Background: The association between visceral adiposity index (VAI) and diabetic complications has been reported in cross-sectional studies, while the effect of VAI on complication development remains unclear. This study aims to evaluate the longitudinal association of VAI and Chinese VAI (CVAI) with the incidence of diabetic nephropathy and retinopathy using a Chinese cohort. Methods: A total of 8 948 participants with type 2 diabetes from Beijing Health Management Cohort were enrolled during 2013–2014, and followed until December 31, 2019. Nephropathy was confirmed by urine albumin/creatinine ratio and estimated glomerular filtration rate; retinopathy was diagnosed using fundus photograph. Results: The mean (SD) age was 53.35 (14.66) years, and 6 154 (68.8%) were men. During a median follow-up of 4.82 years, 467 participants developed nephropathy and 90 participants developed retinopathy. One-SD increase in VAI and CVAI levels were significantly associated with an increased risk of nephropathy, and the adjusted hazard ratios (HR) were 1.127 (95% CI 1.050–1.210) and 1.165 (95% CI 1.003–1.353), respectively. On contrary, VAI and CVAI level were not associated with retinopathy after adjusting confounding factors. Conclusion: VAI and CVAI are independently associated with the development of nephropathy, but not retinopathy in Chinese adults with diabetes.

Background: Arterial stiffness, glycemic control and blood pressure are risk factors of macrovascular complications in type 2 diabetes. This study aimed to investigate the combined association of arterial stiffness, glycemic control and hypertension status with the occurrence of diabetic macrovascular complication. Methods: A total of 1870 patients of diabetes were enrolled from Beijing Health Management Cohort between 2008 and 2018 as baseline, and then followed for macrovascular complication onset. We proposed a composite risk score (0–4) by arterial stiffness severity, pool glycemic control and hypertension status. Cox model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Results: The mean age (SD) of this population was 59.90 (12.29) years. During a median follow-up of 4.0 years, 359 (19.2%) patients developed macrovascular complication. Compared to the normal arterial stiffness and good glycemic control group, patients with severe arterial stiffness and pool glycemic control had the highest risk of macrovascular complications (HR: 2.73; 95% CI: 1.42–5.25). Similarly, those of severe arterial stiffness and hypertension had the highest risk (HR: 2.69; 95% CI: 1.61–4.50). Patients of the composite score > 2 had a significantly increased risk of macrovascular complication. Conclusion: This study suggested the clinical importance of combined evaluation of arterial stiffness, glycemic control and hypertension status for the risk stratification and management of macrovascular complication of type 2 diabetes.

This study aimed to examine gender differences in the association between serum uric acid (SUA) and the risk of prediabetes in a longitudinal cohort. A total of 8237 participants in the Beijing Health Management Cohort study were recruited and surveyed during 2008–2009, and followed up in 2011–2012 and 2014–2015 surveys. Generalized estimating equation (GEE) models were used to evaluate the association between SUA and prediabetes. Furthermore, subgroup analyses assessed the primary outcome according to status of abdominal obesity, age and status of hypertension. During six years of follow-up, we identified 1083 prediabetes events. The GEE analyses confirmed and clarified the association between SUA and prediabetes (RR = 1.362; 95% CI = 1.095–1.696; p = 0.006) after adjusting for other potential confounders, especially in females (RR = 2.109; 95% CI = 1.329–3.347; p = 0.002). In addition, this association was stronger in the subgroup of females aged ≥48 years old (RR = 2.384; 95% CI = 1.417–4.010; p = 0.001). The risk for prediabetes increased significantly with increasing SUA for females in the Chinese population. This association was strongly confirmed in older females aged ≥48 years old rather than in younger females, which may provide clues for pathogenic mechanisms of gender differences in the association between SUA and prediabetes.

Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients’ quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. Objective: The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (<20 mm) based on CT images. Methods: In this study, 834 CT imaging data from 396 patients with small pulmonary nodules were gathered and randomly assigned to the training and validation sets in an 8:2 ratio. ResNet50 and VGG16 algorithms were utilized to extract CT image features, followed by XGBoost, SVM, and Ensemble Voting techniques for classification, for a total of ten different classes of machine learning combinatorial classifiers. Indicators such as accuracy, sensitivity, and specificity were used to assess the models. The collected features are also shown to investigate the contrasts between them. Results: The algorithm we presented, ResNet50-Ensemble Voting, performed best in the test set, with an accuracy of 0.943 (0.938, 0.948) and sensitivity and specificity of 0.964 and 0.911, respectively. VGG16-Ensemble Voting had an accuracy of 0.887 (0.880, 0.894), with a sensitivity and specificity of 0.952 and 0.784, respectively. Conclusion: Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice.

This study aims to establish a simple and non-invasive risk prediction model for hyperuricemia in Chinese adults based on modifiable risk factors.