BackgroundFrailty is a major public health concern associated with adverse outcomes. The remnant cholesterol inflammatory index (RCII), a novel biomarker integrating lipid metabolism and systemic inflammation, has been proposed as an indicator of adverse health outcomes. However, little is known about its relationship with frailty. The study aimed to investigate the longitudinal association between RCII and frailty risk in the UK Biobank.MethodsA total of 402,850 participants from the UK Biobank were included at baseline. RCII was calculated as remnant cholesterol (RC, mg/dL) × C-reactive protein (CRP, mg/L)/10, and frailty was assessed using the Fried frailty phenotype. Cox proportional hazards regression models were applied to evaluate the association between baseline RCII and incident frailty. Restricted cubic spline (RCS) analyses were used to explore potential nonlinear relationships. To capture cumulative exposure, we additionally analyzed 12,895 participants with the same measurements to examine the relationship between cumulative RCII and frailty risk.ResultsDuring a median follow-up of 15.58 years, 2,327 participants (0.58%) developed frailty. Frailty incidence increased progressively across RCII quartiles, from 0.3% in Q1 to 0.9% in Q4 (P for trend <0.001). In the fully adjusted model, each standard deviation increase in RCII was associated with a 11% higher risk of frailty (HR = 1.11, 95% CI: 1.07–1.15). RCS analysis indicated a nonlinear positive relationship, with frailty risk rising more sharply at higher RCII levels. In the 11.68-year subset analysis, 497 participants (3.85%) suffered frailty. Participants in Q4 of cumulative RCII had a significantly higher risk of frailty compared with those in Q1 (HR = 2.34, 95% CI: 1.52–3.60). Subgroup analyses suggested that the association between RCII and frailty was generally consistent across subgroups (P for interaction > 0.05).ConclusionHigher RCII levels, both at baseline and cumulatively, are associated with an increased risk of frailty. RCII may be a promising biomarker for frailty risk stratification and a potential target for early prevention in aging populations.

OBJECTIVE:To develop and externally validate an interpretable machine learning model for predicting 7-day stroke-associated pneumonia (SAP) using routine clinical data collected within 24 h of admission. METHODS:This multicenter study utilized a development cohort from the Henan Stroke Cohort and an independent external validation cohort from three Zhengzhou hospitals. Adult patients with imaging-confirmed ischemic or hemorrhagic stroke were eligible. Patients with infection at admission or who developed SAP within 24 h of admission were excluded. We evaluated 26 candidate predictors obtained within 24 h of admission. Nine machine learning algorithms were trained following recursive feature elimination. Model performance was evaluated based on discrimination, calibration, and clinical utility. The optimal model was interpreted using SHapley Additive exPlanations (SHAP) and deployed as an online calculator. RESULTS:The development and external validation cohorts comprised 1201 and 645 patients, with 7-day SAP incidences of 20.6 % (n = 247) and 24.8 % (n = 160), respectively. Among the nine algorithms, stochastic gradient boosting (SGBT) demonstrated the most balanced overall performance. In internal validation, SGBT achieved an area under the receiver operating characteristic curve (AUC) of 0.947 in the training set and 0.905 in the test set. In external validation, the model yielded an AUC of 0.906, alongside an accuracy of 0.864, sensitivity of 0.712, specificity of 0.918, positive predictive value (PPV) of 0.756, negative predictive value (NPV) of 0.899, and F1-score of 0.733. The final model retained 10 predictors: stroke subtype, fibrinogen, D-dimer, C-reactive protein, uric acid, triglycerides, homocysteine, and clinical scores (ADL, GCS, NIHSS). SHAP analysis identified early neurological impairment and inflammatory burden as the primary contributors to SAP prediction. CONCLUSION:An interpretable SGBT model utilizing routine admission data accurately predicted 7-day SAP and remained robust during external validation. The accompanying online calculator facilitates individualized risk estimation to support early preventive decision-making in hospitalized patients with stroke.

