Identify Key Asthma Risk Factors via 2019–2023 Health Interview Survey Data

Identify Key Asthma Risk Factors via 2019–2023 Health Interview Survey 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 Identify Key Asthma Risk Factors via 2019–2023 Health Interview Survey Data Weili Guo, Siqin Wang, Wenchao Zhang, Xianghua Lin, Qiuxing Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8016017/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Asthma is a common chronic respiratory disease. The risk factors that affected asthma were not clearly understood. Thus, using 2019–2023 National Health Interview Surveys (NHIS) data, this study identified risk factors in asthma, offering a reference for asthma prevention and treatment. Methods A total of 150220 subjects were retrieved from NHIS database (2019–2023). First, data were screened. Participants were divided into two groups: those with asthma and those without. Then, differences in baseline characteristics between two groups were analyzed, and significant variables were selected as candidate variables. Least absolute shrinkage and selection operator (LASSO) method was applied to gain candidate risk factors. Subsequently, multivariable logistic regression analysis was applied to further screen and gain risk factors. A stratified analysis of risk factors was conducted to identify key risk factors. Finally, a nomogram for asthma prediction was constructed. Results After variable screening, 23680 subjects were attained, including 3919 asthma subjects and 19761 healthy subjects. Variables such as sex and ever smoked were found to be significantly associated with asthma. Seven candidate variables were gained. LASSO analysis yielded seven candidate risk factors. Multivariable logistic regression identified hypertension, body mass index (BMI), sex, and ever-married as significant risk factors. Stratified analysis showed that BMI, sex, and marital status were key risk factors for asthma. A nomogram demonstrated better predictive performance for the condition. Conclusions Three key risk factors associated with asthma were identified. A nomogram with good predictive power was constructed. These findings offered valuable insights for asthma prevention and treatment. Asthma NHIS Key risk factors Figures Figure 1 Figure 2 Figure 3 1 Introduction Asthma is a common and highly heterogeneous chronic respiratory disease characterized by airway stenosis, edema and excessive mucus blockage. Over 300 million individuals globally are thought to suffer from asthma, and the prevalence of asthma continues to rise globally[1]. Asthma is the result of complex genetic-environmental interactions, so clinical manifestations and the types and intensity of airway inflammation and remodeling are heterogeneous. Heat waves, cold waves, sandstorms, hurricanes, floods, and storm surges increase the risk of asthma-related outcomes [2]. Asthma has a genetic tendency. The mechanism of the asthma phenotype is strongly correlated with genetics [3,4]. Asthma is likely to be transmitted by multiple genes, locus heterogeneity and several changes in multigene inheritance lead to the multifaceted expression of asthma[5]. Asthma is associated with pulmonary and extrapulmonary complications, which are more common in patients with severe asthma than in patients with mild to moderate disease or the general population[6]. In a study of asthma during pregnancy, asthma was associated with various complications and adverse consequences of mothers and neonates at all stages of pregnancy. The severity of asthma affect the risk of complications, as adverse consequences are more common in pregnant women with moderate to severe asthma [7]. A report on the association between asthma and cardiovascular disease indicates that asthma is associated with cardiovascular disease, especially in older patients and untreated asthma patients, and this interaction is more pronounced[8] . It can be seen that it is crucial to identify relevant risk factors or markers for delaying or preventing asthma. Although many researchers have studied asthma extensively, the changing characteristics of asthma burden and the impact of macro-environmental factors on asthma burden are still unknown[9]. Over the past four decades, the prevalence of asthma among all ages has steadily increased worldwide. Asthma is an increasingly serious health problem among adults >64 years old. In addition, 40% of elderly patients with asthma have the first disease after the age of 40. However, pathophysiological studies of adult asthma are relatively insufficient, so it is important to identify and manage risk factors for asthma to reduce adverse reactions and related mortality in middle-aged people.[10]. The NHIS is a family interview survey conducted through the National Center for Health Statistics (NCHS) under the support of the Centers for Disease Control and Prevention (CDCP), conducted annually in person, involving a statistically representative sample of non-contained population in the United States.Therefore, in order to explore the key risk factors affecting asthma in adults, this study attempts to use NHIS data to explore the relationship between exposure factors and asthma in a nationally representative sample of American adults, hoping to provide more reference for the prevention and control of asthma. This study obtained data from NHIS database (2019-2023). After excluding people with asthma and samples with covariate deletion, 27,628 study subjects were included, divided into two groups with asthma and healthy. Analyses on the baseline characteristics difference between the two groups were conducted, and statistically significant variables were selected as candidate variables. Next, the minimum absolute shrinkage and selection operator (LASSO) method was used to obtain candidate risk factors, and then multivariate logistic regression analysis was performed to further screen the variables to obtain risk factors. Key risk factors were then stratified analyzed. Finally, a nomogram for asthma prediction was constructed. The accuracy of this nomogram is evaluated by drawing calibration curves, providing more reference for the prevention and control of asthma. 2 Materials and methods 2.1 Data acquisition The data for this study were gained from NHIS database ( https://www.cdc.gov/nchs/nhis/index.html ). The NHIS was designed and operated by NCHS. Health-related interviews, examinations, and nutrition surveys were collected for later analysis by the NHIS. All data were accessible via provided online platforms without any additional requirements. A total of 150220 subjects who participated in NHIS survey from 2019 to 2023 were initially included. Additionally, these subjects were screened. Subjects with unclear sunburndiagnosis and missing data wereexcluded. Exclude subjects labeled as missing, rejected, and unaware of covariates. Finally, 27,628 subjects were included in this analysis ( Fig. 1 ) . These variables were screened via dplyr package (v 1.1.4) ( https://cran.r-project.org/web/packages/dplyr/index.html ). 2.2 Variable definition Asthma was assessed in line with question "Have you ever been told by a doctor or other health professional that you had asthma ?" [ 11 ]. If a doctor or other healthcare professional had ever informed respondent that they had asthma, respondent was identified as having asthma (asthma group), while rest were regarded as non-asthmatic (healthy group). The covariates in this study included sex, urban/rural residence, educated, etc. These variables were classified into categorical variables. Different categorical variables were partitioned into different subgroups. For example, gender comprised male and female, and region included Northeast, Midwest, South, and West ( Table 1 ) . Table 1 Definition, numbering and grouping information table of covariates or confounding factors variates serial numbers definitions sex SEX 1 = Male 2 = Female Urban.rural URBRRL 1 = Large central metro 2 = Large fringe metro 3 = Medium and small metro 4 = Nonmetropolita educated EDUCP(2023) 1 = Not graduated from high school 2 = Graduated from high school 3 = Not graduated from university 4 = Graduated from university evermarried EVRMARRIED 1 = yes 2 = no BMI(Body Mass Index) BMICAT 1=(<18.5 kg/m2) 2=(≥18.5 kg/m2 and < 25 kg/m2) 3=(≥25 kg/m2 and < 30 kg/m2) 4=(≥30 kg/m2) smokever SMKEV 1 = yes 2 = no Hypercholesterolemi CHLEV 1 = yes 2 = no Hypertension HYPMED, HYPEV 1 = yes 2 = no region REGION 1 = Northeast 2 = Midwest 3 = South 4 = West The covariates included sex, urban/rural residence, educated, evermarried, BMI(Body Mass Index), smokever, Hypercholesterolemi, Hypertension, region. These variables were classified into categorical variables. Different categorical variables were partitioned into different subgroups. For example, gender comprised male and female, and region included Northeast, Midwest, South, and West. 2.3 Baseline characteristics analysis In line with subjects in NHIS database, chi-square test was harnessed to assess whether there were distinctions in baseline characteristics in asthma group and healthy group (p < 0.05). The baseline table was drawn via tableone package (v 0.13.2)[ 12 ]. Variables with statistical significance were selected as candidate variables for subsequent analysis. 