Cumulative Joint Burden of Functional Limitation and Depressive Symptoms on Cardiovascular Disease Risk ：A Nationwide Bidirectional Mediation and Competing Risks Analysis

Cumulative Joint Burden of Functional Limitation and Depressive Symptoms on Cardiovascular Disease Risk ：A Nationwide Bidirectional Mediation and Competing Risks Analysis | 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 Cumulative Joint Burden of Functional Limitation and Depressive Symptoms on Cardiovascular Disease Risk ：A Nationwide Bidirectional Mediation and Competing Risks Analysis Chuyi Luo, Chun Wang, Chengmin Jiang, Yukun Wang, Yongyan He, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7509404/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objectives: To examine independent, joint, and bidirectionally mediated effects of cumulative average functional limitation (FL) and depressive symptoms (DS) on incident cardiovascular disease (CVD) in a nationally representative cohort of Chinese adults aged ≥45 years. Methods: We analyzed 12,274 adults from the China Health and Retirement Longitudinal Study (2011–2018), free of CVD at their wave-specific baseline. Each participant’s baseline was the first survey wave in which they enrolled (waves 1–4). FL was assessed via activities of daily living/instrumental activities of daily living and DS via the 10-item Center for Epidemiologic Studies Depression Scale. Time‐weighted cumulative averages across waves were computed. Fine–Gray competing risks models estimated subdistribution hazard ratios (SHRs) for CVD, accounting for non‐CVD death. Additive and multiplicative interactions and bidirectional mediation were evaluated. Results: Over median 7.0 years of follow-up, 2,294 (18.7%) participants developed CVD. Compared with neither condition, fully adjusted SHRs were 1.63 (95% CI [1.44, 1.85]) for FL only, 1.35 (95% CI [1.17, 1.55]) for DS only, and 2.14 (95% CI [1.92, 2.40]) for both. Additive interaction was significant (RERI = 0.16). Mediation analyses showed DS mediated 23.6% (95% CI [17.7%, 31.0%]) of the FL–CVD association, and FL mediated 28.2% (95% CI [21.2%, 37.0%]) of the DS–CVD association. Discussion: Persistent co‐occurrence of FL and DS markedly increased CVD risk. Reciprocal mediation highlights the need for integrated strategies targeting functional and mental health to disrupt this reinforcing cycle and reduce CVD burden. Cumulative average exposure Competing risk model Ceiling effect Cardiovascular diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular diseases (CVDs) represent the foremost global health challenge, accounting for an estimated 17.9 million deaths in 2019, or 32% of all global mortality[ 1 ]. This burden is particularly acute in China, where rapid urbanization, lifestyle shifts, and population aging have fueled a dramatic escalation. By 2019, CVD was the leading cause of death, responsible for 46.74% of mortality in rural and 44.26% in urban areas, with over 330 million individuals affected nationwide[ 2 , 3 ]. While traditional risk factors like hypertension and smoking are well-established, other contributors, especially among the burgeoning older adult population, demand deeper investigation[ 3 ] . Among aging populations, functional limitation and depressive symptoms are two of the most prevalent and debilitating conditions. These conditions are not isolated but are intricately linked in a well-documented bidirectional relationship: functional decline significantly predicts the onset of depression (HR = 1.090, 95% CI 1.058–1.123), while baseline depression similarly forecasts future functional impairment (HR = 1.033, 95% CI 1.025–1.042) [ 4 ]. The clinical significance of this link is amplified following major health shocks; for instance, a recent landmark study demonstrated that severe physical disability post-stroke is a powerful determinant of a long-term, high-symptom trajectory of depression[ 5 ]. Recent etiologic models suggest this relationship transcends simple comorbidity[ 6 ]. Late-life depression is increasingly conceptualized not as a discrete psychiatric disorder, but as a manifestation of accelerated biological aging, enmeshed in a vicious cycle of inflammation, vascular injury, and neurodegeneration. Within this framework, functional limitation and frailty act as both consequences of and contributors to this cycle, perpetuating a downward spiral of physiological decline and psychological distress. Independently, both functional limitation (FL) and elevated depressive symptoms (DS) are established risk factors for incident CVD. For example, data from the China Health and Retirement Longitudinal Study (CHARLS) indicate that functional limitation increase CVD risk by 23% (HR = 1.23) [ 7 ]. Similarly, the burden of depressive symptoms, even at subclinical levels, is thought to contribute to CVD pathogenesis. The mechanisms are believed to mirror those identified in studies of major depressive disorder, including systemic inflammation, immune dysregulation, autonomic nervous system dysfunction, and poor treatment adherence [ 8 – 12 ]. However, despite the strong evidence for their individual impacts and their reciprocal relationship, the joint effect of co-occurring FL and DS on the prospective risk of CVD remains poorly understood. Prior research has primarily focused on unidirectional causal pathways[ 7 ] or relied on cross-sectional designs[ 13 ], which cannot elucidate the temporal dynamics of this complex interplay. Therefore, this study aims to dissect the joint burden of long-term functional limitation and depressive symptoms on incident CVD. Leveraging four waves of the China Health and Retirement Longitudinal Study (2011–2018), we will employ competing-risks models to quantify this burden, formally test for interaction on both additive and multiplicative scales, explore underpinning mediation pathways, and identify vulnerable subgroups. Methods Study design and population We analyzed data from the Harmonized China Health and Retirement Longitudinal Study (CHARLS), Version D (released June 2021), which includes four waves conducted in 2011, 2013, 2015, and 2018 and standardizes variables across waves to enhance comparability and reduce measurement bias. [ 14 – 16 ] CHARLS is a nationally representative longitudinal survey of adults aged 45 years or older in China, recruited through multistage probability-proportional-to-size sampling with periodic refreshment samples. The 2020 wave was excluded because a new survey instrument omitted the question essential for defining the study outcome. For each participant, follow-up was calculated from baseline interview to first CVD event, non-CVD death, loss to follow-up, or last available interview, totaling 83,398 person-years (median, 7.0 years; IQR, 6.4–7.0 years). From 25,586 respondents, we excluded individuals younger than 45 years at baseline (n = 8 655), with prevalent CVD (n = 2 419), with non-positive follow-up time (n = 638), or missing covariate data (n = 1 600), resulting in a final analytic cohort of 12,274 participants (Fig. 1 ). Assessment of Functional limitation Functional limitation (FL) was assessed using the Katz activities of daily living (ADL, 6 items: dressing, bathing, eating, transferring, toileting, continence) [ 17 ] and the Lawton instrumental activities of daily living (IADL, 5 items: managing finances, taking medications, shopping, preparing meals, household chores) [ 18 ] scales from the Harmonized CHARLS. The IADL item on telephone use was excluded from all waves to ensure consistency, as it was absent in Wave 1, [ 14 ] following prior CHARLS-based studies. [ 4 , 7 , 19 , 20 ]An FL score (range 0–11) was calculated as the sum of task impairments, with limitation defined as a score ≥ 1. [ 4 ] Missing task data at any wave rendered that wave’s score missing. Long-term functional limitation burden was quantified as a time-weighted cumulative average FL score over follow-up, calculated as the area under the curve of all available FL scores divided by total follow-up time(Supplementary Method1) Depressive Symptoms Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D), which has been validated in this population. [ 21 ] In the China Health and Retirement Longitudinal Study (CHARLS), the CES-D includes 10 items, each scored on a 4-point scale from 0 (“rarely or not at all”) to 3 (“most of the time”), yielding a total score ranging from 0 to 30. This scoring approach is consistent with prior studies using CHARLS data. [ 22 – 24 ] We obtained the pre-computed CES-D total score from the Harmonized CHARLS dataset, with higher scores indicating greater depressive symptom severity. Following previous validation research, [ 13 ] elevated depressive symptoms were defined as a CES-D score ≥ 10 in the primary analyses. Cumulative average exposure to depressive symptoms during follow-up was calculated using the same trapezoidal rule method described for functional limitation. Ascertainment of Outcomes and Follow-up We established a dynamic cohort for this analysis using data from four waves (2011–2018) of the China Health and Retirement Longitudinal Study (CHARLS). Individuals free of cardiovascular disease (CVD) at their baseline interview were eligible, with this date defined as their personal study entry. Follow-up began at the entry date and was calculated in years. The primary outcome was incident CVD, with all-cause mortality treated as a competing risk. Incident CVD was defined as the first self-reported, physician-diagnosed heart disease (including myocardial infarction, coronary heart disease, angina, or congestive heart failure) or stroke occurring at any wave after baseline. [ 25 – 28 ].Mortality status and date of death were obtained from interviewer-verified official records. For competing risk analyses, participant status and follow-up time were classified according to a