Relationship between depression scores and all - cause mortality in an obese population: a cohort study

Relationship between depression scores and all - cause mortality in an obese population: a cohort study | 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 Relationship between depression scores and all - cause mortality in an obese population: a cohort study Jianchao Wu, Lu Zhou, Sijia Yang, Shengbo Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6499114/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Depression is a grave global mental - health issue, commonly found among obese individuals. Obesity and depression are mutually interactive. Thus, investigating their association with all - cause mortality holds substantial significance. Methods: Using NHANES data from 2005 to 2018 in the United States, 9542 obese participants were screened out from 28047 participants. Depressive symptoms were assessed using the PHQ-9, while all-cause and cardiac death served as the outcome indicators. Covariates were accounted for in the analysis through different statistical techniques. Results: 1,761 participants were diagnosed with depression. Depressed patients and non - depressed patients differed significantly in several aspects.The PHQ-9 score was non-linear with all-cause mortality, with a threshold of 6. When the score was lower than 6, the all-cause mortality effect was significant. When it was higher than 6, there was no significant effect. Age, race and other factors influenced the relationship. A significant correlation was found between the depression score and all-cause mortality among people aged 60 or younger, particularly in non-Hispanic Black individuals. Conclusion： In the obese population, the PHQ - 9 score and all - cause mortality exhibit a non - linear association. When the score was lower than 6, the all-cause mortality effect was significant. When it was higher than 6, there was no significant effect. obesity Depression score All-cause mortality 9 items of patient health questionnaire (PHQ-9) Cohort study Figures Figure 1 Figure 2 1. Introduction Depression is considered a serious mental health condition that is commonly found in individuals with obesity. Obesity has been associated with the onset of several chronic diseases, including type 2 diabetes and cardiovascular issues. Moreover, it has been shown to significantly affect mental health, often worsening symptoms of depression[1,2]. The psychosocial challenges faced by obese individuals, such as discrimination and low self-esteem, contribute to the acceleration of depressive symptoms. Individuals with higher BMI are often afflicted with mental disorders [3,4]. From a physiological standpoint, alterations in leptin and cortisol levels, consequent to obesity, are associated with the pathophysiological processes underlying mood disorders [5,6]. The interplay between obesity and its metabolic consequences, compounded by reduced physical activity, forms a vicious cycle that perpetuates the condition. Obesity has been demonstrated to intensify depression, and conversely, depression promotes additional weight gain [7,8].A study analyzing NHANES data with PHQ - 9 metrics reveals that obese individuals tend to have higher PHQ - 9 scores[9]. The potential for depression to heighten mortality risk is attributable to its impact on various physiological systems, including the cardiovascular system, the immune system, and overall lifestyle. Addressing the comorbidity profile of obese individuals is a critical component of reducing overall health burdens [10,11]. Healthcare providers must consider the physical and mental health of obese individuals [8,11]. Utilizing the NHANES data collected from 2005 to 2018, this study delved into the link between the baseline scores of depressive symptoms and both all-cause mortality and cardiac-related mortality among the obese population. Given the hypothesis that the link between obesity and depression isn't a straightforward linear one, a threshold regression model was utilized to pinpoint the inflection point. The aim was to furnish evidence for the management of obesity - related issues. 2. Materials and methods 2.1 Data for the Study and Population Screening The NHANES, which is a large-scale national research project organized by the National Center for Health Statistics, provides its data free of charge on the web. As shown in Figure 1,the dataset covering the period from 2005 to 2018 originally comprised 28,047 participants. However, minors under the age of 18, individuals without PHQ - 9 scores or relevant death data, as well as those whose body mass index (BMI) was lower than 30 kg/m² were excluded from our analysis[12] .were excluded from the analysis. Consequently, 9570 participants were eligible for analysis. 2.2 Outcome Judgement, Depression Evaluation and Covariate Selection The primary outcome was all-cause mortality, and the secondary one was cardiac-cause mortality. Mortality data by Dec 31, 2019, were retrieved via NDI. Cardiac death was identified using ICD-10 codes. The PHQ-9 (0-27) was used to assess depressive symptoms[2,13,14]. The inclusion of age, sex, race, and other covariates was essential for determining disease and physical activity according to the responses provided in the questionnaire. 2.3 Statistical Methods The continuous and categorical variables represent baseline characteristics in a specific way, and they compare differences between non-depressed and depressed groups. The analysis incorporated both the unadjusted model and a multivariate-adjusted model, a sophisticated statistical approach that considers the interplay between multiple variables. The nonlinear relationship between the PHQ-9 score and mortality risk was explored through the generalized additive model and the two-stage Cox regression model. To guarantee the robustness of the results, a stratified analysis was conducted, with R software and its extension package as the analytical tools. A P value less than 0.05 was regarded as statistically significant.[15-17] 3. Results Table 1 presents the foundational attributes of the participants at the outset were presented. This focused on highlighting the distinctions between those with depression and those without. The study encompassed 9,542 participants, among which 1,761 had been diagnosed with depression. The average age for the participants with depression was 48.52 years, and that of the non-depressed ones was 49.35 years.There was a statistically significant age difference between the two groups (P = 0.026). The older group exhibited higher rates of depression and a lower poverty income ratio (PIR). In terms of health status, depressed patients exhibited a higher prevalence of hypertension, diabetes, and other diseases, such as hypertension. While there was minimal variation in ethnicity or cancer diagnosis, depressed individuals exhibited a lower propensity for regular exercise. Table 1. Baseline characteristics of study participants Characteristics Overall No-depression (PHQ-960 2768 (29.01%) 2291 (29.44%) 477 (27.09%) PIR 2.28 ± 1.51 2.40 ± 1.53 1.71 ± 1.27 <0.001 Q1 2366 (24.80%) 1715 (22.04%) 651 (36.97%) <0.001 Q2 2021 (21.18%) 1588 (20.41%) 433 (24.59%) Q3 2768 (29.01%) 2308 (29.66%) 460 (26.12%) Q4 2387 (25.02%) 2170 (27.89%) 217 (12.32%) Sex, n (%) <0.001 Male 3839 (40.23%) 3273 (42.06%) 566 (32.14%) Female 5703 (59.77%) 4508 (57.94%) 1195 (67.86%) Ethnicity, n (%) 0.001 Mexican American people 1651 (17.30%) 1345 (17.29%) 306 (17.38%) Other Hispanic people 934 (9.79%) 720 (9.25%) 214 (12.15%) Non-Hispanic White people 3923 (41.11%) 3200 (41.13%) 723 (41.06%) Non-Hispanic Black people 2451 (25.69%) 2044 (26.27%) 407 (23.11%) Other Race - Including Multi-Racial people 583 (6.09%) 472 (6.07%) 111 (6.20%) Hypertension, n (%) <0.001 yes 4514 (47.35%) 3517 (45.23%) 997 (56.68%) no 5020 (52.65%) 4258 (54.77%) 762 (43.32%) Diabetes, n (%) <0.001 yes 1929 (20.96%) 1441 (19.17%) 488 (28.91%) no 7274 (79.04%) 6074 (80.83%) 1200 (71.09%) Congestive heart failure, n (%) <0.001 yes 452 (4.93%) 319 (4.28%) 133 (7.76%) no 8714 (95.07%) 7134 (95.72%) 1580 (92.24%) Stroke, n (%) <0.001 yes 454 (4.95%) 314 (4.21%) 140 (8.16%) no 8719 (95.05%) 7144 (95.79%) 1575 (91.84%) Emphysema, n (%) <0.001 yes 236 (2.57%) 145 (1.94%) 91 (5.31%) no 8946 (97.43%) 7323 (98.06%) 1623 (94.69%) Chronic bronchitis, n (%) <0.001 yes 866 (9.45%) 577 (7.75%) 289 (16.86%) no 8296 (90.55%) 6871 (92.25%) 1425 (83.14%) Cancer, n (%) 0.048 yes 882 (9.60%) 695 (9.31%) 187 (10.87%) no 8303 (90.40%) 6769 (90.69%) 1534 (89.13%) Physical activity, n (%) <0.001 yes 3793 (39.88%) 3201 (41.24%) 592 (33.87%) no 5717 (60.12%) 4561 (58.76%) 1156 (66.13%) Note: Results in table: Mean+SD/N(%) Table 2 presents the outcomes of an analysis regarding the association between PHQ scores and all-cause mortality, which was evaluated via three different models. In the continuous analysis, Model 1 indicated that higher PHQ scores were correlated with an increased risk of mortality. The hazard ratio (HR) was 1.04, and its 95% confidence interval (CI) ranged from 1.02 to 1.06. Model 2 took age and other factors into account, leading to an increased HR of 1.06 (95% CI: 1.04, 1.08). With more covariates incorporated into Model 3, the HR dropped to 1.02 (95% CI: 0.99, 1.04), suggesting that the association was affected by confounding factors.In the context of classification analysis, it was observed that disparate score ranges exhibited varied relationships with mortality risk across different models. For instance, models 1 and 2 exhibited a discernible association between scores ranging from 10-14 and scores of 15 or higher, while model 3 revealed a diminished association or an absence of statistical significance following adjustment. The findings from the trend test further corroborated the role of confounding factors in this context. Table 2 Associations of PHQSCORE with All-cause mortality Model 1[OR(95% CI)] Model 2[OR(95% CI)] Model 3 [OR(95% CI)] PHQSCORE (continuous) 1.04 (1.02, 1.06) 1.06 (1.04, 1.08) 1.02 (0.99, 1.04) PHQSCORE（categorical） <10 Ref. Ref. Ref. 10-14 0.63 (0.41, 0.97) 0.56 (0.34, 0.92) 0.69 (0.40, 1.18) ≥15 0.57 (0.40, 0.82) 0.45 (0.29, 0.69) 0.84 (0.52, 1.35) P for trend 0.0044 0.0003 0.7714 Model 1: Without adjusting for covariates. Model 2: Adjusted for age, sexual identity, and ethnic group. Model 3: Adjustments include: ethnicity, physical exercise, household income - poverty ratio (PIR), high - blood - pressure (hypertension), diabetes, stroke, chronic bronchitis, coronary artery disease, heart failure, emphysema, age, gender, cancer. As shown in Figure 2, the horizontal axis depicts PHQ scores and the vertical axis represents all - cause mortality. The solid red line illustrates that when PHQ scores ascend from 0 to 25, all-cause mortality goes up, suggesting that more severe depressive symptoms are linked to a greater all-cause mortality risk. Table 3 investigated the association between the PHQ score and two types of mortality, namely all-cause mortality and cardiovascular disease mortality.In Model I, the PHQ score had a significant linear impact on all-cause mortality. The HR was 1.02, and its 95% CI ranged from 1.01 to 1.03, with P = 0.0029. Nevertheless, regarding cardiovascular disease mortality, the effect was not significant . In Model II, the breakpoint of the PHQ score was set at 6. When the score was below 6, both overall mortality and cardiac mortality effects were pronounced.When it exceeded 6, there was no marked impact on all - cause mortality, and the risk of cardiovascular disease death decreased slightly. Log - likelihood ratio tests indicated that the all - cause mortality model was insignificant, while the cardiovascular disease mortality model was significant. Table 3 The Association between PHQ Score and Mortality Results, with a Focus on Overall Mortality and Cardiac Mortality. Outcome: All-cause mortality CVD Death Model I A linear effect 1.02 (1.01, 1.03) 0.0029 1.01 (0.98, 1.03) 0.5366 Model II Break point (K) 6 6 Effect 1 for the segment K 1.01 (0.98, 1.03) 0.4508 0.97 (0.93, 1.01) 0.1214 Difference in effects between 2 and 1 0.96 (0.90, 1.01) 0.1010 0.88 (0.80, 0.97) 0.0083 Log - likelihood ratio test 0.100 0.008 Table 4 presents a sub-analysis on the relationship between PHQ-9 scores and all-cause mortality. Regarding sex, no significant interaction was detected, since the P-value for the corresponding male-to-female ratio was 0.9693.When it came to age, a significant connection was found between PHQ-9 scores and overall mortality among individuals aged 60 years or younger (odds ratio [OR] = 1.04, 95% confidence interval [CI]: 1.01 - 1.07, P = 0.0159). However, for those over 60 years old, no such association could be detected. Subgroup analysis based on poverty status revealed no significant disparities (P = 0.2068). Among different races, a significant association was observed in non-Hispanic blacks, with an OR of 1.05, a 95% CI ranging from 1.01 to 1.08, and a P-value of 0.0478. In contrast, for other ethnic groups, no significant tendency was manifested. Health factors such as a history of stroke showed a potential two - way relationship. Moreover, participation in physical activity did not lead to a significant alteration of the observed association. Table 4 Subgroup analysis of the association between PHQ-9score and all-cause mortality. Subgroup PHQ-9score [OR(95%CI)] P for interaction Sex, N (%) 0.9693 Male 1.02 (0.98, 1.06) Female 1.02 (0.99, 1.04) Age, N (%) 0.0159 ≤60years 1.04 (1.01, 1.07) ＞60years 1.00 (0.97, 1.02) PIR 0.2068 0-1.3 1.04 (0.97, 1.11) 1.4–3.5 1.00 (0.96, 1.03) ＞3.5 1.03 (1.00, 1.06) Ethnicity, N (%) 0.0478 Mexican American people 0.98 (0.94, 1.03) Other Hispanic people 1.05 (0.97, 1.12) Non-Hispanic White people 1.02 (0.99, 1.05) Non-Hispanic Black people 1.05 (1.01, 1.08) Other Race - Including Multi-Racial people 0.91 (0.82, 1.01) Hypertension, N (%) 0.4806 yes 1.01 (0.99, 1.04) no 1.03 (0.99, 1.06) Diabetes, N (%) 0.2208 yes 1.00 (0.97, 1.03) no 1.03 (1.00, 1.05) Congestive heart failure, N (%) 0.8789 yes 1.01 (0.96, 1.07) no 1.02 (0.99, 1.04) Stroke, N (%) 0.0146 yes 0.95 (0.89, 1.01) no 1.03 (1.00, 1.05) Emphysema, N (%) 0.4654 yes 0.99 (0.92, 1.07) no 1.02 (1.00, 1.04) Chronic bronchitis, N (%) 0.8741 yes 1.02 (0.98, 1.07) no 1.02 (0.99, 1.04) Cancer, N (%) 0.4582 yes 1.00 (0.95, 1.05) no 1.02 (1.00, 1.04) Physical activity, N (%) 0.9488 yes 1.02 (0.97, 1.06) no 1.02 (0.99, 1.04) 4. Discussion An examination of NHANES data spanning from 2005 to 2018 revealed a non-linear correlation between PHQ-9 scores and the mortality rate of obese individuals, and a threshold value of 6 was determined.When below this threshold, the link between PHQ - 9 scores and overall mortality presented a statistically significant association ，HR：1.05（1.01, 1.11）.In contrast, when above this threshold, the connection was not statistically significant. Previous literature has largely centered on the linear association between depression score and mortality risk.However, there is a paucity of research that has thoroughly examined the impact of specific depression severity on mortality in diverse populations, particularly obese populations. Previous analyses have not thoroughly investigated the threshold difference in the impact of depression on overall mortality, which has limited the applicability of relevant clinical interventions [20]. This analysis emphasizes a more detailed investigation of depression in obese individuals. For instance, Gariepy et al. underscore the significance of incorporating nonlinear dynamics into the examination of obesity-depression relationships, contending that depression may function as a precursor or consequence of obesity, and that the efficacy of intervention strategies is contingent upon their adaptation to address these interactions [19]. In comparison to earlier studies that may have relied on less sophisticated statistical descriptions, the methods employed in this analysis, particularly the utilization of survival analysis techniques such as logarithmic rank tests, enhance its robustness [21]. Future studies should define a specific threshold where the severity of depression notably impacts mortality outcomes. They should also combine multiple demographic factors and methodologies to thoroughly examine the two-way relationship between obesity and depression and create a comprehensive framework to address the psychological health problems