Proteomics and Metabolomics for AKI Diagnosis

Abstract

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

Non‐invasive differentiation of non‐rejection kidney injury from acute rejection in pediatric renal transplant recipients

“…Candidate biomarkers such as urinary NGAL, KIM‐1, and cystatin C fail to distinguish NRKI from rejection, which limit their utility in transplant populations. Distinctive urinary metabolite changes associated with AKI have been previously reported, but not in the context of kidney transplantation.…”

Urinary Peptidomic Biomarkers in Kidney Diseases

“…Current diagnostic tools do not allow early detection or prediction of the course of AKI, thereby hampering any improvement in patient outcome [49]. The search for biomarkers predicting progression to severe damage has come up with substances that exhibit the presence of AKI but are of limited success in differentiating severe AKI from less severe forms [50].…”

“…The search for biomarkers predicting progression to severe damage has come up with substances that exhibit the presence of AKI but are of limited success in differentiating severe AKI from less severe forms [50]. Most of these biomarkers are closely linked to a single pathologic process, so that they perform poorly in AKI populations with other pathophysiological mechanisms or heterogeneous origins [49].…”

“…Hopefully, new bioinformatics approaches for the elucidation of the molecular events leading to the development of these molecular species and the mapping of molecular interactions will provide causative insights into the underlying molecular mechanisms and pathways involved in AKI [49].…”

Artificial Intelligence in Acute Kidney Injury Risk Prediction

“…Baseline SCr is frequently unknown, and UO assessment is complex without a urinary catheter.Novel biomarkers have been investigated in multiple settings to increase diagnostic accuracy, which so far include cystatin C (Cys-C), neutrophil gelatinase-associated lipocalin (NGAL), N-acetyl-glucosaminidase (NAG), kidney injury molecule 1 (KIM-1), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin 18 (IL-18), liver-type fatty acid-binding protein (L-FABP), calprotectin, urine angiotensinogen (AGT), urine microRNAs, insulin-like growth factor-binding protein 7 (IGFBP7), and tissue inhibitor of metalloproteinases-2 (TIMP-2) [17][18][19][20][21][22][23][24][25][26][27].Important weaknesses have limited the generalization of the use of these biomarkers in clinical practice [19]. These have not consistently distinguished pre-renal from renal AKI; several patient characteristics and comorbidities can produce range variations that limit their validity; cost-effectiveness is limited due to the increased costs associated with these biomarkers and need for multiple assessments, and evidence of outcome improvement is still lacking [28,29]. Given the complexity of AKI, perhaps the use of a panel of several biomarkers covering different stages of the syndrome could provide a better understanding of its pathophysiology and identify future treatment targets [29,30].…”