Objective To construct and validate a risk diagnostic model for diabetes in rural elderly individuals with both hypertension and dyslipidemia, thus providing a scientific basis for the prevention and control of the co-morbidity of hypertension, hyperglycemia, and dyslipidemia in rural elderly people in China.
Methods With the data of 583 rural elderly individuals with hypertension and dyslipidemia from the 2015 China Health and Retirement Longitudinal Study (CHARLS) as the training set, a multivariate unconditional logistic regression model was adopted to analyze the influencing factors of diabetes in rural elderly individuals with hypertension and dyslipidemia and construct a risk diagnostic model. The goodness-of-fit of the model was tested via the Hosmer-Lemeshow method. The physical examination data of 1 185 elderly individuals who aged 65 and above and had both hypertension and dyslipidemia and complete outcome variables and key variables in Anfeng town, Dongtai city, Jiangsu province were used for external validation. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were established. The restricted cubic spline (RCS) model was employed to analyze the dose-response relationships of the Chinese visceral adiposity index (CVAI) and white blood cell count with the prevalence of diabetes in rural elderly individuals with both hypertension and dyslipidemia.
Results The results of the analysis on CHARLS data showed that CVAI, alcohol consumption status, and white blood cell count were the main factors influencing the prevalence of diabetes among rural elderly individuals with both hypertension and dyslipidemia (all P < 0.05). The results of the RCS model analysis showed that CVAI and white blood cell count were linearly positively correlated with the risk of diabetes (non-linear test P = 0.964 and 0.755), and the risk of diabetes increased linearly with the increase in CVAI and white blood cell count. The Hosmer-Lemeshow goodness-of-fit test (P = 0.739), the ROC curve of the external validation set AUC (95%CI): 0.924 (0.910–0.939), the calibration curve, and the decision curve all indicated that the risk diagnostic model constructed in this study for diabetes has good performance.
Conclusions The risk diagnostic model for diabetes in rural elderly individuals with both hypertension and dyslipidemia, which is constructed in this study, has excellent performance and clinical benefits. In this population, drinkers as well as those with higher CVAI and white blood cell count should be regarded as the key groups for diabetes prevention.
DownLoad: