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Recent years have witnessed the unprecedented development and integration of genomics, epigenetics, transcriptomics, proteomics, and metabolomics, as well as a growing interest in novel single biomarkers and process-specific biomarker panels in human renal diseases. In a scenario currently dominated by kidney biopsy and established biomarkers such as serum creatinine, albuminuria, and proteinuria, novel biomarkers could potentially provide vital diagnostic and prognostic information and help to predict response to treatment in several clinical settings, including acute kidney injury, renal transplant, autosomal dominant polycystic kidney disease, and glomerulopathies. However, it is still uncertain whether and to what extent novel biomarkers will succeed in this difficult task. To date, they have generally failed to provide relevant information over and above what is already granted by established, cheap, and easily available biomarkers such as proteinuria, while the complexity and costs of these technology platforms are an important obstacle to their wide adoption. On the other hand, the successful implementation of anti–phospholipase A2 receptor antibodies as a diagnostic and prognostic biomarker of membranous nephropathy, as well as the huge number of ongoing collaborative efforts worldwide, should induce the nephrology community to be rather optimistic about a potential breakthrough in the management of kidney diseases over the next few decades.

Keywords

biomarkers acute kidney injury chronic kidney disease glomerulopathies kidney transplant genomics proteomics metabolomics review

Introduction: The Quest for Novel Biomarkers of Renal Disease

The availability of human genome data and novel technology advances in the fields of proteomics and metabolomics have fueled an impressive number of research projects that aim to identify biomarkers for different health issues, including renal diseases. Biomarkers could prove useful in achieving a timely diagnosis, providing prognostic information, monitoring disease progression, and predicting therapeutic response. This could be of particular relevance to nephrologists, as kidney biopsy—the current gold standard for several renal diseases—is invasive, associated with risks (particularly in patients with advanced renal insufficiency),¹ and sometimes unable to provide a clear-cut association between biopsy appearance and prognosis or response to therapy.² The need for novel and effective biomarkers is further exacerbated by the limitations of established biomarkers of renal disease, such as serum creatinine, albuminuria, and proteinuria. Glomerular filtration rate (GFR) measured by iohexol or iothalamate plasma clearance represents the gold standard for assessing true renal function but is unfortunately not routinely available because of the complexity and costs of measurement protocols. As a consequence, GFR is generally estimated via several creatinine-based equations, including the Cockcroft-Gault formula, the Modification of Diet in Renal Disease (MDRD) study equation, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.³ However, those formulas are unreliable and potentially misleading in a number of conditions that might deeply influence serum creatinine ( Table 1 ), as well as in common clinical scenarios, including acute kidney injury (AKI),⁴ autosomal polycystic kidney disease (ADPKD),⁵ and kidney transplant.⁶ Similarly, although albuminuria and proteinuria have been associated with an increased risk of cardiovascular events⁷ and end-stage renal disease (ESRD)⁸ and represent a suitable surrogate biomarker in chronic kidney disease (CKD) patients,⁹ the Food and Drug Administration (FDA) has generally not accepted effects on proteinuria as evidence of a drug’s effectiveness.¹⁰

Table 1.

Commonly Used Methods for Clinical Assessment of Renal Function and Their Limitations.

Method	Issue(s)	Typical Clinical Scenarios Conditioning Inaccurate Estimates
Serum creatinine	Several factors might severely affect its serum concentration	Advanced age, female sex, malnutrition, inflammation, amputations, vegetarian diet, neuromuscular diseases, liver diseases, drugs, Hispanic ethnicity (decrease); larger muscle mass, intake of cooked meat, drugs, African American ethnicity (increase)
	Nonlinear inverse relationship between serum creatinine and GFR	Patients with mild to moderate CKD might have normal or near-normal serum creatinine levels despite a significant decrease in GFR
	Poor relationship between creatinine levels and acute changes in GFR	AKI patients
Cockcroft-Gault formula: ([140 − age] × weight)/(72 × SCr) × [0.85 if patient is female]	Weight is included to adjust for muscle mass, a determinant of serum creatinine level	Patients with obesity, shifts to the third space, edematous conditions or pregnancy might have a significant body weight increase without any changes in muscle mass
	The formula is derived from a predominantly male population	Females (the 0.85 correction faction is arbitrary)
	The formula estimates creatinine clearance, which overestimates true GFR because of the tubular secretion of creatinine	All patients
	The formula uses serum creatinine, which is not predictive of acute changes in GFR	AKI patients
	The formula uses serum creatinine, which might be affected by factors other than creatinine filtration, and does not incorporate albumin and BUN levels	Sick hospitalized patients
	The formula assumes a steady state with equal production and excretion of creatinine	Older people with a very low muscle mass, infants and children (because of growth and diet), patients receiving drugs who interfere with creatinine secretion (e.g., triple therapy for HIV, chemotherapy, cimetidine, trimethoprim), and/or dosage (diabetic ketoacidosis)
MDRD formula: 186 × SCr−1.154 × Age−0.203 Revised MDRD formula: 175 × SCr−1.154 × Age−0.203	No adjustment for non–African Americans	Patients of Hispanic or Asian ethnicity
	The formulas use serum creatinine, which is not predictive of acute changes in GFR	AKI patients
	Both formulas use serum creatinine, which might be affected by factors other than creatinine filtration, and do not incorporate albumin and BUN levels	Sick hospitalized patients
	The formulas assume a steady state with equal production and excretion of creatinine	Older people with a very low muscle mass, infants and children (because of growth and diet), patients receiving drugs who interfere with creatinine secretion (e.g., triple therapy for HIV, chemotherapy, cimetidine, trimethoprim), and/or dosage (diabetic ketoacidosis)
	Their performance is compromised at higher levels of GFR, as both formulas underestimate true GFR	Healthy subjects, patients with no evidence of parenchymal disease (normal UPCR, normal kidney US scan) and no risk factors for CKD (diabetes, hypertension), people selected for evaluation for kidney donation, ADPKD or diabetic patients with glomerular hyperfiltration
Six-variable MDRD formula: 170 × Scr–0.999 × Age–0.176 × BUN–0.170 × Albumin0.318 × 0.762 (for women) × 1.180 (for blacks)	As above (but slightly more accurate, as the formula includes albumin and BUN)	As above (slightly more reliable in sick hospitalized patients but still unsuitable for clinical application in this population)
CKD-EPI formula: 141 × min(Scr/κ,1)α × max(Scr/κ, 1)–1.209 × 0.993Age × 1.018 (if female) × 1.159 (if black)	As above (but more accurate in patients with GFR between 60 and 120 mL/min/1.73 m²)	As above (but somewhat more reliable in non-hyperfiltering healthy subjects, including people selected for evaluation for kidney donation)

GFR, glomerular filtration rate; CKD, chronic kidney disease; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; HIV, human immunodeficiency virus; MDRD, Modification of Diet in Renal Disease; AKI, acute kidney injury; UPCR, urine protein to creatinine ratio; US, ultrasound; ADPKD, autosomal dominant polycystic kidney disease; BUN, blood urea nitrogen; SCr, serum creatinine; max, maximum of Scr/κ or 1; min, minimum of Scr/κ or 1. κ is 61.9 for females and 79.6 for males; α is −0.329 for females and −0.411 for males.

Several novel biomarker candidates are currently being evaluated in preclinical and clinical models to try to overcome the aforementioned limitations. Among the plethora of urinary and serum biomarkers, microRNAs (miRNAs) appear to be particularly promising. MiRNAs are small, noncoding RNAs that play a key role in multiple cellular functions, such as differentiation, replication, regeneration, and malignant transformation, by regulating protein levels posttranscriptionally. In sharp contrast with most RNAs, miRNAs are surprisingly stable, thus suggesting that secreted miRNAs are likely protected against RNase digestion. As a consequence, they are significantly increased in serum and other body fluids in a wide spectrum of diseases.¹¹ Apart from this desirable feature, circulating miRNAs can readily be detected by PCR and are relatively homogeneous, and the highly specific expression profile of most miRNAs could potentially make it possible to precisely monitor the health status of specific organs—possibly including the kidneys. Proteomic biomarkers also offer great hope for improving the current management of renal patients by achieving early detection and more accurate monitoring of several kidney conditions.¹² In fact, current proteomic tools enable thorough investigation of the human proteome, as recent advances in protein fractionation and labeling techniques are progressively making it possible to detect even the least abundant proteins. Furthermore, the field of proteomics has been enriched by analysis of posttranslational modifications and the possibility of comparing different proteomes.¹³

Role of Novel Biomarkers in Human Renal Diseases

Despite the huge efforts in research, it is still unclear whether and to what extent novel serum and urinary biomarkers will be able to partly or completely replace kidney biopsy or “older” and established biomarkers and provide relevant and otherwise inaccessible information ( Table 2 ). The present review aims to provide an update on serum and urinary biomarkers encompassing a broad range of conditions, from CKD to kidney transplantation, that involve several hundred million people worldwide ( Fig. 1 ).

Table 2.

Established and Novel Biomarkers in Different Renal Diseases.

