multicollinearity was systematically evaluated by comparing variance inflation factors (VIF) across imputed datasets, with tolerance maintained at \u0026gt;\u0026thinsp;0.4 for all variables.\u003c/p\u003e \u003cp\u003ePhase 4: Sensitivity Analyses\u003c/p\u003e \u003cp\u003eThree complementary analyses evaluated the robustness of imputation results: first, complete-case analyses were contrasted with imputed results using the D1 pooling method for regression coefficients; second, alternative imputation strategies (Markov Chain Monte Carlo with 200 burn-in iterations) were tested; third, pattern mixture models with varying delta values (δ\u0026thinsp;=\u0026thinsp;0.1\u0026ndash;0.5) assessed potential bias under different missing-not-at-random scenarios.\u003c/p\u003e \u003cp\u003e This workflow aligns with recent guidelines for transparent reporting of multiply imputed data in observational studies, balancing statistical rigor with computational feasibility. The complete analytic syntax has been archived for reproducibility.\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\u003eComprehensive Multivariable Regression Analysis with Full Core Predictors\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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-imputation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-imputation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e∆\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStatistical Characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;3152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCohen's d\u0026thinsp;=\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoodness-of-fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (adjusted R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071(0.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF-change p\u0026thinsp;=\u0026thinsp;0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoot MSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI overlap\u0026thinsp;=\u0026thinsp;99.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCore Predictors\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002(-0.002-0.001)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.002(-0.002-0.001)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.03,E-value\u0026thinsp;=\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015(0.007,0023)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016(0.009,0.023)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;112 τ\u0026thinsp;=\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHematologic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEosinophils(10*9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127(0.097.0.157)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.117(0.090 0.145)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.57,SMD\u0026thinsp;=\u0026thinsp;0.09\u0026dagger;\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\u003eLymphocytes(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002(-0.006,0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.002 (-0.006, 0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.08, FI\u0026thinsp;=\u0026thinsp;3\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\u003eRed cell distribution width (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005(0.001.0.009)\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006 (0.002, 0.009)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.21,Fragility Index\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometric\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003 (0.002, 0.004)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003 (0.002, 0.004)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.15, RERI\u0026thinsp;=\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBehavioral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017 (0.010, 0.024)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020 (0.014, 0.027)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.09, E-value\u0026thinsp;=\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057 (0.035, 0.079)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071 (0.051, 0.090)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.24, S-value\u0026thinsp;=\u0026thinsp;0.89\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\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009 (-0.003, 0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008 (-0.004, 0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.18, ROB\u0026thinsp;=\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoverty-income ratio (PIR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.017 (-0.024, -0.010)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.017 (-0.024, -0.011)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.31, E-value\u0026thinsp;=\u0026thinsp;1.42\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\u003eCollege Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017 (0.009, 0.025)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016 (0.009, 0.023)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.24, PAF\u0026thinsp;=\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutritional\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitamin D (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001 (-0.002, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001 (-0.002, 0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u0026thinsp;=\u0026thinsp;1.07, BMD\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnostics\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDurbin-Watson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCumming's Δ\u0026thinsp;=\u0026thinsp;0.004\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\u003eShapiro-Wilk test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;0.992, p\u0026thinsp;=\u0026thinsp;0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;0.993, p\u0026thinsp;=\u0026thinsp;0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ-Q plot convergence\u0026thinsp;=\u0026thinsp;92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollinearity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCondition number\u0026thinsp;=\u0026thinsp;12.7\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\u003eTolerance range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEigenvalue ratio\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSensitivity Analysis\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMCAR test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLittle's χ\u0026sup2;=32.1, p\u0026thinsp;=\u0026thinsp;0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePattern mixture model λ\u0026thinsp;=\u0026thinsp;0.013\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\u003eImputation efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative efficiency\u0026thinsp;=\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFraction missing information\u0026thinsp;=\u0026thinsp;0.07\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\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026dagger; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u0026Dagger; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eVIF: Variance inflation factor; E-value: Confounding bias strength threshold; SMD: Standardized mean difference; FI: Fragility index; RERI: Relative excess risk due to interaction; PAF: Population attributable fraction; BMD: Benchmark dose; ROB: Risk of bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data weighting method\u003c/h2\u003e \u003cp\u003e To ensure that our research findings are nationally representative, we used sample weights provided by the NHANES. These weights are adjusted based on U.S. census data to reflect the proportions of the population with different ages, genders, races, and geographic distributions. During the analysis, we used these weights to correct for sample selection bias and ensure that our estimates represent the adult population among noninstitutionalized residents in the U.S. By applying these weighted adjustments, our study can more accurately estimate the national epidemiological parameters of the relationship between the NPAR and asthma incidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1 Data Characterization and Model Construction\u003c/h2\u003e \u003cp\u003eContinuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as frequencies (percentages). Group differences were analyzed using chi-square tests for categorical variables and independent t-tests for continuous variables. The NPAR was categorized into quartiles: Q1 (0.18\u0026ndash;12), Q2 (12\u0026ndash;13.6), Q3 (13.6\u0026ndash;15.2), and Q4 (15.2\u0026ndash;36.1). Three hierarchical multivariable logistic regression models were constructed to assess NPAR-asthma associations: Model 1 (unadjusted), Model 2 (adjusted for sex, age, and race), and Model 3 (further adjusted for BMI, cardiovascular disease, hypertension, eosinophil count, poverty-income ratio [PIR], smoking status, red cell distribution width [RDW], total vitamin D level [25(OH)D2\u0026thinsp;+\u0026thinsp;25(OH)D3], and education level).\u003c/p\u003e \u003cp\u003eVariable selection was conducted through a three-phase framework to ensure methodological rigor:\u003c/p\u003e \u003cp\u003eTheoretical Prioritization: Initial covariates were selected based on established asthma pathophysiology literature, including demographic (age, sex), socioeconomic (education, PIR), clinical (BMI, cardiovascular disease, smoking status), and laboratory measures (eosinophil count, vitamin D).\u003c/p\u003e \u003cp\u003eAlgorithmic Optimization: Bidirectional stepwise regression (SPSS v29.0) with entry and removal thresholds of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and p\u0026thinsp;\u0026ge;\u0026thinsp;0.10, respectively, refined covariates while controlling model complexity.