The association of socioeconomic status and social participation with intrinsic capacity: findings from cross-sectional and longitudinal analyses in CHARLS

The association of socioeconomic status and social participation with intrinsic capacity: findings from cross-sectional and longitudinal analyses in CHARLS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association of socioeconomic status and social participation with intrinsic capacity: findings from cross-sectional and longitudinal analyses in CHARLS Zhaopeng Kang, lin zhang, Xiujuan Feng, Hailong Zhang, wen chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8638767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Socioeconomic status (SES) and social participation (SP) influence intrinsic capacity (IC) in older adults. This study examined their impact on IC using CHARLS data. Methods Cross-sectional analysis included 10,390 CHARLS 2011 participants, with 3,008 for longitudinal evaluation (2011–2015). Univariate and multivariate Cox and logistic regression models examined the relationship between socioeconomic status (SES), social participation (SP), and intrinsic capacity (IC). Nonlinear associations were assessed using restricted cubic spline (RCS) modeling, and stratified analyses explored demographic differences. Least absolute shrinkage and selection operator (LASSO) regression identified key predictors for IC, and a nomogram was developed. Results Among the 10,390 participants included in the analysis, 6,756 (65.0%) were classified as having impaired IC. In all 3 models, SES and SP were both independent protective factors for IC in cross-sectional analysis (odds ratio < 1, P < 0.001). In longitudinal analysis, the graded protective association of SES with IC was consistent with that in cross-sectional analysis, but only for higher SP (hazard ratio < 1, P < 0.001). Both SES and SP demonstrated marked inverse associations with IC, which were either linear or nonlinear. The importance of SES and SP in predicting IC was further highlighted by the fact that LASSO regression revealed SP and SES as the top 2 predictors. Moreover, the nomogram established based on top 7 most important variables in LASSO exhibited good predictive performance. Conclusion SES and SP were identified as protective factors for IC, enhancing precaution of impaired IC and supporting healthy aging in older adults. Trial registration Not applicable. cross-sectional study CHARLS intrinsic capacity longitudinal study socioeconomic status social participation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction With the accelerating pace of global population aging, older adults increasingly face burdens from chronic conditions, functional decline, and psychosocial stressors, collectively threatening their independence and quality of life [ 1 ]. Depression, among these challenges, stands as a highly prevalent and disabling psychiatric disorder in later life. Robust evidence links it to accelerated physical disability, cognitive deterioration, and elevated mortality risk [ 2 – 4 ]. These adverse outcomes underscore the urgent need for multidimensional frameworks to advance understanding and intervention strategies for late-life depression. Within this context, the World Health Organization (WHO) has proposed the concept of intrinsic capacity (IC)—defined as the composite of an individual’s physical and cognitive reserves—to catalyze a pivotal shift in geriatric care from disease-centered treatment toward functional ability optimization [ 5 ]. Emerging evidence positions IC as a critical determinant of depression in aging populations. Recent studies demonstrate that IC impairment correlates with both concurrent depressive symptoms and heightened risk of incident depression, suggesting its potential as a clinically valuable predictor for identifying high-risk individuals [ 6 ]. Data from China and Europe further reveal that declining IC trajectories associate with reduced quality of life, accelerated functional deterioration, and increased depression risk, with social support and regional factors exerting moderating roles [ 7 ]. Crucially, IC is operationalized through five interconnected domains: locomotion, cognition, psychological well-being, sensory capacity (vision/hearing), and vitality, collectively representing the biological substrate of functional reserves [ 8 ]. Population-based studies have shown that impairments in IC are common among older adults, with prevalence estimates varying between 25% and 60% depending on the population and measurement criteria [ 9 ]. Such impairments often manifest heterogeneously, affecting one or multiple domains of IC, and are associated with adverse outcomes including increased disability, higher hospitalization rates, cognitive decline, loss of independence, and elevated mortality risk [ 7 ]. Multiple factors have been identified as determinants of IC, including sociodemographic characteristics (e.g., age, sex, socioeconomic status), lifestyle behaviors (e.g., physical activity, nutrition, sleep), chronic diseases (e.g., diabetes, cardiovascular disease), and psychosocial factors (e.g., social participation, mental health) [ 10 ]. Given its central role in healthy aging, understanding the distribution and determinants of IC is crucial for the development of effective strategies to preserve functional ability, reduce health burdens, and improve quality of life in aging populations. Beyond individual mental health problems such as depression, broader socioeconomic variables also exert tremendous influence on aging trajectories[ 11 ]. A growing body of research indicates that greater socioeconomic status (SES) and more active social participation (SP) are independently associated with reduced risk of depression and better overall health outcomes [ 12 – 14 ]. The association between SES and depression can be described through the stress–strain theoretical paradigm, where stress theory stresses the buffering function of human resources against external stressors, while strain theory highlights the psychological toll of structural restrictions [ 15 ]. Moreover, SES may impact health both directly and indirectly by influencing lifestyle behaviors and the development of chronic disorders [ 16 ]. A cross-sectional study further demonstrated a dose-response association between SES and IC deficits, indicating that individuals with lower SES are more susceptible to impairments in IC [ 17 ]. SP, defined as involvement in social, community, and group activities, enhances physical and mental health through greater social support, a stronger sense of belonging, and stress alleviation [ 18 ]. Among older adults, active SP has also been linked to lower odds of depression, slower cognitive decline, and reduced mortality [ 19 ]. Despite these observations, insufficient research has simultaneously studied SES and SP within the context of IC, especially in longitudinal cohorts of older Chinese adults. Against this background, the present study leverages 3 waves (2011–2015) of nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS) in a combined cross-sectional and longitudinal design to comprehensively examine the link of SES and SP with IC. The findings are expected to provide robust, evidence-based support for developing personalized intervention strategies for middle-aged and older Chinese adults, thereby promoting the transition of healthy aging policies from a predominant focus on “disease treatment” toward “functional maintenance.” 2 Materials and methodology 2.1 Study design and population The China Health and Retirement Longitudinal Study (CHARLS, http://charls.pku.edu.cn/en ) is a nationally representative survey designed to address the challenges of population aging and promote interdisciplinary research on aging in China. It collects high-quality microdata from Chinese households with members aged 45 and above. Approved by the Biomedical Ethics Committee of Peking University (IRB00001052-11015), the study obtained written informed consent from all participants. The baseline survey, conducted in 2011, recruited 17,708 individuals from 10,257 households across 450 villages/communities and 150 counties/districts to ensure national representativeness. Follow-up surveys are administered every two to three years. For this analysis, data from 3 waves (2011–2015) were utilized, comprising 29,020 eligible observations from respondents aged over 45. Observations with missing values in the SES and SP indices, IC measures, or any covariates (e.g., sleep duration, hypertension, and diabetes) were excluded. This resulted in 13,797 individuals contributing 28,500 observations, including 10,390 participants from the 2011 wave who were included in cross-sectional analysis. For the longitudinal analysis, after further excluding individuals who had IC data at baseline or lacked follow-up information, 3,008 subjects were retained (Fig. 1 ). 2.2 Assessment of SES and SP SES was assessed utilizing two indicators: total household wealth and educational achievement. Primary school or less (0 points), secondary education (1 point), and higher education (2 points) were the three levels of educational achievement. The total household wealth was determined by taking into account all assets without liabilities, such as real estate, cars, savings accounts, and business holdings. Wealth was categorized into quartiles and rated on a scale of 0 (lowest) to 3 (highest) [ 20 ]. SP was assessed based on seven types and frequency of activities: interacting with friends, playing mahjong or other board games, participating in sports/social clubs, engaging in community organizations, volunteering or charity work, providing unpaid help to relatives or others, and internet use. Participants were asked whether they had engaged in each activity during the past month, and those who responded “yes” were further asked about their frequency of participation. Given the low participation rates (less than 2%), volunteering or charity work and providing unpaid help were combined into a single variable termed volunteering activities [ 21 ]. For sports/social clubs, community organizations, and internet use, due to low regular engagement (less than 1% participating weekly or more frequently), these variables were dichotomized into no or yes. Frequency of SP was defined as the highest frequency level reported across the six types and classified into three categories: no participation, irregular participation, or participation at least once per week. Diversity was measured as the total number of SP types engaged in, and grouped into 0, 1, 2, and ≥ 3 types [ 18 ]. 