基于生物标志物与列线图快速筛查2型糖尿病肾病老年患者内在能力下降的研究OA

Objective To analyze the factors influencing the decline of intrinsic capacity(IC)in elderly patients with Type 2 Diabetic Kidney Disease(T2DKD)and to construct a nomogram prediction model based on biomarkers.Methods Using a conven-ience sampling method,468 elderly patients with T2DKD who received treatment at the People's Hospital of Xinjiang Uygur Autonomous Region between October 2024 and May 2025 were enrolled as study subjects.They were randomly divided into a training set(328 cases)and a validation set(140 cases)in a 7∶3 ratio.The training set was divided into 257 cases in the IC impaired group and 71 cases in the IC stable group based on IC impairment status.Clinical characteristics were compared between the training set and the validation set,as well as between the IC stable group and the IC impaired group.Binary logistic regression was employed to identify risk factors for IC im-pairment;based on the results,a nomogram was developed using R software,and the model was subsequently validated.Results The results of univariate analysis of clinical data between the IC stable group and the IC impaired group in the training set showed that there were significant differences in age,gender,grip strength,living arrangement,number of chronic diseases,social participation score,fear of disease progression score,25-hydroxyvitamin D,and glycosylated hemoglobin(P<0.05).Binary logistic regression analysis revealed that age,living arrangement(Living alone),grip strength,number of chronic diseases,social participation score,and 25-hydroxyvitamin D level were influencing factors for IC decline in elderly T2DKD patients(P<0.05).Nomogram prediction model validation results showed good predictive performance of the model,the areas under the ROC curve for the training set and validation set were 0.899(95%CI:0.861-0.937,P<0.001)and 0.855(95%CI:0.784-0.926,P<0.001),respectively,both at high levels.The calibration curve results indicated good consistency between the model's predicted probabilities and the actual observed outcomes(P>0.05),demonstra-ting the model's good diagnostic performance.Conclusion This study identified multiple risk factors for IC decline in elderly T2DKD patients and subsequently developed a robust and easily measurable nomogram model.This model can assist clinical healthcare staff in rapidly screening patients at high risk for IC decline,providing a basis for early intervention.