Acute Kidney Injury (AKI) is currently recognized as a life-threatening disease, leading to an exponential increase in morbidity and mortality worldwide. At present, AKI is characterized by a significant increase in serum creatinine (SCr) levels, typically followed by a sudden drop in glomerulus filtration rate (GFR). Changes in urine output are usually associated with the renal inability to excrete urea and other nitrogenous waste products, causing extracellular volume and electrolyte imbalances. Several molecular mechanisms were proposed to be affiliated with AKI development and progression, ultimately involving renal epithelium tubular cell-cycle arrest, inflammation, mitochondrial dysfunction, the inability to recover and regenerate proximal tubules, and impaired endothelial function. Diagnosis and prognosis using state-of-the-art clinical markers are often late and provide poor outcomes at disease onset. Inappropriate clinical assessment is a strong disease contributor, actively driving progression towards end stage renal disease (ESRD). Proteins, as the main functional and structural unit of the cell, provide the opportunity to monitor the disease on a molecular level. Changes in the proteomic profiles are pivotal for the expression of molecular pathways and disease pathogenesis. Introduction of highly-sensitive and innovative technology enabled the discovery of novel biomarkers for improved risk stratification, better and more cost-effective medical care for the ill patients and advanced personalized medicine.
In light of the three stages of AKI, the examined biomarkers are sorted into categories as prerenal, intrinsic renal injuries -intrinsic renal after medical intervention- and postrenal injuries. The biomarkers are also defined under three types as diagnostic, prognostic and monitoring biomarkers according to their characteristics, stated in recent studies. The proteins that are utilized to detect and confirm AKI are named as diagnostic AKI biomarkers. The ones that provide information on AKI stage and affected cells or areas of the kidney are named as prognostic, and the ones that support the research if the treatment effect is different for biomarker positive patients are classified as monitoring biomarkers. Together with the biomarkers, the affected kidney areas and cells are summarized in Table 1 based on the findings of the investigators.
Biomarkers | Biomarker Type | Study Type | Affected Area of Kidney | Affected Kidney Cell Types | AKI Category |
---|---|---|---|---|---|
NGAL | Diagnostic | Urine analysis | Renal pelvis | Collecting duct epithelial cells | Prerenal |
B2M | Diagnostic | Urine analysis | Proximal tubule | Tubular epithelial cells | Intrinsic renal |
SERPINA1 (AAT) | Diagnostic | Urine analysis | Proximal tubule | Tubular epithelial cells | Intrinsic renal |
RBP4 | Diagnostic | Plasma analysis | Proximal tubule | Tubular epithelial cells | Postrenal |
FBG | Diagnostic | Urine analysis | Glomerulus | Tubular epithelial cells | Intrinsic renal (after MI) ** |
GDF15 | Diagnostic | Urine analysis | Nephron | Renal endothelial cells | Intrinsic renal (after MI) ** |
LRG1 | Diagnostic | Urine analysis | Nephron | Renal endothelial cells | Intrinsic renal (after MI) ** |
SPP1 | Diagnostic | Urine analysis | Nephron | Renal endothelial cells | Intrinsic renal (after MI) ** |
ANXA5 | Diagnostic | Urine analysis | Nephron | Renal endothelial cells | Prerenal |
6-PGLS | Diagnostic | Urine analysis | Nephron | Renal endothelial cells | Prerenal |
TIMP-2 IGFBP7 * | Diagnostic | Urine/serum | Proximal tubule | Proximal tubular epithelial cells | Intrinsic renal (after MI) ** |
C3 | Diagnostic or prognostic | Urine analysis | Glomerulus | Tubular epithelial cells | Intrinsic renal (after MI) ** |