predefined hierarchy. Those developing CVD were censored at the interview date when the event was reported (event of interest). Participants who died without prior CVD were considered to have experienced a competing event, with follow-up through their date of death. Individuals alive and free of CVD at their last interview were censored at that date. Each participant was thus assigned to one of three mutually exclusive categories: incident CVD, competing mortality, or censored. As a final data quality step, we excluded individuals with invalid or non-positive follow-up time. Ascertainment of Covariates Potential confounders were selected a priori based on established literature[ 26 , 29 – 32 ] and their availability in the dataset. To align with the dynamic cohort design, all covariates were measured at each participant’s entry wave and treated as baseline characteristics in the models. The multivariable models adjusted for sociodemographic factors (age at entry [45–54, 55–64, 65–74, ≥ 75 years], sex, residence, and education level (Supplementary Method2)) and health behaviors/conditions (smoking, alcohol consumption, physician-diagnosed hypertension, and diabetes). Social connection was evaluated using four distinct indicators—marital status (partnered vs unpartnered), living alone (yes vs no), contact with children (regular contact vs none/no children), and social activity participation (yes vs no)—rather than a composite measure of social isolation, due to the low internal consistency reliability of the latter (Cronbach’s α = 0.43). [ 33 – 37 ] Statistical Analysis Statistical analyses were performed using R, version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria), with two-sided P < .05 considered statistically significant. Missing data were addressed using a prespecified multistage approach tailored to each variable’s analytical role, concluding with complete case analysis (Supplementary Method3). Baseline characteristics were summarized across 4 prespecified health status groups—Neither Condition, Functional Limitation (FL) Only, Depressive Symptoms (DS) Only, and Both Conditions—defined using baseline criteria of FL ≥ 1 and DS score ≥ 10. Continuous variables were described using medians with interquartile ranges (IQRs) for skewed distributions (including all cumulative average exposures) or means with standard deviations (SDs) where appropriate; categorical variables were presented as counts with percentages. Group differences were evaluated using ANOVA or Kruskal–Wallis tests for continuous variables and χ² tests for categorical variables. The primary analysis estimated associations between cumulative average joint health status (primary exposure) and incident cardiovascular disease (CVD) using Fine–Gray competing risk models, accounting for non-CVD death as a competing event, and yielding subdistribution hazard ratios (SHRs) with 95% CIs. Three hierarchical models were constructed: Model 1 (unadjusted), Model 2 (adjusted for age, gender, and residence), and Model 3 (fully adjusted, additionally including education, marital status, living arrangement, social connections, smoking, drinking, hypertension and diabetes history). Effect modification was examined on a multiplicative scale by including interaction terms between the 4-level exposure and each baseline covariate in separate models, followed by prespecified subgroup analyses in a forest plot. Interaction between FL and DS was additionally assessed as continuous, centered variables. Potential mediation pathways were analyzed using two-way causal mediation analysis with an Accelerated Failure Time model (Weibull distribution), estimating direct and indirect effects via nonparametric bootstrapping (1 000 replications). Sensitivity analyses included: (1) substituting baseline for cumulative average exposure in Model 3; (2) repeating the analysis using a standard Cox proportional hazards model censoring competing events; (3) applying a CES-D depressive symptoms cutoff of ≥ 12;[ 38 ] and (4) calculating the E-value adjusted for the observed CVD incidence (18.7%) to assess the possible impact of unmeasured confounding. [ 39 ] Use of Artificial Intelligence–Assisted Tools In accordance with the Committee on Publication Ethics (COPE) and the International Committee of Medical Journal Editors (ICMJE) guidelines on artificial intelligence (AI)–assisted technologies, we transparently disclose the use of AI tools in the preparation of this study. Large language models, including Gemini 2.5 Pro (Google DeepMind, Mountain View, CA), GPT‑5‑Chat‑Latest (OpenAI, San Francisco, CA), and Claude Sonnet 4 20250514 (Anthropic, San Francisco, CA), were used under close human supervision to assist with: (1) drafting portions of the R code for data processing and statistical modeling; and (2) generating preliminary narrative text describing the study rationale, methodology, results, and discussion, based on analysis plans conceived entirely by the authors. All AI-generated code and text were independently reviewed, edited, and verified by the authors prior to inclusion in the manuscript. The final study design, statistical analyses, interpretation of results, and formulation of scientific conclusions were conducted solely by the authors, who retain full responsibility for the integrity, accuracy, and originality of all content. No AI tool is listed as an author, and all quoted or paraphrased materials have been appropriately attributed to human sources. Results Baseline Characteristics of the Study Population Among 12,274 participants, baseline sociodemographic, behavioral, and health characteristics differed significantly across the four prespecified joint health status groups (P < .001 for all) (Table 1 ). Compared with the reference group (Neither Condition), those with functional limitations (FL), depressive symptoms (DS), or both were generally older, more often female, had lower educational attainment, and were more likely to reside in rural areas. Table 1 Baseline Characteristics of Participants According to Joint Health Status (N = 12,274) Characteristic Overall (n = 12,274) Neither condition (n = 6,666) FL only (n = 1,298) Depressive symptoms only (n = 2,542) Both conditions (n = 1,768) p Age group, % < 0.001 45–54 38.0 43.8 21.9 39.8 22.2 55–64 37.5 36.5 36.3 39.7 39.0 65–74 17.8 14.9 27.1 15.8 24.9 ≥ 75 7.1 4.7 14.6 4.7 13.9 Sex, % < 0.001 Male 48.8 55.0 45.8 41.7 37.8 Female 51.2 45.0 54.2 58.3 62.2 Education, % < 0.001 Less than secondary 88.6 83.8 94.0 92.3 97.1 Secondary/vocational 9.8 13.6 5.1 7.1 2.6 Tertiary 1.7 2.6 0.9 0.7 0.3 Residence, % < 0.001 Urban 19.7 25.0 16.3 15.2 8.8 Rural 80.3 75.0 83.7 84.8 91.2 Marital status, % < 0.001 Partnered 87.8 91.8 83.3 85.3 79.6 Unpartnered 12.2 8.2 16.7 14.7 20.4 Living alone, % 14.8 11.6 18.0 16.9 21.6 < 0.001 Child contact, % < 0.001 Has contact 90.3 92.4 89.8 88.4 85.1 No contact 7.6 5.8 8.2 8.9 12.2 No children 2.1 1.8 1.9 2.8 2.7 Social activity, % < 0.001 Participation 46.2 51.5 40.5 42.6 35.7 No participation 53.8 48.5 59.5 57.4 64.3 Smoking status, % < 0.001 Never 59.9 57.0 60.8 63.9 64.7 Former 8.1 8.4 9.8 6.0 9.0 Current 32.0 34.7 29.4 30.1 26.3 Drinking status, % < 0.001 Not current 65.6 61.5 68.9 69.2 73.4 Current 34.4 38.5 31.1 30.8 26.6 Hypertension, % 21.9 19.7 27.2 22.5 25.2 < 0.001 Diabetes, % 4.9 4.2 6.9 5.2 5.9 < 0.001 Notes. Data are presented as % (n). FL = functional limitation; DS = depressive symptoms; CHARLS = China Health and Retirement Longitudinal Study. p values were calculated using one-way analysis of variance (ANOVA) for continuous variables and Pearson’s χ² tests for categorical variables to compare characteristics across the four joint health status groups. A graded pattern of social vulnerability was evident: the Both Conditions group had the highest prevalence of being unpartnered (20.4% vs 8.2% in the Neither group), living alone (21.5% vs 11.6%), and not participating in social activities (64.3% vs 48.5%). Chronic conditions such as hypertension and diabetes were also most common among participants with FL (FL Only or Both Conditions). Cumulative Joint Health Status and Incident CVD Over a median follow-up of 7.0 years, 2 294 participants (18.7%) developed incident CVD. When cumulative average exposure was considered, the risk of CVD increased progressively with a higher burden of FL and/or DS. The Fine–Gray cumulative incidence curves (Fig. 2 ) visually confirmed this dose-response relationship, showing a sustained and widening separation among the four health status groups over the follow-up period (Gray’s test, P < .001). Notably, the risk gradient was most pronounced when using cumulative exposure (Fig. 2 D), where the curve for the Both Conditions group diverged most sharply from the others, reinforcing that long-term burden is a stronger predictor than baseline status (Fig. 2 C). In fully adjusted Fine–Gray models, the subdistribution hazard ratios (SHRs) for incident CVD were 1.63 (95% CI, 1.44–1.85) for FL only, 1.35 (95% CI, 1.17–1.56) for DS only, and 2.14 (95% CI, 1.92–2.40) for both conditions, relative to the group with neither condition (P < .001 for all) (Table 2 ). Table 2 Fine–Gray Models for Association Between Cumulative Joint Health Status and Incident CVD Characteristic Model 1 SHR (95% CI) p-value Model 2 SHR (95% CI) p-value Model 3 SHR (95% CI) p-value Joint health status cumulative 1. Neither Condition (ref) — — — 2. FL Only 1.75 (1.55–1.98) < 0.001 1.66 (1.47–1.89) < 0.001 1.63 (1.44–1.85) < 0.001 3. Depressive Symptoms Only 1.28 (1.11–1.47) < 0.001 1.35 (1.17–1.55) < 0.001 1.35 (1.17–1.56) < 0.001 4. Both Conditions 2.20 (1.98–2.45) < 0.001 2.20 (1.97–2.46) < 0.001 2.14 (1.92–2.40) < 0.001 Age group at entry 45–54 (ref) — — 55–64 1.32 (1.20–1.45) < 0.001 1.27 (1.15–1.40) < 0.001 65–74 1.59 (1.42–1.79) < 0.001 1.47 (1.31–1.66) < 0.001 ≥ 75 1.40 (1.18–1.66) < 0.001 