BACKGROUND:Post-stroke depression (PSD) is a common neuropsychiatric complication in stroke survivors. However, gender differences in the prevalence and correlates of PSD are underexplored. This study examined gender differences in PSD prevalence and their demographic and clinical correlates among older stroke survivors. METHODS:Data from a large national survey conducted in China during 2017-2018 were analyzed. Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale. Univariate and multivariate analyses examined the demographic and clinical correlates of PSD by gender. RESULTS:A total of 1123 older stroke survivors (65 years of age or above), including 578 males (51.5%) and 545 females (48.5%), were included. The overall PSD prevalence was 34.28% (95% CI = 31.52%-37.15%), with significantly higher prevalence in females (38.71%; 95% CI: 34.62%-42.96%) compared to males (30.10%; 95% CI: 26.42%-34.05%). In males, engaging in recent physical exercise (OR: 0.604; 95% CI: 0.389-0.936; p = 0.024) was associated with a lower PSD risk, while more activity limitations (OR: 1.727; 95% CI: 1.131-2.639; p = 0.011) and severe anxiety (OR: 1.455; 95% CI: 1.334-1.586; p < 0.001) were associated with higher risk. In females, recent physical exercise (OR: 0.370; 95% CI: 0.235-0.581; p < 0.001) was linked to lower PSD risk, while heart disease (OR: 1.698; 95% CI: 1.136-2.539; p = 0.010) and severe anxiety (OR: 1.516; 95% CI: 1.372-1.674; p < 0.001) were associated with higher risk. CONCLUSION:This study highlights the gender differences in PSD prevalence and correlates among older Chinese stroke survivors. Tailored interventions are needed to address PSD, with future research focusing on targeted screening and intervention.

To develop and validate a parsimonious risk model for short-term memory decline in older adults and to evaluate its cross-population transportability between Chinese and Japanese cohorts. The model was developed in 5985 cognitively normal older adults from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2015). Seven machine learning algorithms were compared, and a Cox proportional hazards (CoxPH) model was selected for its optimal balance between performance and parsimony. The final model was validated in a temporal CHARLS cohort (2015–2018; n = 1333) and an external Japanese cohort from the Japanese Study of Aging and Retirement (JSTAR, 2007–2009; n = 2798). Performance was assessed using discrimination, calibration, decision curve analysis, and bootstrap-derived confidence intervals. In temporal validation, the model demonstrated good discrimination (C-index = 0.72) with acceptable calibration (slope = 1.40). In the external JSTAR cohort, discriminative performance remained moderate and stable (C-index = 0.68), and calibration was comparable (slope = 0.96) despite differences in baseline incidence and follow-up duration. Decision curve analysis showed net benefit in the temporal cohort and consistent risk stratification in the external cohort. Sensitivity analyses confirmed stable performance across varying follow-up horizons. The six-predictor model consistently stratified short-term memory decline risk across distinct East Asian populations. The findings support its cross-population transportability for relative risk stratification in aging cohorts.

BackgroundMental health professionals (MHPs) are susceptible to fatigue, particularly during public health crises like the COVID-19 pandemic. This study examined nonlinear relationships between fatigue, post-traumatic stress disorder (PTSD), and fear of COVID-19 (FOC) among MHPs.MethodsA multi-site survey was conducted from January to February 2023. Fatigue was assessed using the Fatigue Visual Analogue Scale (VAS), PTSD with the Post-Traumatic Stress Disorder Checklist for Civilians (PCL-C), and FOC with the Fear of COVID-19 Scale (FCV-19S). Data were analyzed using logistic regression and restricted cubic splines to explore non-linear associations.ResultsOf the 9,858 COVID-recovered MHPs, the prevalence of significant PTSD symptoms (PCL-17 ≥ 50) was 6.85% (95% CI: 6.35% - 7.35%), while significant fear of COVID-19 (FOC ≥ 16) was observed in 61.28% (95% CI: 60.32% - 62.24%). Higher fatigue levels were significantly associated with increased odds for exacerbated PTSD symptomatology (OR = 1.75, 95% CI: 1.65 - 1.86, p < 0.001) and FOC severity (OR = 1.19, 95% CI: 1.16 - 1.21, p < 0.001). Restricted cubic splines analysis revealed nonlinear relationships. Specifically, as fatigue rose towards an inflection point of 5.00, its association with PTSD symptoms strengthened, while its association with FOC showed a decelerating growth.ConclusionThis study underscored fatigue as a factor significantly associated with COVID-recovered MHPs, particularly regarding the presence of PTSD and FOC. However, due to the cross-sectional study design, the direction of causality between fatigue, PTSD, and FOC could not be determined. Regular monitoring and targeted interventions are crucial for managing fatigue during public health crises. Healthcare organizations should provide appropriate work-rest schedules and supportive policies during such periods.