2.4 Identification of key risk factors In line with candidate variables, candidate risk factors were gained by learning Least Absolute Shrinkage and Selection Operator (LASSO) via glmnet package (v 4.1-8) [ 13 ]. The response variable was set as binomial and alpha was set as 1. LASSO, a statistical method for data analysis, constructed a penalty function to gain a model. In this process, model compressed some regression coefficients, reducing dimensionality of data and avoiding multicollinearity and overfitting in multiple regression models. Features with stronger importance link to disease were less compressed, while those with weaker importance were compressed to zero. After 10-fold cross-validation, variables with minimum lambda value and non-zero regression coefficients were selected as candidate risk factors. Subsequently, candidate risk factors were further screened via multivariable logistic regression analysis. Variables with p < 0.05 and an effect size greater than 1 were selected as risk factors. Among them, variables with a warming effect size greater than 1 were considered risk factors for asthma, while those with a warming effect size less than 1 were regarded as protective factors for asthma. Finally, to contrast impacts of risk factors on asthma in different subgroups of risk factors, stats package (v 4.4.1 ) ( https://search.r-project.org/R/refmans/stats/html/00Index.html ) was utilized to conduct a stratified analysis on risk factors (p < 0.05) to identify risk factors. The odds ratio (OR) and 95% confidence interval (CI) were calculated. An OR greater than 1 was regarded as a risk factor for asthma, while an OR less than 1 was considered a protective factor for asthma. 2.4 Construction and validation of the nomogram To better predict occurrence of asthma, a nomogram for predicting asthma occurrence was constructed via rms package (v 6.5.0) [ 14 ], with key risk factors as independent variables and asthma as dependent variable. On line segment corresponding to each variable, scales were marked, representing range of possible scores for that variable, and length of line segment reflected contribution of variable to outcome event. Corresponding single-item points were gained for each variable under different values, and total points were calculated by summing up single-item points. In line with total points, likelihood of asthma occurrence was inferred. Moreover, to evaluate accuracy and reliability of predictive performance of nomogram, calibration curve of nomogram was drawn via regplot package(v 1.1) [ 15 ]. The closer slope of calibration curve was to 1, more accurate prediction of nomogram was. Finally, decision curve analysis (DCA) curves were plotted via ggDCA package (v 1.2) ( https://www.rdocumentation.org/packages/ggDCA/versions/1.1 ) and clinical impact curve (CIC) was plotted via rms package (v 6.5.0) to further evaluate clinical utility of nomogram. 2.5 Statistical analysis R language (v 4.2.2) was harnessed to execute all statistical analyses. A p-value less than 0.05 was considered to be statistically significant. The chi-square test was harnessed to assess whether there were distinctions in baseline characteristics in asthma group and healthy group (p < 0.05). 3 Result 3.1 Baseline characteristics of subjects In the NHIS database, a final cohort of 27,628 subjects was gained, including 4619 asthma patients and 23009 healthy individuals. Sex, evermarried, BMI, smokever, Urban/rural, Hypercholesterolemia, and Hypertension were found to be significantly associated with asthma (p < 0.05), and were taken as candidate variables (Table 2 ). Table 2 Baseline characteristics between two groups level ASEV _A Healthy p n 3919 19761 sex (%) 1 1320 (33.7) 8317 (42.1) < 0.001 2 2599 (66.3) 11444 (57.9) educated (%) 1 1661 (42.4) 8410 (42.6) 0.09 2 717 (18.3) 3404 (17.2) 3 201 (5.1) 900 (4.6) 4 1340 (34.2) 7047 (35.7) evermarried (%) 1 2734 (69.8) 14506 (73.4) < 0.001 2 1185 (30.2) 5255 (26.6) BMI (%) 1 40 (1.0) 247 (1.2) < 0.001 2 666 (17.0) 4625 (23.4) 3 1053 (26.9) 6614 (33.5) 4 2160 (55.1) 8275 (41.9) smokever (%) 1 2039 (52.0) 9508 (48.1) < 0.001 2 1880 (48.0) 10253 (51.9) Urban.rural (%) 1 1083 (27.6) 5427 (27.5) 0.035 2 752 (19.2) 4192 (21.2) 3 1336 (34.1) 6506 (32.9) 4 748 (19.1) 3636 (18.4) Hypercholesterolemia (%) 1 2157 (55.0) 10398 (52.6) 0.006 2 1762 (45.0) 9363 (47.4) Hypertension (%) 1 3101 (79.1) 16231 (82.1) < 0.001 2 818 (20.9) 3530 (17.9) region (%) 1 651 (16.6) 3104 (15.7) 0.21 2 875 (22.3) 4410 (22.3) 3 1546 (39.4) 8114 (41.1) 4 847 (21.6) 4133 (20.9) Sex, evermarried, BMI, smokever, urban/rural, hypercholesterolemia and hypertension were found to be significantly associated with asthma (p < 0.05) 3.2 BMI, sex, and evermarried were identified as key risk factors associated with asthma The LASSO results displayed that when value of lambda.min was 0.00107, regression coefficients of Sex, evermarried, BMI, smokever, Urban.rural, Hypercholesterolemia, and told. Hypertension were not zero, and these variables were taken as candidate risk factors (Fig. 2 a, b). In multivariable logistic regression analysis, Hypertension, BMI, sex, and evermarried were identified as risk factors for asthma (warming effect size > 1, p 1) (Fig. 2 d, e, f) . 3.3 Nomogram had better predictive performance for asthma The nomogram constructed in line with BMI, sex, and evermarried displayed that higher total points, greater likelihood of asthma occurrence ( Fig. 3 a ) . The calibration curve demonstrated that slope was close to 1 ( Fig. 3 b ) . The DCA curves displayed that net benefit of nomogram model ( red line) was higher than that of diagonal line (All), horizontal line (None), and risk factors, indicating that nomogram model had better predictive performance and practical application value in clinical decision-making ( Fig. 3 c ) . These results suggested that nomogram had a better predictive performance for asthma. 4. Discussion Asthma is a chronic respiratory disease, caused by the complex interaction of various environmental and genetic factors. Its pathogenesis is complex and related to a variety of diseases. The incidence of asthma is very high, with more than 350 million sufferers worldwide[16]. This study was based on the data from the NHIS database for the years 2019 to 2023. It included the subjects without missing information and those related to the established covariates. Eventually, 23,009 healthy samples and 4,616 asthma samples were included. Baseline statistics revealed that eight covariates-gender, educational level, marital status, BMI index, smoking status, family location, hyperlipidemia, and hypertension-showed significant differences between the control group and the asthma group, suggesting that they might be related to the occurrence of asthma. Using LASSO regression and multivariate logistic regression analyses, the significant variables from baseline statistics were further screened, and finally six core variables were were identified: gender, smoking status, marital status, BMI, hypertension, and hyperlipidemia. The Global Initiative for Asthma (GINA) in 2025 identified obesity as an independent risk factor for the difficulty in treating asthma[17]. Studies have found that in people over 45 years old, BMI has a significant non-linear relationship with the risk of asthma. Whether the weight is too low or too high, it will increase the risk of asthma. Data from the Chinese population shows that when BMI is below 19.9 or above 29.9 kg/m², the risk of asthma significantly increases; the risk threshold for the American population is 21.6 kg/m²[18]. When compared to those who are lean, those who are obese typically have poorly controlled asthma; a roughly 5-fold increased risk of hospitalization; and they do not respond to conventional controller therapy[19]. Even more alarming is that about 60% of patients with severe or refractory asthma are obese, which suggests that we need to re-examine this special asthma phenotype from a new perspective[20]. At least 5% weight loss was associated with significant improvements in important asthma-related outcomes, and in general health-related quality of life[19].A genetic predisposition to high BMI increases the risk of early respiratory tract infections and severe wheezing and asthma attacks[21]. Obese adults with asthma show elevated oxidative stress in corticosteroid insensitivity and the airway[22]. Accumulation in the mediastinum and abdomen might change the respiratory system and consequently interfere with the physiology and function of the lung[23]. Gender differences highlight the heterogeneity of asthma manifestation and underlying mechanisms. Studies have shown that the incidence of asthma differs between men and women, highlighting sex-specific relationships between obesity (as measured by BMI) and asthma. In women, asthma incidence increases monotonically with BMI, reaching the highest levels among severely obese individuals[24].By contrast, in middle-aged and elderly populations, the increase in asthma incidence is more