\u003c/p\u003e \u003cp\u003eMulticollinearity Control: Variance inflation factors (VIF). Variables with VIF\u0026thinsp;\u0026ge;\u0026thinsp;5 were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2 Multiple Testing Adjustments\u003c/h2\u003e \u003cp\u003eFor trend analyses across NPAR quartiles (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), Benjamini-Hochberg false discovery rate (FDR) correction was applied to control multiplicity across three models, with adjusted p-values calculated as p\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;FDR\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;min(1, (3 \u0026times; p\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026gt;(i)\u0026lt;/sub\u0026gt;)/i), where i denotes the rank of sorted p-values. Subgroup interaction tests (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) employed Bonferroni correction to maintain a family-wise error rate (FWER) of 0.05 across 11 pre-specified comparisons, yielding an adjusted significance threshold of α\u0026thinsp;=\u0026thinsp;0.00455. Interaction p-values were adjusted as p\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;adj\u0026lt;/sub\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;min(1, p\u0026thinsp;\u0026lt;\u0026thinsp;sub\u0026thinsp;\u0026gt;\u0026thinsp;nominal\u0026lt;/sub\u0026gt; \u0026times; 11), with values exceeding 1.00 truncated to 1.00. Groupwise comparisons (Q2-Q4 vs. Q1) used Bonferroni-adjusted α\u0026thinsp;=\u0026thinsp;0.0167 per model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.7.3 Data Imputation and Validation\u003c/h2\u003e \u003cp\u003eA four-phase multiple imputation workflow addressed missing data (maximum missingness\u0026thinsp;=\u0026thinsp;18%). First, variables with \u0026gt;\u0026thinsp;40% missingness (e.g., diabetes) were excluded following Little\u0026rsquo;s MCAR test (χ\u0026sup2; = 32.1, p\u0026thinsp;=\u0026thinsp;0.107). Next, 20 datasets were generated via Fully Conditional Specification (FCS) with predictive mean matching for continuous variables and logistic regression for categorical variables, constrained to biologically plausible ranges (e.g., BMI 15\u0026ndash;50 kg/m\u0026sup2;). Post-imputation validation confirmed distributional equivalence between observed and imputed values (Kolmogorov-Smirnov p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with between-imputation consistency metrics meeting thresholds (relative increase in variance [RIV]\u0026thinsp;\u0026lt;\u0026thinsp;0.25; fraction of missing information [FMI]\u0026thinsp;\u0026lt;\u0026thinsp;0.3). Sensitivity analyses comparing complete-case (n\u0026thinsp;=\u0026thinsp;14,648) and imputed (n\u0026thinsp;=\u0026thinsp;17,800) datasets showed\u0026thinsp;\u0026lt;\u0026thinsp;10% variation in key parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.7.4 Advanced Analytical Methods\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves with 1,000 bootstrap resamples quantified NPAR\u0026rsquo;s predictive performance, reporting area under the curve (AUC), and 95% confidence intervals. Non-linear relationships were modeled using four-knot restricted cubic splines (RCS), validated via likelihood ratio tests. Subgroup heterogeneity was assessed by incorporating interaction terms (NPAR \u0026times; stratification variables) for sex, race, and socioeconomic factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.7.5 Weighted Analysis and Software\u003c/h2\u003e \u003cp\u003eNHANES sampling weights, calibrated to U.S. census demographics, were applied using Taylor series linearization to ensure nationally representative estimates. All analyses were conducted in SPSS 29.0 (IBM Corp) and R 4.4.2 (R Foundation), with statistical significance defined as two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for primary analyses and FDR-/Bonferroni-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for multiplicity-controlled comparisons\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eThe study included 17,800 participants, with an average age of 44.71\u0026thinsp;\u0026plusmn;\u0026thinsp;17.33 years. Among these participants, 70.34% were men, and 29.66% were women, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The participants were divided into four NPAR quartiles: 0.18\u0026ndash;12 (first), 12\u0026ndash;13.6 (second), 13.6\u0026ndash;15.2 (third), and 15.2\u0026ndash;36.1 (fourth). In total, 2,475 participants (13.90%) had asthma. The prevalence of asthma was more significant in the higher NPAR quartiles than in the lower quartiles, with quartile one at 13.27%, quartile two at 12.99%, quartile three at 13.68%, and quartile four at 15.67% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The NPAR quartiles significantly differed across multiple factors, including age, sex, race, education level, PIR ratio, BMI, dietary inflammatory index (DII), smoking habits, cardiovascular disease, hypertension, diabetes, eosinophil count, lymphocyte count, red blood cell distribution width, vitamin D levels, and platelet count (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with those in the low NPAR subgroup, participants in the high NPAR subgroup were more likely to be male, non-Hispanic white, nonsmokers, and free of cardiovascular disease. They generally have a higher level of education. No statistically significant differences were found between the two groups regarding alcohol consumption or family history of asthma (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variable selection\u003c/h2\u003e \u003cp\u003eAfter applying the three-phase selection framework (theoretical prioritization, stepwise regression, and multicollinearity control), the final model included NPAR, age, sex, race, BMI, cardiovascular disease, eosinophil count, PIR, smoking status, red cell distribution width, vitamin D level, and education level. All variables exhibited VIF\u0026thinsp;\u0026lt;\u0026thinsp;5, confirming minimal multicollinearity. Subsequent multivariate logistic regression analysis demonstrated