BACKGROUND:Depressive symptoms and sleep problems are prevalent among older adults with depression. To reduce their adverse impact, it is important to understand the changes in symptom patterns as the Coronavirus disease 2019 (COVID-19) pandemic emerged. This longitudinal study examined the interactive changes between depressive symptoms and sleep problems among older adults with depression before and during the COVID-19 pandemic from a network perspective in the USA. METHODS:This network analysis study was based on data from the three waves (2016, 2018, and 2020) of the Health and Retirement Study (HRS). Depressive symptoms were measured using the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8), and sleep problems were assessed with the four-item Jenkins Sleep Scale (JSS-4). The study examined central symptoms and bridge symptoms within the network model. RESULTS:A total of 2905 older adults with depression were included in the analyses. The prevalence of depressive symptoms did not significantly change in the study wave during the COVID-19 compared to the pre-pandemic waves. "Feeling Depressed" was the most central symptom of the depression-sleep problems network in the 2016 wave, while "Feeling Sad" was the most central symptom in both the 2018 and 2020 waves. Additionally, "Feeling Loneliness" was the key bridge symptom of the depression-sleep problems network in the 2016 wave, while "Not Enjoying Life" was the key bridge symptom in the 2018 wave, and "Feeling Rested in Morning" was the key bridge symptom in the 2020 wave. CONCLUSION:The findings highlighted that central and bridge symptoms were potential targets in treating depressive symptoms and sleep problems among older adults with depression across the study period in the USA.

Background:Late-life depression (LLD) is a significant global public health challenge among older adults. Exploring central/influential symptoms with longitudinal study designs can enhance the efficacy of detection, early prevention, and interventions for LLD. This study aimed to identify key symptoms of LLD using a panel graphical vector autoregression (panel-GVAR) model based on longitudinal national survey data. Methods:Data from the China Health and Retirement Longitudinal Study (CHARLS) between 2013 and 2020, encompassing four waves, were utilized to construct a longitudinal depressive symptom network. Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). In expected influence (in-EI) and out expected influence (out-EI) were identified to characterize the interaction of symptoms within the temporal network, while expected influence (EI) was used to examine the interaction of symptoms in both the contemporaneous network and the between-subjects network. Results:A total of 1393 older adults were assessed. A persistently significant increase in the prevalence of depression was observed over time. In the temporal network, "restless sleep" (CESD7) and "could not get going" (CESD10) were the most influential symptom and most influenced symptom, respectively. In both the contemporaneous network and the between-subjects network, "felt depressed" (CESD3) emerged as the most central symptom within the community of depressive symptoms. Conclusions:Given the challenges associated with treating LLD and its adverse effects on daily life for older adults, timely interventions targeting identified key symptoms may help prevent and mitigate depression in this population.

BACKGROUND:Intermittent theta-burst stimulation (iTBS) may hold potential for treating late-life depression (LLD) accompanied by comorbid insomnia; however, its underlying neural mechanisms remain incompletely understood. This study aimed to explore the therapeutic effects of iTBS on depressive and insomnia symptoms in this population and to explore related alterations in brain neural function using resting-state functional magnetic resonance imaging (fMRI). METHODS:In this study, 36 elderly patients with LLD and insomnia were assigned to receive either sham or active iTBS over the left dorsolateral prefrontal cortex (DLPFC) for 3 weeks (15 sessions). Primary outcomes were assessed using the Pittsburgh Sleep Quality Index (PSQI) and Hamilton Depression Rating Scale (HAMD-17) at baseline, as well as at Weeks 1, 2, 3, and 6. Resting-state fMRI data were obtained at baseline and after the 3-week treatment to quantify the amplitude of low-frequency fluctuations (ALFF) and functional connectivity. RESULTS:Active iTBS significantly improved depressive and insomnia symptoms compared with sham stimulation (p < 0.05). Reductions in ALFF were observed in limbic-temporal regions (e.g., parahippocampal gyrus, temporal poles) and correlated with sleep improvement. The connectivity between the visual cortex and the left DLPFC also decreased, with trend-level association to better sleep. LIMITATIONS:Larger multi-center trials with prolonged follow-up are needed to confirm findings and elucidate mechanisms. CONCLUSION:iTBS over the left DLPFC may reduce depression and insomnia in LLD patients, possibly by modulating limbic-temporal activity and prefrontal-sensory connectivity. This small, exploratory, single-center study suggests that iTBS could be a promising adjunctive treatment for this population.

