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Abstract

The diagnosis and prognosis of acute kidney injury (AKI) by current clinical means is inadequate. Biomarkers of kidney injury that are easily measured and unaffected by physiological variables could revolutionize the management of AKI. Our objective was to systematically review the diagnostic and prognostic utility of urine and serum biomarkers of AKI in humans. We searched MEDLINE, PubMed and EMBASE databases (January 2000–August 2009) for biomarker studies that could be classified into the following categories: (a) confirmation of the diagnosis of established AKI, (b) early prediction of AKI, and (c) prognostication of AKI. We identified 54 manuscripts published since 2000 that met our inclusion and exclusion criteria. Urinary interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL) and N-acetyl-β-d-glucosaminidase (NAG) are potentially useful biomarkers for the diagnosis of established AKI. Urinary NGAL, IL-18, and liver-type fatty acid binding protein, and serum NGAL and cystatin C represent the most promising biomarkers for early prediction of AKI. Urinary cystatin C, α1-microglobulin, NAG and retinol-binding protein may be useful to predict severity and outcomes of AKI. In conclusion, we identified several studies of promising biomarkers for the diagnosis, prediction and prognostication of AKI. However, we note several limitations, including small sample sizes, inadequate gold standard, exclusion of patients with chronic kidney disease, incomplete statistical analyses, utilization of research-based assays and a paucity of studies examining prediction for clinical outcomes. Future studies will need to address these limitations in order for further progress to be made.

Introduction

The clinical syndrome of acute kidney injury

The incidence of acute kidney injury (AKI, previously referred to as acute renal failure) has reached epidemic proportions worldwide, and is now estimated at 5–7% of all hospitalized patients.¹ In the critical care setting, AKI affects 5–25% of patients^2,3 and accounts for an overall mortality rate of 50–80%.^3–6 Once established, the treatment of AKI is largely supportive, unsatisfactory and associated with a poor prognosis. In a recent multinational study of AKI in nearly 30,000 critically ill subjects, the overall prevalence of AKI requiring supportive renal replacement therapy was 6% with a mortality rate of 60%.⁷ The increase in morbidity and mortality associated with AKI has now been established in a wide variety of clinical situations,^8–11 including administration of radiocontrast dye,¹² cardiopulmonary bypass,^13–18 mechanical ventilation¹⁹ and sepsis.^20,21 The dramatic negative influence of AKI on overall patient outcomes is also well documented.^22–27 AKI has emerged as a major risk factor for the development of non-renal complications, and as a powerful independent predictor of mortality.^12,28 Furthermore, the treatment of AKI represents an enormous financial burden to society, with annual expenses now estimated at $10 billion in the USA alone.^29,30

Tremendous progress has been made in our understanding of the molecular and cellular mechanisms of AKI, which has yielded novel therapeutic targets that work well in experimental models.^31–34 The translation of these findings to humans has been disappointing, and several of these agents have failed in clinical trials.^35–38 This disparity has been attributed, at least in part, to the paucity of early biomarkers for the prediction of AKI in the clinical setting. In the field of cardiology, the measurement of serum biomarkers such as cardiac troponins, that reflect early myocyte damage rather than diminished cardiac function, has facilitated the development and implementation of novel strategies for the management of coronary insufficiency, which have significantly reduced the morbidity and mortality from acute myocardial infarction. In stark contrast, AKI prevention and treatment studies using variables such as serum creatinine, urine output and serum and urine chemistries, that reflect the delayed diminution in kidney function and not the early structural tubule damage, have predictably not yielded interventions that decrease dialysis requirement or mortality.^39,40 Of those interventions that have been successful in small phase II studies of AKI,^41,42 none were efficacious at reducing dialysis requirement or mortality in larger phase III trials.^36,37,43 Regrettably, these studies were confounded by the use of serum creatinine concentration as an inclusion criterion, resulting in both delayed recognition of AKI and initiation of therapy. Possibly, these and other promising interventions would have been successful if they could be initiated at the onset of structural AKI rather than waiting several days for its functional sequelae such as the increase in serum creatinine.

The flawed ‘gold standard’ for the diagnosis of acute kidney injury

The diagnosis of AKI currently depends on detection of reduced kidney function by the increase in serum creatinine concentration. This is a notoriously delayed and unreliable measure in the acute setting, for a number of reasons. First, there are numerous non-renal factors influencing the serum creatinine concentration such as body weight, race, age, gender, hydration status, drugs, muscle mass, muscle metabolism and diet. Second, in AKI, serum creatinine is an even poorer reflection of kidney function, because the patients are not in steady state, and the increase in serum creatinine concentration lags far behind structural renal injury.^44,45 Third, significant renal disease can exist with no change in serum creatinine because of functional renal reserve, enhanced tubular secretion of creatinine and other factors.^44,46 Indeed, more than 50% of renal function may be lost before serum creatinine rises above the upper reference limit.

Other conventional biomarkers such as urinary casts (a late indicator of tubule damage) and fractional excretion of sodium (a highly variable measure that primarily differentiates volume-responsive prerenal injury from intrinsic AKI) have also proven to be insensitive and non-specific for the early recognition of AKI.^47–50 Thus, a troponin-like biomarker that is easily measured, unaffected by physiological variables, and capable of both early detection and risk stratification could revolutionize the management of AKI.