Renal Disease	Established Renal Biomarker(s)	Benefits	Limitations	Suggested Novel Biomarkers	Potential Advantages	Limitations, Challenges, and Unmet Needs
AKI	Changes in SCr vs. baseline, UO	Cheap, easily assessable; easy classification of AKI into severity classes	Baseline SCr is necessary to define and classify AKI, but frequently unknown in clinical practice. Scr might be influenced by several factors (see Table 1 ). UO assessment is infrequent in non-ICU patients. Sensitivity and specificity of UO can be significantly changed by diuretics.	Several individual urinary and serum biomarkers, including upregulated proteins (NGAL, IL-18, KIM-1, L-FABP), low-molecular-weight proteins (urinary cystatin C), tubular enzymes (NAG, urine GGT, urine AP) Proteomic and miRNA panels	Improved diagnostic accuracy and prognostic stratification	Ability to distinguish between histological AKI (i.e., intrinsic kidney damage) and functional AKI Need for more reliable prediction of AKI development (current AUCs: 0.50 to 0.84) Availability issues, high costs, long turnaround times (particularly for panels)
CKD	SCr, SCr-based eGFR CyC-based eGFR Albuminuria, proteinuria Measured GFR	Cheap, easily assessable, widely available Not affected by muscle mass and dietary protein intake Cheap, easily available, CV and renal risk stratification, targeted therapeutic interventions Accurate estimation (gold standard) of renal function and trends in renal function	Heavily influenced by several factors (see Table 1 ) No conclusive evidence of superiority vs. SCr-based eGFR Higher sensitivity and lower specificity in females than males in predicting clinically relevant proteinuria; more sensitive and less specific with advancing age; total proteinuria cannot be adequately predicted from albuminuria; the latter should be used with caution in nondiabetic CKD Costs, ease of access	Several individual urinary and serum biomarkers, including markers of glomerular (nephrin, podocalyxin, podocin) and tubulointerstitial (NGAL, NAG, KIM-1) injury, oxidative stress (AOPP, TAS, etc.), fibrosis (TGF-β1), inflammation (CRP, hs-CRP, IL-18, PTX3, tenascin, etc.), endothelial dysfunction (ADMA), metabolic factors (FGF-23, etc.) Urinary biomarkers panels (e.g., CKD273 classifier), miRNA panels	Improved diagnostic accuracy and prognostic stratification	Individual biomarkers are still unable to overcome established biomarkers in terms of sensitivity and specificity Availability issues, high costs, long turnaround times (particularly for panels)
Diabetic nephropathy	SCr, SCr-based eGFR CyC-based eGFR	Cheap, easily assessable, broadly available Not affected by muscle mass and dietary protein intake	Heavily influenced by several factors (see Table 1 ) No conclusive evidence of superiority vs. SCr-based eGFR	Several individual urinary and serum biomarkers, including several biomarkers of CKD plus additional markers of fibrosis (MMP-1, 2, 7, 8, 13, leptin, etc.), inflammation (chemokine 1 and 10, TNRF1 and 2, etc.), angiogenesis (endostatin, VEFG-A, HGF), mineral (FGF-23, sclerostin) and lipid (AZGP1) metabolism	Improved diagnostic accuracy, timely diagnosis, better prognostic stratification	Individual biomarkers are still unable to overcome established biomarkers in terms of sensitivity and specificity
	Albuminuria, proteinuria	Cheap, easily available, CV and renal risk stratification, targeted therapeutic interventions	Progressive disease is sometimes present in patients with normoalbuminuria or moderately increased albuminuria; high variability, relatively low sensitivity and specificity	Urinary biomarker panels (e.g., CKD273 classifier)		Availability issues, high costs, long turnaround times (particularly for panels)
ADPKD	SCr, SCr-based eGFRUltrasound imagingTKVGenetic testing	Cheap, easily assessable, broadly availableCheap, broadly availableHigher baseline TKV predicts stage 3 CKD and GFR decline; potential surrogate for therapeutic efficacyCurrent diagnostic gold standard	Poor estimation of true GFR and trends in GFR; unable to identify high-risk hyperfiltering patientsPoor sensitivity in children and young adultsNo data on the association between TKV and subsequent ESRD; needs MRIAvailability issues, high costs; false-negative results in 9% of cases	Markers of endothelial dysfunction or vasopressin signaling; urinary biomarkersUrinary proteomic panels; miRNA panels	Better identification of hyperfiltering patients, better prognostic stratification	Individual biomarkers are still unable to overcome established biomarkers in terms of sensitivity and specificityAvailability issues, high costs, long turnaround times (particularly for panels)
Membranous nephropathy	SCr, SCr-based eGFRProteinuria	Cheap, easily assessable, broadly availableCheap, easily available, risk stratification, targeted therapeutic interventions	Heavily influenced by several factors (see Table 1 )	Anti-PLA2R antibodies	Improved diagnostic accuracy (70% of patients with primary disease)Their serial evaluation might predict the clinical response to rituximab and the risk of relapse	Identification of novel diagnostic biomarkers for anti-PLA2R-negative primary membranous nephropathyIdentification of biomarkers of resistant membranous nephropathy
Kidney transplant	SCr, SCr-based eGFR	Cheap, easily assessable, broadly available	Heavily influenced by several factors (see Table 1 )Poor assessment of true GFR (overestimation) with SCr-based formulas	NGAL, aminoacylase-1, IL-18, CXC-chemokine receptor 4, integrin β2, donor-specific antibodies, microRNAs, etc.	Better assessment and monitoring of graft functionLower risk of kidney allograft rejection	Most available biomarkers do not meet the criteria for biomarker establishmentAbility to discriminate between common clinical conditions (e.g., CNI toxicity vs. acute rejection), thus reducing the need for allograft biopsies
	Albuminuria, proteinuria	Cheap, easily available; prognostic information (risk of graft loss or death)	Influenced by several conditions (ischemia-reperfusion injury, CNI toxicity, chronic allograft nephropathy, recurrent/de novo glomerulonephritis, transplant glomerulopathy, UTIs etc.)		Tailored immuno-suppressive treatment	Ability to predict acute rejection or chronic allograft nephropathy, (immuno-suppression minimization, lower risk of infections and cancer)

AKI, acute kidney injury; SCr, serum creatinine; UO, urine output; ICU, intensive care unit; NGAL, neutrophil gelatinase–associated lipocalin; IL, interleukin; KIM-1, kidney injury molecule 1; L-FABP, liver fatty acid binding protein; NAG, N-acetyl-β-D-glucosaminidase; GGT, gammaglutanyl transpeptidase; AP, alkaline phosphatase; AUC, area under the curve; miRNAs, micro-RNAs; CKD, chronic kidney disease; ADPKD, autosomal dominant polycystic kidney disease; CyC, cystatin C; eGFR, estimated glomerular filtration rate; GFR, glomerular filtration rate; TKV, total kidney volume; MRI, magnetic resonance imaging; PLA2R, phospholipase A2 receptor; CNI, calcineurin inhibitors; UTI, urine tract infection; ESRD, end-stage renal disease; CV, cardiovascular; ADMA, asymmetric dimethtylarginine; CRP, C-reactive protein; hs, high-sensitivity; PTX3, pentraxin 3; AOPP, advanced oxidation protein products; TGF-β1, transforming growth factor-β1; TAS, total antioxidant status; FGF-23, fibroblast growth factor-23; MMP, matrix metallopeptidase; VEGF-A, vascular endothelial growth factor-A; HGF, hepatocyte growth factor; TNFR, tumor necrosis factor receptor; AZGP1, zinc-binding α2-glycoprotein 1.

Figure 1.

The constellation of renal diseases. Worldwide prevalence estimates for different renal diseases. CKD, chronic kidney disease; DN, diabetic nephropathy; ADPKD, autosomal dominant polycystic kidney disease; Tx, kidney transplant. Acute kidney injury is not included, as its occurrence is better described in terms of incidence: about 13.3 million cases every year (original file: https://upload.wikimedia.org/wikipedia/commons/c/cc/Star-sizes.jpg, modified under the terms of Wikimedia Commons, freely licensed media file repository).

Acute Kidney Injury

AKI is a major public health issue worldwide, with an estimated 13.3 million cases every year. The burden is particularly high in developing countries, where the annual incidence is estimated to be 11.3 million cases.¹⁴ It has been estimated that one of five adults and one of three children develop AKI during a hospital stay worldwide.¹⁵ AKI has been associated with increased risk of short- and long-term mortality, CKD, and ESRD,^16–18 even more evident in older patients or those with preexisting CKD.¹⁹ In hospitalized patients, AKI requiring renal replacement therapy (RRT) doubles the long-term risk of mortality and causes a 28-fold increase in the risk of stage 4 to 5 CKD.²⁰ Moreover, even small increases in serum creatinine during hospital stays have been linked with significantly increased in-hospital mortality, increased length of hospitalization, and higher hospital costs.²¹ In the United Kingdom alone, AKI affects between 262,000 and 1 million emergency hospital admissions per year, of which one in four will die, and costs the National Health Service (NHS) £434 to £620 million/year—more than it spends on breast, lung, and skin cancer combined.²² Similarly, despite significant improvements in individual patient outcomes over the past two decades,^23,24 AKI requiring RRT greatly increases the risk of in-hospital mortality in different clinical settings, including patients undergoing cardiovascular surgery²⁵ or admitted into intensive care units (ICUs).²⁶ In light of the above, several research groups have tried to identify serum and urinary biomarkers for diagnosis and the prognostic stratification of AKI. However, to date, biomarkers have failed to achieve any additional benefit compared to the traditional approach in clinical decision making.²⁷ A recent systematic review identified 87 studies on serum and urinary biomarkers of AKI, organized according to five clinical settings: emergency department, critically ill patients in the ICU, cardiac surgery, contrast-induced nephropathy, and pediatrics. The diagnostic and prognostic performance of the biomarkers was extremely heterogeneous, ranging from completely negative to over-optimistic.²⁷ This disappointing scenario might be explained by a number of issues, including the broad array of definitions of AKI, the widely different clinical settings, and the difficulty in making a distinction between histological AKI (i.e., intrinsic kidney damage) and functional AKI. In the ICU setting, some authors have also tried to improve the diagnostic and prognostic performance of biomarkers by using a panel of different biomarkers rather than individual ones. In particular, Metzger and coworkers²⁸ have tested the diagnostic accuracy of 20 urinary peptides in a blinded validation set of 20 ICU patients and 31 stem cell transplantation patients. The proteomic marker pattern was able to detect AKI up to 5 days prior to the increase in serum creatinine, compared to a number of individual markers of AKI, including neutrophil gelatinase–associated lipocalin (NGAL) and urinary kidney injury molecule (KIM-1, interleukin-18, and serum cystatin C). Unfortunately, availability issues, high costs, and long turnaround times are likely to outweigh any potential benefits from this panel. More recently, a pilot study investigated a set of miRNAs as potential biomarkers of AKI in ICU and post–cardiac surgery patients. The miRNA panel had a high diagnostic value, with the area under the curve (AUC) values between 0.9 and 1 at receiver operating characteristic (ROC) analysis, a discriminative power higher than any novel AKI biomarker. Although encouraging, these preliminary results need to be validated by large prospective cohort studies.²⁹ Finally, a number of novel biomarkers, such as KIM-1 and clusterin, are currently being investigated in human drug-induced AKI. In particular, urinary KIM-1 increases may be detected after exposure to several nephrotoxic agents, even in the absence of any changes in serum creatinine concentrations, and it has received regulatory endorsement as a sensitive biomarker of AKI during early drug development.³⁰