OBJECTIVE:24h movement behavior, including physical activity (PA), sedentary behavior (SB), and sleep(SLP), is independently associated with health after stroke. Few studies have explored the trajectory of 24h movement behavior and its effect on functional recovery in stroke survivors. DESIGN:We conducted a prospective longitudinal study from November 2023 to June 2025. SETTING:This study was conducted in five hospitals in Henan province. PARTICIPANTS:The ActiGraph GT3X+ accelerometer was used to collect 24h movement behavior of 353 stroke survivors at acute phase(T0), 3-month recovery phase (T1), and 6-month sequelae phase (T2) after stroke onset, and functional recovery was evaluated at 12 months (T3) post-stroke. INTERVENTIONS:Patients' demographic information, disease-related data, psychological-related indicators and environment-related indicators were collected. MAIN OUTCOME MEASURES:The modified Rankin Scale (mRS) was used to assess their functional recovery 12 months after the onset of the disease. Through a prospective longitudinal study, Parallel-Process Latent Class Growth Model(PP-LCGM) was applied to determine the trajectories of 24h movement behavior, logistic regression was used to explore the effects of different trajectories on functional recovery. RESULTS:298 stroke survivors completed three follow-up visits. Four potential categories of the 24h movement behavior change trajectory were finally identified. Multivariate logistic regression analysis showed that, after adjusting for age, gender, BMI, NRS grade, NIHSS score, BI index, social support, exercise self-efficacy, anxiety, and depression, there was an increased risk of poor prognosis in the 24h movement behavior imbalance group(OR:5.237,95%CI:1.848-14.835) or long-SLP increased-SB decreased-PA group(OR:4.160,95%CI:1.589-10.891) compared with the 24h movement behavior balance group. CONCLUSIONS:The results showed that stroke survivors had different 24h movement behavior trajectories after acute stroke, and there was an increased risk of poor prognosis in the 24h movement behavior imbalance group or long-SLP increased-SB decreased-PA group. 24h movement behavior interventions can be developed accordingly.

BackgroundEvidence regarding the combined influence of healthy plant-based dietary patterns and lifestyle behaviours on obesity-related cancer outcomes remains limited. We prospectively investigated the associations of the healthy plant-based diet-lifestyle (hPDI-Lifestyle) score with obesity-related cancer incidence and mortality in the UK Biobank.MethodsAmong 104,713 eligible participants, 93,763 were included in the primary complete-case analyses. The hPDI-Lifestyle score integrated healthy plant-based dietary quality, physical activity, smoking status, and sleep duration. Restricted cubic spline analyses were used to examine dose–response relationships, and Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for the three outcomes, including sex-stratified analyses. Several sensitivity analyses were conducted to assess the robustness of the findings.ResultsDuring median follow-up periods of 16.3, 15.0, and 15.1 years, respectively, 2,258 incident obesity-related cancers, 1,095 obesity-related cancer deaths, and 6,215 all-cause deaths were documented. Higher hPDI-Lifestyle scores were associated with lower risks of obesity-related cancer mortality and all-cause mortality. For obesity-related cancer incidence, an inverse association was observed overall, whereas sex-stratified analyses showed no statistically significant association among women. Compared with participants in the lowest tertile, those in the highest tertile had lower risks of obesity-related cancer incidence (HR: 0.82, 95% CI: 0.74–0.92), obesity-related cancer-specific mortality (HR: 0.66, 95% CI: 0.56–0.77), and all-cause mortality (HR: 0.64, 95% CI: 0.60–0.68). Each 1-SD increment in the hPDI-Lifestyle score was associated with 8, 18, and 22% lower risks of obesity-related cancer incidence, obesity-related cancer-specific mortality, and all-cause mortality, respectively.ConclusionHigher hPDI-Lifestyle scores were associated with lower risks of obesity-related cancer incidence, cancer-specific mortality, and all-cause mortality. These findings require confirmation in more representative populations and intervention studies before preventive implications can be established.