Tubular proteinuria for the diagnosis of AKI

Proteinuria can result from three basic mechanisms: glomerular, overflow and tubular. Glomerular proteinuria results from increased filtration of macromolecules (predominantly albumin) across the glomerulus. The implication of this type of proteinuria in the detection and management of chronic kidney disease has recently been published in this journal.⁵¹ However, in the clinical setting, albuminuria can occur even in the absence of renal injury. Postulated mechanisms include acute changes in glomerular capillary permeability⁵² and inflammatory insults.⁵³ In addition, clinical situations that lead to AKI may result in albuminuria due to diminished reabsorption by the injured proximal tubules. The potential utility of albuminuria as a test for the early diagnosis of AKI has not been systematically evaluated. Overflow proteinuria refers to the increased excretion of proteins that are markedly overproduced in specific disease states such that the filtered load exceeds the normal tubular reabsorptive capacity. Examples include immunoglobulin light chains in multiple myeloma and myoglobin in rhabdomyolysis.

Tubular proteinuria has traditionally referred to low molecular weight proteins that are normally produced in the body, freely filtered across the glomerulus and normally reabsorbed in the proximal tubule, but appear in the urine due to proximal tubular damage. Examples include α1-microglobulin, β2-microglobulin and retinol binding protein.^54–56 Tubular proteinuria can also result when proximal tubular enzymes and proteins, such as α-glutathione S-transferase (α-GST) and N-acetyl-β-d-glucosaminidase (NAG), are released into the urine as a direct consequence of cell injury.^57–59 In general, these traditional urinary biomarkers have suffered from lack of specificity for AKI and a dearth of standardized assays. Fortunately, the application of innovative technologies such as functional genomics and proteomics to human and animal models of AKI has recently uncovered several novel genes and gene products that represent the early stress response of the kidney tubule to acute injuries.^60–62 The serendipitous appearance of some of these proteins in the urine has dramatically rekindled the pursuit of tubular proteinuria as a diagnostic tool in AKI, and has resulted in the identification and validation of novel biomarkers.^63–67

This review represents a systematic analysis of tubular proteins as biomarkers of AKI in humans, based on data published from January 2000 to July 2009. Both traditional as well as novel urinary biomarkers are critically evaluated. Also included are the most promising serum biomarkers for AKI, since some of the urinary biomarkers are also detected in, and may indeed originate from, the blood. Isolated glomerular proteinuria and biomarkers of chronic kidney disease are specifically excluded.

Methods

The most recent systematic analysis of biomarkers in AKI included studies from 2000 to 2006.⁶⁸ Since then, numerous new studies have substantially added to the field of knowledge. This review reproduces the systematic methods of the previous analysis, and updates the search to include all publications up to August 2009.

Study eligibility

Studies were eligible for this review if they were prospective or retrospective cohort studies, case-control studies or randomized controlled trials that investigated the use of tubular proteinuria or other biomarkers for the diagnosis or risk stratification of AKI. Studies that detected these proteins in either urine or blood samples were eligible. Only studies that examined 20 or more human subjects were included.

Search strategy

We searched MEDLINE, PubMed and EMBASE databases (January 2000–August 2009) using the MeSH terms ‘acute renal failure’ or ‘acute kidney injury’ or ‘creatinine blood level’ and cross referenced with the terms ‘biomarker’ or ‘biological marker’. The following limits were used: humans, 2000–2009 and diagnosis (sensitivity). We further increased the sensitivity of our search by manually searching through the references of eligible articles and by using the ‘related articles’ feature of PubMed. The search was restricted to the English language.

Data abstraction

Data were abstracted and studies were classified into three groups: (a) those evaluating biomarkers for confirming the diagnosis of established AKI, (b) those evaluating biomarkers for the prediction of AKI, and (c) those evaluating biomarkers for the prognosis of AKI. Within each group, studies were further divided by sample type – urine or blood. The primary endpoints abstracted from each study included the sensitivity, specificity, area under receiver operator curve (AUC) and positive likelihood ratios. In cases where the positive likelihood ratio was not reported, it was calculated using reported sensitivity and specificity.

Quality assessment

The methodological quality of each study was assessed using a modified scoring system based on the STARD criteria.^69,70 The 10 criteria used in our quality assessment were identical to those proposed by Coca et al. ⁶⁸

Results

We have previously reported the results of our search strategy covering publications from 2000 to 2006.⁶⁸ For the present review, we updated the search to include all publications from January 2007 to August 2009. Our search identified 344 new citations published since January 2007, of which 317 were excluded upon title and abstract review (Figure 1). The primary reason for exclusion was the subject matter of studies pertaining to neither AKI nor biomarkers. Of the remaining articles, a further four were excluded after full article review; two articles defined AKI with the very biomarkers whose utility we are investigating,^71,72 one article had less than 20 subjects⁷³ and one was a subjective letter to the editor.⁷⁴ The remaining 23 studies,^75–97 in addition to the 31 studies previously found by Coca et al. ^98–128 constitute a total of 54 manuscripts published since 2000 that examine biomarkers in the diagnosis and prognosis of AKI. The studies published since January 2007 examine a further 16 unique biomarkers in addition to the 21 identified by Coca et al. ⁶⁸ Quality assessment revealed deficiencies in reporting of blinding (52% of studies were deficient), participant sampling (28% used convenience sample) and including a representative distribution of disease in the cohort (19% were deficient), as shown in Table 1.

