Association between vision impairment and the risk of comorbidities of heart disease and depression: a population-based cohort study among middle-aged and older adults using the CHARLS 2018 data

Association between vision impairment and the risk of comorbidities of heart disease and depression: a population-based cohort study among middle-aged and older adults using the CHARLS 2018 data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between vision impairment and the risk of comorbidities of heart disease and depression: a population-based cohort study among middle-aged and older adults using the CHARLS 2018 data Jian Liang, Xi Su, Xiaomin He, Penghua You This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7005878/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Researches have shown that vision impairment are linked to various cardiovascular diseases and mental health conditions, particularly in middle-aged and elderly populations. Therefore, our study aimed to investigate the correlation between vision impairments as an exposure factor and the comorbidities of heart disease and depression as an outcome, exploring new strategies for their prevention and treatment. Methods A total of 22,698 participants from the China Health and Retirement Longitudinal Study (CHARLS) database in 2018 were incorporated into the study. After data screening, a baseline table was first created using the weighted chi-square test. Next, three logistic regression models were developed to investigate the correlation between the exposure factor and the outcome. Additionally, risk stratification analysis was conducted. Finally, a ROC curve was plotted to evaluate the predictive ability of the exposure factor for the outcome. Results After excluding ineligible individuals, 3,913 participants remained, consisting of 93 cases and 3,820 controls. The baseline table revealed significant differences between cases and controls regarding vision impairments. A notably positive association was found between vision impairments and comorbidities of heart disease and depression (in model 3, OR = 2.357, 95% CI = 1.037–4.817, P < 0.027). Furthermore, risk stratification analysis confirmed that vision problems were a significant risk factor for the outcome. ROC curves demonstrated strong predictive performance of vision impairments for the outcome, with an area under the curve (AUC) of 0.837. Conclusion The study's findings suggested a link between vision impairments and comorbidities of heart disease and depression, highlighting vision impairments as a potential risk factor for comorbidities of heart disease and depression. This could inform the development of new treatment strategies for the comorbidities of heart disease and depression. Heart disease Depression Vision impairments CHARLS database Comorbidities Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular diseases (CVDs) encompass a range of disorders that impact the heart and blood vessels and represent one of the foremost causes of premature mortality and disability globally. They also constitute a significant portion of the global disease burden. According to the Global Burden of Cardiovascular Diseases Report, the prevalence of these diseases nearly doubled between 1990 and 2019, with cases rising from 271 million to 523 million and associated deaths increasing from 12.1 million to 18.6 million [ 1 ] . As the global population continues to age, over 50% of the elderly are afflicted with multiple chronic conditions, with CVDs being the most prevalent [ 2 ] . Statistics indicate that among individuals aged 70 and above with chronic illnesses, 70% suffer from CVDs, and more than two-thirds of these individuals also have additional chronic conditions [ 3 ] . Beyond traditional risk factors, emerging evidence highlights the role of psychological stress in CVD development. Chronic mental stress has been shown to adversely affect cardiac structure and function, increasing cardiovascular risk [ 4 ] . However, despite this connection, the mental health status of heart disease patients remains understudied in clinical research [ 5 ] . Depression is a persistent psychological disorder affecting over 120 million individuals globally, with a prevalence rate of 13–20% and a lifetime prevalence ranging from 6.1–9.5% [ 6 ] . Over the past three decades, the global incidence of depression has surged by nearly 50% [ 7 ] . Individuals with chronic heart disease are particularly susceptible to anxiety and depression, and those experiencing prolonged depression are at an increased risk of developing coronary heart disease [ 8 ] . Furthermore, the severity, duration, and treatment responsiveness of depression are significantly associated with adverse outcomes in patients with coronary heart disease [ 9 , 10 ] . Vision impairment encompasses a range of conditions characterized by diminished vision function, reduced vision acuity, or a restricted field of view, which collectively hinder the attainment of normal vision and subsequently impact daily life to varying extents [ 11 ] . Evidence suggests that vision impairment is significantly associated with both the symptoms and occurrence of coronary heart disease (CHD) [ 12 ] . Beyond CHD, vision impairment has also been linked to a higher risk of broader cardiovascular diseases (CVDs), including stroke and heart failure [ 13 ] . Early detection of vision impairment, coupled with the implementation of appropriate interventions, may contribute to the prevention of CVDs and alleviate the clinical burden associated with their diagnosis and treatment [ 14 ] . Enhancing awareness of heart disease among individuals with vision impairments and encouraging the adoption of suitable health management strategies may further mitigate their risk of developing CVDs [ 15 ] . Furthermore, vision impairment has been linked to psychological disorders, including depression. Among children, vision impairment is correlated with increased symptoms of depression and anxiety [ 16 ] . Studies focusing on patients with Behcet's uveitis and vision impairment have also demonstrated a high prevalence of depression and anxiety, revealing a significant correlation between vision function and depression [ 17 ] . Collectively, vision impairment is associated with both heart disease and depression. However, the extent to which vision impairment can predict the comorbidity of heart disease and depression remains uncertain. This study employs data from middle-aged and elderly Chinese populations to investigate the relationship between vision impairment and the comorbidity of heart disease and depression, to provide valuable insights for clinical research and the treatment of patients with these conditions. The research population of the China Health and Retirement Longitudinal Study (CHARLS) database comprises middle-aged and elderly individuals aged 45 and above in China [ 18 ] . This study examined the association between vision impairment and the co-occurrence of heart disease and depression utilizing data from the CHARLS database. A weighted chi-square test was employed to assess differences in exposure factors and covariates between the disease and control groups, leading to the construction of a baseline table. Furthermore, three regression models were developed, and a risk stratification analysis was performed. The predictive capability of vision impairment for heart disease combined with depression was subsequently evaluated using receiver operating characteristic (ROC) analysis. Our findings may inform clinical management of comorbid heart disease and depression, and contribute to strategies for healthy aging in visionly impaired older adults. 2. Materials and methods 2.1 Data extraction The CHARLS is a large-scale longitudinal survey designed to collect high-quality microdata representing Chinese households and individuals aged 45 and older. Its primary aim is to analyze issues related to population aging in China and to promote interdisciplinary research on aging [ 19 ] . The CHARLS survey project received approval from the Ethics Review Committee of Peking University, and all participants were required to provide informed consent. In this study, the exposure factor was vision impairment, while the outcome variable was comorbidities of heart disease and depression. Several covariates related to the outcome were also considered. Initially, 22,698 participants from 2018 were included in the analysis. However, certain participants were excluded based on specific criteria: (1) those younger than 45 years, (2) those with missing information on covariates and outcome, and (3) those with missing data on the exposure factor. Ultimately, the 3,913 remaining participants were included for subsequent analysis (Fig. 1 ). 