2.3 Measurement of IC IC was operationalized as a composite measure across five functional domains: locomotion, sensory capacity, cognition, vitality, and psychological status. Locomotion was assessed applying the 5-repetition sit-to-stand test. Participants who completed the test unassisted within 14 seconds were assigned a score of 1; those requiring a longer time received a score of 0. Sensory capacity incorporated both hearing and vision. Self-reported hearing was evaluated using the question: “How is your hearing?” Vision was assessed based on two questions: “How well do you see distant objects?” and “How well do you see objects that are close by?” Responses rated as “fair,” “good,” “very good,” or “excellent” were scored 1, while “poor” responses were scored 0. Cognitive function was measured adopting the telephone interview of cognitive status (TICS), which evaluates memory and mental acuity. Immediate and delayed recall of 10 terminologies was tested (maximum 20 points). Mental status was assessed through orientation (5 points), serial subtraction (5 points), and visuoconstruction (1 point), totaling 11 points. Cognitive impairment was defined as performance below one standard deviation of the mean in either memory or mental status. A score of 1 was given if both domains were unimpaired; otherwise, a score of 0 was assigned. Vitality was evaluated based on body mass index (BMI). Individuals with BMI < 18.5 kg/m² were classified as at nutritional risk and scored 0, while those with BMI ≥ 18.5 kg/m² received a score of 1. Psychological status was assessed utilizing the Center for Epidemiologic Studies Depression Scale (CES-D). Participants scoring below 12 were considered free of significant depressive symptoms and assigned a score of 1; those scoring ≥ 12 received 0. Scores from each domain were summed into a total score ranging from 0 to 6, with higher scores indicating better integrated functional capacity. A total score ≤ 5 was classified as impaired IC [ 22 ]. 2.4 Covariates To account for potential confounding factors, the following covariates were included in the analysis: age, marital status (married and others), gender, drinking, hypertension, smoking, sleep time (hours), and diabetes. 2.5 Statistical analysis For continuous variables in baseline, the Kruskal–Wallis test was adopted (mean ± standard deviation) for group comparison. Categorical variables were compared utilizing the chi-square test (n (%)). Regression models were established via survey (v4.2-2) package [ 23 ] to assess the role of SES and SP for IC. Specifically, Model 1 was unadjusted; Model 2 was adjusted for age, gender, marital status, smoking, and drinking; Model 3 was further adjusted based on all covariates. For the cross-sectional analysis, logistic regression models were employed, while Cox regression models were utilized for the longitudinal analysis. For restricted cubic splines (RCS), the rms (v6.8-0) package [ 24 ] was utilized to investigate the connection between the SES and SP with IC and to test for nonlinearity. Subgroup analyses were then performed inside the fully adjusted Model 3 employing the jstable (v1.3.12) package to evaluate effect heterogeneity across population strata identified by baseline characteristics. The pROC (v1.18.5) package [ 25 ] was applied to create receiver operating characteristic (ROC) curves and measure the area under the curve (AUC) in order to assess the prediction power of three models in cross-sectional study. In order to show the incidence of impaired IC among people stratified by SES and SP levels, cumulative incidence curves were displayed for the longitudinal study. A nomogram was then created to forecast the risk of IC after significant predictors were chosen via least absolute shrinkage and selection operator (LASSO) regression. R (v4.4.3) was used for all statistical analyses, and two-tailed P -values of less than 0.05 were regarded as statistically significant. 3 Results 3.1 Baseline characteristics Among the 10,390 participants included in the analysis, 6,756 (65.0%) were classified as having impaired IC (Table 1 ). Significant differences ( P < 0.001) were observed across all variables between the impaired and normal groups. Notable differences were identified in SES: compared to normal group, a higher proportion of participants with impaired IC fell into the low SES category (30% vs. 15%); while the normal group contained more high-SES individuals (32% vs. 22%). SP also showed marked contrasts: 53% of the impaired group reported no SP, compared to 41% in the normal group. The impaired group was older (60.5 ± 9.1 vs. 56.6 ± 8.0), had a higher proportion of females (56% vs. 42%), and a lower rate of being married (86% vs. 93%). Moreover, drinking was less common in the impaired group (31% vs. 39%), and average nightly sleep duration was shorter (6.2 ± 2.0 vs. 6.7 ± 1.5). Table 1 Baseline characteristics of participants from CHARLS 2011. Variable Overall Impaired Normal P 10390 6756 3634 SES < 0.001 Low 2,579 (25%) 2,038 (30%) 541 (15%) Medium 5,155 (50%) 3,219 (48%) 1,936 (53%) High 2,656 (26%) 1,499 (22%) 1,157 (32%) SP < 0.001 0 5,072 (49%) 3,592 (53%) 1,480 (41%) 1 3,494 (34%) 2,232 (33%) 1,262 (35%) 2 1,394 (13%) 773 (11%) 621 (17%) ≥ 3 430 (4%) 159 (2%) 271 (7%) Age < 0.001 Mean (SD) 59.2 (8.9) 60.5 (9.1) 56.6 (8.0) Gender < 0.001 Female 5,289 (51%) 3,768 (56%) 1,521 (42%) Male 5,101 (49%) 2,988 (44%) 2,113 (58%) Marry < 0.001 Married 9,158 (88%) 5,793 (86%) 3,365 (93%) Other 1,232 (12%) 963 (14%) 269 (7%) Smoking < 0.001 NO 7,067 (68%) 4,721 (70%) 2,346 (65%) YES 3,323 (32%) 2,035 (30%) 1,288 (35%) Drinking < 0.001 NO 6,899 (66%) 4,692 (69%) 2,207 (61%) YES 3,491 (34%) 2,064 (31%) 1,427 (39%) Sleep time < 0.001 Mean (SD) 6.4 (1.8) 6.2 (2.0) 6.7 (1.5) Hypertension < 0.001 NO 7,735 (74%) 4,934 (73%) 2,801 (77%) YES 2,655 (26%) 1,822 (27%) 833 (23%) Diabetes < 0.001 NO 9,747 (94%) 6,293 (93%) 3,454 (95%) YES 643 (6%) 463 (7%) 180 (5%) SES: socioeconomic status; SP: social participation 3.2 SES and SP were both independent protective factors for IC in cross-sectional analysis In all 3 models in cross-sectional analysis, a graded inverse association was observed between SES and the risk of impaired IC ( P < 0.001) (Table 2 ). In Model 1, both medium (odds ratio (OR) 95% confidence interval (CI)) = 0.458 (0.410–0.511) and high SES (0.380 (0.336–0.430)) were strongly associated with reduced impaired IC risk relative to low SES. In Model 2, the effect estimates were slightly attenuated but remained highly significant (medium SES: (0.463–0.582); high SES: 0.406 (0.357–0.461)). Further adjustment for all covariates in Model 3 resulted in minimal changes (medium SES: 0.517 (0.460–0.581); high SES: 0.402 (0.353–0.458)), indicating robust and independent protective effects of higher SES. Subgroup analysis by SES indicated that the protective effect of higher SES on IC remained significant across all population subgroups. An interaction was observed with marital status ( P = 0.022), with a weaker protective effect among unmarried or separated individuals (Fig. 2 A). A noteworthy negative nonlinear connection was observed between SES and IC ( P for overall and nonlinearity < 0.001), as assessed via RCS (Fig. 3 A). Table 2 Logistic regression models examining the associations of SES and SP with IC Model 1 Model 2 Model3 SES OR(95% CI) P OR(95% CI) P OR(95% CI) P Low Ref. Ref. Ref. Medium 0.458 (0.410–0.511) < 0.001 0.519 (0.463–0.582) < 0.001 0.517 (0.460–0.581) < 0.001 High 0.380 (0.336–0.430) < 0.001 0.406 (0.357–0.461) < 0.001 0.402 (0.353–0.458) < 0.001 SP 0 Ref. Ref. Ref. 1 0.746 (0.679–0.819) < 0.001 0.719(0.653–0.792) < 0.001 0.724(0.657–0.798) < 0.001 2 0.541 (0.477–0.613) < 0.001 0.567 (0.499–0.644) < 0.001 0.570 (0.501–0.648) < 0.001 ≥ 3 0.284 (0.231–0.350) < 0.001 0.319 (0.258–0.395) < 0.001 0.318 (0.256–0.394) < 0.001 OR: odds ratio; CI: confidence interval Similarly, SP demonstrated a clear dose-response relationship with IC ( P < 0.001) (Table 2 ). Compared to no participation, engaging in 1, 2, or ≥ 3 types of activities was associated with progressively lower risks across all models. For example, participation in ≥ 3 activities was associated with a pronounced reduction in risk in Model 1 (0.284 (0.231–0.350)) compared to in 1 (0.746 (0.679–0.819)) and 2 (0.541 (0.477–0.613)) activities. This association was slightly attenuated but remained highly marked in Models 2 (0.319 (0.258–0.395)) and 3 (0.318 (0.256–0.394)). The protective effect of SP on IC remained notable across all subgroups except for other marital status individuals and those with diabetes. A significant interaction was observed with hypertension ( P = 0.026) (Fig. 2 B). A marked negative linear connection was shown between SP and IC ( P for overall < 0.05) in RCS (Fig. 3 B). The ROC curve for Model 3 yielded an AUC of 0.706, demonstrating acceptable predictive accuracy for the model (Fig. 3 C). 3.3 SES and SP were both independent protective factors for IC in longitudinal analysis In longitudinal analysis, the graded protective association of SES with IC was consistent with that in cross-sectional analysis ( P < 0.001) (Table 3 ). In Model 1, both medium (hazard ratio (HR) (95% CI) = 0.800 (0.703–0.909)) and high SES (0.690 (0.596–0.799)) were significantly associated with lower impaired IC risk. In Models 2 and 3, the effect estimates remained largely stable. The HR for high SES changed only minimally from 0.690 in Model 1 to 0.708 in Model 3, indicating a robust and independent protective effect. Higher SES had a significant protective effect on IC across nearly all population groupings. There was a decreased protective effect among those with diabetes, and an interaction with diabetes was noted ( P = 0.025) (Fig. 4 A). Higher SES was associated with a lower cumulative incidence of impaired IC ( P < 0.001), and an inverse association was observed between SES and IC ( P for overall < 0.05) (Fig. 5 A-B). Table 3 Cox proportional hazards regression models examining the associations of SES and SP with IC Model 1 Model 2 Model3 SES HR(95% CI) P HR(95% CI) P HR(95% CI) P Low Ref. Ref. Ref. Medium 0.800 (0.703–0.909) 0.001 0.836 (0.733–0.952) 0.007 0.831 (0.729–0.947) 0.006 High 0.690 (0.596–0.799) < 0.001 0.711 (0.613–0.824) < 0.001 0.708 (0.610–0.821) < 0.001 Activity 0 Ref. Ref. Ref. 1 1.026 (0.921–1.143) 0.643 0.999 (0.896–1.113) 0.979 1.002 (0.899–1.117) 0.973 2 0.842 (0.730–0.970) 0.017 0.850 (0.736–0.980) 0.025 0.845 (0.732–0.975) 0.021 ≥ 3 0.692 (0.556–0.860) 0.001 0.729 (0.585–0.907) 0.025 0.722 (0.580–0.900) 0.004 HR: hazard ratio; CI: confidence interval In contrast, the association between SP and IC showed a dose-response relationship only at higher participation levels ( P < 0.05) (Table 3 ). Participation in 2 types (Model 1: 0.842 (0.730–0.970), Model 2: 0.850 (0.736–0.980), Model 3: 0.845 (0.732–0.975)) and especially ≥ 3 types (Model 1: 0.692 (0.556–0.860), Model 2: 0.729 (0.585–0.907), Model 3: 0.722 (0.580–0.900)) was associated with risk reduction. Engagement in ≥ 3 types of SP continued to demonstrate a marked protective effect on IC across the majority of population subgroups (Fig. 4 B). There was a graded inverse correlation between SP and reduced cumulative incidence of impaired IC ( P < 0.001) and an inverse linear connection of SP with impaired IC risk ( P for overall < 0.05) (Fig. 5 C-D). 