Patients with a history of more than 5 years of diabetes without DKD were recruited. At the baseline visit, the patients' serum, plasma and urine were collected after obtaining informed patient consent. Simultaneously, the basic information, anthropometric indicators (including height, weight, waist circumference, hip circumference, blood pressure), past history, family history, menstrual history, birth history, medication history, lifestyle of the patients were registered, and the corresponding laboratory examination and auxiliary examination were carried out according to the diagnostic process. All data and data were entered into the database for later analysis. After 5years of follow up, subjects will be divided into two groups(the new onset DKD group and the non-DKD group), the base line level of MDNBs were tested in the two group to validate the performance of the novel MDNBs in in prediction of the risk for early diabetic nephropathy.
Diabetic kidney disease(DKD) is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) to estimating early diabetic nephropathy, and further validating the performance of the novel systemin in prediction of the risk for early diabetic nephropathy by a nested case-control study.