1.24 (1.03–1.48) 0.022 Gender Male (ref) — — Female 1.13 (1.04–1.22) 0.004 1.17 (1.04–1.31) 0.011 Education level Less than secondary (ref) — Secondary & vocational 1.25 (1.08–1.44) 0.003 Tertiary 1.36 (0.99–1.87) 0.054 Residence at entry Urban (ref) — — Rural 0.63 (0.57–0.70) < 0.001 0.72 (0.65–0.80) < 0.001 Marital status Partnered (ref) — Unpartnered 0.96 (0.73–1.26) 0.8 Smoking status Never (ref) — Former 1.28 (1.09–1.50) 0.003 Current 1.09 (0.96–1.23) 0.2 Drinking status Not current (ref) — Current 0.93 (0.85–1.03) 0.2 Hypertension No (ref) — Yes 1.88 (1.72–2.04) < 0.001 Diabetes No (ref) — Yes 1.22 (1.05–1.41) 0.011 Living alone No (ref) — Yes 1.11 (0.86–1.42) 0.4 Child contact Has contact (ref) — No contact 0.88 (0.76–1.03) 0.11 No children 1.02 (0.77–1.36) 0.9 Social activity Participation (ref) — No participation 0.95 (0.88–1.03) 0.2 Abbreviations: SHR, subdistribution hazard ratio; CI, confidence interval; CVD, Cardiovascular Disease Notes. All models account for the competing risk of non-CVD death. Model 1 is the crude model. Model 2 adjusts for demographic covariates. Model 3 is the fully adjusted model. ref = reference category. Interaction and mediation In competing risks regression using the Fine–Gray model, cumulative average functional limitation and cumulative average depressive symptoms were each independently associated with increased incident CVD risk (per‑point SHR = 1.144; 95% CI, 1.116–1.173 and 1.047; 95% CI, 1.038–1.056, respectively; P < .001 for both), and their interaction was negative and significant (SHR = 0.990; 95% CI, 0.987–0.993; P < .001) (Supplementary Table 1). Stratified analyses revealed opposite patterns: in participants with low FL (0–1 points), higher CES‑D scores predicted monotonically higher CVD risk, whereas in high FL (6–11 points), risk paradoxically decreased with increasing CES‑D(Fig. 3 ). In fully adjusted accelerated failure time (AFT) models, this paradox disappeared—both exposures independently and consistently shortened time to CVD across strata. Bidirectional AFT‑based mediation analysis (1000 bootstraps) indicated that 28.2% (95% CI, 21.2–37.0) of the FL total effect (–0.607; 95% CI, − 0.700 to − 0.510) was mediated through subsequent CES‑D (FL → DS → CVD, ACME = − 0.171), and 23.6% (95% CI, 17.7–31.0) of the CES‑D total effect (–0.210; 95% CI, − 0.252 to − 0.170) was mediated through FL (DS → FL → CVD, ACME = − 0.050) (Supplementary Table 2), supporting a mutually reinforcing pathway between physical and mental health in accelerating CVD onset. Subgroup Analyses We examined the FL–DS interaction across demographic, social, and clinical strata (Fig. 4 ). The interaction term’s SHR was consistently < 1.0 across subgroups defined by sex, age, residence, marital status, social ties, lifestyle, and comorbidities, with no statistically significant interaction with these stratification variables (P for interaction > .05 for all), indicating a broadly generalizable antagonistic effect. An exception was observed in participants with tertiary education (SHR, 1.67; 95% CI, 0.63–2.71), but this was imprecise due to small sample size. Sensitivity Analyses The primary findings were robust across multiple sensitivity analyses. Replacing the Fine–Gray model with a cause-specific Cox model produced similar estimates (both conditions: HR, 2.21; 95% CI, 1.97–2.48; Supplementary Table 3 Panel A). Applying a stricter depressive symptom threshold (CES-D ≥ 12) yielded comparable results (both conditions: SHR, 1.48; 95% CI, 1.31–1.66; Supplementary Table 3 Panel B). Using baseline rather than cumulative exposures also showed significant, albeit slightly attenuated, associations (both conditions: SHR, 1.54; 95% CI, 1.37–1.72; Supplementary Table 3 Panel C). E‑value analysis for the primary finding (both conditions vs. neither) yielded values of 2.77 for the point estimate and 2.51 for the lower bound of the 95% CI, indicating that substantial unmeasured confounding would be required to explain the observed association (Supplementary Table 4). Discussion In this large nationwide cohort, cumulative comorbid burden of functional limitation (FL) and depressive symptoms (DS) was a strong and independent predictor of incident cardiovascular disease (CVD). Absolute risk was highest in individuals with both burdens, despite a sub-multiplicative interaction (“risk saturation”), and persistent co-occurrence—not transient episodes—conferred the greatest hazard. Bidirectional mediation revealed a self-perpetuating cycle: 28.2% of FL-related CVD risk was mediated via DS, and 23.6% of DS-related risk via FL. These findings expand current etiologic models by quantifying reciprocal effects and situating them within the context of accelerated biological aging. The likely mechanisms are multifactorial and overlapping, encompassing systemic inflammation, hypothalamic–pituitary–adrenal axis dysregulation, autonomic imbalance, vascular dysfunction, and immune disturbance—processes well recognized in both late-life depression and frailty. [ 8 – 12 , 26 , 40 – 42 ], Behaviorally, DS may impair physical function via reduced motivation, energy depletion, psychomotor retardation, and diminished self-care, [ 43 – 45 ], whereas FL may predispose to DS through physical discomfort, mobility restriction, social isolation, and loss of autonomy—factors also linked to inflammation and neuroendocrine disruption. [ 42 ] Once one burden sufficiently activates these pathways, the marginal risk contribution of the other diminishes, consistent with a biological “ceiling effect.” [ 46 ] Our results align with and extend prior evidence that cumulative exposure metrics improve prediction of chronic disease outcomes, [ 26 ] and that combined functional impairment and vascular risk factors synergistically increase CVD risk. [ 29 , 30 ]Clinical data reinforce the public health relevance: in the DEPACS trial, [ 47 ]depression screening in acute coronary syndrome patients improved identification of individuals at long-term risk of major cardiac events, and escitalopram treatment was associated with better prognosis over eight years. Similarly, generalized anxiety disorder was predictive of post-surgical cardiovascular events, [ 48 ] suggesting that chronic psychological distress in various forms has sustained cardiovascular consequences. The absence of interaction by age indicates that the deleterious synergy between FL and DS is not confined to older adults: middle-aged (< 65 years) and older (≥ 65 years) individuals were similarly affected. These findings underscore the need for lifespan-wide screening, and the sub-additive interaction further suggests that once both burdens are entrenched, targeting only one may yield diminishing returns. Together, our data advocate for early, integrated, and multifaceted prevention strategies that address physical function and mental health in tandem. Limitations include self-reported exposures and outcomes, which may incur measurement error, though self-report is also pragmatic for low-cost, scalable screening. Residual confounding is possible despite extensive adjustment and an E-value of 2.77. Excluding 4.8% of participants with missing covariates—who were older, less educated, and had earlier events—may have led to healthy-subject bias, yielding conservative estimates. Finally, 2–3‑year survey intervals reduce temporal precision for exposure and outcome onset. Conclusion In conclusion, the cumulative burdens of FL and DS function as interdependent drivers of CVD risk via a self-perpetuating cycle, with evidence for a biological ceiling effect suggestive of shared causal pathways. These findings advocate for a paradigm shift from siloed management to integrated prevention and treatment strategies addressing both domains simultaneously, across all adult age groups. Future studies should elucidate the specific inflammatory and neuroendocrine mechanisms underlying this interplay, and test integrated intervention programs—including rehabilitation, psychosocial support, and lifestyle modification—capable of disrupting the vicious cycle and delaying CVD onset. Abbreviations FL Functional Limitation DS Depressive Symptoms CVD Cardiovascular diseases CHARLS China Health and Retirement Longitudinal Study SHR Subdistribution Hazard Ratio CI Confidence intervals ADL Activities of daily living CES-D the 10-item Center for Epidemiologic Studies Depression Scale IADL instrumental activities of daily living (IADL) Declarations Ethics approval and consent to participate The ethical review board of Peking University meticulously examined and subsequently sanctioned the CHARLS project (IRB00001052-11015). Informed consent was obtained from all subjects prior to their participation of this study. Consent for publication Not applicable. Availability of data and materials Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), conducted by the National School of Development at Peking University. The English-language website is available at https://charls.pku.edu.cn/en/ (if temporarily unavailable, please visit the main site at https://charls.pku.edu.cn/). CHARLS data are publicly accessible upon registration via the official website. Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (grant numbers 82374564 and 82074566); and the Hubei Provincial Joint Fund Project (grant numbers 2025AFD596 and 2023AFD112). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. Author contributions CYL: Conceptualization; Data curation; Formal analysis; Methodology; Software; Validation; Visualization; Writing – original draft; Writing – review & editing. CW: Conceptualization; Data curation; Formal analysis; Methodology; Writing – original draft; Writing – review & editing. CMJ: Conceptualization; Data curation; Formal analysis; Writing – original draft. YKW: Conceptualization; Data curation. YYH: Conceptualization; Data curation. YJD (corresponding author): Conceptualization; Funding acquisition; Methodology; Project administration; Supervision; Writing – original draft; Writing – review & editing. All authors actively participated in the research process, made substantial contributions to manuscript revisions, and reviewed and approved the final version of the manuscript. Chuyi Luo had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Acknowledgements This study is based on baseline and follow-up data from the China Health and Retirement Longitudinal Study (CHARLS). We thank the CHARLS research team, field staff, and every respondent for their time and dedication to the project. Use of Artificial Intelligence–Assisted Tools In accordance with the Committee on Publication Ethics (COPE) and the International Committee of Medical Journal Editors (ICMJE) guidelines on artificial intelligence (AI)–assisted technologies, we transparently disclose the use of AI tools in the preparation of this study. Large language models, including Gemini 2.5 Pro (Google DeepMind, Mountain View, CA), GPT‑5‑Chat‑Latest (OpenAI, San Francisco, CA), and Claude Sonnet 4 20250514 (Anthropic, San Francisco, CA), were used under close human supervision to assist with: (1) drafting portions of the R code for data processing and statistical modeling; and (2) generating preliminary narrative text describing the study rationale, methodology, results, and discussion, based on analysis plans conceived entirely by the authors. All AI-generated code and text were independently reviewed, edited, and verified by the authors prior to inclusion in the manuscript. The final study design, statistical analyses, interpretation of results, and formulation of scientific conclusions were conducted solely by the authors, who retain full responsibility for the integrity, accuracy, and originality of all content. No AI tool is listed as an author, and all quoted or paraphrased materials have been appropriately attributed to human sources. References World Health Organization. Fact sheets: cardiovascular diseases (CVDs).https://www. who.int/ news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 9 May 2025. Hu S-S. Report on cardiovascular health and diseases in China 2021: an updated summary. Journal of Geriatric Cardiology. 2023;20:399–430. https://doi.org/10.26599/1671-5411.2023.06.001. Wu Q, Zhao Y, Liu L, Liu Y, Liu J. 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Supplementary Files Supplementary.docx CHARLSCVDDataPreparation.r CHARLSCVDAnalysisReadyV9.rdata CHARLSCVDStatisticalAnalysis.r CHARLSCVDCausalMediationAnalysis.r CHARLSsubgroupresultsenhancedV10.csv CHARLSCVDSubgroup.r Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 25 Sep, 2025 Editor invited by journal 08 Sep, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 04 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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15:50:08","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172141,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/966cf4c446974ddc915cbcc9.html"},{"id":93795598,"identity":"f8c1a1b0-eb56-478c-baff-1577493f1e90","added_by":"auto","created_at":"2025-10-17 15:50:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Participant Inclusion and Exclusion\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/5a2a1791dc0ab697f96a7ee0.png"},{"id":93798669,"identity":"6d32aca8-d341-4d9e-96d5-76dec71795ad","added_by":"auto","created_at":"2025-10-17 16:14:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative Incidence of Cardiovascular Disease by Health Status Profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote：\u003c/em\u003eShaded areas represent 95% Cls. Panels A and B compare extreme groups (Neither Condition vs Both Conditions).Panels C and D show all four health status combinations. Number at risk for each group is shown below the xaxis for each panel.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/4eeeb42d6ee6353907971eba.png"},{"id":93795602,"identity":"ce83608a-53c9-4a84-8bbd-8349b3f6f876","added_by":"auto","created_at":"2025-10-17 15:50:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction of Depressive Symptoms and Functional Limitation on CVD Risk\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/9079fa4e06722e5df262d8bc.png"},{"id":93797854,"identity":"914d5e1a-533e-411f-ac63-e1c21f0bc552","added_by":"auto","created_at":"2025-10-17 16:06:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":251929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJoint Effect of Functional Limitation and Depressive Symptoms on incident CVD: A Subgroup Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/73ba5308a4e5f8d17369015b.png"},{"id":93798885,"identity":"56fc4cd3-b3d7-433c-a34a-a4a43222bb34","added_by":"auto","created_at":"2025-10-17 16:22:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2153589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/8e7b0d6d-0175-4e6e-ae3b-3eb42fe86779.pdf"},{"id":93797332,"identity":"2fc9ccbc-3d22-465b-8cd1-ec1a36c80d08","added_by":"auto","created_at":"2025-10-17 15:58:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32316,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/30bc4bfec4520639d917504c.docx"},{"id":93795599,"identity":"46a2e3bb-bc89-4220-8133-a06eb9911429","added_by":"auto","created_at":"2025-10-17 15:50:08","extension":"r","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33234,"visible":true,"origin":"","legend":"","description":"","filename":"CHARLSCVDDataPreparation.r","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/0fc8fec6764e6b4a070f23e3.r"},{"id":93797343,"identity":"dbbc6eba-5430-41d3-87ca-581ec6e86e14","added_by":"auto","created_at":"2025-10-17 15:58:08","extension":"rdata","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":158360,"visible":true,"origin":"","legend":"","description":"","filename":"CHARLSCVDAnalysisReadyV9.rdata","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/e628f02bd73eb5f635ed0b31.rdata"},{"id":93797335,"identity":"ff1a31ce-1cf8-455c-98e5-454271831746","added_by":"auto","created_at":"2025-10-17 15:58:08","extension":"r","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":40987,"visible":true,"origin":"","legend":"","description":"","filename":"CHARLSCVDStatisticalAnalysis.r","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/51d4a201f915e6ef84fa4a64.r"},{"id":93795611,"identity":"115fbf7d-3350-47ae-bcea-adf872cc31bf","added_by":"auto","created_at":"2025-10-17 15:50:08","extension":"r","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":7219,"visible":true,"origin":"","legend":"","description":"","filename":"CHARLSCVDCausalMediationAnalysis.r","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/9eedb2b8c8e6734b2820fbec.r"},{"id":93797858,"identity":"a61d5cfa-c03a-4c05-9a7f-fbb26a840ea9","added_by":"auto","created_at":"2025-10-17 16:06:08","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3837,"visible":true,"origin":"","legend":"","description":"","filename":"CHARLSsubgroupresultsenhancedV10.csv","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/ef989b573c6cf1e565318627.csv"},{"id":93795613,"identity":"76eafce5-b9b8-4a07-9248-6465fa9bc3dc","added_by":"auto","created_at":"2025-10-17 15:50:08","extension":"r","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":5397,"visible":true,"origin":"","legend":"","description":"","filename":"CHARLSCVDSubgroup.r","url":"https://assets-eu.researchsquare.com/files/rs-7509404/v1/29f80bfce4679acde67c8521.r"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cumulative Joint Burden of Functional Limitation and Depressive Symptoms on Cardiovascular Disease Risk ：A Nationwide Bidirectional Mediation and Competing Risks Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) represent the foremost global health challenge, accounting for an estimated 17.9\u0026nbsp;million deaths in 2019, or 32% of all global mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This burden is particularly acute in China, where rapid urbanization, lifestyle shifts, and population aging have fueled a dramatic escalation. By 2019, CVD was the leading cause of death, responsible for 46.74% of mortality in rural and 44.26% in urban areas, with over 330\u0026nbsp;million individuals affected nationwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While traditional risk factors like hypertension and smoking are well-established, other contributors, especially among the burgeoning older adult population, demand deeper investigation[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eAmong aging populations, functional limitation and depressive symptoms are two of the most prevalent and debilitating conditions. These conditions are not isolated but are intricately linked in a well-documented bidirectional relationship: functional decline significantly predicts the onset of depression (HR\u0026thinsp;=\u0026thinsp;1.090, 95% CI 1.058\u0026ndash;1.123), while baseline depression similarly forecasts future functional impairment (HR\u0026thinsp;=\u0026thinsp;1.033, 95% CI 1.025\u0026ndash;1.042) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The clinical significance of this link is amplified following major health shocks; for instance, a recent landmark study demonstrated that severe physical disability post-stroke is a powerful determinant of a long-term, high-symptom trajectory of depression[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent etiologic models suggest this relationship transcends simple comorbidity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Late-life depression is increasingly conceptualized not as a discrete psychiatric disorder, but as a manifestation of accelerated biological aging, enmeshed in a vicious cycle of inflammation, vascular injury, and neurodegeneration. Within this framework, functional limitation and frailty act as both consequences of and contributors to this cycle, perpetuating a downward spiral of physiological decline and psychological distress.