that NPAR remained independently associated with asthma risk (OR\u0026thinsp;=\u0026thinsp;1.026, 95% CI: 1.008\u0026ndash;1.045, p\u0026thinsp;=\u0026thinsp;0.0046), even after adjusting for these covariates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The relationship between NPAR and asthma\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the logistic regression model findings, highlighting the association between NPAR and asthma. Initially, treating the NPAR as a continuous variable revealed a significant association with asthma. After controlling for factors such as sex, age, and race, the association between the NPAR and asthma persisted, highlighting the potential role of the NPAR as a significant predictor of asthma. Furthermore, even after accounting for variables such as BMI, CVD, eosinophil count, PIR, smoking status, red cell distribution width (RDW), total vitamin D level, and education level, the associations between NPAR and asthma remained significant. These findings indicate that the NPAR may have an independent effect on the development or exacerbation of asthma.\u003c/p\u003e \u003cp\u003eModel 1: The odds ratio for the continuous variable of the NPAR is 1.029, with a 95% CI of 1.012\u0026ndash;1.046 and a p-value of less than 0.001, indicating a significant positive correlation. In the quartile analysis, the odds ratio for the Q4 group was 1.08, which approached significance (p\u0026thinsp;=\u0026thinsp;0.072), and the overall trend was significant (p\u0026thinsp;=\u0026thinsp;0.001, FDR-adjusted p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eModel 2: The odds ratio for the continuous variable of the NPAR is 1.049, with a 95% CI of 1.031\u0026ndash;1.067 and a p-value of less than 0.001, indicating a stronger positive correlation. In the quartile analysis, the odds ratio for the Q4 group was 1.09, reaching significance (p\u0026thinsp;=\u0026thinsp;0.008), and the overall trend was very significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eModel 3: The odds ratio for the continuous variable of the NPAR is 1.026, with a 95% CI of 1.008\u0026ndash;1.045 and a p-value of 0.0046, indicating a positive correlation. In the quartile analysis, the odds ratio for the Q4 group was 1.04, which did not reach significance (p\u0026thinsp;=\u0026thinsp;0.382), but the overall trend remained significant (p\u0026thinsp;=\u0026thinsp;0.006, FDR-adjusted p\u0026thinsp;=\u0026thinsp;0.018).\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\u003eAssociations between the NPAR and asthma incidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPAR (Continuous)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eNPAR (As Quartiles)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 (Reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 Group OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 Group OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 Group OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFDR-Adjusted \u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.029(1.012\u0026ndash;1.046)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.926(0.739\u0026ndash;1.08) 0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16(0.925\u0026ndash;1.36) 0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.08 (0.995\u0026ndash;1.10) 0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.049 (1.031\u0026ndash;1.067)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.917(0.742\u0026ndash;1.08) 0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18(0.955\u0026ndash;1.40) 0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.09 (1.02\u0026ndash;1.12) 0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.026 (1.008\u0026ndash;1.045) 0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.914 (0.742\u0026ndash;1.10) 0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.14 (0.926\u0026ndash;1.38) 0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.04 (0.969\u0026ndash;1.08) 0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Model 1: no excoriates were adjusted; Model 2: adjusted for sex, age, and race; Model 3: Adjusted for age, sex, race, body mass index (BMI), cardiovascular disease (CVD), eosinophil count, poverty income ratio (PIR), smoking status (categorized as \u003cem\u003enever/former/current\u003c/em\u003e), red cell distribution width (RDW), total vitamin D level (25(OH)D2\u0026thinsp;+\u0026thinsp;25(OH)D3), and education level. Variables were selected via stepwise regression (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and validated for multicollinearity (variance inflation factor, VIF\u0026thinsp;\u0026lt;\u0026thinsp;5). Abbreviation: Q, quartile; NPAR, neutrophil-percentage-to-albumin ratio; CVD: cardiovascular disease; BMI: body mass index; PIR, poverty income ratio. Trend p-values were adjusted via Benjamini-Hochberg FDR (q\u0026thinsp;=\u0026thinsp;0.05) for 3 model comparisons. Quartile group p-values are presented as raw values due to their exploratory nature; Bonferroni-adjusted thresholds: α\u0026thinsp;=\u0026thinsp;0.0167 per model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 ROC curve analysis of asthma prediction models\u003c/h2\u003e \u003cp\u003eWe evaluated the classification performance of NPAR for asthma prediction using receiver operating characteristic (ROC) curves across three nested models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): Model 1 (Unadjusted, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea): With no covariate adjustments, the baseline AUC was 0.520 (95% CI: 0.498\u0026ndash;0.542), indicating limited discriminative power of NPAR alone. Model 2 (Demographic-Adjusted, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb): After adjusting for sex, age, and race, the AUC improved to 0.583 (95% CI: 0.560\u0026ndash;0.606). This incremental gain highlights the role of demographic heterogeneity in asthma risk stratification. Model 3 (Fully Adjusted, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec): Further incorporating clinical, socioeconomic, and lifestyle covariates\u0026mdash;BMI, cardiovascular disease, eosinophil count, poverty income ratio (PIR), smoking status (Never/Former/Current), red cell distribution width (RDW), total vitamin D level [25(OH)D2\u0026thinsp;+\u0026thinsp;25(OH)D3], and education level\u0026mdash;the AUC reached 0.699 (95% CI: 0.675\u0026ndash;0.723). This substantial improvement underscores the multifactorial nature of asthma, where inflammatory, metabolic, and socioeconomic determinants collectively enhance risk prediction. At the optimal threshold of 0.135, Model 3 achieved a sensitivity of 0.721 and a specificity of 0.587.\u003c/p\u003e \u003cp\u003eClinical relevance: High sensitivity ensures effective identification of true asthma cases, while moderate specificity reduces unnecessary interventions in non-asthmatic populations. This balance is critical for deploying NPAR as a cost-effective screening tool in primary care, particularly for high-risk subgroups (e.g., individuals with low PIR or smoking history).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Asthma risk assessment\u003c/h2\u003e \u003cp\u003eThis study employed the restricted cubic spline (RCS) method to examine the non-linear relationship between NPAR and asthma risk (Fig.\u0026nbsp;3). We illustrated the relationship between NPAR and asthma risk with three subplots (a, b, c), each reflecting different variable adjustments. In each subplot, the blue curve shows the trend of asthma risk. The purple-shaded area indicates the confidence interval, showing the uncertainty in the estimates. In subplot a, we observed an apparent U-shaped curve between NPAR and asthma risk without adjusting for variables. These findings indicate that asthma risk is more significant at low and high NPAR values but lower in the middle range. The confidence interval in the middle range is narrower, suggesting greater precision of the estimates. This pattern suggests a complex interaction between NPAR and asthma risk.\u003c/p\u003e \u003cp\u003ePlot b illustrates the relationship between NPAR and asthma risk, adjusting for age, sex, and race. Compared with plot a, the shape of the curve remains consistent. The confidence interval narrows in the lower NPAR value region, indicating improved accuracy after adjustment. Additionally, Plot b shows that the increase in asthma probability slows down when NPAR values exceed 25, suggesting a reduced impact of NPAR on asthma risk in these populations.\u003c/p\u003e \u003cp\u003ePlot c illustrates the relationship between NPAR and asthma probability after adjusting for all relevant variables. The curve indicates that asthma probability is low at low NPAR values, rises with higher NPAR values, and decreases as NPAR approaches 30. After adjustment, the confidence interval across the entire range of NPAR values is relatively wide, particularly at the extremes, suggesting increased uncertainty in the estimates at these values. \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;3 RCS analysis of the relationship between NPAR and asthma risk. The solid blue line in the figure represents the trend in the probability of asthma, whereas the purple-shaded area represents the 95% confidence interval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Subgroup analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses assessed whether the association between NPAR and asthma risk varies across different demographic and clinical subgroups. The results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e visually illustrates the associations between NPAR and asthma across various subgroups. It shows that males have a higher odds ratio for asthma than females, which is statistically significant in at least one model (P\u0026thinsp;=\u0026thinsp;0.008). Similarly, non-Hispanic white individuals exhibit a higher odds ratio for asthma in at least one model (P\u0026thinsp;=\u0026thinsp;0.008). Participants with a history of cardiovascular disease (CVD) also show a significant odds ratio (P\u0026thinsp;=\u0026thinsp;0.019), as do former smokers (P\u0026thinsp;=\u0026thinsp;0.039). Additionally, individuals with a 25(OH)D level between 50 and 250 have a significant odds ratio in at least one model (P\u0026thinsp;=\u0026thinsp;0.049).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a detailed statistical analysis of the subgroup interactions. The table lists the nominal p-values for each subgroup interaction, along with the Bonferroni-adjusted significance. The Bonferroni correction was applied to control the family-wise error rate for the 11 pre-specified subgroup interaction tests, with an adjusted significance threshold of 0.00455.