2.2 Definition of outcome In this study, heart disease was defined based on responses to the question (DA007-7): "Has a doctor ever told you about a heart disease (such as myocardial infarction, coronary heart disease, angina pectoris, congestive heart failure, or other heart diseases)?" Respondents answering "yes" were classified as heart disease patients. Additionally, participants' depression status was assessed using a depression scale comprising 10 questions (SECTION CESD Depression, DC009-DC018), with a total score of ≥ 10 indicating depression [ 20 ] . Responses were scored as follows: “rarely or none of the time” (< 1 day) = 0 points, “some or a little of the time” (1–2 days) = 1 point, “occasionally or a moderate amount of the time” (3–4 days) = 2 points, and “most or all of the time” (5–7 days) = 3 points. Notably, questions DC013 and DC016 were reverse scored. The total score ranged from 0 to 30, with lower scores reflecting fewer depressive symptoms. A total score of ≥ 10 suggested the presence of depression. Overall, based on the above-mentioned definitions, participants identified as having both heart disease and depression were classified as the case group, while all other participants were categorized as the control group. 2.3 Definition of exposure factor and covariates The exposure factor was defined based on responses to the question (DA005_3): "Do you have a vision impairment?" Respondents answering "yes" were considered to have a vision impairment. Additionally, to evaluate the effect of the other potential factors for outcome, several important covariates were selected for this study, including age, gender, nation, marital status, drunk, hypertension, diabetes, smoke, residential address, education, liver disease, asthma, and chronic lung disease. Interestingly, all covariates were categorical variables, and specific information for each covariate was provided in Supplementary Table 1 . Variables Subgroup Number Age 45–59, ≥ 60 BA002 Gender Male, Female BA000_W2_3 Nation Han_Nationality, Hui_Nationality, Manchu, Miao_Nationality, Mongol_Nationality, Tibetan_Nationality, Tujia_Nationality, Uyghur_Nationality, Yi_Nationality, Zhuang_Nationality, Other BG001_W4 Residential_Address Family_Housing, Hospital, Nursing_Home, Other BB001_W3_1 Education Did_not_Finish_Primary_School, Elementary_School, Four-Year_College/Bachelor's_Degree, High_School, Master's_Degree, Middle_School, No_Formal_Education_(Illiterate), Sishu/Home_School, Two-/Three-Year_College/Associate_Degree, Vocational_School BD001_W2_4 Marital_status Married, unmarried BE001 Hypertension Yes, No DA007_1 Diabetes Yes, No DA007_3 Chronic_Lung_Disease Yes, No DA007_5 Liver_Disease Yes, No DA007_6 Asthma Yes, No DA007_14 Smoke Yes, No, Quit DA059, DA061 Drunk Drink_but_less_than_once_a_month, Drink_more_than_once_a_month, None_of_these DA067 2.4 Statistical analysis To compare the differences in the exposure factor and covariates between cases and controls, a weighted chi-square test was conducted and a baseline table was constructed using the "tableone" package [ 21 ] (v 0.13.2) (P < 0.05). Subsequently, to investigate the correlation between the exposure factor and the outcome, three logistic regression models were developed using the "nhanesA" package [ 22 ] (v 1.0)(P < 0.05). Model 1 focused solely on the effects of vision impairment on the outcome, without adjusting for any covariates. Model 2 adjusted for age, gender, and nation. Model 3 further incorporated marital status, drunk, hypertension, diabetes, smoke, residential address, education, liver disease, asthma, and chronic lung disease into the adjustments made in model 2. Notably, OR, 95% CI, and P value were critical parameters for determining the relationships. After that, to assess the stability of the relationship between the exposure factor and the outcome, a stratified logistic regression was conducted in conjunction with model 3 using the "survry" package (v 4.2.1) ( http://cran.fhcrc.org/web/packages/survey/index.html ) (P < 0.05). The ROC curve for model 3 was plotted using the "pROC" package [ 23 ] (v 1.18.0), and the AUC was calculated as a quantitative measure of predictive validity. Besides, to compare the importance of vision impairment and other covariates in predicting the occurrence of the outcome, eXtreme Gradient Boosting (XGBoost) analysis was performed via "xgboost" package [ 24 ] (v 2.0.3.1) . In summary, all analyses were executed via R (v 4.2.2), employing two-tailed tests with a significance level set at P < 0.05. 3. Results 3.1 Vision impairments increased the risk of comorbidities of heart disease and depression The baseline table revealed significant differences between cases (93 participants) and controls (3,820 participants), regarding vision impairment, nation, hypertension, diabetes, smoking, education, liver disease, asthma, and chronic lung disease (P < 0.05) (Table 1 ). Table 1 Comparison of baseline characteristics between the two groups in the study level Control Case P Valiable 3820 93 Nation (%) Han_Nationality 3536 (92.6) 81 (87.1) < 0.001 Hui_Nationality 23 ( 0.6) 1 ( 1.1) Manchu 47 ( 1.2) 2 ( 2.2) Miao_Nationality 27 ( 0.7) 0 ( 0.0) Mongol_Nationality 33 ( 0.9) 1 ( 1.1) Other 48 ( 1.3) 1 ( 1.1) Tibetan_Nationality 24 ( 0.6) 1 ( 1.1) Tujia_Nationality 4 ( 0.1) 0 ( 0.0) Uyghur_Nationality 9 ( 0.2) 4 ( 4.3) Yi_Nationality 11 ( 0.3) 0 ( 0.0) Zhuang_Nationality 58 ( 1.5) 2 ( 2.2) Gender (%) Male 854 (22.4) 18 (19.4) 0.575 Female 2966 (77.6) 75 (80.6) Age (%) >=60 1557 (40.8) 46 (49.5) 0.114 45–59 2263 (59.2) 47 (50.5) Marital_status (%) Married 3377 (88.4) 82 (88.2) 1 unMarried 443 (11.6) 11 (11.8) Drunk (%) Drink_but_less_than_once_a_month 311 ( 8.1) 7 ( 7.5) 0.86 Drink_more_than_once_a_month 646 (16.9) 14 (15.1) None_of_these 2863 (74.9) 72 (77.4) Hypertension (%) No 3354 (87.8) 54 (58.1) < 0.001 Yes 466 (12.2) 39 (41.9) Diabetes (%) No 3668 (96.0) 83 (89.2) 0.003 Yes 152 ( 4.0) 10 (10.8) Smoke (%) No 3550 (92.9) 82 (88.2) 0.043 Quit 114 ( 3.0) 7 ( 7.5) Yes 156 ( 4.1) 4 ( 4.3) Residential_Address (%) Family_Housing 3701 (96.9) 90 (96.8) 0.978 Hospital 4 ( 0.1) 0 ( 0.0) Nursing_Home 3 ( 0.1) 0 ( 0.0) Other 112 ( 2.9) 3 ( 3.2) Education (%) Did_not_Finish_Primary_School 732 (19.2) 28 (30.1) 0.004 Elementary_School 845 (22.1) 13 (14.0) Four-Year_College/Bachelor's_Degree 28 ( 0.7) 0 ( 0.0) High_School 316 ( 8.3) 10 (10.8) Master's_Degree 4 ( 0.1) 0 ( 0.0) Middle_School 803 (21.0) 19 (20.4) No_Formal_Education_(Illiterate) 971 (25.4) 20 (21.5) Sishu/Home_School 2 ( 0.1) 1 ( 1.1) Two-/Three-Year_College/Associate_Degree 51 ( 1.3) 0 ( 0.0) Vocational_School 68 ( 1.8) 2 ( 2.2) Liver_Disease (%) No 3731 (97.7) 85 (91.4) < 0.001 Yes 89 ( 2.3) 8 ( 8.6) Asthma (%) No 3787 (99.1) 87 (93.5) < 0.001 Yes 33 ( 0.9) 6 ( 6.5) Chronic_Lung_Disease (%) No 3688 (96.5) 78 (83.9) < 0.001 Yes 132 ( 3.5) 15 (16.1) Vision _impairment (%) No 3694 (96.7) 83 (89.2) < 0.001 Yes 126 ( 3.3) 10 (10.8) Subsequent regression models demonstrated a significantly positive association between vision impairment and comorbidities of heart disease and depression, with all P values for vision impairment across the three models being less than 0.05. Specifically, model 1 showed OR = 3.532 (95% CI = 1.686–6.659, P < 0.001), model 2 yielded OR = 3.247 (95% CI = 1.524–6.238, P < 0.001), and model 3 indicated OR = 2.357 (95% CI = 1.037–4.817, P < 0.027) (Table 2 ). Table 2 Linear regression models of vision impairment and comorbidities of heart disease and depression exposure model1_OR(95%_CI) model2_OR(95%_CI) model3_OR(95%_CI) Vision_impairment 3.532(1.686–6.659) 3.247(1.524–6.238) 2.357(1.037–4.817) p_value 0.0002737 0.0009395 0.02737 Additionally, risk stratification analysis confirmed the stability of the association between vision problems and increased risk of comorbidities of heart disease and depression (OR = 2.357, 95% CI = 1.037–4.817, P < 0.027) (Fig. 2 ). Other significant covariates included nationality (uyghur nationality), gender (female), education (elementary school, no formal education), hypertension (Yes), liver disease (Yes), and chronic lung disease (Yes) (P < 0.05) (Fig. 2 ). Overall, these findings underscored the critical role of vision problems in increasing the risk of comorbidities of heart disease and depression, highlighting the need for integrated healthcare approaches. 