Figure 1

Study flowchart. AKI, acute kidney injury

Table 1

Scoring system for validity

Validity criterion	Explanation	Scoring	Comments
Participant recruitment	Was recruitment based on presenting symptoms, results from previous tests or fact that participants received index tests?	Presenting sx = 1 Previous tests or index tests = 0	Based on presenting symptoms in all 54 studies.
Participant sampling	Was it study population or a convenience sample or a consecutive series?	Consecutive series = 1 Convenience sample = 0	Based on convenience sample in 15 of 54 studies.
Data collection	Was data collection planned before the index test and reference standard were performed prospectively or retrospectively?	Prospective = 1 Retrospective = 0	Planned and performed prospectively in all but one study.
Reference standard	Was the rationale for the reference standard stated?	Stated = 1 Not stated = 0	Not stated for four studies.
Materials and methods	Were technical specifications of material and methods stated including how and when measurements were taken?	Stated = 1 Not stated = 0	Stated in all but one of the articles.
Index test	Were the definitions of and rationales for the units, cutoffs, and/or categories of the results of the index tests stated?	Stated = 1 Not stated = 0	Not stated in four of the 54 studies.
Blinding	Were readers of index test and reference standard blinded?	Blinded = 1 Not blinded or not stated = 0	Stated that the readers of index test and reference standard were blinded in 28 of the 54 studies.
Completion	Was the number of participants that did not undergo index tests (no. of tests versus sample size) stated?	Stated = 1 Not stated = 0	Stated in 46 of the 54 studies.
Time interval	Was the time interval from index test to reference standard stated?	Stated = 1 Not stated = 0	The time interval between index test and reference standard (clinical diagnosis of AKI or severity endpoint such as dialysis or death) was not stated eight studies.
Distribution of severity of disease	Was there a representative distribution of severity of disease? (mild, moderate, severe AKI; non-oliguric versus oliguric)	Yes = 1 No = 0	A broad distribution of disease severity was present in 10 of the 54 studies.

Biomarkers for the diagnosis of established AKI

Urinary biomarkers that have been quantitatively evaluated in subjects with established AKI include interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), NAG, matrix metalloproteinase-9 (MMP-9) and sodium–hydrogen exchanger isoform-3 (Table 2). Concentrations of urine IL-18 were significantly increased in patients with established AKI in comparison to those with prerenal azotemia, urinary tract infection or chronic kidney disease, with an AUC of 0.95.¹¹⁵ Increased urine KIM-1 has also been documented in subjects with established AKI in comparison to those with urinary tract infection or chronic kidney disease, with an AUC of 0.90.^106,107 In this cohort, urinary NAG was also an excellent biomarker for the identification of established AKI (AUC 0.97), whereas MMP-9 performed only fairly (AUC 0.74). Finally, urinary NGAL was found to be increased greater than 25-fold in patients with established AKI when compared with controls or those with chronic kidney disease, but the biomarker properties were not reported.¹¹³

Table 2

Biomarkers for the diagnosis of established AKI

Reference number	Biomarker name	Patient setting	No of subjects	Sensitivity	Specificity	AUC-ROC (CI)	Likelihood ratio	Quality score
	Urine
¹¹³	NGAL	Hospitalized	46	NR	NR	NR	N/A	5
¹⁰⁰	NGAL	Contrast	35	NR	NR	NR	N/A	9
¹¹⁵	IL-18	Hospitalized	72	0.85	0.88	0.95 (N/A)	7.1	7
¹⁰³	GST, NAG, A1M	Cardiac Surgery	80	NR	NR	NR	N/A	9
¹⁰⁶	KIM-1	Hospitalized	32	NR	NR	NR	12.4*	8
¹⁰⁷	KIM-1	Hospitalized	74	NR	NR	0.9 (0.81–0.96)	N/A	8
¹⁰⁷	MMP-9	Hospitalized	74	NR	NR	0.74 (0.63–0.83)	N/A	8
¹⁰⁷	NAG	Hospitalized	74	NR	NR	0.97 (0.91–1.0)	N/A	8
¹⁰⁴	NHE3	Intensive Care	68	NR	NR	NR	N/A	9
	Serum
⁹⁸	Cystatin C	Cardiac Surgery	60	NR	NR	NR	N/.A	6
¹⁰²	Cystatin C	Liver Transplant	68	0.97	0.85	NR	6.4	9
¹⁰¹	Cystatin C	Cisplatin	41	NR	NR	NR	N/A	9
¹²⁰	Cystatin C	Cisplatin	72	0.87	1.0	0.97 (N/A)	∞	9
¹²⁸	Cystatin C	Cardiac Surgery	60	NR	NR	0.88 (0.82–0.93)	N/A	9
¹²²	Cystatin C	Intensive Care	50	NR	NR	0.93 (0.86–0.99)	N/A	9
¹²⁶	Carb Hb	Hospitalized	41	0.94	0.92	NR	11.8	8
¹¹³	NGAL	Hospitalized	37	NR	NR	NR	N/A	5
¹⁰⁰	NGAL	Contrast	35	NR	NR	NR	N/A	9

AUC-ROC (CI), area under the receiver operating characteristic curve (95% confidence interval); NGAL, neutrophil gelatinase-associated lipocalin; IL, interleukin; GST, glutathione S-transferase; NAG, N-acetyl-β-d-glucosaminidase; A1M, alpha-1 microglobulin; MMP-9, matrix metalloproteinase 9; NHE3, sodium-hydrogen exchanger 3; Carb Hb, carbamylated hemoglobin; NR-not reported; N/A-not applicable

*This value represents the odds ratio reported rather than the positive likelihood ratio

In the serum, cystatin C is emerging as the best performing biomarker for identifying established AKI, with reported AUCs of 0.88–0.97.^{98,101,102,120,122,128} Serum NGAL also correlates highly with the degree of established AKI, but the sensitivities and specificities were not reported.^100,113