pronounced in men than in women[25]. This rapid rise in asthma incidence among men may be associated with the influence of sex hormones on epithelial cell function[26]. However, females demonstrate greater susceptibility to asthma and severe asthma than males[27]. Furthermore, females are more likely than males to experience non-atopic asthma, exhibit poorer responses to corticosteroids, and develop obesity-related steroid-refractory asthma[28]. Sex hormones have been proven to play an important role in airway inflammation, smooth muscle contraction, mucus secretion and the pathogenesis related to asthma. The hormones secreted by the ovaries aggravate airway inflammation and testosterone reduces airway inflammation[29]. The literature highlights the influence of marital status on the quality of life in individuals with asthma. In a study conducted by Wang et al. [30] in Taiwan, utilizing the Chinese version of the St. George’s Respiratory Questionnaire (SGRQ), marital status was found to affect the perception of quality of life.Conversely, in the research by Ferreira et al. [31], single individuals and those who were married exhibited higher quality of life compared to divorced or widowed individuals.Additionally, partners of individuals with specific diseases, such as asthma, are at an elevated risk—approximately a 70% increased risk—of developing the same condition themselves. This suggests shared environmental factors contributing to certain diseases, in addition to genetic predispositions, distant exposures, or shared behaviors related to healthcare-seeking[32]. This may be due to shared diet, the similar intestinal microecological environments[33] or shared exposure to allergens[34]. The Nomogram model constructed based on key risk factors shows that the higher the total score, the greater the possibility of asthma occurrence. This result is consistent with multiple studies, indicating that the Nomogram model has good discrimination ability in asthma prediction[35]. The slope of the calibration curve was close to 1, indicating that the predicted probabilities of the model are highly consistent with the actual observed values. This consistency further validates the reliability of the model and suggests that it has high accuracy in clinical applications, further supporting the calibration ability of the model[36]. The DCA curve indicates good practical application value, and this result is similar to those of other asthma prediction models reported in the literature, indicating the reliability of the results of this study[37]. In conclusion, this study identified three key risk factors: BMI, sex, and marital status, which provide a practical basis for high-risk population screening, early prevention and personalized health management. However, there are still some limitations. Firstly, the limited sample size may affect the accuracy and generalizability of the model's predictions. Future research can address this by expanding the sample size. Secondly, the current study is mainly based on data from specific populations and lacks diversity. In subsequent research, more sources and backgrounds of data can be included to enhance the generalization ability of the model. Conclusion The study identified three key risk factors: BMI, sex, and marital status, which provide practical basis for high-risk population screening, early prevention, and personalized health management. Abbreviations BMI Body Mass Index CI Confidence Interval DCA Decision Curve Analysis CDCP Centers for Disease Control and Prevention GINA Global Initiative for Asthma NHIS National Health Interview Surveys NCHS National Center for Health Statistics OR Odds Ratio SGRQ St. George’s Respiratory Questionnaire Declarations Ethical approval and informed consent statement This study constitutes a secondary analysis of publicly available, de-identified data provided by the National Center for Health Statistics (NCHS). The original data collection by NCHS obtained the necessary ethical approval from its Research Ethics Review Board and secured informed consent from all participants. As the dataset used in this analysis contains no personally identifiable information and is openly accessible to the public, research of this nature is considered exempt from ethics review or falls outside the scope of "human subjects research" in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Clinical trial number Not applicable. Authors' contributions Conceptualization, Siqin Wang. and Weili Guo.; methodology, Weili Guo.; software, Weili Guo.; validation, Weili Guo., Wenchao Zhang. and Wenjin Du.; formal analysis, Weili Guo.; investigation, Xianghua Lin.; resources, Weili Guo.; data curation, Zhaoji Meng.; writing—original draft preparation, Weili Guo.; writing—review and editing, Weili Guo.; visualization,Weili Guo.; supervision, Qiuxing Zhang.; project administration, Siqin Wang.. All authors have read and agreed to the published version of the manuscript. Funding The current study did not receive any funding from a specific grant, funding agency, commercial or not-for-profit sector. Declaration of competing interest The authors declare no conflicts of interest. No funders had any role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Availability of data and materials The dataset analyzed in this study was publicly available from the NHIS database (https://www.cdc.gov/nchs/nhis/index.html). The data used in this study are available on request from the corresponding author. Acknowledgements We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following author: Yafeng,Wang. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. References Wan R, Srikaram P, Guntupalli V, Hu C, Chen Q, Gao P. Cellular senescence in asthma: from pathogenesis to therapeutic challenges. eBioMedicine.2023;94:104717. Makrufardi F, Manullang A, Rusmawatiningtyas D, Chung KF, Lin S-C, Chuang H-C. Extreme weather and asthma: a systematic review and meta-analysis. European Respiratory Review.2023;32:230019. Polyxeni N, Andreas P, Eleftherios Z, Georgina X, Konstantinos S. Genetics and Epigenetics in Asthma. Int J Mol Sci.2021;22:2412. Laura A C, Michael D C, Deepa R. Defining pediatric asthma: phenotypes to endotypes and beyond. Pediatr Res.2020;90:45-51. Hisako M. PATHOPHYSIOLOGY OF ASTHMA. Arerugi.2022;71:1065-1071. Lommatzsch M, Brusselle GG, Levy ML, Canonica GW, Pavord ID, Schatz M, et al. A2BCD: a concise guide for asthma management. The Lancet Respiratory Medicine.2023;11:573-576. Bravo-Solarte DC, Garcia-Guaqueta DP, Chiarella SE. Asthma in pregnancy. Allergy and Asthma Proceedings.2023;44:24-34. Wee JH, Park MW, Min C, Byun SH, Park B, Choi HG. Association between asthma and cardiovascular disease. European Journal of Clinical Investigation.2020;51:e13396. Wang Z, Li Y, Gao Y, Fu Y, Lin J, Lei X, et al. Global, regional, and national burden of asthma and its attributable risk factors from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. Respiratory Research.2023;24:169. Jiwon C, Sun Jae P, Young Jun P, Jaeyi H, Seogsong J, Jooyoung C, et al. Association between antibiotics and asthma risk among adults aged over 40 years: a nationally representative retrospective cohort study. BMJ Open Respir Res.2023;10:e001643. Cynthia A P, Hatice S Z, Xiaoting Q, Carol J, Erik H, Josephine M. Asthma Surveillance - United States, 2006-2018. MMWR Surveill Summ.2021;70:1-32. Agapios P, Dimitris M. TableOne: an online web application and R package for summarising and visualising data. Evid Based Ment Health.2020;23:127-130. Jerome F, Trevor H, Rob T. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw.2010;33:1-22. Michael C S. plotROC: A Tool for Plotting ROC Curves. J Stat Softw.2017;79:1-24. Hongxia Z, Haihong H, Bo H, Wendi Z, Ting Y, Jingdi Z, et al. Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients. Technol Cancer Res Treat.2024;23:1-16. Reddel HK, Bacharier LB, Bateman ED, Brightling CE, Brusselle GG, Buhl R, et al. Global Initiative for Asthma Strategy 2021: Executive Summary and Rationale for Key Changes. American Journal of Respiratory and Critical Care Medicine.2022;205:17-35. Brown N, Sullo N, Tyson N, Eagle-Hemming B, Lai FY, Sheikh S, et al. Acute kidney injury after cardiac surgery is associated with platelet activation. Journal of thrombosis and haemostasis : JTH.2025;23:2776-2789. Weili K, Xiangling Z, Hailing G, Manlin C, Mei L, Xiaoyun Z, et al. Association between BMI and asthma in adults over 45 years of age: analysis of Global Burden of Disease 2021, China Health and Retirement Longitudinal Study, and National Health and Nutrition Examination Survey data. EClinicalMedicine.2025;82:103163. Johnson O, Gerald LB, Harvey J, Roy G, Hazucha H, Large C, et al. An Online Weight Loss Intervention for People With Obesity and Poorly Controlled Asthma. The Journal of Allergy and Clinical Immunology: In Practice.2022;10:1577-1586.e1573. Ewelina R. The Role of Peptides in Asthma-Obesity Phenotype. Int J Mol Sci.2024;25:1-26. Signe Kjeldgaard J, Casper-Emil Tingskov P, Kasper F-R, Mathias Elsner M, Nicklas B, Julie Nyholm K, et al. Genetic predisposition to high BMI increases risk of early life respiratory infections and episodes of severe wheeze and asthma. Eur Respir J.2024;64. Peters U, Dixon AE, Forno E. Obesity and asthma. Journal of Allergy and Clinical Immunology.2018;141:1169-1179. Ye W, Xu X, Ding Y, Li X, Gu W. Trends in disease burden and risk factors of asthma from 1990 to 2019 in Belt and Road Initiative countries: evidence from the Global Burden of Disease Study 2019. Annals of Medicine.2024;56:2399964. Park S, Jung S-Y, Kwon J-W. Sex differences in the association between asthma incidence and modifiable risk factors in Korean middle-aged and older adults: NHIS-HEALS 10-year cohort. BMC Pulmonary Medicine.2019;19:248. Andrea S G, Jun G, Chengning W, Teresa T. Trends in asthma prevalence and incidence in Ontario, Canada, 1996-2005: a population study. Am J Epidemiol.2010;172:728-736. Jain R, Ray JM, Pan J-h, Brody SL. Sex Hormone–Dependent Regulation of Cilia Beat Frequency in Airway Epithelium. American Journal of Respiratory Cell and Molecular Biology.2012;46:446-453. Eileen W, Michael E W, Trung N T, Liam G H, Rupert C J, Andrew N M-G, et al. Characterization of Severe Asthma Worldwide: Data From the International Severe Asthma Registry. Chest.2019;157:790-804. Wei W, Eugene B, Wendy M, William W B, Mario C, Kian Fan C, et al. Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data. J Allergy Clin Immunol.2014;133:1280-1288. Radzikowska U, Golebski K. Sex hormones and asthma: The role of estrogen in asthma development and severity. Allergy.2023;78:620-622. Kwua-Yun W, Chin-Pyng W, Yu-Ying T, Muh-Lan Y. Health-related quality of life in Taiwanese patients with bronchial asthma. J Formos Med Assoc.2004;103:205-211. Ferreira L, Brito U, Ferreira P. Quality of life in asthma patients. Revista portuguesa de pneumologia.2010;16:23-55. Julia H-C, Carol C, Mike P, Nicola C, Vicky H. Married couples' risk of same disease: cross sectional study. BMJ.2002;325:636. Erika vM, Hermelijn H S. Primary prevention of asthma: from risk and protective factors to targeted strategies for prevention. Lancet.396:854-866. Tadech B, Zeynep Celebi S, Pattraporn S, Cezmi A A. Immunologic mechanisms in asthma. Semin Immunol.2019;46:101333. Yang L, Li M, Zheng Q, Ren C, Ma W, Yang Y. A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study. Journal of clinical laboratory analysis.2021;35:e23820. Jiang H, Fu C. Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning. International immunopharmacology.2024;133:112064. Yufan D, Dilixiati N, Mengyu L, Jie S, Jingjing Y, Ziliang H, et al. Development of a nomogram to estimate the risk of community-acquired pneumonia in adults with acute asthma exacerbations. Clin Respir J.2023;17:1169-1181. Additional Declarations No competing interests reported. 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Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3PsUoDQRDG8QkLk2Zi2jmQyyusBBasfJVdTqwipApbBBI42StUbH2MVGp5R2BtVusrE3yB2GmVPEDC3dlZ7K+eP3wDEEX/EI6+qu3O7tOrIq822s7bkzO4ycYcxFiSz+Qm+PYkhYnigRNmxROVbO9Eh2HgNScOe68UlDVLhGFxr5uTXl7K6SeJy8LNavN2Dhw+Vs2JQK15xghh/VKbgCD5tiVBkiWhJKi1mhonOiREF0tymmV9raBbwpgJDqVMHnzGOnhq/WX0LN5/d7ZcPPXz6vvHztNh8dicHKG/nUdRFEUnHQCRXUrIhnlZAAAAAABJRU5ErkJggg==","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Siqin","middleName":"","lastName":"Wang","suffix":""},{"id":558970884,"identity":"7471947c-d2b3-4793-9051-2198fbe9f484","order_by":2,"name":"Wenchao Zhang","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenchao","middleName":"","lastName":"Zhang","suffix":""},{"id":558970885,"identity":"f7a149f3-672b-41a0-bd57-11d304a32965","order_by":3,"name":"Xianghua Lin","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianghua","middleName":"","lastName":"Lin","suffix":""},{"id":558970886,"identity":"32edd46b-df93-4e3a-8fc3-03899f67b55a","order_by":4,"name":"Qiuxing Zhang","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiuxing","middleName":"","lastName":"Zhang","suffix":""},{"id":558970887,"identity":"aeb1a1bb-7eb4-4111-8c11-7083a8eac8dc","order_by":5,"name":"Wenjin Du","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenjin","middleName":"","lastName":"Du","suffix":""},{"id":558970888,"identity":"7c6b5a6c-2511-4cb8-b251-cf985b268407","order_by":6,"name":"Zhaoji Meng","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaoji","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2025-11-03 07:23:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8016017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8016017/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98074146,"identity":"d0069960-906c-4cc3-b9cc-5d63e5474095","added_by":"auto","created_at":"2025-12-12 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16:42:32","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106709,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8016017/v1/50114c17d66751496f07c1de.html"},{"id":98074150,"identity":"1ca17708-12bb-4ec8-9cab-b7208344ce65","added_by":"auto","created_at":"2025-12-12 13:26:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3776304,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow chart 2019 to 2023 National Health Interview Survey.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8016017/v1/7fd6faa99e3796cd45b7dbfd.jpg"},{"id":98074159,"identity":"df204ce9-ee9a-436c-b26f-c6d8c106652f","added_by":"auto","created_at":"2025-12-12 13:26:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28380039,"visible":true,"origin":"","legend":"\u003cp\u003eIdentify the key risk factors and their relationship with asthma. \u003cstrong\u003e(a) \u003c/strong\u003eLASSO Regression Coefficient Path Diagram. \u003cstrong\u003e(b) \u003c/strong\u003eLASSO Regression Cross-Validation Curve\u003c/p\u003e\n\u003cp\u003e. \u003cstrong\u003e(c) \u003c/strong\u003eMultivariable logistic regression analysis. \u003cstrong\u003e(d) \u003c/strong\u003eForest plot of the impact of BMI on asthma in different groups. \u003cstrong\u003e(e) \u003c/strong\u003eForest plot showing the effect of sex on asthma in different groups. \u003cstrong\u003e(f) \u003c/strong\u003eForest plot showing the effect of Evermarried on asthma in different groups.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8016017/v1/879c47787557779dae822322.png"},{"id":98074153,"identity":"81f2246e-033f-48f0-be35-de7fd319fdda","added_by":"auto","created_at":"2025-12-12 13:26:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11325597,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram had better predictive performance for asthma. \u003cstrong\u003e(a)\u003c/strong\u003e The nomogram of BMI, sex, and evermarried. \u003cstrong\u003e(b) \u003c/strong\u003eCalibration curve of Nomogram. \u003cstrong\u003e(c) \u003c/strong\u003eThe DCA curves of Nomogram.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8016017/v1/703f3003179186b115412b4d.png"},{"id":99889675,"identity":"ba222e87-f5ba-4c8b-9353-6ed17797fc74","added_by":"auto","created_at":"2026-01-09 13:24:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5065805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8016017/v1/40e9af6e-f7b3-421e-803a-c5eb757f518d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identify Key Asthma Risk Factors via 2019–2023 Health Interview Survey Data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAsthma is a common and highly heterogeneous chronic respiratory disease characterized by airway stenosis, edema and excessive mucus blockage. Over 300 million individuals globally are thought to suffer from asthma, and the prevalence of asthma continues to rise globally[1]. Asthma is the result of complex genetic-environmental interactions, so clinical manifestations and the types and intensity of airway inflammation and remodeling are heterogeneous. Heat waves, cold waves, sandstorms, hurricanes, floods, and storm surges increase the risk of asthma-related outcomes [2]. Asthma has a genetic tendency. The mechanism of the asthma phenotype is strongly correlated with genetics [3,4]. Asthma is likely to be transmitted by multiple genes, locus heterogeneity and several changes in multigene inheritance lead to the multifaceted expression of asthma[5].\u003c/p\u003e\n\u003cp\u003eAsthma is associated with pulmonary and extrapulmonary complications, which are more common in patients with severe asthma than in patients with mild to moderate disease or the general population[6]. In a study of asthma during pregnancy, asthma was associated with various complications and adverse consequences of mothers and neonates at all stages of pregnancy. The severity of asthma affect the risk of complications, as adverse consequences are more common in pregnant women with moderate to severe asthma [7]. A report on the association between asthma and cardiovascular disease indicates that asthma is associated with cardiovascular disease, especially in older patients and untreated asthma patients, and this interaction is more pronounced[8] . It can be seen that it is crucial to identify relevant risk factors or markers for delaying or preventing asthma. Although many researchers have studied asthma extensively, the changing characteristics of asthma burden and the impact of macro-environmental factors on asthma burden are still unknown[9]. Over the past four decades, the prevalence of asthma among all ages has steadily increased worldwide. Asthma is an increasingly serious health problem among adults \u0026gt;64 years old. In addition, 40% of elderly patients with asthma have the first disease after the age of 40. However, pathophysiological studies of adult asthma are relatively insufficient, so it is important to identify and manage risk factors for asthma to reduce adverse reactions and related mortality in middle-aged people.