ObjectivesThis study aimed to comprehensively investigate the associations between leisure-related sedentary time, physical activity (PA), and the incidence of myocardial infarction (MI) and its subtypes.MethodsThis cohort study included 475,874 individuals free of MI at baseline. Daily behavioral data—including leisure-related sedentary time, PA, and sleep duration—along with baseline demographic characteristics were collected. Three multivariable-adjusted Cox proportional hazards models were constructed to assess the association between leisure-related sedentary time and incident MI. Additionally, a mutually adjusted Cox model incorporating all four behaviors simultaneously was used to estimate hazard ratios for inter-behavior comparisons via coefficient contrasts, evaluating whether higher PA or sleep durations, independent of other measured behaviors, were associated with reduced MI risk.ResultsCompared with the reference group, individuals with leisure-related sedentary time exceeding 4 h per day had an increased risk of MI and its subtypes after full multivariable adjustment. Mutually adjusted analyses demonstrated inverse associations with MI risk across all behaviors. Coefficient contrasts indicated that a 60-min higher duration of sleep, LPA, or MVPA—holding other measured behaviors constant—corresponded to hazard ratios of 0.967 (95% CI: 0.954–0.980) for sleep, 0.987 (95% CI: 0.964–1.011) for LPA, and 0.974 (95% CI: 0.953–0.995) for MVPA. Consistent patterns were observed for 30-min and 15-min contrasts, with estimated risk reductions ranging from approximately 1–6% across all comparisons.ConclusionProlonged leisure-related sedentary time is associated with an increased risk of MI, STEMI, and NSTEMI. Independent of other daily behaviors, higher durations of PA and sleep are inversely associated with MI incidence, with benefits evident even at modest (15–30 min) differences in daily duration.

AIM:To develop and validate a machine learning-based associational prediction model for nurse-led identification of financial toxicity (FT) among stroke patients. BACKGROUND:FT is increasingly recognized among patients with chronic conditions, yet evidence in stroke patients remains limited. Early identification may help nurses provide timely financial, psychosocial, and discharge-related support. METHODS:A total of 575 stroke patients were recruited. Based on Health Ecology Theory, factors were grouped into five levels. The dataset was randomly split 7:3 into training and test sets. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection, and five machine learning models were trained and evaluated on the independent internal test set. The optimal model was interpreted using SHapley Additive exPlanations (SHAP) and evaluated in an external cross-sectional validation dataset of 207 patients from another hospital. A web-based calculator was developed for individualized FT assessment. RESULTS:The prevalence of FT was 62.3%. The eXtreme Gradient Boosting (XGBoost) model demonstrated the best model performance, achieving an AUC of 0.823 in the internal test set and 0.865 in the external cross-sectional validation set. SHAP analysis based on the XGBoost model identified age, fear of progression, complications, primary caregiver, and out-of-pocket cost as the most important associated factors of FT. A web-based calculator based on the XGBoost model was further developed to support individualized FT assessment in nursing practice. CONCLUSIONS:The XGBoost model showed good internal and external performance in identifying FT among stroke patients. The web-based XGBoost tool may support nurse-led contemporaneous FT assessment in routine practice. Longitudinal validation is warranted to establish its clinical utility. IMPLICATIONS FOR NURSING MANAGEMENT:The tool may help nurse leaders embed structured FT assessment into admission and discharge workflows, enabling nurses to identify financially vulnerable stroke patients and initiate timely financial navigation, psychosocial support, and multidisciplinary referral.

Body roundness index (BRI) and biological aging are critical drivers of cardiovascular disease (CVD). However, their joint pathogenic effects and underlying mechanisms in the development of CVD remain elusive. This study aims to investigate the independent and joint effects of BRI and biological age acceleration on the risk of incident CVD among middle-aged and older adults, evaluate their incremental predictive value, and elucidate the mediating role of biological aging within the obesity-CVD pathological pathway. This longitudinal study included 6,389 middle-aged and older participants free of CVD at baseline from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). Multivariate Cox proportional hazards models and restricted cubic splines (RCS) were employed to assess the associations between exposure factors and incident CVD risk. Incremental predictive value was evaluated by calculating the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Furthermore, a bootstrapping-based mediation analysis was applied to explore pathogenic mechanisms. Over a median follow-up of 8.9 years, 1,407 (22.0