3.4 SES and SP were important indicators for predicting the risk of IC The importance of SES and SP in predicting IC was further highlighted by the fact that LASSO regression revealed SP, SES, and gender as the top 3 predictors (Fig. 6 A). A nomogram to predict the probability of impaired IC was then established applying the top 7 most important variables identified by LASSO (Fig. 6 B). The calibration curve demonstrated excellent agreement with the ideal reference line, indicating good predictive performance of the model (Fig. 6 C). 4 Discussion Using nationally representative longitudinal data from CHARLS, we examined how SES and SP influence IC among older adults. In cross-sectional analyses, both higher SES and more active SP were independently associated with a reduced risk of IC impairment, with dose–response relationships suggesting cumulative protective effects. Longitudinal Cox regression analyses further confirmed these findings: among participants with normal IC at baseline, higher SES and greater SP predicted a lower risk of incident IC impairment. These associations remained robust across fully adjusted models, underscoring the long-term protective roles of socioeconomic and social resources in maintaining functional capacity in later life. Our findings align with and extend existing studies investigating SP, SES, and cognitive aspects of IC. Longitudinal studies in China have revealed that greater diversity and frequency of social contacts are favorably associated with cognitive performance across memory and mental status domains [ 26 ]. Moreover, SP has been shown to predict cognitive trajectories more robustly than traditional health indicators, such as depressive symptoms and self-rated health, particularly for episodic memory [ 27 ]. Engagement in social activities exhibits a non-linear, inverse association with the risk of cognitive impairment and depression, while depression itself significantly mediates both cognitive function and social activity levels, highlighting the importance of maintaining both cognitively and physically stimulating activities [ 28 ]. Among cognitively impaired older adults, participation in cognitively demanding leisure activities is linked to better subsequent memory performance [ 23 ]. Importantly, national cohort studies have found that sustaining or increasing social activity over time substantially enhances the likelihood of cognitive improvement, independent of baseline engagement [ 29 , 30 ].On the socioeconomic side, higher community-level SES—reflecting the combined influence of education and income—has been associated with improved cognitive outcomes among middle-aged and older Chinese adults [ 31 ]. In summary, existing studies consistently demonstrate strong associations between SP and the cognitive components of IC. Our study extends this evidence by revealing that greater SP activity is significantly associated with lower risk of overall IC impairment, suggesting that its benefits extend beyond specific domains to the integrated construct of functional capacity. In parallel, SES emerged as another key determinant of IC. Ample evidence has shown that higher SES—typically indexed by education, income, and occupation—is associated with better outcomes across multiple IC domains, particularly cognition, mobility, and psychological well-being [ 32 – 34 ]. Education enhances cognitive reserve and health literacy, enabling older adults to adopt healthier lifestyles, utilize healthcare resources effectively, and adhere to preventive behaviors [ 35 , 36 ]. Furthermore, income secures access to nutritious diets, safe living environments, and health-promoting resources, all of which contribute to sustained physical and mental function [ 37 , 38 ]. Moreover, higher SES buffers against chronic stress exposure and fosters adaptive coping through greater psychosocial resources and perceived control [ 39 ]. Beyond cognitive aspects, longitudinal studies have demonstrated that higher SES is linked to slower declines in physical function, lower risk of disability, and better self-rated health [ 40 , 41 ]. Longitudinal studies based on CHARLS cohorts have demonstrated that higher SES is linked to slower declines in physical function, lower risk of disability, and better self-rated health.Longitudinal evidence from Chinese cohorts has confirmed that individuals with persistently high or upwardly mobile SES exhibit slower declines in cognitive and physical performance than those in persistently low SES groups [ 42 ]. Furthermore, social capital and social network resources have been shown to buffer the adverse effects of low SES on cognitive and functional trajectories among older Chinese adults [ 43 ]. Taken together, these findings indicate that SES influences IC through both material and psychosocial pathways, highlighting the importance of promoting socioeconomic equity to foster healthy aging and maintain functional ability among older adults. Our study extends this evidence by showing that more active SP is significantly associated with a lower risk of overall IC impairment—suggesting that its benefits extend beyond specific domains to the integrated construct of functional capacity. From a public health standpoint, these findings provide critical insights into promoting healthy aging. First, healthy aging initiatives should integrate both socioeconomic and social engagement components to preserve IC [ 44 ]. Second, interventions should prioritize vulnerable groups—including the unmarried, those with lower SES, and individuals living with chronic conditions—who may benefit most from targeted social and economic support [ 45 ]. Third, the LASSO-based predictive model and nomogram established in this study, incorporating SES, social participation, and demographic variables, offer a practical framework for identifying individuals at elevated risk of IC impairment. The observed dose–response associations further support the feasibility of embedding these factors into stratified risk prediction and early-warning systems. Collectively, these results underscore the importance of multi-level policies combining economic, social, and behavioral strategies to sustain IC in aging populations.This study has several notable strengths. It draws upon a large, nationally representative longitudinal dataset, enabling both cross-sectional and prospective analyses [ 38 , 39 ]. The application of diverse analytic approaches—including multivariable regression, subgroup analysis, restricted cubic spline modeling, and LASSO-based predictive modeling—enhanced the robustness and interpretability of results. To our knowledge, this is among the first studies to jointly examine SES and social participation in relation to IC decline within a Chinese context, while also providing a clinically applicable prediction tool for early risk assessment.Several limitations warrant consideration. First, SES and social participation were self-reported, potentially underrepresenting their multidimensionality and quality. Education and income may not fully capture accumulated wealth or neighborhood context, while frequency-based social metrics overlook activity quality and diversity.Second, despite extensive covariate adjustment, residual confounding from unmeasured factors such as diet, genetics, or environmental exposures cannot be excluded.Third, the follow-up duration may be insufficient to capture long-term IC trajectories, and attrition could bias estimates among vulnerable groups.Fourth, the observational nature of CHARLS limits causal inference; randomized or quasi-experimental studies are needed to confirm whether enhancing SES or social engagement mitigates IC decline.Finally, the LASSO-based model lacked external validation, restricting its generalizability beyond the current sample. Future research should employ multidimensional SES and social engagement measures, incorporate biological mediators such as inflammation and cognitive reserve, and conduct intervention and validation studies to build scalable early-warning systems for IC deterioration. 5 Conclusions In this nationally representative cohort of middle-aged and older Chinese individuals, better SES and greater SP were consistently linked with a decreased risk of IC impairment. Subgroup analyses found that SES was less protective among unmarried or divorced individuals, whereas SP conferred stronger protection among those with hypertension, older age, or shorter nocturnal sleep. LASSO-based predictive modeling created a nomogram incorporating SES, SP, age, and marital status, enabling tailored 4-year risk estimation. Kaplan-Meier analyses demonstrated dose-dependent protective effects of both SES and SP. These findings emphasize the potential of integrating social and economic factors into early-warning systems and targeted interventions to prevent IC decline in middle-aged and older adults. Abbreviations WHO World Health Organization IC intrinsic capacity SES socioeconomic status SP social participation CHARLS China Health and Retirement Longitudinal Study TICS telephone interview of cognitive status RCS restricted cubic splines ROC receiver operating characteristic AUC area under the curve LASSO least absolute shrinkage and selection operator OR odds ratio HR hazard ratio Declarations Conflict of interest statement The authors declares that there is no conflict of interest. Funding This work was supported by the Gusu Talent Program under Grant [number:(2024)137]. Data availability statement The data for this study were sourced from CHARLS (http://charls.pku.edu.cn/en). Ethics statement Approved by the Biomedical Ethics Committee of Peking University (IRB00001052-11015), the study obtained written informed consent from all participants. Acknowledgments We thank the CHARLS team for providing open-access data and the anonymous reviewers for their constructive comments. Author contributions Zhaopeng Kang: Conceptualization, Methodology, Software, Formal analysis. Lin Zhang: Data curation, Investigation, Validation. Xiujuan Feng: Writing – Original Draft, Visualization. Hailong Zhang: Data curation, Investigation. Wen Chen: Supervision, Project administration. Cheng Lian: Funding acquisition, Writing – Review & Editing, Supervision, *Corresponding author. References Khan HTA, Addo KM, Findlay H. Public Health Challenges and Responses to the Growing Ageing Populations. Public Health Chall. 2024;3(3):e213. Wei J, Hou R, Zhang X, Xu H, Xie L, Chandrasekar EK, Ying M, Goodman M. 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Effects of personal and health characteristics on the intrinsic capacity of older adults in the community: a cross-sectional study using the healthy aging framework. BMC Geriatr. 2023;23(1):643. Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, Ricceri F, d'Errico A, Barros H, Bochud M, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women. Lancet. 2017;389(10075):1229–37. Agerbo E, Trabjerg BB, Børglum AD, Schork AJ, Vilhjálmsson BJ, Pedersen CB, Hakulinen C, Albiñana C, Hougaard DM, Grove J, et al. Risk of Early-Onset Depression Associated With Polygenic Liability, Parental Psychiatric History, and Socioeconomic Status. JAMA Psychiatry. 