Serum Biomarkers of Renal Fibrosis: A Systematic Review

Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy

BACKGROUND

Kidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.

METHODS

We included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI).

RESULTS

Overall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75-0.99) and 0.74 (95% CI 0.52-0.93) for total parenchymal and cortical ROI features, respectively.

CONCLUSION

Significant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.

Visualization of Renal Glomeruli in Human Native Kidneys With Sensing Ultrasound Localization Microscopy

OBJECTIVES

Kidney diseases significantly impact individuals' quality of life and strongly reduce life expectancy. Glomeruli play a crucial role in kidney function. Current imaging techniques cannot visualize them due to their small size. Sensing ultrasound localization microscopy (sULM) has shown promising results for visualizing in vivo the glomeruli of human kidney grafts. This study aimed to evaluate the ability of sULM to visualize glomeruli in vivo in native human kidneys despite their depth and a shorter duration of ultrasound acquisition limited by the period of the patient's apnea. Sensing ultrasound localization microscopy parameters in native kidneys and kidney grafts and their consequence regarding glomeruli detection were also compared.

MATERIALS AND METHODS

Exploration by sULM was conducted in 15 patients with native kidneys and 5 with kidney allografts. Glomeruli were counted using a normalized distance metric projected onto sULM density maps. The difference in the acquisition time, the kidney depth, and the frame rate between native kidneys and kidney grafts and their consequence regarding glomeruli detection were assessed.

RESULTS

Glomerular visualization was achieved in 12 of 15 patients with native kidneys. It failed due to impossible breath-holding for 2 patients and a too-deep kidney for 1 patient. Sensing ultrasound localization microscopy found 16 glomeruli per square centimeter in the native kidneys (6-31) and 33 glomeruli per square centimeter in kidney transplant patients (18-55).

CONCLUSIONS

This study demonstrated that sULM can visualize glomeruli in native human kidneys in vivo. The proposed method may have many hypothetical applications, including biomarker development, assisting biopsy, or potentially avoiding it. It establishes a framework for improving the detection of local microstructural pathology, influencing the evaluation of allografts, and facilitating disease monitoring in the native kidney.

Serum soluble LYVE1 is a promising non-invasive biomarker of renal fibrosis: a population-based retrospective cross-sectional study

Diagnosis of renal fibrosis can only be verified by kidney biopsy, but biomarkers for non-invasive evaluation remain unsatisfactory. Patients with fibrosis often have abnormalities of the lymphatic vascular system and associated immune function. We describe here a lymphatic marker as a candidate biomarker for fibrosis. After assessing and grading the fibrosis scores, testing serum soluble lymphatic vessel endothelial hyaluronan receptor1 (sLYVE1) level, and collecting clinical information, the association between sLYVE1 and renal fibrosis was analyzed. Logistic regression analysis was used to screen variables. Diagnosis models with or without sLYVE1 were built, and nomograms were plotted. Calibration curve, C-index, and DCA were performed to assess the models. A total of 298 patients were enrolled in the study, of which 199 were included in the training cohort and 99 patients in the validation cohort. Serum sLYVE1 levels markedly elevated with increasing fibrosis grade (p<0.05). ROC analysis of sLYVE1 showed an AUC of 0.791 and 0.846 with optimal cut-off value of 405.25 ng/mL and 498.55 ng/mL for the prediction of moderate-to-severe renal fibrosis (MSF) and severe renal fibrosis (SF), respectively. The diagnostic nomogram model without sLYVE1 (model 1) included traditional clinical determinants (C-index: 0.658 for MSF; 0.603 for SF). A combination of model 1 and sLYVE1 (model 2) improved predictive performance (C-index: 0.847 for MSF; 0.856 for SF). Calibration curve and DCA demonstrated a better consistency accuracy and clinical benefit of model 2 than model 1. Serum sLYVE1 may be identified as a potential biomarker of renal fibrosis. Models incorporating sLYVE1 may be beneficial for a more accurate non-invasive diagnosis of renal fibrosis.

The disordered extracellular matrix landscape induced endometrial fibrosis of sheep: A multi-omics integrative analysis

Endometrial fibrosis leads to the destruction of endometrial function and affects reproductive performance. However, mechanisms underlying the development of endometrial fibrosis in sheep remain unclear. We use transcriptomic, proteomic, and metabolomic studies to reveal the formation mechanisms of endometrial fibrosis. The results showed that the fibrotic endometrial tissue phenotype presented fewer glands, accompanied by collagen deposition. Transcriptomic results indicated alterations in genes associated with the synthesis and degradation of extracellular matrix components, which alter metabolite homeostasis, especially in glycerophospholipid metabolism. Moreover, differentially expressed metabolites may play regulatory roles in key metabolic processes during fibrogenesis, including protein digestion and absorption, and amino acid synthesis. Affected by the aberrant genes, protein levels related to the extracellular matrix components were altered. In addition, based on Kyoto Encyclopedia of Genes and Genomes analysis of differentially expressed genes, metabolites and proteins, amino acid biosynthesis, glutathione, glycerophospholipid, arginine and proline metabolism, and cell adhesion are closely associated with fibrogenesis. Finally, we analyzed the dynamic changes in serum differential metabolites at different time points during fibrosis. Taken together, fibrosis development is related to metabolic obstacles in extracellular matrix synthesis and degradation triggered by disturbed gene and protein levels.

