Multiplex Serum Biomarker Assays Improve Prediction of Renal and Mortality Outcomes in Chronic Kidney Disease

Assessment and Risk Prediction of Chronic Kidney Disease and Kidney Fibrosis Using Non-Invasive Biomarkers

Effective management of chronic kidney disease (CKD), a major health problem worldwide, requires accurate and timely diagnosis, prognosis of progression, assessment of therapeutic efficacy, and, ideally, prediction of drug response. Multiple biomarkers and algorithms for evaluating specific aspects of CKD have been proposed in the literature, many of which are based on a small number of samples. Based on the evidence presented in relevant studies, a comprehensive overview of the different biomarkers applicable for clinical implementation is lacking. This review aims to compile information on the non-invasive diagnostic, prognostic, and predictive biomarkers currently available for the management of CKD and provide guidance on the application of these biomarkers. We specifically focus on biomarkers that have demonstrated added value in prospective studies or those based on prospectively collected samples including at least 100 subjects. Published data demonstrate that several valid non-invasive biomarkers of potential value in the management of CKD are currently available.

A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice

BACKGROUND

Diagnosis and staging of diabetic kidney disease (DKD) via the serial assessment of routine laboratory indices lacks the granularity required to resolve the heterogeneous disease mechanisms driving progression in the individual patient. A systems nephrology approach may help resolve mechanisms underlying this clinically apparent heterogeneity, paving a way for targeted treatment of DKD.

SUMMARY

Given the limited access to kidney tissue in routine clinical care of patients with DKD, data derived from renal tissue in preclinical model systems, including animal and in vitro models, can play a central role in the development of a targeted systems-based approach to DKD. Multi-centre prospective cohort studies, including the Kidney Precision Medicine Project (KPMP) and the European Nephrectomy Biobank (ENBiBA) project, will improve access to human diabetic kidney tissue for research purposes. Integration of diverse data domains from such initiatives including clinical phenotypic data, renal and retinal imaging biomarkers, histopathological and ultrastructural data, and an array of molecular omics (transcriptomics, proteomics, etc.) alongside multi-dimensional data from preclinical modelling offers exciting opportunities to unravel individual-level mechanisms underlying progressive DKD. The application of machine and deep learning approaches may particularly enhance insights derived from imaging and histopathological/ultrastructural data domains.

KEY MESSAGES

Integration of data from multiple model systems (in vitro, animal models, and patients) and from diverse domains (clinical phenotypic, imaging, histopathological/ultrastructural, and molecular omics) offers potential to create a precision medicine approach to DKD care wherein the right treatments are offered to the right patients at the right time.

TNFR2 as a Potential Biomarker for Early Detection and Progression of CKD

The inflammatory pathway driven by TNF-α, through its receptors TNFR1 and TNFR2, is a common feature in the pathogenesis of chronic kidney disease (CKD), regardless of the initial disease cause. Evidence correlates the chronic inflammatory status with decreased renal function. Our aim was to evaluate the potential of TNF receptors as biomarkers for CKD diagnosis and staging, as well as their association with the progression of renal lesions, in rat models of early and moderate CKD. We analyzed the circulating levels of inflammatory molecules—tumor necrosis factor-alpha (TNF-α), tumor necrosis factor receptor 1 (TNFR1) and 2 (TNFR2) and tissue inhibitor of metalloproteinase-1 (TIMP-1)—and studied their associations with TNFR1 and TNFR2 renal expression, glomerular and tubulointerstitial lesions, and with biomarkers of renal (dys)function. An increase in all inflammatory markers was observed in moderate CKD, as compared to controls, but only circulating levels of both TNFR1 and TNFR2 were significantly increased in the early disease; TNFR2 serum levels were negatively correlated with eGFR. However, only TNFR2 renal expression increased with CKD severity and showed correlations with the score of mild and advanced tubular lesions. Our findings suggest that renal TNFR2 plays a role in CKD development, and has potential to be used as a biomarker for the early detection and progression of the disease. Still, the potential value of this biomarker in disease progression warrants further investigation.

Association of redox and inflammation-related biomarkers with prognosis in IgA nephropathy: A prospective observational study

BACKGROUND

IgA nephropathy (IGAN) has a variable prognosis. Risk stratification tools are usually based on clinical parameters combined with histologic Oxford-MEST-C score. Circulating redox- and inflammation-related biomarkers may be related to histological changes in IGAN. Therefore, we studied the performance of these biomarkers in predicting the rate of GFR-loss in IGAN.

METHODS

This was an observational prospective study. Fifty-seven stable patients with IGAN were examined at baseline and after a mean observational time of 5.9 ± 1.1 years. The main outcome measure was eGFR-loss per year with predefined groups, stable (<1.5 ml/min/1,73 m²/year, intermediate (between 1.5 and 2.5), and progressive (>2.5).

RESULTS

Fifteen patients were in the progressive, 11 in the intermediate, and 31 in the stable groups. Positive relationships were detected between eGFR-loss per year and baseline nitrate, oxidized free cysteine, parathyroid hormone, APRIL, TNFR1, CD30, chitinase 3, and LIF-5. The progressive group had elevated concentrations of these markers plus AOPP and osteopontin. Through ROC analysis, it was observed that AOPP, oxidized free cysteine, TNFR1, osteopontin, and LIF-5 had the best ability to identify progressive vs. non-progressive diseases. The combination of urinary albumin/creatinine ratio with AOPP and TNFR1 significantly improved the ability to identify progressive eGFR decline with ROC AUC 95% (adjusted 85%).

CONCLUSIONS

We found prognostic biomarkers related to the rate of eGFR-loss in IGAN. These biomarkers may help identify patients at risk of progressive disease. AOPP, oxidized free cysteine, TNFR1, and osteopontin are promising prognostic biomarkers in IGAN, however, further validation studies are needed.

The Signaling Pathway of TNF Receptors: Linking Animal Models of Renal Disease to Human CKD

Chronic kidney disease (CKD) has been recognized as a global public health problem. Despite the current advances in medicine, CKD-associated morbidity and mortality remain unacceptably high. Several studies have highlighted the contribution of inflammation and inflammatory mediators to the development and/or progression of CKD, such as tumor necrosis factor (TNF)-related biomarkers. The inflammation pathway driven by TNF-α, through TNF receptors 1 (TNFR1) and 2 (TNFR2), involves important mediators in the pathogenesis of CKD. Circulating levels of TNFRs were associated with changes in other biomarkers of kidney function and injury, and were described as predictors of disease progression, cardiovascular morbidity, and mortality in several cohorts of patients. Experimental studies describe the possible downstream signaling pathways induced upon TNFR activation and the resulting biological responses. This review will focus on the available data on TNFR1 and TNFR2, and illustrates their contributions to the pathophysiology of kidney diseases, their cellular and molecular roles, as well as their potential as CKD biomarkers. The emerging evidence shows that TNF receptors could act as biomarkers of renal damage and as mediators of the disease. Furthermore, it has been suggested that these biomarkers could significantly improve the discrimination of clinical CKD prognostic models.