2021;78(4):387–97. Santini ZI, Jose PE, York Cornwell E, Koyanagi A, Nielsen L, Hinrichsen C, Meilstrup C, Madsen KR, Koushede V. 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BMC Public Health. 2025;25(1):1262. Tan F, Wei X, Zhang J, Zhao Y, Zhang Y, Gong H, Michel JP, Gong E, Shao R. Association of objective and subjective socioeconomic status with intrinsic capacity deficits among community-dwelling middle-aged and older adults in China: A cross-sectional study. J Frailty Aging. 2025;14(2):100036. Shao Z, Chen Y, Sun S, Wang M. Association Between Multidimensional Social Participation and Hypertension Among Middle-Aged and Older Adults in China: A Cross-Sectional Analysis From the China Health and Retirement Longitudinal Study. J Clin Hypertens (Greenwich). 2025;27(5):e70059. Ueno T, Nakagomi A, Tsuji T, Kondo K. Association between social participation and hypertension control among older people with self-reported hypertension in Japanese communities. Hypertens Res. 2022;45(8):1263–8. Zhou Y, Kivimäki M, Yan LL, Carrillo-Larco RM, Zhang Y, Cheng Y, Wang H, Zhou M, Xu X. Associations between socioeconomic inequalities and progression to psychological and cognitive multimorbidities after onset of a physical condition: a multicohort study. EClinicalMedicine. 2024;74:102739. Lin W. A study on the factors influencing the community participation of older adults in China: based on the CHARLS2011 data set. Health Soc Care Commun. 2017;25(3):1160–8. Ding H, Li C, Zhao X. The relationship between intrinsic capacity and sarcopenia in middle-aged and older Chinese populations: the mediating influence of a novel nutritional index. Front public health. 2025;13:1605158. Liang J, An H, Hu X, Gao Y, Zhou J, Gong X, Zong J, Liu Y. Correlation between chronic kidney disease and all-cause mortality in diabetic foot ulcers: evidence from the 1999–2004 national health and nutrition examination survey (NHANES). Front Endocrinol. 2025;16:1533087. Wang H, Li Q, Wang H, Song W. Construction and validation of a line chart for gestational diabetes mellitus based on clinical indicators. Lipids Health Dis. 2024;23(1):349. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. Zhou Y, Chen Z, Shaw I, Wu X, Liao S, Qi L, Huo L, Liu Y, Wang R. Association between social participation and cognitive function among middle- and old-aged Chinese: A fixed-effects analysis. J Glob Health. 2020;10(2):020801. Li X, Xu W. A change in social participation affects cognitive function in middle-aged and older Chinese adults: analysis of a Chinese longitudinal study on aging (2011–2018). Front public health. 2024;12:1295433. Yang Q, Lin S, Zhang Z, Du S, Zhou D. Relationship between social activities and cognitive impairment in Chinese older adults: the mediating effect of depressive symptoms. Front public health. 2024;12:1506484. Wang J, Liu J, Wang X, Zhu J, Bai Y, Che Y, Tao J. Association between change in social participation and improved cognitive function among older adults in China: A national prospective cohort study. Health Soc Care Commun. 2022;30(6):e4199–210. Beard JR, Hanewald K, Si Y, Amuthavalli Thiyagarajan J, Moreno-Agostino D. Cohort trends in intrinsic capacity in England and China. Nat Aging. 2025;5(1):87–98. Liu Y, Liu Z, Liang R, Luo Y. The association between community-level socioeconomic status and cognitive function among Chinese middle-aged and older adults: a study based on the China Health and Retirement Longitudinal Study (CHARLS). BMC Geriatr. 2022;22(1):239. Wang X, Bakulski KM, Paulson HL, Albin RL, Park SK. Associations of healthy lifestyle and socioeconomic status with cognitive function in U.S. older adults. Sci Rep. 2023;13(1):7513. Lam PH, Chen E, Chiang JJ, Miller GE. Socioeconomic disadvantage, chronic stress, and proinflammatory phenotype: an integrative data analysis across the lifecourse. PNAS Nexus 2022, 1(4). He Y, Zhou L, Li J, Wu J. An empirical analysis of the impact of income inequality and social capital on physical and mental health - take China's micro-database analysis as an example. Int J Equity Health. 2021;20(1):241. Huang Z, Lai ETC, Hashimoto H, Marmot M, Woo J. Life-course socioeconomic inequalities, social mobility and healthy aging in older adults: A multi-cohort study. Arch Gerontol Geriatr. 2025;133:105829. Salinas-Rodríguez A, Fernández-Niño JA, Rivera-Almaraz A, Manrique-Espinoza B. Intrinsic capacity trajectories and socioeconomic inequalities in health: the contributions of wealth, education, gender, and ethnicity. Int J Equity Health. 2024;23(1):48. Algren MH, Ekholm O, Nielsen L, Ersbøll AK, Bak CK, Andersen PT. Associations between perceived stress, socioeconomic status, and health-risk behaviour in deprived neighbourhoods in Denmark: a cross-sectional study. BMC Public Health. 2018;18(1):250. Lohman MC, Wei J, Bawa EM, Fallahi A, Verma M, Merchant AT. Longitudinal Associations of Diet, Food Insecurity, and Supplemental Nutrition Assistance Program Use with Global Cognitive Performance in Middle-Aged and Older Adults. J Nutr. 2024;154(2):714–21. Mooney CJ, Elliot AJ, Douthit KZ, Marquis A, Seplaki CL. Perceived Control Mediates Effects of Socioeconomic Status and Chronic Stress on Physical Frailty: Findings From the Health and Retirement Study. J Gerontol B Psychol Sci Soc Sci. 2018;73(7):1175–84. Liu Y, Liu Z, Liang R, Luo Y. The association between community-level socioeconomic status and cognitive function among Chinese middle-aged and older adults: a study based on the China Health and Retirement Longitudinal Study (CHARLS). BMC Geriatr. 2022;22(1):239. Li W, Zhang X, Gao H, Tang Q. Heterogeneous effects of socio-economic status on social engagement level among Chinese older adults: evidence from CHARLS 2020. Front public health. 2024;12:1479359. Shi L, Tao L, Chen N, Liang H. Relationship between socioeconomic status and cognitive ability among Chinese older adults: the moderating role of social support. Int J Equity Health. 2023;22(1):70. Luo L, Xing Y, Shang Z, Ren W, Zhang L. Association between diversified social interaction and health among older adults in China: a longitudinal analysis by interaction type and frequency. BMC Geriatr. 2025;25(1):730. Zhang X, Zheng X, Zheng T, Zhang M, Yang L, Xue B, Li X, Wang Y, Zhang C. Associations between leisure activities with trajectories of intrinsic capacity among Chinese older adults: the China health and retirement longitudinal study. Archives Public Health. 2025;83(1):162. Si Y, Hanewald K, Chen S, Li B, Bateman H, Beard JR. Life-course inequalities in intrinsic capacity and healthy ageing, China. Bull World Health Organ. 2023;101(5):307–c316. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 16 Feb, 2026 Editor invited by journal 23 Jan, 2026 Editor assigned by journal 20 Jan, 2026 Submission checks completed at journal 20 Jan, 2026 First submitted to journal 19 Jan, 2026 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8638767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593366856,"identity":"ac05ef62-2be6-4e5a-9a71-8a8f338abafb","order_by":0,"name":"Zhaopeng Kang","email":"","orcid":"","institution":"Suzhou Guangji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaopeng","middleName":"","lastName":"Kang","suffix":""},{"id":593366857,"identity":"03320a18-ace8-4ae6-952b-4c37b20f2928","order_by":1,"name":"lin zhang","email":"","orcid":"","institution":"Suzhou Guangji Hospital","correspondingAuthor":false,"prefix":"","firstName":"lin","middleName":"","lastName":"zhang","suffix":""},{"id":593366858,"identity":"09bc64d6-1971-4361-9d7b-b860dbe8ee05","order_by":2,"name":"Xiujuan Feng","email":"","orcid":"","institution":"Suzhou Guangji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiujuan","middleName":"","lastName":"Feng","suffix":""},{"id":593366859,"identity":"01c9883f-f10c-40a4-a1ff-341348bf301f","order_by":3,"name":"Hailong Zhang","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Zhang","suffix":""},{"id":593366860,"identity":"e7bb98f1-749a-4043-b90c-be61f08cb845","order_by":4,"name":"wen chen","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"wen","middleName":"","lastName":"chen","suffix":""},{"id":593366861,"identity":"134777b3-432a-4d00-9c8a-e32f87ea191f","order_by":5,"name":"cheng lian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACPghlI8fPzHz4AVFa2CBUmrFkO1uaASlaDiduOM+jIEGcFvYDbA9/th02Nj7Mw2DAUGMTTVgLTwK7MW9bupzZYd4DDxiOpeU2ENQiwcAmzdhmbWx2mC/BgLHhMHFaJH+2MSdubuYxkCBaiwRvm3PiBmaitYD8wnMuzVjiMDCQE4jxCz8oxH6UAaOy//DhBx9qbAhrAWr6xsAIjR2GBMLKIW5jYPhDpNJRMApGwSgYmQAA4mg09wmx1p0AAAAASUVORK5CYII=","orcid":"","institution":"The Fourth Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"cheng","middleName":"","lastName":"lian","suffix":""}],"badges":[],"createdAt":"2026-01-19 11:12:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8638767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8638767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103166018,"identity":"f0658b03-8edc-4e5a-82b1-3b4521cf8a81","added_by":"auto","created_at":"2026-02-22 12:36:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2365736,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart showing the selection of the studied population.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8638767/v1/02741c648872f0b5a14ff8d0.png"},{"id":103166021,"identity":"8e757a34-e720-43d9-a210-bcea092516de","added_by":"auto","created_at":"2026-02-22 12:36:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3747905,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship of socioeconomic status (SES) and social participation (SP)with intrinsic capacity (IC) in various populations in cross-sectional study. (\u003cstrong\u003eA\u003c/strong\u003e) The relationship of SES with IC in various populations in cross-sectional study. (\u003cstrong\u003eB\u003c/strong\u003e) The relationship of SP with IC in various populations in cross-sectional study.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8638767/v1/1fdaa61b489fa47113144f94.png"},{"id":103166020,"identity":"b78eada6-1022-40f0-8b19-549db9230ee2","added_by":"auto","created_at":"2026-02-22 12:36:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4398099,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) and receiver operating characteristic (ROC) curve of the impact of SES and SP on IC in cross-sectional study. (\u003cstrong\u003eA\u003c/strong\u003e) RCS analysis of the impact of SES on IC in cross-sectional study. (\u003cstrong\u003eB\u003c/strong\u003e) RCS analysis of the impact of SP on IC in cross-sectional study. (\u003cstrong\u003eC\u003c/strong\u003e) ROC curve of three models for the impact of SES and SP on IC in cross-sectional study.