Loneliness and trajectories of the cognition among Chinese middle and old-aged adults

Loneliness and trajectories of the cognition among Chinese middle and old-aged adults | 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 Article Loneliness and trajectories of the cognition among Chinese middle and old-aged adults Bo Hu, Yueqiang Zhou, Peijia Zhang, Chengyu Gu, Jun Zhang, Ling Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7044155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives Loneliness is widely regarded as a potential risk factor for cognitive decline, yet existing findings remain inconsistent. Analyzing longitudinal patterns of cognitive change, rather than relying on cross-sectional assessments, can provide deeper insights into the dynamic interplay between loneliness and cognitive health. This study explores the relationship between cognitive trajectories and loneliness in middle-aged and older adults across China. Methods The study analyzed data from 4,239 participants aged 45 and older, drawn from four waves (2011–2018) of the China Health and Retirement Longitudinal Study (CHARLS). Loneliness was measured using a single-item scale (1–4 points). Cognitive function was assessed using validated tests (recall, subtraction, figure drawing) standardized to population norms. Results Group-based trajectory modeling identified three distinct cognitive patterns: stable, slow decline, and rapid decline. After adjusting for covariates, binary logistic regression revealed a significant association between these cognitive trajectories and loneliness scores. Conclusion This study delineates three cognitive trajectories (stable, slow decline, rapid decline) in Chinese middle-aged and older adults. Individuals with rapid cognitive decline exhibited accelerated loneliness progression and significantly heightened risks of mild cognitive impairment and dementia. Health sciences/Diseases Health sciences/Health care Health sciences/Neurology Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Loneliness Cognitive decline Group-based trajectory modeling CHARLS Figures Figure 1 Figure 2 Introduction Population aging has emerged as a pressing global challenge, with mainland China experiencing particularly accelerated demographic shifts. Current projections indicate that by 2050, China will account for 400 million citizens aged 65 + years, including 150 million octogenarians - a demographic transformation supported by longitudinal studies (Chen et al., 2018 ). This rapid aging process, evidenced by the Seventh National Population Census revealing nearly one-fifth (18.7%) of the population exceeding 60 years, portends a substantial escalation in age-related disease burden(Ren et al., 2022 ; Fang, 2015 ). As a key indicator of healthy aging(Lu et al., 2021 ; Ballard et al., 2011 ), cognitive function progressively deteriorates with advancing age, significantly elevating the risk of various age-associated pathologies. The intersection of accelerated population aging and neurocognitive decline positions this dual phenomenon as a critical public health priority requiring multidisciplinary intervention strategies. The escalating global burden of cognitive impairment manifests through its multistage neurodegenerative progression, where age-related decline evolves from subjective dysfunction to mild cognitive impairment (MCI), with 10–15% annual conversion to dementia, severely compromising occupational competence, social engagement, and instrumental daily living capacities(Sohn et al., 2023 ). Given that 35%-40% of dementia cases are attributable to modifiable risk factors, early identification of cognitive impairment holds critical clinical significance for implementing preventive strategies and optimizing intervention timelines. Global research on heterogeneous cognitive trajectories reveals distinct aging patterns across populations. A decade-long Australian study of adults over 55 identified three trajectories: high-stable (87.4%), low-stable (11.3%), and progressive decline (1.3%), demonstrating predominant cognitive resilience in Western populations(Yuan et al., 2022 ). Conversely, analysis of 266,000 U.S. seniors uncovered three persistent states: severe impairment (35.5%), moderate impairment (31.8%), and preserved/mild impairment (32.7%), highlighting significant subpopulation vulnerabilities(Ye L., 2022 ). The China Health and Retirement Longitudinal Survey (CHARLS) cohort data from China revealed unique patterns - high baseline with decline (64.2%), medium baseline with improvement (19.2%), and low baseline with deterioration (16.6%) - suggesting cultural influences on cognitive plasticity. underscore the complexity of cognitive aging. The terminal manifestation of cognitive decline - dementia - now constitutes a global public health emergency. Projections indicate 61.2 million diagnosed cases by 2025 with 12.7 million annual incident cases, generating direct medical costs exceeding $ 1.6 trillion USD alongside caregiver productivity losses(Mose et al., 2023 ). As the seventh leading cause of mortality, dementia induces multidimensional functional deterioration while overwhelming social support systems. Loneliness, defined as the perceived discrepancy between desired and actual social relationships, manifests as a multidimensional psychosocial stressor with systemic health consequences(Tiwari, 2013 ). Emerging evidence positions chronic loneliness as a critical determinant of allostatic load, exhibiting dose-dependent associations with cardiovascular morbidity (OSG, 2023 ; Wei et al., 2022 ), accelerated neurocognitive aging (0.5% annual global cognition decline in seniors), and premature mortality (comparable to daily 15-cigarette smoking). Longitudinal cohort studies reveal bidirectional relationships with mental health disorders: loneliness amplifies depression risk through prefrontal-amygdala dysregulation(Lemay et al., 2024 ). Notably, older adults demonstrate particular susceptibility, with meta-analytic data showing 64% faster progression from subjective cognitive decline to MCI (Tiwari, 2013 ), and 3.2-fold increased dementia conversion rates compared to socially integrated peers(Lemay et al., 2024 ). Longitudinal evidence elucidating the loneliness-cognition nexus reveals complex temporospatial dynamics across populations. Seminal work by Ayalon et al. (Ayalon et al., 2016 )employing structural equation modeling in U.S. adults ≥ 50 years identified unidirectional predictive pathways: baseline memory deficits significantly predicted 4-year loneliness escalation, whereas reciprocal effects were nonsignificant. This directional specificity was corroborated by Lee et al.(Lee et al., 2022 )through multilevel trajectory analysis in a nationally representative U.S. cohort, demonstrating stable loneliness trajectories among cognitively impaired individuals despite baseline elevation. Contrastingly, Zhong et al. (Zhong et al., 2017 )revealed bidirectional neuropsychological coupling in the CHARLS Chinese cohort using latent growth curve modeling: loneliness predicted 18-month cognitive decline while baseline cognitive impairment amplified loneliness progression. This cultural divergence is further complicated by Wang et al. (Wang & Collaboration, 2020 ) multistate modeling in the UK 75 + population, showing null associations across two-decade follow-up despite sufficient statistical power. Current investigations into loneliness and cognitive decline predominantly rely on cross-sectional designs or single-timepoint assessments, failing to capture the cumulative effects of dynamic loneliness fluctuations(Souza et al., 2023 ). Notably, the study revealed that participants with persistent loneliness exhibited significantly accelerated cognitive decline compared to those with transient loneliness, concomitant with reduced gray matter volume (GMV) in the right posterior cingulate cortex. However, extant research has not elucidated how these dynamic fluctuations interact with heterogeneous cognitive trajectories (e.g., stable vs. precipitous decline clusters), potentially underestimating the neurotoxic impact of chronic loneliness on high-risk subgroups characterized by cerebrovascular pathology. Therefore, the present study aimed to first describe the differences in cognitive trajectories in Chinese middle-aged and older adults using the CHARLS data. A data-driven method of group-based trajectory modeling (GBTM) was adopted to describe cognitive trajectories. This method can help investigate how cognitive changes over multiple time points and identify meaningful clusters of individuals that follow distinctive developmental trajectories of cognitive. Methods 2.1 Data Source and Sample The data were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of Chinese adults aged 45 + across 28 provinces (Zhao et al., 2014 ). This longitudinal study, launched in 2011, conducted follow-up surveys in 2013, 2015, 2018, and 2020. Detailed information on sampling methodology and study design is available at the official website ( http://charls.pku.edu.cn/ ). Our analysis utilized five waves of CHARLS data (2011–2020), with 2011 as baseline.To be included in the analysis, participants were required to have complete baseline demographic data (including age, gender, education, and registered residence) and valid cognitive measures across all five survey waves. Additionally, eligible participants needed to be free of loneliness at baseline (2011) and have recorded loneliness measurements in 2020. The participant selection process is detailed in Fig. 1 . For the cognitive trajectory analysis specifically, we adopted an inclusive approach by not applying loneliness-related exclusion criteria, aiming to maximize sample size and better characterize cognitive change patterns. From the initial pool of 17,708 baseline participants, exclusion criteria were applied sequentially: (1) 1,585 participants with incomplete baseline demographic data; (2) 2,554 participants lacking complete follow-up data; and (3) 5,475 participants with missing loneliness scores during the study period. This selection process resulted in a final analytical sample of 4,239 participants for cognitive trajectory examination. The CHARLS study was approved by Peking University’s Institutional Review Board. All participants gave informed consent. The ethical approval of data collection was from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). Every participant signed an informed consent before investigation, and their information were kept anonymous. 