Association between biological aging and Asthma-COPD overlap based on Nhanes 2005-2018

Association between biological aging and Asthma-COPD overlap based on Nhanes 2005-2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between biological aging and Asthma-COPD overlap based on Nhanes 2005-2018 Tongyao Sun, Shengzhen Yang, Shitao Li, Huiwen Li, Jianjian Yu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598620/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 Aging is an important factor in the pathogenesis of various respiratory diseases, and biological aging can better reflect the systemic functional status of individual organisms. The purpose of this study was to analyze the association between biological aging and Asthma-COPD Overlap (ACO) ,and to explore its potential causal relationship. METHODS The present study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning from 2005 to 2018. Three biological ages [Klemera-Doubal method (KDM), phenotypic age (PhenoAge) and homeostatic dysregulation (HD)] and two measures of biological acceleration of aging (BioAgeAccel and PhenoAgeAccel) were selected as the main exposure factors for analysis. Weighted logistic regression and restricted cubic spline regression were used to analyze the association between biological aging and ACO. RESULTS In our study, phenotypic age was positively associated with the incidence of ACO, and the degree of phenotypic age acceleration was also a risk factor for ACO prevalence. After further adjustment for demographic characteristics, both remained an important risk factor for ACO. CONCLUSION This study provides some evidence for the association of biological aging in the development of ACO. In addition, preventive strategies targeting aging have a potential role in reducing the risk of ACO. Asthma-COPD Overlap Biological age Aging NHANES Figures Figure 1 Figure 2 1 Introduction Asthma and chronic obstructive pulmonary disease (COPD) are two common respiratory diseases of the respiratory system, the etiology of their onset is not the same, and the clinical manifestations of the two are also different. Clinically, asthma is usually associated with airway hyperresponsiveness and airway inflammation, and patients often experience wheezing, shortness of breath, chest tightness, and cough 1 . In contrast, COPD is often caused by airway lesions and / or alveolar lesions caused by smoking, and is characterized by persistent ( often progressive ) airflow limitation, which is characterized by chronic respiratory symptoms ( wheezing, cough, and increased expectoration ) 2 . And in 2015 by the GINA report and GOLD report together proposed to suggest that patients with concurrent airway hyperresponsiveness, airway inflammation, and airflow limitation can be seen clinically, and defined this group of patients with both asthma and COPD as asthma-chronic obstructive pulmonary overlap (ACO) syndrome 3 . Although the GOLD report in 2021 recommended to stop using the name 'ACO ' 4 , ACO as a medical condition or feature is still a clinical problem. In addition, ACO patients have worse quality of life, and the acute exacerbation of related symptoms is more frequent 5 .Due to the aggravation of symptoms, the use rate of respiratory drugs, admission rate and mortality are higher 6 , 7 .Therefore, this type of patients need more attention in clinical practice. Aging, as one of the pathogenic factors that has been of continuous concern, has been associated with a variety of respiratory diseases in terms of its severity, and an increase in age often implies a decline in lung function and structural changes 8 . However, under certain conditions, the degree of frailty of the body cannot be reasonably described by age alone. Therefore, this study assessed the degree of aging by measuring biological age through three calculation methods, which were Klemera-Doubal method (KDM), PhenoAge and homeostatic dysregulation (HD). In addition, BioAgeAccel and PhenoAgeAccel were introduced to further clarify the degree of individual aging, BioAgeAccel is defined as the residual of the linear regression of KDM to age. Similarly, PhenoAgeAccel is the residual of the linear regression of PhenoAge to age. The positive value indicates accelerated aging, and the negative value indicates the opposite 9 – 11 .Previous studies have pointed out that the increase of age is accompanied by the increase of the risk of ACO, but no study has explored the association between biological age and ACO.Therefore, this study first explored the association between biological age and the risk of ACO by analyzing the data of nhanes 2005–2018 observational study 12 – 15 . In this study, the data of patients recruited in the National Health and Nutrition Examination Survey ( NHANES ) were used to calculate the biological age and aging degree of the corresponding individuals as exposure, and multiple covariates were introduced to construct a complex model. Two methods of logistic regression and drawing restricted cubic spline curve were used to explore the relationship between aging and asthma-copd overlap, and the possible linear relationship between them. 2 Materials and Methods 2.1 Data Sources NHANES is a long-term, nationwide research project approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS) to assess the health and nutritional status of Americansand, and all patients were recruited with an informed consent forms at the time of recruitment. Therefore, this study does not require additional ethical approval. A total of 70,190 participants were enrolled from 2005 through 2018. After excluding individuals younger than 18 years of age and those with incomplete or unreliable data on key variables such as biological age, asthma, and chronic obstructive pulmonary disease, our study included 22,654 eligible participants. Figure 1. For the remaining patients with missing values we used predictive mean matching (pmm) method to supplement the missing data. Figure 1 Participant selection process diagram for the NHANES study 2.2 Calculation of biological age The results of the three biological age measurement methods in this study were calculated using the “ bioage “ package developed based on r4.3.2, which introduced albumin, alkaline phosphatase, blood urea nitrogen, creatinine, uric acid, lymphocyte percentage, mean cell volume, white blood cell count. Eleven variables including systolic blood pressure, total cholesterol, and hemoglobin A1C were calculated. In different years, the measurement methods of some biomarkers have changed, and these data have been normalized according to the changes of the methods 16 . 2.3 Definition of ACO in this study According to the diagnostic criteria of ACO in previous studies, patients who met the diagnosis of ACO in this study must meet the clinical manifestations of both COPD and asthma 17,18 . 2.3.1 Definition of COPD Based on previous studies 19,20 , patients who meet one of the following criteria can be defined as having COPD : 1) FEV1/FVC<0.7 after inhalation of bronchodilators ; 2) patients who answered "yes" when asked "Has a doctor or other health professional ever told you that you had chronic bronchitis?" or "Has a doctor or other health professional ever told you that you had emphysema?" ; 3) patients who answered “yes" when asked "Has a doctor or other health professional ever told you that you had COPD?". 