of obese individuals[22, 23]. From a physiological standpoint, elements like elevated leptin levels and modified cortisol levels, which are often linked to obesity, might play a part in the physiological processes of mood disorders. Leptin resistance is thought to disrupt neurotransmitter systems related to mood regulation. Meanwhile, persistent changes in cortisol levels can cause alterations to brain structure and function, contributing to an increased risk of mortality [24,25]. Behavioral effects of depression include a reduction in physical activity levels, which are further exacerbated by the physical limitations associated with obesity, creating a detrimental cycle that exacerbates health problems and increases the risk of mortality [16,26]. From a psychological standpoint, the adverse effects of social discrimination and low self-esteem that frequently afflict obese individuals may intensify depressive symptoms. This, in turn, can complicate health management and lifestyle choices, indirectly leading to an increased risk of death [27,28]. Subgroup analysis showed that the relationship between depression (measured by PHQ - 9) and all - cause mortality in obese individuals displayed significant heterogeneity across different demographics and clinical classifications. Stratification by age demonstrated that depression was positively correlated with the mortality risk among individuals aged 60 years or younger (Odds Ratio: 1.04, 95% Confidence Interval: 1.01 - 1.07). This correlation became more evident in younger obese people, likely because they have fewer comorbidities and higher baseline physical function. In contrast, for the elderly population, this effect was weakened due to competing risks and decreased physiological reserve [29,30].Concerning racial disparities, depression has been shown to have a significant association with elevated mortality in non - Hispanic black individuals (OR]：1.05, 95%CI: 1.01 - 1.08). However, this link was not observed in Hispanic individuals and it was lacking statistical significance. Differences in obesity and mental health caused by racial and cultural factors, along with systemic differences, may account for these findings.Non-Hispanic blacks face systemic differences that make them more susceptible to depression, while Hispanics may be less affected by protective factors or differences in medical utilization [31-33]. A potential interaction effect was identified in individuals with a history of stroke ， severe underlying conditions, or modality. This relationship between depression and mortality risk may mask the role of depressive symptoms, which is consistent with previous comorbidities [29,30]. These results emphasize the intricate nature of the interconnection among obesity, depression, and mortality, thereby underscoring the necessity for targeted interventions. Subsequent studies should adopt a comprehensive approach to analyze these relationships and formulate prevention strategies to decrease the risk of death in high-risk groups [31,32]. In summary, this research employs NHANES data to explore the link between depression and mortality in obese individuals. This study is strengthened by its large sample size, national representation, and comprehensive consideration of covariates. Nevertheless, the cross-sectional design is subject to limitations, precluding the determination of causality and potentially influenced by confounding factors [34,35]. Consequently, longitudinal studies are required to elucidate causal mechanisms and develop more efficacious intervention strategies to reduce the risk of depression and mortality in obese individuals. 5. Conclusion In this study, we detected a non - linear correlation between the PHQ - 9 score and the mortality rate of the obese population, with a threshold value of 6. When the score was beneath 6, a notable association existed between lower PHQ - 9 scores and all - cause mortality . Conversely, when the score exceeded 6, the association was not statistically significant . Declarations Ethics approval and consent to participate This study has been approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS). The research strictly adheres to local laws and relevant institutional guidelines. According to national and institutional regulations, participants and their legal representatives are not required to provide written consent to participate in this study. Consent for publication All authors (Jianchao Wu, Lu Zhou, Sijia Yang, Shengbo Zhang) consent to the publication of this manuscript "Relationship between depression scores and all - cause mortality in an obese population: a cohort study" in Archives of Public Health. They confirm the originality of the paper and grant the journal all necessary publication rights. Availability of data and materials The datasets used in this study are publicly accessible. You can access the relevant data by visiting the official website of the National Health and Nutrition Examination Survey (NHANES) at https://www.cdc.gov/nchs/nhanes/index.html. Competing interests All authors declare that there are no commercial or financial relationships that could be regarded as a conflict of interest during the conduct of this study. The research was not influenced by such factors. Funding The authors received no monetary funding during the implementation of this study, the writing of this paper, and the dissemination of this article. Authors' contributions JW: Responsible for the initial concept development, methodology construction, data management, formal analysis, software use, manuscript drafting, participation in the verification process, conducting research, assisting in verification, providing feedback, supervising the methodology, supervising writing and editing, and conducting critical review and editing. LZ: Involved in data management, formal analysis, software use, and manuscript drafting. SZ: Participated in the verification process, research implementation, and manuscript revision. SY: Assisted in verification and provided feedback. Acknowledgements We would like to express our gratitude to the National Medical Research Center within the National Institute for the Prevention and Control of Diseases for making the data of the National Health and Nutrition Examination Survey publicly available, which provides important data support for this study. Authors' information (optional) No additional information about the authors is provided in this article. References Farrell, E.P.; Nadglowski, J.; Hollmann, E.; Roux, C.W.L.; McGillicuddy, D. The Nature of the Relationship Between Obesity and Mental Health: An IMI2 SOPHIA Qualitative Study. 2024 , doi:10.21203/rs.3.rs-4248258/v1. Zare, H.; Fugal, A.; Azadi, M.; Gaskin, D.J. How Income Inequality and Race Concentrate Depression in Low-Income Women in the US; 2005–2016. Healthcare-Basel 2022 , 10, 1424, doi:10.3390/healthcare10081424. Adam, M.Y. Social Support and Mental Health Among Obese and Non-Obese. 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Race and Socioeconomic Differences in Obesity and Depression Among Black and Non-Hispanic White Americans. J Health Care Poor U 2014 , 25, 257-275, doi:10.1353/hpu.2014.0038. Liu, H.; Dong, H.; Zhou, Y.; Jin, M.; Hao, H.; Yuan, Y.; Jia, H. The Association Between Metabolic Score for Visceral Fat and Depression in Overweight or Obese Individuals: Evidence From NHANES. Front Endocrinol 2024 , 15, doi:10.3389/fendo.2024.1482003. Mehta, T.; Pajewski, N.M.; Keith, S.W.; Fontaine, K.R.; Allison, D.B. Role of a Plausible Nuisance Contributor in the Declining Obesity-Mortality Risks Over Time. Exp Gerontol 2016 , 86, 14-21, doi:10.1016/j.exger.2016.09.015. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6499114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463486882,"identity":"f13bd513-18f0-447d-bc43-cb4a233b26eb","order_by":0,"name":"Jianchao Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie2RsWrDMBCGZQTpckGrDOk7qAQSCsZ5FQlBJpMlLyAIOEtCV3dpX6FdQsYzhnQR7WrIEj9AwB4LbamzFiJnLETfcMPxf/wcR4jH8z+ReBqEUMQaImDMXKz0VJUNpoMwwwurCIHhEKIiEka6o6KUFX5u4xlbmhGH5AMEwaBuEqci87XVc25xyrndw5gaGj5uziujVsF+isqUcsfvVnu4N9ij/Q4l/26V51KlXP28g0DZrRSnlpdSU4GA3crEHmRxm2r1andBZUBDmOUL5y3hMtHNMY3V09tDXXxBPGFskdeNQ2nfIf9uAuPKt9xgR8Dj8Xiunl/U/1+2JCHnjAAAAABJRU5ErkJggg==","orcid":"","institution":"Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University)","correspondingAuthor":true,"prefix":"","firstName":"Jianchao","middleName":"","lastName":"Wu","suffix":""},{"id":463486884,"identity":"36f5ef73-d3c3-4e37-9ffd-50aabd0d8258","order_by":1,"name":"Lu Zhou","email":"","orcid":"","institution":"Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University)","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Zhou","suffix":""},{"id":463486885,"identity":"53bea32e-e1ca-4b9c-a548-428052657ad6","order_by":2,"name":"Sijia Yang","email":"","orcid":"","institution":"Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University)","correspondingAuthor":false,"prefix":"","firstName":"Sijia","middleName":"","lastName":"Yang","suffix":""},{"id":463486887,"identity":"365c9bd6-973c-4a58-8f3f-f9bc963092bd","order_by":3,"name":"Shengbo Zhang","email":"","orcid":"","institution":"Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University)","correspondingAuthor":false,"prefix":"","firstName":"Shengbo","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-22 00:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6499114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6499114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83772929,"identity":"6b1f2be0-fea5-4891-8024-4ca01dee32c5","added_by":"auto","created_at":"2025-06-02 13:07:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41691,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant selection flowchart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499114/v1/d5f2e529476daa650baf8ed1.jpg"},{"id":83772930,"identity":"1e4e9685-bb7c-40a1-b43d-1e603c3582e8","added_by":"auto","created_at":"2025-06-02 13:07:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26177,"visible":true,"origin":"","legend":"\u003cp\u003eFitting of the curve for PHQ score and all - cause mortality\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499114/v1/2db2271526f976de1027a684.jpg"},{"id":88858981,"identity":"5025c0b8-92ec-426a-9d3a-fad43d02a022","added_by":"auto","created_at":"2025-08-12 07:23:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":864480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6499114/v1/97f42f3e-832c-43c8-9d90-49f514d0f0cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between depression scores and all - cause mortality in an obese population: a cohort study","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eDepression is considered a serious mental health condition that is commonly found in individuals with obesity. Obesity has been associated with the onset of several chronic diseases, including type 2 diabetes and cardiovascular issues. Moreover, it has been shown to significantly affect mental health, often worsening symptoms of depression[1,2]. The psychosocial challenges faced by obese individuals, such as discrimination and low self-esteem, contribute to the acceleration of depressive symptoms. Individuals with higher BMI are often afflicted with mental disorders [3,4].\u003c/p\u003e\n\u003cp\u003eFrom a physiological standpoint, alterations in leptin and cortisol levels, consequent to obesity, are associated with the pathophysiological processes underlying mood disorders [5,6]. The interplay between obesity and its metabolic consequences, compounded by reduced physical activity, forms a vicious cycle that perpetuates the condition. Obesity has been demonstrated to intensify depression, and conversely, depression promotes additional weight gain [7,8].A study analyzing NHANES data with PHQ - 9 metrics reveals that obese individuals tend to have higher PHQ - 9 scores[9]. The potential for depression to heighten mortality risk is attributable to its impact on various physiological systems, including the cardiovascular system, the immune system, and overall lifestyle. Addressing the comorbidity profile of obese individuals is a critical component of reducing overall health burdens [10,11]. Healthcare providers must consider the physical and mental health of obese individuals [8,11].\u003c/p\u003e\n\u003cp\u003eUtilizing the NHANES data collected from 2005 to 2018, this study delved into the link between the baseline scores of depressive symptoms and both all-cause mortality and cardiac-related mortality among the obese population. Given the hypothesis that the link between obesity and depression isn\u0026apos;t a straightforward linear one, a threshold regression model was utilized to pinpoint the inflection point. The aim was to furnish evidence for the management of obesity - related issues.\u003c/p\u003e"},{"header":"2.\tMaterials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data for the Study and Population Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES, which is a large-scale national research project organized by the National Center for Health Statistics, provides its data free of charge on the web. As shown in Figure 1,the dataset covering the period from 2005 to 2018 originally comprised 28,047 participants. However, minors under the age of 18, individuals without PHQ - 9 scores or relevant death data, as well as those whose body mass index (BMI) was lower than 30 kg/m\u0026sup2; were excluded from our analysis[12] .were excluded from the analysis. Consequently, 9570 participants were eligible for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Outcome Judgement, Depression Evaluation and Covariate Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality, and the secondary one was cardiac-cause mortality. Mortality data by Dec 31, 2019, were retrieved via NDI. Cardiac death was identified using ICD-10 codes. The PHQ-9 (0-27) was used to assess depressive symptoms[2,13,14]. The inclusion of age, sex, race, and other covariates was essential for determining disease and physical activity according to the responses provided in the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Statistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe continuous and categorical variables represent baseline characteristics in a specific way, and they compare differences between non-depressed and depressed groups. The analysis incorporated both the unadjusted model and a multivariate-adjusted model, a sophisticated statistical approach that considers the interplay between multiple variables. The nonlinear relationship between the PHQ-9 score and mortality risk was explored through the generalized additive model and the two-stage Cox regression model. To guarantee the robustness of the results, a stratified analysis was conducted, with R software and its extension package as the analytical tools. A P value less than 0.05 was regarded as statistically significant.[15-17]\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003eTable 1 presents the foundational attributes of the participants at the outset were presented. This focused on highlighting the distinctions between those with depression and those without. The study encompassed 9,542 participants, among which 1,761 had been diagnosed with depression. The average age for the participants with depression was 48.52 years, and that of the non-depressed ones was 49.35 years.There was a statistically significant age difference between the two groups (P = 0.026). The older group exhibited higher rates of depression and a lower poverty income ratio (PIR). In terms of health status, depressed patients exhibited a higher prevalence of hypertension, diabetes, and other diseases, such as hypertension. While there was minimal variation in ethnicity or cancer diagnosis, depressed individuals exhibited a lower propensity for regular exercise.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of study participants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9292%;\"\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3097%;\"\u003e\u003cstrong\u003eNo-depression\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e(PHQ-9\u0026lt;10)\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.885%;\"\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e(PHQ-9\u0026ge;10)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.55752%;\"\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003eN\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e9542\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e7781\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1761\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003eAge(years )\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e48.52 \u0026plusmn; 17.