2.2 Measures 2.2.1 Loneliness The study employed a single-item loneliness measure adapted from the CES-D 10-item scale (Joyce et al., 2021 ). Participants reported their loneliness frequency during the previous week using a 4-point Likert scale: 1 ("rarely or none [< 1 day]"), 2 ("some or a little [1–2 days]"), 3 ("occasionally [3–4 days]"), and 4 ("most [5–7 days]"). Scores were summed such that higher values indicated more severe loneliness experiences. This brief assessment tool has been widely validated in loneliness research (Shiovitz-Ezra & Ayalon, 2010 ), demonstrating strong psychometric properties while maintaining practicality for population-based epidemiological investigations. Its efficiency makes it particularly suitable for large-scale studies where comprehensive assessment may be impractical. 2.2.2 Assessment of cognitive function Cognitive functioning was assessed across two distinct domains following established methodologies (Luo et al., 2019 ; Lei, 2014 ). The first domain evaluated episodic memory capacity, operationalized through two parallel tasks: immediate verbal recall (score range 0–10) and delayed verbal recall (score range 0–10). The second domain measured mental status using the Telephone Interview for Cognitive Status (TICS) battery, a validated instrument designed to evaluate cognitive integrity through three core components: ①Temporal-spatial orientation (0–5 points), assessed by accurate identification of current date (month, day, year, season) and weekday; ②Visuoconstructional ability (0–1 point), evaluated via figure reproduction from visual memory; ③Sustained attention (0–5 points), measured through five consecutive trials of serial subtraction (100-7 sequence). Total cognitive performance scores (range 0–31) were derived from the summation of both domain scores, with higher composite scores indicating superior cognitive functioning. This multidimensional approach aligns with contemporary neuropsychological assessment frameworks that emphasize differentiated cognitive domain evaluation. All statistical analyses were employed the “traj” package in R (version 4.3.1) Results 3.1. Study sample A total of 4239 middle-aged and elderly people were included in the analysis. The average age was 56.19 ± 7.54 years, among them, there are 2350 (55.4)males and 1889 (44.6)females. The loneliness score of 4239 middle-aged and elderly people is 1.40 ± 0.84, the detailed characteristics were presented in Table 1 . Table 1 Baseline Characteristics of participants in 2013 Baseline characteristic N = 4239 Age, M(SD) 56.19 (7.54) Gender, n (%) Female 1889 (44.6) Male 2350 (55.4) Education, n (%) Illiterate 902 (21.3) Primary school 1141 (26.9) Junior high school 1385 (32.7) Senior high school and above 811 (19.1) Region, n (%) Urban 2463 (58.1) Rural 1776 (41.9) Being married, n (%) Yes 3974 (93.7) No 265 ( 6.3) Drinking, n (%) Never 2414 (56.9) Ever 1825 (43.1) Smoking, n (%) Never 2419 (57.1) Ever 1820 (42.9) Chronic disease, n (%) Yes 1457 (34.4) No 2782 (65.6) Sleep duration, M(SD) 6.53 (1.67) Number of surviving children, M(SD) 2.33 (1.16) 3.2 Cognitive Trajectories In Group-Based Trajectory Model (GBTM), Entropy and Odds of Correct Classification (OCC) are two important evaluation indexes in GBTM. Entropy is used to measure the discrimination between trajectory groups or the complexity of the model, while OCC is used to measure the classification accuracy of the model. OCC > 5 is generally considered as a good model for classification (Nagin, 2005 ). Thus, taking both the Bayesian information criterion (BIC) and average posterior probability (AvePP) indexes into account, we chose the three-trajectory mode as the best-fit model (Nagin, 2001 ). Subsequently, in order to optimize the fitting effect and interpretation of the model, we carefully adjusted the order of the polynomial. After many attempts and model evaluation, we found that the overall performance of the model is the best when the three trajectory groups are fitted with quadratic, quadratic and zeroth polynomials.the detailed characteristics were presented in Table 3 . Table 3 Model evaluation index Order AIC BIC Entropy OCC AvePP Proportion (%) 1 2 -52366.59 -52379.30 - - 1 100 2 2,2 -49498.83 -49524.24 0.825 27.642/10.406 0.922/0.960 30.49/ 69.51 3 2,2,2 -48752.03 -48790.14 0.777 87.170/10.505/10.387 0.912/0.876/0.908 11.00/40.81/48.19 4 2,2,2,2 -48550.40 -48601.21 0.719 153.034/17.873/4.889/16.413 0.899/0.847/0.817/0.830 5.84/23.96/45.78/24.42 5 2,2,2,2,2 -48459.81 -48523.33 0.713 157.054/34.192/15.162/4.707/16.904 0.897/0.728/0.746/0.813/0.835 5.36/7.96/16.93/45.28/24.47 3 (1) 2,2,1 -48751.68 -48786.62 0.777 89.270/10.537/10.294 0.913/0.876/0.908 10.99/40.79/48.22 3 (2) 2,2,0 -48750.69 -48782.45 0.777 89.402/10.453/10.379 0.913/0.876/0.908 11.00/40.80/48.20 Table 4 shows the three distinct trajectories of cognitive, which were called Stable (n = 2076, 48.97%), Slow Decline (n = 1713, 40.41%), and Rapid Decline (n = 450, 10.62%). The stable trajectory was characterized by the persistently lowest level of cognitive, The Slow decline trajectory consisted of participants reporting moderate levels of cognitive scores that declining to nearly 1 point over time. In the Rapid decline group, the initial cognitive scores were lowest and declining to nearly 2 points across the five waves. Table 4 Baseline data of 4239 middle-aged and elderly people with different cognitive trajectories. CHARACTERISTIC Total(n = 4239) TRAJECTORY GROUP P Rapid decline(n = 450) Slow decline(n = 1713) Stable(n = 2076) Loneliness score , M (SD) 1.40 (0.84) 1.61 (1.04) 1.47 (0.90) 1.30 (0.71) 1) 960 (22.6) 384 (18.5) 136 (30.2) 440 (25.7) < 0.001 No(Score = 1) 3279 (77.4) 1692 (81.5) 314 (69.8) 1273 (74.3) Age , M (SD) 56.19 (7.54) 58.54 (7.47) 57.13 (7.69) 54.91 (7.18) < 0.001 Gender , n (%) Female 1889 (44.6) 249 (55.3) 748 (43.7) 892 (43.0) < 0.001 Male 2350 (55.4) 201 (44.7) 965 (56.3) 1184 (57.0) Region , n (%) Rural 1776 (41.9) 125 (27.8) 616 (36.0) 1035 (49.9) < 0.001 Urban 2463 (58.1) 325 (72.2) 1097 (64.0) 1041 (50.1) Being married , n (%) Yes 265 ( 6.3) 56 (12.4) 106 ( 6.2) 103 ( 5.0) < 0.001 No 3974 (93.7) 394 (87.6) 1607 (93.8) 1973 (95.0) Education , n (%) Illiterate 902 (21.3) 259 (57.6) 436 (25.5) 207 (10.0) < 0.001 Primary school 1141 (26.9) 130 (28.9) 587 (34.3) 424 (20.4) Junior high school 1385 (32.7) 47 (10.4) 510 (29.8) 828 (39.9) Senior high school and above 811 (19.1) 14 ( 3.1) 180 (10.5) 617 (29.7) Drinking , n (%) Never 2414 (56.9) 263 (58.4) 976 (57.0) 1175 (56.6) 0.773 Ever 1825 (43.1) 187 (41.6) 737 (43.0) 901 (43.4) Smoking , n (%) Never 2419 (57.1) 264 (58.7) 949 (55.4) 1206 (58.1) 0.192 Ever 1820 (42.9) 186 (41.3) 764 (44.6) 870 (41.9) Chronic disease , n (%) Yes 1457 (34.4) 151 (33.6) 557 (32.5) 749 (36.1) 0.066 No 2782 (65.6) 299 (66.4) 1156 (67.5) 1327 (63.9) IADL score , M(SD) 0.17 (0.60) 0.26 (0.74) 0.24 (0.72) 0.09 (0.42) < 0.001 Number of surviving children , M(SD) 2.33 (1.16) 2.68 (1.22) 2.47 (1.17) 2.14 (1.10) < 0.001 Sleep duration , M(SD) 6.53 (1.67) 6.45 (2.00) 6.43 (1.74) 6.64 (1.51) < 0.001 3.3 Effects of Loneliness on Cognitive Trajectories As shown in Table 4 , Univariate analysis revealed statistically significant differences ( p < 0.001) across cognitive trajectory groups in middle-aged and older adults regarding loneliness scores, age, gender, residence, marital status, educational attainment, activities of daily living (ADL) scores, number of living children, and sleep duration. Compared to stable group, people with cognitive Slow decline ( OR = 1.52, 95% CI [1.30, 1.78], p < 0.001) and cognitive Rapid decline ( OR = 1.91, 95% CI [1.52, 2.40], p < 0.001) were significantly associated with higher odds of having loneliness when considering the effects of cognitive trajectory types only. Such associations remained significant after adjusting for other social connection factors and demographic factors at baseline (Table 5 ). Table 5 Disordered multi-classification logistic regression Stable vs Rapid decline Stable vs Slow decline OR 95%CI P OR 95%CI P Model 1 Loneliness score 1.507 1.350,1.682 < 0.001 1.291 1.192,1.399 < 0.001 Loneliness no Reference Reference yes 1.908 1.516,2.401 < 0.001 1.522 1.304,1.778 < 0.001 Model 2 Loneliness score 1.347 1.186,1.531 < 0.001 1.265 1.158,1.382 < 0.001 Loneliness no Reference Reference yes 1.649 1.268,2.145 < 0.001 1.488 1.258,1.761 < 0.001 Model 3 Loneliness score 1.295 1.294,1.295 < 0.001 1.213 1.212,1.213 < 0.001 Loneliness no Reference Reference yes 1.522 1.522,1.523 < 0.001 1.374 1.373,1.374 < 0.001 Model 1: Unadjusted model Model 2:Adjust age, sex, place of residence, education and marriage. Model 3:Adjust smoking, drinking, chronic diseases, ADL, IADL, per capita household consumption, number of surviving children, sleep time and social activities on the basis of Model 2. Discussion In this study, we used a nationally representative sample, three different cognitive trajectories were identified for Chinese adults aged 45 years or older over a 9-year follow-up: cognitive stable group, cognitive Slow decline, and cognitive Rapid decline. Compared with the stable group, both cognitive Slow decline and cognitive Rapid decline were related to Loneliness score. In this research, 48.2% of middle-aged and older adults were classified into the stable group, whose cognitive scores remained stable without significant changes from 2011 to 2020. 40.8% of middle-aged and older adults were categorized into the slow decline group, exhibiting a gradual decline in cognitive scores within a 1-point range. 