[10].\u003c/p\u003e\n\u003cp\u003eThe NHIS is a family interview survey conducted through the National Center for Health Statistics (NCHS) under the support of the Centers for Disease Control and Prevention (CDCP), conducted annually in person, involving a statistically representative sample of non-contained population in the United States.Therefore, in order to explore the key risk factors affecting asthma in adults, this study attempts to use NHIS data to explore the relationship between exposure factors and asthma in a nationally representative sample of American adults, hoping to provide more reference for the prevention and control of asthma.\u003c/p\u003e\n\u003cp\u003eThis study obtained data from NHIS database (2019-2023). After excluding people with asthma and samples with covariate deletion, 27,628 study subjects were included, divided into two groups with asthma and healthy. Analyses on the baseline characteristics difference between the two groups were conducted, and statistically significant variables were selected as candidate variables. Next, the minimum absolute shrinkage and selection operator (LASSO) method was used to obtain candidate risk factors, and then multivariate logistic regression analysis was performed to further screen the variables to obtain risk factors. Key risk factors were then stratified analyzed. Finally, a nomogram for asthma prediction was constructed. The accuracy of this nomogram is evaluated by drawing calibration curves, providing more reference for the prevention and control of asthma.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data acquisition\u003c/h2\u003e\u003cp\u003eThe data for this study were gained from NHIS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhis/index.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhis/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The NHIS was designed and operated by NCHS. Health-related interviews, examinations, and nutrition surveys were collected for later analysis by the NHIS. All data were accessible via provided online platforms without any additional requirements. A total of 150220 subjects who participated in NHIS survey from 2019 to 2023 were initially included. Additionally, these subjects were screened. Subjects with unclear sunburndiagnosis and missing data wereexcluded. Exclude subjects labeled as missing, rejected, and unaware of covariates. Finally, 27,628 subjects were included in this analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These variables were screened via dplyr package (v 1.1.4) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/dplyr/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/dplyr/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Variable definition\u003c/h2\u003e\u003cp\u003eAsthma was assessed in line with question \"Have you ever been told by a doctor or other health professional that you had asthma ?\" [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. If a doctor or other healthcare professional had ever informed respondent that they had asthma, respondent was identified as having asthma (asthma group), while rest were regarded as non-asthmatic (healthy group). The covariates in this study included sex, urban/rural residence, educated, etc. These variables were classified into categorical variables. Different categorical variables were partitioned into different subgroups. For example, gender comprised male and female, and region included Northeast, Midwest, South, and West \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\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\u003eDefinition, numbering and grouping information table of covariates or confounding factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003evariates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eserial numbers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edefinitions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003esex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Male\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eUrban.rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eURBRRL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Large central metro\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Large fringe metro\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;Medium and small metro\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;Nonmetropolita\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eeducated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEDUCP(2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Not graduated from high school\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Graduated from high school\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;Not graduated from university\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;Graduated from university\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eevermarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEVRMARRIED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBMI(Body Mass Index)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBMICAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1=(\u0026lt;18.5 kg/m2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2=(\u0026ge;18.5 kg/m2 and \u0026lt;\u0026thinsp;25 kg/m2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3=(\u0026ge;25 kg/m2 and \u0026lt;\u0026thinsp;30 kg/m2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4=(\u0026ge;30 kg/m2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003esmokever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSMKEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHypercholesterolemi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCHLEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHYPMED, HYPEV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eregion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eREGION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Northeast\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Midwest\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;South\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;West\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\u003eThe covariates included sex, urban/rural residence, educated, evermarried, BMI(Body Mass Index), smokever, Hypercholesterolemi, Hypertension, region. These variables were classified into categorical variables. Different categorical variables were partitioned into different subgroups. For example, gender comprised male and female, and region included Northeast, Midwest, South, and West.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Baseline characteristics analysis\u003c/h2\u003e\u003cp\u003eIn line with subjects in NHIS database, chi-square test was harnessed to assess whether there were distinctions in baseline characteristics in asthma group and healthy group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The baseline table was drawn via tableone package (v 0.13.2)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Variables with statistical significance were selected as candidate variables for subsequent analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Identification of key risk factors\u003c/h2\u003e\u003cp\u003eIn line with candidate variables, candidate risk factors were gained by learning Least Absolute Shrinkage and Selection Operator (LASSO) via glmnet package (v 4.1-8) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The response variable was set as binomial and alpha was set as 1. LASSO, a statistical method for data analysis, constructed a penalty function to gain a model. In this process, model compressed some regression coefficients, reducing dimensionality of data and avoiding multicollinearity and overfitting in multiple regression models. Features with stronger importance link to disease were less compressed, while those with weaker importance were compressed to zero. After 10-fold cross-validation, variables with minimum lambda value and non-zero regression coefficients were selected as candidate risk factors.\u003c/p\u003e\u003cp\u003eSubsequently, candidate risk factors were further screened via multivariable logistic regression analysis. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an effect size greater than 1 were selected as risk factors. Among them, variables with a warming effect size greater than 1 were considered risk factors for asthma, while those with a warming effect size less than 1 were regarded as protective factors for asthma.