3.2 Vision impairments exhibited excellent predictive ability for comorbidities of heart disease and depression The ROC curve for model 3 demonstrated that vision impairments had excellent predictive ability for comorbidities of heart disease and depression, with an AUC of 0.837 (Fig. 3 A). Furthermore, XGBoost analysis revealed that education was the most important variable for predicting the occurrence of comorbidities of heart disease and depression. Although vision problems ranked eighth among all variables, they still played a significant role in the risk assessment of comorbidities of heart disease and depression (Fig. 3 B). In summary, these findings highlighted the multifaceted nature of factors, with vision impairments playing significant roles in predicting the risk of comorbidities of heart disease and depression. 4. Discussion CVDs represent the most prevalent chronic conditions encountered in clinical practice, characterized by elevated morbidity and mortality rates, and frequently associated with mental health complications [ 25 ] . Within the population of patients afflicted by CVDs, the elderly constitute a substantial proportion and often experience concomitant vision impairments [ 26 ] . Existing research indicates that vision impairments may increase the susceptibility of heart disease patients to depression [ 27 ] . Nevertheless, there is a notable deficiency in research exploring the association between vision impairment and the comorbidity of heart disease and depression, rendering this an underexplored domain. This study aims to examine the relationship between vision impairment and the concurrent presence of heart disease and depression, utilizing data from the CHARLS database. A total of 3,913 participants were included in the analysis, and significant differences in vision impairment were observed between the disease group and the control group, as evidenced by baseline statistical tables. These findings suggest that vision impairment may influence the comorbidity of heart disease and depression. The relationship between vision impairments and the co-occurrence of heart disease and depression was further examined using three analytical models. When combined with risk stratification analysis, findings indicated that vision impairments constitute a significant risk factor for heart disease accompanied by depression. These results offer insights and strategies for the early prevention of heart disease with concurrent depression within the Chinese population. Psychological health exerts a substantial influence on CVD outcomes. Depression is linked to an increased global disease burden and elevated mortality risk in Western populations [ 28 ] . Research suggests that individuals with major depressive disorder face a significantly heightened risk of developing CVDs, along with diminished treatment efficacy, as evidenced by increased morbidity and mortality rates [ 29 ] . Consequently, for patients with pre-existing chronic health conditions, particularly those afflicted by both CVD and depression, treatment and management present even greater challenges [ 30 ] . Studies have demonstrated an association between depression and myocardial infarction, congestive heart failure, or any CVD [ 31 ] . Depression exerts a substantial adverse influence on both the progression and outcomes of CVD [ 32 ] . Furthermore, in elderly male patients, the administration of antidepressants not only enhances mood but is also correlated with a decreased risk of cardiovascular events [ 33 ] . vision impairment adversely affects an individual's work efficiency and quality of life and may precipitate a range of psychological and social issues, particularly in middle-aged and elderly populations [ 15 ] . Research conducted on the elderly population in the United States indicates that individuals with vision impairments are more susceptible to depression, with a significant bidirectional and longitudinal relationship observed between vision impairments and mental health symptoms [ 34 ] . Additionally, vision impairments are linked to various CVDs and mental health conditions [ 35 ] . The coexistence of heart disease and depression presents patients with heightened physical and life challenges. vision impairment can significantly hinder individuals' access to medical information, adherence to treatment protocols, and engagement in rehabilitation exercises. These limitations may exacerbate the progression of heart disease, increase psychological burden, and contribute to the onset and development of depression. Consequently, vision impairment may be intricately linked with heart disease comorbid with depression. This study identifies an independent association between vision impairment and the comorbidity of heart disease and depression through a comprehensive multi-model analysis system (p < 0.01), underscoring its scientific significance when compared to multidisciplinary research. Unlike the studies by Donato [ 36 ] and Barber [ 37 ] , which concentrate on single-disease mechanisms, our triple-model validation (AUC = 0.837) not only corroborates the independent predictive value of vision impairment but also elucidates its critical role (ranked 8th) within a multifactorial predictive framework using the XGBoost model. This finding aligns with the interdisciplinary research conducted by the Smith team, which explores the specific connection between retinopathy and cardiovascular disease [ 38 ] . Notably, similar patterns of multisystem associations have been documented in other chronic disease studies: for instance, diabetes intensifies coronary artery lesions through inflammatory responses (SII values) [ 39 ] , air pollutants concurrently affect cardiovascular and mental health in asthma patients via systemic inflammation [ 40 ] , and there exists a vicious cycle of mutual exacerbation between asthma and coronary heart disease [ 41 ] . Collectively, these studies delineate a pathological network of interconnected organ systems. In conclusion, this study provides evidence for the association between vision impairment and depression in conjunction with heart disease among middle-aged and elderly individuals in China. vision impairment may serve as a risk factor for heart disease when combined with depression and can offer a theoretical foundation for the early prevention and treatment of this comorbidity. However, this study has certain limitations. It is specific to the Chinese population, and future research should include a broader range of countries and social groups to enhance the generalizability of the findings. Additionally, the covariates considered may not be sufficiently comprehensive, and other confounding factors could potentially influence the study's outcomes. Therefore, large-scale clinical trials are necessary to further elucidate the underlying mechanisms, thereby improving clinical practice and extending benefits to a wider patient population. Declarations Conflict of interest The authors declare no conflict of interest, financial or otherwise. Funding This work was supported by the Natural Science Foundation of Shaanxi Provincial (2022JM-540) and the Science and Technology Plan Project of xi’an( 24YXYJ0136). Human Ethics and Consent to Participate declarations This study complied with the provisions of the Declaration of Helsinki. Since it did not involve human subjects, ethical approval was not required. Participate declaration Not Applicable. Consent for publication Not Applicable. Author Contribution L.J. and Y.P.H. wrote the main manuscript text, S.X. and H.X.M. prepared all figures and tables. All authors reviewed and approved the final manuscript. References Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. 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Psychiatr Clin North Am. 2018;41(1):29–37. 10.1016/j.psc.2017.10.003 . PMID: 29412846. Cao H, Zhao H, Shen L. Depression increased risk of coronary heart disease: A meta-analysis of prospective cohort studies. Front Cardiovasc Med. 2022;9:913888. 