Chronic Kidney Disease

CKD is a complex spectrum of functional and structural changes that gradually progress to ESRD. According to the Kidney Disease Improving Global Outcomes (KDIGO) 2012 guidelines, CKD is defined by the presence of abnormalities of kidney structure or function for >3 months and classified based on cause, GFR category, and albuminuria category (CGA staging).³¹ The rate of progression of CKD is extremely variable—from years to decades—and might largely be due to independent risk factors, including high blood pressure, proteinuria, and phosphate.³² CKD affects 8% to 16% of the global population³³ and puts a huge burden on health care systems, due to increased cardiovascular morbidity and mortality, progression to ESRD requiring RRT, and CKD-associated complications such as anemia,³⁴ mineral bone disorder and fractures,³⁵ and cognitive decline.³⁶ As a consequence, there is a huge interest in identifying new serum and urinary biomarkers for the early diagnosis, prognosis, and management of CKD. To date, however, all novel individual biomarkers have failed to overcome more established biomarkers, such as serum creatinine or urinary albumin and protein. Despite their limitations,^37–39 those “older” biomarkers are cheap, easy to assess, and almost universally available, thus representing the cornerstone of large-scale cardiorenal screening and management strategies,⁴⁰ as well as valuable surrogate end points for clinical trials.⁹ On the other hand, a recently published study on 273 specific urinary peptides (CKD273 panel), including fragments of extracellular matrix and blood-derived proteins, showed a mild improvement in detecting and predicting progression of CKD compared to the combination of albuminuria and baseline eGFR (AUC = 0.831 vs. 0.758).⁴¹ However, at present, these findings are insufficient for generating a new paradigm of renal function monitoring, given the constraints of the study design (cross-sectional), the limited mechanistic information, the unknown performance in older people, and the absence of data on hard renal end points, including ESRD.⁴²

Novel biomarkers are generally identified from experimental studies before investigation in clinical studies. As renal cells use a broad range of signaling pathways to regulate their activity, experimental studies have focused on different pathophysiological processes, including glomerular and tubulointerstitial injury, metabolic factors, endothelial dysfunction, oxidative stress, inflammation, and fibrosis.^12,43 Among this extensive and ever growing list, a few compounds showed some promise as biomarkers of CKD progression—particularly serum and urinary NGAL⁴⁴—or kidney function, CKD progression, and associated cardiovascular risk, particularly cystatin C.^45,46 However, further qualification is needed in different and larger populations before any biomarkers can used in daily clinical practice. The prospective Canadian Study of Prediction of Death, Dialysis and Interim Cardiovascular Events (CanPREDDICT) is currently trying to provide new evidence of the association between several traditional biomarkers (eGFR, urine albumin creatinine ratio [uACR], hemoglobin, phosphate, and albumin) and novel biomarkers—asymmetric dimethylarginine (ADMA), N-terminal pro–brain natriuretic peptide (NT-pro-BNP), troponin I, cystatin C, high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and transforming growth factor β1 (TGFβ1)—and RRT, cardiovascular (CV) events, and death in CKD patients. This information could then be used to better understand biological variation in outcomes, to develop clinical prediction models, and to inform enrollment into interventional studies, which may lead to novel treatments.⁴⁷

ADPKD

ADPKD is an inherited systemic disorder that affects 12 million people worldwide and accounts for 8% to 10% of ESRD patients requiring RRT.⁴⁸ About 85% of patients carry germline mutations in the PKD1 gene, while PKD2 gene mutations have been reported in the remaining 15% of cases.⁴⁹ The hallmark phenotype of ADPKD is the progressive enlargement of both kidneys, caused by the sustained expansion of multiple cysts originating from any segment of the renal tubule, which gradually replace normal renal parenchyma.^50–52 So far, clinical management of ADPKD has mainly focused on conservative therapies and treating complications.⁵³ As happened in other fields of nephrology, recent years have seen an extensive search for biomarkers that could play a diagnostic and prognostic role in ADPKD patients. Classically, ADPKD diagnosis relied on detecting cysts by ultrasound scan in patients with a family history of ADPKD.⁵⁴ Unfortunately, ultrasound imaging has poor sensitivity in children and young adults, particularly in the presence of PKD2 mutations. As a result, ADPKD cannot be excluded by renal ultrasound in patients younger than 30 years, and even genetic testing may be inconclusive, as screening of ADPKD patients fails to detect mutations in up to 9% of cases.⁵⁵ Higher baseline height-adjusted total kidney volume (TKV) may predict the onset of stage 3 CKD and GFR decline, but unfortunately there are no data on the association between TKV and the risk of ESRD.⁵⁶ In addition, the inverse association between GFR and kidney volume is greatly variable in individual ADPKD patients, thus limiting the role of GFR as a biomarker.⁵⁷ In this context, proteomic analysis of urine could play an important role in diagnosing and managing ADPKD by detecting multiple changes in the expression and processing of proteins.⁵⁸ In particular, a recent multicentric study characterized the urinary peptidomic pattern of subjects with relatively early stages of ADPKD. In total, 657 peptides with significantly altered excretion were identified, of which 209 could be sequenced by tandem mass spectrometry. A diagnostic biomarker model based on the 142 most consistent peptide markers achieved a sensitivity of 84.5% and a specificity of 94.2% in an independent validation cohort of 251 ADPKD patients from five centers and 86 healthy controls.⁵⁹ In addition, the authors were able to identify a number of biomarkers for disease severity and progression and to develop a proteomic severity score that aimed to predict height-adjusted total kidney volume (htKTV). However, the diagnostic biomarker model showed lower sensitivity in young patients and in patients with the PKD2 genotype, a limit shared with ultrasound imaging. Thus, unfortunately, the usefulness of this biomarker approach in daily clinical practice is questionable, as renal ultrasound scan is very accurate in patients with the PKD1 genotype aged 30 years or older and certainly more easily available and less costly. Other authors have tried a different approach by profiling urinary microRNAs in 20 ADPKD patients and 20 matched CKD patients, in an attempt to identify suitable biomarkers of ADPKD progression.⁶⁰ Interestingly, several specific microRNAs in cellular and extracellular (exosome) specimens were present only in ADPKD patients. In particular, the authors hypothesized that decreased levels of mir-1(4) and mir-133b(2) might play a role in the pathogenesis of ADPKD, thus making them potential biomarkers for disease progression and therapeutic response. However, large-scale prospective studies are needed to corroborate these preliminary findings.

Podocytopathies

The glomerulus is the functional unit of the kidney that produces the ultrafiltrate from the afferent blood flow, and it is a selective filter that ultimately determines which compounds will be further processed throughout the nephron. Traditionally, the glomerular filtration barrier has been described as a three-layer structure: the capillary endothelium, the glomerular basement membrane (GBM), and the visceral epithelium, made up by podocytes, terminally differentiated cells whose foot processes encircle the GBM. More recently, a large number of experimental studies highlighted the importance of at least two additional layers, the endothelial surface layer⁶¹ and the subpodocyte space,⁶² as well as the key role of podocytes as both the regulators of glomerular development and the determinants of proteinuria and progressive glomerulosclerosis.⁶³ Unsurprisingly, a large number of urinary biomarkers of podocyte injury have been investigated so far, including proteins (synaptopodin, podocin, podocalyxin, nephrin, mindin, CD59, CD80, complement receptor 1, glomerular epithelial protein 1, etc.), podocyte-specific mRNAs, the exosomal transcription factor WT1, and podocalyxin-positive granular structures.⁶⁴ In addition, slit diaphragm proteins have been suggested as potential biomarkers given their important role in regulating both podocyte function and structure, through complex interplay with the podocyte actin network.^65–67 All the aforementioned biomarkers, however, need further evaluation in large clinical studies.

Diabetic Nephropathy

Diabetic nephropathy, one the most feared long-term complications of diabetes mellitus, is heralded by the persistence of moderately increased albuminuria (formerly called microalbuminuria; albumin excretion rate: 30–300 mg/24 h; albumin-to-creatinine ratio: 30–300 mg/g).⁶⁸ The subsequent development of severely increased albuminuria (formerly called macroalbuminuria; albumin excretion rate >300 mg/24 h or albumin-to-creatinine ratio >300 mg/g)⁶⁸ is typically paralleled by a progressive decline in GFR, which eventually results in the development of ESRD. Diabetic nephropathy is still the leading cause of ESRD requiring RRT in most of the wealthiest countries countries, including the United States, European Union, and Japan,^69,70 despite a trend toward incidence stabilization that is not evident in poorer countries.^71,72 Diabetic nephropathy occurs in 25% to 40% of diabetic patients within 20 to 25 years of disease and often coexists with some degree of diabetic retinopathy,⁷³ the leading cause of legal blindness,⁷⁴ and a staggering increase in CV morbidity and mortality,⁷⁵ to the point that most patients with diabetic nephropathy will die of CV diseases before progressing to ESRD.^76,77 The increased risk of renal and CV complications eventually results in a hefty socioeconomic burden, particularly in specific ethnic groups⁷⁸ and in low- or middle-income countries, where most ESRD patients do not have access to RRT.⁷⁹

As a result, timely diagnosis and early therapeutic intervention might halt or significantly slow the progression of diabetic nephropathy toward ESRD and ensure substantial socioeconomic benefits, particularly in developing countries and newly industrialized countries, such as China and India.⁸⁰ In addition, albuminuria is a nonspecific marker, as it may increase in other kidney diseases, such as hypertensive nephrosclerosis⁸¹ and interstitial nephritis,⁸² as well as in other common diseases, such as congestive heart failure.^83,84 Finally, the slope of renal function decline in diabetic nephropathy is not always proportional to the progression in albuminuria.^85,86 Thus, there is an unmet need for a novel biomarker that might allow early detection of diabetic nephropathy with higher accuracy and better prognostic evaluation.

