The Association Between Depression Trajectories and Asthma Risk: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

The Association Between Depression Trajectories and Asthma Risk: Evidence from the China Health and Retirement Longitudinal Study (CHARLS) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association Between Depression Trajectories and Asthma Risk: Evidence from the China Health and Retirement Longitudinal Study (CHARLS) Rui Zhang, Pengyu Zhou, Jinnan Wu, Shuwen Zhang, Liqin Xu, Xianghong Ming This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8069992/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Previous studies show asthma comorbid with depression significantly impacts quality of life, yet research on the long-term trajectory between depressive symptoms and asthma in middle-aged and elderly Chinese remains limited. Objective This study aims to understand the different trajectories of depressive symptoms and their influencing factors in middle-aged and elderly asthmatic patients in China. Methods This study uses data from the China Health and Retirement Longitudinal Study (CHARLS) collected between 2011 and 2018, involving 484 participants aged 45 and above. Depressive symptoms were assessed with the Center for Epidemiological Studies Depression Scale (CESD-10). A group-based trajectory model (GBTM) was constructed to identify long-term patterns of depressive symptom trajectories; Factors influencing these trajectories were analyzed via a multivariate logistic regression model. Results During the entire follow-up, we identified three depressive symptom trajectories: the "Low-stable" group, "Middle-fluctuating" group, and "High-increasing" group. We found differences in the basic characteristics across subgroups with distinct depressive trajectories, while unmarried, having no contact with children, and short sleep duration were key indicators for identifying populations requiring focus in depressive symptom prevention and treatment. Conclusions Depressive symptom trajectories in middle-aged and elderly Chinese asthmatics are heterogeneous, so it is necessary to focus on the trajectory characteristics of different subgroups. Asthma Depression trajectories Group-based trajectory modeling CHARLS Figures Figure 1 Figure 2 Introduction Asthma is one of the most prevalent chronic respiratory diseases worldwide, characterized primarily by airway inflammation, airway remodeling, increased airway responsiveness, and reversible airflow limitation [ 1 ] . According to the survey by the World Health Organization (WHO) in2019, the projected global number of people living with asthma reached 262 million, with the disease causing 455,000 deaths annually—imposing a heavy burden on the economies and public health systems of countries across the world [ 2 , 3 ] . While asthma care and preventative techniques have been developed to lower prevalence and mortality, the worldwide burden of asthma remains a major concern. Although there has been much study on asthma, its specific pathophysiology is not well understood. Multiple epidemiological studies have confirmed that depression is a significant risk factor for asthma exacerbations, and there is a bidirectional relationship between the two conditions: on the one hand, depressive symptoms can interfere with asthma control and lower patients' quality of life; on the other hand, recurrent asthma exacerbations can exacerbate patients' psychological burden and increase their risk of developing depression. For instance, a study by Moussavi et al. indicated that the prevalence of depression among asthma patients is as high as 18.1% [ 4 ] . Another study based on NHANES data found that compared with non-depressed individuals, the risk of asthma in people with severe depression is significantly increased by 2.4 times (OR = 2.41, 95% CI: 1.37–4.24); additionally, a higher depression score correlates with more severe depressive symptoms, which in turn elevates the risk of respiratory symptoms [ 5 ] .Furthermore, the presence of depression exerts a negative impact on asthma prognosis, contributing to a higher rate of asthma exacerbations, accelerated decline in lung function, and increased mortality [ 6 – 8 ] . Asthma and depressive symptoms are intertwined and interact reciprocally, severely compromising asthma prognosis and reducing patients’ quality of life. Therefore, enhancing the assessment of asthma-depression comorbidity is of great significance for advancing relevant public health prevention and control measures and improving patients’ health outcomes. Previous studies on depressive symptoms and asthma have rarely considered individual characteristics of changes in depressive symptoms. They also treated depressive status at a single time point as the exposure factor, ignoring its variability or reversibility, which prevents a comprehensive assessment of the potential complex relationship between depression and asthma [ 9 – 11 ] . Long-term examination of depression levels is necessary to determine the relationship between depressed trajectory changes and asthma and to better understand when symptoms rise or diminish. As a finite mixture modeling technique, GBTM studies dynamically capture long-term trends in depressive symptom changes. They simultaneously estimate multiple trajectories, accurately identify subgroups with distinct depressive development trajectories within the study population, fully reveal the heterogeneity of individual trajectories, and uncover long-term patterns of depressive symptoms in the population more comprehensively and deeply—providing a more precise basis for subgroup classification in subsequent targeted intervention [ 12 , 13 ] . Based on this, this study uses longitudinal data from CHARLS to describe the distinct trajectory patterns of depressive symptoms and their heterogeneity among middle-aged and elderly Chinese asthmatics. It further classifies these depressive symptom trajectories, explores the relationship between these patterns and asthma, and aims to provide theoretical basis and practical guidance for the management of depressive symptoms as well as the treatment and prevention of asthma. Methods Study participants The data of this study were derived from the China Health and Retirement Longitudinal Study (CHARLS), a national longitudinal survey focusing on the health and socioeconomic status of Chinese middle-aged and elderly people aged 45 years and above. Adopting a scientific multistage stratified random sampling strategy, the project launched its baseline survey across 28 provinces in China from June 2011 to March 2012, covering 150 county-level units and 450 villages/urban communities, with a total of 17,708 participants enrolled. Following the baseline survey, follow-up investigations have been conducted every two years, with data collection completed for 2013, 2015, and 2018; currently, the follow-up work is ongoing until 2020 [14] . The CHARLS project has obtained approval from the Biomedical Ethics Committee of Peking University(IRB00001052-11015), and all participants have signed written informed consent forms. Details regarding its study design and questionnaire have been published in other literatures. In this manuscript, we utilized four waves of data from the China Health and Retirement Longitudinal Study (CHARLS), spanning 2011 (Wave 1) to 2018 (Wave 4), to systematically examine the trajectories of depressive symptoms among middle-aged and elderly patients with asthma. An initial pool of 17,708 individuals was included. After quality control, exclusions were made for the following groups: 777 participants under 45 years old or with missing basic information; 16,176 non-asthma patients or those with incomplete asthma-related data; 90 individuals lacking sociodemographic variables. Additionally, 4 participants had missing data on depressive symptoms at Wave 1, while another 177 individuals lacked relevant depressive symptom information during the follow-up period. A final analytical cohort of 484 participants was obtained after all exclusions (Fig. 1) . Detailed exclusion criteria are available in Figure 1. Assessment of depressive symptoms The Epidemiological Studies Depression Scale (CES-D10) measured depression. Studies in Chinese people have validated this scale's reliability and validity[15,16]. The 10-item CESD-10 assesses previous week's depressed symptoms. Using a 4-point Likert scale (0 to 3), answers to all 10 items were added to get the total score, which ranged from 0 to 30. Higher overall scores indicate more severe depression. Using a 12-point threshold, respondents with a total score of 12 or more were classed as depressed (coded as 1), while those with scores below 12 were classified as non-depressed (coded as 0). Asthma assessment This study drew on the validated CHARLS baseline questionnaire, with self-reported doctor-diagnosed asthma selected as the core outcome of interest. Assessment of this outcome was based on the questionnaire query: “Have you ever been diagnosed with asthma by a doctor?” [17,18]. Participants who answered “Yes” were identified as asthma patients. Covariates We included sociodemographic characteristics and health-related behaviors as covariates in the analysis. The former comprised age, sex, residence type (urban/rural), educational level (illiterate, primary school or below, secondary to vocational school, university or above), and marital status (married, divorced, unmarried); the latter covered smoking status (yes/no), alcohol consumption patterns, and sleep duration. For this study, smoking was defined as lifetime consumption of over 100 cigarettes: ex-smokers (previously smoked but quit) and current smokers were both grouped as "smokers" in analysis. Alcohol consumption was categorized into three tiers: (1) >1 time/month, (2) <1 time/month, (3) no consumption. Statistical analyses Group-based trajectory modeling (GBTM) was applied to identify potential trajectories of depressive symptoms observed between 2011 and 2018. This model proceeds on the assumption that heterogeneous trajectories exist within the study population, leveraging maximum likelihood estimation to detect clusters of individuals who share similar developmental patterns of depressive symptoms [13,19]. Concurrently, it estimates the probabilities associated with multiple trajectories, and participants are grouped according to their posterior probabilities of belonging to each specific trajectory. Model fit was evaluated using four key metrics: Bayesian Information Criterion (BIC), average posterior probability of assignment (AvePP), and Group proportion . Specifically, BIC values that are closer to zero signify a superior model fit; an AvePP exceeding 0.7 across all trajectory groups indicates acceptable certainty in participant assignment; Additionally, a minimum of 5% of the total sample was required for each trajectory group to ensure statistical robustness [20]. Potential confounding factors in this study included sociodemographic variables—age, gender, marital status, residence, and education level—and health-related factors, namely smoking status, drinking status, and sleep duration, all of which were accounted for in the analyses. For the group-based trajectory modeling (GBTM) analysis, the Traj plugin within Stata 17 software was utilized[21]. All remaining statistical analyses were carried out using R 4.2.2. A two-tailed test was applied for all statistical assessments, with statistical significance determined by a P-value of less than 0.05. Results Depressive trajectory modeling The track of depressive symptoms was fitted according to different groups (1 ~ 4 groups) and different function forms (intercept, linearity, square, cubic). By comparing BIC and AvePP, the optimal number of trajectory groups is 3 groups (BIC = -5780.53), and 2, 3, 1 as polynomial orders, and the optimal combination of trajectory types of each group is obtained. The trajectory model parameters are shown in Table 1 . The specific characteristics of each group regarding the change trajectories of depressive symptoms in middle-aged and elderly individuals are shown in Fig. 2 . Group 1 (n=240, 49.6%) had a low average depression score at baseline. It remained slowly decreasing in the early stage of the survey and then started to show a slow upward trend from 