BACKGROUND:Coronary artery disease (CAD) remains a major cause of morbidity and mortality worldwide. Variants in the lipid metabolism gene SCARB1 may influence CAD susceptibility, but existing evidence is inconsistent. METHODS:We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, and Scopus up to 13 February 2026 for case-control studies on SCARB1 polymorphisms and CAD risk. Three polymorphisms, rs5888, rs4238001, and rs10846744, were included. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated under multiple genetic models. Subgroup analyses were conducted for rs5888 by sex, ethnicity, and clinical outcome. Heterogeneity was assessed using I2. RESULTS:12 eligible case-control studies involving 3947 CAD cases and 5076 controls were included. Overall, rs5888 was not significantly associated with CAD in any genetic model, either in the pooled analysis or in subgroup analyses by ethnicity and clinical outcome. In sex-stratified analyses, males carrying the TT genotype had a significantly lower CAD risk under the recessive model (OR = 0.73, 95% CI: 0.57-0.93), whereas no significant association was observed in females. No significant association was found between rs4238001 and CAD under any model. In contrast, the rs10846744 G allele was significantly associated with reduced CAD risk under the allelic (OR = 0.78, 95% CI: 0.64-0.94), dominant (OR = 0.68, 95% CI: 0.50-0.93), homozygote (OR = 0.65, 95% CI: 0.44-0.94), and additive models (OR = 0.80, 95% CI: 0.67-0.96). CONCLUSIONS:SCARB1 rs5888 showed a male-specific association with CAD, while rs10846744 showed a suggestive inverse association that requires further validation.

INTRODUCTION:Socioeconomic status (SES) is a key risk factor for depression in older adults, while cognitive function, lifestyle and social participation also have an impact on depression. This study aimed to investigate the mediating role of cognitive function, lifestyle and social participation in the association between SES and depressive symptoms among older adults in China. METHODS:Data were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) (2017-2018). A total of 7595 community-dwelling adults aged ≥65 years were included. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). SES was measured as a composite index incorporating education level, occupation, and self-rated economic status. Cognitive function was evaluated via the Mini Mental State Examination (MMSE). Lifestyle and social participation scores were constructed based on relevant questionnaire items. Mediation analysis was performed to explore the indirect effects of cognitive function, lifestyle, and social participation on the association between SES and depressive symptoms. RESULTS:The prevalence rate of depressive symptoms (CES-D-10 score ≥10) was 41.1%. After adjusting for sociodemographic and health-related covariates, SES was negatively associated with depressive symptoms (β = -0.887, P < 0.001). SES had a significant mediating effect on depression in older adults respectively, through cognitive function (relative mediating effect = 8.0%, β = -0.071, 95%CI: -0.095 ~ -0.048), lifestyle (19.9%, β = -0.177, 95%CI: -0.213 ~ -0.140) and social participation (7.6%, β = -0.068, 95%CI: -0.095 ~ -0.042). Additionally, sequential mediating effects were observed for "cognitive function → lifestyle" (1.0%, β = -0.009, 95%CI: -0.012 ~ -0.006), "cognitive function → social participation" (1.0%, β = -0.009, 95%CI: -0.014 ~ -0.006), "lifestyle → social participation" (1.4%, β = -0.012, 95%CI: -0.018 ~ -0.007), and "cognitive function → lifestyle → social participation" (0.1%, β = -0.001, 95%CI: -0.001 ~ -0.001). CONCLUSION:SES influences depressive symptoms in Chinese older adults through both direct and indirect pathways. The findings highlight the need for multifaceted interventions targeting cognitive function enhancement, healthy lifestyle promotion, and social participation facilitation, particularly among socioeconomically disadvantaged older populations, to mitigate depressive symptoms and promote healthy aging.