\u003c/p\u003e\u003cp\u003eIndependently, both functional limitation (FL) and elevated depressive symptoms (DS) are established risk factors for incident CVD. For example, data from the China Health and Retirement Longitudinal Study (CHARLS) indicate that functional limitation increase CVD risk by 23% (HR\u0026thinsp;=\u0026thinsp;1.23) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, the burden of depressive symptoms, even at subclinical levels, is thought to contribute to CVD pathogenesis. The mechanisms are believed to mirror those identified in studies of major depressive disorder, including systemic inflammation, immune dysregulation, autonomic nervous system dysfunction, and poor treatment adherence [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, despite the strong evidence for their individual impacts and their reciprocal relationship, the joint effect of co-occurring FL and DS on the prospective risk of CVD remains poorly understood. Prior research has primarily focused on unidirectional causal pathways[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or relied on cross-sectional designs[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which cannot elucidate the temporal dynamics of this complex interplay.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to dissect the joint burden of long-term functional limitation and depressive symptoms on incident CVD. Leveraging four waves of the China Health and Retirement Longitudinal Study (2011\u0026ndash;2018), we will employ competing-risks models to quantify this burden, formally test for interaction on both additive and multiplicative scales, explore underpinning mediation pathways, and identify vulnerable subgroups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eWe analyzed data from the Harmonized China Health and Retirement Longitudinal Study (CHARLS), Version D (released June 2021), which includes four waves conducted in 2011, 2013, 2015, and 2018 and standardizes variables across waves to enhance comparability and reduce measurement bias. [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] CHARLS is a nationally representative longitudinal survey of adults aged 45 years or older in China, recruited through multistage probability-proportional-to-size sampling with periodic refreshment samples. The 2020 wave was excluded because a new survey instrument omitted the question essential for defining the study outcome. For each participant, follow-up was calculated from baseline interview to first CVD event, non-CVD death, loss to follow-up, or last available interview, totaling 83,398 person-years (median, 7.0 years; IQR, 6.4\u0026ndash;7.0 years). From 25,586 respondents, we excluded individuals younger than 45 years at baseline (n\u0026thinsp;=\u0026thinsp;8 655), with prevalent CVD (n\u0026thinsp;=\u0026thinsp;2 419), with non-positive follow-up time (n\u0026thinsp;=\u0026thinsp;638), or missing covariate data (n\u0026thinsp;=\u0026thinsp;1 600), resulting in a final analytic cohort of 12,274 participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of Functional limitation\u003c/h3\u003e\n\u003cp\u003eFunctional limitation (FL) was assessed using the Katz activities of daily living (ADL, 6 items: dressing, bathing, eating, transferring, toileting, continence) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and the Lawton instrumental activities of daily living (IADL, 5 items: managing finances, taking medications, shopping, preparing meals, household chores) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] scales from the Harmonized CHARLS. The IADL item on telephone use was excluded from all waves to ensure consistency, as it was absent in Wave 1, [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] following prior CHARLS-based studies. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]An FL score (range 0\u0026ndash;11) was calculated as the sum of task impairments, with limitation defined as a score\u0026thinsp;\u0026ge;\u0026thinsp;1. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Missing task data at any wave rendered that wave\u0026rsquo;s score missing.\u003c/p\u003e\u003cp\u003eLong-term functional limitation burden was quantified as a time-weighted cumulative average FL score over follow-up, calculated as the area under the curve of all available FL scores divided by total follow-up time(Supplementary Method1)\u003c/p\u003e\n\u003ch3\u003eDepressive Symptoms\u003c/h3\u003e\n\u003cp\u003eDepressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D), which has been validated in this population. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] In the China Health and Retirement Longitudinal Study (CHARLS), the CES-D includes 10 items, each scored on a 4-point scale from 0 (\u0026ldquo;rarely or not at all\u0026rdquo;) to 3 (\u0026ldquo;most of the time\u0026rdquo;), yielding a total score ranging from 0 to 30. This scoring approach is consistent with prior studies using CHARLS data. [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] We obtained the pre-computed CES-D total score from the Harmonized CHARLS dataset, with higher scores indicating greater depressive symptom severity. Following previous validation research, [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] elevated depressive symptoms were defined as a CES-D score\u0026thinsp;\u0026ge;\u0026thinsp;10 in the primary analyses. Cumulative average exposure to depressive symptoms during follow-up was calculated using the same trapezoidal rule method described for functional limitation.\u003c/p\u003e\n\u003ch3\u003eAscertainment of Outcomes and Follow-up\u003c/h3\u003e\n\u003cp\u003eWe established a dynamic cohort for this analysis using data from four waves (2011\u0026ndash;2018) of the China Health and Retirement Longitudinal Study (CHARLS). Individuals free of cardiovascular disease (CVD) at their baseline interview were eligible, with this date defined as their personal study entry. Follow-up began at the entry date and was calculated in years.\u003c/p\u003e\u003cp\u003eThe primary outcome was incident CVD, with all-cause mortality treated as a competing risk. Incident CVD was defined as the first self-reported, physician-diagnosed heart disease (including myocardial infarction, coronary heart disease, angina, or congestive heart failure) or stroke occurring at any wave after baseline. [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].Mortality status and date of death were obtained from interviewer-verified official records.\u003c/p\u003e\u003cp\u003eFor competing risk analyses, participant status and follow-up time were classified according to a predefined hierarchy. Those developing CVD were censored at the interview date when the event was reported (event of interest). Participants who died without prior CVD were considered to have experienced a competing event, with follow-up through their date of death. Individuals alive and free of CVD at their last interview were censored at that date. Each participant was thus assigned to one of three mutually exclusive categories: incident CVD, competing mortality, or censored. As a final data quality step, we excluded individuals with invalid or non-positive follow-up time.\u003c/p\u003e\n\u003ch3\u003eAscertainment of Covariates\u003c/h3\u003e\n\u003cp\u003ePotential confounders were selected a priori based on established literature[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and their availability in the dataset. To align with the dynamic cohort design, all covariates were measured at each participant\u0026rsquo;s entry wave and treated as baseline characteristics in the models.\u003c/p\u003e\u003cp\u003eThe multivariable models adjusted for sociodemographic factors (age at entry [45\u0026ndash;54, 55\u0026ndash;64, 65\u0026ndash;74, \u0026ge;\u0026thinsp;75 years], sex, residence, and education level (Supplementary Method2)) and health behaviors/conditions (smoking, alcohol consumption, physician-diagnosed hypertension, and diabetes). Social connection was evaluated using four distinct indicators\u0026mdash;marital status (partnered vs unpartnered), living alone (yes vs no), contact with children (regular contact vs none/no children), and social activity participation (yes vs no)\u0026mdash;rather than a composite measure of social isolation, due to the low internal consistency reliability of the latter (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.43). [\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R, version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria), with two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;.05 considered statistically significant.\u003c/p\u003e\u003cp\u003eMissing data were addressed using a prespecified multistage approach tailored to each variable\u0026rsquo;s analytical role, concluding with complete case analysis (Supplementary Method3).\u003c/p\u003e\u003cp\u003eBaseline characteristics were summarized across 4 prespecified health status groups\u0026mdash;Neither Condition, Functional Limitation (FL) Only, Depressive Symptoms (DS) Only, and Both Conditions\u0026mdash;defined using baseline criteria of FL\u0026thinsp;\u0026ge;\u0026thinsp;1 and DS score\u0026thinsp;\u0026ge;\u0026thinsp;10. Continuous variables were described using medians with interquartile ranges (IQRs) for skewed distributions (including all cumulative average exposures) or means with standard deviations (SDs) where appropriate; categorical variables were presented as counts with percentages. Group differences were evaluated using ANOVA or Kruskal\u0026ndash;Wallis tests for continuous variables and χ\u0026sup2; tests for categorical variables.