The Role of Glomerular Epithelial Injury in Kidney Function Decline in Patients With Diabetic Kidney Disease in the TRIDENT Cohort.

diabetic kidney disease end-stage kidney disease glomerular epithelial injury kidney function pathological descriptors

Journal

Kidney international reports
ISSN: 2468-0249
Titre abrégé: Kidney Int Rep
Pays: United States
ID NLM: 101684752

Informations de publication

Date de publication:
Apr 2021
Historique:
received: 01 11 2020
revised: 11 01 2021
accepted: 18 01 2021
entrez: 29 4 2021
pubmed: 30 4 2021
medline: 30 4 2021
Statut: epublish

Résumé

Although diabetic kidney disease (DKD) is responsible for more than half of all chronic and end-stage kidney disease (ESKD), the association of light (LM) and electron microscopic (EM) structural changes with clinical parameters and prognosis in DKD is incompletely understood. This is an interim analysis of 62 patients diagnosed with biopsy-confirmed DKD from the multicenter TRIDENT (Transformative Research in Diabetic Nephropathy) study. Twelve LM and 8 EM descriptors, representing changes in glomeruli, tubulointerstitium, and vasculature were analyzed for their relationship with clinical measures of renal function. Patients were followed every 6 months. Multivariable linear regression analysis revealed that estimated glomerular filtration rate (eGFR) upon enrollment correlated the best with interstitial fibrosis. On the other hand, the rate of kidney function decline (eGFR slope) correlated the most with glomerular lesions including global glomerulosclerosis and mesangiolysis. Unbiased clustering analysis based on histopathologic data identified 3 subgroups. The first cluster, encompassing subjects with the mildest histologic lesions, had the most preserved kidney function. The second and third clusters had similar degrees of kidney dysfunction and structural damage, but differed in the degree of glomerular epithelial cell and podocyte injury (podocytopathy DKD subtype). Cox proportional hazard analysis showed that subjects in cluster 2 had the highest risk to reach ESKD (hazard ratio: 17.89; 95% confidence interval: 2.13-149.79). Glomerular epithelial hyperplasia and interstitial fibrosis were significant predictors of ESKD in the multivariate model. The study highlights the association between fibrosis and kidney function and identifies the role of glomerular epithelial changes and kidney function decline.

Identifiants

pubmed: 33912757
doi: 10.1016/j.ekir.2021.01.025
pii: S2468-0249(21)00028-0
pmc: PMC8071659
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1066-1080

Subventions

Organisme : NIDDK NIH HHS
ID : K23 DK120811
Pays : United States

Informations de copyright

© 2021 International Society of Nephrology. Published by Elsevier Inc.

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Auteurs

Matthew B Palmer (MB)

Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Amin Abedini (A)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Institute of Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Casey Jackson (C)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Institute of Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Shira Blady (S)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Shatakshee Chatterjee (S)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Institute of Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Katie Marie Sullivan (KM)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Institute of Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Raymond R Townsend (RR)

Institute of Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Jens Brodbeck (J)

Inflammation & Respiratory Therapeutics, Gilead Sciences Inc., Foster City, California, United States.

Salem Almaani (S)

Division of Nephrology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.

Anand Srivastava (A)

Division of Nephrology and Hypertension, Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.

Rupali Avasare (R)

Department of Medicine, Nephrology, Oregon Health & Science University, Portland, Oregon, USA.

Michael J Ross (MJ)

Division of Nephrology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, New York, USA.

Amy K Mottl (AK)

University of North Carolina Kidney Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Christos Argyropoulos (C)

Department of Internal Medicine, Division of Nephrology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA.

Jonathan Hogan (J)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Katalin Susztak (K)

Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Institute of Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Classifications MeSH