Background: Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability.Methods: Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, –2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model.Results: Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power.Conclusion: Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN.

Develop a diabetic nephropathy incidence risk nomogram in a Chinese population with type 2 diabetes mellitus. Predictors included systolic blood pressure, diastolic blood pressure, fasting blood glucose, glycosylated hemoglobin A1c, total triglycerides, serum creatinine, blood urea nitrogen and body mass index. The model displayed medium predictive power with a C-index of 0.744 and an area under curve of 0.744. Internal verification of C-index reached 0.737. The decision curve analysis showed the risk threshold was 20%. The value of net reclassification improvement and integrated discrimination improvement were 0.131, 0.05, and that the nomogram could be applied in clinical practice. Diabetic nephropathy incidence risk nomogram incorporating 8 features is useful to predict diabetic nephropathy incidence risk in type 2 diabetes mellitus patients. Questionnaires, physical examinations and biochemical tests were performed on 3489 T2DM patients in six communities in Shanghai. LASSO regression was used to optimize feature selection by running cyclic coordinate descent. Logistic regression analysis was applied to build a prediction model incorporating the selected features. The C-index, calibration plot, curve analysis, forest plot, net reclassification improvement, integrated discrimination improvement and internal validation were used to validate the discrimination, calibration and clinical usefulness of the model.

Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.

The impact of psychosocial factors on increasing the risk of HIV infection among men who have sex with men (MSM) has attracted increasing attention. We aimed to develop and validate an integrated prediction model, especially incorporating emerging psychosocial variables, for predicting the risk of HIV infection among MSM. We surveyed and collected sociodemographic, psychosocial, and behavioral information from 547 MSM in China. The participants were split into a training set and a testing set in a 3:1 theoretical ratio. The prediction model was constructed by introducing the important variables selected with the least absolute shrinkage and selection operator (LASSO) regression, applying multivariate logistic regression, and visually assessing the risk of HIV infection through the nomogram. Receiver operating characteristic curves (ROC), Kolmogorov–Smirnov test, calibration plots, Hosmer–Lemeshow test and population stability index (PSI) were performed to test validity and stability of the model. Four of the 15 selected variables—unprotected anal intercourse, multiple sexual partners, involuntary subordination and drug use before sex—were included in the prediction model. The results indicated that the comprehensive prediction model we developed had relatively good predictive performance and stability in identifying MSM at high-risk for HIV infection, thus providing targeted interventions for high-risk MSM.

Introduction Non-alcoholic fatty liver disease (NAFLD) is becoming more prevalent in patients with type 2 diabetes mellitus (T2DM) and can contribute to serious liver damage in this patient population. The aim of this study was to develop a risk nomogram for NAFLD in a Chinese population with T2DM. Methods A questionnaire survey, physical examination and biochemical indicator testing were performed on 874 patients with T2DM, and the collected data were used to evaluate the risk to develop NAFLD in T2DM patients. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. Multivariable logistic regression analysis was applied to build a predictive model by introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. A calibration plot, receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to validate the model, with further assessment by external validation. Results A total of nine predictors, namely sex, age, total cholesterol (TC), body mass index (BMI), waistline, diastolic blood pressure (DBP), serum uric acid (SUA), course of disease and high-density lipoprotein-cholesterol (HDL-C), were identified by LASSO regression analysis from a total of 24 variables studied. The model constructed using these nine predictors displayed medium prediction ability, with an area under the ROC of 0.848 in the training set and 0.809 in the validation set. The DCA curve showed that the nomogram could be applied clinically if the risk threshold was between 48 and 91%, which was found to be between 44 and 82% in the external validation. Conclusion Introducing sex, age, TC, BMI, waistline, DBP, SUA, course of disease and HDL-C into the risk nomogram increased its usefulness for predicting NAFLD risk in patients with T2DM.

Purpose: The purpose of this study was to identify diabetic nephropathy risk factors in type 2 diabetes mellitus obese people based on community type 2 diabetes mellitus patients. Patients and Methods: In the community in Shanghai, we conduct a questionnaire, physical examination, and biochemical examination. The 406 patients included in the analysis were divided into two groups based on whether or not they had diabetic nephropathy. The influencing factors of type 2 diabetes mellitus obese patients were screened by the least absolute shrinkage and selection operator method, and then the influencing factors detected by the least absolute shrinkage and selection operator method were included in the binary logistic regression analysis, and the risk factors for diabetic nephropathy in obese people with type 2 diabetes mellitus were obtained. Finally, the nomogram and forest plot are used to visualize the binary logistic regression results, and the calibration plot and receiver-operating characteristic curve are used to verify the result. Results: The results showed that family history of diabetes (OR= 2.091, P= 0.002), disease course (OR=1.050, P= 0.007). hypertension (OR=1.768, P=0.042), hyperuricemia (OR=2.263, P=0.003), systolic blood pressure (OR=1.027, P< 0.001), and glycosylated haemoglobin A1c (OR=1.358, P< 0.001) were risk factors for diabetic nephropathy. Conclusion: For obese patients with type 2 diabetes mellitus, they should pay attention to family history of diabetes, disease course and hyperuricemia. Hypertension should be concerned and strictly controlled. Systolic blood pressure and glycosylated haemoglobin A1c will help prolong the survival of diabetic nephropathy patients.