2.3.1 Definition of asthma When the patient answers “ yes ” when asked the following question, the patient can be defined as asthma : 1 ) “ Has a doctor or other health professional ever told you that you have asthma ? ” ; 2 ) “ Do you still have asthma ? ” ; 3 ) “ In the past 12 months have you had wheezing or whistling in your chest ? ” 2.4 Covariates In this study, baseline covariates were primarily selected based on demographics, social factors, lifestyle habits, and comorbidities. In addition to demographic data such as gender, age, and race, social factors including household income, education level, and marital status were also taken into account. As part of the study, data on smoking and alcohol consumption behaviours were collected. Furthermore, variables related to medical comorbidities such as BMI, hypertension, diabetes, and hyperlipidaemia were also collected. The definitions of relevant covariates can be found after Table 1. For patients with remaining missing values, we used predictive mean matching (PMM) to supplement the missing data. 2.5 Statistical Analysis Statistical analysis was conducted using R4.3.2 software. A weighted logistic regression model was utilised to explore the relationship between bioage and ACO. A restricted cubic spline analysis was employed to assess the non-linear relationship between COPD variables and bioage. Statistical significance was defined as a p-value less than 0.05. 3 Result 3.1 Baseline characteristics of NHANES participants A total of 22,654 subjects aged 18 years and above, with biological age and ACO data, participated in the analysis. Table 1 lists the general characteristics of the participants, including demographic factors, biological age and ACO prevalence. There were 1326 participants with ACO. Among them, 11033 were males and 11621 were females. The calendar ages of the ACO group and the non-ACO group were ( 53.77 ± 16.67 ) years and ( 49.46 ± 17.90 ) years, respectively. There were significant differences in age, gender, race, education level, marital status, poverty level, alcohol using, smoking, obesity, hypertension, hyperlipidemia and diabetes between ACO patients and non-ACO patients. In addition, compared with non-ACO patients, ACO patients had a higher biological age ( HD : 2.41 vs.2.20 ; kDM : 48.97 vs. 44.65 ; phenoAge : 52.69 vs. 46.95 ).Table 1. Variables ACO(N=1326) NON-ACO(N=21328) P-Value Age ,mean(SD) 53.77(16.67) 49.46(17.90) <0.001 Age ,N(%) <40 302(22.8) 7308(34.3) 60 551(41.6) 7144(33.5) hd ,mean(SD) 2.41(1.02) 2.20(0.98) <0.001 kdm ,mean(SD) 48.97(20.18) 44.65(20.40) <0.001 phenoage ,mean(SD) 52.69(17.59) 46.95(18.84) <0.001 BioAgeAccel ,mean(SD) -4.80(15.95) -4.81(15.05) 0.978 BioAgeAccel ,N(%) YES 474(35.7) 7418(34.8) 0.492 NO 852(64.3) 13910(65.2) PhenoAgeAccel ,mean(SD) -1.08(4.99) -2.52(4.73) <0.001 PhenoAgeAccel,N(%) YES 509(38.4) 5816(27.3) <0.001 NO 817(61.6) 15512(72.7) Gender ,N(%) male 590(44.5) 10443(49.0) 0.002 female 736(55.5) 10885(51.0) Race ,N(%) Mexican American 86(6.5) 3898(18.3) <0.001 Non-Hispanic White 800(60.3) 9282(43.5) Non-Hispanic Black 241(18.2) 4200(19.7) Other 199(15.0) 3948(18.5) EDUcation ,N(%) Below high school 381(28.7) 5334(25.0) 0.003 High School or above 945(71.3) 15994(75.0) Marital ,N(%) Yes 722(54.4) 13155(61.7) <0.001 No 604(45.6) 8173(38.3) Poverty ,N(%) Poor 358(27.0) 4110(19.3) <0.001 Not poor 968(73.0) 17218(80.7) Obesity ,N(%) Obesity 587(44.3) 8174(38.3) <0.001 Overweight 396(29.9) 7228(33.9) Normal 343(25.9) 5926(27.8) Drink ,N(%) Yes 989(74.6) 15050(70.6) 0.002 No 336(25.4) 6264(29.4) Smoke,N(%) Yes 530(40.0) 4111(19.3) <0.001 No 796(60.0) 17217(80.7) Diabetes ,N(%) YES 310(23.4) 3479(16.3) <0.001 NO 1016(76.6) 17849(83.7) Hyptersion ,N(%) Yes 730(55.1) 8812(41.3) <0.001 No 596(44.9) 12516(58.7) Hyperlipidemia ,N(%) YES 1125(84.8) 17048(79.9) <0.001 NO 201(15.2) 4280(20.1) Table 1. Baseline characteristics of the NHANES participants. Continuous variables were presented as mean (SE); Categorical variables were presented as N (%); means (SE) and % were weight-adjusted. KDM: KlemeraDoubal method; hd: homeostatic dysregulation. * Patients who are married and cohabiting with their partners are defined as married; patients with a family income index below 1 are defined as poor; those with a body mass index over 30 are defined as obese, between 25 and 30 are defined as overweight, and below 25 are classified as normal; individuals meeting any of the following criteria are defined as diabetic patients: history of diabetes, insulin injection, oral hypoglycaemic medication, Glycohemoglobin level >=6.5, Glucose level >=126; individuals meeting any of the following conditions are defined as hypertensive patients: currently diagnosed with hypertension, SBP >=140, DBP >=90; individuals meeting any of the following conditions are diagnosed as hyperlipidemia patients, with TC >200, TG >150, male HDL <40, female HDL =130, or with hypercholesterolaemia. 3.2 Logistic regression analysis of the association between biological aging and ACO Table 2 shows the relationship between ACO and biological age. After adjusting for age, gender and race ( model 1 ), we found that there was a significant association between biological aging and ACO. Adjusted educational level, marital status, ratio of family income to poverty, obesity, alcohol consumption, and cigarette use, the association between BioAgeAccel and the incidence of ACO became no longer significant, and the remaining results were consistent with the results of model 1 in the multivariate regression results of model 2. After further adjustment of the disease status in Model 3, it was found that HD, kdm, and bioageaccel were not significantly associated with the incidence of ACO. In model 3, for every 1 year increase in phenotypic age, the risk of ACO increased by 1 % ( OR 1.01,95 % CI 1.01-1.02, P<0.001 ). Acceleration of biological aging is defined as the residual error of the regression of biological age to calendar age, including BioAgeAccel and PhenoAgeAccel, where Phenoageaccel is associated with the onset of ACO. The acceleration of biological aging indicates that the biological age is older. Compared with PhenoAgeAccel-negative individuals, PhenoAgeAccel-positive individuals were 4 % more likely to develop ACO ( OR 1.04,95 % CI 1.02-1.05, p < 0.001 ). Biological aging Model 1 Model 2 Model 3 OR (95% CI) P OR (95% CI) P OR (95% CI) P HD 1.17 (1.10-1.24) <0.001 1.13 (1.07-1.20) <0.001 1.01 (0.94-1.08) 0.734 KDM 1.00 (1.00-1.01) 0.022 1.00 (1.00-1.01) 0.019 1.00 (0.99-1.00) 0.357 PhenoAge 1.01 (1.01-1.02) <0.001 1.02 (1.01-1.03) <0.001 1.01(1.01-1.02) <0.001 BioAgeAccel 1.00 (1.00-1.01) 0.018 1.00 (1.00-1.00) 0.744 1.00 (0.99-1.00) 0.209 PhenoAgeAccel 1.04 (1.02-1.05) <0.001 1.04 (1.02-1.05) <0.001 1.04(1.02-1.05) <0.001 Table2 Associations of biological ages and accelerated biological aging with ACO. Model 1: Adjusting for age, gender, race; Model 2: Model 1+adjusted for educational level, marital status, ratio of family income to poverty, obesity, alcohol consumption, and cigarette use. Model 3: Model 2+adjusted for Diabetes, hypertension, and hyperlipidemia.HD: homeostatic dysregulation; KDM: Klemera–Doubal method; OR: Odds ratio; CI: Confidence interval; The exposure was further grouped into categorical variables according to the quartile, and the P value of the trend was estimated. In model3, higher phenotypic age ( Q4 ) and higher degree of phenotypic aging ( Q4 ) were more likely to cause ACO, with OR values of 2.33 ( 1.59-3.42,95 % CI ) and 1.64 ( 1.36-1.98,95 % CI ), respectively, and P trend was less than 0.001.In addition, lower kdm biological age acceleration ( Q2 ) could reduce the risk of ACO ( OR : 0.79 ( 0.67-0.93 ), 95 % CI ).Table 3 OR (95% CI) P value PhenoAgeAccel, (continuous) 1.04 (1.02-1.05) <0.001 PhenoAgeAccel, (quartile) Quartile 1 1(REF) Quartile 2 1.32 (1.09-1.59) 0.004 Quartile 3 1.40 (1.16-1.69) <0.001 Quartile 4 1.64 (1.36-1.98) <0.001 P trend <0.001 KdmAgeAccel, (continuous) 1.00 (0.99-1.00) 0.209 KdmAgeAccel, (quartile) Quartile 1 1(REF) Quartile 2 0.79 (0.67-0.93) 0.006 Quartile 3 0.97 (0.82-1.14) 0.714 Quartile 4 0.88 (0.74-1.04) 0.144 P trend 0.49 PhenoAge, (continuous) 1.01 (1.01-1.02) 0.001 PhenoAge, (quartile) Quartile 1 1(REF) Quartile 2 1.44 (1.14-1.83) 0.002 Quartile 3 1.79 (1.30-2.46) <0.001 Quartile 4 2.33 (1.59-3.42) <0.001 P