BACKGROUND:Cognitive frailty, defined as the co-occurrence of physical frailty/prefrailty and cognitive impairment in the absence of dementia, is a potentially reversible condition that may signal high risk for disability and dementia. This systematic review and meta-analysis estimated the global prevalence of cognitive frailty among community-dwelling older adults and examined methodological and contextual moderators of prevalence differences. METHODS:We searched for relevant studies published up to October 22, 2025 in international (PubMed, Web of Science, Embase, PsycINFO) and Chinese (CNKI, Wanfang) databases. Pooled prevalence rates and 95% confidence intervals (CIs) were calculated with random-effects models. Subgroup and meta-regression analyses examined possible sociodemographic, methodological, and clinical moderators. Study quality was assessed with the Joanna Briggs Institute checklist. RESULTS:Sixty-six studies of 127 556 participants were included. The pooled prevalence of cognitive frailty was 12.2% (95% CI: 9.4%-15.7%). Prevalence was higher in studies using the "Fatigue, Resistance, Ambulation, Illness, and Loss of weight" (FRAIL) scale or Fried phenotype and those using the Montreal Cognitive Assessment (MoCA) or composite cognitive criteria, in upper-middle-income countries, and in studies published after 2021. In meta-regression analyses, alcohol use status was significantly associated with cognitive frailty prevalence but most sociodemographic factors were not related to rates of cognitive frailty. Trim-and-fill analysis suggested that potential publication bias may have led to underestimation of prevalence. CONCLUSION:Cognitive frailty is common among community-dwelling older adults worldwide. Given its potential reversibility and strong links to adverse outcomes, systematic identification and targeted multi-domain interventions should be prioritized in aging societies.

Background Post-stroke depression (PSD) and post-stroke cognitive impairment (PSCI) are prevalent neuropsychiatric problems that are associated with high disability burden and low quality of life (QoL). This study explored the PSD-PSCI network, along with the interaction and association with QoL among Chinese older stroke survivors.Methods Data from the 2017-2018 wave of the Chinese Longitudinal Healthy Longevity Survey were obtained to investigate the inter-relationship between PSD and PSCI among older stroke survivors. Central and bridge symptoms within the PSD-PSCI network and their association with QoL were explored. Depressive symptoms, cognitive impairment and QoL were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), Mini-Mental State Examination and the WHO QoL-brief version, respectively.Results The prevalence of PSD and PSCI among older stroke survivors was 31.5% and 22.1%, respectively. In the PSD-PSCI network, 'feeling blue/depressed' (CESD3, strength: 1.117) and 'Attention and calculation' (At_C, strength: 0.972) were the most influential symptoms, while 'Naming' (Nam, bridge strength: 0.175) was the most significant bridge symptom. Notably, 'Sleep disturbances' (CESD10) had the strongest association with lower QoL.Conclusion This study revealed that both PSD and PSCI were prevalent among older stroke survivors. The key central and bridge symptoms in the PSD-PSCI network, along with those symptoms that negatively impact on QoL, should be prioritised in targeted interventions to enhance treatment outcomes in this population.

BACKGROUND:Depression is prevalent among older adults. Understanding the network structure of depression across diverse cultural contexts is essential to preventing and treating depression. This study evaluated the prevalence and combined network structure of depression among older adults based on national surveys from five countries. METHODS:This study combined data from five national cohort studies. The Center for Epidemiologic Studies Depression (CESD) scale was used to assess depressive symptoms. Meta-analysis was used to estimate the overall prevalence of depression, while network models were constructed using Ising models. The most central depressive symptoms were identified using the Expected Influence (EI) index. RESULTS:In total, 102,202 older adults were included. The pooled prevalence of depression was 18.9% (95% confidence interval (CI):10.3%-27.4%). In the combined network model, the most central symptoms were "Feeling depressed" (CESD1), "Feeling sad" (CESD4), "Lack of happiness" (CESD6) and Loneliness (CESD3), while the strongest positive edge was "Not enjoy life" (CESD7) - "Lack of happiness" (CESD6). CONCLUSIONS:Our findings indicated depressive symptoms are common among older adults across several countries. Moreover, interventions to address feelings of sadness, depressed mood, and lack of happiness as well as loneliness may be beneficial in alleviating depression across older adults in these countries.