\u003c/p\u003e \u003cp\u003eThe results indicate that the interaction between sex and NPAR shows a nominal p-value of 0.029, which does not meet the Bonferroni-adjusted significance threshold. Similarly, the interaction between race and NPAR has a nominal p-value of 0.086, which is also not significant after adjustment. The poverty income ratio (PIR) interaction has a nominal p-value of 0.034, again not meeting the adjusted significance threshold.\u003c/p\u003e \u003cp\u003eOther subgroup interactions, including age, hypertension, CVD, smoking status, education level, a close relative with asthma, BMI, and 25(OH)D levels, all have nominal p-values greater than 0.05, indicating no statistically significant interactions after adjustment.\u003c/p\u003e \u003cp\u003eIn summary, the subgroup analyses suggest that while there are some differences in the odds ratios across subgroups, these differences are not statistically significant after applying the Bonferroni correction. This implies that the association between NPAR and asthma risk is consistent across the different demographic and clinical subgroups considered in this study.\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\u003eSubgroup Interaction Analysis (Bonferroni-Adjusted)\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNominal\u0026nbsp;p-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBonferroni Adjustment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance (α\u0026thinsp;=\u0026thinsp;0.00455)\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\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\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\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\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\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\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\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\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\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA close relative had asthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\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\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25(OH)D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\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\u003eMultiplicity Adjustment: Bonferroni correction was applied to control the family-wise error rate for 11 pre-specified subgroup interaction tests. The adjusted significance threshold was calculated as 0.05 divided by 11 (approximately 0.00455). Statistical Significance: A subgroup interaction was considered statistically significant if the nominal (unadjusted) p-value was less than the adjusted threshold of 0.00455. Equivalently, adjusted p-values (calculated by multiplying the nominal p-value by 11) were compared to the original alpha level 0.05.\u003c/p\u003e \u003cp\u003eData Truncation: Adjusted p-values exceeding 1.00 were capped at 1.00 to maintain valid probability bounds. Interpretation: All nominal p-values greater than 0.00455 (adjusted p-values greater than 0.05) indicate no statistically significant effect modification, supporting consistent NPAR-asthma associations across demographic and clinical subgroups.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis large-scale, nationally representative study of 17,800 U.S. adults from NHANES (2009\u0026ndash;2018) provides the first epidemiological evidence that NPAR is a novel, independent biomarker for asthma risk, exhibiting a characteristic U-shaped dose-response relationship. In fully adjusted models accounting for 12 demographic, clinical, and socioeconomic confounders, each unit increase in NPAR was associated with a 2.6% elevation in asthma prevalence (adjusted OR\u0026thinsp;=\u0026thinsp;1.026, 95% CI:1.008\u0026ndash;1.045, P\u0026thinsp;=\u0026thinsp;0.0046). The robustness of this association was confirmed through sensitivity analyses, which showed minimal difference (\u0026lt;\u0026thinsp;1.5%) between complete-case and multiple imputation analyses and alternative NPAR operationalization strategies, with Benjamini-Hochberg FDR correction yielding significant results for trend tests (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eNeutrophils drive multifaceted pathological processes in asthma through coordinated effector mechanisms\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Neutrophil elastase (NE) disrupts airway epithelial barrier function through proteolytic cleavage of tight junction proteins, including occludin and E-cadherin, as demonstrated by in vitro polarized epithelial monolayers and murine asthma models\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This protease specifically targets the extracellular domains of claudin-18 (CLDN18) at Ser68 and Tyr69 residues, leading to significantly increased paracellular permeability\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Concurrently, neutrophil elastase (NE)-mediated proteolytic degradation of zonula occludens-1 (ZO-1) triggers MAPK/ERK signaling pathway activation, thereby promoting IL-33 release and enhancing the paracellular penetration of aeroallergens such as house dust mite-derived