Background Frailty is the third most common complication of diabetes after macrovascular and microvascular complications. The aim of this study was to develop a validated risk prediction model for frailty in patients with diabetes. Methods The research used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Twenty-five indicators, including socio-demographic variables, behavioral factors, health status, and mental health parameters, were analyzed in this study. The study cohort was randomly divided into a training set and a validation set at a ratio of 70 to 30%. LASSO regression analysis was used to screen the variables for the best predictors of the model based on a 10-fold cross-validation. The logistic regression model was applied to explore the associated factors of frailty in patients with diabetes. A nomogram was constructed to develop the prediction model. Calibration curves were applied to evaluate the accuracy of the nomogram model. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance. Results One thousand four hundred thirty-six patients with diabetes from the CHARLS database collected in 2013 (n = 793) and 2015 (n = 643) were included in the final analysis. A total of 145 (10.9%) had frailty symptoms. Multivariate logistic regression analysis showed that marital status, activities of daily living, waist circumference, cognitive function, grip strength, social activity, and depression as predictors of frailty in people with diabetes. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.912 (95%CI 0.887–0.937) and 0.881 (95% CI 0.829–0.934). Hosmer–Lemeshow test values were P = 0.824 and P = 0.608 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance. Conclusions Comprehensive nomogram constructed in this study was a promising and convenient tool to evaluate the risk of frailty in patients with diabetes, and contributed clinicians to screening the high-risk population.

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Background Subjective memory impairment (SMI) is common in the elderly. We aimed to reveal the interaction effect of midday napping duration and depressive symptoms on the risk of SMI. Methods Using a dataset representative of the Chinese population from a longitudinal study of health and retirement in China, subjects with SMI were screened using the question “how do you feel about your memory now?” and the Mini-Mental State Examination. A logistic regression model was applied to explore the factors affecting SMI. Additive and multiplicative models were used to analyze the interaction effect of midday napping duration and depressive symptoms on the risk of SMI. Results We enrolled 8,254 subjects included and the incidence of SMI was 63.9%. Depressive symptoms, nap time, and physical activity were influencing factors of SMI. Midday napping duration and depressive symptoms had positive additive interaction effects on the risk of SMI. When extended-length naps and depressive symptoms coexisted, the risk of SMI was 1.06 times greater than that for either alone (RERI = 0.27, 95% CI = 0.07–0.43; AP = 0.14, 95% CI = 0.01–0.23; S = 1.06, 95% CI = 0.57–1.62). When short naps and depressive symptoms coexisted, the risk of SMI was 1.2 times higher than that for either alone (RERI = 0.12, 95% CI=-0.14–0.39; AP = 0.13, 95% CI=-0.07–0.22; S = 1.20, 95% CI = 0.79–1.82). Limitations Since this was a cross-sectional study, the cause-and-effect relationships between the associated variables cannot be inferred. Conclusions The interaction effect that exists between nap time and depressive symptoms in the elderly is important for the identification and early intervention of people at risk for SMI.

Background: Detecting cognitive impairment in its earliest stages can prevent or delay the onset of dementia. Despite many policies that support cognitive screening, the screening rate is only 10.6% in China. Limited evidence is available on the cognitive screening intention of populations at high risk for dementia and the predictors of cognitive screening. This study aimed to assess the cognitive screening intention of people at high risk for dementia and to identify potential predictors of their cognitive screening intention. Methods: A cross-sectional survey involving 205 subjects at high risk for dementia was conducted between November 2021 and April 2022 in two large communities in Guangzhou, China. Data were collected using a self-designed questionnaire based on the planned behavior theory, including demographic characteristics and the theory of planned behavior (TPB) structures. Structural equation modeling (SEM) was used to identify predictors of cognitive screening intention. Results: The mean total score of cognitive screening intention was 8.54 ± 3.60, and 54% of the total number of subjects showed a intention to under cognitive screening. Religion, residence, family history of dementia, and health status were influencing factors of cognitive screening intention. Subject norms (r = 0.468, P < 0.01), perceived behavioral control (r = 0.695, P < 0.01), and attitude (r = 0.647, P < 0.01) were positively correlated with the intention. Fit indices indicated that the final structural model was well reflected, and that the research model was satisfactory. It was found that all hypotheses were supported. Based on the findings of the structural equation model, attitudes (path coefficient = 0.24, P < 0.01), subjective norms (path coefficient = 0.25, P < 0.01), and perceived behavioral control (path coefficient = 0.43, P < 0.01) had significantly positive effects on behavioral intentions.Conclusions: Half of the population at high-risk for dementia in the present study had the intention to undergo cognitive screening. The screening behavioral attitude, subjective norms, and perceived behavioral control have a direct positive effect on screening behavior intentions. The perception of behavioral control was the strongest predictor of intent to undergo cognitive screening. Further studies are needed to establish the interventions to facilitate cognitive screening intention, and their perceived behavioral control deserves more attention.