PURPOSE:This study used cross-lagged panel network analysis to examine longitudinal symptom networks in patients with esophageal cancer undergoing chemotherapy, aiming to identify stage-specific influential symptoms and directed temporal associations across treatment stages. METHOD:This longitudinal study was conducted from January to September 2025 in four Grade III Level A hospitals in Henan, China. Patients receiving chemotherapy for esophageal cancer were assessed at three time points using the M.D. Anderson Symptom Inventory Gastrointestinal Cancer Module (MDASI-GI). Cross-lagged panel network models were applied to estimate symptom-to-symptom temporal associations and quantify symptom influence using network centrality indices. RESULTS:A total of 339 participants completed all three assessments. During the T1→T2 interval, S1 (Pain) and S18 (Difficulty Swallowing) exhibited the highest out-expected influence (out-EI = 1.825 and 1.717, respectively), with the strongest temporal association observed from S1 (Pain) to S6 (Shortness of Breath) (β = 0.282). During the T2→T3 interval, S2 (Fatigue) and S3 (Nausea) emerged as the most influential symptoms (out-EI = 2.172 and 1.937, respectively), with the strongest association identified from S2 (Fatigue) to S6 (Shortness of Breath) (β = 0.454). CONCLUSIONS:This study identified stage-specific influential symptoms and directed temporal associations within symptom networks among patients with esophageal cancer undergoing chemotherapy. Pain and difficulty swallowing were most influential early, whereas fatigue and nausea became more influential later. These findings support a network-based, stage-sensitive perspective on symptom management and may inform time-specific supportive care prioritization.

ObjectiveTo explore the latent trajectory classes of objective sleep quality in stroke patients and their impact on neurological functional recovery.MethodsA multicenter cluster sampling method was used to recruit 362 stroke patients from the neurology departments of 5 tertiary hospitals in China between November 2023 and July 2024. Baseline data were collected using a general information questionnaire and related scales. Objective sleep data were obtained using ActiGraph GT3X triaxial accelerometers during the acute (T0), recovery (T1), and chronic (T2) phases of stroke. Neurological recovery was assessed at 12 months post-onset (T3) using the modified Rankin Scale. Parallel-process latent class growth modeling was used to identify trajectory classes. Binary logistic regression examined the association between sleep trajectories and neurological recovery.ResultsA total of 306 patients were followed up. Four distinct trajectory classes were identified: Consistently good sleep quality group (34.31%), Short sleep-increased efficiency-improved fragmentation group (49.02%), Long sleep-reduced efficiency-deteriorated fragmentation group (7.84%), and Consistently poor sleep quality group (8.82%). Compared to the consistently good sleep quality group, patients in the Long sleep-reduced efficiency-deteriorated fragmentation group and Consistently poor sleep quality group had 5.728 (95% confidence interval [CI]: 2.124-15.444) and 6.769 (95% CI: 2.580-17.758) times higher risks of poor neurological recovery, respectively.ConclusionStroke patients exhibit heterogeneous sleep quality trajectories, with differential impacts on neurological recovery. Healthcare providers should implement personalized sleep management strategies to optimize both sleep quality and functional outcomes.

Introduction:We aimed to develop a dynamic model for predicting 3-month recurrence after first-ever ischemic stroke using multimodal longitudinal data. Methods:Patients with first-ever ischemic stroke admitted to a tertiary hospital in Zhengzhou, China between January 2023 and January 2025 were enrolled. Data on static baseline characteristics, discharge variables, and 1-month follow-up variables were collected. Imaging phenotypes were derived from diffusion-weighted imaging (DWI) and Fluid-Attenuated Inversion Recovery (FLAIR) using automated lesion segmentation and unsupervised clustering. The dataset was divided into training and validation sets (8∶2) using a fixed random seed. Seven models were trained and evaluated: logistic regression, random forest (RF), support vector machine (SVM), XGBoost, multi-layer perceptron (MLP), standard Long Short-Term Memory (LSTM), and two-stream attention-LSTM. External validation was conducted at three independent hospitals. The predictive performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, and Brier score. Results:Among 625 patients, recurrence occurred in 79 (12.64%) patients 3 months after discharge. In the validation set, the AUCs were ranked as follows: two-stream attention-LSTM (0.857); SVM (0.838); XGBoost (0.808); MLP (0.768); RF (0.761); standard LSTM (0.754); logistic regression with follow-up features (0.748); and discharge-only logistic regression (0.686). Two-stream attention LSTM identified key predictors, including dynamic changes in C-reactive protein, systolic blood pressure, and imaging phenotypes. External validation showed stable discrimination (pooled AUC=0.83) and good calibration. Conclusion:Two-stream attention-LSTM improved prediction of 3-month recurrence after first-ever ischemic stroke and may support early post-discharge risk stratification.