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e48.34 \u0026plusmn; 17.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e49.35 \u0026plusmn; 15.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e0.026\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003e\u0026le;60\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e6774 (70.99%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e5490 (70.56%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1284 (72.91%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e0.049\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003e\u0026gt;60\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e2768 (29.01%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e2291 (29.44%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e477 (27.09%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003ePIR\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e2.28 \u0026plusmn; 1.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e2.40 \u0026plusmn; 1.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1.71 \u0026plusmn; 1.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003eQ1\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e2366 (24.80%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e1715 (22.04%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e651 (36.97%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003eQ2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e2021 (21.18%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e1588 (20.41%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e433 (24.59%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003eQ3\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e2768 (29.01%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e2308 (29.66%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e460 (26.12%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.3186%;\"\u003eQ4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e2387 (25.02%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e2170 (27.89%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e217 (12.32%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eSex,\u0026nbsp;n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e3839 (40.23%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e3273 (42.06%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e566 (32.14%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e5703 (59.77%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e4508 (57.94%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1195 (67.86%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eEthnicity,\u0026nbsp;n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eMexican American people\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e1651 (17.30%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e1345 (17.29%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e306 (17.38%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eOther Hispanic people\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e934 (9.79%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e720 (9.25%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e214 (12.15%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eNon-Hispanic White people\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e3923 (41.11%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e3200 (41.13%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e723 (41.06%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eNon-Hispanic Black people\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e2451 (25.69%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e2044 (26.27%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e407 (23.11%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eOther Race - Including Multi-Racial people\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e583 (6.09%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e472 (6.07%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e111 (6.20%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eHypertension, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e4514 (47.35%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e3517 (45.23%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e997 (56.68%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e5020 (52.65%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e4258 (54.77%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e762 (43.32%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eDiabetes, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e1929 (20.96%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e1441 (19.17%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e488 (28.91%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e7274 (79.04%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e6074 (80.83%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1200 (71.09%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eCongestive heart failure, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e452 (4.93%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e319 (4.28%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e133 (7.76%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e8714 (95.07%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e7134 (95.72%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1580 (92.24%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eStroke, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e454 (4.95%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e314 (4.21%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e140 (8.16%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e8719 (95.05%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e7144 (95.79%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1575 (91.84%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003e\u0026nbsp;Emphysema, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e236 (2.57%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e145 (1.94%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e91 (5.31%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e8946 (97.43%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e7323 (98.06%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1623 (94.69%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003e\u0026nbsp;Chronic bronchitis, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e866 (9.45%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e577 (7.75%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e289 (16.86%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e8296 (90.55%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e6871 (92.25%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1425 (83.14%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eCancer, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e0.048\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e882 (9.60%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e695 (9.31%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e187 (10.87%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e8303 (90.40%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e6769 (90.69%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1534 (89.13%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003ePhysical activity, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e3793 (39.88%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e3201 (41.24%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e592 (33.87%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3186%;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.9292%;\"\u003e5717 (60.12%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.3097%;\"\u003e4561 (58.76%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.885%;\"\u003e1156 (66.13%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.55752%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Results in table: Mean+SD/N(%)\u003c/p\u003e\n\u003cp\u003eTable 2 presents the outcomes of an analysis regarding the association between PHQ scores and all-cause mortality, which was evaluated via three different models. In the continuous analysis, Model 1 indicated that higher PHQ scores were correlated with an increased risk of mortality. The hazard ratio (HR) was 1.04, and its 95% confidence interval (CI) ranged from 1.02 to 1.06. Model 2 took age and other factors into account, leading to an increased HR of 1.06 (95% CI: 1.04, 1.08). With more covariates incorporated into Model 3, the HR dropped to 1.02 (95% CI: 0.99, 1.04), suggesting that the association was affected by confounding factors.In the context of classification analysis, it was observed that disparate score ranges exhibited varied relationships with mortality risk across different models. For instance, models 1 and 2 exhibited a discernible association between scores ranging from 10-14 and scores of 15 or higher, while model 3 revealed a diminished association or an absence of statistical significance following adjustment. The findings from the trend test further corroborated the role of confounding factors in this context.\u003c/p\u003e\n\u003cp\u003eTable 2 Associations of PHQSCORE with All-cause mortality\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.7003%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0871%;\"\u003eModel 1[OR(95% CI)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6098%;\"\u003eModel 2[OR(95% CI)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6028%;\"\u003eModel 3 [OR(95% CI)]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.7003%;\"\u003ePHQSCORE (continuous)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.0871%;\"\u003e1.04 (1.02, 1.06)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.6098%;\"\u003e1.06 (1.04, 1.08)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21.6028%;\"\u003e1.02 (0.99, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.7003%;\"\u003ePHQSCORE（categorical）\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.0871%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.6098%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21.6028%;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.7003%;\"\u003e\u0026lt;10\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.0871%;\"\u003eRef.\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.6098%;\"\u003eRef.\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21.6028%;\"\u003eRef.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.7003%;\"\u003e10-14\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.0871%;\"\u003e0.63 (0.41, 0.97)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.6098%;\"\u003e0.56 (0.34, 0.92)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21.6028%;\"\u003e0.69 (0.40, 1.18)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.7003%;\"\u003e\u0026ge;15\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.0871%;\"\u003e0.57 (0.40, 0.82)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.6098%;\"\u003e0.45 (0.29, 0.69)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21.6028%;\"\u003e0.84 (0.52, 1.35)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.7003%;\"\u003eP for trend\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.0871%;\"\u003e0.0044\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25.6098%;\"\u003e\u0026nbsp;0.0003\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21.6028%;\"\u003e0.7714\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: Without adjusting for covariates.\u003c/p\u003e\n\u003cp\u003eModel 2: Adjusted for age, sexual identity, and ethnic group.\u003c/p\u003e\n\u003cp\u003eModel 3: Adjustments include: ethnicity, physical exercise, household income - poverty ratio (PIR), high - blood - pressure (hypertension), diabetes, stroke, chronic bronchitis, coronary artery disease, heart failure, emphysema, age, gender, cancer.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, the horizontal axis depicts PHQ scores and the vertical axis represents all - cause mortality. The solid red line illustrates that when PHQ scores ascend from 0 to 25, all-cause mortality goes up, suggesting that more severe depressive symptoms are linked to a greater all-cause mortality risk.\u003c/p\u003e\n\u003cp\u003eTable 3 investigated the association between the PHQ score and two types of mortality, namely all-cause mortality and cardiovascular disease mortality.In Model I, the PHQ score had a significant linear impact on all-cause mortality. The HR was 1.02, and its 95% CI ranged from 1.01 to 1.03, with P = 0.0029. Nevertheless, regarding cardiovascular disease mortality, the effect was not significant .\u003c/p\u003e\n\u003cp\u003eIn Model II, the breakpoint of the PHQ score was set at 6. When the score was below 6, both overall mortality and cardiac mortality effects were pronounced.When it exceeded 6, there was no marked impact on all - cause mortality, and the risk of cardiovascular disease death decreased slightly. Log - likelihood ratio tests indicated that the all - cause mortality model was insignificant, while the cardiovascular disease mortality model was significant.\u003c/p\u003e\n\u003cp\u003eTable 3 The Association between PHQ Score and Mortality Results, with a Focus on Overall Mortality and Cardiac Mortality.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eOutcome:\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eAll-cause mortality\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eCVD Death\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eModel I\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eA linear effect\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.02 (1.01, 1.03) 0.0029\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.01 (0.98, 1.03) 0.5366\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eModel II\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u0026nbsp; Break point (K)\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e6\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e6\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eEffect 1 for the segment \u0026lt; K\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.05(1.01, 1.11) 0.0134\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.11 (1.03, 1.19) 0.0070\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eEffect 2 for the segment \u0026gt; K\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.01 (0.98, 1.03) 0.4508\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.97 (0.93, 1.01) 0.1214\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eDifference in effects between 2 and 1\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.96 (0.90, 1.01) 0.1010\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.88 (0.80, 0.97) 0.0083\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eLog - likelihood ratio test\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.100\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.008\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 presents a sub-analysis on the relationship between PHQ-9 scores and all-cause mortality. Regarding sex, no significant interaction was detected, since the P-value for the corresponding male-to-female ratio was 0.9693.When it came to age, a significant connection was found between PHQ-9 scores and overall mortality among individuals aged 60 years or younger (odds ratio [OR] = 1.04, 95% confidence interval [CI]: 1.01 - 1.07, P = 0.0159). However, for those over 60 years old, no such association could be detected. Subgroup analysis based on poverty status revealed no significant disparities (P = 0.2068). Among different races, a significant association was observed in non-Hispanic blacks, with an OR of 1.05, a 95% CI ranging from 1.01 to 1.08, and a P-value of 0.0478. In contrast, for other ethnic groups, no significant tendency was manifested. Health factors such as a history of stroke showed a potential two - way relationship. Moreover, participation in physical activity did not lead to a significant alteration of the observed association.