In this regard, urinary biomarkers are particularly attractive, as a large volume of urine can be collected noninvasively. A large number of urinary compounds have been investigated, including urinary microRNAs,⁸⁷ exosomes,⁸⁸ and markers of epithelial-mesenchymal transition,^89,90 tubulointerstitial damage,^91–93 increased glomerular permeability,^94,95 extracellular matrix,^96–98 and podocyte damage.^99,100 To date, none of those urinary biomarkers have been shown to be equal or superior to the current gold standard of moderately increased albuminuria. Despite this, the future implementation of a urinary proteomic-based classifier might potentially enable the timely identification of early stages of diabetic nephropathy, even before the onset of moderately increased albuminuria.^101–103 In particular, the PRIORITY (Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early DN In TYpe 2 diabetic patients with normoalbuminuria) trial is currently evaluating the usefulness of a urinary peptide-based classifier composed of 273 different urinary peptides (CKD273) for detecting individuals at high risk of developing diabetic nephropathy.¹⁰⁴ Among serum biomarkers, cystatin C,¹⁰⁵ fibroblast growth factor (FGF)–21,¹⁰⁶ FGF-23,¹⁰⁷ and soluble tumor necrosis factor receptors¹⁰⁸ have been recommended as novel biomarkers for predicting the progression of diabetic nephropathy but need to be validated by large-scale studies.

Membranous Nephropathy

Primary (“idiopathic”) membranous nephropathy (MN) is the leading cause of nephrotic syndrome in white adults, is more common in men than in women, and is most commonly diagnosed during the fourth or fifth decade of life.¹⁰⁹ Primary MN is a chronic disease, characterized by spontaneous remissions—particularly during the first 2 years after onset—and relapses. At 8 years, about 25% of patients reach ESRD requiring RRT; furthermore, nephrotic patients who do not progress toward ESRD have a higher risk of developing CV events. For patients with primary MN, anti–phospholipase A2 receptor (PLA2R) antibodies are a promising diagnostic¹¹⁰ and prognostic¹¹¹ biomarker. These nephritogenic antibodies bind to a 180-kDa M-type transmembrane phospholipase A2 receptor located on podocytes. Anti-PLA2R antibodies are present in about 70% of adult patients with primary MN,¹¹⁰ making them a powerful tool for differentiating between primary and secondary MN and monitoring disease activity and response to therapy.¹¹² In addition, familiar clustering of primary MN has been described in different ethnicities, including Caucasians^113,114 and Asians,¹¹⁵ and has been taken to suggest that genetic factors may play a role in the production of anti-PLA2R antibodies. In light of this, a recent study aimed to investigate the relationship between treatment effect, anti-PLA2R antibodies, and genetic polymorphisms in 132 consecutive patients with primary MN and persistent nephritic syndrome, treated with the anti-CD20 monoclonal antibody rituximab. Among the 81 patients with anti-PLA2R antibodies, lower anti-PLA2R antibody titer at baseline and full antibody depletion at 6 months after rituximab strongly predicted remission over a median follow-up of 30.8 months. Notably, all 25 complete remissions were preceded by full anti-PLA2R antibody depletion. Those findings strongly suggest that serial evaluation of circulating anti-PLA2R antibodies might help to predict both the clinical response to rituximab and the risk of relapse in patients with primary MN and longstanding nephrotic syndrome.¹¹¹ In patients with primary MN, urinary biomarkers have no clear prognostic value because of the poor sensitivity and specificity, the small sample size of the studies, and the lack of adequately powered qualification studies.^116–121

Other Glomerular Diseases

In immunoglobulin A (IgA) nephropathy, both serum galactose-deficient IgA1 (Gd-IgA1)¹²² and antiglycan antibodies targeting the hinge region of Gd-IgA1¹²³ have been suggested as diagnostic and prognostic^124,125 biomarkers. However, family studies showed that a number of asymptomatic relatives of IgA nephropathy patients might have elevated levels of Gd-IgA1 in the absence of any renal involvement, consistent with the hypothesis that the abnormal O-glycosylation of the IgA1 “first hit” is not sufficient per se to induce IgA nephropathy.¹²⁶ Moreover, other studies failed to confirm any association between serum Gd-IgA1 and the magnitude of proteinuria in children with IgA nephropathy.¹²⁷ Thus, further studies are needed to evaluate whether and to what extent a combination of serum Gd-IgA1 and antiglycan antibodies—a likely candidate for the “second hit”¹²³—plays any diagnostic or prognostic role in IgA nephropathy. To date, no urinary biomarkers showed any clinical applicability in IgA nephropathy, despite the use of novel approaches such as urinary RNA profiling¹²⁸ and the extensive study of several compounds, including urinary markers of complement activation^129,130 and urinary IgA1-containing immune complexes.¹³¹

Focal segmental glomerulosclerosis (FSGS) should not be viewed as a single disease but rather as a histologic pattern of renal damage that initially affects the glomerulus and the tubulointerstitium.¹³² The hallmark of primary (idiopathic) FSGS is podocyte injury, while secondary FSGS might occur as a result of genetic mutations, viral infections, drugs, reduced renal mass, hypertension, and obesity.¹³³ At least in some cases, FSGS might be caused by a circulating permeability factor; among several candidate molecules, the soluble urokinase receptor (suPAR) recently emerged as a promising diagnostic biomarker, as suPAR is increased in about two-thirds of patients with primary FSGS.¹³⁴ In addition, high suPAR levels before transplantation have been associated with an increased risk of FSGS relapse after transplant. Finally, suPAR might play a prognostic role, as a reduction in circulating suPAR after immunosuppressive treatment could be predictive of proteinuria reduction and complete remission.^135,136 Unfortunately, suPAR has poor overall sensitivity in secondary FSGS and might be normal in one-third of subjects with primary FSGS or even more—up to 45%. In addition, a nonspecific increase in suPAR might occur in advanced CKD, ESRD, proinflammatory states, and malignancies.^137,138 Thus, current assays—including those using urinary biomarkers¹³⁹—cannot be recommended for daily clinical practice. More recently, the levels of urinary microRNAs (miR196a, miR-30a-5p, and miR-490) were found to be increased in active FSGS but not in healthy controls or in patients with FSGS in remission. In addition, after steroid treatment, their levels were lower in steroid-responsive patients but remained unchanged in steroid-resistant patients.¹⁴⁰ Unfortunately, the decrease in urinary microRNAs was paralleled by a reduction in proteinuria, so these novel urinary biomarkers failed to achieve any advantage over this “older” but cheap and easily accessible biomarker.

In lupus nephritis, putative prognostic biomarkers^141–145 failed to precede the onset of proteinuria, thus resulting in confirmation rather than prediction of damage.

Kidney Transplantation

Kidney transplant is the most desired and cost-effective RRT modality for ESRD patients. However, despite significant advances in surgical techniques and postoperative care over recent decades, and even though kidney transplants unequivocally offer the best survival and quality of life for successful candidates across all demographic groups, broad applicability has been limited by a relative shortage of available organs, graft rejection, and side effects of immunosuppressant drugs.¹⁴⁶ In principle, effective implementation of biomarkers could enable better assessment and monitoring of graft function, minimize the risk of kidney allograft rejection, and allow tailored immunosuppressive therapy in kidney transplant recipients.¹⁴⁷ Unfortunately, despite the identification of several putative biomarkers, including NGAL,^148,149 aminoacylase-1,¹⁵⁰ interleukin-18,¹⁵¹ CXC-chemokine receptor 4, chemokine (C-C motif) ligand 5, integrin β2,¹⁵² donor-specific antibodies,¹⁵³ and microRNAs,^154,155 most data from experimental and clinical studies failed to be translated into daily clinical practice. This unsatisfactory scenario might partly be explained by the short follow-up times and the lack of reliable end points in qualification studies. For example, although numerous formulas have been developed to estimate renal function from biochemical, demographic, and anthropometric data, prediction equations do not allow a rigorous assessment of renal function in kidney transplant recipients. In a group of 81 renal transplant recipients enrolled in the Mycophenolate Mofetil Steroid Sparing (MYSS) trial, 12 different prediction equations were carried out in all patients at months 6, 9, and 21 after surgery and compared with GFR measurement by plasma iohexol clearance as the reference method. All equations showed a tendency toward GFR overestimation; moreover, a significantly higher rate of GFR decline, ranging from −5.0 to − 7.4 mL/min/1.73 m²/y, was estimated by all the equations compared to iohexol clearance (−3.0 mL/min/1.73 m²/y).⁶ Along the same lines, the intrinsic variability in synthesis or tubular secretion of serum creatinine makes it a poor marker of true renal function in the transplant setting.¹⁵⁶ An additional issue is that most of the compounds proposed as biomarkers in human renal transplantation do not fully meet the criteria for biomarker establishment, including evaluation in an independent set of samples; appraisal in different clinical settings to allow the identification of sensitivity, specificity, and negative and positive predictive values; and the assessment of interpatient variability and reproducibility.¹⁵⁷