trend <0.001 KdmAge, (continuous) 1.00 (0.99-1.00) 0.357 KdmAge, (quartile) Quartile 1 1(REF) Quartile 2 1.04 (0.85-1.27) 0.713 Quartile 3 1.06 (0.86-1.30) 0.599 Quartile 4 1.00 (0.80-1.26) 0.978 P trend 0.964 Table3 Associations of biological ages and accelerated biological aging with ACO (Quartile divided groups) 3.3 Restricted cubic spline regression analysis of the association between biological aging and ACO Figure2 shows that there is a significant nonlinear positive correlation between the age of the three organisms and ACO ( nonlinear P < 0.001 ). The odds ratio ( OR ) shows that when the HD value exceeds 1.97, it will gradually increase and slowly stabilize. Regarding the non-linear relationship between KDM and ACO, the figure shows that the risk of ACO increases rapidly in the lower range of KDM ( < 42.53 years old ). After KDM reached 42.53 years old, the rate of risk increase slowed down and gradually stabilized. In addition, the risk of ACO increased rapidly before the PhenoAge value reached about 46.39 years old, and then began to increase slowly and gradually entered the plateau period. The results of rcs analysis of BioAgeAccel accelerated biological aging were generally meaningless ( P-overall = 0.186 ). In addition, the results of rcs analysis of PhenoAgeAccel showed that there was a linear relationship between exposure and outcome ( P for nonlinear = 0.699 ), and the critical values of two kinds of accelerated biological aging were-5.33 and-2.69, respectively. Figure 2. Restricted cubic spline model of the odds ratios of ACO with HD (a), KDM (b), PhenoAge (c), BioAgeAccel (d), and PhenoAgeAccel (e). HD: homeostatic dysregulation; KDM: Klemera–Doubal method; OR: Odds ratio; CI: Confidence interval. 4 Discussion As one of the continuously increasing and irreversible indicators, age has been an important topic in the field of respiratory disease research. Continuously increasing age brings with it a continuous decline in lung function, changes in lung remodelling function, and a decrease in cellular regeneration 21 , and these changes imply an increase in the susceptibility to diseases such as chronic obstructive pulmonary disease (COPD), asthma, and interstitial fibrosis 8 , 22 , and therefore Age has long been regarded as an important predictor of respiratory diseases and a criterion for the development of management strategies, however, since individuals of the same age may show differences in health status due to different organic conditions (e.g., individuals who exercise regularly perform better than the average individual in lung function), compared with calendar age alone, the introduction of multiple indicators such as blood markers and the use of three methods of calculating the Therefore, compared with calendar age alone, biological age calculated by three methods using multiple blood markers and other indicators has a higher application value 23 – 26 .Wang and other researchers extracted and analysed data from 308,592 patients in the UK Biobank and concluded that PhenoAgeAccel was significantly associated with the risk of chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and idiopathic pulmonary fibrosis (IPF), as well as with the decline in lung function 27 . It has been similarly noted that biological age is associated with the incidence of several age-related diseases and that the calculation of biological age can be used for the prediction of age-related diseases 28 .ACO is recognised clinically by its common features with asthma and chronic obstructive pulmonary disease, and numerous patients are often incorrectly diagnosed with simple asthma or COPD by ignoring a symptom, with a consequent higher risk of exacerbation, greater burden of hospitalisation and medication, and higher mortality 13 , 29 – 32 . In this study, we found that patients with high calendar age, female, low education, poverty, higher body weight, possessing bad habits such as smoking and drinking, and comorbidities such as hypertension and hyperlipidaemia had a higher rate of ACO in terms of baseline data. In addition, after initial adjustment for sex, age, and race as covariates, we found that the biological age of the three calculations and the results of the two biological age accelerations were positively associated with ACO, and after adjusting for all covariates, the positive association between phenotypic age and phenotypic age acceleration and the prevalence of ACO remained, and quartile grouping showed that both high phenotypic age and higher phenotypic age aging were ACO These results suggest that higher phenotypic age may increase the probability of ACO, and the results of the restricted cubic spline regression analysis are consistent with those of the logistic regression. All these results suggest that biological age can be used as a meaningful indicator to predict the occurrence of ACO. However, our study also has some limitations. First, since the NHANES data were collected through a questionnaire, subjective judgement and recall bias of the respondents may have affected the results. In addition, the definition of ACO relied only on basic surveys rather than comprehensive measurements, which may have reduced the robustness of the results. Finally, the data for this study were obtained from the NHANES database, which is a cross-sectional study, and the dynamics of disease prevalence as well as changes in physical status of the same individuals over time were not available, so prospective studies are needed to further refine the validation. 5 Conclusion This study provides some evidence to prove the causal association of biological aging in the pathogenesis of ACO. The phenotypic age and accelerated phenotypic aging in this study have the potential to be used as predictors of ACO, and the prevention strategies for aging have a potential role in reducing the risk of ACO. Declarations Declarations There is no conflict of interest in this study. Funding This study was funded by the following projects : Shandong Province Traditional Chinese Medicine Science and Technology Project ( Q-2023041 ) ; the provincial-bureau joint construction of traditional Chinese medicine science and technology projects ( GZY-KJS-SD-2023-048 ) ; Shandong Province Medical and Health Science and Technology Project ( 202303021034 ) Author Contribution All authors read and approved the final manuscript. Tongyao Sun and Shengzhen Yang performed the analysis. Tongyao Sun and Shitao Li wrote a draft of this article. Jianjian Yu, Huiwen Li and Jun Wang conceived the research design. All authors contributed to the interpretation of the results, critically revised the important knowledge of the manuscript, and approved the final version of the manuscript. Data Availability Relevant supplementary materials and codes can be obtained by contacting the author. 