Biomarkers for the early prediction of AKI

The quest for biomarkers that can provide an early diagnosis of AKI is an area of intense scholarly interest, with 15 new studies published since 2007 (Table 3). In the urine, NGAL and IL-18 continue to be the most extensively studied biomarkers. Overall, urine NGAL is emerging as an excellent standalone urinary biomarker for the early prediction of AKI, but the results have been mixed depending on the clinical setting and type of population studied. In children undergoing cardiopulmonary bypass surgery, urine NGAL is an outstanding biomarker, with reported AUCs in the 0.93–1 range.^84,112 In adults undergoing percutaneous coronary intervention or cardiopulmonary bypass surgery, the AUCs have ranged widely from 0.61 to 0.96.^{76,82,83,90,97,123} In a heterogeneous population of critically ill children, urine NGAL was predictive of subsequent AKI with an AUC of 0.78.¹²⁷ In general, the predictive value of urine NGAL appears to be enhanced in children in comparison with adults, with five recent studies reporting AUCs of 0.78–1.00.^{84,86,87,112,123} In the setting of adult and paediatric kidney transplantation, urine NGAL was highly successful at predicting delayed graft function with an AUC of 0.9.¹¹⁶ Finally, a large study of 635 adults admitted to the emergency department illustrated the value of urine NGAL as an initial screening tool for the subsequent development of AKI, with high sensitivity (0.90), high specificity (0.995) and an outstanding AUC of 0.95.⁹¹ This study also examined several other urinary biomarkers, which also performed well but proved to be inferior to NGAL (AUCs of 0.89, 0.83 and 0.71 for α-1 microglobulin, α-1 acid glycoprotein and NAG, respectively).