10.3389/fcvm.2022.913888 . PMID: 36110417. Krittanawong C, Maitra NS, Qadeer YK, Wang Z, Fogg S, Storch EA et al. Association of Depression and Cardiovascular Disease. Am J Med. 2023;136(9):881 – 95. 10.1016/j.amjmed.2023.04.036 . PMID: 37247751. Almeida OP, Ford AH, Hankey GJ, Golledge J, Yeap BB, Flicker L. Depression, antidepressants and the risk of cardiovascular events and death in older men. Maturitas. 2019;128:4–9. 10.1016/j.maturitas.2019.06.009 . PMID: 31561821. Frank CR, Xiang X, Stagg BC, Ehrlich JR. Longitudinal Associations of Self-reported Vision Impairment With Symptoms of Anxiety and Depression Among Older Adults in the United States. JAMA Ophthalmol. 2019;137(7):793–800. 10.1001/jamaophthalmol.2019.1085 . PMID: 31095253. Lee W, Chang Y, Shin H, Ryu S. Hearing Loss and Risk of Overall, Injury-Related, and Cardiovascular Mortality: The Kangbuk Samsung Health Study. J Clin Med. 2020;9(5):1415. 10.3390/jcm9051415 . PMID: 32397655. Leo DG, Islam U, Lotto RR, Lotto A, Lane DA. Psychological interventions for depression in adolescent and adult congenital heart disease. Cochrane Database Syst Rev. 2023;10(10):CD004372. 10.1002/14651858.CD004372.pub3 . PMID: 37787122. Barber C, Gould C, Guillermo G, Dupree J, McLeer M, Benevides T et al. Interventions in the Scope of Occupational Therapy to Improve Psychosocial Well-Being in Older Adults with Low Vision and Mental Health Concerns: A Systematic Review. Occup Ther Health Care. 2021;35(4):397–423. 10.1080/07380577.2021.1946733 . PMID: 34369234. Smith RT, Olsen TW, Chong V, Kim J, Hammer M, Lema G et al. Subretinal drusenoid deposits, age-related macular degeneration, and cardiovascular disease. Asia Pac J Ophthalmol (Phila). 2024 Jan-Feb;13(1):100036. 10.1016/j.apjo.2024.100036 . PMID: 38244930. Wang H, Huang Z, Wang J, Yue S, Hou Y, Ren R et al. Predictive value of system immune-inflammation index for the severity of coronary stenosis in patients with coronary heart disease and diabetes mellitus. Sci Rep. 2024;14(1):31370. 10.1038/s41598-024-82826-5 . PMID: 39732905. Kim B, Ha Y, Hwang J, Kim HJ. Association between chronic ambient heavy metal exposure and mental health in Korean adult patients with asthma and the general population. Chemosphere. 2025;370:144002. 10.1016/j.chemosphere.2024.144002 . PMID: 39708948. Cheng X, Wu X, Ye W, Chen Y, Fu P, Jia W et al. All-Cause and Cause-Specific Burden of Asthma in a Transitioning City in China: Population Study. JMIR Public Health Surveill. 2024;10:e44845. 10.2196/44845 . PMID: 39621867. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers invited by journal 11 Aug, 2025 Editor invited by journal 10 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 29 Jun, 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-7005878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500697322,"identity":"69479d09-279e-4317-a549-f683de3fcb01","order_by":0,"name":"Jian Liang","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Liang","suffix":""},{"id":500697323,"identity":"f381c0ee-ca5a-4256-980f-4bec77b6a4b6","order_by":1,"name":"Xi Su","email":"","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Su","suffix":""},{"id":500697324,"identity":"cbeca20e-fa8b-4181-929b-b2444960c572","order_by":2,"name":"Xiaomin He","email":"","orcid":"","institution":"the Hospital of Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"He","suffix":""},{"id":500697325,"identity":"54b068ca-e8b7-4757-9c0e-f6eadd4a0892","order_by":3,"name":"Penghua You","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACfvnjBx9+qKixY2NvIFKL5AyeZGOJM8eS+XkOEKnF4AaDmQRvGzPjzBkJxLrsdkOahAQbG7PBzccbbzDU2EQT1ME45+BhiwIeGT6D22nFFgzH0nIbCGlhZkhIvAG0htngdo6ZBGPDYcJa2BgSDCR4DJgZN9w8Q6QWHokEIwmeBJD3eYjUIsFzBhjIB0CBDPRLAjF+sT/efvDhx3+gqDy88caHGhvCWpCBgUQCKcohWkjVMQpGwSgYBSMDAABexj4lhtC9fwAAAABJRU5ErkJggg==","orcid":"","institution":"Shaanxi Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Penghua","middleName":"","lastName":"You","suffix":""}],"badges":[],"createdAt":"2025-06-30 03:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7005878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7005878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89378932,"identity":"d78c7ab8-076e-4f9b-98f6-143acc093a13","added_by":"auto","created_at":"2025-08-19 11:34:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33025,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of sample selection and the exclusion criteria.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7005878/v1/a97516c9c6bf73a27b4579d5.png"},{"id":89380204,"identity":"6bc51d77-7ff3-4397-9f42-c04c2f690193","added_by":"auto","created_at":"2025-08-19 11:42:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156053,"visible":true,"origin":"","legend":"\u003cp\u003eThe stratified logistic regression analysis of the impact of exposure factors on comorbidities of heart disease and depression based on model 3.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7005878/v1/acfc984e839564c7f1cebfb0.png"},{"id":89380192,"identity":"d23cbcfa-b1f4-4101-aaa4-b4f66d7c4116","added_by":"auto","created_at":"2025-08-19 11:42:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA： \u003c/strong\u003eThe ROC curve based on model 3 for vision impairment predicting comorbidities of heart disease and depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: \u003c/strong\u003eThe XGBoost analysis of the relative importance of the impact of exposure factors on the comorbidities of heart disease and depression\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7005878/v1/37b4ceeaa6478e1d4292f05d.png"},{"id":89382041,"identity":"ddb70fe4-4dff-4998-93e6-c92474001237","added_by":"auto","created_at":"2025-08-19 12:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1130591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7005878/v1/222dcb95-b631-4dbf-934a-abab5e3a157f.pdf"},{"id":89378933,"identity":"ec292df9-996c-4175-af76-f8f33deeeec3","added_by":"auto","created_at":"2025-08-19 11:34:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14874,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7005878/v1/43c98a9e94fcb68117e7babb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between vision impairment and the risk of comorbidities of heart disease and depression: a population-based cohort study among middle-aged and older adults using the CHARLS 2018 data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) encompass a range of disorders that impact the heart and blood vessels and represent one of the foremost causes of premature mortality and disability globally. They also constitute a significant portion of the global disease burden. According to the Global Burden of Cardiovascular Diseases Report, the prevalence of these diseases nearly doubled between 1990 and 2019, with cases rising from 271\u0026nbsp;million to 523\u0026nbsp;million and associated deaths increasing from 12.1\u0026nbsp;million to 18.6\u0026nbsp;million \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As the global population continues to age, over 50% of the elderly are afflicted with multiple chronic conditions, with CVDs being the most prevalent\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Statistics indicate that among individuals aged 70 and above with chronic illnesses, 70% suffer from CVDs, and more than two-thirds of these individuals also have additional chronic conditions\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Beyond traditional risk factors, emerging evidence highlights the role of psychological stress in CVD development. Chronic mental stress has been shown to adversely affect cardiac structure and function, increasing cardiovascular risk \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, despite this connection, the mental health status of heart disease patients remains understudied in clinical research\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Depression is a persistent psychological disorder affecting over 120\u0026nbsp;million individuals globally, with a prevalence rate of 13\u0026ndash;20% and a lifetime prevalence ranging from 6.1\u0026ndash;9.5%\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Over the past three decades, the global incidence of depression has surged by nearly 50%\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Individuals with chronic heart disease are particularly susceptible to anxiety and depression, and those experiencing