Background Given the increased use of network analysis in sleep studies, this systematic review and statistical evaluation aimed to aggregate network studies to identify the most central symptoms in composite network models of sleep-related symptoms. Methods A systematic search of cross-sectional network studies focused exclusively on sleep within community or clinical samples was conducted across PubMed, Web of Science (WOS), PsycINFO, and EMBASE databases up to March 5, 2025. Studies were categorized by topic and measurement instruments. Statistical evaluations extracted the most central symptoms across network models. Results The review included 23 studies of 84,510 participants and 29 network models. Explored topics included insomnia/sleep disturbances, sleep quality, sleep attitudes and behaviors, daytime function, and dream content. Regarding main analyses, key central symptoms in network models of insomnia were "Difficulty staying asleep" [median rank:1.5, Interquartile range (IQR): 1-2], "Distress caused by the sleep difficulties" (median rank:2, IQR: 2-3) and "Interference with daytime functioning" (median rank:3.5, IQR: 2.25-4). For sleep quality, "Subjective sleep quality" (median rank:1, IQR: 1-1), "Daytime dysfunction" (median rank:3, IQR: 2-5.25) and "Sleep disturbance" (median rank:3.5, IQR: 2-4.5) were the most central experiences. Conclusions Identified central symptoms offer plausible targets for intervention across populations and guide future research directions.

BACKGROUND:Post-stroke depression, anxiety, and cognitive impairment are prevalent in older adults and may impede recovery. We evaluated the comparative efficacy of exercise interventions for these outcomes using network meta-analysis. METHODS:We systematically searched five international and two Chinese databases from inception to July 2025 for randomized controlled trials enrolling older adults with stroke receiving exercise-based interventions. Primary outcomes were depression, anxiety, and cognitive function. Treatment effects were estimated as standardized mean differences (SMDs) with 95% credible intervals (CrIs), and interventions were ranked using surface under the cumulative ranking curve (SUCRA). Risk of bias and confidence in estimates were assessed with RoB 2 and CINeMA. RESULTS:Fifty-two trials involving 4,170 participants were included; 11 were at low risk of bias. For depression, dual-task training (DTT), mind-body exercise (MBE), and balance/coordination training (BCT) ranked highest and showed statistically credible benefits versus usual care. For anxiety, MBE, resistance/strength training (RST), and DTT ranked highest, with statistically credible benefits observed for RST and DTT, whereas EFR was associated with worse anxiety outcomes. For cognitive function, MBE, general aerobic exercise (GA), and moderate-intensity continuous training (MICT) ranked highest, whereas BCT was the only intervention showing a statistically credible benefit versus usual care. Meta-regression suggested that longer time since stroke was associated with smaller benefits for anxiety and cognitive outcomes. CONCLUSIONS:Exercise interventions may yield outcome-specific benefits after stroke in older adults. These findings support individualized, phase-sensitive exercise prescription in post-stroke rehabilitation.

BACKGROUND:Loneliness is increasingly recognized as a critical factor influencing health outcomes. Previous studies have reported associations between loneliness and increased mortality risk across chronic medical conditions. However, findings remain inconsistent, particularly regarding the predictive role of loneliness on all-cause mortality within mid- to late-life populations. METHODS:This prospective cohort study utilized 2010 to 2020 data from the Health and Retirement Study (HRS) to investigate the impact of loneliness on all-cause mortality among U.S. adults aged 50 years and older. Loneliness was assessed using the 11-item University of California, Los Angeles Loneliness Scale (UCLA-11), and total scores were grouped into quintiles. Cox proportional hazards models were employed to estimate hazard ratios (HRs) for all-cause mortality, adjusting for sociodemographic and health-related covariates. Subgroup and sensitivity analyses were performed to assess interactions and the robustness of findings. RESULTS:A total of 6807 participants were included in this study, with 1561 deaths recorded over a mean follow-up of 7.8 years. Higher baseline loneliness levels (i.e., UCLA-11 total score of ≥15) were associated with increased all-cause mortality across chronic disease groups, with significant HRs for Quintiles 3 to 5 compared to Quintile 1. The loneliness symptom "[not] a lot in common with friends" was significantly associated with all-cause mortality in the diabetes subgroup (adjusted HR: 1.14, 95% CI:1.00-1.30; P = 0.05). Subgroup analyses indicated the association between baseline loneliness scores and all-cause mortality among participants with heart conditions was more pronounced among women (adjusted HR, 1.55; 95% CI:1.22-1.97), compared to men (adjusted HR, 1.04; 95% CI: 0.83-1.31), p for interaction = 0.02. CONCLUSION:Loneliness is a significant predictor of all-cause mortality among middle-aged and older U.S. adults with chronic medical conditions, especially among women with heart conditions. Findings highlight the importance of assessment and tailored interventions to address loneliness in clinical settings, particularly for vulnerable mid- to late-life subgroups. Future research should focus on refining screening and intervention strategies to mitigate loneliness-related mortality risks.

