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

Abstract

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.

Keywords: biomarker; fibrosis; kidney disease; progression; response prediction.

Figure 1

Graphical depiction of a model of chronic kidney disease (CKD) progression and the current needs and opportunities for improvement in patient management. In the lower panel, the progression of kidney disease is depicted and the specific context of biomarker use is indicated. Healthy nephrons (green) experience an impact that results in initial reversible damage (indicated in yellow). Without appropriate intervention, damage is expected to become irreversible (indicated in red), leading to destruction and replacement by fibrotic tissue (black). The upper panel indicates the potential benefits of applying molecular biomarkers. Ideally, these biomarkers should enable early detection and targeted intervention before irreversible organ damage occurs. Abbreviations: eGFR: estimated glomerular filtration rate.

Figure 2

Biomarker-guided approach towards personalized intervention in chronic kidney disease (CKD). Biomarkers can be used to determine the risk of CKD onset and progression. In the case of increased risk, the impact of treatment with different drugs on biomarkers can be modeled based on previous data. Based on these models, optimal personalized intervention can be suggested [58]. Abbreviations: ARB: angiotensin receptor blockers; DPP4i: Dipeptidyl peptidase-4 inhibitor; eGFR: estimated glomerular filtration rate; GLP1RA: glucagon-like peptide 1 receptor agonist; MRA: mineralocorticoid receptor antagonist; SGLT2i: sodium-glucose cotransporter 2 inhibitor.

Figure 3

Fibrosis results from imbalanced homeostasis of extracellular matrix (ECM). While current efforts to optimize therapeutic intervention mostly target synthesis of ECM and, in many cases, different types of collagens, accumulating evidence suggests that attenuation of degradation may be a key factor driving fibrosis. The figure was adapted from Latosinska et al. [64]. Abbreviations: ECM: extracellular matrix; MMPs: matrix metalloproteinases.