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.

Purpose: This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample. Methods: We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model. Results: The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice. Conclusion: This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.

The study aimed to identify diseases that exhibit significant differences between hyperuricaemia (HUA) and non-hyperuricaemia (NHUA) groups and analyse the risk factors for HUA based on the related diseases in type 2 diabetes mellitus (T2DM). Methods: A total of 3264 T2DM patients were investigated from 2013 to 2017 in the Jinyang and Sanlin communities by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 3000 patients (264 patients were missing during follow-up) were investigated with questionnaires, physical examinations and biochemical index tests. After removing missing values, 2899 patients were divided into HUA and NHUA groups. The chi-square test was used to identify diseases with differences. Using Lasso analysis and logistic regression analysis, risk factors for HUA based on the related diseases were obtained. The C-index, receiver operating characteristic (ROC) curve and calibration plot were used to validate the discrimination and accuracy of the factors. Results: The chi-square test showed that there were significant differences in coronary heart disease (CHD) and diabetic nephropathy (DN) between the HUA group and the NHUA group. Through Lasso regression, glycosylated haemoglobin A1c (HbA1c), triglyceride (TG), blood urea nitrogen (BUN) and serum creatinine (SCR) were screened in the CHD group. Body mass index (BMI), HbA1c, total cholesterol (TC), TG, BUN, SCR and urine microalbumin (UMA) were screened in the DN group. The P-value of all the variables was less than 0.05. Through the C-index, calibration, and ROC curve analyses, these risk factors had medium accuracy. Conclusion: HUA was significantly related to CHD and DN. The level of UA was correlated with HbA1c, TG, BUN, and SCR based on CHD. The level of UA was associated with BMI, HbA1c, TC, TG, BUN, SCR, and UMA based on DN.

Introduction This study aimed to study risk factors for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) patients and establish a clinical prediction model. Research Design and Methods A total of 3402 T2DM patients were diagnosed by clinical doctors and recorded in the electronic medical record system (EMRS) of six Community Health Center Hospitals from 2015 to 2017, including the communities of Huamu, Jinyang, Yinhang, Siping, Sanlin and Daqiao. From September 2018 to September 2019, 3361 patients (41 patients were missing) were investigated using a questionnaire, physical examination, and biochemical index test. After excluding the uncompleted data, 3214 participants were included in the study and randomly divided into a training set (n = 2252) and a validation set (n = 962) at a ratio of 3:1. Through lead absolute shrinkage and selection operator (LASSO) regression analysis and logistic regression analysis of the training set, risk factors were determined and included in a nomogram. The C-index, receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were used to validate the distinction, calibration and clinical practicality of the model. Results Age, T2DM duration, hypertension (HTN), hyperuricaemia (HUA), body mass index (BMI), glycosylated haemoglobin A1c (HbA1c), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C) were significant factors in this study. The C-index was 0.750 (0.724–0.776) based on the training set and 0.767 (0.726–0.808) based on the validation set. Through ROC analysis, the set area was 0.750 for the training set and 0.755 for the validation set. The calibration test indicated that the S:P of the prediction model was 0.982 in the training set and 0.499 in the validation set. The decision curve analysis showed that the threshold probability of the model was 16–69% in the training set and 16–73% in the validation set. Conclusion Based on community surveys and data analysis, a prediction model of CHD in T2DM patients was established.