Background and purpose: This study aims to explore whether different types of oral conditions are associated with incident stroke risk. Methods and results: This cohort study included 476,868 individuals without outcome events at baseline, multivariable models were constructed using Cox proportional hazard regression to assess the association between different oral conditions and the incidence of stroke, ischemic stroke (IS) and myocardial infarction (MI). Our fully adjusted model showed that individuals with painful gums [HRpainful gums: 1.31 (1.06, 1.63)], loose teeth [HRloose teeth: 1.45 (1.20, 1.77)] and dentures [HRdentures: 1.23 (1.12, 1.36)] have increased risk for stroke incidence. Conclusions: Individuals with painful gums, loose teeth and dentures have increased risk for stroke, IS and MI incidence, this study helps to identify high-risk stroke participants.

Depression, anxiety, and stress are major mental health challenges during children’s development. In China, heightened sleep disorders (SD), parental expectations, and academic stress highlight the importance of this issue. A total of 6,635 SD child-parent dyads were included in analysis. Sleep quality and psychological variables were assessed using Pittsburgh Sleep Quality Index (PSQI), Depression Anxiety Stress Scales (DASS) and Satisfaction with Life Scale (SWLS). Children with SD exhibited significantly higher rates of depression, anxiety, stress and life dissatisfaction. We identified the core and bridge symptoms within the Chinese children mental health symptoms and explored their associations with parental mental health. Worthlessness and palpitations, exhibiting the highest centrality values, were identified as core symptoms in the SD group. Palpitations were associated with elevated parental anxiety and stress. Impending collapse associated with parental anxiety, emerging as a bridge symptom may aggravate the occurrence of depression, anxiety and stress comorbidity in children. These symptoms provide theoretical basis for the targeted psychological intervention of Chinese children with SD in the future.

BACKGROUND:Psychological health is crucial for the physical and mental well-being of primary and secondary school students, however, the research on the interrelationships among negative emotions is still limited. This study aims to investigate the network structure of depression, anxiety, and stress, and explore their correlation with screen time in these populations. METHOD:This study was conducted from March to July 2022 in 157 counties across 18 cities in Henan Province. The Depression Anxiety Stress Scale (DASS-21) was used to assess the negative emotions experienced by students. Firstly, "Expected Influence" and "Bridge Expected Influence" were considered as key indicators within the symptom network to depict the structure of depressive, anxiety and stress symptoms. Secondly. a case-dropping bootstrap procedure was applied to assess the stability of the network. Finally, this study identified the central and bridge symptoms in the network, and explored their relationships with screen time. RESULTS:The study included 52,782 students with an average age of 11.23±2.56 years. Network analysis showed that feelings of worthlessness, panic, and agitation are the predominant symptoms in the negative emotional networks. Depression, anxiety, and stress were interconnected through feelings of low mood, overreaction, and trembling. Additionally, lack of initiative and dry mouth exhibited the most significant direct associations with screen time. CONCLUSION:The central and bridge symptoms identified in the negative emotion networks can serve as potential focal points for future research on negative emotions among primary and secondary school students.

Physical frailty has been recognized as reversible and an important risk factor for cardiovascular disease (CVD). However, little is known about sustained frailty remission and its impact on CVD. The study aimed to assess the association between sustained frailty remission and incident CVD. Data were derived from the UK Biobank. Physical frailty was evaluated by the Fried frailty phenotype, and frailty transition patterns across three waves that is instance 0, instance 1, and instance 2 were defined. Incident CVD was defined by the International Classification of Diseases, 10th Revision (codes I00-I99). Cox proportional hazard models were used to calculate the hazard ratio (HR) and 95