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Arch Gerontol Geriatr 107:104905. https://doi.org/10.1016/j.archger.2022.104905 Liu W-S, You J, Ge Y-J, Wu B-S, Zhang Y, Chen S-D, Zhang Y-R, Huang S-Y, Ma L-Z, Feng J-F, Cheng W, Yu J-T (2023) Association of Biological Age with Health Outcomes and Its Modifiable Factors. Aging Cell 22(12):e13995. https://doi.org/10.1111/acel.13995 Wang T, Duan W, Jia X, Huang X, Liu Y, Meng F, Ni C (2024) Associations of Combined Phenotypic Ageing and Genetic Risk with Incidence of Chronic Respiratory Diseases in the UK Biobank: A Prospective Cohort Study. Eur Respir J 63(2):2301720. https://doi.org/10.1183/13993003.01720-2023 Bae C-Y, Kim I-H, Kim B-S, Kim J-H, Kim J-H (2022) Predicting the Incidence of Age-Related Diseases Based on Biological Age: The 11-Year National Health Examination Data Follow-Up. Arch Gerontol Geriatr 103:104788. https://doi.org/10.1016/j.archger.2022.104788 Hikichi M, Hashimoto S, Gon Y, Asthma (2018) Overlap Pathophysiology of ACO. Allergol Int Off J Jpn Soc Allergol 67(2):179–186. https://doi.org/10.1016/j.alit.2018.01.001 Dasgupta S, Ghosh N, Bhattacharyya P, Roy Chowdhury S, Chaudhury K (2023) Metabolomics of Asthma, COPD, and Asthma-COPD Overlap: An Overview. Crit Rev Clin Lab Sci 60(2):153–170. https://doi.org/10.1080/10408363.2022.2140329 Cosío BG, Dacal D, de Pérez L (2018) Asthma-COPD Overlap: Identification and Optimal Treatment. Ther Adv Respir Dis 12:1753466618805662. https://doi.org/10.1177/1753466618805662 Leung JM, Sin DD, Asthma (2017) -COPD Overlap Syndrome: Pathogenesis, Clinical Features, and Therapeutic Targets. BMJ 358 , j3772. https://doi.org/10.1136/bmj.j3772 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4598620","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320637172,"identity":"97bd3853-211e-4cd6-b4ee-3605cecc0b10","order_by":0,"name":"Tongyao Sun","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tongyao","middleName":"","lastName":"Sun","suffix":""},{"id":320637175,"identity":"55bf889e-435d-4a75-84a2-00ad564b448c","order_by":1,"name":"Shengzhen Yang","email":"","orcid":"","institution":"Rizhao Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shengzhen","middleName":"","lastName":"Yang","suffix":""},{"id":320637176,"identity":"bbf82dbd-8f0d-4633-b28c-bb1444f1e7be","order_by":2,"name":"Shitao Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shitao","middleName":"","lastName":"Li","suffix":""},{"id":320637177,"identity":"c7ead6ad-880e-44b9-a8b7-6c23a23282de","order_by":3,"name":"Huiwen Li","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huiwen","middleName":"","lastName":"Li","suffix":""},{"id":320637178,"identity":"fcfdecd2-eb34-4730-8c8b-3cf9139adbe6","order_by":4,"name":"Jianjian Yu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jianjian","middleName":"","lastName":"Yu","suffix":""},{"id":320637179,"identity":"f6f50789-4065-4d80-a654-2a24e54ec475","order_by":5,"name":"Jun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACxgYGBoMPFRJybOzNBw58+EGkFsMZZ2yM+XmOJR6c2UOkTcycLWmJkjN8jA9zsBGjfHaPQTFjw+EEgxs8Hw4z8DDI84sdIOCwOWcMjAt3HM4zuN274XCBBYPhzNkJBLTMyN1gPPPM4WKDO2c3HJ7Bw5BgcJsYLbxthxM33Mh5cJiHjXgtaYkzZ+QwEKsl/wMskA2AgSxB2C+GM9LSYFH5+MOHHzby/NKEtDQwsBkg8SXwKwcBeWDUPCCsbBSMglEwCkY0AADyck5qgPe0hQAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-18 08:47:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4598620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4598620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60447484,"identity":"7a59af66-b5ac-4fa5-89ad-ee5d99458e32","added_by":"auto","created_at":"2024-07-16 22:03:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant selection process diagram for the NHANES study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4598620/v1/75602477d2ce6fee3770cab4.png"},{"id":60447483,"identity":"f31add3a-5cf9-4535-a8ed-f5645cbbb733","added_by":"auto","created_at":"2024-07-16 22:03:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96117,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline model of the odds ratios of ACO with HD (a), KDM (b), PhenoAge (c), BioAgeAccel (d), and PhenoAgeAccel (e). HD: homeostatic dysregulation; KDM: Klemera–Doubal method; OR: Odds ratio; CI: Confidence interval.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-4598620/v1/960fdb06b157754453235bfa.png"},{"id":74564585,"identity":"36de6007-1f38-4b81-b15e-3de53bf36559","added_by":"auto","created_at":"2025-01-23 13:23:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1662876,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598620/v1/a6c06680-ea54-45cf-b051-c814e2dc3d0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between biological aging and Asthma-COPD overlap based on Nhanes 2005-2018","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAsthma and chronic obstructive pulmonary disease (COPD) are two common respiratory diseases of the respiratory system, the etiology of their onset is not the same, and the clinical manifestations of the two are also different. Clinically, asthma is usually associated with airway hyperresponsiveness and airway inflammation, and patients often experience wheezing, shortness of breath, chest tightness, and cough \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In contrast, COPD is often caused by airway lesions and / or alveolar lesions caused by smoking, and is characterized by persistent ( often progressive ) airflow limitation, which is characterized by chronic respiratory symptoms ( wheezing, cough, and increased expectoration )\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. And in 2015 by the GINA report and GOLD report together proposed to suggest that patients with concurrent airway hyperresponsiveness, airway inflammation, and airflow limitation can be seen clinically, and defined this group of patients with both asthma and COPD as asthma-chronic obstructive pulmonary overlap (ACO) syndrome \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although the GOLD report in 2021 recommended to stop using the name 'ACO ' \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, ACO as a medical condition or feature is still a clinical problem. In addition, ACO patients have worse quality of life, and the acute exacerbation of related symptoms is more frequent \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e .Due to the aggravation of symptoms, the use rate of respiratory drugs, admission rate and mortality are higher \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e .Therefore, this type of patients need more attention in clinical practice.\u003c/p\u003e \u003cp\u003eAging, as one of the pathogenic factors that has been of continuous concern, has been associated with a variety of respiratory diseases in terms of its severity, and an increase in age often implies a decline in lung function and structural changes \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, under certain conditions, the degree of frailty of the body cannot be reasonably described by age alone. Therefore, this study assessed the degree of aging by measuring biological age through three calculation methods, which were Klemera-Doubal method (KDM), PhenoAge and homeostatic dysregulation (HD). In addition, BioAgeAccel and PhenoAgeAccel were introduced to further clarify the degree of individual aging, BioAgeAccel is defined as the residual of the linear regression of KDM to age. Similarly, PhenoAgeAccel is the residual of the linear regression of PhenoAge to age. The positive value indicates accelerated aging, and the negative value indicates the opposite \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e .Previous studies have pointed out that the increase of age is accompanied by the increase of the risk of ACO, but no study has explored the association between biological age and ACO.Therefore, this study first explored the association between biological age and the risk of ACO by analyzing the data of nhanes 2005\u0026ndash;2018 observational study \u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eIn this study, the data of patients recruited in the National Health and Nutrition Examination Survey ( NHANES ) were used to calculate the biological age and aging degree of the corresponding individuals as exposure, and multiple covariates were introduced to construct a complex model. Two methods of logistic regression and drawing restricted cubic spline curve were used to explore the relationship between aging and asthma-copd overlap, and the possible linear relationship between them.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e2.1 Data Sources\u003c/p\u003e\n\u003cp\u003eNHANES is a long-term, nationwide research project approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS) to assess the health and nutritional status of Americansand, and all patients were recruited with an informed consent forms at the time of recruitment. Therefore, this study does not require additional ethical approval. A total of 70,190 participants were enrolled from 2005 through 2018. After excluding individuals younger than 18 years of age and those with incomplete or unreliable data on key variables such as biological age, asthma, and chronic obstructive pulmonary disease, our study included 22,654 eligible participants. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1. For the remaining patients with missing values we used predictive mean matching (pmm) method to supplement the missing data.\u003c/p\u003e\n\u003cp\u003eFigure 1 Participant selection process diagram for the NHANES study\u003c/p\u003e\n\u003cp\u003e2.2 Calculation of biological age\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the three biological age measurement methods in this study were calculated using the \u0026ldquo; bioage \u0026ldquo; package developed based on r4.3.2, which introduced albumin, alkaline phosphatase, blood urea nitrogen, creatinine, uric acid, lymphocyte percentage, mean cell volume, white blood cell count. Eleven variables including systolic blood pressure, total cholesterol, and hemoglobin A1C were calculated. In different years, the measurement methods of some biomarkers have changed, and these data have been normalized according to the changes of the methods\u0026nbsp;\u003csup\u003e16\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003e2.3 Definition of ACO in this study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the diagnostic criteria of ACO in previous studies, patients who met the diagnosis of ACO in this study must meet the clinical manifestations of both COPD and asthma\u0026nbsp;\u003csup\u003e17,18\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003e2.3.1 Definition of COPD\u003c/p\u003e\n\u003cp\u003eBased on previous studies\u0026nbsp;\u003csup\u003e19,20\u003c/sup\u003e , patients who meet one of the following criteria can be defined as having COPD : 1) FEV1/FVC\u0026lt;0.7 after inhalation of bronchodilators ; 2) patients who answered \u0026quot;yes\u0026quot; when asked \u0026quot;Has a doctor or other health professional ever told you that you had chronic bronchitis?\u0026quot; or \u0026quot;Has a doctor or other health professional ever told you that you had emphysema?\u0026quot; ; 3) patients who answered \u0026ldquo;yes\u0026quot; when asked \u0026quot;Has a doctor or other health professional ever told you that you had COPD?\u0026quot;.\u003c/p\u003e\n\u003cp\u003e2.3.1 Definition of asthma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen the patient answers \u0026ldquo; yes \u0026rdquo; when asked the following question, the patient can be defined as asthma : 1 ) \u0026ldquo; Has a doctor or other health professional ever told you that you have asthma ? \u0026rdquo; ; 2 ) \u0026ldquo; Do you still have asthma ? \u0026rdquo; ; 3 ) \u0026ldquo; In the past 12 months have you had wheezing or whistling in your chest ? \u0026rdquo;\u003c/p\u003e\n\u003cp\u003e2.4 Covariates\u003c/p\u003e\n\u003cp\u003eIn this study, baseline covariates were primarily selected based on demographics, social factors, lifestyle habits, and comorbidities. In addition to demographic data such as gender, age, and race, social factors including household income, education level, and marital status were also taken into account. As part of the study, data on smoking and alcohol consumption behaviours were collected. Furthermore, variables related to medical comorbidities such as BMI, hypertension, diabetes, and hyperlipidaemia were also collected. The definitions of relevant covariates can be found after Table 1. For patients with remaining missing values, we used predictive mean matching (PMM) to supplement the missing data.\u003c/p\u003e\n\u003cp\u003e2.5 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using R4.3.2 software. A weighted logistic regression model was utilised to explore the relationship between bioage and ACO. A restricted cubic spline analysis was employed to assess the non-linear relationship between COPD variables and bioage. Statistical significance was defined as a p-value less than 0.05.\u003c/p\u003e"},{"header":"3 Result","content":"\u003cp\u003e3.1 Baseline characteristics of NHANES participants\u003c/p\u003e\n\u003cp\u003eA total of 22,654 subjects aged 18 years and above, with biological age and ACO data, participated in the analysis. Table 1 lists the general characteristics of the participants, including demographic factors, biological age and ACO prevalence. There were 1326 participants with ACO. Among them, 11033 were males and 11621 were females. The calendar ages of the ACO group and the non-ACO group were ( 53.77 \u0026plusmn; 16.67 ) years and ( 49.46 \u0026plusmn; 17.90 ) years, respectively. There were significant differences in age, gender, race, education level, marital status, poverty level, alcohol using, smoking, obesity, hypertension, hyperlipidemia and diabetes between ACO patients and non-ACO patients. In addition, compared with non-ACO patients, ACO patients had a higher biological age ( HD : 2.41 vs.2.20 ; kDM : 48.97 vs. 44.65 ; phenoAge : 52.69 vs. 46.95 ).Table 1.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"542\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003eACO(N=1326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003eNON-ACO(N=21328)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eAge ,mean(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e53.77(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e49.46(17.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eAge ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e302(22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e7308(34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;40-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e473(35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e6876(32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e551(41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e7144(33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003ehd ,mean(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e2.41(1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e2.20(0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003ekdm ,mean(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e48.97(20.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e44.65(20.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003ephenoage ,mean(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e52.69(17.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e46.95(18.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eBioAgeAccel ,mean(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e-4.80(15.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e-4.81(15.