Background Late-life depression (LLD) is a growing public health concern in aging populations. Although digital mindfulness interventions show promise for depression, anxiety, and insomnia, their efficacy and electroencephalogram (EEG) correlates in older adults with LLD remain unclear. This study evaluated the FocusZen Mindfulness Stress Reduction System, a digital mindfulness intervention with EEG feedback, in mild-to-moderate LLD. Methods Fifty-four participants with mild-to-moderate LLD were randomly assigned to a 6-week intervention group (n = 27; daily FocusZen sessions) or a control group (n = 27; general health education). The primary outcome was the change in HAMD-17 score. Secondary outcomes included anxiety, sleep quality, cognition, and frontal EEG spectral activity. Data were analyzed using mixed-effects models and intention-to-treat principles. Results The intervention group demonstrated significant reductions in depressive symptoms [HAMD-17: F(3, 132.69) = 8.83, P < 0.001], anxiety [HAMA: F(3, 129.95) = 8.34, P < 0.001], and sleep disturbances [PSQI: F(3, 128.91) = 5.55, P = 0.01], alongside improved cognition [MOCA: F(3, 133.19) = 5.14, P = 0.01]. Response and remission rates were higher in the intervention group. Exploratory EEG analysis showed increased frontal theta [F(1.96, 43.12) = 25.28, P < 0.001] and alpha activity [F(1.44, 31.73) = 22.92, P < 0.001]. Conclusions FocusZen-based digital mindfulness reduced depressive, anxiety, and sleep symptoms and improved cognition in mild-to-moderate LLD, potentially accompanied by enhanced frontal theta and alpha activity. Trial registration Chinese Clinical Trial Registry Identifier: ChiCTR2400086063; https://www.chictr.org.cn/

BACKGROUND:Depressive and anxiety symptoms (depression and anxiety hereafter), and suicidality are common among psychiatric patients. This study examined the network structure of depressive and anxiety symptoms and suicidality among psychiatric patients across Asia. METHODS:Data were drawn from the Research on Asian Psychotropic Prescription Patterns for Antidepressants Phase 3 study (REAP-AD3), which included 2,455 psychiatric patients from 11 Asian countries and territories. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality (i.e., suicidal thoughts or acts) was assessed by a clinical interview. Expected Influence (EI) and Bridge EI were used as centrality indices in the symptom network to characterize the structure of the symptoms. RESULTS:The point prevalence of suicidality was 30.1% (95% confidence interval [CI] = 28.2, 31.9) among the psychiatric patients. The network analysis identified PHQ2 ("Sad mood") as the most central symptom, followed by GAD2 ("Uncontrollable worry"). Additionally, PHQ8 ("Motor disturbances") and S ("Suicidality") were identified as bridge nodes linking depression and anxiety with suicidality. The flow network indicated that PHQ6 ("Guilt") and PHQ2 ("Sad mood") had the strongest positive associations with suicidality. CONCLUSIONS:Suicidality was common among psychiatric patients across Asia. The central and bridge symptoms might represent potential clinical markers and generate hypotheses for longitudinal and interventional research on depression, anxiety, and suicidality in the future.