2015. Therefore, this group was named the "Low-stable depressive symptoms group". Compared with Group 1, the middle-aged and elderly participants in Group 3 (n=50, 10.3%) saw a sharp rise starting from a relatively high baseline, displaying a trajectory of rapid growth. This group has therefore been designated as the "High-Growth Group." While Group 2 (n = 194, 40.1%) had an average score at a moderate level, which fluctuated over time, so it was named the "Middle-fluctuating group". Table 1. Goodness-of-fit statistics of group-based trajectory analysis. Number of groups Trajectory shape BIC for total number of observations Group proportion (%) Average posterior probabilities 2 3 3 -5822.86 60.56/39.44 0.94/0.91 3 3 3 3 -5788.22 47.92/40.87/11.21 0.91/0.87/0.86 4 3 3 3 3 -5791.65 44.44/26.06/18.03/11.46 0.89/0.71/0.74/0.86 3 2 3 1 -5780.53 48.31/40.96/10.72 0.91/0.87/0.86 Baseline characteristics of trajectory groups Table 2 shows baseline characteristics of study participants per depression trajectory group. Age (p=0.910) and education (p=0.476) were similar among the three groups. Gender differences were significant (p<0.001), with women making up 34.6% of the low-stable group, 47.4% of the middle-fluctuating group, and 60.0% of the high-increasing group. Marriage status also differed (p=0.023), with married people making up 89.6%, 80.9%, and 80.0% of the groups. Non-smokers made up 42.5%, 54.1%, and 66.0% of groups (p=0.003). Drinking behavior varied significantly (p<0.001), with non-drinkers comprising 47.9%, 56.2%, and 80.0% of the groups. The depression trajectory groups showed substantial variations in sleep duration (p<0.001), with 48.3%, 65.5%, and 82.0% sleeping fewer than 7 hours. No significant differences were seen in living situation (p=0.295), child interaction (p=0.114), or social activity engagement (p=0.140). Table 2. Patient demographics and baseline characteristics . Characteristic Depression group p -value Low-stable N = 240 Middle-fluctuating N = 194 High-increasing N = 50 Age, n (%) 0.910 < 60 95 (39.6%) 75 (38.7%) 21 (42.0%) ≥ 60 145 (60.4%) 119 (61.3%) 29 (58.0%) Gender, n (%) <0.001 man 157 (65.4%) 102 (52.6%) 20 (40.0%) woman 83 (34.6%) 92 (47.4%) 30 (60.0%) Education, n (%) 0.476 High school and below 230 (95.8%) 190 (97.9%) 49 (98.0%) Above high school 10 (4.2%) 4 (2.1%) 1 (2.0%) Living status, n (%) 0.295 Urban Community 85 (35.4%) 64 (33.0%) 12 (24.0%) Rural Village 155 (64.6%) 130 (67.0%) 38 (76.0%) Marital status, n (%) 0.023 Married 215 (89.6%) 157 (80.9%) 40 (80.0%) Non-married 25 (10.4%) 37 (19.1%) 10 (20.0%) Contact Kids, n (%) 0.114 No 23 (9.6%) 30 (15.5%) 4 (8.0%) Yes 217 (90.4%) 164 (84.5%) 46 (92.0%) Participate social activities, n (%) 0.140 No 114 (47.5%) 103 (53.1%) 31 (62.0%) Yes 126 (52.5%) 91 (46.9%) 19 (38.0%) Smoke status, n (%) 0.003 No 102 (42.5%) 105 (54.1%) 33 (66.0%) Yes 138 (57.5%) 89 (45.9%) 17 (34.0%) Drink status, n (%) <0.001 No 115 (47.9%) 109 (56.2%) 40 (80.0%) Yes 125 (52.1%) 85 (43.8%) 10 (20.0%) Sleep, n (%) <0.001 < 7 116 (48.3%) 127 (65.5%) 41 (82.0%) ≥ 7 124 (51.7%) 67 (34.5%) 9 (18.0%) Association between asthma and depressive trajectories We utilized the “Low-stable” group as a reference(Table 3). Multivariate logistic regression analysis showed that in the moderate fluctuation group, “Non-Married” (OR=1.93, P=0.026), “No contact with adult children” (OR=1.84, P=0.049), and “Sleeping less than 7 hours” (OR=2.07, P<0.001) significantly increased the risk. Factors related to insufficient sleep suggest an association with an increased likelihood of the "high-increasing" trajectory. The results showed that "Sleeping less than 7 hours" could significantly increase the risk of onset in the high-level growth group(OR=5.133, P<0.001). Table 3 . Results of multiple logistic regression on the factors associated with outcomes. Variable Category Middle-fluctuating P value High-increasing P value Age <60 ≥60 1.073 (0.709~1.624) 0.739 1.062 (0.539~2.093) 0.862 Gender Man Woman 1.307 (0.736~2.320) 0.361 1.129 (0.440~2.893) 0.801 Education status Above high school Education status High school and below 1.471 (0.430~5.031) 0.539 1.192 (0.128~11.079) 0.877 Living status Urban Community Living status Rural Village 1.047 (0.686~1.599) 0.831 1.809 (0.851~3.842) 0.123 Marital status Married Marital status Non-Married 1.927 (1.084~3.426) 0.026 2.012 (0.839~4.829) 0.117 Contact Kids Yes Contact Kids No 1.839 (1.004~3.369) 0.049 0.896 (0.281~2.852) 0.852 Participate social activities Yes Participate social activities No 1.196 (0.805~1.778) 0.375 1.887 (0.969~3.672) 0.062 Smoke status No Smoke status Yes 0.775 (0.450~1.333) 0.357 0.693 (0.275~1.748) 0.437 Drinks status No Drinks status Yes 0.913 (0.582~1.433) 0.692 0.280 (0.120~0.652) 0.003 Sleep status ≥7 Sleep status <7 2.074 (1.390~3.094) <0.001 5.133 (2.326~11.328) <0.001 Discussion A nationally representative sample of 484 asthmatic middle-aged and elderly (45 years and older) Chinese patients was evaluated for depressed trajectories. We found three depressed symptom trajectories over 9 years: low-stable, middle-fluctuating, and high-increasing. Depression may increase with unmarried status, no adult children, and little sleep. Depression prevention and therapy should target high-increasing populations. Early detection and tailored therapy may reduce depression. This research demonstrated varied depressed symptom development in middle-aged and elderly asthma patients in China. This shows that these diverse symptom trajectories might be used to develop differentiated policies and interventions to promote asthma patients' mental health. This study further explores the factors influencing the trajectories of depressive symptoms among middle-aged and elderly asthma patients. Analysis of participants' baseline characteristics showed significant intergroup differences in gender, marital status, smoking and drinking status, and sleep duration. Results from multiple logistic regression analysis then show that unmarried, having no contact with adult children, and short sleep duration all significantly affect depressive symptoms in this population (all P < 0.05). In terms of demographic characteristics, being male, married, and having contact with children act as protective factors against depressive symptoms in middle-aged and elderly asthma patients. Studies have identified marital status as one of the key social factors influencing depression, with married individuals generally exhibiting better mental health than those who are single, widowed, separated, or divorced [ 22 , 23 ] .Empirical evidence supports this: a cross-country, two-stage analysis by Zhai et al [ 24 ] , using nationally representative data from seven countries, confirmed that unmarried individuals face a significantly higher risk of depressive symptoms than married counterparts. Similarly, a cross-sectional study on Europeans aged 65 and above highlighted that being unmarried is a major risk factor for late-life depression [ 25 ] .Mechanistically, spouses serve as the core source of social support for married individuals, playing a crucial role in safeguarding the mental health of older adults [ 26 ] . This helps explain why singles are more depressed. Adult depression prognoses differ by marital status. Unmarried participants reported more severe depression symptoms 3–4 months after baseline evaluation in a European meta-analysis, suggesting a worse prognosis [ 27 ] . Within the social support system, intergenerational support serves as a key protective factor against depression in older adults [ 28 , 29 ] . Intergenerational relationships refer to the interactive bonds between adjacent generations, particularly between older adults and their adult children [ 30 ] . Given that adult children often act as primary caregivers for their elderly parents, the link between intergenerational support and depressive symptoms holds special significance for safeguarding the mental health of older adults [ 31 ] . Studies have shown a significant correlation between intergenerational support and reduced depressive symptoms in this population [ 32 , 33 ] , with its effects primarily exerted through three dimensions: economic support, emotional support, and care support [ 34 ] . Emotional support effectively alleviates loneliness among older adults, enhances their life satisfaction and mental well-being [ 35 , 36 ] . Meanwhile, emotional comfort and practical assistance from family members reduce the occurrence of psychological distress, thereby lowering the severity of depression [ 37 ] .Economic support eases the burden of expenses for older adults—such as medical costs, housing fees, and daily living expenditures—alleviating anxiety caused by life pressures. This provides a sense of stability and happiness, reducing the likelihood of developing depressive symptoms [ 38 , 39 ] . Care support, as a vital form of direct guarantee for older adults’ quality of life, also plays a positive role in relieving depressive emotions [ 40 ] .Synthesizing the above research evidence, the nature and quality of interactions between older adults and their adult children exert a significant impact on their mental health in later life. Depression develops with age, while middle-aged and elderly people's ideal sleep length decreases [ 41 ] . In our study, we found that compared with middle-aged and elderly patients with low-stable, individuals in both middle-fluctuating group and high-increasing group with a significant reduction in sleep duration who slept less than 7 hours had a significantly higher risk of developing depressive symptoms. The association between sleep disorders and depression has been well established in previous research. A study from the U.S. National Health and Nutrition Examination Survey (NHANES) indicated that short sleep duration is linked to depressive symptoms [ 42 ] . Furthermore, among individuals with sleep disorders, the proportion of elderly people experiencing depressive symptoms is significantly higher than that of young people [ 43 ] . It is generally accepted that sleep disorders typically precede depression: sleep disruption exacerbates depressive symptoms, which in turn impair sleep quality, and this bidirectional interaction may form a vicious cycle [ 44 ] . However, due to the complexity of the relationship between sleep and depression, the exact mechanism by which sleep disorders lead to depression remains to be further elucidated. Psychosocially, decreasing sleep duration causes daytime drowsiness and exhaustion, which may lead to less social activity and more social isolation and loneliness [ 45 ] . Both factors are critical risk factors for depression. From a physiological standpoint, sleep disorders may elevate the risk of depression through two pathways: first, by altering neural sensitivity to inflammation, and second, by disrupting key neurotransmitter systems involved in emotional regulation, such as serotonin and dopamine [ 46 , 47 ] . In conclusion, middle-aged and elderly asthma patients with depressed symptoms should be monitored for sleep issues and treated to reduce depressive symptoms. Populations with various depression trajectories had diverse demographics in this research. Individualized prevention and control may be applied for groups with specific trajectory aspects. Unmarried, childless, and short-sleeper populations are crucial for depressive symptom prevention and treatment, according to a thorough review. Notably, while a significant intergroup difference in gender was observed when analyzing the baseline characteristics of participants in this study, no association between gender and an elevated risk of depressive symptoms was found in the subsequent multivariate logistic regression analysis that adjusted for confounding factors. This result differs somewhat from the findings of previous research. Prior studies have shown that among clinical populations with depression, the incidence rate is significantly higher in women across all age groups, approximately twice that of men [ 48 , 49 ] . Furthermore, compared to male patients, female patients typically exhibit more severe clinical symptoms, higher rates of disease recurrence, and lower quality of life [ 50 – 52 ] .Two primary factors may account for this discrepancy: First, there is an inherent difference in the study samples. Previous