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Kidney Int Rep. 2025;10(7):2296-2310.

Complement Proteins Identify Rapidly Progressive Diabetic Kidney Disease.

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제1저자 윤동환 · 교신저자 한승석, Afkarian M

PMID 40677326 PMC12266264 DOI 10.1016/j.ekir.2025.04.061 IF 6.2 신장내과
소속 (11)
  1. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
  2. Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.
  3. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA.
  4. Division of Nephrology, Department of Medicine, University of California, Davis, California, USA.
  5. Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Korea.
  6. School of Nursing, University of California, Davis, Sacramento, California, USA.
  7. Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.
  8. Department of Pathology, Johns Hopkins University, Maryland, USA.
  9. Department of Internal Medicine, University of California, Berkeley, Berkeley, California, USA.
  10. Department of Medicine-Quantitative Health, University of Florida College of Medicine, Gainesville, Forida, USA.
  11. Department of Internal Medicine, University of California, Davis, California, USA.

Abstract

INTRODUCTION Mechanisms underlying diabetic kidney disease (DKD) progression remain incompletely understood. This study used untargeted and targeted mass spectrometry-based proteomics in 2 independent cohorts to capture rapidly progressive DKD.

METHODS We conducted untargeted and targeted mass spectrometry on urine samples from Korean patients with type 2 diabetes and biopsy-confirmed diabetic nephropathy (SNUH-DN cohort; n = 64) and a DKD subgroup of the Chronic Renal Insufficiency Cohort (CRIC-T2D; n = 282), respectively. Urine proteins associated with kidney disease progression (doubling of serum creatinine, ≥ 50% decrease in estimated glomerular filtration rates [eGFRs], or progression to end-stage kidney disease[ESKD]) were identified after adjusting for eGFR, proteinuria, and other clinical variables.

RESULTS In the SNUH-DN patients, urine proteins clustered into 2 groups, with cluster 1 exhibiting a 4.6-fold higher hazard of disease progression (95% confidence interval [CI]: 1.9-11.5) than cluster 0. Proteins in cluster 1 mapped to 10 pathways, 4 of the top 5 being complement-related. A high complement score, derived from urine complement protein abundance, correlated with histopathologic features of DKD and conferred a 2.4-fold greater hazard of disease progression (95% CI: 1.0-5.4) than a low complement score. In CRIC-T2D, targeted mass spectrometry similarly confirmed that complement score stratified patients into rapid and slow DKD progression groups. In both cohorts, complement score exhibited a linear association with disease progression.

CONCLUSION The strong association between complement activation and rapid DKD progression highlights the need to explore complement inhibition as a potential therapeutic strategy for DKD.

© 2025 International Society of Nephrology. Published by Elsevier Inc.

Keywords

biomarkerscomplement system proteinsdiabetic nephropathiespathologyproteomics

Figures

Figure 1
Figure 1 Unsupervised clustering based on untargeted proteomics in the SNUH-DN cohort. (a) Uniform manifold approximation and projection (UMAP) plot with 2 distinct clusters (clusters 0 and 1). (b) Multivariable-adjusted cumulative incidence of kidney disease progression according to clusters, after adjustment for age, sex, body mass index, hypertension, diabetes duration, urine protein-to-creatine ratio, and eGFR. (c) Sankey plot illustrating the relationships among glomerular classes, clusters, and kidney disease progression within 1 year. eGFR, estimated glomerular filtration rate.
Figure 2
Figure 2 Complement pathway and kidney disease progression in the SNUH-DN cohort. (a) Pathway analysis of the differentially abundant proteins in cluster 1 compared with cluster 0. (b) Volcano plot of the differentially abundant proteins in cluster 1 compared with cluster 0. The dashed line denotes the threshold of significance. Red and blue dots represent proteins increased and decreased in cluster 1, respectively, compared with cluster 0. (c) Heatmap of urinary complement component abundance categorized by clustering, kidney disease progression within 1 year, and complement function.
Figure 3
Figure 3 Complement score and kidney disease progression in the SNUH-DN cohort. (a) Bar plot of complement score in clusters 0 and 1. ∗∗∗P < 0.001. (b) Multivariable-adjusted cumulative incidence of kidney disease progression according to complement score groups after adjustment for age, sex, body mass index, hypertension, diabetes duration, urine protein-to-creatinine ratio, and eGFR. (c) Penalized spline regression according to complement score after adjusting for age, sex, body mass index, hypertension, diabetes duration, urine protein-to-creatinine ratio, and eGFR. eGFR, estimated glomerular filtration rate.
Figure 4
Figure 4 Complement score and kidney disease progression in the CRIC-T2D subcohort. (a) Bar plot of complement score in high and low score groups. ∗∗∗P < 0.001. (b) Multivariable-adjusted cumulative incidence of kidney disease progression according to complement score groups after adjustment for age, sex, body mass index, race, hypertension, 24-hour urine protein, and eGFR. (c) Penalized spline regression according to complement score after adjusting for age, sex, body mass index, race, hypertension, 24-hour urine protein, and eGFR. eGFR, estimated glomerular filtration rate.

그림 출처: PubMed Central 공개 논문 (PMC12266264)

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출판 형태
Journal Article
저널
Kidney international reports · eISSN 2468-0249
전자 출판
2025-05-06
참고문헌
61편