11.0% of older adults were classified into the rapid decline group, demonstrating a more pronounced cognitive score decrease ranging between 1–2 points. People in the cognitive Rapid decline group had a persistently higher level of loneliness from the very beginning than people in the other two groups, indicating the differences and diversity of different individuals in the development of cognitive function. This study further investigated determinants influencing cognitive trajectories in middle-aged and older adults. Multivariate logistic regression analyses revealed that age, gender, region, marital status, educational attainment, instrumental ability of daily living (IADL) scores, number of surviving children, and sleep duration significantly predicted cognitive status. From a demographic perspective, female sex, rural residence, being married, higher education levels, better IADL performance, fewer surviving children, and longer sleep duration emerged as protective factors for cognitive function (Zhao et al., 2025 ; Bruderer-Hofstetter et al., 2022 ; Du Y et al., 2023 ; Yang et al., 2024 ). It is worth noting that rural residence and fewer living children exhibited heterogeneous associations. The study suggests that residing in areas with higher green space coverage may significantly decelerate cognitive decline (Zhang et al., 2022 ), whereas urban populations demonstrate more prevalent sedentary behaviors—a known risk factor for cerebral hypoxia and subsequent neural damage accelerating cognitive deterioration (Z. Yang et al., 2024 ). Additionally, in multi-child families, parents are required to allocate more energy and face increased caregiving responsibilities (Yang et al., 2022 ); such chronic stress accumulation may exacerbate cognitive aging processes. Multiple theoretical models provide a biological explanatory framework for elucidating the association between social factors and cognitive function. The primary mechanism focuses on the restructuring of social networks unique to middle-aged and older populations—the exit from occupational roles and disruption of emotional bonds significantly compromise the stability of social support systems, rendering this demographic particularly vulnerable to the dual risk of chronic social isolation and emotional loneliness (Casey & Holmes, 1995 ; Wrzus et al., 2013 ). This can also be used to explain that the age of the stable group in this study is significantly lower than that of the slow decline group and the rapid decline group. This sustained psychosocial stress triggers a cascade of neuroendocrine responses, including persistent activation of the hypothalamic-pituitary-adrenal (HPA) axis, glucocorticoid receptor resistance, and upregulation of pro-inflammatory gene expression(Ren et al., 2023 ). These pathophysiological alterations have been demonstrated to accelerate neurodegenerative processes by directly impairing neural plasticity (Golaszewski et al., 2022 ; Song Y, 2023 ). Notably, these neurobiological modifications exhibit bidirectional associations with cardiovascular and cerebrovascular events such as atherosclerosis and hypertension(Valtorta et al., 2016 ). Furthermore, these vascular risk factors synergistically interact with metabolic disorders like diabetes, collectively constituting a multidimensional pathological basis for cognitive decline. The secondary pathways emphasize the mediating role of health behaviors. Empirical studies demonstrate that individuals experiencing chronic social isolation are more prone to developing unhealthy lifestyle patterns characterized by physical inactivity and poor dietary habits (Leigh-Hunt et al., 2017 ). Such behavioral patterns not only directly impair cerebrovascular autoregulation but also substantially elevate the comorbidity risk of cardiovascular disorders and cognitive impairment through mechanisms involving vascular endothelial dysfunction and systemic inflammatory responses (Xia & Li, 2018 ). Behavioral epidemiology data indicate that these modifiable lifestyle factors account for approximately 30%-40% of attributable risk for cognitive decline, underscoring the critical importance of behavioral interventions in cognitive preservation strategies. Longitudinal cohort studies substantiate that impairments in global cognitive performance and specific cognitive domains (e.g., social cognition and executive function) significantly predict subsequent levels of perceived social isolation (Zhong et al., 2017 ; Cachon-Alonso et al., 2023 ; Okely & Deary, 2019 ). Clinical neuropsychological investigations further reveal that young-onset dementia patients frequently exhibit characteristic sociobehavioral alterations, including pathological social withdrawal, impaired socio-environmental adaptation, and deficits in social cue interpretation. These behavioral manifestations fundamentally reflect the externalization of frontotemporal neural network degeneration(Poey et al., 2017 ). Notably, this discovery suggests that subtle sociobehavioral changes emerging during the preclinical phase of dementia may serve as early-stage biomarkers, offering novel perspectives for prodromal disease prediction. Limitations This study has several limitations. First, loneliness was measured using a single question with a four-point scale, which may confine the possible variation of loneliness. Second, the reliance on self-reported measures of cognitive performance and loneliness scores introduces potential biases due to inherent subjectivity in such assessments. Finally, there are possibilities of reverse causality because of the observational nature of the study. Future research should prioritize methodological innovations to address current limitations, including the adoption of multidimensional loneliness assessments combined with biomarker validation to enhance measurement precision and reduce self-report bias. Objective cognitive evaluations through standardized neuropsychological batteries and linkage with clinical diagnostic records could refine outcome characterization. Advanced causal inference approaches, such as Mendelian randomization or high-frequency ecological momentary assessment designs, would help disentangle bidirectional relationships between loneliness and cognitive decline. Furthermore, integrating multi-omics data with computational social science methods could elucidate mechanistic pathways across biological, psychological, and socio-environmental levels, ultimately informing targeted interventions to mitigate cognitive risks associated with chronic loneliness. Conclusion The present study identified three distinct cognitive trajectories among Chinese middle-aged and older adults: stable group, slow decline, and rapid decline. Individuals exhibiting rapid declining cognitive trajectories demonstrated significantly accelerated loneliness decline, with those in the rapid-declining group showing particularly elevated risks of mild cognitive impairment and dementia subtypes. These findings advance research on healthy cognitive aging by elucidating bidirectional psychobiological pathways—where rapid cognitive decline exacerbates loneliness through impaired social cognition, while escalating loneliness further depletes neural reserves, creating a vicious cycle that accelerates neurodegeneration. Prioritizing routine monitoring of cognitive trajectories could enable early detection of at-risk populations. Declarations Author contributions ：Study conception and design: BH; data analysis: BH, LJ, JZ; drafting the article: LFF, RS; data preparation: WZ, YQZ; data collection: BH, PJZ; revision of the article: BH, PJZ, WZ, YQZ; all authors read and approved the final manuscript. Funding ：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Zhejiang Traditional Chinese Medicine Science and Technology Plan in 2025 (County Special Project) (No.2025ZX265) and the second batch of science and technology projects in Haining in 2024(No.2024090) Data availability ：The raw data supporting the conclusions of this article will be made available by the corresponding authors. Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants. Consent for publication Not applicable. Conflict of interest The authors declare no conflict of interest. Clinical trial number:not applicable. References Ayalon, L., Shiovitz-Ezra, S., & Roziner, I. (2016). 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General Psychiatry , 35(1), e100751. http://doi.org/10.1136/gpsych-2022-100751 Ren, Y., Savadlou, A., Park, S., Siska, P., Epp, J. R., & Sargin, D. (2023). The impact of loneliness and social isolation on the development of cognitive decline and Alzheimer's Disease. Frontiers in Neuroendocrinology , 69, 101061. http://doi.org/10.1016/j.yfrne.2023.101061 Shiovitz-Ezra, S., & Ayalon, L. (2010). Situational versus chronic loneliness as risk factors for all-cause mortality. International Psychogeriatrics , 22(3), 455-462. http://doi.org/10.1017/S1041610209991426 Sohn, M., Yang, J., Sohn, J., & Lee, J. H. (2023). Digital healthcare for dementia and cognitive impairment: A scoping review. International Journal of Nursing Studies , 140, 104413. http://doi.org/10.1016/j.ijnurstu.2022.104413 Song Y, Z. C. S. B. (2023). Social isolation, loneliness, and incident type 2 diabetes mellitus: results from two large prospective cohorts in Europe and East Asia and Mendelian randomization. EClinicalMedicine , 21(64) http://doi.org/10.1016/j.eclinm.2023.102236 Souza, J. G., Farias-Itao, D. S., Aliberti, M., Bertola, L., de Andrade, F. B., Lima-Costa, M. F., Ferri, C. P., & Suemoto, C. K. (2023). Social Isolation, Loneliness, and Cognitive Performance in Older Adults: Evidence From the ELSI-Brazil Study. American Journal of Geriatric Psychiatry , 31(8), 610-620. http://doi.org/10.1016/j.jagp.2023.03.013 Tiwari, S. C. (2013). Loneliness: A disease?. Indian Journal of Psychiatry , 55(4), 320-322. http://doi.org/10.4103/0019-5545.120536 Valtorta, N. K., Kanaan, M., Gilbody, S., Ronzi, S., & Hanratty, B. (2016). Loneliness and social isolation as risk factors for coronary heart disease and stroke: systematic review and meta-analysis of longitudinal observational studies. Heart , 102(13), 1009-1016. http://doi.org/10.1136/heartjnl-2015-308790 Wang, H. L. C. H., & Collaboration. (2020). Longitudinal analysis of the impact of loneliness on cognitive function over a 20-year follow-up. Aging & Mental Health , 24(11), 1815-1821. http://doi.org/https://doi.org/10.1080/13607863.2019.1655704 Wei, Z. Y., Yang, J. G., Qian, H. Y., & Yang, Y. J. (2022). Impact of Marital Status on Management and Outcomes of Patients With Acute Myocardial Infarction: Insights From the China Acute Myocardial Infarction Registry. Journal of the American Heart Association , 11(23), e25671. http://doi.org/10.1161/JAHA.122.025671 Wrzus, C., Hanel, M., Wagner, J., & Neyer, F. J. (2013). Social network changes and life events across the life span: a meta-analysis. Psychological Bulletin , 139(1), 53-80. http://doi.org/10.1037/a0028601 Xia, N., & Li, H. (2018). Loneliness, Social Isolation, and Cardiovascular Health. Antioxidants & Redox Signaling , 28(9), 837-851. http://doi.org/10.1089/ars.2017.7312 Yang, C., Mao, Z., Wu, S., Yin, S., Sun, Y., & Cui, D. (2024). Influencing factors, gender differences and the decomposition of inequalities in cognitive function in Chinese older adults: a population-based cohort study. Bmc Geriatrics , 24(1), 371. http://doi.org/10.1186/s12877-024-04857-x Yang, H. L., Zhang, S. Q., Zhang, S., Wu, Y. Y., & Luo, R. D. (2022). Fertility experiences and later-life cognitive function among older adults in China. American Journal of Human Biology , 34(10), e23786. http://doi.org/10.1002/ajhb.23786 Yang, Z., Hotterbeex, P., Marent, P. J., Cerin, E., Thomis, M., & van Uffelen, J. (2024). Physical activity, sedentary behaviour, and cognitive function among older adults: A bibliometric analysis from 2004 to 2024. Ageing Research Reviews , 97, 102283. http://doi.org/10.1016/j.arr.2024.102283 Ye L., Q. L. X. B. (2022). Heterogeneous growth trajectories of cognitive function and influencing factors for elderly adults. Health Statist , 183-187. http://doi.org/10.3969/j.issn.1002-3674.2021.02.006 Yuan, Y., Lapane, K. L., Tjia, J., Baek, J., Liu, S. H., & Ulbricht, C. M. (2022). Trajectories of physical frailty and cognitive impairment in older adults in United States nursing homes. Bmc Geriatrics , 22(1), 339. http://doi.org/10.1186/s12877-022-03012-8 Zhang, L., Luo, Y., Zhang, Y., Pan, X., Zhao, D., & Wang, Q. (2022). Green Space, Air Pollution, Weather, and Cognitive Function in Middle and Old Age in China. Frontiers in Public Health , 10, 871104. http://doi.org/10.3389/fpubh.2022.871104 Zhao, Y. L., Hao, Y. N., Ge, Y. J., Zhang, Y., Huang, L. Y., Fu, Y., Zhang, D. D., Ou, Y. N., Cao, X. P., Feng, J. F., Cheng, W., Tan, L., & Yu, J. T. (2025). Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis. Alzheimers Research & Therapy , 17(1), 13. http://doi.org/10.1186/s13195-025-01670-5 Zhao, Y., Hu, Y., Smith, J. P., Strauss, J., & Yang, G. (2014). Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). International Journal of Epidemiology , 43(1), 61-68. http://doi.org/10.1093/ije/dys203 Zhong, B. L., Chen, S. L., Tu, X., & Conwell, Y. (2017). Loneliness and Cognitive Function in Older Adults: Findings From the Chinese Longitudinal Healthy Longevity Survey. Journals of Gerontology Series B-Psychological Sciences and Social Sciences , 72(1), 120-128. http://doi.org/10.1093/geronb/gbw037 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-7044155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488811567,"identity":"0bb36eb0-79f2-4dcc-9d10-768759003e10","order_by":0,"name":"Bo 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University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peijia","middleName":"","lastName":"Zhang","suffix":""},{"id":488811574,"identity":"111b5aec-c4e8-4cc3-b116-bf3ac2765272","order_by":3,"name":"Chengyu Gu","email":"","orcid":"","institution":"The Fourth People’s Hospital Of Haining","correspondingAuthor":false,"prefix":"","firstName":"Chengyu","middleName":"","lastName":"Gu","suffix":""},{"id":488811575,"identity":"69b51bb8-f052-4ab7-8f0c-9166bd2cdfe0","order_by":4,"name":"Jun Zhang","email":"","orcid":"","institution":"The Fourth People’s Hospital Of Haining","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhang","suffix":""},{"id":488811578,"identity":"b5b530ee-2865-4b96-8f56-a1be297d45a7","order_by":5,"name":"Ling Jiang","email":"","orcid":"","institution":"The Fourth People’s Hospital Of Haining","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Jiang","suffix":""},{"id":488811580,"identity":"79ee0aa5-193e-49aa-9282-b21bc90764c9","order_by":6,"name":"Luning Zhang","email":"","orcid":"","institution":"The Fourth People’s Hospital Of Haining","correspondingAuthor":false,"prefix":"","firstName":"Luning","middleName":"","lastName":"Zhang","suffix":""},{"id":488811582,"identity":"3148004b-0b58-4d78-bdae-93bb4210f678","order_by":7,"name":"Wei Zhang","email":"","orcid":"","institution":"The Fourth People’s Hospital Of Haining","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-07-04 07:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7044155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7044155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87347127,"identity":"302d60c1-d6cd-42e0-a0ca-7a660de30f61","added_by":"auto","created_at":"2025-07-23 02:38:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":370241,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of participants\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7044155/v1/42ab566e4f8320a0a0f48547.png"},{"id":87347130,"identity":"9a77d4cb-e995-4519-b53e-36f6ee987183","added_by":"auto","created_at":"2025-07-23 02:38:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206841,"visible":true,"origin":"","legend":"\u003cp\u003eCognitive trajectory\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7044155/v1/1a00376a43c22835a15cae7d.png"},{"id":103399985,"identity":"3e910e86-580b-4df7-9ba9-db94ee9ae1b0","added_by":"auto","created_at":"2026-02-25 09:13:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1559283,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7044155/v1/7f86f7d9-1a75-44b3-be72-6560fc53a99d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Loneliness and trajectories of the cognition among Chinese middle and old-aged adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation aging has emerged as a pressing global challenge, with mainland China experiencing particularly accelerated demographic shifts. Current projections indicate that by 2050, China will account for 400\u0026nbsp;million citizens aged 65\u0026thinsp;+\u0026thinsp;years, including 150\u0026nbsp;million octogenarians - a demographic transformation supported by longitudinal studies (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This rapid aging process, evidenced by the Seventh National Population Census revealing nearly one-fifth (18.7%) of the population exceeding 60 years, portends a substantial escalation in age-related disease burden(Ren et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As a key indicator of healthy aging(Lu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ballard et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), cognitive function progressively deteriorates with advancing age, significantly elevating the risk of various age-associated pathologies. The intersection of accelerated population aging and neurocognitive decline positions this dual phenomenon as a critical public health priority requiring multidisciplinary intervention strategies.\u003c/p\u003e\u003cp\u003eThe escalating global burden of cognitive impairment manifests through its multistage neurodegenerative progression, where age-related decline evolves from subjective dysfunction to mild cognitive impairment (MCI), with 10\u0026ndash;15% annual conversion to dementia, severely compromising occupational competence, social engagement, and instrumental daily living capacities(Sohn et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given that 35%-40% of dementia cases are attributable to modifiable risk factors, early identification of cognitive impairment holds critical clinical significance for implementing preventive strategies and optimizing intervention timelines.\u003c/p\u003e\u003cp\u003eGlobal research on heterogeneous cognitive trajectories reveals distinct aging patterns across populations. A decade-long Australian study of adults over 55 identified three trajectories: high-stable (87.4%), low-stable (11.3%), and progressive decline (1.3%), demonstrating predominant cognitive resilience in Western populations(Yuan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, analysis of 266,000 U.S. seniors uncovered three persistent states: severe impairment (35.5%), moderate impairment (31.8%), and preserved/mild impairment (32.7%), highlighting significant subpopulation vulnerabilities(Ye L., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The China Health and Retirement Longitudinal Survey (CHARLS) cohort data from China revealed unique patterns - high baseline with decline (64.2%), medium baseline with improvement (19.2%), and low baseline with deterioration (16.6%) - suggesting cultural influences on cognitive plasticity. underscore the complexity of cognitive aging. The terminal manifestation of cognitive decline - dementia - now constitutes a global public health emergency. Projections indicate 61.2\u0026nbsp;million diagnosed cases by 2025 with 12.7\u0026nbsp;million annual incident cases, generating direct medical costs exceeding \u003cspan\u003e$\u003c/span\u003e1.6 trillion USD alongside caregiver productivity losses(Mose et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As the seventh leading cause of mortality, dementia induces multidimensional functional deterioration while overwhelming social support systems.