\u003c/p\u003e\u003cp\u003eThe primary analysis estimated associations between cumulative average joint health status (primary exposure) and incident cardiovascular disease (CVD) using Fine\u0026ndash;Gray competing risk models, accounting for non-CVD death as a competing event, and yielding subdistribution hazard ratios (SHRs) with 95% CIs. Three hierarchical models were constructed: Model 1 (unadjusted), Model 2 (adjusted for age, gender, and residence), and Model 3 (fully adjusted, additionally including education, marital status, living arrangement, social connections, smoking, drinking, hypertension and diabetes history).\u003c/p\u003e\u003cp\u003eEffect modification was examined on a multiplicative scale by including interaction terms between the 4-level exposure and each baseline covariate in separate models, followed by prespecified subgroup analyses in a forest plot. Interaction between FL and DS was additionally assessed as continuous, centered variables. Potential mediation pathways were analyzed using two-way causal mediation analysis with an Accelerated Failure Time model (Weibull distribution), estimating direct and indirect effects via nonparametric bootstrapping (1 000 replications).\u003c/p\u003e\u003cp\u003eSensitivity analyses included: (1) substituting baseline for cumulative average exposure in Model 3; (2) repeating the analysis using a standard Cox proportional hazards model censoring competing events; (3) applying a CES-D depressive symptoms cutoff of \u0026ge;\u0026thinsp;12;[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and (4) calculating the E-value adjusted for the observed CVD incidence (18.7%) to assess the possible impact of unmeasured confounding. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUse of Artificial Intelligence–Assisted Tools\u003c/h3\u003e\n\u003cp\u003e In accordance with the Committee on Publication Ethics (COPE) and the International Committee of Medical Journal Editors (ICMJE) guidelines on artificial intelligence (AI)\u0026ndash;assisted technologies, we transparently disclose the use of AI tools in the preparation of this study. Large language models, including Gemini 2.5 Pro (Google DeepMind, Mountain View, CA), GPT‑5‑Chat‑Latest (OpenAI, San Francisco, CA), and Claude Sonnet 4 20250514 (Anthropic, San Francisco, CA), were used under close human supervision to assist with: (1) drafting portions of the R code for data processing and statistical modeling; and (2) generating preliminary narrative text describing the study rationale, methodology, results, and discussion, based on analysis plans conceived entirely by the authors.\u003c/p\u003e\u003cp\u003eAll AI-generated code and text were independently reviewed, edited, and verified by the authors prior to inclusion in the manuscript. The final study design, statistical analyses, interpretation of results, and formulation of scientific conclusions were conducted solely by the authors, who retain full responsibility for the integrity, accuracy, and originality of all content. No AI tool is listed as an author, and all quoted or paraphrased materials have been appropriately attributed to human sources.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics of the Study Population\u003c/h2\u003e\n \u003cp\u003eAmong 12,274 participants, baseline sociodemographic, behavioral, and health characteristics differed significantly across the four prespecified joint health status groups (P\u0026thinsp;\u0026lt;\u0026thinsp;.001 for all) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with the reference group (Neither Condition), those with functional limitations (FL), depressive symptoms (DS), or both were generally older, more often female, had lower educational attainment, and were more likely to reside in rural areas.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics of Participants According to Joint Health Status (N\u0026thinsp;=\u0026thinsp;12,274)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;12,274)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNeither condition (n\u0026thinsp;=\u0026thinsp;6,666)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFL only (n\u0026thinsp;=\u0026thinsp;1,298)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDepressive symptoms only (n\u0026thinsp;=\u0026thinsp;2,542)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBoth conditions\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,768)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u0026ndash;54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e55\u0026ndash;64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u0026ndash;74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess than secondary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary/vocational\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartnered\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnpartnered\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving alone, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild contact, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHas contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial activity, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo participation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNever\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFormer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking status, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot current\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes.\u003c/em\u003e Data are presented as % (n). FL\u0026thinsp;=\u0026thinsp;functional limitation; DS\u0026thinsp;=\u0026thinsp;depressive symptoms; CHARLS\u0026thinsp;=\u0026thinsp;China Health and Retirement Longitudinal Study. p values were calculated using one-way analysis of variance (ANOVA) for continuous variables and Pearson\u0026rsquo;s \u0026chi;\u0026sup2; tests for categorical variables to compare characteristics across the four joint health status groups.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eA graded pattern of social vulnerability was evident: the Both Conditions group had the highest prevalence of being unpartnered (20.4% vs 8.2% in the Neither group), living alone (21.5% vs 11.6%), and not participating in social activities (64.3% vs 48.5%). Chronic conditions such as hypertension and diabetes were also most common among participants with FL (FL Only or Both Conditions).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCumulative Joint Health Status and Incident CVD\u003c/h2\u003e\n \u003cp\u003eOver a median follow-up of 7.0 years, 2 294 participants (18.7%) developed incident CVD. When cumulative average exposure was considered, the risk of CVD increased progressively with a higher burden of FL and/or DS.\u003c/p\u003e\n \u003cp\u003eThe Fine\u0026ndash;Gray cumulative incidence curves (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) visually confirmed this dose-response relationship, showing a sustained and widening separation among the four health status groups over the follow-up period (Gray\u0026rsquo;s test, P\u0026thinsp;\u0026lt;\u0026thinsp;.001). Notably, the risk gradient was most pronounced when using cumulative exposure (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD), where the curve for the Both Conditions group diverged most sharply from the others, reinforcing that long-term burden is\u003c/p\u003e\n \u003cp\u003ea stronger predictor than baseline status (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eIn fully adjusted Fine\u0026ndash;Gray models, the subdistribution hazard ratios (SHRs) for incident CVD were 1.63 (95% CI, 1.44\u0026ndash;1.85) for FL only, 1.35 (95% CI, 1.17\u0026ndash;1.56) for DS only, and 2.14 (95% CI, 1.92\u0026ndash;2.40) for both conditions, relative to the group with neither condition (P\u0026thinsp;\u0026lt;\u0026thinsp;.001 for all) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFine\u0026ndash;Gray Models for Association Between Cumulative Joint Health Status and Incident CVD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1 SHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2 SHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3 SHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJoint health status cumulative\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. Neither Condition (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. FL Only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75 (1.55\u0026ndash;1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (1.47\u0026ndash;1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63 (1.44\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. Depressive Symptoms Only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (1.11\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.17\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.17\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4. Both Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20 (1.98\u0026ndash;2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20 (1.97\u0026ndash;2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14 (1.92\u0026ndash;2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group at entry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u0026ndash;54 (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e55\u0026ndash;64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (1.20\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (1.15\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u0026ndash;74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 (1.42\u0026ndash;1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (1.31\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026thinsp;75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (1.18\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (1.03\u0026ndash;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (1.04\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (1.04\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess than secondary (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary \u0026amp; vocational\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.08\u0026ndash;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTertiary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (0.99\u0026ndash;1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence at entry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63 (0.57\u0026ndash;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72 (0.65\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartnered (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnpartnered\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.73\u0026ndash;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNever (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFormer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (1.09\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (0.96\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot current (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.85\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88 (1.72\u0026ndash;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1.05\u0026ndash;1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving alone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.86\u0026ndash;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHas contact (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.76\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.77\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipation (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo participation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.88\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e SHR, subdistribution hazard ratio; CI, confidence interval; CVD, Cardiovascular Disease\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNotes.\u003c/em\u003e All models account for the competing risk of non-CVD death. Model 1 is the crude model. Model 2 adjusts for demographic covariates. Model 3 is the fully adjusted model. ref = reference category.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eInteraction and mediation\u003c/h2\u003e\n \u003cp\u003eIn competing risks regression using the Fine\u0026ndash;Gray model, cumulative average functional limitation and cumulative average depressive symptoms were each independently associated with increased incident CVD risk (per‑point SHR\u0026thinsp;=\u0026thinsp;1.144; 95% CI, 1.116\u0026ndash;1.173 and 1.047; 95% CI, 1.038\u0026ndash;1.056, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;.001 for both), and their interaction was negative and significant (SHR\u0026thinsp;=\u0026thinsp;0.990; 95% CI, 0.987\u0026ndash;0.993; P\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Supplementary Table 1). Stratified analyses revealed opposite patterns: in participants with low FL (0\u0026ndash;1 points), higher CES‑D scores predicted monotonically higher CVD risk, whereas in high FL (6\u0026ndash;11 points), risk paradoxically decreased with increasing CES‑D(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn fully adjusted accelerated failure time (AFT) models, this paradox disappeared\u0026mdash;both exposures independently and consistently shortened time to CVD across strata. Bidirectional AFT‑based mediation analysis (1000 bootstraps) indicated that 28.2% (95% CI, 21.2\u0026ndash;37.0) of the FL total effect (\u0026ndash;0.607; 95% CI, \u0026minus;\u0026thinsp;0.700 to \u0026minus;\u0026thinsp;0.510) was mediated through subsequent CES‑D (FL \u0026rarr; DS \u0026rarr; CVD, ACME = \u0026minus;\u0026thinsp;0.171), and 23.6% (95% CI, 17.7\u0026ndash;31.0) of the CES‑D total effect (\u0026ndash;0.210; 95% CI, \u0026minus;\u0026thinsp;0.252 to \u0026minus;\u0026thinsp;0.170) was mediated through FL (DS \u0026rarr; FL \u0026rarr; CVD, ACME = \u0026minus;\u0026thinsp;0.050) (Supplementary Table 2), supporting a mutually reinforcing pathway between physical and mental health in accelerating CVD onset.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup Analyses\u003c/h2\u003e\n \u003cp\u003eWe examined the FL\u0026ndash;DS interaction across demographic, social, and clinical strata (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The interaction term\u0026rsquo;s SHR was consistently\u0026thinsp;\u0026lt;\u0026thinsp;1.0 across subgroups defined by sex, age, residence, marital status, social ties, lifestyle, and comorbidities, with no statistically significant interaction with these stratification variables (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;.05 for all), indicating a broadly generalizable antagonistic effect. An exception was observed in participants with tertiary education (SHR, 1.67; 95% CI, 0.63\u0026ndash;2.71), but this was imprecise due to small sample size.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e\n \u003cp\u003eThe primary findings were robust across multiple sensitivity analyses. Replacing the Fine\u0026ndash;Gray model with a cause-specific Cox model produced similar estimates (both conditions: HR, 2.21; 95% CI, 1.97\u0026ndash;2.48; Supplementary Table\u0026nbsp;3 Panel A). Applying a stricter depressive symptom threshold (CES-D\u0026thinsp;\u0026ge;\u0026thinsp;12) yielded comparable results (both conditions: SHR, 1.48; 95% CI, 1.31\u0026ndash;1.66; Supplementary Table\u0026nbsp;3 Panel B). Using baseline rather than cumulative exposures also showed significant, albeit slightly attenuated, associations (both conditions: SHR, 1.54; 95% CI, 1.37\u0026ndash;1.72; Supplementary Table\u0026nbsp;3 Panel C). E‑value analysis for the primary finding (both conditions vs. neither) yielded values of 2.77 for the point estimate and 2.51 for the lower bound of the 95% CI, indicating that substantial unmeasured confounding would be required to explain the observed association (Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large nationwide cohort, cumulative comorbid burden of functional limitation (FL) and depressive symptoms (DS) was a strong and independent predictor of incident cardiovascular disease (CVD). Absolute risk was highest in individuals with both burdens, despite a sub-multiplicative interaction (\u0026ldquo;risk saturation\u0026rdquo;), and persistent co-occurrence\u0026mdash;not transient episodes\u0026mdash;conferred the greatest hazard. Bidirectional mediation revealed a self-perpetuating cycle: 28.2% of FL-related CVD risk was mediated via DS, and 23.6% of DS-related risk via FL. These findings expand current etiologic models by quantifying reciprocal effects and situating them within the context of accelerated biological aging.\u003c/p\u003e\u003cp\u003eThe likely mechanisms are multifactorial and overlapping, encompassing systemic inflammation, hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis dysregulation, autonomic imbalance, vascular dysfunction, and immune disturbance\u0026mdash;processes well recognized in both late-life depression and frailty. [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], Behaviorally, DS may impair physical function via reduced motivation, energy depletion, psychomotor retardation, and diminished self-care, [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], whereas FL may predispose to DS through physical discomfort, mobility restriction, social isolation, and loss of autonomy\u0026mdash;factors also linked to inflammation and neuroendocrine disruption. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] Once one burden sufficiently activates these pathways, the marginal risk contribution of the other diminishes, consistent with a biological \u0026ldquo;ceiling effect.\u0026rdquo; [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOur results align with and extend prior evidence that cumulative exposure metrics improve prediction of chronic disease outcomes, [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and that combined functional impairment and vascular risk factors synergistically increase CVD risk. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]Clinical data reinforce the public health relevance: in the DEPACS trial, [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]depression screening in acute coronary syndrome patients improved identification of individuals at long-term risk of major cardiac events, and escitalopram treatment was associated with better prognosis over eight years. Similarly, generalized anxiety disorder was predictive of post-surgical cardiovascular events, [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] suggesting that chronic psychological distress in various forms has sustained cardiovascular consequences.\u003c/p\u003e\u003cp\u003eThe absence of interaction by age indicates that the deleterious synergy between FL and DS is not confined to older adults: middle-aged (\u0026lt;\u0026thinsp;65 years) and older (\u0026ge;\u0026thinsp;65 years) individuals were similarly affected. These findings underscore the need for lifespan-wide screening, and the sub-additive interaction further suggests that once both burdens are entrenched, targeting only one may yield diminishing returns. Together, our data advocate for early, integrated, and multifaceted prevention strategies that address physical function and mental health in tandem.\u003c/p\u003e\u003cp\u003eLimitations include self-reported exposures and outcomes, which may incur measurement error, though self-report is also pragmatic for low-cost, scalable screening. Residual confounding is possible despite extensive adjustment and an E-value of 2.77. Excluding 4.8% of participants with missing covariates\u0026mdash;who were older, less educated, and had earlier events\u0026mdash;may have led to healthy-subject bias, yielding conservative estimates. Finally, 2\u0026ndash;3‑year survey intervals reduce temporal precision for exposure and outcome onset.