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eBioAgeAccel ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;YES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e474(35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e7418(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e852(64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e13910(65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003ePhenoAgeAccel ,mean(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e-1.08(4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e-2.52(4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003ePhenoAgeAccel,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;YES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e509(38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e5816(27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e817(61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e15512(72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eGender ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e590(44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e10443(49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e736(55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e10885(51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eRace ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e86(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e3898(18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e800(60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e9282(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e241(18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e4200(19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e199(15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e3948(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eEDUcation ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Below high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e381(28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e5334(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High School or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e945(71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e15994(75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eMarital ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e722(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e13155(61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e604(45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e8173(38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003ePoverty ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e358(27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e4110(19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Not poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e968(73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e17218(80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eObesity ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e587(44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e8174(38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Overweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e396(29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e7228(33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e343(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e5926(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eDrink ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e989(74.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e15050(70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e336(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e6264(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eSmoke,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e530(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e4111(19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e796(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e17217(80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eDiabetes ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;YES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e310(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e3479(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e1016(76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e17849(83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eHyptersion ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e730(55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e8812(41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e596(44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e12516(58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003eHyperlipidemia ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;YES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e1125(84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e17048(79.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.90036900369004%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;NO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.66420664206642%\"\u003e\n \u003cp\u003e201(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.15129151291513%\"\u003e\n \u003cp\u003e4280(20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.284132841328413%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of the NHANES participants. Continuous variables were presented as mean (SE); Categorical variables were presented as N (%); means (SE) and % were weight-adjusted. KDM: KlemeraDoubal method; hd: homeostatic dysregulation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e* \u0026nbsp;Patients who are married and cohabiting with their partners are defined as married; patients with a family income index below 1 are defined as poor; those with a body mass index over 30 are defined as obese, between 25 and 30 are defined as overweight, and below 25 are classified as normal; individuals meeting any of the following criteria are defined as diabetic patients: history of diabetes, insulin injection, oral hypoglycaemic medication, Glycohemoglobin level \u0026gt;=6.5, Glucose level \u0026gt;=126; individuals meeting any of the following conditions are defined as hypertensive patients: currently diagnosed with hypertension, SBP \u0026gt;=140, DBP \u0026gt;=90; individuals meeting any of the following conditions are diagnosed as hyperlipidemia patients, with TC \u0026gt;200, TG \u0026gt;150, male HDL \u0026lt;40, female HDL \u0026lt;50, LDL \u0026gt;=130, or with hypercholesterolaemia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.2 Logistic regression analysis of the association between biological aging and ACO\u003c/p\u003e\n\u003cp\u003eTable 2\u0026nbsp;shows the relationship between ACO and biological age. After adjusting for age, gender and race ( model 1 ), we found that there was a significant association between biological aging and ACO. Adjusted educational level, marital status, ratio of family income to poverty, obesity, alcohol consumption, and cigarette use, the association between BioAgeAccel and the incidence of ACO became no longer significant, and the remaining results were consistent with the results of model 1 in the multivariate regression results of model 2. After further adjustment of the disease status in Model 3, it was found that HD, kdm, and bioageaccel were not significantly associated with the incidence of ACO. In model 3, for every 1 year increase in phenotypic age, the risk of ACO increased by 1 % ( OR 1.01,95 % CI 1.01-1.02, P\u0026lt;0.001 ). Acceleration of biological aging is defined as the residual error of the regression of biological age to calendar age, including BioAgeAccel and PhenoAgeAccel, where Phenoageaccel is associated with the onset of ACO. The acceleration of biological aging indicates that the biological age is older. Compared with PhenoAgeAccel-negative individuals, PhenoAgeAccel-positive individuals were 4 % more likely to develop ACO ( OR 1.04,95 % CI 1.02-1.05, p \u0026lt; 0.001 ).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.73156342182891%\" rowspan=\"2\"\u003e\n \u003cp\u003eBiological aging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.28613569321534%\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.43362831858407%\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.548672566371682%\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.463768115942027%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.05072463768116%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.82608695652174%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.05072463768116%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.557971014492754%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.05072463768116%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.7039764359352%\"\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.262150220913107%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.17 (1.10-1.