prolonged depression are at an increased risk of developing coronary heart disease\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the severity, duration, and treatment responsiveness of depression are significantly associated with adverse outcomes in patients with coronary heart disease\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eVision impairment encompasses a range of conditions characterized by diminished vision function, reduced vision acuity, or a restricted field of view, which collectively hinder the attainment of normal vision and subsequently impact daily life to varying extents\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Evidence suggests that vision impairment is significantly associated with both the symptoms and occurrence of coronary heart disease (CHD)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Beyond CHD, vision impairment has also been linked to a higher risk of broader cardiovascular diseases (CVDs), including stroke and heart failure\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Early detection of vision impairment, coupled with the implementation of appropriate interventions, may contribute to the prevention of CVDs and alleviate the clinical burden associated with their diagnosis and treatment\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Enhancing awareness of heart disease among individuals with vision impairments and encouraging the adoption of suitable health management strategies may further mitigate their risk of developing CVDs\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Furthermore, vision impairment has been linked to psychological disorders, including depression. Among children, vision impairment is correlated with increased symptoms of depression and anxiety\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Studies focusing on patients with Behcet's uveitis and vision impairment have also demonstrated a high prevalence of depression and anxiety, revealing a significant correlation between vision function and depression\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Collectively, vision impairment is associated with both heart disease and depression. However, the extent to which vision impairment can predict the comorbidity of heart disease and depression remains uncertain. This study employs data from middle-aged and elderly Chinese populations to investigate the relationship between vision impairment and the comorbidity of heart disease and depression, to provide valuable insights for clinical research and the treatment of patients with these conditions.\u003c/p\u003e\u003cp\u003eThe research population of the China Health and Retirement Longitudinal Study (CHARLS) database comprises middle-aged and elderly individuals aged 45 and above in China\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This study examined the association between vision impairment and the co-occurrence of heart disease and depression utilizing data from the CHARLS database. A weighted chi-square test was employed to assess differences in exposure factors and covariates between the disease and control groups, leading to the construction of a baseline table. Furthermore, three regression models were developed, and a risk stratification analysis was performed. The predictive capability of vision impairment for heart disease combined with depression was subsequently evaluated using receiver operating characteristic (ROC) analysis. Our findings may inform clinical management of comorbid heart disease and depression, and contribute to strategies for healthy aging in visionly impaired older adults.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data extraction\u003c/h2\u003e\u003cp\u003eThe CHARLS is a large-scale longitudinal survey designed to collect high-quality microdata representing Chinese households and individuals aged 45 and older. Its primary aim is to analyze issues related to population aging in China and to promote interdisciplinary research on aging\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The CHARLS survey project received approval from the Ethics Review Committee of Peking University, and all participants were required to provide informed consent.\u003c/p\u003e\u003cp\u003eIn this study, the exposure factor was vision impairment, while the outcome variable was comorbidities of heart disease and depression. Several covariates related to the outcome were also considered. Initially, 22,698 participants from 2018 were included in the analysis. However, certain participants were excluded based on specific criteria: (1) those younger than 45 years, (2) those with missing information on covariates and outcome, and (3) those with missing data on the exposure factor. Ultimately, the 3,913 remaining participants were included for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Definition of outcome\u003c/h2\u003e\u003cp\u003eIn this study, heart disease was defined based on responses to the question (DA007-7): \"Has a doctor ever told you about a heart disease (such as myocardial infarction, coronary heart disease, angina pectoris, congestive heart failure, or other heart diseases)?\" Respondents answering \"yes\" were classified as heart disease patients. Additionally, participants' depression status was assessed using a depression scale comprising 10 questions (SECTION CESD Depression, DC009-DC018), with a total score of \u0026ge;\u0026thinsp;10 indicating depression\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Responses were scored as follows: \u0026ldquo;rarely or none of the time\u0026rdquo; (\u0026lt;\u0026thinsp;1 day)\u0026thinsp;=\u0026thinsp;0 points, \u0026ldquo;some or a little of the time\u0026rdquo; (1\u0026ndash;2 days)\u0026thinsp;=\u0026thinsp;1 point, \u0026ldquo;occasionally or a moderate amount of the time\u0026rdquo; (3\u0026ndash;4 days)\u0026thinsp;=\u0026thinsp;2 points, and \u0026ldquo;most or all of the time\u0026rdquo; (5\u0026ndash;7 days)\u0026thinsp;=\u0026thinsp;3 points. Notably, questions DC013 and DC016 were reverse scored. The total score ranged from 0 to 30, with lower scores reflecting fewer depressive symptoms. A total score of \u0026ge;\u0026thinsp;10 suggested the presence of depression. Overall, based on the above-mentioned definitions, participants identified as having both heart disease and depression were classified as the case group, while all other participants were categorized as the control group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Definition of exposure factor and covariates\u003c/h2\u003e\u003cp\u003eThe exposure factor was defined based on responses to the question (DA005_3): \"Do you have a vision impairment?\" Respondents answering \"yes\" were considered to have a vision impairment. Additionally, to evaluate the effect of the other potential factors for outcome, several important covariates were selected for this study, including age, gender, nation, marital status, drunk, hypertension, diabetes, smoke, residential address, education, liver disease, asthma, and chronic lung disease. Interestingly, all covariates were categorical variables, and specific information for each covariate was provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;59, \u0026ge; 60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBA002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBA000_W2_3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHan_Nationality, Hui_Nationality, Manchu, Miao_Nationality, Mongol_Nationality, Tibetan_Nationality, Tujia_Nationality, Uyghur_Nationality, Yi_Nationality, Zhuang_Nationality, Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBG001_W4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential_Address\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFamily_Housing, Hospital, Nursing_Home, Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBB001_W3_1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDid_not_Finish_Primary_School, Elementary_School, Four-Year_College/Bachelor's_Degree, High_School, Master's_Degree, Middle_School, No_Formal_Education_(Illiterate), Sishu/Home_School, Two-/Three-Year_College/Associate_Degree, Vocational_School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBD001_W2_4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital_status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried, unmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBE001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDA007_1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDA007_3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic_Lung_Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDA007_5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver_Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDA007_6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDA007_14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, No, Quit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDA059, DA061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrunk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrink_but_less_than_once_a_month, Drink_more_than_once_a_month, None_of_these\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDA067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eTo