\u003c/p\u003e\u003cp\u003eFinally, to contrast impacts of risk factors on asthma in different subgroups of risk factors, stats package (v 4.4.1 ) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.r-project.org/R/refmans/stats/html/00Index.html\u003c/span\u003e\u003cspan address=\"https://search.r-project.org/R/refmans/stats/html/00Index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to conduct a stratified analysis on risk factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to identify risk factors. The odds ratio (OR) and 95% confidence interval (CI) were calculated. An OR greater than 1 was regarded as a risk factor for asthma, while an OR less than 1 was considered a protective factor for asthma.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Construction and validation of the nomogram\u003c/h2\u003e\u003cp\u003eTo better predict occurrence of asthma, a nomogram for predicting asthma occurrence was constructed via rms package (v 6.5.0) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with key risk factors as independent variables and asthma as dependent variable. On line segment corresponding to each variable, scales were marked, representing range of possible scores for that variable, and length of line segment reflected contribution of variable to outcome event. Corresponding single-item points were gained for each variable under different values, and total points were calculated by summing up single-item points. In line with total points, likelihood of asthma occurrence was inferred. Moreover, to evaluate accuracy and reliability of predictive performance of nomogram, calibration curve of nomogram was drawn via regplot package(v 1.1) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The closer slope of calibration curve was to 1, more accurate prediction of nomogram was. Finally, decision curve analysis (DCA) curves were plotted via ggDCA package (v 1.2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rdocumentation.org/packages/ggDCA/versions/1.1\u003c/span\u003e\u003cspan address=\"https://www.rdocumentation.org/packages/ggDCA/versions/1.1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and clinical impact curve (CIC) was plotted via rms package (v 6.5.0) to further evaluate clinical utility of nomogram.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eR language (v 4.2.2) was harnessed to execute all statistical analyses. A p-value less than 0.05 was considered to be statistically significant. The chi-square test was harnessed to assess whether there were distinctions in baseline characteristics in asthma group and healthy group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of subjects\u003c/h2\u003e\u003cp\u003eIn the NHIS database, a final cohort of 27,628 subjects was gained, including 4619 asthma patients and 23009 healthy individuals. Sex, evermarried, BMI, smokever, Urban/rural, Hypercholesterolemia, and Hypertension were found to be significantly associated with asthma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and were taken as candidate variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eBaseline characteristics between two groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASEV _A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealthy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esex (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1320 (33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8317 (42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599 (66.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11444 (57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeducated (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1661 (42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8410 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e717 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3404 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e900 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1340 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7047 (35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eevermarried (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2734 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14506 (73.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1185 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5255 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e247 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e666 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4625 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1053 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6614 (33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2160 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8275 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esmokever (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2039 (52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9508 (48.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1880 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10253 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban.rural (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1083 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5427 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e752 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4192 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1336 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6506 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e748 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3636 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercholesterolemia (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2157 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10398 (52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1762 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9363 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3101 (79.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16231 (82.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e818 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3530 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eregion (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e651 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3104 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e875 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4410 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1546 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8114 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e847 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4133 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSex, evermarried, BMI, smokever, urban/rural, hypercholesterolemia and hypertension were found to be significantly associated with asthma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 BMI, sex, and evermarried were identified as key risk factors associated with asthma\u003c/h2\u003e\u003cp\u003eThe LASSO results displayed that when value of lambda.min was 0.00107, regression coefficients of Sex, evermarried, BMI, smokever, Urban.rural, Hypercholesterolemia, and told. Hypertension were not zero, and these variables were taken as candidate risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). In multivariable logistic regression analysis, Hypertension, BMI, sex, and evermarried were identified as risk factors for asthma (warming effect size\u0026thinsp;\u0026gt;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The results of stratified analysis indicated that BMI, sex, and evermarried were still recognized as risk factors for asthma in different subgroups (OR\u0026thinsp;\u0026gt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, e, f) .\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Nomogram had better predictive performance for asthma\u003c/h2\u003e\u003cp\u003eThe nomogram constructed in line with BMI, sex, and evermarried displayed that higher total points, greater likelihood of asthma occurrence \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The calibration curve demonstrated that slope was close to 1 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. The DCA curves displayed that net benefit of nomogram model ( red line) was higher than that of diagonal line (All), horizontal line (None), and risk factors, indicating that nomogram model had better predictive performance and practical application value in clinical decision-making \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. These results suggested that nomogram had a better predictive performance for asthma.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAsthma is a chronic respiratory disease, caused by the complex interaction of various environmental and genetic factors. Its pathogenesis is complex and related to a variety of diseases. The incidence of asthma is very high, with more than 350 million sufferers worldwide[16]. This study was based on the data from the NHIS database for the years 2019 to 2023. It included the subjects without missing information and those related to the established covariates. Eventually, 23,009 healthy samples and 4,616 asthma samples were included. Baseline statistics revealed that eight covariates-gender, educational level, marital status, BMI index, smoking status, family location, hyperlipidemia, and hypertension-showed significant differences between the control group and the asthma group, suggesting that they might be related to the occurrence of asthma. Using LASSO regression and multivariate logistic regression analyses, the significant variables from baseline statistics were further screened, and finally six core variables were were identified: gender, smoking status, marital status, BMI, hypertension, and hyperlipidemia.