research mostly focused on patients with a confirmed diagnosis of depression [ 53 , 54 ] , whereas this study targeted the general population. Multiple large-scale studies have confirmed that no significant gender differences in the level of depressive symptoms are observed in non-clinical samples [ 55 – 57 ] . Second, China exhibits substantial regional disparities in economic development and educational attainment, and men and women differ in how they express depressive symptoms [ 58 , 59 ] . These factors may lead to variations in the public’s awareness and acceptance of mental illnesses, thereby resulting in differences in the assessment of "prevalence rates". All the aforementioned factors may have impacted the validity of the scale assessment results in this study. It uses a sample 9-year longitudinal follow-up dataset from throughout China to improve study reliability and generalizability. Second, it classifies depression trajectories using GBTM (Growth Mixture Modeling). This method clearly shows the range of depressed symptom trajectories and partially explains the link between middle-aged and elderly asthmatics and depressive symptom trajectories. Third, the research used a large Chinese cohort of middle-aged and older persons, making the findings applicable to them. This study also has limitations. First, the assessment of depressive symptoms relies solely on self-reported scales, which may introduce information bias due to subjective factors. Second, the study excluded samples with missing baseline demographic data and those lost to follow-up during the research period, which may lead to selection bias. Third, the sample is mainly composed of Chinese people, which limits the generalization of the results to more diverse populations. Future longitudinal studies involving multicultural and multi-ethnic backgrounds may further improve the applicability of the research results to broader communities. Conclusions There is significant heterogeneity in the developmental trajectories of depressive symptoms among middle-aged and elderly asthma patients in China. Therefore, it is essential to focus on the characteristics of different trajectory subgroups. This focus not only helps identify various risk factors and classify high-risk groups more accurately but also provides a basis for formulating more effective monitoring and intervention strategies, ultimately improving the mental health of this population. Declarations Ethics approval The data utilized in this study were not part of the primary research but were obtained from published or publicly available sources. Ethical clearances for each of the primary studies that contributed to this data may be found in their respective original articles. Consent to participate Not applicable. Consent for publication Not applicable. Conflict of interest The authors declare no conflict of interest. Funding This study was supported by grants from Jiangsu Provincial Research Hospital(YJXYY202204-XKA02) Author Contribution Conceived and designed the research: XH.MWrote the paper: R.Z.Analyzed the data: R.Z. and PY.Z. Revised the paper:R.Z. PY.Z JN.W SW. Z LQ.X Data Availability Data in the article can be obtained from the CHARLS database (https://charls.pku.edu.cn/). The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Reddel HK, Bacharier LB, Bateman ED, Brightling CE, Brusselle GG, Buhl R, et al. Global initiative for asthma strategy 2021: executive summary and rationale for key changes. Am J Respir Crit Care Med. 2022;205:17–35. https://doi.org/10.1164/rccm.202109-2205PP. Porsbjerg C, Melén E, Lehtimäki L, Shaw D. Asthma. Lancet Lond Engl. 2023;401:858–73. https://doi.org/10.1016/S0140-6736(22)02125-0. Stern J, Pier J, Litonjua AA. Asthma epidemiology and risk factors. Semin Immunopathol. 2020;42:5–15. https://doi.org/10.1007/s00281-020-00785-1. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the world health surveys. Lancet Lond Engl. 2007;370:851–8. https://doi.org/10.1016/S0140-6736(07)61415-9. Sun Y, Zhang Y, Bai W, Liu X. Relationship between depression severity and respiratory symptoms in US adults: a national cross-sectional study. Respir Med. 2023;220:107451. https://doi.org/10.1016/j.rmed.2023.107451. Li Y, Jiang Q, Ji Y, Cao C. Anxiety and depression may associate with poorer control and quality of life in adults with asthma. Allergy. 2020;75:1759–62. https://doi.org/10.1111/all.14189. Bardach NS, Neel C, Kleinman LC, McCulloch CE, Thombley R, Zima BT, et al. Depression, anxiety, and emergency department use for asthma. Pediatrics. 2019;144:e20190856. https://doi.org/10.1542/peds.2019-0856. Lin P, Li X, Liang Z, Wang T. Association between depression and mortality in persons with asthma: a population-based cohort study. Allergy Asthma Clin Immunol Off J Can Soc Allergy Clin Immunol. 2022;18:29. https://doi.org/10.1186/s13223-022-00672-4. Han Y-Y, Yan Q, Chen W, Celedón JC. Child maltreatment, anxiety and depression, and asthma among british adults in the UK biobank. Eur Respir J. 2022;60:2103160. https://doi.org/10.1183/13993003.03160-2021. Stubbs MA, Clark VL, Gibson PG, Yorke J, McDonald VM. Associations of symptoms of anxiety and depression with health-status, asthma control, dyspnoea, dysfunction breathing and obesity in people with severe asthma. Respir Res. 2022;23:341. https://doi.org/10.1186/s12931-022-02266-5. Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health. 2013;34:119–38. https://doi.org/10.1146/annurev-publhealth-031912-114409. Elmer J, Jones BL, Nagin DS. Using the beta distribution in group-based trajectory models. BMC Med Res Methodol. 2018;18:152. https://doi.org/10.1186/s12874-018-0620-9. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38. https://doi.org/10.1146/annurev.clinpsy.121208.131413. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43:61–8. https://doi.org/10.1093/ije/dys203. Chen H, Mui AC. Factorial validity of the center for epidemiologic studies depression scale short form in older population in China. Int Psychogeriatr. 2014;26:49–57. https://doi.org/10.1017/S1041610213001701. Cheng S-T, Chan ACM, Fung HH. Factorial structure of a short version of the center for epidemiologic studies depression scale. Int J Geriatr Psychiatry. 2006;21:333–6. https://doi.org/10.1002/gps.1467. Song P, Zha M, Xia W, Zeng C, Zhu Y. Asthma-chronic obstructive pulmonary disease overlap in China: prevalence, associated factors and comorbidities in middle-aged and older adults. Curr Med Res Opin. 2020;36:667–75. https://doi.org/10.1080/03007995.2020.1722082. Jin C, Zhang T, Li Y, Shi W. Early-life exposure to malnutrition from the chinese famine on risk of asthma and chronic obstructive pulmonary disease in adulthood. Front Nutr. 2022;9:848108. https://doi.org/10.3389/fnut.2022.848108. Kiadaliri A, Englund M. Variability in end-of-life healthcare use in patients with osteoarthritis: a population-based matched cohort study. Osteoarthritis Cartilage. 2021;29:1418–25. https://doi.org/10.1016/j.joca.2021.07.001. Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res. 2018;27:2015–23. https://doi.org/10.1177/0962280216673085. Nagin DS, Jones BL, Elmer J. Recent advances in group-based trajectory modeling for clinical research. Annu Rev Clin Psychol. 2024;20:285–305. https://doi.org/10.1146/annurev-clinpsy-081122-012416. Bulloch AG, Williams JV, Lavorato DH, Patten SB. The relationship between major depression and marital disruption is bidirectional. Depress Anxiety. 2009;26:1172–7. https://doi.org/10.1002/da.20618. Marital status and depressive symptoms over time: age and gender variations - LaPierre - 2009 - family relations - wiley online library. https://onlinelibrary.wiley.com/doi/10.1111/j.1741-3729.2009.00562.x. Accessed 13 Oct 2025. Zhai X, Tong HHY, Lam CK, Xing A, Sha Y, Luo G, et al. Association and causal mediation between marital status and depression in seven countries. Nat Hum Behav. 2024;8:2392–405. https://doi.org/10.1038/s41562-024-02033-0. Prince MJ, Beekman AT, Deeg DJ, Fuhrer R, Kivela SL, Lawlor BA, et al. Depression symptoms in late life assessed using the EURO-D scale. Effect of age, gender and marital status in 14 european centres. Br J Psychiatry J Ment Sci. 1999;174:339–45. https://doi.org/10.1192/bjp.174.4.339. Bulloch AGM, Williams JVA, Lavorato DH, Patten SB. The depression and marital status relationship is modified by both age and gender. J Affect Disord. 2017;223:65–8. https://doi.org/10.1016/j.jad.2017.06.007. Buckman JEJ, Saunders R, Stott J, Arundell L-L, O’Driscoll C, Davies MR, et al. Role of age, gender and marital status in prognosis for adults with depression: an individual patient data meta-analysis. Epidemiol Psychiatr Sci. 2021;30:e42. https://doi.org/10.1017/S2045796021000342. Schwarzbach M, Luppa M, Forstmeier S, König H-H, Riedel-Heller SG. Social relations and depression in late life-a systematic review. Int J Geriatr Psychiatry. 2014;29:1–21. https://doi.org/10.1002/gps.3971. Children and the mental health of older adults in China: what matters? | population research and policy review. https://link.springer.com/article/10.1007/s11113-018-9495-z. Accessed 13 Oct 2025. The third age: class, cohort or generation? | ageing & society | cambridge core. https://www.cambridge.org/core/journals/ageing-and-society/article/abs/third-age-class-cohort-or-generation/98008ABD6DFEB30BE2BE1FD62047CA78. Accessed 13 Oct 2025. Poulin J, Deng R, Ingersoll TS, Witt H, Swain M. Perceived family and friend support and the psychological well-being of american and chinese elderly persons. J Cross-Cult Gerontol. 2012;27:305–17. https://doi.org/10.1007/s10823-012-9177-y. Li C, Jiang S, Zhang X. Intergenerational relationship, family social support, and depression among chinese elderly: a structural equation modeling analysis. J Affect Disord. 2019;248:73–80. https://doi.org/10.1016/j.jad.2019.01.032. Gautam R, Saito T, Houde SC, Kai I. Social interactions and depressive symptoms among community dwelling older adults in Nepal: a synergic effect model. Arch Gerontol Geriatr. 2011;53:24–30. https://doi.org/10.1016/j.archger.2010.06.007. Chao SF. Assessing social support and depressive symptoms in older chinese adults: a longitudinal perspective. Aging Ment Health. 2011;15:765–74. https://doi.org/10.1080/13607863.2011.562182. Krause N, Liang J. Stress, social support, and psychological distress among the chinese elderly. J Gerontol. 1993;48:P282-291. https://doi.org/10.1093/geronj/48.6.p282. Zeng Y, Brasher MS, Gu D, Vaupel JW. Older parents benefit more in health outcome from daughters’ than sons’ emotional care in China. J Aging Health. 2016;28:1426–47. https://doi.org/10.1177/0898264315620591. Jacobson NC, Lord KA, Newman MG. Perceived emotional social support in bereaved spouses mediates the relationship between anxiety and depression. J Affect Disord. 2017;211:83–91. https://doi.org/10.1016/j.jad.2017.01.011. Wang C, Liu Z, Chen T, Wang J, Zhang X, Han B. Intergenerational support and depressive symptoms in old age: the difference between urban and rural china. Front Public Health. 2022;10:1007408. https://doi.org/10.3389/fpubh.2022.1007408. Pandey NM, Tripathi RK, Kar SK, Vidya KL, Singh N. Mental health promotion for elderly populations in world health organization south-east Asia region: needs and resource gaps. World J Psychiatry. 2022;12:117–27. https://doi.org/10.5498/wjp.v12.i1.117. Li C, Han Q, Hu J, Han Z, Yang H. Impact of intergenerational support and medical expenditures on depression: evidence from rural older adults in China. Front Public Health. 2022;10:840864. https://doi.org/10.3389/fpubh.2022.840864. Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004;27:1255–73. https://doi.org/10.1093/sleep/27.7.1255. Chunnan L, Shaomei S, Wannian L. The association between sleep and depressive symptoms in US adults: data from the NHANES (2007-2014). Epidemiol Psychiatr Sci. 2022;31:e63. https://doi.org/10.1017/S2045796022000452. Cho HJ, Lavretsky H, Olmstead R, Levin MJ, Oxman MN, Irwin MR. Sleep disturbance and depression recurrence in community-dwelling older adults: a prospective study. Am J Psychiatry. 