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8638767/v1/e389d4df077193b71829225c.png"},{"id":103166023,"identity":"6b348b84-c30d-479f-abab-f3b91134fc2b","added_by":"auto","created_at":"2026-02-22 12:36:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3588575,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship of SES and SP with IC in various populations in longitudinal study. (\u003cstrong\u003eA\u003c/strong\u003e) The relationship of SES with IC in various populations in longitudinal study. (\u003cstrong\u003eB\u003c/strong\u003e) The relationship of SP with IC in various populations in longitudinal study.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8638767/v1/f3e62c836cc3931c7dcd8246.png"},{"id":103504732,"identity":"1a72ada4-1ed5-48b5-a2af-74a020dc5436","added_by":"auto","created_at":"2026-02-26 13:21:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3242505,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence curves and RCS of the impact of SES and SP on IC in longitudinal study. (\u003cstrong\u003eA\u003c/strong\u003e) Cumulative incidence curves of the impact of SES on IC in longitudinal study. (\u003cstrong\u003eB\u003c/strong\u003e) Cumulative incidence curves of the impact of SP on IC in longitudinal study. (\u003cstrong\u003eC\u003c/strong\u003e) RCS analysis of the impact of SES on IC in longitudinal study. (\u003cstrong\u003eD\u003c/strong\u003e) RCS analysis of the impact of SP on IC in longitudinal study.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8638767/v1/967185cea5fa3a5ada032c2d.png"},{"id":103166019,"identity":"76c8311a-8253-4ed5-a9b4-124fc8407475","added_by":"auto","created_at":"2026-02-22 12:36:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2130008,"visible":true,"origin":"","legend":"\u003cp\u003eLeast absolute shrinkage and selection operator (LASSO) regression and nomogram construction. (\u003cstrong\u003eA\u003c/strong\u003e) The importance of variables in the LASSO model. (\u003cstrong\u003eB\u003c/strong\u003e) A nomogram constructed based on the top 7 most important variables in LASSO. (\u003cstrong\u003eC\u003c/strong\u003e) Calibration curve of nomogram.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8638767/v1/c845c822123efb57657a4bba.png"},{"id":103509397,"identity":"1f36b6b4-1a16-4744-9df6-e39f305d8a6a","added_by":"auto","created_at":"2026-02-26 13:58:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29331642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8638767/v1/6bf8dab2-38d9-4241-b28a-98994455f7b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association of socioeconomic status and social participation with intrinsic capacity: findings from cross-sectional and longitudinal analyses in CHARLS","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the accelerating pace of global population aging, older adults increasingly face burdens from chronic conditions, functional decline, and psychosocial stressors, collectively threatening their independence and quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Depression, among these challenges, stands as a highly prevalent and disabling psychiatric disorder in later life. Robust evidence links it to accelerated physical disability, cognitive deterioration, and elevated mortality risk [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These adverse outcomes underscore the urgent need for multidimensional frameworks to advance understanding and intervention strategies for late-life depression. Within this context, the World Health Organization (WHO) has proposed the concept of intrinsic capacity (IC)\u0026mdash;defined as the composite of an individual\u0026rsquo;s physical and cognitive reserves\u0026mdash;to catalyze a pivotal shift in geriatric care from disease-centered treatment toward functional ability optimization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Emerging evidence positions IC as a critical determinant of depression in aging populations. Recent studies demonstrate that IC impairment correlates with both concurrent depressive symptoms and heightened risk of incident depression, suggesting its potential as a clinically valuable predictor for identifying high-risk individuals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Data from China and Europe further reveal that declining IC trajectories associate with reduced quality of life, accelerated functional deterioration, and increased depression risk, with social support and regional factors exerting moderating roles [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Crucially, IC is operationalized through five interconnected domains: locomotion, cognition, psychological well-being, sensory capacity (vision/hearing), and vitality, collectively representing the biological substrate of functional reserves [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePopulation-based studies have shown that impairments in IC are common among older adults, with prevalence estimates varying between 25% and 60% depending on the population and measurement criteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Such impairments often manifest heterogeneously, affecting one or multiple domains of IC, and are associated with adverse outcomes including increased disability, higher hospitalization rates, cognitive decline, loss of independence, and elevated mortality risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Multiple factors have been identified as determinants of IC, including sociodemographic characteristics (e.g., age, sex, socioeconomic status), lifestyle behaviors (e.g., physical activity, nutrition, sleep), chronic diseases (e.g., diabetes, cardiovascular disease), and psychosocial factors (e.g., social participation, mental health) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Given its central role in healthy aging, understanding the distribution and determinants of IC is crucial for the development of effective strategies to preserve functional ability, reduce health burdens, and improve quality of life in aging populations.\u003c/p\u003e \u003cp\u003eBeyond individual mental health problems such as depression, broader socioeconomic variables also exert tremendous influence on aging trajectories[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A growing body of research indicates that greater socioeconomic status (SES) and more active social participation (SP) are independently associated with reduced risk of depression and better overall health outcomes [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The association between SES and depression can be described through the stress\u0026ndash;strain theoretical paradigm, where stress theory stresses the buffering function of human resources against external stressors, while strain theory highlights the psychological toll of structural restrictions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, SES may impact health both directly and indirectly by influencing lifestyle behaviors and the development of chronic disorders [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A cross-sectional study further demonstrated a dose-response association between SES and IC deficits, indicating that individuals with lower SES are more susceptible to impairments in IC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. SP, defined as involvement in social, community, and group activities, enhances physical and mental health through greater social support, a stronger sense of belonging, and stress alleviation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Among older adults, active SP has also been linked to lower odds of depression, slower cognitive decline, and reduced mortality [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite these observations, insufficient research has simultaneously studied SES and SP within the context of IC, especially in longitudinal cohorts of older Chinese adults.\u003c/p\u003e \u003cp\u003eAgainst this background, the present study leverages 3 waves (2011\u0026ndash;2015) of nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS) in a combined cross-sectional and longitudinal design to comprehensively examine the link of SES and SP with IC. The findings are expected to provide robust, evidence-based support for developing personalized intervention strategies for middle-aged and older Chinese adults, thereby promoting the transition of healthy aging policies from a predominant focus on \u0026ldquo;disease treatment\u0026rdquo; toward \u0026ldquo;functional maintenance.\u0026rdquo;\u003c/p\u003e"},{"header":"2 Materials and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and population\u003c/h2\u003e \u003cp\u003eThe China Health and Retirement Longitudinal Study (CHARLS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/en\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a nationally representative survey designed to address the challenges of population aging and promote interdisciplinary research on aging in China. It collects high-quality microdata from Chinese households with members aged 45 and above. Approved by the Biomedical Ethics Committee of Peking University (IRB00001052-11015), the study obtained written informed consent from all participants. The baseline survey, conducted in 2011, recruited 17,708 individuals from 10,257 households across 450 villages/communities and 150 counties/districts to ensure national representativeness. Follow-up surveys are administered every two to three years. For this analysis, data from 3 waves (2011\u0026ndash;2015) were utilized, comprising 29,020 eligible observations from respondents aged over 45. Observations with missing values in the SES and SP indices, IC measures, or any covariates (e.g., sleep duration, hypertension, and diabetes) were excluded. This resulted in 13,797 individuals contributing 28,500 observations, including 10,390 participants from the 2011 wave who were included in cross-sectional analysis. For the longitudinal analysis, after further excluding individuals who had IC data at baseline or lacked follow-up information, 3,008 subjects were retained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of SES and SP\u003c/h2\u003e \u003cp\u003eSES was assessed utilizing two indicators: total household wealth and educational achievement. Primary school or less (0 points), secondary education (1 point), and higher education (2 points) were the three levels of educational achievement. The total household wealth was determined by taking into account all assets without liabilities, such as real estate, cars, savings accounts, and business holdings. Wealth was categorized into quartiles and rated on a scale of 0 (lowest) to 3 (highest) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. SP was assessed based on seven types and frequency of activities: interacting with friends, playing mahjong or other board games, participating in sports/social clubs, engaging in community organizations, volunteering or charity work, providing unpaid help to relatives or others, and internet use. Participants were asked whether they had engaged in each activity during the past month, and those who responded \u0026ldquo;yes\u0026rdquo; were further asked about their frequency of participation. Given the low participation rates (less than 2%), volunteering or charity work and providing unpaid help were combined into a single variable termed volunteering activities [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For sports/social clubs, community organizations, and internet use, due to low regular engagement (less than 1% participating weekly or more frequently), these variables were dichotomized into no or yes. Frequency of SP was defined as the highest frequency level reported across the six types and classified into three categories: no participation, irregular participation, or participation at least once per week. Diversity was measured as the total number of SP types engaged in, and grouped into 0, 1, 2, and \u0026ge;\u0026thinsp;3 types [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement of IC\u003c/h2\u003e \u003cp\u003eIC was operationalized as a composite measure across five functional domains: locomotion, sensory capacity, cognition, vitality, and psychological status. Locomotion was assessed applying the 5-repetition sit-to-stand test. Participants who completed the test unassisted within 14 seconds were assigned a score of 1; those requiring a longer time received a score of 0. Sensory capacity incorporated both hearing and vision. Self-reported hearing was evaluated using the question: \u0026ldquo;How is your hearing?