compare the differences in the exposure factor and covariates between cases and controls, a weighted chi-square test was conducted and a baseline table was constructed using the \"tableone\" package\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e (v 0.13.2) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, to investigate the correlation between the exposure factor and the outcome, three logistic regression models were developed using the \"nhanesA\" package\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e (v 1.0)(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Model 1 focused solely on the effects of vision impairment on the outcome, without adjusting for any covariates. Model 2 adjusted for age, gender, and nation. Model 3 further incorporated marital status, drunk, hypertension, diabetes, smoke, residential address, education, liver disease, asthma, and chronic lung disease into the adjustments made in model 2. Notably, OR, 95% CI, and P value were critical parameters for determining the relationships. After that, to assess the stability of the relationship between the exposure factor and the outcome, a stratified logistic regression was conducted in conjunction with model 3 using the \"survry\" package (v 4.2.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran.fhcrc.org/web/packages/survey/index.html\u003c/span\u003e\u003cspan address=\"http://cran.fhcrc.org/web/packages/survey/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The ROC curve for model 3 was plotted using the \"pROC\" package\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e (v 1.18.0), and the AUC was calculated as a quantitative measure of predictive validity. Besides, to compare the importance of vision impairment and other covariates in predicting the occurrence of the outcome, eXtreme Gradient Boosting (XGBoost) analysis was performed \u003cem\u003evia\u003c/em\u003e \"xgboost\" package\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e (v 2.0.3.1) .\u003c/p\u003e\u003cp\u003eIn summary, all analyses were executed via R (v 4.2.2), employing two-tailed tests with a significance level set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Vision impairments increased the risk of comorbidities of heart disease and depression\u003c/h2\u003e\u003cp\u003eThe baseline table revealed significant differences between cases (93 participants) and controls (3,820 participants), regarding vision impairment, nation, hypertension, diabetes, smoking, education, liver disease, asthma, and chronic lung disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of baseline characteristics between the two groups in the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValiable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNation (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHan_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3536 (92.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (87.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHui_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 ( 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 ( 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManchu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 ( 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 ( 2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiao_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 ( 0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMongol_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 ( 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 ( 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 ( 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 ( 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTibetan_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 ( 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 ( 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTujia_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 ( 0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUyghur_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 ( 0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 ( 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYi_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 ( 0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZhuang_Nationality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 ( 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 ( 2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e854 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2966 (77.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (80.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;=60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1557 (40.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2263 (59.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (50.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital_status (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3377 (88.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (88.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e443 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrunk (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrink_but_less_than_once_a_month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e311 ( 8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 ( 7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrink_more_than_once_a_month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e646 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone_of_these\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2863 (74.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (77.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3354 (87.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e466 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3668 (96.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (89.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 ( 4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3550 (92.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (88.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 ( 3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 ( 7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156 ( 4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 ( 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential_Address (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFamily_Housing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3701 (96.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90 (96.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 ( 0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNursing_Home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 ( 0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 ( 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 ( 3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDid_not_Finish_Primary_School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e732 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (30.