Unique Biomarkers of Collagen Type III Remodeling Reflect Different Information Regarding Pathological Kidney Tissue Alterations in Patients with IgA Nephropathy.
Nadja SpardingMichaela NeprasovaDita MaixnerovaFederica GenoveseMorten Asser KarsdalMarek KollarHelena KoprivovaZdenka HruskovaVladimir TesarPublished in: Biomolecules (2023)
Kidney fibrosis is the hallmark of chronic kidney disease (CKD) and is characterized by an imbalanced extracellular matrix (ECM) remodeling. Collagen type III is one of the main ECM components of the interstitial matrix of the kidney. We hypothesized that measuring three biomarkers of collagen type III reflecting different aspects of this protein turnover (C3M, C3C, and PRO-C3) may provide different information about the fibrotic burden in patients with IgA nephropathy (IgAN). We examined a cohort of 134 patients with IgAN. The three collagen type III biomarkers were measured in serum (S) and in urine (U) samples taken on the same day before kidney biopsy was performed. Biopsies were evaluated for interstitial fibrosis and tubular atrophy, according to the Banff and MEST-C scores. S-PRO-C3 and S-C3C correlated with the degree of fibrosis in the biopsy, whereas U-C3M/Cr had an inverse correlation with fibrosis. U-C3M/Cr had the highest discrimination ability for advanced fibrosis, which was maintained after adjustment for the other collagen type III biomarkers, proteinuria, and serum creatinine. The data presented in this study indicate that measuring the different fragments of the same ECM protein and in different matrices provides a variety of information regarding pathological kidney tissue alterations in patients with IgAN.
Keyphrases
- type iii
- extracellular matrix
- chronic kidney disease
- wound healing
- tissue engineering
- end stage renal disease
- ultrasound guided
- liver fibrosis
- health information
- binding protein
- machine learning
- protein protein
- anti inflammatory
- electronic health record
- metabolic syndrome
- big data
- amino acid
- peritoneal dialysis
- endothelial cells
- body composition
- deep learning