\u0026rdquo; Vision was assessed based on two questions: \u0026ldquo;How well do you see distant objects?\u0026rdquo; and \u0026ldquo;How well do you see objects that are close by?\u0026rdquo; Responses rated as \u0026ldquo;fair,\u0026rdquo; \u0026ldquo;good,\u0026rdquo; \u0026ldquo;very good,\u0026rdquo; or \u0026ldquo;excellent\u0026rdquo; were scored 1, while \u0026ldquo;poor\u0026rdquo; responses were scored 0. Cognitive function was measured adopting the telephone interview of cognitive status (TICS), which evaluates memory and mental acuity. Immediate and delayed recall of 10 terminologies was tested (maximum 20 points). Mental status was assessed through orientation (5 points), serial subtraction (5 points), and visuoconstruction (1 point), totaling 11 points. Cognitive impairment was defined as performance below one standard deviation of the mean in either memory or mental status. A score of 1 was given if both domains were unimpaired; otherwise, a score of 0 was assigned. Vitality was evaluated based on body mass index (BMI). Individuals with BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2; were classified as at nutritional risk and scored 0, while those with BMI\u0026thinsp;\u0026ge;\u0026thinsp;18.5 kg/m\u0026sup2; received a score of 1. Psychological status was assessed utilizing the Center for Epidemiologic Studies Depression Scale (CES-D). Participants scoring below 12 were considered free of significant depressive symptoms and assigned a score of 1; those scoring\u0026thinsp;\u0026ge;\u0026thinsp;12 received 0.\u003c/p\u003e \u003cp\u003eScores from each domain were summed into a total score ranging from 0 to 6, with higher scores indicating better integrated functional capacity. A total score\u0026thinsp;\u0026le;\u0026thinsp;5 was classified as impaired IC [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eTo account for potential confounding factors, the following covariates were included in the analysis: age, marital status (married and others), gender, drinking, hypertension, smoking, sleep time (hours), and diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eFor continuous variables in baseline, the Kruskal\u0026ndash;Wallis test was adopted (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) for group comparison. Categorical variables were compared utilizing the chi-square test (n (%)). Regression models were established via survey (v4.2-2) package [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to assess the role of SES and SP for IC. Specifically, Model 1 was unadjusted; Model 2 was adjusted for age, gender, marital status, smoking, and drinking; Model 3 was further adjusted based on all covariates. For the cross-sectional analysis, logistic regression models were employed, while Cox regression models were utilized for the longitudinal analysis. For restricted cubic splines (RCS), the rms (v6.8-0) package [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was utilized to investigate the connection between the SES and SP with IC and to test for nonlinearity. Subgroup analyses were then performed inside the fully adjusted Model 3 employing the jstable (v1.3.12) package to evaluate effect heterogeneity across population strata identified by baseline characteristics. The pROC (v1.18.5) package [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was applied to create receiver operating characteristic (ROC) curves and measure the area under the curve (AUC) in order to assess the prediction power of three models in cross-sectional study. In order to show the incidence of impaired IC among people stratified by SES and SP levels, cumulative incidence curves were displayed for the longitudinal study. A nomogram was then created to forecast the risk of IC after significant predictors were chosen via least absolute shrinkage and selection operator (LASSO) regression. R (v4.4.3) was used for all statistical analyses, and two-tailed \u003cem\u003eP\u003c/em\u003e-values of less than 0.05 were regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eAmong the 10,390 participants included in the analysis, 6,756 (65.0%) were classified as having impaired IC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were observed across all variables between the impaired and normal groups. Notable differences were identified in SES: compared to normal group, a higher proportion of participants with impaired IC fell into the low SES category (30% vs. 15%); while the normal group contained more high-SES individuals (32% vs. 22%). SP also showed marked contrasts: 53% of the impaired group reported no SP, compared to 41% in the normal group. The impaired group was older (60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1 vs. 56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0), had a higher proportion of females (56% vs. 42%), and a lower rate of being married (86% vs. 93%). Moreover, drinking was less common in the impaired group (31% vs. 39%), and average nightly sleep duration was shorter (6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 vs. 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants from CHARLS 2011.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpaired\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10390\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6756\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3634\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,579 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,038 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e541 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,155 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,219 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,936 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,656 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,499 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,157 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,072 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,592 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,480 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,494 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,232 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,262 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,394 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e773 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e621 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e271 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.2 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.5 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.6 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,289 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,768 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,521 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,101 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,988 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,113 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,158 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,793 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,365 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,232 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e963 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,067 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,721 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,346 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,323 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,035 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,288 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,899 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,692 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,207 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,491 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,064 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,427 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,735 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,934 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,801 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,655 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,822 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e833 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,747 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,293 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,454 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e643 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSES: socioeconomic status; SP: social participation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 SES and SP were both independent protective factors for IC in cross-sectional analysis\u003c/h2\u003e \u003cp\u003eIn all 3 models in cross-sectional analysis, a graded inverse association was observed between SES and the risk of impaired IC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In Model 1, both medium (odds ratio (OR) 95% confidence interval (CI))\u0026thinsp;=\u0026thinsp;0.458 (0.410\u0026ndash;0.511) and high SES (0.380 (0.336\u0026ndash;0.430)) were strongly associated with reduced impaired IC risk relative to low SES. In Model 2, the effect estimates were slightly attenuated but remained highly significant (medium SES: (0.463\u0026ndash;0.582); high SES: 0.406 (0.357\u0026ndash;0.461)). Further adjustment for all covariates in Model 3 resulted in minimal changes (medium SES: 0.517 (0.460\u0026ndash;0.581); high SES: 0.402 (0.353\u0026ndash;0.458)), indicating robust and independent protective effects of higher SES. Subgroup analysis by SES indicated that the protective effect of higher SES on IC remained significant across all population subgroups. An interaction was observed with marital status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), with a weaker protective effect among unmarried or separated individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). A noteworthy negative nonlinear connection was observed between SES and IC (\u003cem\u003eP\u003c/em\u003e for overall and nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as assessed via RCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression models examining the associations of SES and SP with IC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.458 (0.410\u0026ndash;0.511)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.519 (0.463\u0026ndash;0.582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.517 (0.460\u0026ndash;0.