The influence of social participation and depressive symptoms on cognition among middle-aged and older adults.

Background: The global aging phenomenon has raised concerns about the cognitive abilities of older individuals. This study aimed to explore the relationship between social participation, depressive symptoms, and cognitive function among middle-aged and older adults. Methods: This study utilized data from the China Longitudinal Study of Health and Retirement (CHARLS) from wave 1 to wave 4. We used linear regression and generalized estimation equations to investigate the correlation between social participation, depressive symptoms, and cognitive function. Moreover, three models were constructed by adjusting covariates, and we used the sobel test and bootstrap method to analyze the mediating effects of depressive symptoms on social activities and cognitive function. Results: The results of both linear regression and generalized estimation equation showed that social participation had a positive correlation with cognitive function (P < 0.05), and the impact of social participation on cognition increased with the number of social activity types. Meanwhile, depressive symptoms had a negative association with cognitive function (P < 0.05). Furthermore, there was no interaction between social participation and depressive symptoms on cognitive function. Finally, after adjusting the model, social participation could affect cognitive function by affecting depressive symptoms (P < 0.05). Conclusion: The study emphasizes the mediating role of depressive symptoms in the relationship between social participation and cognitive function. Notably, no interaction was observed between social participation and depressive symptoms. These findings highlight the potential of active social participation in reducing depressive symptoms and enhancing cognitive function in middle-aged and older adults.

Development of the RF-GSEA Method for Identifying Disulfidptosis-Related Genes and Application in Hepatocellular Carcinoma.

Disulfidptosis is a newly discovered cellular programmed cell death mode. Presently, a considerable number of genes related to disulfidptosis remain undiscovered, and its significance in hepatocellular carcinoma remains unrevealed. We have developed a powerful analytical method called RF-GSEA for identifying potential genes associated with disulfidptosis. This method draws inspiration from gene regulation networks and graph theory, and it is implemented through a combination of random forest regression model and Gene Set Enrichment Analysis. Subsequently, to validate the practical application value of this method, we applied it to hepatocellular carcinoma. Based on the RF-GSEA method, we developed a disulfidptosis-related signature. Lastly, we looked into how the disulfidptosis-related signature is connected to HCC prognosis, the tumor microenvironment, the effectiveness of immunotherapy, and the sensitivity of chemotherapy drugs. The RF-GSEA method identified a total of 220 disulfidptosis-related genes, from which 7 were selected to construct the disulfidptosis-related signature. The high-disulfidptosis-related score group had a worse prognosis compared to the low-disulfidptosis-related score group and showed lower infiltration levels of immune-promoting cells. The high-disulfidptosis-related score group had a higher likelihood of benefiting from immunotherapy compared to the low-disulfidptosis-related score group. The RF-GSEA method is a powerful tool for identifying disulfidptosis-related genes. The disulfidptosis-related signature effectively predicts HCC prognosis, immunotherapy response, and drug sensitivity.

Study on the trajectory of depression among middle-aged and elderly disabled people in China: Based on group-based trajectory model.

Background: Previous studies have shown that middle-aged and elderly adults with disabilities are at higher risk for depressive symptoms. However, there are few studies on the long-term trajectories of depressive symptoms in the Chinese middle-aged and elderly disabled population. Objective: This study aimed to identify the different development trajectories of depressive symptoms and their influencing factors in middle-aged and elderly people with disabilities in China. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS) in 2011, 2013, 2015, and 2018, a longitudinal cohort was formed for the study. A total of 2053 participants underwent at least two measures of depressive symptoms, assessed using the Center for Epidemiological Studies Depression Scale (CES-D10), a depression symptom assessment scale. We constructed a Group-Based Trajectory Model (GBTM) to identify the development trajectory of depressive symptoms in 2053 middle-aged and elderly disabled individuals, screened the potential predictors using lasso regression, and analyzed the factors affecting the development trajectory of depression through multivariate logistic regression. Results: We identified four depression symptom trajectories throughout the follow-up process: "low depressive symptom group", "worsening depressive symptom group", "relieved depressive symptom group", and "high depressive symptom group". We found that there were differences in basic characteristics among different subgroups of depression trajectory. However, middle-aged and elderly disabled women living in rural areas, with limited ADL or IADL, physical pain, poor self-reported health and self-reported memory, short sleep time, and no relatives and friends to take care of them were the key groups for the prevention and treatment of depressive symptoms. Conclusion: There is heterogeneity in the trajectories of depressive symptoms in the Chinese middle-aged and elderly disabled population, it is necessary to focus on the characteristics of the trajectories of different subgroups.