In conclusion, the broad use of genomics, epigenetics, transcriptomics, proteomics, and metabolomics will probably translate into a major breakthrough in the management of kidney diseases over the next decades. At the moment, however, the complexity and costs of these technology platforms represent formidable obstacles to their usage in everyday clinical practice. Thus, we concur with De Vriese and Fervenza² that, with the exception of serum anti-PLA2R antibodies in most cases of primary membranous nephropathy, novel biomarkers have no additional value compared to conventional ones such as creatinine, albuminuria, and proteinuria. This scenario is likely justified by the fact that until recently, several research groups have focused their efforts on single serum or urinary biomarkers rather than a combination of different biomarkers. Indeed, any single biomarker of disease will always be hindered by the high degree of intra- and interpatient variability in each specific disease and each sample type,¹⁵⁸ especially in urine, wherein the intraindividual reproducibility of the results during the day might be as low as 70%.¹⁵⁹ On the other hand, a combination of biomarkers could be more robust and specific¹⁶⁰ and potentially lead to the generation of different diagnostic, prognostic, or therapeutic response patterns. In support of this statement, albeit preliminary, the recently suggested diagnostic accuracy of a miRNA set in AKI patients highlights that the use of biomarker panels is probably the right way forward.²⁹ Finally, there is an unmet need for novel and broad data integration approaches that might effectively deal with the large amount of information generated by the “omics” tools, improve the comparability of study results across different populations, and confirm or reject findings across studies.¹⁶¹ This crucial step in biomarker discovery becomes even more apparent in light of the fact that many of the available studies have been performed in small patient groups, thus resulting in a poor degree of overlap between proposed biomarkers among different studies. The large amount, complexity, and heterogeneity of data have led several research groups to create multicentric consortiums for qualifying serum and urinary biomarkers. For example, in the recent Clinical Trials in Organ Transplantation-01, a multicenter observational study in 280 adult and pediatric renal transplant recipients, low urinary CXCL9 protein at 6-month follow-up identified the individuals least likely to develop future acute rejection or a decrement in estimated GFR between 6 and 24 months posttransplant from urine obtained from stable allograft recipients.^162,163 The NEFRONA project is currently recruiting over 2000 CKD patients into a prospective observational study aimed at investigating the relationship between biomarkers, cardiovascular events, and mortality,¹⁶⁴ while the Canadian Study of Prediction of Risk and Evolution to Dialysis, Death and Interim cardiovascular Events Over Time (CaNPREDDICT) is assessing a number of biomarkers in about 2500 patients with moderate to advanced CKD over a 36-month follow-up period.¹⁶⁵ Moreover, the Nephrotic Syndrome Study Network (NEPTUNE), the CureGN prospective cohort study, the EURenOmics study, the UK registry for rare renal diseases (RADAR), and the European Renal cDNA bank are studying several biomarkers in patients with different glomerular diseases.¹⁶⁶ We hope that all of those collaborative projects will eventually succeed in qualifying novel and effective diagnostic, prognostic, and therapeutic response biomarkers for kidney diseases—hopefully in a few years.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Whittier

W. L.

Korbet

S. M.

Timing of Complications in Percutaneous Renal Biopsy. J. Am. Soc. Nephrol. 2004, 15, 142–147.

De Vriese

A. S.

Fervenza

F. C.

Con: Biomarkers in Glomerular Diseases: Putting the Cart Before the Wheel?

Nephrol. Dial Transplant 2015, 30, 885–890.

Levey

A. S.

Stevens

L. A.

Schmid

C. H.

. A New Equation to Estimate Glomerular Filtration Rate. Ann. Intern. Med. 2009, 150, 604–612.

Poggio

E. D.

Nef

P. C.

Wang

. Performance of the Cockcroft-Gault and Modification of Diet in Renal Disease Equations in Estimating GFR in Ill Hospitalized Patients. Am. J. Kidney Dis. 2005, 46, 242–252.

Ruggenenti

Gaspari

Cannata

. Measuring and Estimating GFR and Treatment Effect in ADPKD Patients: Results and Implications of a Longitudinal Cohort Study. PLoS One 2012, 7, e32533.

Gaspari

Ferrari

Stucchi

. Performance of Different Prediction Equations for Estimating Renal Function in Kidney Transplantation. Am. J. Transplant 2004, 4, 1826–1835.

Ruggenenti

Porrini

Motterlini

. Measurable Urinary Albumin Predicts Cardiovascular Risk among Normoalbuminuric Patients with Type 2 Diabetes. J. Am. Soc. Nephrol. 2012, 23, 1717–1724.

Hallan

S. I.

Ritz

Lydersen

. Combining GFR and Albuminuria to Classify CKD Improves Prediction of ESRD. J. Am. Soc. Nephrol. 2009, 20, 1069–1077.

Cravedi

Ruggenenti

Remuzzi

Proteinuria Should Be Used as a Surrogate in CKD. Nat. Rev. Nephrol. 2012, 8, 301–306.

10.

Thompson

Proteinuria as a Surrogate End Point—More Data Are Needed. Nat. Rev. Nephrol. 2012, 8, 306–309.

11.

Etheridge

Lee

Hood

. Extracellular MicroRNA: A New Source of Biomarkers. Mutat. Res. 2011, 717, 85–90.

12.

Mischak

Delles

Vlahou

. Proteomic Biomarkers in Kidney Disease: Issues in Development and Implementation. Nat. Rev. Nephrol. 2015, 11, 221–232.

13.

Chandramouli

Qian

P. Y.

Proteomics: Challenges, Techniques and Possibilities to Overcome Biological Sample Complexity. Hum Genomics Proteomics 2009, 2009, 239204.

14.

Perico

Remuzzi

Acute Kidney Injury in Poor Countries Should No Longer Be a Death Sentence: The ISN ‘0 by 25’ Project. Ann. Nutr. Metab. 2015, 66(Suppl 3), 42–44.

15.

Susantitaphong

Cruz

D. N.

Cerda

. World Incidence of AKI: A Meta-Analysis. Clin. J. Am. Soc. Nephrol. 2013, 8, 1482–1493.

16.

Coca

S. G.

Yusuf

Shlipak

M. G.

. Long-Term Risk of Mortality and Other Adverse Outcomes after Acute Kidney Injury: A Systematic Review and Meta-Analysis. Am. J. Kidney Dis. 2009, 53, 961–973.

17.

Chao

C. T.

Hou

C. C.

V. C.

. The Impact of Dialysis-Requiring Acute Kidney Injury on Long-Term Prognosis of Patients Requiring Prolonged Mechanical Ventilation: Nationwide Population-Based Study. PLoS One 2012, 7, e50675.

18.

Coca

S. G.

Singanamala

Parikh

C. R.

Chronic Kidney Disease after Acute Kidney Injury: A Systematic Review and Meta-Analysis. Kidney Int. 2012, 81, 442–448.

19.

Coca

S. G.

Acute Kidney Injury in Elderly Persons. Am. J. Kidney Dis. 2010, 56, 122–131.

20.

L. J.

A. S.

Chertow

G. M.

. Dialysis-Requiring Acute Renal Failure Increases the Risk of Progressive Chronic Kidney Disease. Kidney Int. 2009, 76, 893–899.

21.

Chertow

G. M.

Burdick

Honour

. Acute Kidney Injury, Mortality, Length of Stay, and Costs in Hospitalized Patients. J. Am. Soc. Nephrol. 2005, 16, 3365–3370.

22.

National Institute for Health and Care Excellence (NICE). Acute Kidney Injury: Prevention, Detection and Management of Acute Kidney Injury up to the Point of Renal Replacement Therapy. http://guidance.nice.org.uk/CG169. Accessed May 2, 2015.

23.

Waikar

S. S.

Curhan

G. C.

Wald

. Declining Mortality in Patients with Acute Renal Failure, 1988 to 2002. J. Am. Soc. Nephrol. 2006, 17, 1143–1150.

24.

Lenihan

C. R.

Montez-Rath

M. E.

Mora Mangano

C. T.

. Trends in Acute Kidney Injury, Associated Use of Dialysis, and Mortality after Cardiac Surgery, 1999 to 2008. Ann. Thorac. Surg. 2013, 95, 20–28.

25.

Ivert

Holzmann

M. J.

Sartipy

Survival in Patients with Acute Kidney Injury Requiring Dialysis after Coronary Artery Bypass Grafting. Eur. J. Cardiothorac. Surg. 2014, 45, 312–317.

26.

Thakar

C. V.

Christianson

Freyberg

. Incidence and Outcomes of Acute Kidney Injury in Intensive Care Units: A Veterans Administration Study. Crit. Care Med. 2009, 37, 2552–2558.

27.

Vanmassenhove

Vanholder

Nagler

. Urinary and Serum Biomarkers for the Diagnosis of Acute Kidney Injury: An In-Depth Review of the Literature. Nephrol. Dial Transplant 2013, 28, 254–273.

28.

Metzger

Kirsch

Schiffer

. Urinary Excretion of Twenty Peptides Forms an Early and Accurate Diagnostic Pattern of Acute Kidney Injury. Kidney Int. 2010, 78, 1252–1262.

29.

Aguado-Fraile

Ramos

Conde

. A Pilot Study Identifying a Set of microRNAs As Precise Diagnostic Biomarkers of Acute Kidney Injury. PLoS One 2015, 10, e0127175.

30.

Waring

W. S.

Moonie

Earlier Recognition of Nephrotoxicity Using Novel Biomarkers of Acute Kidney Injury. Clin. Toxicol. (Phila) 2011, 49, 720–728.

31.

The KDIGO Working Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. Suppl. 2013, 3, 1–150.

32.

Cozzolino

Gentile

Mazzaferro

. Blood Pressure, Proteinuria, and Phosphate as Risk Factors for Progressive Kidney Disease: A Hypothesis. Am. J. Kidney Dis. 2013, 62, 984–992.

33.

Jha

Garcia-Garcia

Iseki

. Chronic Kidney Disease: Global Dimension and Perspectives. Lancet 2013, 382, 260–272.

34.

Babitt

J. L.

Lin

H. Y.

Mechanisms of Anemia in CKD. J. Am. Soc. Nephrol. 2012, 23, 1631–1634.

35.

Nickolas

T. L.

Leonard

M. B.

Shane

Chronic Kidney Disease and Bone Fracture: A Growing Concern. Kidney Int. 2008, 74, 721–731.

36.

Bugnicourt

J.-M.

Godefroy

Chillon

J.-M.