\u003c/p\u003e\u003cp\u003eLoneliness, defined as the perceived discrepancy between desired and actual social relationships, manifests as a multidimensional psychosocial stressor with systemic health consequences(Tiwari, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Emerging evidence positions chronic loneliness as a critical determinant of allostatic load, exhibiting dose-dependent associations with cardiovascular morbidity (OSG, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), accelerated neurocognitive aging (0.5% annual global cognition decline in seniors), and premature mortality (comparable to daily 15-cigarette smoking). Longitudinal cohort studies reveal bidirectional relationships with mental health disorders: loneliness amplifies depression risk through prefrontal-amygdala dysregulation(Lemay et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, older adults demonstrate particular susceptibility, with meta-analytic data showing 64% faster progression from subjective cognitive decline to MCI (Tiwari, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and 3.2-fold increased dementia conversion rates compared to socially integrated peers(Lemay et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLongitudinal evidence elucidating the loneliness-cognition nexus reveals complex temporospatial dynamics across populations. Seminal work by Ayalon et al. (Ayalon et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)employing structural equation modeling in U.S. adults\u0026thinsp;\u0026ge;\u0026thinsp;50 years identified unidirectional predictive pathways: baseline memory deficits significantly predicted 4-year loneliness escalation, whereas reciprocal effects were nonsignificant. This directional specificity was corroborated by Lee et al.(Lee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)through multilevel trajectory analysis in a nationally representative U.S. cohort, demonstrating stable loneliness trajectories among cognitively impaired individuals despite baseline elevation. Contrastingly, Zhong et al. (Zhong et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)revealed bidirectional neuropsychological coupling in the CHARLS Chinese cohort using latent growth curve modeling: loneliness predicted 18-month cognitive decline while baseline cognitive impairment amplified loneliness progression. This cultural divergence is further complicated by Wang et al. (Wang \u0026amp; Collaboration, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) multistate modeling in the UK 75\u0026thinsp;+\u0026thinsp;population, showing null associations across two-decade follow-up despite sufficient statistical power.\u003c/p\u003e\u003cp\u003eCurrent investigations into loneliness and cognitive decline predominantly rely on cross-sectional designs or single-timepoint assessments, failing to capture the cumulative effects of dynamic loneliness fluctuations(Souza et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, the study revealed that participants with persistent loneliness exhibited significantly accelerated cognitive decline compared to those with transient loneliness, concomitant with reduced gray matter volume (GMV) in the right posterior cingulate cortex. However, extant research has not elucidated how these dynamic fluctuations interact with heterogeneous cognitive trajectories (e.g., stable vs. precipitous decline clusters), potentially underestimating the neurotoxic impact of chronic loneliness on high-risk subgroups characterized by cerebrovascular pathology. Therefore, the present study aimed to first describe the differences in cognitive trajectories in Chinese middle-aged and older adults using the CHARLS data. A data-driven method of group-based trajectory modeling (GBTM) was adopted to describe cognitive trajectories. This method can help investigate how cognitive changes over multiple time points and identify meaningful clusters of individuals that follow distinctive developmental trajectories of cognitive.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source and Sample\u003c/h2\u003e\u003cp\u003eThe data were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of Chinese adults aged 45\u0026thinsp;+\u0026thinsp;across 28 provinces (Zhao et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This longitudinal study, launched in 2011, conducted follow-up surveys in 2013, 2015, 2018, and 2020. Detailed information on sampling methodology and study design is available at the official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur analysis utilized five waves of CHARLS data (2011\u0026ndash;2020), with 2011 as baseline.To be included in the analysis, participants were required to have complete baseline demographic data (including age, gender, education, and registered residence) and valid cognitive measures across all five survey waves. Additionally, eligible participants needed to be free of loneliness at baseline (2011) and have recorded loneliness measurements in 2020.\u003c/p\u003e\u003cp\u003eThe participant selection process is detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For the cognitive trajectory analysis specifically, we adopted an inclusive approach by not applying loneliness-related exclusion criteria, aiming to maximize sample size and better characterize cognitive change patterns. From the initial pool of 17,708 baseline participants, exclusion criteria were applied sequentially: (1) 1,585 participants with incomplete baseline demographic data; (2) 2,554 participants lacking complete follow-up data; and (3) 5,475 participants with missing loneliness scores during the study period. This selection process resulted in a final analytical sample of 4,239 participants for cognitive trajectory examination.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e The CHARLS study was approved by Peking University\u0026rsquo;s Institutional Review Board. All participants gave informed consent. The ethical approval of data collection was from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). Every participant signed an informed consent before investigation, and their information were kept anonymous.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measures\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Loneliness\u003c/h2\u003e\u003cp\u003eThe study employed a single-item loneliness measure adapted from the CES-D 10-item scale (Joyce et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Participants reported their loneliness frequency during the previous week using a 4-point Likert scale: 1 (\"rarely or none [\u0026lt;\u0026thinsp;1 day]\"), 2 (\"some or a little [1\u0026ndash;2 days]\"), 3 (\"occasionally [3\u0026ndash;4 days]\"), and 4 (\"most [5\u0026ndash;7 days]\"). Scores were summed such that higher values indicated more severe loneliness experiences. This brief assessment tool has been widely validated in loneliness research (Shiovitz-Ezra \u0026amp; Ayalon, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), demonstrating strong psychometric properties while maintaining practicality for population-based epidemiological investigations. Its efficiency makes it particularly suitable for large-scale studies where comprehensive assessment may be impractical.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Assessment of cognitive function\u003c/h2\u003e\u003cp\u003eCognitive functioning was assessed across two distinct domains following established methodologies (Luo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lei, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The first domain evaluated episodic memory capacity, operationalized through two parallel tasks: immediate verbal recall (score range 0\u0026ndash;10) and delayed verbal recall (score range 0\u0026ndash;10). The second domain measured mental status using the Telephone Interview for Cognitive Status (TICS) battery, a validated instrument designed to evaluate cognitive integrity through three core components: ①Temporal-spatial orientation (0\u0026ndash;5 points), assessed by accurate identification of current date (month, day, year, season) and weekday; ②Visuoconstructional ability (0\u0026ndash;1 point), evaluated via figure reproduction from visual memory; ③Sustained attention (0\u0026ndash;5 points), measured through five consecutive trials of serial subtraction (100-7 sequence). Total cognitive performance scores (range 0\u0026ndash;31) were derived from the summation of both domain scores, with higher composite scores indicating superior cognitive functioning. This multidimensional approach aligns with contemporary neuropsychological assessment frameworks that emphasize differentiated cognitive domain evaluation. All statistical analyses were employed the \u0026ldquo;traj\u0026rdquo; package in R (version 4.3.1)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Study sample\u003c/h2\u003e\u003cp\u003eA total of 4239 middle-aged and elderly people were included in the analysis. The average age was 56.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54 years, among them, there are 2350 (55.4)males and 1889 (44.6)females. The loneliness score of 4239 middle-aged and elderly people is 1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84, the detailed characteristics were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of participants in 2013\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4239\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, \u003cem\u003eM(SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.19 (7.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGender, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1889 (44.6)\u003c/p\u003e\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2350 (55.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEducation, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e902 (21.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1141 (26.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1385 (32.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior high school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e811 (19.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRegion, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2463 (58.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1776 (41.