2008;165:1543–50. https://doi.org/10.1176/appi.ajp.2008.07121882. Peakman G, Karunatilake N, Seynaeve M, Perera G, Aarsland D, Stewart R, et al. Clinical factors associated with progression to dementia in people with late-life depression: A cohort study of patients in secondary care. BMJ Open. 2020;10:e035147. https://doi.org/10.1136/bmjopen-2019-035147. Holding BC, Sundelin T, Schiller H, Åkerstedt T, Kecklund G, Axelsson J. Sleepiness, sleep duration, and human social activity: an investigation into bidirectionality using longitudinal time-use data. Proc Natl Acad Sci U S A. 2020;117:21209–17. https://doi.org/10.1073/pnas.2004535117. Irwin MR, Opp MR. Sleep health: reciprocal regulation of sleep and innate immunity. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2017;42:129–55. https://doi.org/10.1038/npp.2016.148. Weicker H, Strüder HK. Influence of exercise on serotonergic neuromodulation in the brain. Amino Acids. 2001;20:35–47. https://doi.org/10.1007/s007260170064. Parker G, Brotchie H. Gender differences in depression. Int Rev Psychiatry Abingdon Engl. 2010;22:429–36. https://doi.org/10.3109/09540261.2010.492391. Nöbbelin L, Bogren M, Mattisson C, Brådvik L. Incidence of melancholic depression by age of onset and gender in the lundby population, 1947-1997. Eur Arch Psychiatry Clin Neurosci. 2023;273:1163–73. https://doi.org/10.1007/s00406-022-01506-5. Kornstein SG, Schatzberg AF, Thase ME, Yonkers KA, McCullough JP, Keitner GI, et al. Gender differences in chronic major and double depression. J Affect Disord. 2000;60:1–11. https://doi.org/10.1016/s0165-0327(99)00158-5. Young MA, Scheftner WA, Fawcett J, Klerman GL. Gender differences in the clinical features of unipolar major depressive disorder. J Nerv Ment Dis. 1990;178:200–3. https://doi.org/10.1097/00005053-199003000-00008. Williams JB, Spitzer RL, Linzer M, Kroenke K, Hahn SR, deGruy FV, et al. Gender differences in depression in primary care. Am J Obstet Gynecol. 1995;173:654–9. https://doi.org/10.1016/0002-9378(95)90298-8. Seedat S, Scott KM, Angermeyer MC, Berglund P, Bromet EJ, Brugha TS, et al. Cross-national associations between gender and mental disorders in the world health organization world mental health surveys. Arch Gen Psychiatry. 2009;66:785–95. https://doi.org/10.1001/archgenpsychiatry.2009.36. Luppa M, Sikorski C, Luck T, Ehreke L, Konnopka A, Wiese B, et al. Age- and gender-specific prevalence of depression in latest-life--systematic review and meta-analysis. J Affect Disord. 2012;136:212–21. https://doi.org/10.1016/j.jad.2010.11.033. Hammen CL, Padesky CA. Sex differences in the expression of depressive responses on the beck depression inventory. J Abnorm Psychol. 1977;86:609–14. https://doi.org/10.1037//0021-843x.86.6.609. Kim SJ, Lee YJ, Cho S-J, Cho I-H, Lim W, Lim W. Relationship between weekend catch-up sleep and poor performance on attention tasks in korean adolescents. Arch Pediatr Adolesc Med. 2011;165:806–12. https://doi.org/10.1001/archpediatrics.2011.128. Thomas J, Altareb B. Cognitive vulnerability to depression: an exploration of dysfunctional attitudes and ruminative response styles in the United Arab Emirates. Psychol Psychother. 2012;85:117–21. https://doi.org/10.1111/j.2044-8341.2011.02015.x. Wang K, Lu H, Cheung EFC, Neumann DL, Shum DHK, Chan RCK. “female preponderance” of depression in non-clinical populations: a meta-analytic study. Front Psychol. 2016;7:1398. https://doi.org/10.3389/fpsyg.2016.01398. Cavanagh A, Wilson CJ, Kavanagh DJ, Caputi P. Differences in the expression of symptoms in men versus women with depression: a systematic review and meta-analysis. Harv Rev Psychiatry. 2017;25:29–38. https://doi.org/10.1097/HRP.0000000000000128. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Dec, 2025 Reviewers agreed at journal 14 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers invited by journal 05 Dec, 2025 Editor assigned by journal 03 Dec, 2025 Editor invited by journal 14 Nov, 2025 Submission checks completed at journal 14 Nov, 2025 First submitted to journal 14 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8069992","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555964500,"identity":"8c9d3e70-d426-4a76-aec1-bf2506ae7b85","order_by":0,"name":"Rui Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Zhang","suffix":""},{"id":555964502,"identity":"c6411b8f-c107-49ea-8c11-8d4274b25986","order_by":1,"name":"Pengyu Zhou","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Pengyu","middleName":"","lastName":"Zhou","suffix":""},{"id":555964503,"identity":"5cd8415a-5b6a-40ac-801b-8e97d6c2a994","order_by":2,"name":"Jinnan Wu","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Jinnan","middleName":"","lastName":"Wu","suffix":""},{"id":555964504,"identity":"3b91e76b-0d58-416e-b98c-2893e6bc5e6e","order_by":3,"name":"Shuwen Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Shuwen","middleName":"","lastName":"Zhang","suffix":""},{"id":555964505,"identity":"092663b4-4fbb-4e79-b75b-02098f8ec895","order_by":4,"name":"Liqin Xu","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Liqin","middleName":"","lastName":"Xu","suffix":""},{"id":555964506,"identity":"aef3224a-73ec-41d7-b186-a7e918205f50","order_by":5,"name":"Xianghong Ming","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFACHgaGDww2PPz8DSRoYZzBkCYjOeMACVqYORgO2xg0JBCpQXdG7gFmxrbzPAYMBxg/fMwhQovZmXMJzIVtt3nMmRuYJWduI0bL8R4D5plALZYNB9iYeYnScpjHgJm37RyPwYEEYrWAbOFtO0CKljNnDBhnnEvmkZxxsJlIv9zIMWD4UGZnz8/ffPDDR2K0AAH7D0Y2EM3YQJx6CPhDiuJRMApGwSgYcQAAyII0pcYN5A4AAAAASUVORK5CYII=","orcid":"","institution":"Medical School of Nantong University","correspondingAuthor":true,"prefix":"","firstName":"Xianghong","middleName":"","lastName":"Ming","suffix":""}],"badges":[],"createdAt":"2025-11-09 15:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8069992/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8069992/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97811942,"identity":"c907802e-9aa0-407c-baa5-5474e106fbe9","added_by":"auto","created_at":"2025-12-09 16:05:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":491086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/ae8d300ad437730278741ccb.docx"},{"id":97811937,"identity":"0320782b-8c8d-49ad-9450-d679c6ed3d3c","added_by":"auto","created_at":"2025-12-09 16:05:36","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7350,"visible":true,"origin":"","legend":"","description":"","filename":"7f068323eb0441bdb1de06e29dfa98f3.json","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/6e8c2e1eae0128fe2d227253.json"},{"id":97898754,"identity":"23fb5c3c-2f64-4ffa-88d1-9040bf8d976b","added_by":"auto","created_at":"2025-12-10 15:39:34","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150385,"visible":true,"origin":"","legend":"","description":"","filename":"7f068323eb0441bdb1de06e29dfa98f31enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/1c258b422b6a8ca079145a84.xml"},{"id":97898412,"identity":"c1fe2ea3-d02a-424c-bf59-9919125d155c","added_by":"auto","created_at":"2025-12-10 15:39:08","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5685354,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/14491e258e0b5039f45b873b.jpeg"},{"id":97897479,"identity":"0d4fa9a7-bade-4460-abce-75b70e007d29","added_by":"auto","created_at":"2025-12-10 15:37:52","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":349197,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/6ba9d3f94bae63e3c776de3a.jpeg"},{"id":97811940,"identity":"ba9a34ae-6f33-45ab-81ef-1e8cdcb4045e","added_by":"auto","created_at":"2025-12-09 16:05:37","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":58334,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/af5126b69e3711e4a29f97d6.png"},{"id":97898630,"identity":"2cb76800-7191-4cd3-bae0-7ba45c618c5b","added_by":"auto","created_at":"2025-12-10 15:39:23","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47421,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/3ea417ef7bbcfea9d9525646.png"},{"id":97811946,"identity":"4ccd649c-045e-4fae-87a9-e0185f935241","added_by":"auto","created_at":"2025-12-09 16:05:37","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147889,"visible":true,"origin":"","legend":"","description":"","filename":"7f068323eb0441bdb1de06e29dfa98f31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/74eb18bd126fc816fa891948.xml"},{"id":97811948,"identity":"9ab4283d-6675-423f-91e3-a3926402b200","added_by":"auto","created_at":"2025-12-09 16:05:37","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158510,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/fcfad411d88591826f12a9ca.html"},{"id":97811939,"identity":"f163dafc-3ab5-48ed-8b8b-55dfb316d410","added_by":"auto","created_at":"2025-12-09 16:05:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":246318,"visible":true,"origin":"","legend":"\u003cp\u003eStudy participant screening process.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/1313989a1e79814d7ed0b6d1.png"},{"id":97898225,"identity":"28170f63-961c-4b6f-98ea-5b291d355e6d","added_by":"auto","created_at":"2025-12-10 15:38:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":431237,"visible":true,"origin":"","legend":"\u003cp\u003eChange track of subjective well-being of the elderly.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/1d416b210c1a9fd6d4f22441.png"},{"id":97903261,"identity":"9ce44621-7472-4519-b577-262ab139a8fc","added_by":"auto","created_at":"2025-12-10 15:54:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1751215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8069992/v1/860dcb0d-6598-4f7e-84f1-831f19b6fb3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Depression Trajectories and Asthma Risk: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAsthma is one of the most prevalent chronic respiratory diseases worldwide, characterized primarily by airway inflammation, airway remodeling, increased airway responsiveness, and reversible airflow limitation\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to the survey by the World Health Organization (WHO) in2019, the projected global number of people living with asthma reached 262\u0026nbsp;million, with the disease causing 455,000 deaths annually\u0026mdash;imposing a heavy burden on the economies and public health systems of countries across the world\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. While asthma care and preventative techniques have been developed to lower prevalence and mortality, the worldwide burden of asthma remains a major concern.\u003c/p\u003e\u003cp\u003eAlthough there has been much study on asthma, its specific pathophysiology is not well understood. Multiple epidemiological studies have confirmed that depression is a significant risk factor for asthma exacerbations, and there is a bidirectional relationship between the two conditions: on the one hand, depressive symptoms can interfere with asthma control and lower patients' quality of life; on the other hand, recurrent asthma exacerbations can exacerbate patients' psychological burden and increase their risk of developing depression. For instance, a study by Moussavi et al. indicated that the prevalence of depression among asthma patients is as high as 18.1%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Another study based on NHANES data found that compared with non-depressed individuals, the risk of asthma in people with severe depression is significantly increased by 2.4 times (OR\u0026thinsp;=\u0026thinsp;2.41, 95% CI: 1.37\u0026ndash;4.24); additionally, a higher depression score correlates with more severe depressive symptoms, which in turn elevates the risk of respiratory symptoms\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.Furthermore, the presence of depression exerts a negative impact on asthma prognosis, contributing to a higher rate of asthma exacerbations, accelerated decline in lung function, and increased mortality\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Asthma and depressive symptoms are intertwined and interact reciprocally, severely compromising asthma prognosis and reducing patients\u0026rsquo; quality of life. Therefore, enhancing the assessment of asthma-depression comorbidity is of great significance for advancing relevant public health prevention and control measures and improving patients\u0026rsquo; health outcomes.