Background Type 2 diabetes mellitus is an expanding global public health issue, especially in developing countries. This study aimed to investigate the prevalence, awareness and control rate of type 2 diabetes mellitus, and assess its risk factors in elderly Chinese individuals. Methods The health screening data of 376,702 individuals aged ≥ 65 years in Wuhan, China, were collected to analyse the prevalence, awareness, and control rates of diabetes. Indices, including fasting plasma glucose and other biochemical indicators, were measured for all participants using standard methods at the central laboratory. Multilevel logistic regression analysis was performed to assess the key determinants of the prevalence, awareness, and control rates of diabetes. Results The prevalence, awareness, and control rates of diabetes in the Chinese individuals aged ≥ 65 years were 18.80%, 77.14%, and 41.33%, respectively. There were statistically significant differences in the prevalence, awareness, and control rates by gender. Factors associated with diabetes prevalence were age, body mass index (BMI), and central obesity; while those associated with awareness and control were gender, education level, marital status, physical activity, alcohol consumption, BMI, and central obesity. Conclusions Diabetes is an important public health problem in the elderly in China. The awareness and control rates have improved, but overall remained poor. Therefore, effective measures to raise awareness and control the rates of diabetes should be undertaken to circumvent the growing disease burden in elderly Chinese people.
Identifying people with a high risk of developing diabetes among those with prediabetes may facilitate the implementation of a targeted lifestyle and pharmacological interventions. We aimed to establish machine learning models based on demographic and clinical characteristics to predict the risk of incident diabetes. We used data from the free medical examination service project for elderly people who were 65 years or older to develop logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) machine learning models for the follow-up results of 2019 and 2020 and performed internal validation. The receiver operating characteristic (ROC), sensitivity, specificity, accuracy, and F1 score were used to select the model with better performance. The average annual progression rate to diabetes in prediabetic elderly people was 14.21%. Each model was trained using eight features and one outcome variable from 9607 prediabetic individuals, and the performance of the models was assessed in 2402 prediabetes patients. The predictive ability of four models in the first year was better than in the second year. The XGBoost model performed relatively efficiently (ROC: 0.6742 for 2019 and 0.6707 for 2020). We established and compared four machine learning models to predict the risk of progression from prediabetes to diabetes. Although there was little difference in the performance of the four models, the XGBoost model had a relatively good ROC value, which might perform well in future exploration in this field.
Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.
ObjectiveThis study aimed to examine the relationship between social support and its sub-domains and cognitive performance, and the association with cognitive impairment among older adults in China.DesignA cross-sectional study.Setting and participantsWe included 865 community-based individuals aged 65 and above from Hubei province, China.MethodsThe level of social support was evaluated using the social support rating scale (SSRC). The Mini-Mental State Examination was adopted to assess cognitive function, and its cut-offs were used to determine cognitive impairment among the participants. Multiple linear regression models and logistic regression models were used to estimate the β and odds ratios (ORs) and their 95% CIs, respectively.ResultsThe participants were divided into quartiles 1–4 (Q1–Q4), according to the total scores of SSRC. After adjusting for sociodemographic characteristics, lifestyle factors, and history of diseases, for MMSE scores, compared to these in Q1, the β of Q2–Q4 were −0.22 (−0.88, 0.43), 0.29 (−0.35, 0.94), and 0.86 (0.19, 1.53), respectively; For cognitive impairment, the ORs of Q2–Q4 were 1.21 (0.80, 1.82), 0.62 (0.40, 0.94), and 0.50 (0.32, 0.80), respectively. Considering SSRC scores as the continuous variable, per 1-unit increase, the β was 0.05 (0.02, 0.09) for the cognitive score, and the OR was 0.95 (0.92, 0.98) for cognitive impairment. In addition, higher levels of both subjective support and support utilization were related to better MMSE performance and lower risks of cognitive impairment.Conclusion and implicationsAmong the older adults in China, as expected, there is a positive relationship between social support and cognitive performance, and high levels of social support, particularly in support utilization, were related to low risks of cognitive impairment. More social support should be provided in this population to improve cognitive function and reduce the risks of cognitive impairment.
Background: Type 2 diabetes mellitus is an expanding global public health issue, especially in developing countries. This study aimed to investigate the prevalence, awareness and control rate of type 2 diabetes mellitus, and assess its risk factors in elderly Chinese individuals.Methods: The health screening data of 376,702 individuals aged ≥65 years in Wuhan, China, were collected to analyse the prevalence, awareness, and control rates of diabetes. Indices, including fasting plasma glucose and other biochemical indicators, were measured for all participants using standard methods at the central laboratory. Multilevel logistic regression analysis was performed to assess the key determinants of the prevalence, awareness, and control rates of diabetes.Results: The prevalence, awareness, and control rates of diabetes in the Chinese individuals aged ≥65 years were 18.80%, 77.14%, and 41.33%, respectively. There were statistically significant differences in the prevalence, awareness, and control rates by gender. Factors associated with diabetes prevalence were age, body mass index (BMI), and central obesity; while those associated with awareness and control were gender, education level, marital status, physical activity, alcohol consumption, BMI, and central obesity.Conclusions: Diabetes is an important public health problem in the elderly in China. The awareness and control rates have improved, but overall remained poor. Therefore, effective measures to raise awareness and control the rates of diabetes should be undertaken to circumvent the growing disease burden in elderly Chinese people.
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