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Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction.

ABSTRACT: Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): p-value < 0.05), but none showed significantly better performance than albumin. To improve the predictive power by multivariate analysis, five proteins (ACP2, CTSA, GM2A, MUC1, and SPARCL1) were selected as significant by an AUC-based random forest method. The application of two classifiers-support vector machine and random forest-showed that the multivariate model performed better than univariate analysis of mucin-1 (AUC: 0.935 vs. 0.791) and albumin (AUC: 1.0 vs. 0.722). The urinary proteome can reflect kidney function directly and can predict the prognosis of patients with chronic kidney dysfunction. Classification based on five urinary proteins may better predict the prognosis of DKD patients than urinary albumin concentration or eGFR.

SUBMITTER: Ahn HS

PROVIDER: S-EPMC7352871 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction.

Ahn Hee-Sung HS Kim Jong Ho JH Jeong Hwangkyo H Yu Jiyoung J Yeom Jeonghun J Song Sang Heon SH Kim Sang Soo SS Kim In Joo IJ Kim Kyunggon K

International journal of molecular sciences 20200614 12

Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alky ...[more]

PMID: 32545899

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Project description:Diabetic nephropathy is characterized by excessive accumulation of extracellular matrix (ECM) leading to renal fibrosis, progressive deterioration of renal function, and eventually to end stage renal disease. Matrix metalloproteinases (MMPs) are known to regulate synthesis and degradation of the ECM. Earlier, we demonstrated that imbalanced MMPs promote adverse ECM remodeling leading to renal fibrosis in type-1 diabetes. Moreover, elevated macrophage infiltration, pro-inflammatory cytokines and epithelial‒mesenchymal transition (EMT) are known to contribute to the renal fibrosis. Various bioactive compounds derived from the medicinal plant, Azadirachta indica (neem) are shown to regulate inflammation and ECM proteins in different diseases. Nimbidiol is a neem-derived diterpenoid that is considered as a potential anti-diabetic compound due to its glucosidase inhibitory properties. We investigated whether Nimbidiol mitigates adverse ECM accumulation and renal fibrosis to improve kidney function in type-1 diabetes and the underlying mechanism. Wild-type (C57BL/6J) and type-1 diabetic (C57BL/6-Ins2Akita/J) mice were treated either with saline or with Nimbidiol (0.40 mg kg-1 d-1) for eight weeks. Diabetic kidney showed increased accumulation of M1 macrophages, elevated pro-inflammatory cytokines and EMT. In addition, upregulated MMP-9 and MMP-13, excessive collagen deposition in the glomerular and tubulointerstitial regions, and degradation of vascular elastin resulted to renal fibrosis in the Akita mice. These pathological changes in the diabetic mice were associated with functional impairments that include elevated resistive index and reduced blood flow in the renal cortex, and decreased glomerular filtration rate. Furthermore, TGF-β1, p-Smad2/3, p-P38, p-ERK1/2 and p-JNK were upregulated in diabetic kidney compared to WT mice. Treatment with Nimbidiol reversed the changes to alleviate inflammation, ECM accumulation and fibrosis and thus, improved renal function in Akita mice. Together, our results suggest that Nimbidiol attenuates inflammation and ECM accumulation and thereby, protects kidney from fibrosis and dysfunction possibly by inhibiting TGF-β/Smad and MAPK signaling pathways in type-1 diabetes.

Dataset Information

Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction.

Publications

Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction.

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