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElementary_School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e845 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour-Year_College/Bachelor's_Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 ( 0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh_School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e316 ( 8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster's_Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 ( 0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle_School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e803 (21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo_Formal_Education_(Illiterate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e971 (25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSishu/Home_School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 ( 0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 ( 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo-/Three-Year_College/Associate_Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 ( 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 ( 0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVocational_School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 ( 1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 ( 2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver_Disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3731 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (91.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 ( 2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 ( 8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3787 (99.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (93.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 ( 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 ( 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic_Lung_Disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3688 (96.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132 ( 3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVision _impairment (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3694 (96.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (89.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 ( 3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSubsequent regression models demonstrated a significantly positive association between vision impairment and comorbidities of heart disease and depression, with all P values for vision impairment across the three models being less than 0.05. Specifically, model 1 showed OR\u0026thinsp;=\u0026thinsp;3.532 (95% CI\u0026thinsp;=\u0026thinsp;1.686\u0026ndash;6.659, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), model 2 yielded OR\u0026thinsp;=\u0026thinsp;3.247 (95% CI\u0026thinsp;=\u0026thinsp;1.524\u0026ndash;6.238, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and model 3 indicated OR\u0026thinsp;=\u0026thinsp;2.357 (95% CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;4.817, P\u0026thinsp;\u0026lt;\u0026thinsp;0.027) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear regression models of vision impairment and comorbidities of heart disease and depression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eexposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emodel1_OR(95%_CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emodel2_OR(95%_CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emodel3_OR(95%_CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVision_impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.532(1.686\u0026ndash;6.659)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.247(1.524\u0026ndash;6.238)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.357(1.037\u0026ndash;4.817)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep_value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0002737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0009395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, risk stratification analysis confirmed the stability of the association between vision problems and increased risk of comorbidities of heart disease and depression (OR\u0026thinsp;=\u0026thinsp;2.357, 95% CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;4.817, P\u0026thinsp;\u0026lt;\u0026thinsp;0.027) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther significant covariates included nationality (uyghur nationality), gender (female), education (elementary school, no formal education), hypertension (Yes), liver disease (Yes), and chronic lung disease (Yes) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, these findings underscored the critical role of vision problems in increasing the risk of comorbidities of heart disease and depression, highlighting the need for integrated healthcare approaches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Vision impairments exhibited excellent predictive ability for comorbidities of heart disease and depression\u003c/h2\u003e\u003cp\u003eThe ROC curve for model 3 demonstrated that vision impairments had excellent predictive ability for comorbidities of heart disease and depression, with an AUC of 0.837 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eFurthermore, XGBoost analysis revealed that education was the most important variable for predicting the occurrence of comorbidities of heart disease and depression. Although vision problems ranked eighth among all variables, they still played a significant role in the risk assessment of comorbidities of heart disease and depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eIn summary, these findings highlighted the multifaceted nature of factors, with vision impairments playing significant roles in predicting the risk of comorbidities of heart disease and depression.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCVDs represent the most prevalent chronic conditions encountered in clinical practice, characterized by elevated morbidity and mortality rates, and frequently associated with mental health complications\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Within the population of patients afflicted by CVDs, the elderly constitute a substantial proportion and often experience concomitant vision impairments\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Existing research indicates that vision impairments may increase the susceptibility of heart disease patients to depression \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, there is a notable deficiency in research exploring the association between vision impairment and the comorbidity of heart disease and depression, rendering this an underexplored domain. This study aims to examine the relationship between vision impairment and the concurrent presence of heart disease and depression, utilizing data from the CHARLS database. A total of 3,913 participants were included in the analysis, and significant differences in vision impairment were observed between the disease group and the control group, as evidenced by baseline statistical tables. These findings suggest that vision impairment may influence the comorbidity of heart disease and depression. The relationship between vision impairments and the co-occurrence of heart disease and depression was further examined using three analytical models. When combined with risk stratification analysis, findings indicated that vision impairments constitute a significant risk factor for heart disease accompanied by depression. These results offer insights and strategies for the early prevention of heart disease with concurrent depression within the Chinese population.