The long-term impact of adverse childhood experiences (ACEs) on the development of psychiatric disorders in older adults remains unclear. This study examined associations between ACEs and incident psychiatric disorders in older adults. Data from the 2000-2022 wave of Health and Retirement Study in the USA were analyzed. Time-varying Cox regression and multistate Markov models were applied to explore the impact of ACEs on transitions across five health states: healthy, physical conditions (PC), mental health symptoms (MS), comorbid PC & MS, and psychiatric disorders. Models were adjusted for demographic, behavioral, and disease-related factors. A total of 8628 participants were included, with a mean age of 64.75 years (SD = 8.01). During a mean follow-up duration of 16.8 years, 1429 developed psychiatric disorders (incidence: 9.85 per 1000 person-years). ACEs were significantly associated with higher risk of incident psychiatric disorders in a dose-response relationship. Participants with ACEs had higher transition percentages and intensities from healthy to less healthy states, notably from PC & MS to psychiatric disorders (percentages: 3.7 vs. 3.2%) and from the healthy state to MS (intensities: 0.130 vs. 0.104). They also spent less time in the healthy state and more time in comorbid states, with a 33% higher 22-year cumulative probability of psychiatric disorders (25.3 vs. 19.0%). ACEs appear to have enduring adverse impacts on mental health in later life by accelerating the progression to comorbidity and the development of psychiatric disorders. Early clinical screening and physical-mental health interventions are essential for prevention.

BACKGROUND:Depression and cognitive impairment frequently co-occur in older adults. Network analysis can elucidate inter-relationship between psychiatric disturbances at the symptom level. This study examined the network structure of depressive symptoms and impaired cognitive function among adults aged 60 years or older in India. METHODS:Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Cognitive function was evaluated across six domains: Memory, Orientation, Retrieval fluency, Arithmetic function, Executive function, and Object naming. Central symptoms and bridge symptoms were identified using Expected Influence (EI) and Bridge EI, respectively. A flow network was employed to identify symptoms directly associated with cognitive impairment. RESULTS:In this study, 29 224 participants were included. The prevalence of depression (CESD-10 total score ≥ 4) was 27.46% (95% CI: 26.95%-27.97%), while the prevalence of cognitive impairment was 15.55% (95% CI: 15.13%-15.97%). CESD2 ("Felt depressed") emerged as the most central symptom, followed by Ari ("Arithmetic function") and Ori ("Orientation"). Moreover, CESD8 ("Everything was an effort"), CESD2 ("Felt depressed"), and CESD7 ("Bothered by things") served as bridge nodes linking the communities of depressive symptoms and cognitive functions. The flow network indicated that the strongest connections to cognitive impairment were observed for CESD1 ("Trouble focusing"), CESD3 ("Could not get going"), and CESD10 ("Felt unhappy"). CONCLUSION:This study documented the inter-relationship between particular depressive symptoms and impaired cognition among older adults in India. The central symptoms and bridge symptoms identified in this study should be tested in intervention studies aiming to improve depression and cognition among older adults.

BACKGROUND:As the most prevalent malignancy among women, breast cancer has a potentially shattering impact on quality of life (QoL). However, studies comparing QoL between breast cancer survivors and cancer-free peers have been inconsistent and little is known about possible moderators that contribute to inconsistent findings or specific QoL domains that affect breast cancer survivors most. OBJECTIVES:This meta-analysis examined QoL differences between breast cancer survivors and controls without breast cancer ("controls" hereafter) across multiple QoL instruments and domains. METHODS:We searched major international and Chinese databases and identified 36 eligible case-control studies comprising 29,433 participants (12,261 survivors, 17,172 controls). Standardized mean differences were calculated using a random-effects model; subgroup analyses and meta-regression examined potential moderators. RESULTS:QoL impairments varied by assessment instrument. Medical Outcomes Study Short Form surveys showed lower physical component scores in breast cancer survivors (moderate effect size, SMD = -0.52) and small deficits in physical function, emotional role limitations, mental components, and general health domains. The European Organization for Research and Treatment of Cancer questionnaire revealed lower scores in insomnia (SMD = 0.80) and financial difficulties (SMD = 0.77). Regarding the Functional Assessment of Cancer Therapy-General, breast cancer survivors displayed comparatively large impairments across emotional, functional, and physical well-being domains. Short-term survivors (≤ 5 years post-diagnosis) experienced significantly greater deficits than long-term survivors did in physical role limitations and mental health domains. CONCLUSIONS:Breast cancer survivors experience lower QoL than controls do, particularly in physical and emotional domains. This meta-analysis highlights the importance of developing effective interventions targeting specific QoL domains at different survivorship stages.