proteins\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Concurrently, neutrophil-derived reactive oxygen species (ROS) amplify oxidative stress, activating the NLRP3 inflammasome to enhance IL-1β/IL-18 secretion, which synergizes with Th2 cytokines (IL-4/IL-13) to sustain eosinophilic inflammation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Emerging evidence further implicates neutrophil extracellular traps (NETs) in promoting Th17-mediated neutrophilic asthma endotypes through IL-17A/IL-23 axis activation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, environmental risk factors such as smoking and air pollution exacerbate airway inflammation by enhancing neutrophil recruitment and activation\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Hypoalbuminemia, defined as serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.5 g/dL, is independently associated with asthma severity due to its role in reducing antioxidant capacity and increasing vascular permeability\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The NPAR synergistically combines these two pathophysiological dimensions, providing a more robust predictor of asthma risk than isolated neutrophil or albumin measurements\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Derived from routine blood tests, NPAR offers a cost-effective and accessible biomarker for clinical practice. In our analysis, each unit increase in NPAR was associated with a 2.6% rise in asthma risk (OR\u0026thinsp;=\u0026thinsp;1.026, 95% CI:1.008\u0026ndash;1.045), even after adjusting for socioeconomic and clinical confounders. This is consistent with previous studies, indicating the predictive value of NPAR in chronic respiratory diseases, where the AUC for predicting the five-year mortality rate in COPD is 0.808, while the AUC for NLR is 0.799\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study is the first to systematically explore the NPAR-asthma link in a nationally representative adult population. Our regression analyses identified NPAR as an independent predictor post-adjustment for key confounders. In contrast ROC curve analysis highlighted its added predictive value alongside traditional risk factors, with AUC improving from 0.520 to 0.699. The U-shaped dose-response relationship, with minimal risk at NPAR 12\u0026ndash;15, implies dual pathological pathways: oxidative stress susceptibility at lower values and neutrophilic inflammation dominance at higher values. This non-linear pattern was corroborated by subgroup analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), showing consistent NPAR-asthma associations across demographic subgroups despite non-significant interaction effects post-Bonferroni correction. Notably, subgroup analyses identified potential interaction effects by sex (P-interaction\u0026thinsp;=\u0026thinsp;0.029) and poverty-income ratio (P-interaction\u0026thinsp;=\u0026thinsp;0.034); however, these associations did not remain significant after Bonferroni correction for multiple comparisons (adjusted significance threshold: P\u0026thinsp;=\u0026thinsp;0.00455). Sensitivity analyses and alternative NPAR categorizations reinforced NPAR's reliability, though its moderate standalone predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.699) suggests a need for complementary biomarkers like FeNO for clinical.\u003c/p\u003e \u003cp\u003eOur study has several notable strengths. To our knowledge, this is the first large-scale epidemiological investigation to establish the NPAR as an independent biomarker for asthma risk in the general adult population. Second, our multivariable models rigorously adjusted for 12 potential confounders, including demographic, socioeconomic, and clinical variables (e.g., BMI, smoking status, cardiovascular disease), ensuring robust control of confounding effects. Third, using NHANES data (2009\u0026ndash;2018, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17,800) with nationally representative sampling weights enhances the generalizability of our findings to noninstitutionalized U.S. adults. However, several limitations should be acknowledged. The cross-sectional design precludes causal inference, and longitudinal studies are needed to determine whether NPAR elevation precedes asthma onset or results from chronic inflammation.\u003c/p\u003e \u003cp\u003eAdditionally, NPAR was calculated from single-timepoint laboratory measurements, which may not capture dynamic inflammatory changes over time. Asthma diagnoses relied on self-reported questionnaires rather than spirometry-confirmed criteria, potentially introducing misclassification bias. Furthermore, we lacked data on airway-specific biomarkers (e.g., fractional exhaled nitric oxide [FeNO], eosinophil cationic protein [ECP]) and pulmonary function tests, which could refine asthma phenotyping and mechanistic interpretations. Future research should integrate serial NPAR measurements, multi-omics profiling (e.g., transcriptomics of neutrophilic asthma subtypes), and randomized interventions targeting NPAR modulation to validate its clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.B. and H.K. conceived and designed the study. L.B. performed the data analysis and wrote the initial draft of the manuscript. J.L. assisted with data collection and validation. H.K. revised the manuscript and provided critical feedback. L.B. prepared figures 1-4. All authors contributed to the final version of the manuscript and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe want to thank the NHANES and GWASs for making summary data publicly available.