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13 (1.07-1.20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1.01 (0.94-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.7039764359352%\"\u003e\n \u003cp\u003eKDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.262150220913107%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00 (1.00-1.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00 (1.00-1.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.7039764359352%\"\u003e\n \u003cp\u003ePhenoAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.262150220913107%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.01 (1.01-1.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.02 (1.01-1.03)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.01(1.01-1.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.7039764359352%\"\u003e\n \u003cp\u003eBioAgeAccel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.262150220913107%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00 (1.00-1.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1.00 (1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.7039764359352%\"\u003e\n \u003cp\u003ePhenoAgeAccel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.262150220913107%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04 (1.02-1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04 (1.02-1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04(1.02-1.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.98379970544919%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable2 Associations of biological ages and accelerated biological aging with ACO. Model 1: Adjusting for age, gender, race; Model 2: Model 1+adjusted for educational level, marital status, ratio of family income to poverty, obesity, alcohol consumption, and cigarette use. Model 3: Model 2+adjusted for Diabetes, hypertension, and hyperlipidemia.HD: homeostatic dysregulation; KDM: Klemera\u0026ndash;Doubal method; OR: Odds ratio; CI: Confidence interval;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exposure was further grouped into categorical variables according to the quartile, and the P value of the trend was estimated. In model3, higher phenotypic age ( Q4 ) and higher degree of phenotypic aging ( Q4 ) were more likely to cause ACO, with OR values of 2.33 ( 1.59-3.42,95 % CI ) and 1.64 ( 1.36-1.98,95 % CI ), respectively, and P trend was less than 0.001.In addition, lower kdm biological age acceleration ( Q2 ) could reduce the risk of ACO ( OR : 0.79 ( 0.67-0.93 ), 95 % CI ).Table 3\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003ePhenoAgeAccel, (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (1.02-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003ePhenoAgeAccel, (quartile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1(REF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.32 (1.09-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.40 (1.16-1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.64 (1.36-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eP trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.5609756097561%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eKdmAgeAccel, (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eKdmAgeAccel, (quartile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1(REF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 (0.67-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.82-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 (0.74-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eP trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.5609756097561%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003ePhenoAge, (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (1.01-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003ePhenoAge, (quartile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1(REF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.44 (1.14-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.79 (1.30-2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e2.33 (1.59-3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eP trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.5609756097561%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eKdmAge, (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eKdmAge, (quartile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1(REF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (0.85-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 (0.86-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Quartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21951219512195%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (0.80-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341463414634145%\" valign=\"top\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.4390243902439%\" valign=\"top\"\u003e\n \u003cp\u003eP trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.5609756097561%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable3 \u0026nbsp;Associations of biological ages and accelerated biological aging with ACO (Quartile divided groups)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.3 Restricted cubic spline regression analysis of the association between biological aging and ACO\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure2 shows that there is a significant nonlinear positive correlation between the age of the three organisms and ACO ( nonlinear P \u0026lt; 0.001 ). The odds ratio ( OR ) shows that when the HD value exceeds 1.97, it will gradually increase and slowly stabilize. Regarding the non-linear relationship between KDM and ACO, the figure shows that the risk of ACO increases rapidly in the lower range of KDM ( \u0026lt; 42.53 years old ). After KDM reached 42.53 years old, the rate of risk increase slowed down and gradually stabilized. In addition, the risk of ACO increased rapidly before the PhenoAge value reached about 46.39 years old, and then began to increase slowly and gradually entered the plateau period. The results of rcs analysis of BioAgeAccel accelerated biological aging were generally meaningless ( P-overall = 0.186 ). In addition, the results of rcs analysis of PhenoAgeAccel showed that there was a linear relationship between exposure and outcome ( P for nonlinear = 0.699 ), and the critical values of two kinds of accelerated biological aging were-5.33 and-2.69, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2. Restricted cubic spline model of the odds ratios of ACO with HD (a), KDM (b), PhenoAge (c), BioAgeAccel (d), and PhenoAgeAccel (e). HD: homeostatic dysregulation; KDM: Klemera\u0026ndash;Doubal method; OR: Odds ratio; CI: Confidence interval.\u003c/strong\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAs one of the continuously increasing and irreversible indicators, age has been an important topic in the field of respiratory disease research. Continuously increasing age brings with it a continuous decline in lung function, changes in lung remodelling function, and a decrease in cellular regeneration \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and these changes imply an increase in the susceptibility to diseases such as chronic obstructive pulmonary disease (COPD), asthma, and interstitial fibrosis \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and therefore Age has long been regarded as an important predictor of respiratory diseases and a criterion for the development of management strategies, however, since individuals of the same age may show differences in health status due to different organic conditions (e.g., individuals who exercise regularly perform better than the average individual in lung function), compared with calendar age alone, the introduction of multiple indicators such as blood markers and the use of three methods of calculating the Therefore, compared with calendar age