Older adults with heart disease experience higher rates of depression and insomnia compared with heart disease–free peers. Aside from these psychological disturbances, overall health satisfaction, as a key indicator of subjective health status, may be affected by heart disease status. In spite of these overall associations, symptom-level relationships between depression, insomnia, and health satisfaction remain unclear. We aimed to compare the prevalence and symptom network differences of these variables between older adults in the United States with and without heart disease. Network analyses were conducted on data from the 2022 wave of the Health and Retirement Study. Propensity score matching identified 2 demographically similar groups: 2861 cohorts with heart disease and 2861 heart disease–free peers. Depression was measured using the 8-item dichotomous version of the Center for Epidemiologic Studies Depression Scale. Insomnia was assessed using the 4-item Jenkins Sleep Scale. Health satisfaction was evaluated with a standardized self-report item querying perceived overall health status on a 5-point Likert scale ranging from “poor” to “excellent.” Central and bridge symptoms were identified using expected influence and bridge expected influence metrics. Depression prevalence was higher in the heart disease group (19.8%; 95% confidence interval [CI], 18.4%–21.3%) than in the heart disease–free group (11.8%; 95% CI, 10.7%–13.1%; P < .001), with more severe depressive symptoms in the heart disease group (1.8 ± 2.18 vs 1.3 ± 1.83, P < .001). Similarly, the prevalence of having at least 1 insomnia symptom was significantly higher in the heart disease group (48.2%; 95% CI, 46.4%–50.1%) than the heart disease–free group (36.3%; 95% CI, 34.6%–38.1%; P < .001), with more severe insomnia symptoms in the heart disease group (0.9 ± 1.13 vs 0.6 ± 0.92) (P < .001). Network models revealed similar structures between groups. Key central symptoms across these groups included “feeling sad,” “lack of happiness,” and “feeling depressed.” Bridge symptoms were “feeling tired in the morning” and “trouble falling asleep.” “Everything was an effort” was strongly associated with lower health satisfaction across groups. Older adults with heart disease exhibited a higher prevalence of depression and more severe overall depressive and insomnia symptoms. Identified central and bridge symptoms may be potential markers of co-occurring conditions and could inform future intervention research aimed at reducing comorbidity. Given the similar symptom structures, interventions developed for heart disease–free adults may also be applicable to those with heart disease, although randomized control trials are needed to establish causal effects.

OBJECTIVES:Lack of formal schooling remains prevalent among older adults in China, particularly in rural areas. This study investigates the cognitive function trajectory and influencing factors in older adults without formal schooling from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). METHODS:The study included 2159 individuals without formal schooling (NFS) and 2234 individuals with formal schooling (FS), all cognitively healthy and aged over 60 at the first observation from the 2008 - 2018 CLHLS cohort. Cognitive function was measured using the Chinese version of the Mini-Mental State Examination (MMSE). Group-based trajectory modeling was used to identify potential heterogeneity of longitudinal changes over the 10 years. Logistic regression was used to investigate associations between baseline characteristics (age, sex, marital status, functional abilities, leisure activity, and health status and behaviors) and trajectory classes. RESULTS:NFS individuals were generally older (80 vs. 75.3 years), more likely to be female (72.2% vs. 29.9%), unmarried (43.1% vs. 68.2%), and underweight (27.3% vs. 17.8%). They also had higher prevalence of hearing impairment (40.1% vs. 30.5%), functional limitations (39.6% vs. 19.2%), and extreme sleep length, while lower baseline cognitive function (MMSE score: 26.5 vs. 28.2). Additionally, they were less likely to engage in exercise, leisure activities, or alcohol consumption. Three trajectories (labeled stable, slow decline, and rapid decline) were identified according to the changes in MMSE scores for both groups. For the NFS group, both the slow and rapid decline groups accounted for a larger proportion (15.0% and 12.3%, respectively) than the FS decline groups (6.5% and 5.3%, respectively), and the NFS individuals had a lower baseline MMSE score with a faster decline. In the multivariable logistic regression analyses, older age, hearing impairment, poorer functional abilities, and lower baseline MMSE scores were significantly associated with cognitive decline in both groups compared to the stable group. For the NFS individuals, female sex was a risk factor for slow decline, while marital status was associated with rapid decline. CONCLUSIONS:These findings underscore the importance of considering formal schooling status in cognitive aging research. They also emphasize the need to address educational disparities and promote social and economic well-being, particularly for vulnerable populations, to mitigate the risk of cognitive decline and dementia.