\u003c/p\u003e\u003cp\u003ePsychological health exerts a substantial influence on CVD outcomes. Depression is linked to an increased global disease burden and elevated mortality risk in Western populations \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Research suggests that individuals with major depressive disorder face a significantly heightened risk of developing CVDs, along with diminished treatment efficacy, as evidenced by increased morbidity and mortality rates \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Consequently, for patients with pre-existing chronic health conditions, particularly those afflicted by both CVD and depression, treatment and management present even greater challenges \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Studies have demonstrated an association between depression and myocardial infarction, congestive heart failure, or any CVD \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Depression exerts a substantial adverse influence on both the progression and outcomes of CVD \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Furthermore, in elderly male patients, the administration of antidepressants not only enhances mood but is also correlated with a decreased risk of cardiovascular events\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. vision impairment adversely affects an individual's work efficiency and quality of life and may precipitate a range of psychological and social issues, particularly in middle-aged and elderly populations\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Research conducted on the elderly population in the United States indicates that individuals with vision impairments are more susceptible to depression, with a significant bidirectional and longitudinal relationship observed between vision impairments and mental health symptoms\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Additionally, vision impairments are linked to various CVDs and mental health conditions\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. The coexistence of heart disease and depression presents patients with heightened physical and life challenges. vision impairment can significantly hinder individuals' access to medical information, adherence to treatment protocols, and engagement in rehabilitation exercises. These limitations may exacerbate the progression of heart disease, increase psychological burden, and contribute to the onset and development of depression. Consequently, vision impairment may be intricately linked with heart disease comorbid with depression.\u003c/p\u003e\u003cp\u003eThis study identifies an independent association between vision impairment and the comorbidity of heart disease and depression through a comprehensive multi-model analysis system (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), underscoring its scientific significance when compared to multidisciplinary research. Unlike the studies by Donato\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e and Barber\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, which concentrate on single-disease mechanisms, our triple-model validation (AUC\u0026thinsp;=\u0026thinsp;0.837) not only corroborates the independent predictive value of vision impairment but also elucidates its critical role (ranked 8th) within a multifactorial predictive framework using the XGBoost model. This finding aligns with the interdisciplinary research conducted by the Smith team, which explores the specific connection between retinopathy and cardiovascular disease\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Notably, similar patterns of multisystem associations have been documented in other chronic disease studies: for instance, diabetes intensifies coronary artery lesions through inflammatory responses (SII values)\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, air pollutants concurrently affect cardiovascular and mental health in asthma patients via systemic inflammation\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, and there exists a vicious cycle of mutual exacerbation between asthma and coronary heart disease\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Collectively, these studies delineate a pathological network of interconnected organ systems.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides evidence for the association between vision impairment and depression in conjunction with heart disease among middle-aged and elderly individuals in China. vision impairment may serve as a risk factor for heart disease when combined with depression and can offer a theoretical foundation for the early prevention and treatment of this comorbidity. However, this study has certain limitations. It is specific to the Chinese population, and future research should include a broader range of countries and social groups to enhance the generalizability of the findings. Additionally, the covariates considered may not be sufficiently comprehensive, and other confounding factors could potentially influence the study's outcomes. Therefore, large-scale clinical trials are necessary to further elucidate the underlying mechanisms, thereby improving clinical practice and extending benefits to a wider patient population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Shaanxi Provincial (2022JM-540) and the Science and Technology Plan Project of xi\u0026rsquo;an( 24YXYJ0136).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the provisions of the Declaration of Helsinki. Since it did not involve human subjects, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.J. and Y.P.H. wrote the main manuscript text, S.X. and H.X.M. prepared all figures and tables. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The Global Burden of Cardiovascular Diseases and Risk: A Compass for Future Health. 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PMID: 39621867.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heart disease, Depression, Vision impairments, CHARLS database, Comorbidities","lastPublishedDoi":"10.21203/rs.3.rs-7005878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7005878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eResearches have shown that vision impairment are linked to various cardiovascular diseases and mental health conditions, particularly in middle-aged and elderly populations. Therefore, our study aimed to investigate the correlation between vision impairments as an exposure factor and the comorbidities of heart disease and depression as an outcome, exploring new strategies for their prevention and treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e A total of 22,698 participants from the China Health and Retirement Longitudinal Study (CHARLS) database in 2018 were incorporated into the study. After data screening, a baseline table was first created using the weighted chi-square test. Next, three logistic regression models were developed to investigate the correlation between the exposure factor and the outcome. Additionally, risk stratification analysis was conducted. Finally, a ROC curve was plotted to evaluate the predictive ability of the exposure factor for the outcome.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAfter excluding ineligible individuals, 3,913 participants remained, consisting of 93 cases and 3,820 controls. The baseline table revealed significant differences between cases and controls regarding vision impairments. A notably positive association was found between vision impairments and comorbidities of heart disease and depression (in model 3, OR\u0026thinsp;=\u0026thinsp;2.357, 95% CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;4.817, P\u0026thinsp;\u0026lt;\u0026thinsp;0.027). Furthermore, risk stratification analysis confirmed that vision problems were a significant risk factor for the outcome. ROC curves demonstrated strong predictive performance of vision impairments for the outcome, with an area under the curve (AUC) of 0.837.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe study's findings suggested a link between vision impairments and comorbidities of heart disease and depression, highlighting vision impairments as a potential risk factor for comorbidities of heart disease and depression. This could inform the development of new treatment strategies for the comorbidities of heart disease and depression.\u003c/p\u003e","manuscriptTitle":"Association between vision impairment and the risk of comorbidities of heart disease and depression: a population-based cohort study among middle-aged and older adults using the CHARLS 2018 data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 11:34:50","doi":"10.21203/rs.3.rs-7005878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-20T07:49:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28752111296878280775611975262564724936","date":"2025-08-11T12:36:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T08:28:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-10T08:52:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T09:42:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T09:40:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-06-30T03:22:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88ae21d0-3bb2-4d87-8d1d-f35121611105","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-19T11:34:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 11:34:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7005878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7005878","identity":"rs-7005878","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}