\u003c/p\u003e\u003cp\u003ePrevious studies on depressive symptoms and asthma have rarely considered individual characteristics of changes in depressive symptoms. They also treated depressive status at a single time point as the exposure factor, ignoring its variability or reversibility, which prevents a comprehensive assessment of the potential complex relationship between depression and asthma\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. Long-term examination of depression levels is necessary to determine the relationship between depressed trajectory changes and asthma and to better understand when symptoms rise or diminish.\u003c/p\u003e\u003cp\u003eAs a finite mixture modeling technique, GBTM studies dynamically capture long-term trends in depressive symptom changes. They simultaneously estimate multiple trajectories, accurately identify subgroups with distinct depressive development trajectories within the study population, fully reveal the heterogeneity of individual trajectories, and uncover long-term patterns of depressive symptoms in the population more comprehensively and deeply\u0026mdash;providing a more precise basis for subgroup classification in subsequent targeted intervention\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on this, this study uses longitudinal data from CHARLS to describe the distinct trajectory patterns of depressive symptoms and their heterogeneity among middle-aged and elderly Chinese asthmatics. It further classifies these depressive symptom trajectories, explores the relationship between these patterns and asthma, and aims to provide theoretical basis and practical guidance for the management of depressive symptoms as well as the treatment and prevention of asthma.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study were derived from the China Health and Retirement Longitudinal Study (CHARLS), a national longitudinal survey focusing on the health and socioeconomic status of Chinese middle-aged and elderly people aged 45 years and above. Adopting a scientific multistage stratified random sampling strategy, the project launched its baseline survey across 28 provinces in China from June 2011 to March 2012, covering 150 county-level units and 450 villages/urban communities, with a total of 17,708 participants enrolled. Following the baseline survey, follow-up investigations have been conducted every two years, with data collection completed for 2013, 2015, and 2018; currently, the follow-up work is ongoing until 2020\u003csup\u003e[14]\u003c/sup\u003e. The CHARLS project has obtained approval from the Biomedical Ethics Committee of Peking University(IRB00001052-11015), and all participants have signed written informed consent forms. Details regarding its study design and questionnaire have been published in other literatures.\u003c/p\u003e\n\u003cp\u003eIn this manuscript, we\u0026nbsp;utilized four waves of data from the China Health and Retirement Longitudinal Study (CHARLS), spanning 2011 (Wave 1) to 2018 (Wave 4), to systematically examine the trajectories of depressive symptoms among middle-aged and elderly patients with asthma. An initial pool of 17,708 individuals was included. After quality control, exclusions were made for the following groups: 777 participants under 45 years old or with missing basic information; 16,176 non-asthma patients or those with incomplete asthma-related data; 90 individuals lacking sociodemographic variables. Additionally, 4 participants had missing data on depressive symptoms at Wave 1, while another 177 individuals lacked relevant depressive symptom information during the follow-up period. A final analytical cohort of 484 participants was obtained after all exclusions\u003cstrong\u003e(Fig. 1)\u003c/strong\u003e. Detailed exclusion criteria are available in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Epidemiological Studies Depression Scale (CES-D10) measured depression. \u0026nbsp;Studies in Chinese people have validated this scale\u0026apos;s reliability and validity[15,16]. The 10-item CESD-10 assesses previous week\u0026apos;s depressed symptoms. Using a 4-point Likert scale (0 to 3), answers to all 10 items were added to get the total score, which ranged from 0 to 30. \u0026nbsp;Higher overall scores indicate more severe depression. \u0026nbsp;Using a 12-point threshold, respondents with a total score of 12 or more were classed as depressed (coded as 1), while those with scores below 12 were classified as non-depressed (coded as 0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAsthma assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study drew on the validated CHARLS baseline questionnaire, with self-reported doctor-diagnosed asthma selected as the core outcome of interest. Assessment of this outcome was based on the questionnaire query: \u0026ldquo;Have you ever been diagnosed with asthma by a doctor?\u0026rdquo; [17,18]. Participants who answered \u0026ldquo;Yes\u0026rdquo; were identified as asthma patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included sociodemographic characteristics and health-related behaviors as covariates in the analysis. The former comprised age, sex, residence type (urban/rural), educational level (illiterate, primary school or below, secondary to vocational school, university or above), and marital status (married, divorced, unmarried); the latter covered smoking status (yes/no), alcohol consumption patterns, and sleep duration. For this study, smoking was defined as lifetime consumption of over 100 cigarettes: ex-smokers (previously smoked but quit) and current smokers were both grouped as \u0026quot;smokers\u0026quot; in analysis. Alcohol consumption was categorized into three tiers: (1) \u0026gt;1 time/month, (2) \u0026lt;1 time/month, (3) no consumption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup-based trajectory modeling (GBTM) was applied to identify potential trajectories of depressive symptoms observed between 2011 and 2018. This model proceeds on the assumption that heterogeneous trajectories exist within the study population, leveraging maximum likelihood estimation to detect clusters of individuals who share similar developmental patterns of depressive symptoms [13,19]. Concurrently, it estimates the probabilities associated with multiple trajectories, and participants are grouped according to their posterior probabilities of belonging to each specific trajectory. Model fit was evaluated using four key metrics: Bayesian Information Criterion (BIC), average posterior probability of assignment (AvePP), and Group proportion . Specifically, BIC values that are closer to zero signify a superior model fit; an AvePP exceeding 0.7 across all trajectory groups indicates acceptable certainty in participant assignment; Additionally, a minimum of 5% of the total sample was required for each trajectory group to ensure statistical robustness [20]. Potential confounding factors in this study included sociodemographic variables\u0026mdash;age, gender, marital status, residence, and education level\u0026mdash;and health-related factors, namely smoking status, drinking status, and sleep duration, all of which were accounted for in the analyses.\u003c/p\u003e\n\u003cp\u003eFor the group-based trajectory modeling (GBTM) analysis, the Traj plugin within Stata 17 software was utilized[21]. All remaining statistical analyses were carried out using R 4.2.2. A two-tailed test was applied for all statistical assessments, with statistical significance determined by a P-value of less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDepressive trajectory modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe track of depressive symptoms was fitted according to different groups (1 ~ 4 groups) and different function forms (intercept, linearity, square, cubic). By comparing BIC and AvePP, the optimal number of trajectory groups is 3 groups (BIC = -5780.53), and 2, 3, 1 as polynomial orders, and the optimal combination of trajectory types of each group is obtained. The trajectory model parameters are shown in\u003cstrong\u003e\u0026nbsp;Table 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe specific characteristics of each group regarding the change trajectories of depressive symptoms in middle-aged and elderly individuals are shown in \u003cstrong\u003eFig. 2\u003c/strong\u003e.\u0026nbsp;Group 1 (n=240, 49.6%) had a low average depression score at baseline. It remained slowly decreasing in the early stage of the survey and then started to show a slow upward trend from 2015. Therefore, this group was named the \u0026quot;Low-stable depressive symptoms group\u0026quot;. Compared with Group 1, the middle-aged and elderly participants in Group 3 (n=50, 10.3%) saw a sharp rise starting from a relatively high baseline, displaying a trajectory of rapid growth. This group has therefore been designated as the \u0026quot;High-Growth Group.\u0026quot; While Group 2 (n = 194, 40.1%) had an average score at a moderate level, which fluctuated over time, so it was named the \u0026quot;Middle-fluctuating group\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Goodness-of-fit statistics of group-based trajectory analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of groups\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrajectory shape\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIC for total number of observations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup proportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage posterior probabilities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5822.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.56/39.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94/0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 3 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5788.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.92/40.87/11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91/0.87/0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 3 3 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5791.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.44/26.06/18.03/11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89/0.71/0.74/0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 3 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5780.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.31/40.96/10.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91/0.87/0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline characteristics of trajectory groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows baseline characteristics of study participants per depression trajectory group. \u0026nbsp; Age (p=0.910) and education (p=0.476) were similar among the three groups. \u0026nbsp;Gender differences were significant (p\u0026lt;0.001), with women making up 34.6% of the low-stable group, 47.4% of the middle-fluctuating group, and 60.0% of the high-increasing group. \u0026nbsp;Marriage status also differed (p=0.023), with married people making up 89.6%, 80.9%, and 80.0% of the groups. \u0026nbsp;Non-smokers made up 42.5%, 54.1%, and 66.0% of groups (p=0.003). \u0026nbsp; Drinking behavior varied significantly (p\u0026lt;0.001), with non-drinkers comprising 47.9%, 56.2%, and 80.0% of the groups. \u0026nbsp;The depression trajectory groups showed substantial variations in sleep duration (p\u0026lt;0.001), with 48.3%, 65.5%, and 82.0% sleeping fewer than 7 hours. \u0026nbsp;No significant differences were seen in living situation (p=0.295), child interaction (p=0.114), or social activity engagement (p=0.140).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Patient demographics and baseline characteristics\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-stable\u003cbr\u003e\u0026nbsp;N = 240\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle-fluctuating\u003cbr\u003e\u0026nbsp;N = 194\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-increasing\u003cbr\u003e\u0026nbsp;N = 50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026lt; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e95 (39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e75 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e21 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026ge; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e145 (60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e119 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e29 (58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e157 (65.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e102 (52.