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBeing married, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3974 (93.7)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265 ( 6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDrinking, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2414 (56.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1825 (43.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSmoking, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2419 (57.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1820 (42.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic disease, \u003cem\u003en\u003c/em\u003e (%)\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\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1457 (34.4)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2782 (65.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration, \u003cem\u003eM(SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.53 (1.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of surviving children, \u003cem\u003eM(SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.33 (1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Cognitive Trajectories\u003c/h2\u003e\u003cp\u003eIn Group-Based Trajectory Model (GBTM), Entropy and Odds of Correct Classification (OCC) are two important evaluation indexes in GBTM. Entropy is used to measure the discrimination between trajectory groups or the complexity of the model, while OCC is used to measure the classification accuracy of the model. OCC\u0026thinsp;\u0026gt;\u0026thinsp;5 is generally considered as a good model for classification (Nagin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Thus, taking both the Bayesian information criterion (BIC) and average posterior probability (AvePP) indexes into account, we chose the three-trajectory mode as the best-fit model (Nagin, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Subsequently, in order to optimize the fitting effect and interpretation of the model, we carefully adjusted the order of the polynomial. After many attempts and model evaluation, we found that the overall performance of the model is the best when the three trajectory groups are fitted with quadratic, quadratic and zeroth polynomials.the detailed characteristics were presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel evaluation index\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrder\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOCC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAvePP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eProportion (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-52366.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-52379.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-49498.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-49524.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.642/10.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.922/0.960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.49/ 69.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,2,2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-48752.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-48790.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.170/10.505/10.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.912/0.876/0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.00/40.81/48.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,2,2,2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-48550.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-48601.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e153.034/17.873/4.889/16.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.899/0.847/0.817/0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.84/23.96/45.78/24.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,2,2,2,2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-48459.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-48523.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157.054/34.192/15.162/4.707/16.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.897/0.728/0.746/0.813/0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.36/7.96/16.93/45.28/24.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,2,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-48751.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-48786.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.270/10.537/10.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.913/0.876/0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.99/40.79/48.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3 (2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2,2,0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-48750.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-48782.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.777\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e89.402/10.453/10.379\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.913/0.876/0.908\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e11.00/40.80/48.20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the three distinct trajectories of cognitive, which were called Stable (n\u0026thinsp;=\u0026thinsp;2076, 48.97%), Slow Decline (n\u0026thinsp;=\u0026thinsp;1713, 40.41%), and Rapid Decline (n\u0026thinsp;=\u0026thinsp;450, 10.62%). The stable trajectory was characterized by the persistently lowest level of cognitive, The Slow decline trajectory consisted of participants reporting moderate levels of cognitive scores that declining to nearly 1 point over time. In the Rapid decline group, the initial cognitive scores were lowest and declining to nearly 2 points across the five waves.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline data of 4239 middle-aged and elderly people with different cognitive trajectories.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCHARACTERISTIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;4239)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eTRAJECTORY GROUP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRapid decline(n\u0026thinsp;=\u0026thinsp;450)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSlow decline(n\u0026thinsp;=\u0026thinsp;1713)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStable(n\u0026thinsp;=\u0026thinsp;2076)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLoneliness score\u003c/b\u003e, \u003cb\u003eM (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.40 (0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.61 (1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.47 (0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.30 (0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003cb\u003eLoneliness\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes(Score\u0026thinsp;\u0026gt;\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e960 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e384 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e440 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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(Score\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3279 (77.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1692 (81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e314 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1273 (74.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, \u003cb\u003eM (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56.19 (7.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.54 (7.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.13 (7.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.91 (7.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003cb\u003eGender\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1889 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e249 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e748 (43.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e892 (43.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2350 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e201 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e965 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1184 (57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1776 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125 (27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e616 (36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1035 (49.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2463 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e325 (72.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1097 (64.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1041 (50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBeing married\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e265 ( 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106 ( 6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e103 ( 5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3974 (93.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e394 (87.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1607 (93.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1973 (95.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e902 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e259 (57.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e436 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e207 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003ePrimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1141 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e587 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1385 (32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e510 (29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e828 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior high school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e811 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 ( 3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e180 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e617 (29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2414 (56.