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the cumulative burdens of FL and DS function as interdependent drivers of CVD risk via a self-perpetuating cycle, with evidence for a biological ceiling effect suggestive of shared causal pathways. These findings advocate for a paradigm shift from siloed management to integrated prevention and treatment strategies addressing both domains simultaneously, across all adult age groups. Future studies should elucidate the specific inflammatory and neuroendocrine mechanisms underlying this interplay, and test integrated intervention programs\u0026mdash;including rehabilitation, psychosocial support, and lifestyle modification\u0026mdash;capable of disrupting the vicious cycle and delaying CVD onset.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFL Functional Limitation\u003c/p\u003e\n\u003cp\u003eDS Depressive Symptoms\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CVD Cardiovascular diseases\u003c/p\u003e\n\u003cp\u003eCHARLS China Health and Retirement Longitudinal Study \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSHR Subdistribution Hazard Ratio\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CI Confidence intervals\u003c/p\u003e\n\u003cp\u003eADL Activities of daily living\u003c/p\u003e\n\u003cp\u003eCES-D the 10-item Center for Epidemiologic Studies Depression Scale\u003c/p\u003e\n\u003cp\u003eIADL instrumental activities of daily living (IADL)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical review board of Peking University meticulously examined and subsequently sanctioned the CHARLS project (IRB00001052-11015). Informed consent was obtained from all subjects prior to their participation of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were obtained from the China Health and Retirement Longitudinal Study (CHARLS), conducted by the National School of Development at Peking University. The English-language website is available at https://charls.pku.edu.cn/en/ (if temporarily unavailable, please visit the main site at https://charls.pku.edu.cn/). CHARLS data are publicly accessible upon registration via the official website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant numbers 82374564 and 82074566); and the Hubei Provincial Joint Fund Project (grant numbers 2025AFD596 and 2023AFD112). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCYL: Conceptualization; Data curation; Formal analysis; Methodology; Software; Validation; Visualization; Writing – original draft; Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eCW: Conceptualization; Data curation; Formal analysis; Methodology; Writing – original draft; Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eCMJ: Conceptualization; Data curation; Formal analysis; Writing – original draft.\u003c/p\u003e\n\u003cp\u003eYKW: Conceptualization; Data curation.\u003c/p\u003e\n\u003cp\u003eYYH: Conceptualization; Data curation.\u003c/p\u003e\n\u003cp\u003eYJD (corresponding author): Conceptualization; Funding acquisition; Methodology; Project administration; Supervision; Writing – original draft; Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll authors actively participated in the research process, made substantial contributions to manuscript revisions, and reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eChuyi Luo had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based on baseline and follow-up data from the China Health and Retirement Longitudinal Study (CHARLS). We thank the CHARLS research team, field staff, and every respondent for their time and dedication to the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of Artificial Intelligence–Assisted Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the Committee on Publication Ethics (COPE) and the International Committee of Medical Journal Editors (ICMJE) guidelines on artificial intelligence (AI)–assisted technologies, we transparently disclose the use of AI tools in the preparation of this study. Large language models, including Gemini 2.5 Pro (Google DeepMind, Mountain View, CA), GPT‑5‑Chat‑Latest (OpenAI, San Francisco, CA), and Claude Sonnet 4 20250514 (Anthropic, San Francisco, CA), were used under close human supervision to assist with: (1) drafting portions of the R code for data processing and statistical modeling; and (2) generating preliminary narrative text describing the study rationale, methodology, results, and discussion, based on analysis plans conceived entirely by the authors.\u003c/p\u003e\n\u003cp\u003eAll AI-generated code and text were independently reviewed, edited, and verified by the authors prior to inclusion in the manuscript. The final study design, statistical analyses, interpretation of results, and formulation of scientific conclusions were conducted solely by the authors, who retain full responsibility for the integrity, accuracy, and originality of all content. No AI tool is listed as an author, and all quoted or paraphrased materials have been appropriately attributed to human sources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Fact sheets: cardiovascular diseases (CVDs).https://www. who.int/ news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 9 May 2025.\u003c/li\u003e\n\u003cli\u003eHu S-S. Report on cardiovascular health and diseases in China 2021: an updated summary. Journal of Geriatric Cardiology. 2023;20:399\u0026ndash;430. https://doi.org/10.26599/1671-5411.2023.06.001.\u003c/li\u003e\n\u003cli\u003eWu Q, Zhao Y, Liu L, Liu Y, Liu J. Trend, regional variation and socioeconomic inequality in cardiovascular disease among the elderly population in China: evidence from a nationwide longitudinal study during 2011\u0026ndash;2018. BMJ Glob Health. 2023;8:e013311. https://doi.org/10.1136/bmjgh-2023-013311.\u003c/li\u003e\n\u003cli\u003eYan W, Wang L, Li C, Meng Y, Guo Q, Li H. Bidirectional association between ADL disability and depressive symptoms among older adults: longitudinal evidence from CHARLS. Sci Rep. 2025;15:7125. https://doi.org/10.1038/s41598-025-91680-y.\u003c/li\u003e\n\u003cli\u003eLiu L, Li X, Marshall IJ, Bhalla A, Wang Y, O\u0026rsquo;Connell MDL. Trajectories of depressive symptoms 10 years after stroke and associated risk factors: a prospective cohort study. The Lancet. 2023;402:S64. https://doi.org/10.1016/S0140-6736(23)02111-6.\u003c/li\u003e\n\u003cli\u003eSzymkowicz SM, Gerlach AR, Homiack D, Taylor WD. Biological factors influencing depression in later life: role of aging processes and treatment implications. Transl Psychiatry. 2023;13:160. https://doi.org/10.1038/s41398-023-02464-9.\u003c/li\u003e\n\u003cli\u003eHu Z, Zheng B, Kaminga AC, Zhou F, Xu H. 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BioPsychoSocial Med. 2015;9:14. https://doi.org/10.1186/s13030-015-0041-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cumulative average exposure, Competing risk model, Ceiling effect, Cardiovascular diseases","lastPublishedDoi":"10.21203/rs.3.rs-7509404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7509404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine independent, joint, and bidirectionally mediated effects of cumulative average functional limitation (FL) and depressive symptoms (DS) on incident cardiovascular disease (CVD) in a nationally representative cohort of Chinese adults aged ≥45 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed 12,274 adults from the China Health and Retirement Longitudinal Study (2011–2018), free of CVD at their wave-specific baseline. Each participant’s baseline was the first survey wave in which they enrolled (waves 1–4). FL was assessed via activities of daily living/instrumental activities of daily living and DS via the 10-item Center for Epidemiologic Studies Depression Scale. Time‐weighted cumulative averages across waves were computed. Fine–Gray competing risks models estimated subdistribution hazard ratios (SHRs) for CVD, accounting for non‐CVD death. Additive and multiplicative interactions and bidirectional mediation were evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver median 7.0 years of follow-up, 2,294 (18.7%) participants developed CVD. Compared with neither condition, fully adjusted SHRs were 1.63 (95% CI [1.44, 1.85]) for FL only, 1.35 (95% CI [1.17, 1.55]) for DS only, and 2.14 (95% CI [1.92, 2.40]) for both. Additive interaction was significant (RERI = 0.16). Mediation analyses showed DS mediated 23.6% (95% CI [17.7%, 31.0%]) of the FL–CVD association, and FL mediated 28.2% (95% CI [21.2%, 37.0%]) of the DS–CVD association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePersistent co‐occurrence of FL and DS markedly increased CVD risk. Reciprocal mediation highlights the need for integrated strategies targeting functional and mental health to disrupt this reinforcing cycle and reduce CVD burden.\u003c/p\u003e","manuscriptTitle":"Cumulative Joint Burden of Functional Limitation and Depressive Symptoms on Cardiovascular Disease Risk ：A Nationwide Bidirectional Mediation and Competing Risks Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:50:03","doi":"10.21203/rs.3.rs-7509404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-06T12:59:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T18:52:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T11:27:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T03:36:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-05T03:33:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5fff031c-8e1e-4659-b59c-f6c365e0ff43","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T15:50:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:50:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7509404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7509404","identity":"rs-7509404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}