Table 3

Biomarkers for the early prediction of AKI

Reference number	Biomarker name	Patient setting	No. of subjects	Sensitivity	Specificity	AUC-ROC (CI)	Likelihood ratio	Quality score
	Urine
¹¹²	NGAL	Paed. cardiac surgery	71	1.0	0.98	0.99 (N/A)	50.0	10
¹²³	NGAL	Cardiac surgery	81	0.73	0.78	0.8 (0.57–1.03)	3.3	9
¹²⁷	NGAL	Paed. intensive care	150	0.77	0.72	0.78 (0.62–0.95)	2.8	10
¹¹⁶	NGAL	Kidney transplant	63	0.9	0.83	0.9 (0.71–1.0)	5.3	10
⁹¹	NGAL	Emergency room	635	0.9	0.99	0.95 (0.88–1.0)	181.5	9
¹⁰⁰	NGAL	Contrast	100	NR	NR	NR	N/A	7
⁸²	NGAL	Cardiac surgery	426	NR	NR	0.61 (0.54–0.68)	N/A	8
⁷⁶	NGAL	Cardiac surgery	72	48.5	78.9	0.69 (0.57–0.82)	2.3	10
⁹⁰	NGAL	Contrast	40	0.77	0.71	0.73 (0.54–0.93)	2.6	6
⁸³	NGAL	Cardiac surgery	33	0.71	0.73	0.88 (N/A)	2.6	8
⁷⁸	NGAL	Intensive care	31	0.91	0.95	0.98 (0.82–0.98)	18.2	6
⁸⁴	NGAL	Paed. cardiac surgery	196	0.82	0.9	0.93 (N/A)	8.2	10
⁸⁶	NGAL	Paed. contrast	91	0.73	1.0	0.92 (N/A)	∞	10
⁸⁷	NGAL	Paed cardiac surgery	40	1.0	1.0	1.0 (N/A)	∞	10
⁹⁷	NGAL	Cardiac surgery	50	0.93	0.78	0.96 (0.9–1.0)	4.17	7
⁹⁵	NGAL	Premature infants	20	0.29	0.88	NR	2.42	7
¹¹⁶	IL-18	Kidney transplant	63	NR	NR	0.9 (0.81–0.98)	N/A	10
¹¹⁴	IL-18	Intensive care	138	0.5	0.85	0.73 (N/A)	3.3	9
¹¹⁷	IL-18	Cardiac surgery	71	0.5	0.94	0.75 (N/A)	8.3	10
¹²⁴	IL-18	Paed. intensive care	137	0.25	0.81	0.54 (N/A)	1.3	10
⁹⁰	IL-18	Contrast	40	0.69	0.74	0.75 (0.58–0.92)	2.7	6
⁸³	IL-18	Cardiac surgery	33	0.82	0.82	0.89 (N/A)	4.4	8
⁷⁵	IL-18	Cardiac surgery	100	NR	NR	0.55 (0.4–0.71)	N/A	10
⁷⁹	L-FABP	Contrast	66	NR	NR	NR	N/A	7
⁸⁷	L-FABP	Paed. cardiac surgery	40	0.71	0.68	0.81 (N/A)	2.3	10
¹⁰⁷	KIM-1	Paed. cardiac surgery	40	0.74	0.9	0.83 (0.67–0.96)	3.1	8
¹⁰⁷	NAG	Paed. cardiac surgery	40	1.0	0.3	0.69 (0.52–0.85)	1.4	8
¹⁰⁷	MMP-9	Paed. cardiac surgery	40	NR	NR	0.5 (0.50–0.62)	N/A	8
¹⁰⁵	GST	Cardiac surgery	84	NR	NR	NR	N/A	9
¹²⁵	GGT	Intensive care	26	1.0	0.9	0.95 (0.79–1.0)	10.0	9
¹²⁵	π-GST	Intensive care	26	1.0	0.9	0.93 (0.74–0.99)	10.0	9
¹²⁵	α-GST	Intensive care	26	0.75	0.9	0.89 (0.69–0.98)	7.5	9
¹²⁵	Alk Phos	Intensive care	26	0.5	0.95	0.86 (0.68–0.97)	10.0	9
¹²⁵	NAG	Intensive care	26	1.0	0.81	0.85 (0.64–0.96)	5.3	9
¹²⁵	LDH	Intensive care	26	0.5	0.95	0.69 (0.49–0.87)	10.0	9
⁹¹	NAG	Emergency room	635	0.7	0.63	0.71 (0.62–0.81)	1.9	9
⁹¹	A1M	Emergency room	635	0.8	0.81	0.89 (0.84–0.93)	4.1	9
⁹¹	A1AGP	Emergency room	635	0.87	0.61	0.83 (0.77–0.89)	2.2	9
⁷⁶	Cystatin C	Cardiac surgery	72	0.67	0.76	0.73 (0.62–0.85)	2.81	10
⁹⁶	Aprotinin	Paed. cardiac surgery	106	0.92	0.96	0.98 (N/A)	23	7
⁹²	sE-Selectin	Intensive care	33	NR	NR	NR	N/A	7
⁹²	sICAM-1	Intensive care	33	NR	NR	NR	N/A	7
	Serum
¹¹²	NGAL	Paed. cardiac surgery	71	0.7	0.94	0.91 (N/A)	11.6	10
⁷²	NGAL	Contrast	100	NR	NR	NR	N/A	7
⁷⁶	NGAL	Cardiac surgery	72	NR	NR	0.54 (0.4–0.67)	N/A	10
⁸⁵	NGAL	Paed. cardiac surgery	120	0.84	0.94	0.96 (0.94–0.99)	14	10
⁸⁶	NGAL	Paed. contrast	91	0.73	1.0	0.91 (N/A)	∞	10
⁸⁸	NGAL	Paed. intensive care	143	0.86	0.39	0.68 (0.56–0.79)	1.4	9
⁹⁷	NGAL	Cardiac surgery	50	0.8	0.67	0.85 (0.73–0.97)	2.4	7
⁹³	NGAL	Cardiac surgery	100	0.79	0.78	0.8 (0.63–0.96)	3.6	8
⁹⁹	Cystatin C	Intensive care	202	NR	NR	0.90 (N/A)	N/A	9
¹⁰⁸	Cystatin C	Intensive care	85	0.82	0.95	NR	16.4	9
¹¹¹	Cystatin C	Intensive care	29	0.5	0.5	NR	1.0	6
⁷²	Cystatin C	Contrast	100	NR	NR	NR	N/A	7
⁷⁶	Cystatin C	Cardiac surgery	72	NR	NR	0.62 (0.49–0.75)	N/A	10
⁹⁴	Cystatin C	Intensive care	25	0.85	0.63	0.85 (0.7–1.0)	2.3	7
⁹³	Cystatin C	Cardiac surgery	100	0.77	0.86	0.83 (0.68–0.98)	5.5	8
¹¹¹	ProANP	Intensive care	29	NR	NR	NR	N/A	6
¹¹⁸	CD11b	Cardiac Surgery	75	NR	NR	NR	62.6*	8
⁷⁷	IL-6	Intensive care	734	NR	NR	NR	1.31*	7
⁷⁷	Il-8	Intensive care	755	NR	NR	NR	1.11*	7
⁷⁷	IL-10	Intensive care	572	NR	NR	NR	1.16*	7
⁷⁷	TNF	Intensive care	362	NR	NR	NR	1.36*	7
⁷⁷	sTNFR-1	Intensive care	543	NR	NR	NR	3.02*	7
⁷⁷	sTNFR-2	Intensive care	361	NR	NR	NR	2.57*	7
⁷⁷	ICAM-1	Intensive care	754	NR	NR	NR	1.51*	7
⁷⁷	SP-A	Intensive care	546	NR	NR	NR	1.13*	7
⁷⁷	SP-D	Intensive care	546	NR	NR	NR	1.04*	7
⁷⁷	vWF	Intensive care	540	NR	NR	NR	1.22*	7
⁷⁷	Protein C	Intensive care	755	NR	NR	NR	0.84*	7
⁷⁷	PAI-1	Intensive care	755	NR	NR	NR	2.08*	7
⁹⁴	β2-M	Intensive care	25	0.85	0.54	0.80 (0.63–0.98)	1.8	7

AUC-ROC (CI), area under the receiver operating characteristic curve (95% confidence interrval); NGAL, neutrophil gelatinase-associated lipocalin; IL, interleukin; L-FABP, liver-type fatty acid binding protein; A1M, alpha-1 microglobulin; A1AGP, alpha-1 acid glycoprotein; KIM-1, kidney injury molecule 1; GST, glutathione S-transferase; NAG, N-acetyl-β-d-glucosaminidase; A1M, alpha-1 microglobulin; MMP-9, matrix metalloproteinase 9; vWF, von Willebrand factor; ProANP, pro-atrial natriuretic peptide; NR, not reported; N/A, not applicable; Paed., pediatric

*This value represents the odds ratio reported rather than the positive likelihood ratio

Seven studies of urine IL-18 as an early predictor of AKI have been published to date.^{75,83,90,114,116,117,124} The AUCs have ranged from 0.54 to 0.90. In the setting of cardiac surgery, three studies reported very good to excellent AUCs in the 0.75–0.90 range^83,90,117 whereas one study reported poor performance with an AUC of 0.55.⁷⁵ Urine IL-18 was an excellent predictor of delayed graft function in subjects undergoing kidney transplantation, with an AUC of 0.90.¹¹⁶ In general, increases in urinary IL-18 were rarely false-positive (i.e. high specificity), but many patients who develop AKI do not have elevations in urine IL-18 (i.e. low sensitivity).

