OBJECTIVE: To explore the genetic basis for a patient with clinically suspected neurofibromatosis type I, alopecia areata and vitiligo. METHODS: Variant of the NF1 gene was detected by chip capture and high-throughput sequencing. Candidate variant was verified by Sanger sequencing of the family trio. RESULTS: The patient was found to harbor a novel missense c.1885G>A (p.Gly629Arg) variant of the NF1 gene, for which neither parent was carrier. The variant was not recorded in the public database. Based on the guidelines for genetic variation of the American College of Medical Genetics and Genomics, the c.1885G>A missense variant was predicted to be pathogenic (PS1+PS2+PM2+PP3+PP4). CONCLUSION The c.1885G>A missense variant probably underlay the disease in this child. Above finding has enriched the spectrum of the NF1 gene variants.
Alopecia Areata/genetics* ; Child ; Genomics ; Humans ; Mutation ; Neurofibromatosis 1/genetics* ; Vitiligo/genetics*

Objective:To explore the incidence and risk factors of preoperative deep vein thrombosis (DVT) of elective total joint arthroplasty (TJA).Methods:Data of 500 patients before TJA from March 2015 to August 2016 who underwent ultrasound surveillance were retrospectively analyzed. All patients were divided into DVT group and non-DVT group according to results of ultrasound. Parameters including demographic data, basic medical history, and surgical information and laboratory indexes were collected. Risk factors were assessed via univariate, multivariate and logistic regression analysis.Results:Preoperative DVT was detected in 23 cases (4.6%, 23/500), all of which occurred in the intermuscular vein with no symptom, and among them there were 16 cases (5.6%, 16/285) before total knee arthroplasty and 7 cases (3.3%, 7/215) before total hip arthroplasty. Univariate analysis showed that age ( t=2.266, P=0.024), female patients ( χ2=4.028, P=0.045), history of hypertension ( χ2=7.907, P=0.005), D-dimer ≥0.5 μg/ml ( χ2=13.171, P < 0.001) were significantly higher than those in non-DVT group, and the differences were statistically significant. Multivariate analysis showed that D-dimer ≥0.5 μg/ml [ OR=6.655, 95% CI (1.929, 22.960), P=0.003] and history of hypertension [ OR=2.715, 95% CI (1.017, 7.250), P=0.046] were independent risk factors for preoperative DVT. Among them, the thrombus of 14 cases located in the operation side, 6 cases in non-operation side, and 3 cases in bilateral sides. Postoperative ultrasound showed that newly DVT occurred in 9 patients of whom 5 cases located in the contralateral muscular veins and 4 cases in the nearby muscular veins. After discharge, 22 patients (95.7%) with preoperative DVT were further evaluated by ultrasound. The average follow-up time was 3.0 months (range from 6 weeks to 9 months). The results showed that thrombus of 7 cases were completely dissolved, 13 cases were partially dissolved, and 2 cases remained unchanged. Thrombus extensions to proximal veins or symptomatic PE were not found. Conclusion:The incidence of preoperative DVT in patients with elective joint replacement was about 4.6%, among which D-dimer ≥0.5 μg/ml and history of hypertension were the risk factors for preoperative thrombosis.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.