. Cognitive Disorders and Dementia in CKD: The Neglected Kidney-Brain Axis. J. Am. Soc. Nephrol. 2013, 24, 353–363.

37.

Levey

A. S.

Cattran

Friedman

. Proteinuria as a Surrogate Outcome in CKD: Report of a Scientific Workshop Sponsored by the National Kidney Foundation and the US Food and Drug Administration. Am. J. Kidney Dis. 2009, 54, 205–226.

38.

Nair

Mishra

Hayden

. The Four-Variable Modification of Diet in Renal Disease Formula Underestimates Glomerular Filtration Rate in Obese Type 2 Diabetic Individuals with Chronic Kidney Disease. Diabetologia 2011, 54, 1304–1307.

39.

Macisaac

R. J.

Jerums

Diabetic Kidney Disease with and without Albuminuria. Curr. Opin. Nephrol. Hypertens. 2011, 20, 246–257.

40.

Kuritzky

Toto

Van Buren

Identification and Management of Albuminuria in the Primary Care Setting. J. Clin. Hypertens. 2011, 13, 438–449.

41.

Schanstra

J. P.

Zürbig

Alkhalaf

. Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides. J. Am. Soc. Nephrol. 2015, 26, 1999–2010.

42.

Merchant

M. L.

Can the Urinary Peptidome Outperform Creatinine and Albumin to Predict Renal Function Decline?

J. Am. Soc. Nephrol. 2015, 26, 1760–1761.

43.

Fassett

R. G.

Venuthurupalli

S. K.

Gobe

G. C.

. Biomarkers in Chronic Kidney Disease: A Review. Kidney Int. 2011, 80, 806–821.

44.

Bolignano

Lacquaniti

Coppolino

. Neutrophil Gelatinase–Associated Lipocalin (NGAL) and Progression of Chronic Kidney Disease. Clin. J. Am. Soc. Nephrol. 2009, 4, 337–344.

45.

Taglieri

Koenig

Kaski

J. C.

Cystatin C and Cardiovascular Risk. Clin. Chem. 2009, 55, 1932–1943.

46.

Rifkin

D. E.

Katz

Chonchol

. Albuminuria, Impaired Kidney Function and Cardiovascular Outcomes or Mortality in the Elderly. Nephrol. Dial. Transplant 2010, 25, 1560–1567.

47.

Levin

Rigatto

Brendan

. Cohort Profile: Canadian Study of Prediction of Death, Dialysis and Interim Cardiovascular Events (CanPREDDICT). BMC Nephrol. 2013, 14, 121.

48.

Gabow

P. A.

Autosomal Dominant Polycystic Kidney Disease. N. Engl. J. Med. 1993, 329, 332–342.

49.

Perico

Remuzzi

Do mTOR Inhibitors Still Have a Future in ADPKD?

Nat. Rev. Nephrol. 2010, 6, 696–698.

50.

King

B. F.

Reed

J. E.

Bergstralh

E. J.

. Quantification and Longitudinal Trends of Kidney, Renal Cyst, and Renal Parenchyma Volumes in Autosomal Dominant Polycystic Kidney Disease. J. Am. Soc. Nephrol. 2000, 11, 1505–1511.

51.

Sullivan

L. P.

Wallace

D. P.

Grantham

J. J.

Chloride and Fluid Secretion in Polycystic Kidney Disease. J. Am. Soc. Nephrol. 1998, 9, 903–916.

52.

Sullivan

L. P.

Wallace

D. P.

Grantham

J. J.

Epithelial Transport in Polycystic Kidney Disease. Physiol. Rev. 1998, 78, 1165–1191.

53.

Grantham

J. J.

Clinical Practice: Autosomal Dominant Polycystic Kidney Disease. N. Engl. J. Med. 2008, 359, 1477–1485.

54.

Pei

Obaji

Dupuis

. Unified Criteria for Ultrasonographic Diagnosis of ADPKD. J. Am. Soc. Nephrol. 2009, 20, 205–212.

55.

Harris

P. C.

Rossetti

Molecular Diagnostics for Autosomal Dominant Polycystic Kidney Disease. Nat. Rev. Nephrol. 2010, 6, 197–206.

56.

Alam

Dahl

N. K.

Lipschutz

J. H.

. Total Kidney Volume in Autosomal Dominant Polycystic Kidney Disease: A Biomarker of Disease Progression and Therapeutic Efficacy. Am. J. Kidney Dis. 2015, 66, 564–576.

57.

Hateboer

v Dijk

M. A.

Bogdanova

. Comparison of Phenotypes of Polycystic Kidney Disease Types 1 and 2. European PKD1-PKD2 Study Group. Lancet 1999, 353, 103–107.

58.

Fliser

Novak

Thongboonkerd

. Advances in Urinary Proteome Analysis and Biomarker Discovery. J. Am. Soc. Nephrol. 2007, 18, 1057–1071.

59.

Kistler

A. D.

Serra

A. L.

Siwy

. Urinary Proteomic Biomarkers for Diagnosis and Risk Stratification of Autosomal Dominant Polycystic Kidney Disease: A Multicentric Study. PLoS One 2013, 8, e53016.

60.

Ben-Dov

I. Z.

Tan

Y. C.

Morozov

. Urine MicroRNA as Potential Biomarkers of Autosomal Dominant Polycystic Kidney Disease Progression: Description of miRNA Profiles at Baseline. PLoS One 2014, 9, e86856.

61.

Dane

M. J.

van den Berg

B. M.

Avramut

M. C.

. Glomerular Endothelial Surface Layer Acts as a Barrier against Albumin Filtration. Am. J. Pathol. 2013, 182, 1532–1540.

62.

Salmon

A. H.

Neal

C. R.

Harper

S. J.

New Aspects of Glomerular Filtration Barrier Structure and Function: Five Layers (at Least) Not Three. Curr. Opin. Nephrol. Hypertens. 2009, 18, 197–205.

63.

Wiggins

R. C.

The Spectrum of Podocytopathies: A Unifying View of Glomerular Diseases. Kidney Int. 2007, 71, 1205–1214.

64.

Sekulic

Pichler Sekulic

A Compendium of Urinary Biomarkers Indicative of Glomerular Podocytopathy. Patholog. Res. Int. 2013, 2013, 782395.

65.

New

L. A.

Martin

C. E.

Jones

Advances in Slit Diaphragm Signaling. Curr. Opin. Nephrol. Hypertens. 2014, 23, 420–430.

66.

Saito

Miyauchi

Hashimoto

. Neurexin-1, a Presynaptic Adhesion Molecule, Localizes at the Slit Diaphragm of the Glomerular Podocytes in Kidneys. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2011, 300, R340–R348.

67.

Biederer

Sudhof

T. C.

Cask and Protein 4.1 Support F-Actin Nucleation on Neurexins. J. Biol. Chem. 2001, 276, 47869–47876.

68.

Kidney Disease Improving Global Outcomes (KDIGO) Study Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Available at http://www.kdigo.org/clinical_practice_guidelines/pdf/CKD/KDIGO_2012_CKD_GL.pdf. Accessed May 21, 2015.

69.

Parving

H. H.

Hovind

Rossing

. Evolving Strategies for Renoprotection: Diabetic Nephropathy. Curr. Opin. Nephrol. Hypertens. 2001, 10, 515–522.

70.

Remuzzi

Schieppati

Ruggenenti

Clinical Practice: Nephropathy in Patients with Type 2 Diabetes. N. Engl. J. Med. 2002, 346, 1145–1151.

71.

Burrows

N. R.

Geiss

L. S.

Incidence of Treatment for End-Stage Renal Disease among Individuals with Diabetes in the U.S. Continues to Decline. Diabetes Care 2010, 33, 73–77.

72.

Jones

C. A.

Krolewski

A. S.

Rogus

. Epidemic of End-Stage Renal Disease in People with Diabetes in the United States Population: Do We Know the Cause? Kidney Int. 2005, 67, 1684–1691.

73.

Fong

D. S.

Aiello

Gardner

T. W.

. Retinopathy in Diabetes. Diabetes Care 2004, 27, s84–s87.

74.

Ding

Wong

T. Y.

Current Epidemiology of Diabetic Retinopathy and Diabetic Macular Edema. Curr. Diabetes Rep. 2012, 12, 346–354.

75.

Karalliedde

Viberti

Microalbuminuria and Cardiovascular Risk. Am. J. Hypertens. 2004, 17, 986–993.

76.

Berl

Henrich

Kidney-Heart Interactions: Epidemiology, Pathogenesis, and Treatment. Clin. J. Am. Soc. Nephrol. 2006, 1, 8–18.

77.

Dinneen

S. F.

Gerstein

H. C.

The Association of Microalbuminuria and Mortality in Non-Insulin-Dependent Diabetes Mellitus: A Systematic Overview of the Literature. Arch. Intern. Med. 1997, 157, 1413–1418.

78.

Spanakis

Golden

Race/Ethnic Difference in Diabetes and Diabetic Complications. Curr. Diabetes Rep. 2013, 13, 814–823.

79.

Couser

W. G.

Remuzzi

Mendis

. The Contribution of Chronic Kidney Disease to the Global Burden of Major Noncommunicable Diseases. Kidney Int. 2011, 80, 1258–1270.

80.

Susan van

Beulens

J. W. J.

Yvonne T. van der

. The Global Burden of Diabetes and Its Complications: An Emerging Pandemic. Eur. J. Cardiovasc. Prev. Rehab. 2010, 17, s3–s8.

81.

Rodicio

J. L.

Campo

Ruilope

L. M.

Microalbuminuria in Essential Hypertension. Kidney Int. 1998, 54, S51–S54.

82.

de Jong

P. E.

Curhan

G. C.

Screening, Monitoring, and Treatment of Albuminuria: Public Health Perspectives. J. Am. Soc. Nephrol. 2006, 17, 2120–2126.

83.

Dobre

Nimade

de Zeeuw

Albuminuria in Heart Failure: What Do We Really Know?