Characterizing hub biomarkers for post-transplant renal fibrosis and unveiling their immunological functions through RNA sequencing and advanced machine learning techniques

BACKGROUND

Kidney transplantation stands out as the most effective renal replacement therapy for patients grappling with end-stage renal disease. However, post-transplant renal fibrosis is a prevalent and irreversible consequence, imposing a substantial clinical burden. Unfortunately, the clinical landscape remains devoid of reliable biological markers for diagnosing post-transplant renal interstitial fibrosis.

METHODS

We obtained transcriptome and single-cell sequencing datasets of patients with renal fibrosis from NCBI Gene Expression Omnibus (GEO). Subsequently, we employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify potential genes by integrating core modules and differential genes. Functional enrichment analysis was conducted to unveil the involvement of potential pathways. To identify key biomarkers for renal fibrosis, we utilized logistic analysis, a LASSO-based tenfold cross-validation approach, and gene topological analysis within Cytoscape. Furthermore, histological staining, Western blotting (WB), and quantitative PCR (qPCR) experiments were performed in a murine model of renal fibrosis to verify the identified hub genes. Moreover, molecular docking and molecular dynamics simulations were conducted to explore possible effective drugs.

RESULTS

Through WGCNA, the intersection of core modules and differential genes yielded a compendium of 92 potential genes. Logistic analysis, LASSO-based tenfold cross-validation, and gene topological analysis within Cytoscape identified four core genes (CD3G, CORO1A, FCGR2A, and GZMH) associated with renal fibrosis. The expression of these core genes was confirmed through single-cell data analysis and validated using various machine learning methods. Wet experiments also verified the upregulation of these core genes in the murine model of renal fibrosis. A positive correlation was observed between the core genes and immune cells, suggesting their potential role in bolstering immune system activity. Moreover, four potentially effective small molecules (ZINC000003830276-Tessalon, ZINC000003944422-Norvir, ZINC000008214629-Nonoxynol-9, and ZINC000085537014-Cobicistat) were identified through molecular docking and molecular dynamics simulations.

CONCLUSION

Four potential hub biomarkers most associated with post-transplant renal fibrosis, as well as four potentially effective small molecules, were identified, providing valuable insights for studying the molecular mechanisms underlying post-transplant renal fibrosis and exploring new targets.

Mannose and glycine: Metabolites with potentially causal implications in chronic kidney disease pathogenesis

BACKGROUND

Chronic Kidney Disease (CKD) represents a global health challenge, with its etiology and underlying mechanisms yet to be fully elucidated. Integrating genomics with metabolomics can offer insights into the putatively causal relationships between serum metabolites and CKD.

METHODS

Utilizing bidirectional Mendelian Randomization (MR), we assessed the putatively causal associations between 486 serum metabolites and CKD. Genetic data for these metabolites were sourced from comprehensive genome-wide association studies, and CKD data were obtained from the CKDGen Consortium.

RESULTS

Our analysis identified four metabolites with a robust association with CKD risk, of which mannose and glycine showed the most reliable causal relationships. Pathway analysis spotlighted five significant metabolic pathways, notably including "Methionine Metabolism" and "Arginine and Proline Metabolism", as key contributors to CKD pathogenesis.

CONCLUSION

This study underscores the potential of certain serum metabolites as biomarkers for CKD and illuminates pivotal metabolic pathways in CKD's pathogenesis. Our findings lay the groundwork for potential therapeutic interventions and warrant further research for validation.

Kidney fibrosis: Emerging diagnostic and therapeutic strategies

An increasing number of patients worldwide suffers from chronic kidney disease (CKD). CKD is accompanied by kidney fibrosis, which affects all compartments of the kidney, i.e., the glomeruli, tubulointerstitium, and vasculature. Fibrosis is the best predictor of progression of kidney diseases. Currently, there is no specific anti-fibrotic therapy for kidney patients and invasive renal biopsy remains the only option for specific detection and quantification of kidney fibrosis. Here we review emerging diagnostic approaches and potential therapeutic options for fibrosis. We discuss how translational research could help to establish fibrosis-specific endpoints for clinical trials, leading to improved patient stratification and potentially companion diagnostics, and facilitating and optimizing development of novel anti-fibrotic therapies for kidney patients.