\u003c/p\u003e\n\u003cp\u003eTable 4 Subgroup analysis of the association between PHQ-9score and all-cause mortality.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eSubgroup\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u0026nbsp;PHQ-9score [OR(95%CI)]\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eP for interaction\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eSex, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.9693\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.98, 1.06)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.99, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eAge, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.0159\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\u0026le;60years\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.04 (1.01, 1.07)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e＞60years\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.00 (0.97, 1.02)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003ePIR\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.2068\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e0-1.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.04 (0.97, 1.11)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e1.4\u0026ndash;3.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.00 (0.96, 1.03)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e＞3.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.03 (1.00, 1.06)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eEthnicity, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.0478\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eMexican American people\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e0.98 (0.94, 1.03)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eOther Hispanic people\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.05 (0.97, 1.12)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eNon-Hispanic White people\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.99, 1.05)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eNon-Hispanic Black people\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.05 (1.01, 1.08)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eOther Race - Including Multi-Racial people\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e0.91 (0.82, 1.01)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eHypertension, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.4806\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.01 (0.99, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.03 (0.99, 1.06)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eDiabetes, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.2208\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.00 (0.97, 1.03)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.03 (1.00, 1.05)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eCongestive heart failure, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.8789\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.01 (0.96, 1.07)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.99, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eStroke, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.0146\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e0.95 (0.89, 1.01)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.03 (1.00, 1.05)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\u0026nbsp;Emphysema, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.4654\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e0.99 (0.92, 1.07)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (1.00, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003e\u0026nbsp;Chronic bronchitis, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.8741\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.98, 1.07)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.99, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eCancer, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.4582\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.00 (0.95, 1.05)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (1.00, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003ePhysical activity, N (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e0.9488\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eyes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.97, 1.06)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 220px;\"\u003eno\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e1.02 (0.99, 1.04)\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eAn examination of NHANES data spanning from 2005 to 2018 revealed a non-linear correlation between PHQ-9 scores and the mortality rate of obese individuals, and a threshold value of 6 was determined.When below this threshold, the link between PHQ - 9 scores and overall mortality presented a statistically significant association\u0026nbsp;，HR：1.05（1.01, 1.11）.In contrast, when above this threshold, the connection was not statistically significant.\u003c/p\u003e\n\u003cp\u003ePrevious literature has largely centered on the linear association between depression score and mortality risk.However, there is a paucity of research that has thoroughly examined the impact of specific depression severity on mortality in diverse populations, particularly obese populations. Previous analyses have not thoroughly investigated the threshold difference in the impact of depression on overall mortality, which has limited the applicability of relevant clinical interventions [20]. This analysis emphasizes a more detailed investigation of depression in obese individuals. For instance, Gariepy et al. underscore the significance of incorporating nonlinear dynamics into the examination of obesity-depression relationships, contending that depression may function as a precursor or consequence of obesity, and that the efficacy of intervention strategies is contingent upon their adaptation to address these interactions [19]. In comparison to earlier studies that may have relied on less sophisticated statistical descriptions, the methods employed in this analysis, particularly the utilization of survival analysis techniques such as logarithmic rank tests, enhance its robustness [21]. Future studies should define a specific threshold where the severity of depression notably impacts mortality outcomes. They should also combine multiple demographic factors and methodologies to thoroughly examine the two-way relationship between obesity and depression and create a comprehensive framework to address the psychological health problems of obese individuals[22, 23].\u003c/p\u003e\n\u003cp\u003eFrom a physiological standpoint, elements like elevated leptin levels and modified cortisol levels, which are often linked to obesity, might play a part in the physiological processes of mood disorders. Leptin resistance is thought to disrupt neurotransmitter systems related to mood regulation. Meanwhile, persistent changes in cortisol levels can cause alterations to brain structure and function, contributing to an increased risk of mortality [24,25]. Behavioral effects of depression include a reduction in physical activity levels, which are further exacerbated by the physical limitations associated with obesity, creating a detrimental cycle that exacerbates health problems and increases the risk of mortality [16,26]. From a psychological standpoint, the adverse effects of social discrimination and low self-esteem that frequently afflict obese individuals may intensify depressive symptoms. This, in turn, can complicate health management and lifestyle choices, indirectly leading to an increased risk of death [27,28].