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.380 (0.336\u0026ndash;0.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.406 (0.357\u0026ndash;0.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.402 (0.353\u0026ndash;0.458)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.746 (0.679\u0026ndash;0.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.719(0.653\u0026ndash;0.792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.724(0.657\u0026ndash;0.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.541 (0.477\u0026ndash;0.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.567 (0.499\u0026ndash;0.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.570 (0.501\u0026ndash;0.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.284 (0.231\u0026ndash;0.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.319 (0.258\u0026ndash;0.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.318 (0.256\u0026ndash;0.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOR: odds ratio; CI: confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, SP demonstrated a clear dose-response relationship with IC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to no participation, engaging in 1, 2, or \u0026ge;\u0026thinsp;3 types of activities was associated with progressively lower risks across all models. For example, participation in \u0026ge;\u0026thinsp;3 activities was associated with a pronounced reduction in risk in Model 1 (0.284 (0.231\u0026ndash;0.350)) compared to in 1 (0.746 (0.679\u0026ndash;0.819)) and 2 (0.541 (0.477\u0026ndash;0.613)) activities. This association was slightly attenuated but remained highly marked in Models 2 (0.319 (0.258\u0026ndash;0.395)) and 3 (0.318 (0.256\u0026ndash;0.394)). The protective effect of SP on IC remained notable across all subgroups except for other marital status individuals and those with diabetes. A significant interaction was observed with hypertension (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A marked negative linear connection was shown between SP and IC (\u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in RCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe ROC curve for Model 3 yielded an AUC of 0.706, demonstrating acceptable predictive accuracy for the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 SES and SP were both independent protective factors for IC in longitudinal analysis\u003c/h2\u003e \u003cp\u003eIn longitudinal analysis, the graded protective association of SES with IC was consistent with that in cross-sectional analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Model 1, both medium (hazard ratio (HR) (95% CI)\u0026thinsp;=\u0026thinsp;0.800 (0.703\u0026ndash;0.909)) and high SES (0.690 (0.596\u0026ndash;0.799)) were significantly associated with lower impaired IC risk. In Models 2 and 3, the effect estimates remained largely stable. The HR for high SES changed only minimally from 0.690 in Model 1 to 0.708 in Model 3, indicating a robust and independent protective effect. Higher SES had a significant protective effect on IC across nearly all population groupings. There was a decreased protective effect among those with diabetes, and an interaction with diabetes was noted (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Higher SES was associated with a lower cumulative incidence of impaired IC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and an inverse association was observed between SES and IC (\u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazards regression models examining the associations of SES and SP with IC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.800 (0.703\u0026ndash;0.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.836 (0.733\u0026ndash;0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.831 (0.729\u0026ndash;0.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.690 (0.596\u0026ndash;0.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.711 (0.613\u0026ndash;0.824)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.708 (0.610\u0026ndash;0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.026 (0.921\u0026ndash;1.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999 (0.896\u0026ndash;1.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.002 (0.899\u0026ndash;1.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.842 (0.730\u0026ndash;0.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.850 (0.736\u0026ndash;0.980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.845 (0.732\u0026ndash;0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.692 (0.556\u0026ndash;0.860)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.729 (0.585\u0026ndash;0.907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.722 (0.580\u0026ndash;0.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHR: hazard ratio; CI: confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the association between SP and IC showed a dose-response relationship only at higher participation levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Participation in 2 types (Model 1: 0.842 (0.730\u0026ndash;0.970), Model 2: 0.850 (0.736\u0026ndash;0.980), Model 3: 0.845 (0.732\u0026ndash;0.975)) and especially\u0026thinsp;\u0026ge;\u0026thinsp;3 types (Model 1: 0.692 (0.556\u0026ndash;0.860), Model 2: 0.729 (0.585\u0026ndash;0.907), Model 3: 0.722 (0.580\u0026ndash;0.900)) was associated with risk reduction. Engagement in \u0026ge;\u0026thinsp;3 types of SP continued to demonstrate a marked protective effect on IC across the majority of population subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). There was a graded inverse correlation between SP and reduced cumulative incidence of impaired IC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and an inverse linear connection of SP with impaired IC risk (\u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 SES and SP were important indicators for predicting the risk of IC\u003c/h2\u003e \u003cp\u003eThe importance of SES and SP in predicting IC was further highlighted by the fact that LASSO regression revealed SP, SES, and gender as the top 3 predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). A nomogram to predict the probability of impaired IC was then established applying the top 7 most important variables identified by LASSO (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The calibration curve demonstrated excellent agreement with the ideal reference line, indicating good predictive performance of the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eUsing nationally representative longitudinal data from CHARLS, we examined how SES and SP influence IC among older adults. In cross-sectional analyses, both higher SES and more active SP were independently associated with a reduced risk of IC impairment, with dose\u0026ndash;response relationships suggesting cumulative protective effects. Longitudinal Cox regression analyses further confirmed these findings: among participants with normal IC at baseline, higher SES and greater SP predicted a lower risk of incident IC impairment. These associations remained robust across fully adjusted models, underscoring the long-term protective roles of socioeconomic and social resources in maintaining functional capacity in later life.\u003c/p\u003e \u003cp\u003eOur findings align with and extend existing studies investigating SP, SES, and cognitive aspects of IC. Longitudinal studies in China have revealed that greater diversity and frequency of social contacts are favorably associated with cognitive performance across memory and mental status domains [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, SP has been shown to predict cognitive trajectories more robustly than traditional health indicators, such as depressive symptoms and self-rated health, particularly for episodic memory [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Engagement in social activities exhibits a non-linear, inverse association with the risk of cognitive impairment and depression, while depression itself significantly mediates both cognitive function and social activity levels, highlighting the importance of maintaining both cognitively and physically stimulating activities [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Among cognitively impaired older adults, participation in cognitively demanding leisure activities is linked to better subsequent memory performance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Importantly, national cohort studies have found that sustaining or increasing social activity over time substantially enhances the likelihood of cognitive improvement, independent of baseline engagement [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].On the socioeconomic side, higher community-level SES\u0026mdash;reflecting the combined influence of education and income\u0026mdash;has been associated with improved cognitive outcomes among middle-aged and older Chinese adults [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In summary, existing studies consistently demonstrate strong associations between SP and the cognitive components of IC. Our study extends this evidence by revealing that greater SP activity is significantly associated with lower risk of overall IC impairment, suggesting that its benefits extend beyond specific domains to the integrated construct of functional capacity.