#AI reads Urine# Urinary Complement proteome strongly linked to diabetic kidney disease progression

Published 18 August, 2025

This document mainly discusses a study on the progression of diabetic kidney disease (DKD), with the core finding that complement proteins in urine are closely related to the deterioration of diabetic kidney disease.

Researchers conducted long-term follow-up on people with type 1 or type 2 diabetes and kidney disease at different stages, analyzing their urinary complement proteins using techniques such as high-throughput proteomics. The results showed that six complement proteins in urine, namely C2, C5a, CL-K1, C6, CFH, and C7, can well predict the risk of kidney failure within 10 years. Moreover, this association is not affected by other clinical factors, is more obvious in the advanced stage of kidney disease, applies to both type 1 and type 2 diabetes patients, and has a much stronger predictive effect than complement proteins in the blood.

They also verified these findings in patients with early to moderate kidney disease. Although the association strength is weaker, it is still significant. In addition, the study found that the content of these complement proteins and the expression of related genes in kidney tissue also increase, indicating that the kidney may be the source of these urinary complement proteins, rather than simply leaking from the blood into the urine.

Overall, this study indicates that the complement system is involved in the progression of diabetic kidney disease, and these urinary complement proteins are expected to become biomarkers for predicting the deterioration of kidney disease, laying the foundation for the development of targeted treatment methods.

Nat Commun. 2025 Aug 7;16(1):7291. doi: 10.1038/s41467-025-62101-5.

 

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

 

For earlier AI Reads Urine articles:

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/

 

 

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