Construction And Evaluation Of Nomogram Model For The Effect Of Advanced Glycation End-Products On The Risk Of Sarcopenia In Type 2 Diabetes Patients

Objective To explore the risk factors of sarcopenia in patients with type 2 diabetes(T2DM),establish a nomogram model to predict the risk of skin detected advanced glycation end products(AGEs)affecting sarcopenia in patients with T2DM,and evaluate the risk,so as to provide scientific basis for the prevention and treatment of sarcopenia.Methods In this study,180 patients with type 2 diabetes who were hospitalized in the Department of Endocrinology of Hefei Second People’s Hospital from October2020 to May 2022 were selected for cross-sectional study.Collect general data of all enrolled patients(including course of disease,blood pressure,blood lipid and body mass index BMI,fasting blood glucose FPG,fasting C-peptide,HbA1c,blood calcium,phosphorus,urine A/C ratio UACR,etc.);The skeletal muscle mass of the extremities was measured by dual energy X-ray absorptiometry to calculate the skeletal muscle mass index(ASMI)of the extremities,the muscle strength was measured by the grip strength device,and the skin AGEs level was measured by the AGE Pro type non-invasive advanced glycation end products detector.According to the 2019 Asian consensus on the diagnosis of sarcopenia,all the enrolled patients were divided into sarcopenia group and non sarcopenia group,and the differences in general data such as blood pressure,blood glucose,blood lipid,and body mass index between the two groups were compared;All data were analyzed by single factor logistic regression.The variables with P<0.05 in the single factor analysis results were included in the multi-factor logistic regression analysis.The variables were screened by the entry method,and the output results were used to explore the independent risk factors of sarcopenia in T2DM patients;The screened independent risk factors were used to construct an nomogram prediction model with R software to predict the risk of sarcopenia in T2DM patients,and the discrimination and accuracy of the model were evaluated.Results A total of 180 patients with type 2 diabetes were included in this study,including 34 patients with sarcopenia and 146 patients with non sarcopenia.1.General data showed that age,course of disease,AGEs,muscle strength,time of 5times of standing test,ASMI,HbA1c,ACR were significantly different between the two groups(P<0.05).In the sarcopenia group,the age,course of disease,AGEs,time of five stand up tests,HbA1c and ACR levels were significantly higher than those in the non sarcopenia group,and the muscle strength,ASMI and BMI were significantly lower than those in the non sarcopenia group(P<0.05).2.Taking sarcopenia as the dependent variable,and taking age,AGEs,muscle strength,time of five stand up tests,ASMI,BMI,HbA1c and other indicators as independent variables,we conducted a single factor logistic regression analysis.The results showed that age,AGEs,muscle strength,time of five stand up tests,ASMI,BMI,HbA1c and other indicators had statistically significant effects(P<0.05).3.With sarcopenia as the dependent variable,the variables with P<0.05 in the multi-factor logistic regression analysis results: age,AGEs,muscle strength,time of five standing tests,ASMI,BMI,HbA1c and other indicators as independent variables,the results of multi-factor logistic regression analysis showed that BMI(OR=0.75,P=0.022),muscle strength(OR=0.76,P < 0.001),AGEs(OR=1.05,P < 0.001)were independent risk factors of sarcopenia.4.The nomogram model Model 1(BMI,AGEs,muscle strength)was established based on the results of multi-factor logistic regression analysis.The calibration curve was close to the Y=X straight line,and the model calibration was good.The C index of Model 1 group was 0.933,and the corrected C index was 0.927,indicating that the model fit was good;The 95% confidence interval corresponding to the AUC of 0.933 under the ROC curve drawn by the model is0.897-0.970,which is of good predictive value;DCA evaluation results of clinical benefits Model 1 nomogram prediction model has higher net income and better clinical application value.Conclusions AGEs,BMI and strength are independent risk factors for sarcopenia in T2DM patients;The multivariate model 1 nomogram prediction model suggests that AGEs has a good predictive role in the diagnosis of sarcopenia in T2DM patients.T2DM patients with higher AGEs,low BMI and low strength are high-risk groups for sarcopenia,and comprehensive intervention should be given to these patients in a timely manner to improve their diagnosis and detection.