The ability of urinary KIM-1 to predict AKI has only been evaluated in one published study to date.¹⁰⁷ In a small cohort of children undergoing cardiac surgery, urine KIM-1 predicted subsequent AKI with an AUC of 0.83, which was superior to that of NAG (AUC 0.69) or MMP-9 (AUC 0.50).

Liver-type fatty acid binding protein (L-FABP) has recently been described as another promising urinary biomarker for the prediction of AKI. Urine L-FABP concentrations correlated with serum creatinine in subjects undergoing percutaneous coronary intervention.⁷⁹ In children undergoing cardiopulmonary bypass, early urine L-FABP measurements predicted AKI with an AUC of 0.81.⁸⁷ Several additional promising urinary biomarkers that predict AKI have been reported in critically ill subjects in a single publication,¹²⁵ including γ-glutamyl transpeptidase (AUC 0.95), α-GST (AUC 0.89) and π-glutathione S-transferase (AUC 0.93).

Among serum/plasma biomarkers, NGAL and cystatin C are the most promising for the early prediction of AKI. Four studies in the cardiac surgery setting^85,93,97,112 and one in subjects who developed contrast nephropathy⁸⁶ have illustrated the excellent predictive value of early plasma NGAL measurements, with AUCs in the 0.80–0.96 range. In a more heterogeneous critical care setting, the results were less robust, with a reported AUC of 0.68.⁸⁸ Two studies in the intensive care setting demonstrated excellent accuracy of plasma cystatin C for the early diagnosis of AKI, with an AUC of 0.90.^99,108 However, a recent publication showed plasma cystatin C to be a poor predictor of AKI in subjects undergoing cardiac surgery, with an AUC of 0.62.⁷⁶ In a large study of 876 patients with acute lung injury, a panel of plasma biomarkers that included interleukin-6 (IL-6), soluble tumour necrosis factor -1 and -2 and plasminogen activator inhibitor-1 were found to have significant associations with AKI, but biomarker characteristics were not reported.⁷⁷

Biomarkers for the prognosis of AKI

Urinary NGAL within the first five days of hospitalization demonstrated high sensitivity but low specificity for the subsequent need for dialysis in children with haemolytic uraemic syndrome¹²¹ (Table 4). In patients undergoing cardiac surgery, urine NGAL correlated weakly with the duration of AKI (r ² = 0.22; P = 0.005), and early urine NGAL levels were predictive of the need for dialysis as well as mortality, but the number of events were very small.^84,117 In a heterogeneous critically ill cohort,¹²⁷ urine NGAL was only minimally useful for prediction of AKI persistence (AUC 0.63) or for worsening of AKI (AUC 0.61). Similar weakly positive correlations have been reported for urine IL-18 to predict AKI severity.^117,124 Other tubular proteins and enzymes may be of greater utility for prognostication after AKI. Urinary cystatin C (AUC 0.92), α1-microglobulin (AUC 0.86), NAG (AUC 0.81) and retinol-binding protein (AUC 0.80) were all found to predict dialysis requirement in the intensive care setting.¹⁰⁹ In another study of hospitalized patients, there was a dose-dependent association between urinary NAG and the composite endpoint of dialysis or death.¹¹⁰

Table 4

Biomarkers for the prediction of AKI severity

Reference number	Biomarker name	Patient setting	No of subjects	Severity definition	Sensitivity/Specificity	AUC-ROC (CI)	Likelihood ratio	Quality score
	Urine
¹²¹	NGAL	Paed HUS	34	RRT	0.9/0.54	NR	2.0	9
¹²⁷	NGAL	Paed. intensive care	150	>48 h	NR/NR	0.63 (0.44–0.82)	NR	10
⁸⁴	NGAL	Paed. cardiac surgery	196	RRT	NR/NR	0.86 (N/A)	N/A	10
⁸⁴	NGAL	Paed. cardiac surgery	196	Death	NR/NR	0.91 (N/A)	N/A	10
¹¹⁴	IL-18	Intensive care	138	Death	NR/NR	NR	1.6*	9
¹²⁴	IL-18	Paed. intensive care	137	>48 h	0.53/0.71	NR	1.8	10
¹⁰⁹	Cystatin C	Intensive care	73	RRT	0.92/0.83	0.92 (0.86–0.96)	5.4	10
¹⁰⁹	A1M	Intensive care	73	RRT	0.88/0.81	0.86 (0.78–0.92)	4.6	10
¹⁰⁹	RBP	Intensive care	73	RRT	NR/NR	0.8 (0.72–0.87)	N/A	10
¹⁰⁹	β-2M	Intensive care	73	RRT	NR/NR	0.51 (0.42–0.60)	N/A	10
¹⁰⁹	NAG	Intensive care	73	RRT	0.85/0.62	0.81 (0.73-.88)	2.2	10
¹⁰⁹	α-GST	Intensive care	73	RRT	NR/NR	0.64 (0.55–0.72)	N/A	10
¹⁰⁹	GGT	Intensive care	73	RRT	NR/NR	0.64 (0.55–0.73)	N/A	10
¹⁰⁹	LDH	Intensive care	73	RRT	NR/NR	0.59 (0.5–0.69)	N/A	10
¹¹⁰	NAG	Hospitalized	100	RRT/Death	NR/NR	0.71 (0.63–0.78)	3.0*	9
¹¹⁰	KIM-1	Hospitalized	100	RRT/Death	NR/NR	0.61 (0.53–0.69)	1.4*	9
	Serum
⁹⁹	Cystatin C	Intensive care	71	Death	NR/NR	0.62 (N/A)	N/A	9
¹⁰⁸	Cystatin C	Intensive care	85	RRT	0.82/0.93	0.76 (0.69–0.85)	11.7	9
⁹³	Cystatin C	Cardiac surgery	100	RRT/Death	1.0/0.9	0.99 (0.98–0.99)	10	8
¹²¹	NGAL	Paed. HUS	34	RRT	NR/NR	NR	N/A	9
⁹³	NGAL	Cardiac surgery	100	RRT/Death	0.8/0.97	0.95 (0.86–0.99)	26.7	8
¹¹⁹	IL-6	Intensive care	98	Death	NR/NR	NR	1.65*	9
¹¹⁹	IL-8	Intensive care	98	Death	NR/NR	NR	1.54*	9
¹¹⁹	IL-10	Intensive care	98	Death	NR/NR	NR	1.34*	9
⁸⁰	Prealbumin	Hospitalized	161	Death	NR/NR	NR	2.1^†	9
⁸¹	CRP	Hospitalized	159	Death	NR/NR	NR	2.1^†	8

