Hearing Loss and Subjective Well-Being Among Older Adults: Mediating Effect of Social Activity and Cognitive Function.

OBJECTIVES: To examine the relationship between hearing loss and subjective well-being (SWB) and to investigate the mediating roles of social activity and cognitive function in the link between hearing loss and SWB. METHODS: An analysis of 11,949 older Chinese adults was conducted from the 2018 wave of the Chinese Longitudinal Health Longevity Survey. Multiple linear regression and mediation analysis were conducted. RESULTS: Hearing loss had a significant negative association with SWB (B = -0.787; 95% CI: -0.961, -0.613). Hearing loss influenced older adults' SWB in the following three ways: first, via the partial mediating effect of social activity (B = -0.021, 95% CI: -0.036, -0.009); second, via the partial mediating effect of cognitive function (B = -0.275, 95% CI: -0.347, -0.252); and third, via the serial mediating effects of social activity and cognitive function (-0.016, 95% CI: -0.021, -0.011). CONCLUSIONS: Social activity and cognitive function play a serial intermediary role in the relationship between hearing loss and SWB among older Chinese adults. CLINICAL IMPLICATIONS: Multidimensional health and social interventions aimed at improving mental health and social inclusion among adults with hearing loss should be recommended.

Waist-to-calf circumstance ratio and cognitive function among Chinese older adults: Mediating roles of physical performance and social activity.

Background: In light of the potentially detrimental effects of central fat and decreased muscle mass on cognitive function, it would be beneficial to learn more about the mediating mechanisms underpinning the association between the two. The purpose of this study is to determine the association between waist-to-calf circumstance ratio (WCR) and cognitive function, as well as to investigate whether physical performance and social activity mediate the relationship between WCR and cognitive function among older Chinese adults. Methods: An analysis of 9,652 older Chinese adults was conducted during the 2018 wave of the Chinese Longitudinal Health Longevity Survey (CLHLS). The Mini-Mental State Examination (MMSE) and a self-reported scale were used to measure cognitive function, physical performance, and social activity, respectively. Multiple linear regression and mediation analyses were conducted. Results: The findings suggest that a high WCR had a significant negative association with cognitive function (B = -0.535, 95% CI: -0.754, -0.317). Mediation analysis revealed that a high WCR influenced old adults' cognitive function in three ways: first, through the partial mediating effect of physical performance (B = -0.270; 95% CI: -0.340, -0.203); second, through the partial mediating effect of social activity (B = -0.035; 95% CI: -0.055, -0.017); and third, through the serial mediating effects of physical performance and social activity (B = -0.021, 95% CI: -0.029, -0.015). Conclusion: The study results suggest the adverse impact of a high WCR on older adults' cognitive function, and the possible mechanisms of physical performance and social activity by which the association takes place. Multidimensional health and social interventions aimed at improving physical, social, and cognitive functioning among older adults with sarcopenic obesity are recommended.

Comparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening.

Purpose: Using machine learning method to predict and judge unknown data offers opportunity to improve accuracy by exploring complex interactions between risk factors. Therefore, we evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for predicting the risk of renal function decline (RFD) using routine clinical data. Patients and Methods: This retrospective cohort study includes datasets from 2166 subjects, aged 35-74 years old, provided by an adult health screening follow-up program between 2010 and 2020. Seven different ML models were considered - random forest, gradient boosting, multilayer perceptron, support vector machine, K-nearest neighbors, adaptive boosting, and decision tree - and were compared with standard logistic regression. There were 24 independent variables, and the baseline estimate glomerular filtration rate (eGFR) was used as the predictive variable. Results: A total of 2166 participants (mean age 49.2±11.2 years old, 63.3% males) were enrolled and randomly divided into a training set (n=1732) and a test set (n=434). The area under receiver operating characteristic curve (AUROC) for detecting RFD corresponding to the different models were above 0.85 during the training phase. The gradient boosting algorithms exhibited the best average prediction accuracy (AUROC: 0.914) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the RFD prediction performance, compared to logistic regression model (AUROC:0.882), except the K-nearest neighbors and decision tree algorithms (AUROC:0.854 and 0.824, respectively). However, the improvement differences with logistic regression were small (less than 4%) and nonsignificant. Conclusion: Our results indicate that the proposed health screening dataset-based RFD prediction model using ML algorithms is readily applicable, produces validated results. But logistic regression yields as good performance as ML models to predict the risk of RFD with simple clinical predictors.

Scoring model to predict risk of chronic kidney disease in Chinese health screening examinees with type 2 diabetes.

PURPOSE: As health screening continues to increase in China, there is an opportunity to integrate a large number of demographic as well as subjective and objective clinical data into risk prediction modeling. The aim of this study was to develop and validate a prediction model for chronic kidney disease (CKD) in Chinese health screening examinees with type 2 diabetes mellitus (T2DM). METHODS: We conducted a retrospective cohort study consisting of 2051 Chinese T2DM patients between 35 and 78 years old who were enrolled in the XY3CKD Follow-up Program between 2009 and 2010. All participants were randomly assigned into a derivation set or a validation set at a 2:1 ratio. Cox proportional hazards regression model was selected for the analysis of risk factors for the development of the proposed risk model of CKD. We established a prediction model with a scoring system following the steps proposed by the Framingham Heart Study. RESULTS: The mean follow-up was 8.52 years, with a total of 315 (23.20%) and 189 (27.27%) incident CKD cases in the derivation set and validation set, respectively. We identified the following risk factors: age, gender, body mass index, duration of type 2 diabetes, variation of fasting blood glucose, stroke, and hypertension. The points were summed to obtain individual scores (from 0 to 15). The areas under the curve of 3-, 5- and 10-year CKD risks were 0.843, 0.799 and 0.780 in the derivation set and 0.871, 0.803 and 0.785 in the validation set, respectively. CONCLUSIONS: The proposed scoring system is a promising tool for further application of assisting Chinese medical staff for early prevention of T2DM complications among health screening examinees.

Relationship between dietary patterns and sleep disorders among health screening population in Changsha. / é•¿æ²™åœ°åŒºéƒ¨åˆ†å¥åº·ä½“æ£€äººç¾¤é¥®é£Ÿæ¨¡å¼ä¸Žç¡çœ éšœç¢çš„å ³ç³».

OBJECTIVES: Sleep disorders directly affect health-related quality of life, so it is of great significance to investigate the risk factors of sleep disorders and to actively intervene. This study aims to investigate the relationship between dietary patterns and associated factors and sleep disorders among the health screening populations in Changsha. METHODS: A cross-sectional study was carried out in 86 073 subjects aged 18-70 years old who underwent the health screening. The association between dietary patterns and sleep disorders was analyzed. The associated factors for sleep disorders were identified via by principal component analysis and classification tree model. RESULTS: The overall prevalence of reporting sleep disorders was 18.64%. Four major dietary patterns (healthy, snacks, whole-grain, and fried food patterns) were identified. In logistic regression, snacks and fried food patterns had higher risk of sleep disorders. The whole-grain pattern was a protective factor for sleep disorders. Nine associated factors including age, susceptibility to anxiety, snacking parterns, feelings of depression, chronic pain, physical activity, educational level, gender, and weight, and 9 groups at high risk for sleep disorders were identified by classification tree model. CONCLUSIONS: Sleep disorders are prevalent in the health screening population of Changsha. There is a close association between snacks dietary patterns and sleep disorders. It is necessary to promote healthy and reasonable diet, and keep good lifestyle for the prevention and control of sleep disorders. Health management after physical examination should take different health interventions for high-risk groups with different characteristics of sleep disorders.