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e263 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e976 (57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1175 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1825 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e187 (41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e737 (43.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e901 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2419 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e264 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e949 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1206 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1820 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e186 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e764 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e870 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic disease\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1457 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e151 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e557 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e749 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.066\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2782 (65.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e299 (66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1156 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1327 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIADL score\u003c/b\u003e, \u003cb\u003eM(SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.17 (0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.26 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.24 (0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09 (0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003cb\u003eNumber of surviving children\u003c/b\u003e, \u003cb\u003eM(SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.33 (1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.68 (1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.47 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.14 (1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003cb\u003eSleep duration\u003c/b\u003e, \u003cb\u003eM(SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.53 (1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.45 (2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.43 (1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.64 (1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.3 Effects of Loneliness on Cognitive Trajectories\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Univariate analysis revealed statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across cognitive trajectory groups in middle-aged and older adults regarding loneliness scores, age, gender, residence, marital status, educational attainment, activities of daily living (ADL) scores, number of living children, and sleep duration. Compared to stable group, people with cognitive Slow decline (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.52, 95% \u003cem\u003eCI\u003c/em\u003e [1.30, 1.78], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cognitive Rapid decline (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.91, 95% \u003cem\u003eCI\u003c/em\u003e [1.52, 2.40], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with higher odds of having loneliness when considering the effects of cognitive trajectory types only. Such associations remained significant after adjusting for other social connection factors and demographic factors at baseline (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDisordered multi-classification logistic regression\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eStable vs Rapid decline\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eStable vs Slow decline\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoneliness score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.350,1.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.192,1.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eLoneliness\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\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\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\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.516,2.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.304,1.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003cb\u003eModel 2\u003c/b\u003e\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\u003eLoneliness score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.186,1.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.158,1.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eLoneliness\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\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\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\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.268,2.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.258,1.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003cb\u003eModel 3\u003c/b\u003e\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\u003eLoneliness score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.294,1.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.212,1.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eLoneliness\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\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\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\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.522,1.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.373,1.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\"\u003eModel 1: Unadjusted model\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 2:Adjust age, sex, place of residence, education and marriage.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 3:Adjust smoking, drinking, chronic diseases, ADL, IADL, per capita household consumption, number of surviving children, sleep time and social activities on the basis of Model 2.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used a nationally representative sample, three different cognitive trajectories were identified for Chinese adults aged 45 years or older over a 9-year follow-up: cognitive stable group, cognitive Slow decline, and cognitive Rapid decline. Compared with the stable group, both cognitive Slow decline and cognitive Rapid decline were related to Loneliness score.\u003c/p\u003e\u003cp\u003eIn this research, 48.2% of middle-aged and older adults were classified into the stable group, whose cognitive scores remained stable without significant changes from 2011 to 2020. 40.8% of middle-aged and older adults were categorized into the slow decline group, exhibiting a gradual decline in cognitive scores within a 1-point range. 11.0% of older adults were classified into the rapid decline group, demonstrating a more pronounced cognitive score decrease ranging between 1\u0026ndash;2 points. People in the cognitive Rapid decline group had a persistently higher level of loneliness from the very beginning than people in the other two groups, indicating the differences and diversity of different individuals in the development of cognitive function.\u003c/p\u003e\u003cp\u003eThis study further investigated determinants influencing cognitive trajectories in middle-aged and older adults. Multivariate logistic regression analyses revealed that age, gender, region, marital status, educational attainment, instrumental ability of daily living (IADL) scores, number of surviving children, and sleep duration significantly predicted cognitive status. From a demographic perspective, female sex, rural residence, being married, higher education levels, better IADL performance, fewer surviving children, and longer sleep duration emerged as protective factors for cognitive function (Zhao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bruderer-Hofstetter et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Du Y et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is worth noting that rural residence and fewer living children exhibited heterogeneous associations. The study suggests that residing in areas with higher green space coverage may significantly decelerate cognitive decline (Zhang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), whereas urban populations demonstrate more prevalent sedentary behaviors\u0026mdash;a known risk factor for cerebral hypoxia and subsequent neural damage accelerating cognitive deterioration (Z. Yang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, in multi-child families, parents are required to allocate more energy and face increased caregiving responsibilities (Yang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); such chronic stress accumulation may exacerbate cognitive aging processes.\u003c/p\u003e\u003cp\u003eMultiple theoretical models provide a biological explanatory framework for elucidating the association between social factors and cognitive function. The primary mechanism focuses on the restructuring of social networks unique to middle-aged and older populations\u0026mdash;the exit from occupational roles and disruption of emotional bonds significantly compromise the stability of social support systems, rendering this demographic particularly vulnerable to the dual risk of chronic social isolation and emotional loneliness (Casey \u0026amp; Holmes, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Wrzus et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This can also be used to explain that the age of the stable group in this study is significantly lower than that of the slow decline group and the rapid decline group. This sustained psychosocial stress triggers a cascade of neuroendocrine responses, including persistent activation of the hypothalamic-pituitary-adrenal (HPA) axis, glucocorticoid receptor resistance, and upregulation of pro-inflammatory gene expression(Ren et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These pathophysiological alterations have been demonstrated to accelerate neurodegenerative processes by directly impairing neural plasticity (Golaszewski et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Song Y, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, these neurobiological modifications exhibit bidirectional associations with cardiovascular and cerebrovascular events such as atherosclerosis and hypertension(Valtorta et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, these vascular risk factors synergistically interact with metabolic disorders like diabetes, collectively constituting a multidimensional pathological basis for cognitive decline.