\u003c/p\u003e \u003cp\u003eIn parallel, SES emerged as another key determinant of IC. Ample evidence has shown that higher SES\u0026mdash;typically indexed by education, income, and occupation\u0026mdash;is associated with better outcomes across multiple IC domains, particularly cognition, mobility, and psychological well-being [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Education enhances cognitive reserve and health literacy, enabling older adults to adopt healthier lifestyles, utilize healthcare resources effectively, and adhere to preventive behaviors [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, income secures access to nutritious diets, safe living environments, and health-promoting resources, all of which contribute to sustained physical and mental function [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, higher SES buffers against chronic stress exposure and fosters adaptive coping through greater psychosocial resources and perceived control [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Beyond cognitive aspects, longitudinal studies have demonstrated that higher SES is linked to slower declines in physical function, lower risk of disability, and better self-rated health [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Longitudinal studies based on CHARLS cohorts have demonstrated that higher SES is linked to slower declines in physical function, lower risk of disability, and better self-rated health.Longitudinal evidence from Chinese cohorts has confirmed that individuals with persistently high or upwardly mobile SES exhibit slower declines in cognitive and physical performance than those in persistently low SES groups [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, social capital and social network resources have been shown to buffer the adverse effects of low SES on cognitive and functional trajectories among older Chinese adults [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Taken together, these findings indicate that SES influences IC through both material and psychosocial pathways, highlighting the importance of promoting socioeconomic equity to foster healthy aging and maintain functional ability among older adults. Our study extends this evidence by showing that more active SP is significantly associated with a lower risk of overall IC impairment\u0026mdash;suggesting that its benefits extend beyond specific domains to the integrated construct of functional capacity.\u003c/p\u003e \u003cp\u003eFrom a public health standpoint, these findings provide critical insights into promoting healthy aging. First, healthy aging initiatives should integrate both socioeconomic and social engagement components to preserve IC [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, interventions should prioritize vulnerable groups\u0026mdash;including the unmarried, those with lower SES, and individuals living with chronic conditions\u0026mdash;who may benefit most from targeted social and economic support [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Third, the LASSO-based predictive model and nomogram established in this study, incorporating SES, social participation, and demographic variables, offer a practical framework for identifying individuals at elevated risk of IC impairment. The observed dose\u0026ndash;response associations further support the feasibility of embedding these factors into stratified risk prediction and early-warning systems. Collectively, these results underscore the importance of multi-level policies combining economic, social, and behavioral strategies to sustain IC in aging populations.This study has several notable strengths. It draws upon a large, nationally representative longitudinal dataset, enabling both cross-sectional and prospective analyses [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The application of diverse analytic approaches\u0026mdash;including multivariable regression, subgroup analysis, restricted cubic spline modeling, and LASSO-based predictive modeling\u0026mdash;enhanced the robustness and interpretability of results. To our knowledge, this is among the first studies to jointly examine SES and social participation in relation to IC decline within a Chinese context, while also providing a clinically applicable prediction tool for early risk assessment.Several limitations warrant consideration. First, SES and social participation were self-reported, potentially underrepresenting their multidimensionality and quality. Education and income may not fully capture accumulated wealth or neighborhood context, while frequency-based social metrics overlook activity quality and diversity.Second, despite extensive covariate adjustment, residual confounding from unmeasured factors such as diet, genetics, or environmental exposures cannot be excluded.Third, the follow-up duration may be insufficient to capture long-term IC trajectories, and attrition could bias estimates among vulnerable groups.Fourth, the observational nature of CHARLS limits causal inference; randomized or quasi-experimental studies are needed to confirm whether enhancing SES or social engagement mitigates IC decline.Finally, the LASSO-based model lacked external validation, restricting its generalizability beyond the current sample.\u003c/p\u003e \u003cp\u003eFuture research should employ multidimensional SES and social engagement measures, incorporate biological mediators such as inflammation and cognitive reserve, and conduct intervention and validation studies to build scalable early-warning systems for IC deterioration.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn this nationally representative cohort of middle-aged and older Chinese individuals, better SES and greater SP were consistently linked with a decreased risk of IC impairment. Subgroup analyses found that SES was less protective among unmarried or divorced individuals, whereas SP conferred stronger protection among those with hypertension, older age, or shorter nocturnal sleep. LASSO-based predictive modeling created a nomogram incorporating SES, SP, age, and marital status, enabling tailored 4-year risk estimation. Kaplan-Meier analyses demonstrated dose-dependent protective effects of both SES and SP. These findings emphasize the potential of integrating social and economic factors into early-warning systems and targeted interventions to prevent IC decline in middle-aged and older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintrinsic capacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esocioeconomic status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esocial participation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTICS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etelephone interview of cognitive status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erestricted cubic splines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Gusu Talent Program under Grant [number:(2024)137].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were sourced from CHARLS (http://charls.pku.edu.cn/en).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproved by the Biomedical Ethics Committee of Peking University (IRB00001052-11015), the study obtained written informed consent from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the CHARLS team for providing open-access data and the anonymous reviewers for their constructive comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhaopeng Kang: Conceptualization, Methodology, Software, Formal analysis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLin Zhang: Data curation, Investigation, Validation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXiujuan Feng: Writing\u0026nbsp;–\u0026nbsp;Original Draft, Visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHailong Zhang: Data curation, Investigation.\u003c/p\u003e\n\u003cp\u003eWen Chen: Supervision, Project administration. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCheng Lian: Funding acquisition, Writing\u0026nbsp;–\u0026nbsp;Review \u0026amp; Editing, Supervision, *Corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhan HTA, Addo KM, Findlay H. 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Bull World Health Organ. 2023;101(5):307\u0026ndash;c316.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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":"cross-sectional study, CHARLS, intrinsic capacity, longitudinal study, socioeconomic status, social participation","lastPublishedDoi":"10.21203/rs.3.rs-8638767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8638767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSocioeconomic status (SES) and social participation (SP) influence intrinsic capacity (IC) in older adults. This study examined their impact on IC using CHARLS data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCross-sectional analysis included 10,390 CHARLS 2011 participants, with 3,008 for longitudinal evaluation (2011\u0026ndash;2015). Univariate and multivariate Cox and logistic regression models examined the relationship between socioeconomic status (SES), social participation (SP), and intrinsic capacity (IC). Nonlinear associations were assessed using restricted cubic spline (RCS) modeling, and stratified analyses explored demographic differences. Least absolute shrinkage and selection operator (LASSO) regression identified key predictors for IC, and a nomogram was developed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 10,390 participants included in the analysis, 6,756 (65.0%) were classified as having impaired IC. In all 3 models, SES and SP were both independent protective factors for IC in cross-sectional analysis (odds ratio\u0026thinsp;\u0026lt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In longitudinal analysis, the graded protective association of SES with IC was consistent with that in cross-sectional analysis, but only for higher SP (hazard ratio\u0026thinsp;\u0026lt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both SES and SP demonstrated marked inverse associations with IC, which were either linear or nonlinear. The importance of SES and SP in predicting IC was further highlighted by the fact that LASSO regression revealed SP and SES as the top 2 predictors. Moreover, the nomogram established based on top 7 most important variables in LASSO exhibited good predictive performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSES and SP were identified as protective factors for IC, enhancing precaution of impaired IC and supporting healthy aging in older adults.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"The association of socioeconomic status and social participation with intrinsic capacity: findings from cross-sectional and longitudinal analyses in CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:36:17","doi":"10.21203/rs.3.rs-8638767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-03T04:59:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174925536816231695803885403239796105477","date":"2026-02-23T16:51:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-16T12:12:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-23T17:00:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-20T13:21:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T13:16:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-19T10:55:53+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":"bf9c929e-04b1-46ce-b82e-8d4e218b11c0","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T12:36:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:36:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8638767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8638767","identity":"rs-8638767","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}