C4 | Diagnostic or prognostic | Urine analysis | Glomerulus | Tubular epithelial cells | Intrinsic renal (after MI) ** |
GAL-3BP | Prognostic | Urine analysis | Glomerulus | Tubular epithelial cells | Intrinsic renal (after MI) ** |
Cys C | Prognostic | Plasma analysis | Proximal tubule | Tubular epithelial cells | Prerenal |
S100P | Prognostic | Urine analysis | Glomerulus | Urothelium cells | Prerenal |
α2M | Prognostic | Urine analysis | Glomerulus | Tubular epithelial cells | Intrinsic renal (after MI) ** |
CD26 * | Prognostic | Urine analysis | Glomerulus/Proximal tubule | Renal brush border epithelium | Intrinsic renal (after MI) ** |
sTNFR1, sTNFR2 | Monitoring | Plasma analysis | Glomerulus | Tubular epithelial & mesangial cells | Intrinsic renal |
ANXA-2 | Monitoring | Urine analysis | Glomerulus | Renal glomerular endothelial cells | Intrinsic renal |
CRP | Monitoring | Blood analysis | Renal cortex | Renal Cortical Epithelial Cells | Intrinsic renal (after MI) ** |
OPN | Monitoring | Blood analysis | Nephron-loop of Henle | Renal epithelial cells | Intrinsic renal (after MI) ** |
CD5 & Factor VII * | Monitoring | Blood analysis | Nephron | Filtrating cells | Intrinsic renal (after MI) ** |
IgHM | Monitoring | Urine analysis | Glomerulus | Tubular epithelial cells | Intrinsic renal (after MI) ** |
Serotransferrin | Monitoring | Urine analysis | Glomerulus | Tubular epithelial cells | Intrinsic renal (after MI) ** |
HRG | Monitoring | Urine analysis | Glomerulus | Proximal tubule epithelial cells | Intrinsic renal (after MI) ** |
CFB | Monitoring | Urine analysis | Glomerulus | Proximal tubule epithelial cells | Intrinsic renal (after MI) ** |
CD59 | Monitoring | Urine analysis | Glomerulus/Proximal tubule | Renal brush border epithelium | Intrinsic renal (after MI) ** |
AGT | Monitoring | Urine analysis | Glomerulus | Proximal tubule epithelial cells | Intrinsic renal (after MI) ** |
KRK1 * | Monitoring | Urine analysis | Glomerulus | Proximal tubule epithelial cells | Intrinsic renal (after MI) ** |
Authors | Biofluid | Method | Patient Cohort | Investigated Biomarkers | Most Significant Biomarkers | Conclusion |
---|---|---|---|---|---|---|
Ibrahim et al. [20,21] | Blood | Luminex xMAP immunoassay | 44 AKI 745 non- AKI |
109 | CRP; OPN; CD5; FACTOR VII | The biomarker panel using machine learning was developed and showed a performance with an AUC of 0.79 for predicting procedural AKI. The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; p < 0.001). |
Zhu et al. [21,22] | Urine | LC-MS/MS | 4 CI-AKI 20 CI-non AKI |
99 | NGAL; S100- P; ANXA2; B2M; SERPINA1; RBP4 | In relatively small patient cohort, urine proteome of CI-AKI vs. non-CI-AKI were compared. Upregulation was observed in CI-AKI with ratio of 7.40 (B2M), 6.63(S100-P), 4.25 (NGAL) and 4.27 (SERPINA1). |
Awdishu et al. [23] | Urine/blood | LC-MS/MS | 10 V-AKI 12 HC |
251 | C3; C4; GAL-3BP, FBG, α2M; IgHM; SEROTRANSFERRIN |
Urinary exosome proteins in response to V-AKI might provide vulnerable molecular information that helps elucidate mechanisms of injury and identify novel biomarkers among patients with confirmed drug-induced kidney injury. |
Jung et al. [24] | Urine | LC-MS/MS | 14 AKI 14 non-AKI |
174 | NGAL; ANXA5;GAL3; 6-PGLS; S100-P | Proteomic urinary-based biomarkers that can predict early AKI occurrences in infants were identified. Three biomarkers performed well, showing AUC values of 0.75, 0.88 and 0.74 for NGAL, ANXA5 and S100-P, respectively. There was higher beneficial effect of the classifier performance when NGAL + AXA5 (AUC of 0.92) and NGAL + AXA5 + S100-P (AUC of 0.93) were applied. |
Du et al. [25] | Urine | Flow cytometry | 133 AKI 68 non-AKI |