\u003c/p\u003e\n\u003cp\u003eThe Global Initiative for Asthma (GINA) in 2025 identified obesity as an independent risk factor for the difficulty in treating asthma[17]. Studies have found that in people over 45 years old, BMI has a significant non-linear relationship with the risk of asthma. Whether the weight is too low or too high, it will increase the risk of asthma. Data from the Chinese population shows that when BMI is below 19.9 or above 29.9 kg/m², the risk of asthma significantly increases; the risk threshold for the American population is 21.6 kg/m²[18]. When compared to those who are lean, those who are obese typically have poorly controlled asthma; a roughly 5-fold increased risk of hospitalization; and they do not respond to conventional controller therapy[19]. Even more alarming is that about 60% of patients with severe or refractory asthma are obese, which suggests that we need to re-examine this special asthma phenotype from a new perspective[20]. At least 5% weight loss was associated with significant improvements in important asthma-related outcomes, and in general health-related quality of life[19].A genetic predisposition to high BMI increases the risk of early respiratory tract infections and severe wheezing and asthma attacks[21]. Obese adults with asthma show elevated oxidative stress in corticosteroid insensitivity and the airway[22]. Accumulation in the mediastinum and abdomen might change the respiratory system and consequently interfere with the physiology and function of the lung[23].\u003c/p\u003e\n\u003cp\u003eGender differences highlight the heterogeneity of asthma manifestation and underlying mechanisms. Studies have shown that the incidence of asthma differs between men and women, highlighting sex-specific relationships between obesity (as measured by BMI) and asthma. In women, asthma incidence increases monotonically with BMI, reaching the highest levels among severely obese individuals[24].By contrast, in middle-aged and elderly populations, the increase in asthma incidence is more pronounced in men than in women[25]. This rapid rise in asthma incidence among men may be associated with the influence of sex hormones on epithelial cell function[26]. However, females demonstrate greater susceptibility to asthma and severe asthma than males[27]. Furthermore, females are more likely than males to experience non-atopic asthma, exhibit poorer responses to corticosteroids, and develop obesity-related steroid-refractory asthma[28]. Sex hormones have been proven to play an important role in airway inflammation, smooth muscle contraction, mucus secretion and the pathogenesis related to asthma. The hormones secreted by the ovaries aggravate airway inflammation and testosterone reduces airway inflammation[29].\u003c/p\u003e\n\u003cp\u003eThe literature highlights the influence of marital status on the quality of life in individuals with asthma. In a study conducted by Wang et al. [30] in Taiwan, utilizing the Chinese version of the St. George’s Respiratory Questionnaire (SGRQ), marital status was found to affect the perception of quality of life.Conversely, in the research by Ferreira et al. [31], single individuals and those who were married exhibited higher quality of life compared to divorced or widowed individuals.Additionally, partners of individuals with specific diseases, such as asthma, are at an elevated risk—approximately a 70% increased risk—of developing the same condition themselves. This suggests shared environmental factors contributing to certain diseases, in addition to genetic predispositions, distant exposures, or shared behaviors related to healthcare-seeking[32]. This may be due to shared diet, the similar intestinal microecological environments[33] or shared exposure to allergens[34].\u003c/p\u003e\n\u003cp\u003eThe Nomogram model constructed based on key risk factors shows that the higher the total score, the greater the possibility of asthma occurrence. This result is consistent with multiple studies, indicating that the Nomogram model has good discrimination ability in asthma prediction[35]. The slope of the calibration curve was close to 1, indicating that the predicted probabilities of the model are highly consistent with the actual observed values. This consistency further validates the reliability of the model and suggests that it has high accuracy in clinical applications, further supporting the calibration ability of the model[36]. The DCA curve indicates good practical application value, and this result is similar to those of other asthma prediction models reported in the literature, indicating the reliability of the results of this study[37].\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified three key risk factors: BMI, sex, and marital status, which provide a practical basis for high-risk population screening, early prevention and personalized health management. However, there are still some limitations. Firstly, the limited sample size may affect the accuracy and generalizability of the model's predictions. Future research can address this by expanding the sample size. Secondly, the current study is mainly based on data from specific populations and lacks diversity. In subsequent research, more sources and backgrounds of data can be included to enhance the generalization ability of the model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study identified three key risk factors: BMI, sex, and marital status, which provide practical basis for high-risk population screening, early prevention, and personalized health management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCDCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCenters for Disease Control and Prevention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlobal Initiative for Asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNational Health Interview Surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNCHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNational Center for Health Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSGRQ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSt. George’s Respiratory Questionnaire\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study constitutes a secondary analysis of publicly available, de-identified data provided by the National Center for Health Statistics (NCHS). The original data collection by NCHS obtained the necessary ethical approval from its Research Ethics Review Board and secured informed consent from all participants. As the dataset used in this analysis contains no personally identifiable information and is openly accessible to the public, research of this nature is considered exempt from ethics review or falls outside the scope of \u0026quot;human subjects research\u0026quot; in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Siqin Wang. and Weili Guo.; methodology, Weili Guo.; software, Weili Guo.; validation, Weili Guo., Wenchao Zhang. and Wenjin Du.; formal analysis, Weili Guo.; investigation, Xianghua Lin.; resources, Weili Guo.; data curation, Zhaoji Meng.; writing\u0026mdash;original draft preparation, Weili Guo.; writing\u0026mdash;review and editing, Weili Guo.; visualization,Weili Guo.; supervision, Qiuxing Zhang.; project administration, Siqin Wang.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study did not receive any funding from a specific grant, funding agency, commercial or not-for-profit sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest. No funders had any role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed in this study was publicly available from the NHIS database (https://www.cdc.gov/nchs/nhis/index.html). The data used in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following author: Yafeng,Wang. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWan R, Srikaram P, Guntupalli V, Hu C, Chen Q, Gao P. Cellular senescence in asthma: from pathogenesis to therapeutic challenges. eBioMedicine.2023;94:104717.\u003c/li\u003e\n\u003cli\u003eMakrufardi F, Manullang A, Rusmawatiningtyas D, Chung KF, Lin S-C, Chuang H-C. Extreme weather and asthma: a systematic review and meta-analysis. European Respiratory Review.2023;32:230019.\u003c/li\u003e\n\u003cli\u003ePolyxeni N, Andreas P, Eleftherios Z, Georgina X, Konstantinos S. Genetics and Epigenetics in Asthma. Int J Mol Sci.2021;22:2412.\u003c/li\u003e\n\u003cli\u003eLaura A C, Michael D C, Deepa R. Defining pediatric asthma: phenotypes to endotypes and beyond. Pediatr Res.2020;90:45-51.\u003c/li\u003e\n\u003cli\u003eHisako M. PATHOPHYSIOLOGY OF ASTHMA. Arerugi.2022;71:1065-1071.