alone, biological age calculated by three methods using multiple blood markers and other indicators has a higher application value \u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e .Wang and other researchers extracted and analysed data from 308,592 patients in the UK Biobank and concluded that PhenoAgeAccel was significantly associated with the risk of chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and idiopathic pulmonary fibrosis (IPF), as well as with the decline in lung function \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. It has been similarly noted that biological age is associated with the incidence of several age-related diseases and that the calculation of biological age can be used for the prediction of age-related diseases \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e .ACO is recognised clinically by its common features with asthma and chronic obstructive pulmonary disease, and numerous patients are often incorrectly diagnosed with simple asthma or COPD by ignoring a symptom, with a consequent higher risk of exacerbation, greater burden of hospitalisation and medication, and higher mortality \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eIn this study, we found that patients with high calendar age, female, low education, poverty, higher body weight, possessing bad habits such as smoking and drinking, and comorbidities such as hypertension and hyperlipidaemia had a higher rate of ACO in terms of baseline data. In addition, after initial adjustment for sex, age, and race as covariates, we found that the biological age of the three calculations and the results of the two biological age accelerations were positively associated with ACO, and after adjusting for all covariates, the positive association between phenotypic age and phenotypic age acceleration and the prevalence of ACO remained, and quartile grouping showed that both high phenotypic age and higher phenotypic age aging were ACO These results suggest that higher phenotypic age may increase the probability of ACO, and the results of the restricted cubic spline regression analysis are consistent with those of the logistic regression. All these results suggest that biological age can be used as a meaningful indicator to predict the occurrence of ACO.\u003c/p\u003e \u003cp\u003eHowever, our study also has some limitations. First, since the NHANES data were collected through a questionnaire, subjective judgement and recall bias of the respondents may have affected the results. In addition, the definition of ACO relied only on basic surveys rather than comprehensive measurements, which may have reduced the robustness of the results. Finally, the data for this study were obtained from the NHANES database, which is a cross-sectional study, and the dynamics of disease prevalence as well as changes in physical status of the same individuals over time were not available, so prospective studies are needed to further refine the validation.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study provides some evidence to prove the causal association of biological aging in the pathogenesis of ACO. The phenotypic age and accelerated phenotypic aging in this study have the potential to be used as predictors of ACO, and the prevention strategies for aging have a potential role in reducing the risk of ACO.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003eThere is no conflict of interest in this study.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the following projects : Shandong Province Traditional Chinese Medicine Science and Technology Project ( Q-2023041 ) ; the provincial-bureau joint construction of traditional Chinese medicine science and technology projects ( GZY-KJS-SD-2023-048 ) ; Shandong Province Medical and Health Science and Technology Project ( 202303021034 )\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors read and approved the final manuscript. Tongyao Sun and Shengzhen Yang performed the analysis. Tongyao Sun and Shitao Li wrote a draft of this article. Jianjian Yu, Huiwen Li and Jun Wang conceived the research design. All authors contributed to the interpretation of the results, critically revised the important knowledge of the manuscript, and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRelevant supplementary materials and codes can be obtained by contacting the author. The data in this study are publicly available on the Internet for use by data users and researchers ( www.cdc.gov / nchs / nhanes / ).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003e2024 GINA Main Report - Global Initiative for Asthma - GINA\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ginasthma.org/2024-report/\u003c/span\u003e\u003cspan address=\"https://ginasthma.org/2024-report/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 2024-06-18)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003e2024 GOLD Report\u003c/em\u003e. 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Ther Adv Respir Dis 12:1753466618805662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1753466618805662\u003c/span\u003e\u003cspan address=\"10.1177/1753466618805662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung JM, Sin DD, Asthma (2017) -COPD Overlap Syndrome: Pathogenesis, Clinical Features, and Therapeutic Targets. \u003cem\u003eBMJ 358\u003c/em\u003e, j3772. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj.j3772\u003c/span\u003e\u003cspan address=\"10.1136/bmj.j3772\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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-COPD Overlap, Biological age, Aging, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4598620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4598620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eAging is an important factor in the pathogenesis of various respiratory diseases, and biological aging can better reflect the systemic functional status of individual organisms. The purpose of this study was to analyze the association between biological aging and Asthma-COPD Overlap (ACO) ,and to explore its potential causal relationship.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eThe present study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning from 2005 to 2018. Three biological ages [Klemera-Doubal method (KDM), phenotypic age (PhenoAge) and homeostatic dysregulation (HD)] and two measures of biological acceleration of aging (BioAgeAccel and PhenoAgeAccel) were selected as the main exposure factors for analysis. Weighted logistic regression and restricted cubic spline regression were used to analyze the association between biological aging and ACO.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eIn our study, phenotypic age was positively associated with the incidence of ACO, and the degree of phenotypic age acceleration was also a risk factor for ACO prevalence. After further adjustment for demographic characteristics, both remained an important risk factor for ACO.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eThis study provides some evidence for the association of biological aging in the development of ACO. In addition, preventive strategies targeting aging have a potential role in reducing the risk of ACO.\u003c/p\u003e","manuscriptTitle":"Association between biological aging and Asthma-COPD overlap based on Nhanes 2005-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 22:03:14","doi":"10.21203/rs.3.rs-4598620/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":"173395ff-4d2d-4ae8-9123-2c30822152f7","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-23T13:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 22:03:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4598620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4598620","identity":"rs-4598620","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}