AUC-ROC (CI), area under the receiver operating characteristic curve (95% confidence interval); NGAL, neutrophil gelatinase-associated lipocalin; IL, interleukin; L-FABP, liver-type fatty acid binding protein; A1M, alpha-1 microglobulin; CRP, C-reactive protein; KIM-1, kidney injury molecule 1; GST, glutathione S-transferase; GGT, gamma-glutamyl transpeptidase; NAG, N-acetyl-β-D-glucosaminidase; LDH, lactate dehydrogenase; HUS, haemolytic uraemic syndrome; NR, not reported; N/A, not applicable; Paed., pediatric; RRT, renal replacement therapy

*This value represents the odds ratio reported rather than the positive likelihood ratio

^†This value represents the hazard ratio of death reported rather than the positive likelihood ratio

Few recent studies have examined serum biomarkers as prognosticators of AKI (Table 4). In critically ill patients with AKI, elevated serum cystatin C levels predicted the need for dialysis with an AUC of 0.76.¹⁰⁸ Other studies of patients with AKI revealed that both low serum prealbumin and increased serum C-reactive protein concentrations were associated with an increased risk of death, with a hazard ratio of 2.1.^80,81

Discussion

This review represents a systematic analysis and critical appraisal of the current status of tubular proteinuria (and the corresponding proteins in the serum) as biomarkers of AKI in humans. This has been an area of very active contemporary research, with 54 high quality manuscripts published since January 2000. These publications describe several promising biomarkers that have been identified, undergone initial clinical testing and are being systematically validated, thereby recapitulating the recently elucidated phases of the diagnostic test development process.^129,130 The biological sources and functions of these biomarkers are shown in Table 5.

Table 5

Characteristics of promising biomarkers of acute kidney injury

Biomarker	Origin/source	Biological function
Urine
NGAL	Distal tubule and collecting duct	Early marker of injury, binds iron, promotes tubule cell survival and proliferation, limits tubule cell apoptosis
IL-18	Proximal tubule	Reflects activation of apoptotic cascades
L-FABP	Proximal tubule	Marker of ischaemic and oxidative damage
KIM-1	Proximal tubule	Marker of epithelial dedifferentiation and regeneration
Cystatin C	Systemic and proximal tubule	Marker of increased glomerular permeability and/or impaired tubular reabsorption
α-GST	Proximal tubule	Marker of membrane disruption and cytosol leakage
π-GST	Distal tubule	Marker of membrane disruption and cytosol leakage
NAG	Proximal tubule	Marker of lysosomal enzyme release
A1M	Systemic and proximal tubule	Marker of increased glomerular permeability and/or impaired tubular reabsorption
A1AGP	Systemic and proximal tubule	Marker of increased glomerular permeability and/or impaired tubular reabsorption
β2-Microglobulin	Systemic and proximal tubule	Marker of increased glomerular permeability and/or impaired tubular reabsorption
GGT	Proximal tubule	Marker of epithelial cell membrane disruption
Serum/plasma
NGAL	Neutrophils, macrophages, epithelial cells in the lung and liver	Acute phase reactant, marker of organ cross-talk in acute kidney injury
Cystatin C	All nucleated cells	Marker of decreased glomerular filtration

NGAL, neutrophil gelatinase-associated lipocalin; IL, interleukin; L-FABP, liver-type fatty acid binding protein; A1M, alpha-1 microglobulin; KIM-1, kidney injury molecule 1; GGT, gamma-glutamyl transpeptidase; NAG, N-acetyl-β-d-glucosaminidase; GST, glutathione S-transferase; A1AGP, α-1 acid glycoprotein