\u003c/li\u003e\n\u003cli\u003eLommatzsch M, Brusselle GG, Levy ML, Canonica GW, Pavord ID, Schatz M, et al. A2BCD: a concise guide for asthma management. The Lancet Respiratory Medicine.2023;11:573-576.\u003c/li\u003e\n\u003cli\u003eBravo-Solarte DC, Garcia-Guaqueta DP, Chiarella SE. Asthma in pregnancy. Allergy and Asthma Proceedings.2023;44:24-34.\u003c/li\u003e\n\u003cli\u003eWee JH, Park MW, Min C, Byun SH, Park B, Choi HG. Association between asthma and cardiovascular disease. European Journal of Clinical Investigation.2020;51:e13396.\u003c/li\u003e\n\u003cli\u003eWang Z, Li Y, Gao Y, Fu Y, Lin J, Lei X, et al. Global, regional, and national burden of asthma and its attributable risk factors from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. Respiratory Research.2023;24:169.\u003c/li\u003e\n\u003cli\u003eJiwon C, Sun Jae P, Young Jun P, Jaeyi H, Seogsong J, Jooyoung C, et al. Association between antibiotics and asthma risk among adults aged over 40 years: a nationally representative retrospective cohort study. BMJ Open Respir Res.2023;10:e001643.\u003c/li\u003e\n\u003cli\u003eCynthia A P, Hatice S Z, Xiaoting Q, Carol J, Erik H, Josephine M. Asthma Surveillance - United States, 2006-2018. MMWR Surveill Summ.2021;70:1-32.\u003c/li\u003e\n\u003cli\u003eAgapios P, Dimitris M. TableOne: an online web application and R package for summarising and visualising data. Evid Based Ment Health.2020;23:127-130.\u003c/li\u003e\n\u003cli\u003eJerome F, Trevor H, Rob T. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw.2010;33:1-22.\u003c/li\u003e\n\u003cli\u003eMichael C S. plotROC: A Tool for Plotting ROC Curves. J Stat Softw.2017;79:1-24.\u003c/li\u003e\n\u003cli\u003eHongxia Z, Haihong H, Bo H, Wendi Z, Ting Y, Jingdi Z, et al. Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients. Technol Cancer Res Treat.2024;23:1-16.\u003c/li\u003e\n\u003cli\u003eReddel HK, Bacharier LB, Bateman ED, Brightling CE, Brusselle GG, Buhl R, et al. Global Initiative for Asthma Strategy 2021: Executive Summary and Rationale for Key Changes. American Journal of Respiratory and Critical Care Medicine.2022;205:17-35.\u003c/li\u003e\n\u003cli\u003eBrown N, Sullo N, Tyson N, Eagle-Hemming B, Lai FY, Sheikh S, et al. Acute kidney injury after cardiac surgery is associated with platelet activation. Journal of thrombosis and haemostasis : JTH.2025;23:2776-2789.\u003c/li\u003e\n\u003cli\u003eWeili K, Xiangling Z, Hailing G, Manlin C, Mei L, Xiaoyun Z, et al. Association between BMI and asthma in adults over 45 years of age: analysis of Global Burden of Disease 2021, China Health and Retirement Longitudinal Study, and National Health and Nutrition Examination Survey data. EClinicalMedicine.2025;82:103163.\u003c/li\u003e\n\u003cli\u003eJohnson O, Gerald LB, Harvey J, Roy G, Hazucha H, Large C, et al. An Online Weight Loss Intervention for People With Obesity and Poorly Controlled Asthma. The Journal of Allergy and Clinical Immunology: In Practice.2022;10:1577-1586.e1573.\u003c/li\u003e\n\u003cli\u003eEwelina R. The Role of Peptides in Asthma-Obesity Phenotype. Int J Mol Sci.2024;25:1-26.\u003c/li\u003e\n\u003cli\u003eSigne Kjeldgaard J, Casper-Emil Tingskov P, Kasper F-R, Mathias Elsner M, Nicklas B, Julie Nyholm K, et al. Genetic predisposition to high BMI increases risk of early life respiratory infections and episodes of severe wheeze and asthma. Eur Respir J.2024;64.\u003c/li\u003e\n\u003cli\u003ePeters U, Dixon AE, Forno E. Obesity and asthma. Journal of Allergy and Clinical Immunology.2018;141:1169-1179.\u003c/li\u003e\n\u003cli\u003eYe W, Xu X, Ding Y, Li X, Gu W. Trends in disease burden and risk factors of asthma from 1990 to 2019 in Belt and Road Initiative countries: evidence from the Global Burden of Disease Study 2019. Annals of Medicine.2024;56:2399964.\u003c/li\u003e\n\u003cli\u003ePark S, Jung S-Y, Kwon J-W. Sex differences in the association between asthma incidence and modifiable risk factors in Korean middle-aged and older adults: NHIS-HEALS 10-year cohort. BMC Pulmonary Medicine.2019;19:248.\u003c/li\u003e\n\u003cli\u003eAndrea S G, Jun G, Chengning W, Teresa T. Trends in asthma prevalence and incidence in Ontario, Canada, 1996-2005: a population study. Am J Epidemiol.2010;172:728-736.\u003c/li\u003e\n\u003cli\u003eJain R, Ray JM, Pan J-h, Brody SL. Sex Hormone\u0026ndash;Dependent Regulation of Cilia Beat Frequency in Airway Epithelium. American Journal of Respiratory Cell and Molecular Biology.2012;46:446-453.\u003c/li\u003e\n\u003cli\u003eEileen W, Michael E W, Trung N T, Liam G H, Rupert C J, Andrew N M-G, et al. Characterization of Severe Asthma Worldwide: Data From the International Severe Asthma Registry. Chest.2019;157:790-804.\u003c/li\u003e\n\u003cli\u003eWei W, Eugene B, Wendy M, William W B, Mario C, Kian Fan C, et al. Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data. J Allergy Clin Immunol.2014;133:1280-1288.\u003c/li\u003e\n\u003cli\u003eRadzikowska U, Golebski K. Sex hormones and asthma: The role of estrogen in asthma development and severity. Allergy.2023;78:620-622.\u003c/li\u003e\n\u003cli\u003eKwua-Yun W, Chin-Pyng W, Yu-Ying T, Muh-Lan Y. Health-related quality of life in Taiwanese patients with bronchial asthma. J Formos Med Assoc.2004;103:205-211.\u003c/li\u003e\n\u003cli\u003eFerreira L, Brito U, Ferreira P. Quality of life in asthma patients. Revista portuguesa de pneumologia.2010;16:23-55.\u003c/li\u003e\n\u003cli\u003eJulia H-C, Carol C, Mike P, Nicola C, Vicky H. Married couples\u0026apos; risk of same disease: cross sectional study. BMJ.2002;325:636.\u003c/li\u003e\n\u003cli\u003eErika vM, Hermelijn H S. Primary prevention of asthma: from risk and protective factors to targeted strategies for prevention. Lancet.396:854-866.\u003c/li\u003e\n\u003cli\u003eTadech B, Zeynep Celebi S, Pattraporn S, Cezmi A A. Immunologic mechanisms in asthma. Semin Immunol.2019;46:101333.\u003c/li\u003e\n\u003cli\u003eYang L, Li M, Zheng Q, Ren C, Ma W, Yang Y. A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study. Journal of clinical laboratory analysis.2021;35:e23820.\u003c/li\u003e\n\u003cli\u003eJiang H, Fu C. Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning. International immunopharmacology.2024;133:112064.\u003c/li\u003e\n\u003cli\u003eYufan D, Dilixiati N, Mengyu L, Jie S, Jingjing Y, Ziliang H, et al. Development of a nomogram to estimate the risk of community-acquired pneumonia in adults with acute asthma exacerbations. Clin Respir J.2023;17:1169-1181.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Asthma, NHIS, Key risk factors","lastPublishedDoi":"10.21203/rs.3.rs-8016017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8016017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAsthma is a common chronic respiratory disease. The risk factors that affected asthma were not clearly understood. Thus, using 2019\u0026ndash;2023 National Health Interview Surveys (NHIS) data, this study identified risk factors in asthma, offering a reference for asthma prevention and treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 150220 subjects were retrieved from NHIS database (2019\u0026ndash;2023). First, data were screened. Participants were divided into two groups: those with asthma and those without. Then, differences in baseline characteristics between two groups were analyzed, and significant variables were selected as candidate variables. Least absolute shrinkage and selection operator (LASSO) method was applied to gain candidate risk factors. Subsequently, multivariable logistic regression analysis was applied to further screen and gain risk factors. A stratified analysis of risk factors was conducted to identify key risk factors. Finally, a nomogram for asthma prediction was constructed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAfter variable screening, 23680 subjects were attained, including 3919 asthma subjects and 19761 healthy subjects. Variables such as sex and ever smoked were found to be significantly associated with asthma. Seven candidate variables were gained. LASSO analysis yielded seven candidate risk factors. Multivariable logistic regression identified hypertension, body mass index (BMI), sex, and ever-married as significant risk factors. Stratified analysis showed that BMI, sex, and marital status were key risk factors for asthma. A nomogram demonstrated better predictive performance for the condition.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThree key risk factors associated with asthma were identified. A nomogram with good predictive power was constructed. These findings offered valuable insights for asthma prevention and treatment.\u003c/p\u003e","manuscriptTitle":"Identify Key Asthma Risk Factors via 2019–2023 Health Interview Survey Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 13:25:51","doi":"10.21203/rs.3.rs-8016017/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd82e5e2-1a4e-481a-a66a-9918f00e3ccf","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T13:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 13:25:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8016017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8016017","identity":"rs-8016017","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}