\u003c/p\u003e\u003cp\u003eThe secondary pathways emphasize the mediating role of health behaviors. Empirical studies demonstrate that individuals experiencing chronic social isolation are more prone to developing unhealthy lifestyle patterns characterized by physical inactivity and poor dietary habits (Leigh-Hunt et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such behavioral patterns not only directly impair cerebrovascular autoregulation but also substantially elevate the comorbidity risk of cardiovascular disorders and cognitive impairment through mechanisms involving vascular endothelial dysfunction and systemic inflammatory responses (Xia \u0026amp; Li, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Behavioral epidemiology data indicate that these modifiable lifestyle factors account for approximately 30%-40% of attributable risk for cognitive decline, underscoring the critical importance of behavioral interventions in cognitive preservation strategies.\u003c/p\u003e\u003cp\u003eLongitudinal cohort studies substantiate that impairments in global cognitive performance and specific cognitive domains (e.g., social cognition and executive function) significantly predict subsequent levels of perceived social isolation (Zhong et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cachon-Alonso et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Okely \u0026amp; Deary, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Clinical neuropsychological investigations further reveal that young-onset dementia patients frequently exhibit characteristic sociobehavioral alterations, including pathological social withdrawal, impaired socio-environmental adaptation, and deficits in social cue interpretation. These behavioral manifestations fundamentally reflect the externalization of frontotemporal neural network degeneration(Poey et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Notably, this discovery suggests that subtle sociobehavioral changes emerging during the preclinical phase of dementia may serve as early-stage biomarkers, offering novel perspectives for prodromal disease prediction.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. First, loneliness was measured using a single question with a four-point scale, which may confine the possible variation of loneliness. Second, the reliance on self-reported measures of cognitive performance and loneliness scores introduces potential biases due to inherent subjectivity in such assessments. Finally, there are possibilities of reverse causality because of the observational nature of the study. Future research should prioritize methodological innovations to address current limitations, including the adoption of multidimensional loneliness assessments combined with biomarker validation to enhance measurement precision and reduce self-report bias. Objective cognitive evaluations through standardized neuropsychological batteries and linkage with clinical diagnostic records could refine outcome characterization. Advanced causal inference approaches, such as Mendelian randomization or high-frequency ecological momentary assessment designs, would help disentangle bidirectional relationships between loneliness and cognitive decline. Furthermore, integrating multi-omics data with computational social science methods could elucidate mechanistic pathways across biological, psychological, and socio-environmental levels, ultimately informing targeted interventions to mitigate cognitive risks associated with chronic loneliness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study identified three distinct cognitive trajectories among Chinese middle-aged and older adults: stable group, slow decline, and rapid decline. Individuals exhibiting rapid declining cognitive trajectories demonstrated significantly accelerated loneliness decline, with those in the rapid-declining group showing particularly elevated risks of mild cognitive impairment and dementia subtypes. These findings advance research on healthy cognitive aging by elucidating bidirectional psychobiological pathways\u0026mdash;where rapid cognitive decline exacerbates loneliness through impaired social cognition, while escalating loneliness further depletes neural reserves, creating a vicious cycle that accelerates neurodegeneration. Prioritizing routine monitoring of cognitive trajectories could enable early detection of at-risk populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e：Study conception and design: BH; data analysis: BH, LJ, JZ; drafting the article: LFF, RS; data preparation: WZ, YQZ; data collection: BH, PJZ; revision of the article: BH, PJZ, WZ, YQZ; all authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Zhejiang Traditional Chinese Medicine Science and Technology Plan in 2025 (County Special Project) (No.2025ZX265) and the second batch of science and technology projects in Haining in 2024(No.2024090)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e：The raw data supporting the conclusions of this article will be made available by the corresponding authors.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eClinical trial number:not applicable."},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAyalon, L., Shiovitz-Ezra, S., \u0026amp; Roziner, I. (2016). A cross-lagged model of the reciprocal associations of loneliness and memory functioning. \u003cem\u003ePsychology and Aging\u003c/em\u003e, 31(3), 255-261. http://doi.org/10.1037/pag0000075\u003c/li\u003e\n\u003cli\u003eBallard, C., Gauthier, S., Corbett, A., Brayne, C., Aarsland, D., \u0026amp; Jones, E. (2011). 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F., Cheng, W., Tan, L., \u0026amp; Yu, J. T. (2025). Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis. \u003cem\u003eAlzheimers Research \u0026amp; Therapy\u003c/em\u003e, 17(1), 13. http://doi.org/10.1186/s13195-025-01670-5\u003c/li\u003e\n\u003cli\u003eZhao, Y., Hu, Y., Smith, J. P., Strauss, J., \u0026amp; Yang, G. (2014). Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e, 43(1), 61-68. http://doi.org/10.1093/ije/dys203\u003c/li\u003e\n\u003cli\u003eZhong, B. L., Chen, S. L., Tu, X., \u0026amp; Conwell, Y. (2017). Loneliness and Cognitive Function in Older Adults: Findings From the Chinese Longitudinal Healthy Longevity Survey. \u003cem\u003eJournals of Gerontology Series B-Psychological Sciences and Social Sciences\u003c/em\u003e, 72(1), 120-128. http://doi.org/10.1093/geronb/gbw037\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Loneliness, Cognitive decline, Group-based trajectory modeling, CHARLS","lastPublishedDoi":"10.21203/rs.3.rs-7044155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7044155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eLoneliness is widely regarded as a potential risk factor for cognitive decline, yet existing findings remain inconsistent. Analyzing longitudinal patterns of cognitive change, rather than relying on cross-sectional assessments, can provide deeper insights into the dynamic interplay between loneliness and cognitive health. This study explores the relationship between cognitive trajectories and loneliness in middle-aged and older adults across China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe study analyzed data from 4,239 participants aged 45 and older, drawn from four waves (2011\u0026ndash;2018) of the China Health and Retirement Longitudinal Study (CHARLS). Loneliness was measured using a single-item scale (1\u0026ndash;4 points). Cognitive function was assessed using validated tests (recall, subtraction, figure drawing) standardized to population norms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eGroup-based trajectory modeling identified three distinct cognitive patterns: stable, slow decline, and rapid decline. After adjusting for covariates, binary logistic regression revealed a significant association between these cognitive trajectories and loneliness scores.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study delineates three cognitive trajectories (stable, slow decline, rapid decline) in Chinese middle-aged and older adults. Individuals with rapid cognitive decline exhibited accelerated loneliness progression and significantly heightened risks of mild cognitive impairment and dementia.\u003c/p\u003e","manuscriptTitle":"Loneliness and trajectories of the cognition among Chinese middle and old-aged adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 02:38:05","doi":"10.21203/rs.3.rs-7044155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"301e5aad-5df9-49b9-a746-d03352c57739","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51889794,"name":"Health sciences/Diseases"},{"id":51889795,"name":"Health sciences/Health care"},{"id":51889796,"name":"Health sciences/Neurology"},{"id":51889797,"name":"Biological sciences/Neuroscience"},{"id":51889798,"name":"Biological sciences/Psychology"},{"id":51889799,"name":"Social science/Psychology"},{"id":51889800,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-02-25T09:12:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 02:38:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7044155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7044155","identity":"rs-7044155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}