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e20 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e83 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e92 (47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e30 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHigh school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e230 (95.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e190 (97.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e49 (98.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e10 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e4 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eUrban Community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e85 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e64 (33.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e12 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eRural Village\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e155 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e130 (67.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e38 (76.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e215 (89.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e157 (80.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e40 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNon-married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e37 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e10 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContact Kids, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e23 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e30 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e217 (90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e164 (84.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e46 (92.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipate social activities, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e114 (47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e103 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e31 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e126 (52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e91 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e19 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoke status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e102 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e105 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e33 (66.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e138 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e89 (45.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e17 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrink status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e115 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e109 (56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e40 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e125 (52.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e85 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e10 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026lt; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e116 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e127 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e41 (82.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026ge; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e124 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e67 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9 (18.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between asthma and depressive trajectories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the \u0026ldquo;Low-stable\u0026rdquo; group as a reference(Table 3). Multivariate logistic regression analysis showed that in the moderate fluctuation group, \u0026ldquo;Non-Married\u0026rdquo; (OR=1.93, P=0.026), \u0026ldquo;No contact with\u0026nbsp;adult children\u0026rdquo; (OR=1.84, P=0.049), and \u0026ldquo;Sleeping less than 7 hours\u0026rdquo; (OR=2.07, P\u0026lt;0.001) significantly increased the risk.\u0026nbsp;Factors related to insufficient sleep suggest an association with an increased likelihood of the \u0026quot;high-increasing\u0026quot; trajectory. The results showed that \u0026quot;Sleeping less than 7 hours\u0026quot; could significantly increase the risk of onset in the high-level growth group(OR=5.133,\u0026nbsp;P\u0026lt;0.001).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e. Results of multiple logistic regression on the factors associated with outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle-fluctuating\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-increasing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.073 (0.709~1.624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.062 (0.539~2.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.307 (0.736~2.320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.129 (0.440~2.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHigh school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.471 (0.430~5.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.192 (0.128~11.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUrban Community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRural Village\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.047 (0.686~1.599)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.809 (0.851~3.842)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNon-Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.927 (1.084~3.426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.012 (0.839~4.829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eContact Kids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eContact Kids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.839 (1.004~3.369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.896 (0.281~2.852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipate social activities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipate social activities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.196 (0.805~1.778)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.887 (0.969~3.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoke status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoke status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.775 (0.450~1.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.693 (0.275~1.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinks status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinks status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.913 (0.582~1.433)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.280 (0.120~0.652)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026ge;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.074 (1.390~3.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.133 (2.326~11.328)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eA nationally representative sample of 484 asthmatic middle-aged and elderly (45 years and older) Chinese patients was evaluated for depressed trajectories. We found three depressed symptom trajectories over 9 years: low-stable, middle-fluctuating, and high-increasing. Depression may increase with unmarried status, no adult children, and little sleep. Depression prevention and therapy should target high-increasing populations. Early detection and tailored therapy may reduce depression. This research demonstrated varied depressed symptom development in middle-aged and elderly asthma patients in China. This shows that these diverse symptom trajectories might be used to develop differentiated policies and interventions to promote asthma patients' mental health.\u003c/p\u003e\u003cp\u003eThis study further explores the factors influencing the trajectories of depressive symptoms among middle-aged and elderly asthma patients. Analysis of participants' baseline characteristics showed significant intergroup differences in gender, marital status, smoking and drinking status, and sleep duration. Results from multiple logistic regression analysis then show that unmarried, having no contact with adult children, and short sleep duration all significantly affect depressive symptoms in this population (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIn terms of demographic characteristics, being male, married, and having contact with children act as protective factors against depressive symptoms in middle-aged and elderly asthma patients. Studies have identified marital status as one of the key social factors influencing depression, with married individuals generally exhibiting better mental health than those who are single, widowed, separated, or divorced\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.Empirical evidence supports this: a cross-country, two-stage analysis by Zhai et al\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, using nationally representative data from seven countries, confirmed that unmarried individuals face a significantly higher risk of depressive symptoms than married counterparts. Similarly, a cross-sectional study on Europeans aged 65 and above highlighted that being unmarried is a major risk factor for late-life depression\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.Mechanistically, spouses serve as the core source of social support for married individuals, playing a crucial role in safeguarding the mental health of older adults\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This helps explain why singles are more depressed. Adult depression prognoses differ by marital status. Unmarried participants reported more severe depression symptoms 3\u0026ndash;4 months after baseline evaluation in a European meta-analysis, suggesting a worse prognosis\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWithin the social support system, intergenerational support serves as a key protective factor against depression in older adults\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Intergenerational relationships refer to the interactive bonds between adjacent generations, particularly between older adults and their adult children\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Given that adult children often act as primary caregivers for their elderly parents, the link between intergenerational support and depressive symptoms holds special significance for safeguarding the mental health of older adults\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Studies have shown a significant correlation between intergenerational support and reduced depressive symptoms in this population\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, with its effects primarily exerted through three dimensions: economic support, emotional support, and care support\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Emotional support effectively alleviates loneliness among older adults, enhances their life satisfaction and mental well-being\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, emotional comfort and practical assistance from family members reduce the occurrence of psychological distress, thereby lowering the severity of depression\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.Economic support eases the burden of expenses for older adults\u0026mdash;such as medical costs, housing fees, and daily living expenditures\u0026mdash;alleviating anxiety caused by life pressures. This provides a sense of stability and happiness, reducing the likelihood of developing depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Care support, as a vital form of direct guarantee for older adults\u0026rsquo; quality of life, also plays a positive role in relieving depressive emotions\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.Synthesizing the above research evidence, the nature and quality of interactions between older adults and their adult children exert a significant impact on their mental health in later life.