BACKGROUND:Each year, there are approximately 10.3 million new stroke cases worldwide, with 2 million occurring in China. Post-stroke depression (PSD) is a common complication that negatively affects rehabilitation outcomes and increases long-term mortality. OBJECTIVE:This study used network analysis to investigate the cross-sectional and longitudinal networks between depressive symptoms in new-onset stroke patients with PSD, aiming to identify the key symptoms and predictive relationships among distinct symptoms during the acute phase and 6 months after the stroke. METHODS:This longitudinal descriptive study collected data from October 2022 to December 2023, including eligible new-onset stroke patients. Depressive symptoms were assessed using the CES-D scale, and network analysis was used to analyze the interactions between symptoms. RESULTS:613 participants completed the data collection. The study found that D3 (Felt sadness) emerged as the central depressive symptom at both baseline and follow-up (EI value = 1.215 and 1.168, respectively). In the longitudinal network analysis, D7 (Sleep quality) displayed the strongest out-Expected Influence (value = 1.728), while D4 (Everything was an effort) showed the strongest in-Expected Influence (value = 1.322). LIMITATIONS:The self-report measure is adopted for all depressive symptoms in the study, and there may be some deviation. CONCLUSION:These symptom-level associations at cross-sectional and longitudinal networks extend our understanding of PSD symptoms in new-onset stroke patients by pointing to specific key depressive symptoms that may aggravate PSD. Recognizing these symptoms is imperative for the development of targeted interventions and treatments aimed at addressing PSD in new-onset stroke patients.

As the process of population aging accelerates and the pressure on family caregiving continues to increase in China, building a comprehensive service system that can both meet patients’ home healthcare needs and effectively alleviate the burden on family caregivers has become an urgent task. Hospital at Home, as an important model of integrated healthcare and eldercare, not only provide continuous home medical care for those with mobility impairments or chronic diseases but also help alleviate the strain on healthcare resources to some extent. However, despite the gradual promotion of this service model across various regions, its implementation is still constrained by multiple factors. Following the concept mapping methodology, this study systematically explores the main barriers to the promotion of Hospital at Home through in-depth interviews with the medical professionals of different community medical institution. The study first classifies the identified barrier factors and uses expert scoring methods to assess the priority and feasibility of addressing each factor. Next, through multidimensional scaling and hierarchical cluster analysis, a concept map is constructed to analyze the relationships between the various factors. Finally, based on this analysis, the study explores the integration mechanism of respite care, aiming to achieve a complementary and collaborative relationship between medical support and social services. The theoretical significance of this study lies in two aspects: on the one hand, it provides empirical evidence to improve the Hospital at Home system, and on the other, it offers theoretical guidance for promoting the implementation of respite care services in primary healthcare institutions.

BACKGROUND:Multimorbidity may influence biological aging, particularly in acute ischemic stroke (AIS) patients with high comorbidity burden. However, evidence on associations between multimorbidity and biological aging in AIS remains limited, with unclear differential impacts of specific multimorbidity clusters. This study evaluated latent multimorbidity patterns in AIS patients and quantified relationships between multimorbidity and biological age (BA) acceleration. METHODS:This study included AIS patients from the Ischemic Cerebrovascular Disease Database of the First Affiliated Hospital of Zhengzhou University between 2018 and 2019. Biological age was assessed using the Klemera-Doubal method biological age (KDM-BA) and Phenotypic Age. Latent class analysis (LCA) identified multimorbidity clusters. A generalized linear model evaluated associations between multimorbidity and BA acceleration. RESULTS:A total of 2539 AIS patients were included, with 90% exhibiting multimorbidity (≥2 comorbidities). Each additional chronic condition was associated with a 3.78-year increase in KDM-based age acceleration (95%CI: 3.00-4.55, fully adjusted) and a 0.78-year increase in phenotypic age acceleration (95%CI: 0.56-1.00, fully adjusted). Among multimorbidity patterns, the hyperglycemia-hypertension pattern showed the strongest association with KDM-AA (β = 11.59, 95% CI: 9.61-13.58), followed by cardiac dysfunction (β = 7.89, 95% CI: 3.11-12.66). CONCLUSION:The overwhelming majority of AIS patients exhibit multimorbidity, which is associated with accelerated biological aging. Metabolic-vascular multimorbidity shows the strongest links to this association. Prospective studies are needed to further explore the causal relationship between multimorbidity and biological aging acceleration.

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