1 | CD26 | Urinary exosomal CD26 was negatively correlated with AKI compared with non-AKI patients (β = −15.95, p < 0.001). Similar results were obtained for the AKI cohort with major adverse events. On the other hand, AKI survivors exhibited high-CD26 levels compared AKI patients with low-CD26 levels for early reversal, recovery and reversal, respectively, after adjustment for clinical factors (ORs (95% CI) were 4.73 (1.77–11.48), 5.23 (1.72–13.95) and 6.73 (2.00–19.67), respectively). Prediction performance was moderate for AKI survivors (AUC 0.65; 95% CI, 0.53–0.77; p = 0.021) but improved for non-septic AKI survivors (AUC, 0.83; 95% CI, 0.70–0.97; p = 0.003) |
Wilson et al. [22] | Plasma | Randox’s multiplexed Biochip Arrays | 500 AKI | 11 | sTNFR1; sTNFR2; CYSTATIN C; NGAL |
A multivariable panel containing sTNFR1, sTNFR2, cystatin C, and eGFR discriminated between those with and without kidney disease progression (AUC 0.79 [95% CI, 0.70–0.83]). Optimization of the panel showed 95% sensitivity and a negative predictive value of 92% used to stratify patients at low risk for disease severity. |
Merchant et al. [26] | Urine | ELISA | 15 AKI 32 non-AKI |
29 | HRG; CFB; CD59; C3; AGT | Two proteins, HRG and CFB were upregulated in AKI patients, showing moderate predictive performance (AUC 0.79; 95% CI, 0.65–0.94; p = 0.001 and AUC 0.75; 95% CI, 0.57–0.93; p = 0.007). Significant improvement in the risk prediction for primary outcome was observed, specifically for NRI, IDI in addition to CFB and HRG. Only HRG was a significant predictor in the 21 patients with AKI defined by KDIGO criteria. |
Coca et al. [27] | Serum | Randox’s multiplexed Biochip Arrays | 769 AKI 769 non-AKI |
2 | sTNFR1; sTNFR2 | Plasma sTNFR1 and sTNFR2 measured 3 months after discharge were associated with renal deterioration independent of AKI (HR 4.7, 95% CI, 2.6–8.6) and significant association with renal failure. In this regards, clinical classifier performance was with AUC of 0.83. There was also association of the both biomarkers with Heart failure ((sTNFR1-1.9 (95% CI, 1.4–2.5) and sTNFR2-1.5 (95% CI, 1.2–2.0)) and death ((sTNFR1- 3.3 (95% CI, 2.5–4.2) and sTNFR2-1.5 (95% CI, 1.9–3.1)). |
Jiang et al. [28] | Urine | LC-MS/MS | 90 CP-AKI | 12 | GDF15; LRG1; SPP1 | Urinary proteomic profiles of GDF15 (1.77-fold) and LRG1 (4.25-fold) were significantly elevated by CP treatment compared to the baseline. |
Di Leo et al. [29] | Urine/serum | NephroCheck® (NC) Immunoassay | 719 patients at ICU | 2 | TIMP-2; IGFBP7 | TIMP-2 and IGFBP7 levels yielded good performance in prediction AKI development at first 4 days at ICU and in all critically ill patients (AUC of 0.65). The Kaplan-Meier analysis predicted lower risk for AKI development only for those patients who NC test was negative. |
Navarrete et al. [30] | Urine/serum | ELISA assay | 21 AKI 21 non-AKI |
1 | PLA2G15/LPLA2 | Urinary PLA2G15/LPLA2 activity was associated with subsequent AKI development during/ongoing CPB. There was similar association with PLA2G15/LPLA2 activity from serum. No association was observed between PLA2G15/LPLA2 activity from both biofluids, suggesting that this biomarker might be an early sign of renal response to CPB events. |
Navarrete et al. [31] | Urine | Nano RPLC-MS/MS | 8 AKI 8 non-AKI |
28 | KRK1 | Investigation on KLK1, confirmed the activity of this enzyme in AKI and non- AKI patients. In fact, increased action of KLK1 was confirmed only in AKI patients who arrived at ICU and had highest activity in comparison to other enzymes, hence providing novel finding related to intraoperative events in human ischemia reperfusion injury during CPB. |
This entry is adapted from the peer-reviewed paper 10.3390/diagnostics13162648