Urinary Microvesicle-Bound Uromodulin: A Potential Molecular Biomarker in Diabetic Kidney Disease.

This study was designed to investigate the changes of urinary microvesicle-bound uromodulin and total urinary uromodulin levels in human urine and the correlations with the severity of diabetic kidney disease (DKD). 31 healthy subjects without diabetes and 100 patients with type 2 diabetes mellitus (T2DM) were included in this study. The patients with T2DM were divided into three groups based on the urinary albumin/creatinine ratio (UACR): normoalbuminuria group (DM, n = 46); microalbuminuria group (DN1, n = 32); and macroalbuminuria group (DN2, n = 22). We use a specific monoclonal antibody AD-1 to capture the urinary microvesicles. Urinary microvesicle-bound uromodulin and total urinary uromodulin levels were determined by enzyme-linked immunosorbent assay (ELISA). Our results showed that the levels of urinary microvesicle-bound uromodulin in DN1 and DN2 groups were significantly higher than those in control group and DM group (P < 0.01). Multiple stepwise linear regression analysis showed that UACR was independent determinant for urinary microvesicle-bound uromodulin (P < 0.05) but not for total urinary uromodulin. These findings suggest that the levels of urinary microvesicle-bound uromodulin are associated with the severity of DKD. The uromodulin in urinary microvesicles may be a specific marker of DKD and potentially may be used to predict the onset and/or monitor the progression of DKD.

Evaluation of a risk factor scoring model in screening for undiagnosed diabetes in China population.

OBJECTIVE: To develop a risk scoring model for screening for undiagnosed type 2 diabetes in Chinese population. METHODS: A total of 5348 subjects from two districts of Jinan City, Shandong Province, China were enrolled. Group A (2985) included individuals from east of the city and Group B (2363) from west of the city. Screening questionnaires and a standard oral glucose tolerance test (OGTT) were completed by all subjects. Based on the stepwise logistic regression analysis of Group A, variables were selected to establish the risk scoring model. The validity and effectiveness of this model were evaluated in Group B. RESULTS: Based on stepwise logistic regression analysis performed with data of Group A, variables including age, body mass index (BMI), waist-to-hip ratio (WHR), systolic pressure, diastolic pressure, heart rate, family history of diabetes, and history of high glucose were accepted into the risk scoring model. The risk for having diabetes increased along with aggregate scores. When Youden index was closest to 1, the optimal cutoff value was set up at 51. At this point, the diabetes risk scoring model could identify diabetes patients with a sensitivity of 83.3% and a specificity of 66.5%, making the positive predictive value 12.83% and negative predictive value 98.53%. We compared our model with the Finnish and Danish model and concluded that our model has superior validity in Chinese population. CONCLUSIONS: Our diabetes risk scoring model has satisfactory sensitivity and specificity for identifying undiagnosed diabetes in our population, which might be a simple and practical tool suitable for massive diabetes screening.