\u003c/p\u003e\n\u003cp\u003eSubgroup analysis showed that the relationship between depression (measured by PHQ - 9) and all - cause mortality in obese individuals displayed significant heterogeneity across different demographics and clinical classifications. Stratification by age demonstrated that depression was positively correlated with the mortality risk among individuals aged 60 years or younger (Odds Ratio: 1.04, 95% Confidence Interval: 1.01 - 1.07). This correlation became more evident in younger obese people, likely because they have fewer comorbidities and higher baseline physical function. In contrast, for the elderly population, this effect was weakened due to competing risks and decreased physiological reserve [29,30].Concerning racial disparities, depression has been shown to have a significant association with elevated mortality in non - Hispanic black individuals (OR]：1.05, 95%CI: 1.01 - 1.08). However, this link was not observed in Hispanic individuals and it was lacking statistical significance. Differences in obesity and mental health caused by racial and cultural factors, along with systemic differences, may account for these findings.Non-Hispanic blacks face systemic differences that make them more susceptible to depression, while Hispanics may be less affected by protective factors or differences in medical utilization [31-33]. A potential interaction effect was identified in individuals with a history of stroke\u0026nbsp;，\u0026nbsp;severe underlying conditions, or modality. This relationship between depression and mortality risk may mask the role of depressive symptoms, which is consistent with previous comorbidities [29,30]. These results emphasize the intricate nature of the interconnection among obesity, depression, and mortality, thereby underscoring the necessity for targeted interventions. Subsequent studies should adopt a comprehensive approach to analyze these relationships and formulate prevention strategies to decrease the risk of death in high-risk groups [31,32].\u003c/p\u003e\n\u003cp\u003eIn summary, this research employs NHANES data to explore the link between depression and mortality in obese individuals. This study is strengthened by its large sample size, national representation, and comprehensive consideration of covariates. Nevertheless, the cross-sectional design is subject to limitations, precluding the determination of causality and potentially influenced by confounding factors [34,35]. Consequently, longitudinal studies are required to elucidate causal mechanisms and develop more efficacious intervention strategies to reduce the risk of depression and mortality in obese individuals.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we detected a non - linear correlation between the PHQ - 9 score and the mortality rate of the obese population, with a threshold value of 6. When the score was beneath 6, a notable association existed between lower PHQ - 9 scores and all - cause mortality . Conversely, when the score exceeded 6, the association was not statistically significant .\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS). The research strictly adheres to local laws and relevant institutional guidelines. According to national and institutional regulations, participants and their legal representatives are not required to provide written consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors (Jianchao Wu, Lu Zhou, Sijia Yang, Shengbo Zhang) consent to the publication of this manuscript \u0026quot;Relationship between depression scores and all - cause mortality in an obese population: a cohort study\u0026quot; in Archives of Public Health. They confirm the originality of the paper and grant the journal all necessary publication rights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are publicly accessible. You can access the relevant data by visiting the official website of the National Health and Nutrition Examination Survey (NHANES) at https://www.cdc.gov/nchs/nhanes/index.html.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there are no commercial or financial relationships that could be regarded as a conflict of interest during the conduct of this study. The research was not influenced by such factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no monetary funding during the implementation of this study, the writing of this paper, and the dissemination of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJW: Responsible for the initial concept development, methodology construction, data management, formal analysis, software use, manuscript drafting, participation in the verification process, conducting research, assisting in verification, providing feedback, supervising the methodology, supervising writing and editing, and conducting critical review and editing.\u003c/p\u003e\n\u003cp\u003eLZ: Involved in data management, formal analysis, software use, and manuscript drafting.\u003c/p\u003e\n\u003cp\u003eSZ: Participated in the verification process, research implementation, and manuscript revision.\u003c/p\u003e\n\u003cp\u003eSY: Assisted in verification and provided feedback.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the National Medical Research Center within the National Institute for the Prevention and Control of Diseases for making the data of the National Health and Nutrition Examination Survey publicly available, which provides important data support for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo additional information about the authors is provided in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFarrell, E.P.; Nadglowski, J.; Hollmann, E.; Roux, C.W.L.; McGillicuddy, D. The Nature of the Relationship Between Obesity and Mental Health: An IMI2 SOPHIA Qualitative Study. \u003cstrong\u003e2024\u003c/strong\u003e, doi:10.21203/rs.3.rs-4248258/v1.\u003c/li\u003e\n\u003cli\u003eZare, H.; Fugal, A.; Azadi, M.; Gaskin, D.J. How Income Inequality and Race Concentrate Depression in Low-Income Women in the US; 2005\u0026ndash;2016. \u003cem\u003eHealthcare-Basel\u003c/em\u003e \u003cstrong\u003e2022\u003c/strong\u003e, 10, 1424, doi:10.3390/healthcare10081424.\u003c/li\u003e\n\u003cli\u003eAdam, M.Y. Social Support and Mental Health Among Obese and Non-Obese. \u003cem\u003eChettinad Health City Medical Journal\u003c/em\u003e \u003cstrong\u003e2022\u003c/strong\u003e, 11, 36-42, doi:10.24321/2278.2044.202227.\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;hlig, Y.; Antel, J.; F\u0026ouml;cker, M.; Hebebrand, J. Are Bidirectional Associations of Obesity and Depression Already Apparent in Childhood and Adolescence as Based on High‐quality Studies? 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Obesity and depression are mutually interactive. Thus, investigating their association with all - cause mortality holds substantial significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing NHANES data from 2005 to 2018 in the United States, 9542 obese participants were screened out from 28047 participants. Depressive symptoms were assessed using the PHQ-9, while all-cause and cardiac death served as the outcome indicators. Covariates were accounted for in the analysis through different statistical techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e1,761 participants were diagnosed with depression. Depressed patients and non - depressed patients differed significantly in several aspects.The PHQ-9 score was non-linear with all-cause mortality, with a threshold of 6. When the score was lower than 6, the all-cause mortality effect was significant. When it was higher than 6, there was no significant effect. Age, race and other factors influenced the relationship. A significant correlation was found between the depression score and all-cause mortality among people aged 60 or younger, particularly in non-Hispanic Black individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003eIn the obese population, the PHQ - 9 score and all - cause mortality exhibit a non - linear association. When the score was lower than 6, the all-cause mortality effect was significant. When it was higher than 6, there was no significant effect.\u003c/p\u003e","manuscriptTitle":"Relationship between depression scores and all - cause mortality in an obese population: a cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 13:06:56","doi":"10.21203/rs.3.rs-6499114/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b0fdb5d-410f-4422-8144-0f82320c5b10","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-12T07:23:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 13:06:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6499114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6499114","identity":"rs-6499114","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}