Abstract
BackgroundType 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the ‘gold standard’ for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress.MethodsIn this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs.ResultsWe quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3.ConclusionsOur study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.
Highlights
Type 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and endstage renal diseases (ESRD)
We explored the possibility of inferring the uncomplicated diabetes to “pre-DKD3” progression based on the difference between diabetes and these 13 false-positive DKD3 samples
In this study, we have established a urinary proteomic workflow to conduct a cross-sectional investigation of uncomplicated diabetes, diabetic kidney disease (DKD), and CKD patients
Summary
Type 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and endstage renal diseases (ESRD). Kidney biopsy is considered as the ‘gold standard’ for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress. In 2019, more than 463 million people worldwide were estimated to be living with diabetes, representing 9.3% of the global adult population (20–79 years) [1] Among these people, 20% to 40% will progress to DKD [2], which remains a leading cause of morbidity and mortality in people with type 2 diabetes [3,4,5]. It is important to find a noninvasive test method that can complement or replace renal puncture
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