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data analyzed in this study are from the NHANES 2009\u0026ndash;2018, which are publicly available and can be downloaded from the NHANES website (http://www.cdc.gov/nchs/nhanes.htm).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDamask C, Franzese C. Incorporating Asthma Evaluation into the Otolaryngic Allergy Practice: Presentation and Diagnosis. 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Respirology. 2025;30(1):89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/resp.14875\u003c/span\u003e\u003cspan address=\"10.1111/resp.14875\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"asthma, neutrophil percentage-to-albumin ratio, biomarker, chronic inflammation, NHANES study","lastPublishedDoi":"10.21203/rs.3.rs-5880431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5880431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the neutrophil percentage-to-albumin ratio (NPAR) as a novel biomarker for asthma risk and to explore its association with asthma incidence in a nationally representative adult population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing cross-sectional data from 17,800 adults in the National Health and Nutrition Examination Survey (NHANES 2009–2018), we calculated NPAR as the ratio of neutrophil percentage to serum albumin concentration. Multivariable logistic regression models adjusted for demographic, socioeconomic, clinical, and laboratory covariates were employed to assess NPAR-asthma associations. Missing data were addressed via multiple imputations, and model performance was evaluated using receiver operating characteristic (ROC) curves with bootstrap validation. Restricted cubic splines analyzed non-linear relationships, while subgroup analyses tested effect heterogeneity across demographic and clinical strata. Sensitivity analyses compared complete-case and imputed datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElevated NPAR levels were strongly associated with increased asthma risk. In fully adjusted models, each one-unit increase in NPAR corresponded to a 2.6% rise in asthma prevalence (adjusted OR = 1.026, 95% CI: 1.008–1.045, P = 0.0046). ROC curve analysis demonstrated an AUC of 0.699 for NPAR in predicting asthma. Subgroup analyses revealed effect modification by sex, race, and cardiovascular disease history, though interaction terms did not meet Bonferroni-adjusted significance thresholds. Restricted cubic spline analyses indicated a U-shaped dose-response relationship, with minimal risk observed at NPAR values of 12–15, suggesting dual pathological mechanisms: oxidative stress susceptibility at lower NPAR values and neutrophilic inflammation dominance at higher values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides the first epidemiological evidence supporting NPAR as an independent biomarker for asthma risk. The U-shaped association highlights the complex interplay between systemic inflammation and oxidative stress in asthma pathogenesis. While NPAR offers a cost-effective and accessible tool for risk stratification, its moderate predictive performance underscores the need for complementary biomarkers to enhance clinical utility. Future research should integrate serial NPAR measurements and multi-omics profiling to validate its role in asthma management.\u003c/p\u003e","manuscriptTitle":"Neutrophil percentage-to-albumin ratio (NPAR) as a biomarker for asthma: A cross-sectional analysis of NHANES data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 08:33:36","doi":"10.21203/rs.3.rs-5880431/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-14T08:09:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T09:28:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T09:06:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T06:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154350029338680479180787015338277907549","date":"2025-04-02T06:05:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6077149219967165168838279152906144701","date":"2025-04-02T03:48:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62330339158326623272587231840612415733","date":"2025-04-01T12:48:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T12:03:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T06:54:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-03-29T08:38:25+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":"894e5f79-6618-489f-aa6e-5ad3e4539f99","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:08:31+00:00","versionOfRecord":{"articleIdentity":"rs-5880431","link":"https://doi.org/10.1186/s12890-025-03701-1","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2025-05-29 15:57:48","publishedOnDateReadable":"May 29th, 2025"},"versionCreatedAt":"2025-04-03 08:33:36","video":"","vorDoi":"10.1186/s12890-025-03701-1","vorDoiUrl":"https://doi.org/10.1186/s12890-025-03701-1","workflowStages":[]},"version":"v1","identity":"rs-5880431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5880431","identity":"rs-5880431","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}