Causal association between major depressive disorder and coronary heart disease: a two-sample bidirectional mendelian randomization study.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous mental illness and a major public health problem worldwide. A large number of observational studies have demonstrated a clear association between MDD and coronary heart disease (CHD), and some studies have even suggested that the relationship is bidirectional. However, it was unknown whether any causal relationship existed between them and whether causality was bidirectional in such an instance. Thus, we aimed to determine whether there is a bidirectional causal relationship between major depressive disorders and coronary heart disease. METHODS: Our two-sample Bidirectional Mendelian Randomization Study consisted of two parts: forward MR analysis regarded MDD as exposure and CHD as the outcome, and reverse MR analysis considered CHD as exposure and MDD as the outcome. Summary data on MDD and CHD were obtained from the IEU Open GWAS database. After screening criteria(P < [Formula: see text]), 47 MDD-associated SNPs and 39 CHD-associated SNPs were identified. The inverse-variance weighted (IVW) method, ME-Egger regression, and weighted median method were used to estimate causality. In addition, sensitivity methods, including the heterogeneity test, horizontal pleiotropy test, and leave-one-out method, were applied to ensure the robustness of causal estimation. RESULTS: Based on the MR-Egger regression intercept test results, there did not appear to be any horizontal pleiotropy in this study (MDD: intercept = -0.0000376, P = 0.9996; CHD: intercept = -0.0002698, P = 0.920). Accordingly, IVW results suggested consistent estimates of causal effect values. The results showed that people with MDD increased the risk of CHD by 14.7% compared with those without MDD (OR = 1.147, 95%CI: 1.045-1.249, P = 0.009). But there was no direct evidence that CHD would increase the risk of MDD(OR = 1.008, 95%CI: 0.985-1.031, P = 0.490). The heterogeneity test and funnel plot showed no heterogeneity in 47 SNPs of MDD (Q = 42.28, [Formula: see text]=0, P = 0.629), but there was heterogeneity in 39 SNPs of CHD (Q = 62.48, [Formula: see text]=39.18%, P = 0.007). The leave-one-out method failed to identify instances where a single SNP was either biased toward or dependent on the causation. CONCLUSION: Our study supports a one-way causal relationship between MDD and CHD, but there is no bidirectional causal relationship. MDD increases the risk of CHD, but there is no evidence that CHD increases the risk of MDD. Therefore, the influence of psychological factors should also be considered in the prevention and treatment of CHD. For MDD patients, it is necessary to prevent cardiovascular diseases.

A Deep Learning-Based Model for Predicting Abnormal Liver Function in Workers in the Automotive Manufacturing Industry: A Cross-Sectional Survey in Chongqing, China.

To identify the influencing factors and develop a predictive model for the risk of abnormal liver function in the automotive manufacturing industry works in Chongqing. Automotive manufacturing workers in Chongqing city surveyed during 2019-2021 were used as the study subjects. Logistic regression analysis was used to identify the influencing factors of abnormal liver function. A restricted cubic spline model was used to further explore the influence of the length of service. Finally, a deep neural network-based model for predicting the risk of abnormal liver function among workers was developed. Of all 6087 study subjects, a total of 1018 (16.7%) cases were detected with abnormal liver function. Increased BMI, length of service, DBP, SBP, and being male were independent risk factors for abnormal liver function. The risk of abnormal liver function rises sharply with increasing length of service below 10 years. AUC values of the model were 0.764 (95% CI: 0.746-0.783) and 0.756 (95% CI: 0.727-0.786) in the training and test sets, respectively. The other four evaluation indices of the DNN model also achieved good values.