A pressing need in the field has been for biomarkers that predict AKI early, well before the increase in serum creatinine concentration, so that clinical trials with timely interventions can be initiated. The evidence suggests that a number of urinary (best exemplified by NGAL and IL-18) and plasma (most prominently cystatin C and NGAL) proteins could serve this purpose, and can now be considered candidates for larger phase 3 and 4 validation studies. NGAL has been the most extensively investigated biomarker for the early diagnosis of AKI. A recent meta-analysis of data from 19 publications involving 2538 patients out of whom 487 developed AKI revealed a diagnostic odds ratio of 18.6 (95% confidence interval [CI] 9.0–38.1) and an AUC of 0.82 (95% CI 0.73–0.89) of NGAL to predict AKI in all clinical settings,¹³¹ suggesting its utility as an excellent standalone marker. It is likely that a combination of NGAL with other strategically selected markers will further enhance this performance, but this is yet to be tested. Panels of biomarkers may also assist in determining the timing of the original insult and duration of the ongoing injury, since their appearance follows a temporal pattern determined by the kidney's response to the noxious stimulus. This concept is similar to the panel used for the prediction of acute myocardial infarction (composed of myoglobin, creatine phosphokinase and troponin), and is illustrated in Figure 2. The data are from previously published manuscripts examining urinary biomarkers following cardiac surgery. NGAL¹¹² and L-FABP⁸⁷ displayed a pattern of rapid increase and subsequent decline, whereas IL-18¹¹⁷ and KIM-1¹⁰⁷ showed a slightly later rise in concentration. However, all these biomarkers preceded the increase in serum creatinine, which was usually noted 1–3 days after cardiac surgery. Thus, a panel of these novel biomarkers could be useful to determine the timing and duration of AKI, which in turn could assist in determining the primarily aetiology and the most effective treatment strategy.

Figure 2

Temporal patterns of increases in urinary biomarker concentrations in subjects who developed acute kidney injury after cardio-pulmonary bypass (CPB). NGAL, neutrophil gelatinase-associated lipocalin¹¹²; L-FABP, liver-type fatty acid binding protein⁸⁷; IL-18 interleukin-18¹¹⁷; KIM, kidney injury molecule-1¹⁰⁷

Such panels may also enable us to determine the principal nephron segment that is injured in any given subject, as shown in Table 5.

Despite the optimism in the field, there are important limitations that exist in the published literature that must be acknowledged. First, this systematic review found that the majority of studies reported were from single centres that enrolled small numbers of subjects. Validation of the published results in large multicentre studies will be essential. Second, most studies reported to date did not include patients with chronic kidney disease. This is problematic, not only because it excludes a large proportion of subjects who frequently develop AKI in clinical practice, but also because chronic kidney disease in itself can result in increased concentrations of most of the biomarkers examined, thereby representing a confounding variable. Third, many studies reported only statistical associations (odds ratio or relative risk), but did not report sensitivity, specificity and AUCs for the diagnosis of AKI, which are essential to determine the accuracy of the biomarkers. Fourth, the majority of biomarkers discovered and investigated to date rely on research-based ELISA-type assays, which are impractical from the clinical utility standpoint. However, assays for cystatin C¹⁰⁸ and NGAL^84,85 are now widely available on standardized clinical platforms, which could facilitate their rapid validation and widespread adoption in the scientific and clinical communities. Fifth, only a few studies with a small number of cases have investigated biomarkers for the prediction of AKI severity, morbidity and mortality – results of testing these biomarkers as predictors of clinical outcomes in large multicentre studies that are already under way are keenly awaited.

Finally, the definition of AKI in the published studies varied widely, but was based largely on increases in serum creatinine, which raises the conundrum of using a flawed outcome variable to analyse the performance of a novel assay. The studies of biomarkers for the diagnosis of AKI may have yielded different results had there been a true ‘gold standard’ for AKI. Instead, using AKI as defined by a change in serum creatinine concentration sets up the biomarker assay for lack of accuracy due to either false-positives (true tubular injury but no significant change in serum creatinine) or false-negatives (absence of true tubular injury, but elevations in serum creatinine due to prerenal causes or any of a number of confounding variables that haunt this measurement). It will be crucial in future studies to understand the clinical outcomes of subjects who may be prone to AKI and are ‘biomarker-positive’ but ‘creatinine-negative’, since this will determine whether the biomarkers are sufficiently sensitive. Since the gold standard for true AKI (tissue biopsy) is highly unlikely to be feasible in a research setting, it is vital that large enough future studies demonstrate (a) the association between biomarkers and hard outcomes such as dialysis, cardiovascular events and death, and (b) that randomization to a treatment for AKI based on high biomarker concentrations results in an improvement in kidney function and reduction of clinical outcomes. This should be the next quest for the field. Additional studies that merely examine the association between biomarkers and clinical AKI will likely not lead to significant progress.

DECLARATION

Conflicts of interest: CRP is a co-inventor on patents on IL-18 as a biomarker of acute kidney injury. PD is a co-inventor on patents on NGAL as a biomarker of acute kidney injury, which has been licensed by both Biosite^® and Abbott Diagnostics for the development of commercial assays. PD has received honoraria for speaking assignments from both companies.

Funding: CRP is supported by grants (RO1 HL08676, UO1 DK082185) from the NIH, and PD is supported by grants (RO1 DK069749) from the NIH.

Ethical approval: Individual studies quoted in this review were approved by the individual institutional review boards from where the studies originated.

Guarantor: PD is the senior author and guarantor.

Contributorship: PD and CRP envisioned the idea and drafted the outline. All four authors performed the literature searches and grading of the studies. All four authors contributed to the writing of the manuscript.

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Tubular proteinuria in acute kidney injury: a critical evaluation of current status and future promise

Abstract

Introduction

The clinical syndrome of acute kidney injury

The flawed ‘gold standard’ for the diagnosis of acute kidney injury

Tubular proteinuria for the diagnosis of AKI

Methods

Study eligibility

Search strategy

Data abstraction

Quality assessment

Results

Biomarkers for the diagnosis of established AKI

Biomarkers for the early prediction of AKI

Biomarkers for the prognosis of AKI

Discussion

DECLARATION

References