Curr. Opin. Cardiol. 2009, 24, 148–154.

84.

Figueiredo

E. L.

Leao

F. V.

Oliveira

L. V.

. Microalbuminuria in Nondiabetic and Nonhypertensive Systolic Heart Failure Patients. Congest. Heart Fail. 2008, 14, 234–238.

85.

Wild

Roglic

Green

. Global Prevalence of Diabetes: Estimates for the Year 2000 and Projections for 2030. Diabetes Care 2004, 27, 1047–1053.

86.

Lee

C. H.

Lam

K. S.

Biomarkers of Progression in Diabetic Nephropathy: The Past, Present and Future. J. Diabetes Investig. 2015, 6, 247–249.

87.

Argyropoulos

Wang

McClarty

. Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes. PLoS One 2013, 8, e54662.

88.

Kalani

Mohan

Godbole

M. M.

. Wilm’s Tumor-1 Protein Levels in Urinary Exosomes from Diabetic Patients with or without Proteinuria. PLoS One 2013, 8, e60177.

89.

Diamant

Hanemaaijer

Verheijen

J. H.

. Elevated Matrix Metalloproteinase-2 and -9 in Urine, but Not in Serum, Are Markers of Type 1 Diabetic Nephropathy. Diabetes Med. 2001, 18, 423–424.

90.

Zheng

L. L.

Cao

Y. H.

. Urinary mRNA Markers of Epithelial-Mesenchymal Transition Correlate with Progression of Diabetic Nephropathy. Clin. Endocrinol. (Oxf) 2012, 76, 657–664.

91.

Kamijo-Ikemori

Sugaya

Yasuda

. Clinical Significance of Urinary Liver-Type Fatty Acid–Binding Protein in Diabetic Nephropathy of Type 2 Diabetic Patients. Diabetes Care 2011, 34, 691–696.

92.

Nielsen

S. E.

Andersen

Zdunek

. Tubular Markers Do Not Predict the Decline in Glomerular Filtration Rate in Type 1 Diabetic Patients with Overt Nephropathy. Kidney Int. 2011, 79, 1113–1118.

93.

Titan

S. M.

Vieira

J. M.

Jr. Dominguez

W. V.

. Urinary MCP-1 and RBP: Independent Predictors of Renal Outcome in Macroalbuminuric Diabetic Nephropathy. J. Diabetes Complications 2012, 26, 546–553.

94.

Kim

S. S.

Song

S. H.

Kim

I. J.

. Urinary Cystatin C and Tubular Proteinuria Predict Progression of Diabetic Nephropathy. Diabetes Care 2013, 36, 656–661.

95.

Hellemons

M. E.

Kerschbaum

Bakker

S. J.

. Validity of Biomarkers Predicting Onset or Progression of Nephropathy in Patients with Type 2 Diabetes: A Systematic Review. Diabetes Med. 2012, 29, 567–577.

96.

Nguyen

T. Q.

Tarnow

Andersen

. Urinary Connective Tissue Growth Factor Excretion Correlates with Clinical Markers of Renal Disease in a Large Population of Type 1 Diabetic Patients with Diabetic Nephropathy. Diabetes Care 2006, 29, 83–88.

97.

Tam

F. W.

Riser

B. L.

Meeran

. Urinary Monocyte Chemoattractant Protein-1 (MCP-1) and Connective Tissue Growth Factor (CCN2) as Prognostic Markers for Progression of Diabetic Nephropathy. Cytokine 2009, 47, 37–42.

98.

Fagerudd

J. A.

Groop

P. H.

Honkanen

. Urinary Excretion of TGF-Beta 1, PDGF-BB and Fibronectin in Insulin-Dependent Diabetes Mellitus Patients. Kidney Int. Suppl. 1997, 63, S195–S197.

99.

Nakamura

Ushiyama

Suzuki

. Urinary Excretion of Podocytes in Patients with Diabetic Nephropathy. Nephrol. Dial. Transplant 2000, 15, 1379–1383.

100.

Patari

Forsblom

Havana

. Nephrinuria in Diabetic Nephropathy of Type 1 Diabetes. Diabetes 2003, 52, 2969–2974.

101.

Roscioni

S. S.

de Zeeuw

Hellemons

M. E.

. A Urinary Peptide Biomarker Set Predicts Worsening of Albuminuria in Type 2 Diabetes Mellitus. Diabetologia 2013, 56, 259–267.

102.

Moresco

R. N.

Sangoi

M. B.

De Carvalho

J. A.

. Diabetic Nephropathy: Traditional to Proteomic Markers. Clin. Chim. Acta 2013, 421, 17–30.

103.

Zurbig

Jerums

Hovind

. Urinary Proteomics for Early Diagnosis in Diabetic Nephropathy. Diabetes 2012, 61, 3304–3313.

104.

Siwy

Schanstra

J. P.

Argiles

. Multicentre Prospective Validation of a Urinary Peptidome-Based Classifier for the Diagnosis of Type 2 Diabetic Nephropathy. Nephrol. Dial. Transplant 2014, 29, 1563–1570.

105.

Krolewski

A. S.

Warram

J. H.

Forsblom

. Serum Concentration of Cystatin C and Risk of End-Stage Renal Disease in Diabetes. Diabetes Care 2012, 35, 2311–2316.

106.

Lee

C. H.

Hui

E. Y.

Woo

Y. C.

. Circulating Fibroblast Growth Factor 21 Levels Predict Progressive Kidney Disease in Subjects with Type 2 Diabetes and Normoalbuminuria. J. Clin. Endocrinol. Metab. 2015, 100, 1368–1375.

107.

Titan

S. M.

Zatz

Graciolli

F. G.

. FGF-23 as a Predictor of Renal Outcome in Diabetic Nephropathy. Clin. J. Am. Soc. Nephrol. 2011, 6, 241–247.

108.

Niewczas

M. A.

Gohda

Skupien

. Circulating TNF Receptors 1 and 2 Predict ESRD in Type 2 Diabetes. J. Am. Soc. Nephrol. 2012, 23, 507–515.

109.

Fervenza

F. C.

Sethi

Specks

Idiopathic Membranous Nephropathy: Diagnosis and Treatment. Clin. J. Am. Soc. Nephrol. 2008, 3, 905–919.

110.

Beck

L. H.

Jr. Bonegio

R. G.

Lambeau

. M-Type Phospholipase A2 Receptor as Target Antigen in Idiopathic Membranous Nephropathy. N. Engl. J. Med. 2009, 361, 11–21.

111.

Ruggenenti

Debiec

Ruggiero

. Anti-Phospholipase A2 Receptor Antibody Titer Predicts Post-Rituximab Outcome of Membranous Nephropathy. J. Am. Soc. Nephrol. 2015, 26, 2545–2558.

112.

Cravedi

Ruggenenti

Remuzzi

Circulating Anti-PLA2R Autoantibodies to Monitor Immunological Activity in Membranous Nephropathy. J. Am. Soc. Nephrol. 2011, 22, 1400–1402.

113.

Stanescu

H. C.

Arcos-Burgos

Medlar

. Risk HLA-DQA1 and PLA(2)R1 Alleles in Idiopathic Membranous Nephropathy. N. Engl. J. Med. 2011, 364, 616–626.

114.

Bullich

Ballarin

Oliver

. HLA-DQA1 and PLA2R1 Polymorphisms and Risk of Idiopathic Membranous Nephropathy. Clin. J. Am. Soc. Nephrol. 2014, 9, 335–343.

115.

Hou

Zhou

. Interaction between PLA2R1 and HLA-DQA1 Variants Associates with Anti-PLA2R Antibodies and Membranous Nephropathy. J. Am. Soc. Nephrol. 2013, 24, 1323–1329.

116.

Hofstra

J. M.

Deegens

J. K.

Steenbergen

E. J.

. Urinary Excretion of Fatty Acid–Binding Proteins in Idiopathic Membranous Nephropathy. Nephrol. Dial. Transplant 2008, 23, 3160–3165.

117.

Branten

A. J.

du Buf-Vereijken

P. W.

Klasen

I. S.

. Urinary Excretion of Beta2-Microglobulin and IgG Predict Prognosis in Idiopathic Membranous Nephropathy: A Validation Study. J. Am. Soc. Nephrol. 2005, 16, 169–174.

118.

Reichert

L. J.

Koene

R. A.

Wetzels

J. F.

Urinary Excretion of Beta 2-Microglobulin Predicts Renal Outcome in Patients with Idiopathic Membranous Nephropathy. J. Am. Soc. Nephrol. 1995, 6, 1666–1669.

119.

Honkanen

Teppo

A. M.

Tornroth

. Urinary Transforming Growth Factor–Beta 1 in Membranous Glomerulonephritis. Nephrol. Dial. Transplant 1997, 12, 2562–2568.

120.

Kon

S. P.

Coupes

Short

C. D.

. Urinary C5b-9 Excretion and Clinical Course in Idiopathic Human Membranous Nephropathy. Kidney Int. 1995, 48, 1953–1958.

121.

Bazzi

Petrini

Rizza

. Urinary N-Acetyl-Beta-Glucosaminidase Excretion Is a Marker of Tubular Cell Dysfunction and a Predictor of Outcome in Primary Glomerulonephritis. Nephrol. Dial. Transplant 2002, 17, 1890–1896.

122.

Moldoveanu

Wyatt

R. J.

Lee

J. Y.

. Patients with IgA Nephropathy Have Increased Serum Galactose-Deficient IgA1 Levels. Kidney Int. 2007, 71, 1148–1154.

123.

Suzuki

Fan

Zhang

. Aberrantly Glycosylated IgA1 in IgA Nephropathy Patients Is Recognized by IgG Antibodies with Restricted Heterogeneity. J. Clin. Invest. 2009, 119, 1668–1677.

124.

Zhao

Hou

. The Level of Galactose-Deficient IgA1 in the Sera of Patients with IgA Nephropathy Is Associated with Disease Progression. Kidney Int. 2012, 82, 790–796.

125.