\u003c/p\u003e\u003cp\u003eDepression develops with age, while middle-aged and elderly people's ideal sleep length decreases\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. In our study, we found that compared with middle-aged and elderly patients with low-stable, individuals in both middle-fluctuating group and high-increasing group with a significant reduction in sleep duration who slept less than 7 hours had a significantly higher risk of developing depressive symptoms. The association between sleep disorders and depression has been well established in previous research. A study from the U.S. National Health and Nutrition Examination Survey (NHANES) indicated that short sleep duration is linked to depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Furthermore, among individuals with sleep disorders, the proportion of elderly people experiencing depressive symptoms is significantly higher than that of young people\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. It is generally accepted that sleep disorders typically precede depression: sleep disruption exacerbates depressive symptoms, which in turn impair sleep quality, and this bidirectional interaction may form a vicious cycle\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. However, due to the complexity of the relationship between sleep and depression, the exact mechanism by which sleep disorders lead to depression remains to be further elucidated. Psychosocially, decreasing sleep duration causes daytime drowsiness and exhaustion, which may lead to less social activity and more social isolation and loneliness\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Both factors are critical risk factors for depression. From a physiological standpoint, sleep disorders may elevate the risk of depression through two pathways: first, by altering neural sensitivity to inflammation, and second, by disrupting key neurotransmitter systems involved in emotional regulation, such as serotonin and dopamine\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. In conclusion, middle-aged and elderly asthma patients with depressed symptoms should be monitored for sleep issues and treated to reduce depressive symptoms. Populations with various depression trajectories had diverse demographics in this research. Individualized prevention and control may be applied for groups with specific trajectory aspects. Unmarried, childless, and short-sleeper populations are crucial for depressive symptom prevention and treatment, according to a thorough review.\u003c/p\u003e\u003cp\u003eNotably, while a significant intergroup difference in gender was observed when analyzing the baseline characteristics of participants in this study, no association between gender and an elevated risk of depressive symptoms was found in the subsequent multivariate logistic regression analysis that adjusted for confounding factors. This result differs somewhat from the findings of previous research. Prior studies have shown that among clinical populations with depression, the incidence rate is significantly higher in women across all age groups, approximately twice that of men\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Furthermore, compared to male patients, female patients typically exhibit more severe clinical symptoms, higher rates of disease recurrence, and lower quality of life\u003csup\u003e[\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e.Two primary factors may account for this discrepancy: First, there is an inherent difference in the study samples. Previous research mostly focused on patients with a confirmed diagnosis of depression\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e, whereas this study targeted the general population. Multiple large-scale studies have confirmed that no significant gender differences in the level of depressive symptoms are observed in non-clinical samples\u003csup\u003e[\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Second, China exhibits substantial regional disparities in economic development and educational attainment, and men and women differ in how they express depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. These factors may lead to variations in the public\u0026rsquo;s awareness and acceptance of mental illnesses, thereby resulting in differences in the assessment of \"prevalence rates\". All the aforementioned factors may have impacted the validity of the scale assessment results in this study.\u003c/p\u003e\u003cp\u003eIt uses a sample 9-year longitudinal follow-up dataset from throughout China to improve study reliability and generalizability. Second, it classifies depression trajectories using GBTM (Growth Mixture Modeling). This method clearly shows the range of depressed symptom trajectories and partially explains the link between middle-aged and elderly asthmatics and depressive symptom trajectories. Third, the research used a large Chinese cohort of middle-aged and older persons, making the findings applicable to them.\u003c/p\u003e\u003cp\u003eThis study also has limitations. First, the assessment of depressive symptoms relies solely on self-reported scales, which may introduce information bias due to subjective factors. Second, the study excluded samples with missing baseline demographic data and those lost to follow-up during the research period, which may lead to selection bias. Third, the sample is mainly composed of Chinese people, which limits the generalization of the results to more diverse populations. Future longitudinal studies involving multicultural and multi-ethnic backgrounds may further improve the applicability of the research results to broader communities.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere is significant heterogeneity in the developmental trajectories of depressive symptoms among middle-aged and elderly asthma patients in China. Therefore, it is essential to focus on the characteristics of different trajectory subgroups. This focus not only helps identify various risk factors and classify high-risk groups more accurately but also provides a basis for formulating more effective monitoring and intervention strategies, ultimately improving the mental health of this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThe data utilized in this study were not part of the primary research but were obtained from published or publicly available sources. Ethical clearances for each of the primary studies that contributed to this data may be found in their respective original articles.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by grants from Jiangsu Provincial Research Hospital(YJXYY202204-XKA02)\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceived and designed the research: XH.MWrote the paper: R.Z.Analyzed the data: R.Z. and PY.Z. Revised the paper:R.Z. PY.Z JN.W SW. Z LQ.X\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData in the article can be obtained from the CHARLS database (https://charls.pku.edu.cn/). The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eReddel HK, Bacharier LB, Bateman ED, Brightling CE, Brusselle GG, Buhl R, et al. Global initiative for asthma strategy 2021: executive summary and rationale for key changes. Am J Respir Crit Care Med. 2022;205:17\u0026ndash;35. https://doi.org/10.1164/rccm.202109-2205PP.\u003c/li\u003e\n\u003cli\u003ePorsbjerg C, Mel\u0026eacute;n E, Lehtim\u0026auml;ki L, Shaw D. Asthma. Lancet Lond Engl. 2023;401:858\u0026ndash;73. https://doi.org/10.1016/S0140-6736(22)02125-0.\u003c/li\u003e\n\u003cli\u003eStern J, Pier J, Litonjua AA. Asthma epidemiology and risk factors. Semin Immunopathol. 2020;42:5\u0026ndash;15. https://doi.org/10.1007/s00281-020-00785-1.\u003c/li\u003e\n\u003cli\u003eMoussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the world health surveys. Lancet Lond Engl. 2007;370:851\u0026ndash;8. https://doi.org/10.1016/S0140-6736(07)61415-9.\u003c/li\u003e\n\u003cli\u003eSun Y, Zhang Y, Bai W, Liu X. Relationship between depression severity and respiratory symptoms in US adults: a national cross-sectional study. Respir Med. 2023;220:107451. https://doi.org/10.1016/j.rmed.2023.107451.\u003c/li\u003e\n\u003cli\u003eLi Y, Jiang Q, Ji Y, Cao C. Anxiety and depression may associate with poorer control and quality of life in adults with asthma. Allergy. 2020;75:1759\u0026ndash;62. https://doi.org/10.1111/all.14189.\u003c/li\u003e\n\u003cli\u003eBardach NS, Neel C, Kleinman LC, McCulloch CE, Thombley R, Zima BT, et al. Depression, anxiety, and emergency department use for asthma. Pediatrics. 2019;144:e20190856. https://doi.org/10.1542/peds.2019-0856.\u003c/li\u003e\n\u003cli\u003eLin P, Li X, Liang Z, Wang T. Association between depression and mortality in persons with asthma: a population-based cohort study. Allergy Asthma Clin Immunol Off J Can Soc Allergy Clin Immunol. 2022;18:29. https://doi.org/10.1186/s13223-022-00672-4.\u003c/li\u003e\n\u003cli\u003eHan Y-Y, Yan Q, Chen W, Celed\u0026oacute;n JC. Child maltreatment, anxiety and depression, and asthma among british adults in the UK biobank. Eur Respir J. 2022;60:2103160. https://doi.org/10.1183/13993003.03160-2021.\u003c/li\u003e\n\u003cli\u003eStubbs MA, Clark VL, Gibson PG, Yorke J, McDonald VM. Associations of symptoms of anxiety and depression with health-status, asthma control, dyspnoea, dysfunction breathing and obesity in people with severe asthma. Respir Res. 2022;23:341. https://doi.org/10.1186/s12931-022-02266-5.\u003c/li\u003e\n\u003cli\u003eKessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health. 2013;34:119\u0026ndash;38. https://doi.org/10.1146/annurev-publhealth-031912-114409.\u003c/li\u003e\n\u003cli\u003eElmer J, Jones BL, Nagin DS. Using the beta distribution in group-based trajectory models. BMC Med Res Methodol. 2018;18:152. https://doi.org/10.1186/s12874-018-0620-9.\u003c/li\u003e\n\u003cli\u003eNagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109\u0026ndash;38. https://doi.org/10.1146/annurev.clinpsy.121208.131413.\u003c/li\u003e\n\u003cli\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43:61\u0026ndash;8. https://doi.org/10.1093/ije/dys203.\u003c/li\u003e\n\u003cli\u003eChen H, Mui AC. Factorial validity of the center for epidemiologic studies depression scale short form in older population in China. Int Psychogeriatr. 2014;26:49\u0026ndash;57. https://doi.org/10.1017/S1041610213001701.\u003c/li\u003e\n\u003cli\u003eCheng S-T, Chan ACM, Fung HH. Factorial structure of a short version of the center for epidemiologic studies depression scale. Int J Geriatr Psychiatry. 2006;21:333\u0026ndash;6. https://doi.org/10.1002/gps.1467.\u003c/li\u003e\n\u003cli\u003eSong P, Zha M, Xia W, Zeng C, Zhu Y. Asthma-chronic obstructive pulmonary disease overlap in China: prevalence, associated factors and comorbidities in middle-aged and older adults. Curr Med Res Opin. 2020;36:667\u0026ndash;75. https://doi.org/10.1080/03007995.2020.1722082.\u003c/li\u003e\n\u003cli\u003eJin C, Zhang T, Li Y, Shi W. Early-life exposure to malnutrition from the chinese famine on risk of asthma and chronic obstructive pulmonary disease in adulthood. Front Nutr. 2022;9:848108. https://doi.org/10.3389/fnut.2022.848108.\u003c/li\u003e\n\u003cli\u003eKiadaliri A, Englund M. Variability in end-of-life healthcare use in patients with osteoarthritis: a population-based matched cohort study. Osteoarthritis Cartilage. 2021;29:1418\u0026ndash;25. https://doi.org/10.1016/j.joca.2021.07.001.\u003c/li\u003e\n\u003cli\u003eNagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res. 2018;27:2015\u0026ndash;23. https://doi.org/10.1177/0962280216673085.\u003c/li\u003e\n\u003cli\u003eNagin DS, Jones BL, Elmer J. Recent advances in group-based trajectory modeling for clinical research. Annu Rev Clin Psychol. 2024;20:285\u0026ndash;305. https://doi.org/10.1146/annurev-clinpsy-081122-012416.\u003c/li\u003e\n\u003cli\u003eBulloch AG, Williams JV, Lavorato DH, Patten SB. The relationship between major depression and marital disruption is bidirectional. Depress Anxiety. 2009;26:1172\u0026ndash;7. https://doi.org/10.1002/da.20618.\u003c/li\u003e\n\u003cli\u003eMarital status and depressive symptoms over time: age and gender variations - LaPierre - 2009 - family relations - wiley online library. https://onlinelibrary.wiley.com/doi/10.1111/j.1741-3729.2009.00562.x. Accessed 13 Oct 2025.\u003c/li\u003e\n\u003cli\u003eZhai X, Tong HHY, Lam CK, Xing A, Sha Y, Luo G, et al. Association and causal mediation between marital status and depression in seven countries. Nat Hum Behav. 2024;8:2392\u0026ndash;405. https://doi.org/10.1038/s41562-024-02033-0.