As global aging accelerates, frailty and depressive symptoms have emerged as critical contributors to cardiovascular disease (CVD) risk among older adults. However, the dynamic interplay between these factors remains underexplored. Here, we examine the associations among frailty, depressive symptoms, and incident CVD using data from five international cohorts (HRS, CHARLS, SHARE, ELSA, MHAS) involving individuals aged 50 and above. Our findings reveal that frailty significantly increases CVD risk, with depressive symptoms partially mediating this relationship. Transitions into frailty elevate CVD risk, while improvements reduce it. Cross-lagged panel network analysis identifies consistent CVD predictors, including hypertension, diabetes, and mobility issues. Subgroups with stronger associations include frail males, older individuals, working or retired people, and those with unhealthy lifestyles. These results underscore the need for integrated interventions targeting frailty and depressive symptoms to prevent CVD in aging populations.

Background: Mental health is emerging as an increasingly prevalent public issue globally. There is an urgent need in mental health for efficient detection methods, effective treatments, affordable privacy-focused health care solutions, and increased access to specialized psychiatrists. The emergence and rapid development of large language models (LLMs) have shown the potential to address these mental health demands. However, a comprehensive review summarizing the application areas, processes, and performance comparisons of LLMs in mental health has been lacking until now. Objective: This review aimed to summarize the applications of LLMs in mental health, including trends, application areas, performance comparisons, challenges, and prospective future directions. Methods: A scoping review was conducted to map the landscape of LLMs’ applications in mental health, including trends, application areas, comparative performance, and future trajectories. We searched 7 electronic databases, including Web of Science, PubMed, Cochrane Library, IEEE Xplore, Weipu, CNKI, and Wanfang, from January 1, 2019, to August 31, 2024. Studies eligible for inclusion were peer-reviewed articles focused on LLMs’ applications in mental health. Studies were excluded if they (1) were not peer-reviewed or did not focus on mental health or mental disorders or (2) did not use LLMs; studies that used only natural language processing or long short-term memory models were also excluded. Relevant information on application details and performance metrics was extracted during the data charting of eligible articles. Results: A total of 95 articles were drawn from 4859 studies using LLMs for mental health tasks. The applications were categorized into 3 key areas: screening or detection of mental disorders (67/95, 71%), supporting clinical treatments and interventions (31/95, 33%), and assisting in mental health counseling and education (11/95, 12%). Most studies used LLMs for depression detection and classification (33/95, 35%), clinical treatment support and intervention (14/95, 15%), and suicide risk prediction (12/95, 13%). Compared with nontransformer models and humans, LLMs demonstrate higher capabilities in information acquisition and analysis and efficiently generating natural language responses. Various series of LLMs also have different advantages and disadvantages in addressing mental health tasks. Conclusions: This scoping review synthesizes the applications, processes, performance, and challenges of LLMs in the mental health field. These findings highlight the substantial potential of LLMs to augment mental health research, diagnostics, and intervention strategies, underscoring the imperative for ongoing development and ethical deliberation in clinical settings.

Background: Previous studies have focused on the comorbidity of appearance anxiety and social anxiety, but few studies have focused on the protective role of self-compassion as underlying this mechanism, in young people like University students. With the increase of prevalence of appearance anxiety and social anxiety in this age group, it is necessary to explore factors that can buffer against the symptoms of these disorders. Therefore, the aims of this study were to research the effect of appearance anxiety and social anxiety, then to examine whether self-compassion has a protective effect on social anxiety.Method: The study was cross-sectional and conducted online from October 2021 to November 2021 in Jilin Province, China. A total of 63 Universities in the province participated in this study, totaling 96,218 participants, of which 40,065 were males (41.64%) and 56,153 females (58.36%), the mean age of the sample was 19.59 (±1.74). The Appearance Anxiety Scale-Brief Version was used to measure appearance anxiety. The Social Anxiety subscale of the Self-Consciousness Scale was used to measure social anxiety. The Self-Compassion Scale-Short Form was used to measure self-compassion. A structural equation model (SEM) was run to examine the mediating effect of self-compassion on the relationship between appearance anxiety and social anxiety.Result: Overall, appearance anxiety was positively associated with social anxiety [β = 0.334, 95% CI = (0.328, 0.341), p < 0.001], and self-compassion could mediate the effect of appearance anxiety on social anxiety [β = 0.128, 95% CI = (0.124, 0.132), p < 0.001]. Self-compassion played a partial mediating role between appearance anxiety and social anxiety.Conclusion: Individuals with high appearance anxiety are also at higher risk of social anxiety, but self-compassion can buffer against this relationship. These findings begin to explore novel approaches to treat social anxiety and can provide valuable insights for self-compassion training.

Introduction: It remains unclear whether depressive symptoms are associated with increased all-cause mortality and to what extent depressive symptoms are associated with chronic disease and all-cause mortality. The study aims to explore the relationship between depressive symptoms and all-cause mortality, and how depressive symptoms may, in turn, affect all-cause mortality among Chinese middle-aged and older people through chronic diseases.Methods: Data were collected from the China Health and Retirement Longitudinal Study (CHARLS). This cohort study involved 13,855 individuals from Wave 1 (2011) to Wave 6 (2020) of the CHARLS, which is a nationally representative survey that collects information from Chinese residents ages 45 and older to explore intrinsic mechanisms between depressive symptoms and all-cause mortality. The Center for Epidemiological Studies Depression Scale (CES-D-10) was validated through the CHARLS. Covariates included socioeconomic variables, living habits, and self-reported history of chronic diseases. Kaplan–Meier curves depicted mortality rates by depressive symptom levels, with Cox proportional hazards regression models estimating the hazard ratios (HRs) of all-cause mortality.Results: Out of the total 13,855 participants included, the median (Q1, Q3) age was 58.00 (51.00, 63.00) years. Adjusted for all covariates, middle-aged and older adults with depressive symptoms had a higher all-cause mortality rate (HR = 1.20 [95% CI, 1.09–1.33]). An increased rate was observed for 55–64 years old (HR = 1.23 [95% CI, 1.03–1.47]) and more than 65 years old (HR = 1.32 [95% CI, 1.18–1.49]), agricultural Hukou (HR = 1.44, [95% CI, 1.30–1.59]), and nonagricultural workload (HR = 1.81 [95% CI, 1.61–2.03]). Depressive symptoms increased the risks of all-cause mortality among patients with hypertension (HR = 1.19 [95% CI, 1.00–1.40]), diabetes (HR = 1.41[95% CI, 1.02–1.95]), and arthritis (HR = 1.29 [95% CI, 1.09–1.51]).Conclusion: Depressive symptoms raise all-cause mortality risk, particularly in those aged 55 and above, rural household registration (agricultural Hukou), nonagricultural workers, and middle-aged and older people with hypertension, diabetes, and arthritis. Our findings through the longitudinal data collected in this study offer valuable insights for interventions targeting depression, such as early detection, integrated chronic disease care management, and healthy lifestyles; and community support for depressive symptoms may help to reduce mortality in middle-aged and older people.