Berthoux

Suzuki

Thibaudin

. Autoantibodies Targeting Galactose-Deficient IgA1 Associate with Progression of IgA Nephropathy. J. Am. Soc. Nephrol. 2012, 23, 1579–1587.

126.

Gharavi

A. G.

Moldoveanu

Wyatt

R. J.

. Aberrant IgA1 Glycosylation Is Inherited in Familial and Sporadic IgA Nephropathy. J. Am. Soc. Nephrol. 2008, 19, 1008–1014.

127.

Hastings

M. C.

Afshan

Sanders

J. T.

. Serum Galactose-Deficient IgA1 Level Is Not Associated with Proteinuria in Children with IgA Nephropathy. Int. J. Nephrol. 2012, 2012, 7.

128.

Wang

Kwan

B. C.

Lai

F. M.

. Expression of MicroRNAs in the Urinary Sediment of Patients with IgA Nephropathy. Dis. Markers 2010, 28, 79–86.

129.

Liu

L. L.

Jiang

Wang

L. N.

. Urinary Mannose-Binding Lectin Is a Biomarker for Predicting the Progression of Immunoglobulin (Ig)A Nephropathy. Clin. Exp. Immunol. 2012, 169, 148–155.

130.

Onda

Ohsawa

Ohi

. Excretion of Complement Proteins and Its Activation Marker C5b-9 in IgA Nephropathy in Relation to Renal Function. BMC Nephrol. 2011, 12, 64.

131.

Matousovic

Novak

Yanagihara

. IgA-Containing Immune Complexes in the Urine of IgA Nephropathy Patients. Nephrol. Dial. Transplant 2006, 21, 2478–2484.

132.

Meyrier

Mechanisms of Disease: Focal Segmental Glomerulosclerosis. Nat. Clin. Pract. Nephrol. 2005, 1, 44–54.

133.

D’Agati

V. D.

Fogo

A. B.

Bruijn

J. A.

. Pathologic Classification of Focal Segmental Glomerulosclerosis: A Working Proposal. Am. J. Kidney Dis. 2004, 43, 368–382.

134.

Wei

El Hindi

. Circulating Urokinase Receptor as a Cause of Focal Segmental Glomerulosclerosis. Nat. Med. 2011, 17, 952–960.

135.

Wei

Trachtman

. Circulating suPAR in Two Cohorts of Primary FSGS. J. Am. Soc. Nephrol. 2012, 23, 2051–2059.

136.

Wei

Moller

C. C.

Altintas

M. M.

. Modification of Kidney Barrier Function by the Urokinase Receptor. Nat. Med. 2008, 14, 55–63.

137.

Franco Palacios

C. R.

Lieske

J. C.

Wadei

H. M.

. Urine but Not Serum Soluble Urokinase Receptor (suPAR) May Identify Cases of Recurrent FSGS in Kidney Transplant Candidates. Transplantation 2013, 96, 394–399.

138.

Maas

R. J.

Deegens

J. K.

Wetzels

J. F.

Serum suPAR in Patients with FSGS: Trash or Treasure?

Pediatr. Nephrol. 2013, 28, 1041–1048.

139.

Kalantari

Nafar

Samavat

. Urinary Prognostic Biomarkers in Patients with Focal Segmental Glomerulosclerosis. Nephrourol. Mon. 2014, 6, e16806.

140.

Zhang

Chen

. Evaluation of microRNAs miR-196a, miR-30a-5P, and miR-490 as Biomarkers of Disease Activity among Patients with FSGS. Clin. J. Am. Soc. Nephrol. 2014, 9, 1545–1552.

141.

Rovin

B. H.

Zhang

Biomarkers for Lupus Nephritis: The Quest Continues. Clin. J. Am. Soc. Nephrol. 2009, 4, 1858–1865.

142.

Brunner

H. I.

Bennett

M. R.

Mina

. Association of Noninvasively Measured Renal Protein Biomarkers with Histologic Features of Lupus Nephritis. Arthritis Rheum. 2012, 64, 2687–2697.

143.

Tucci

Narain

. Urinary Biomarkers in Lupus Nephritis. Autoimmun. Rev. 2006, 5, 383–388.

144.

Bruschi

Sinico

R. A.

Moroni

. Glomerular Autoimmune Multicomponents of Human Lupus Nephritis In Vivo: Alpha-Enolase and Annexin AI. J. Am. Soc. Nephrol. 2014, 25, 2483–2498.

145.

Singh

R. G.

Usha Rathore

S. S.

. Urinary MCP-1 as Diagnostic and Prognostic Marker in Patients with Lupus Nephritis Flare. Lupus 2012, 21, 1214–1218.

146.

Abecassis

Bartlett

S. T.

Collins

A. J.

. Kidney Transplantation as Primary Therapy for End-Stage Renal Disease: a National Kidney Foundation/Kidney Disease Outcomes Quality Initiative (NKF/KDOQITM) Conference. Clin. J. Am. Soc. Nephrol. 2008, 3, 471–480.

147.

Sawitzki

Pascher

Babel

. Can We Use Biomarkers and Functional Assays to Implement Personalized Therapies in Transplantation? Transplantation 2009, 87, 1595–1601.

148.

Jochmans

Monbaliu

Pirenne

Neutrophil Gelatinase–Associated Lipocalin, a New Biomarker Candidate in Perfusate of Machine-Perfused Kidneys: A Porcine Pilot Experiment. Transplant Proc. 2011, 43, 3486–3489.

149.

Kohei

Ishida

Tanabe

. Neutrophil Gelatinase–Associated Lipocalin Is a Sensitive Biomarker for the Early Diagnosis of Acute Rejection after Living-Donor Kidney Transplantation. Int. Urol. Nephrol. 2013, 45, 1159–1167.

150.

Welberry Smith

M. P.

Zougman

Cairns

D. A.

. Serum Aminoacylase-1 Is a Novel Biomarker with Potential Prognostic Utility for Long-Term Outcome in Patients with Delayed Graft Function following Renal Transplantation. Kidney Int. 2013, 84, 1214–1225.

151.

Hoogland

E. R.

de Vries

E. E.

Christiaans

M. H.

. The Value of Machine Perfusion Biomarker Concentration in DCD Kidney Transplantations. Transplantation 2013, 95, 603–610.

152.

Scian

M. J.

Maluf

D. G.

Archer

K. J.

. Identification of Biomarkers to Assess Organ Quality and Predict Posttransplantation Outcomes. Transplantation 2012, 94, 851–858.

153.

Hoshino

Kaneku

Everly

M. J.

. Using Donor-Specific Antibodies to Monitor the Need for Immunosuppression. Transplantation 2012, 93, 1173–1178.

154.

Danger

Paul

Giral

. Expression of miR-142-5p in Peripheral Blood Mononuclear Cells from Renal Transplant Patients with Chronic Antibody-Mediated Rejection. PLoS One 2013, 8, e60702.

155.

Anglicheau

Sharma

V. K.

Ding

. MicroRNA Expression Profiles Predictive of Human Renal Allograft Status. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 5330–5335.

156.

Kaplan

Schold

Srinivas

. Effect of Sirolimus Withdrawal in Patients with Deteriorating Renal Function. Am. J. Transplant 2004, 4, 1709–1712.

157.

Schold

J. D.

Kaplan

The Elephant in the Room: Failings of Current Clinical Endpoints in Kidney Transplantation. Am. J. Transplant 2010, 10, 1163–1166.

158.

Kolch

Neususs

Pelzing

. Capillary Electrophoresis–Mass Spectrometry as a Powerful Tool in Clinical Diagnosis and Biomarker Discovery. Mass Spectrom. Rev. 2005, 24, 959–977.

159.

Rodriguez-Suarez

Siwy

Zurbig

. Urine as a Source for Clinical Proteome Analysis: From Discovery to Clinical Application. Biochim. Biophys. Acta 2014, 1844, 884–898.

160.

Argiles

Siwy

Duranton

. CKD273, a New Proteomics Classifier Assessing CKD and Its Prognosis. PLoS One 2013, 8, e62837.

161.

Mas

V. R.

Mueller

T. F.

Archer

K. J.

. Identifying Biomarkers as Diagnostic Tools in Kidney Transplantation. Expert Rev. Mol. Diagn. 2011, 11, 183–196.

162.

Hricik

D. E.

Nickerson

Formica

R. N.

. Multicenter Validation of Urinary CXCL9 as a Risk-Stratifying Biomarker for Kidney Transplant Injury. Am. J. Transplant 2013, 13, 2634–2644.

163.

Srinivas

T. R.

Kaplan

Urinary Biomarkers and Kidney Transplant Rejection: Fine-Tuning the Radar. Am. J. Transplant 2013, 13, 2519–2521.

164.

Junyent

Martinez

Borras

. [Usefulness of Imaging Techniques and Novel Biomarkers in the Prediction of Cardiovascular Risk in Patients with Chronic Kidney Disease in Spain: the NEFRONA Project]. Nefrologia 2010, 30, 119–126.

165.

Levin

Rigatto

Brendan

. Cohort Profile: Canadian Study of Prediction of Death, Dialysis and Interim Cardiovascular Events (CanPREDDICT). BMC Nephrol. 2013, 14, 121.

166.

Mariani

L. H.

Kretzler

Pro: ‘The Usefulness of Biomarkers in Glomerular Diseases’. The Problem: Moving from Syndrome to Mechanism—Individual Patient Variability in Disease Presentation, Course and Response to Therapy. Nephrol. Dial. Transplant 2015, 30, 892–898.

Novel Biomarkers for Renal Diseases? None for the Moment (but One)

Abstract

Keywords

Introduction: The Quest for Novel Biomarkers of Renal Disease

Role of Novel Biomarkers in Human Renal Diseases

Acute Kidney Injury

Chronic Kidney Disease

ADPKD

Podocytopathies

Diabetic Nephropathy

Membranous Nephropathy

Other Glomerular Diseases

Kidney Transplantation

Footnotes

Declaration of Conflicting Interests

Funding

References