\u003c/li\u003e\n\u003cli\u003ePrince MJ, Beekman AT, Deeg DJ, Fuhrer R, Kivela SL, Lawlor BA, et al. Depression symptoms in late life assessed using the EURO-D scale. Effect of age, gender and marital status in 14 european centres. Br J Psychiatry J Ment Sci. 1999;174:339\u0026ndash;45. https://doi.org/10.1192/bjp.174.4.339.\u003c/li\u003e\n\u003cli\u003eBulloch AGM, Williams JVA, Lavorato DH, Patten SB. The depression and marital status relationship is modified by both age and gender. J Affect Disord. 2017;223:65\u0026ndash;8. https://doi.org/10.1016/j.jad.2017.06.007.\u003c/li\u003e\n\u003cli\u003eBuckman JEJ, Saunders R, Stott J, Arundell L-L, O\u0026rsquo;Driscoll C, Davies MR, et al. Role of age, gender and marital status in prognosis for adults with depression: an individual patient data meta-analysis. Epidemiol Psychiatr Sci. 2021;30:e42. https://doi.org/10.1017/S2045796021000342.\u003c/li\u003e\n\u003cli\u003eSchwarzbach M, Luppa M, Forstmeier S, K\u0026ouml;nig H-H, Riedel-Heller SG. Social relations and depression in late life-a systematic review. Int J Geriatr Psychiatry. 2014;29:1\u0026ndash;21. https://doi.org/10.1002/gps.3971.\u003c/li\u003e\n\u003cli\u003eChildren and the mental health of older adults in China: what matters? | population research and policy review. https://link.springer.com/article/10.1007/s11113-018-9495-z. Accessed 13 Oct 2025.\u003c/li\u003e\n\u003cli\u003eThe third age: class, cohort or generation? | ageing \u0026amp; society | cambridge core. https://www.cambridge.org/core/journals/ageing-and-society/article/abs/third-age-class-cohort-or-generation/98008ABD6DFEB30BE2BE1FD62047CA78. Accessed 13 Oct 2025.\u003c/li\u003e\n\u003cli\u003ePoulin J, Deng R, Ingersoll TS, Witt H, Swain M. Perceived family and friend support and the psychological well-being of american and chinese elderly persons. J Cross-Cult Gerontol. 2012;27:305\u0026ndash;17. https://doi.org/10.1007/s10823-012-9177-y.\u003c/li\u003e\n\u003cli\u003eLi C, Jiang S, Zhang X. Intergenerational relationship, family social support, and depression among chinese elderly: a structural equation modeling analysis. J Affect Disord. 2019;248:73\u0026ndash;80. https://doi.org/10.1016/j.jad.2019.01.032.\u003c/li\u003e\n\u003cli\u003eGautam R, Saito T, Houde SC, Kai I. Social interactions and depressive symptoms among community dwelling older adults in Nepal: a synergic effect model. Arch Gerontol Geriatr. 2011;53:24\u0026ndash;30. https://doi.org/10.1016/j.archger.2010.06.007.\u003c/li\u003e\n\u003cli\u003eChao SF. Assessing social support and depressive symptoms in older chinese adults: a longitudinal perspective. Aging Ment Health. 2011;15:765\u0026ndash;74. https://doi.org/10.1080/13607863.2011.562182.\u003c/li\u003e\n\u003cli\u003eKrause N, Liang J. Stress, social support, and psychological distress among the chinese elderly. J Gerontol. 1993;48:P282-291. https://doi.org/10.1093/geronj/48.6.p282.\u003c/li\u003e\n\u003cli\u003eZeng Y, Brasher MS, Gu D, Vaupel JW. Older parents benefit more in health outcome from daughters\u0026rsquo; than sons\u0026rsquo; emotional care in China. J Aging Health. 2016;28:1426\u0026ndash;47. https://doi.org/10.1177/0898264315620591.\u003c/li\u003e\n\u003cli\u003eJacobson NC, Lord KA, Newman MG. Perceived emotional social support in bereaved spouses mediates the relationship between anxiety and depression. J Affect Disord. 2017;211:83\u0026ndash;91. https://doi.org/10.1016/j.jad.2017.01.011.\u003c/li\u003e\n\u003cli\u003eWang C, Liu Z, Chen T, Wang J, Zhang X, Han B. Intergenerational support and depressive symptoms in old age: the difference between urban and rural china. Front Public Health. 2022;10:1007408. https://doi.org/10.3389/fpubh.2022.1007408.\u003c/li\u003e\n\u003cli\u003ePandey NM, Tripathi RK, Kar SK, Vidya KL, Singh N. Mental health promotion for elderly populations in world health organization south-east Asia region: needs and resource gaps. World J Psychiatry. 2022;12:117\u0026ndash;27. https://doi.org/10.5498/wjp.v12.i1.117.\u003c/li\u003e\n\u003cli\u003eLi C, Han Q, Hu J, Han Z, Yang H. Impact of intergenerational support and medical expenditures on depression: evidence from rural older adults in China. Front Public Health. 2022;10:840864. https://doi.org/10.3389/fpubh.2022.840864.\u003c/li\u003e\n\u003cli\u003eOhayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004;27:1255\u0026ndash;73. https://doi.org/10.1093/sleep/27.7.1255.\u003c/li\u003e\n\u003cli\u003eChunnan L, Shaomei S, Wannian L. The association between sleep and depressive symptoms in US adults: data from the NHANES (2007-2014). Epidemiol Psychiatr Sci. 2022;31:e63. https://doi.org/10.1017/S2045796022000452.\u003c/li\u003e\n\u003cli\u003eCho HJ, Lavretsky H, Olmstead R, Levin MJ, Oxman MN, Irwin MR. Sleep disturbance and depression recurrence in community-dwelling older adults: a prospective study. Am J Psychiatry. 2008;165:1543\u0026ndash;50. https://doi.org/10.1176/appi.ajp.2008.07121882.\u003c/li\u003e\n\u003cli\u003ePeakman G, Karunatilake N, Seynaeve M, Perera G, Aarsland D, Stewart R, et al. Clinical factors associated with progression to dementia in people with late-life depression: A cohort study of patients in secondary care. BMJ Open. 2020;10:e035147. https://doi.org/10.1136/bmjopen-2019-035147.\u003c/li\u003e\n\u003cli\u003eHolding BC, Sundelin T, Schiller H, \u0026Aring;kerstedt T, Kecklund G, Axelsson J. Sleepiness, sleep duration, and human social activity: an investigation into bidirectionality using longitudinal time-use data. Proc Natl Acad Sci U S A. 2020;117:21209\u0026ndash;17. https://doi.org/10.1073/pnas.2004535117.\u003c/li\u003e\n\u003cli\u003eIrwin MR, Opp MR. Sleep health: reciprocal regulation of sleep and innate immunity. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2017;42:129\u0026ndash;55. https://doi.org/10.1038/npp.2016.148.\u003c/li\u003e\n\u003cli\u003eWeicker H, Str\u0026uuml;der HK. Influence of exercise on serotonergic neuromodulation in the brain. Amino Acids. 2001;20:35\u0026ndash;47. https://doi.org/10.1007/s007260170064.\u003c/li\u003e\n\u003cli\u003eParker G, Brotchie H. Gender differences in depression. Int Rev Psychiatry Abingdon Engl. 2010;22:429\u0026ndash;36. https://doi.org/10.3109/09540261.2010.492391.\u003c/li\u003e\n\u003cli\u003eN\u0026ouml;bbelin L, Bogren M, Mattisson C, Br\u0026aring;dvik L. Incidence of melancholic depression by age of onset and gender in the lundby population, 1947-1997. Eur Arch Psychiatry Clin Neurosci. 2023;273:1163\u0026ndash;73. https://doi.org/10.1007/s00406-022-01506-5.\u003c/li\u003e\n\u003cli\u003eKornstein SG, Schatzberg AF, Thase ME, Yonkers KA, McCullough JP, Keitner GI, et al. Gender differences in chronic major and double depression. J Affect Disord. 2000;60:1\u0026ndash;11. https://doi.org/10.1016/s0165-0327(99)00158-5.\u003c/li\u003e\n\u003cli\u003eYoung MA, Scheftner WA, Fawcett J, Klerman GL. Gender differences in the clinical features of unipolar major depressive disorder. J Nerv Ment Dis. 1990;178:200\u0026ndash;3. https://doi.org/10.1097/00005053-199003000-00008.\u003c/li\u003e\n\u003cli\u003eWilliams JB, Spitzer RL, Linzer M, Kroenke K, Hahn SR, deGruy FV, et al. Gender differences in depression in primary care. Am J Obstet Gynecol. 1995;173:654\u0026ndash;9. https://doi.org/10.1016/0002-9378(95)90298-8.\u003c/li\u003e\n\u003cli\u003eSeedat S, Scott KM, Angermeyer MC, Berglund P, Bromet EJ, Brugha TS, et al. Cross-national associations between gender and mental disorders in the world health organization world mental health surveys. Arch Gen Psychiatry. 2009;66:785\u0026ndash;95. https://doi.org/10.1001/archgenpsychiatry.2009.36.\u003c/li\u003e\n\u003cli\u003eLuppa M, Sikorski C, Luck T, Ehreke L, Konnopka A, Wiese B, et al. Age- and gender-specific prevalence of depression in latest-life--systematic review and meta-analysis. J Affect Disord. 2012;136:212\u0026ndash;21. https://doi.org/10.1016/j.jad.2010.11.033.\u003c/li\u003e\n\u003cli\u003eHammen CL, Padesky CA. Sex differences in the expression of depressive responses on the beck depression inventory. J Abnorm Psychol. 1977;86:609\u0026ndash;14. https://doi.org/10.1037//0021-843x.86.6.609.\u003c/li\u003e\n\u003cli\u003eKim SJ, Lee YJ, Cho S-J, Cho I-H, Lim W, Lim W. Relationship between weekend catch-up sleep and poor performance on attention tasks in korean adolescents. Arch Pediatr Adolesc Med. 2011;165:806\u0026ndash;12. https://doi.org/10.1001/archpediatrics.2011.128.\u003c/li\u003e\n\u003cli\u003eThomas J, Altareb B. Cognitive vulnerability to depression: an exploration of dysfunctional attitudes and ruminative response styles in the United Arab Emirates. Psychol Psychother. 2012;85:117\u0026ndash;21. https://doi.org/10.1111/j.2044-8341.2011.02015.x.\u003c/li\u003e\n\u003cli\u003eWang K, Lu H, Cheung EFC, Neumann DL, Shum DHK, Chan RCK. \u0026ldquo;female preponderance\u0026rdquo; of depression in non-clinical populations: a meta-analytic study. Front Psychol. 2016;7:1398. https://doi.org/10.3389/fpsyg.2016.01398.\u003c/li\u003e\n\u003cli\u003eCavanagh A, Wilson CJ, Kavanagh DJ, Caputi P. Differences in the expression of symptoms in men versus women with depression: a systematic review and meta-analysis. Harv Rev Psychiatry. 2017;25:29\u0026ndash;38. https://doi.org/10.1097/HRP.0000000000000128.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Asthma, Depression trajectories, Group-based trajectory modeling, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-8069992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8069992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePrevious studies show asthma comorbid with depression significantly impacts quality of life, yet research on the long-term trajectory between depressive symptoms and asthma in middle-aged and elderly Chinese remains limited.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aims to understand the different trajectories of depressive symptoms and their influencing factors in middle-aged and elderly asthmatic patients in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study uses data from the China Health and Retirement Longitudinal Study (CHARLS) collected between 2011 and 2018, involving 484 participants aged 45 and above. Depressive symptoms were assessed with the Center for Epidemiological Studies Depression Scale (CESD-10). A group-based trajectory model (GBTM) was constructed to identify long-term patterns of depressive symptom trajectories; Factors influencing these trajectories were analyzed via a multivariate logistic regression model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDuring the entire follow-up, we identified three depressive symptom trajectories: the \"Low-stable\" group, \"Middle-fluctuating\" group, and \"High-increasing\" group. We found differences in the basic characteristics across subgroups with distinct depressive trajectories, while unmarried, having no contact with children, and short sleep duration were key indicators for identifying populations requiring focus in depressive symptom prevention and treatment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDepressive symptom trajectories in middle-aged and elderly Chinese asthmatics are heterogeneous, so it is necessary to focus on the trajectory characteristics of different subgroups.\u003c/p\u003e","manuscriptTitle":"The Association Between Depression Trajectories and Asthma Risk: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 16:05:32","doi":"10.21203/rs.3.rs-8069992/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-15T02:44:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13849540959161111323275347979442964013","date":"2025-12-14T13:36:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70994193688916294084008748814581738495","date":"2025-12-05T13:29:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-05T09:50:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T12:52:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-14T07:37:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-14T06:36:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-11-14T06:32:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8983a0f-d552-4339-b29d-66cae7fb2989","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T16:05:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 16:05:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8069992","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8069992","identity":"rs-8069992","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}