**3. Chronic kidney disease (CKD) and biomarkers**

Several novel biomarkers have also been identified in the evaluation of childhood CKD: especially CKD secondary to idiopathic nephrotic syndrome and diabetic nephropathy (DN) [2, 50]. Remarkably, an overlap exists between some of the biomarkers of CKD and those of AKI and acute pyelonephritis. For instance, urine NAG, NGAL, cystatin C and L-FABP all play a role in the evaluation of both CKD and these acute kidney diseases. Similar to the grouping of biomarkers of AKI, biomarkers of CKD can be broadly classified into *biomarkers of kidney function* and *biomarkers of kidney damage*.

GFR is the most important marker of kidney function in CKD, although it is poorly estimated in most clinical and research settings. Thus, equations for its estimation are predicated upon filtration biomarkers such as serum creatinine and serum cystatin C [51]. However, equations for eGFR based on cystatin C appear more reliable because this biomarker is not affected by muscle mass and gives a better representation of GFR, in addition to having a more stable rate of production compared to creatinine [52].

Although albuminuria represents the traditional biomarker of kidney damage, it may only be present after significant damage has occurred, or may be absent in other types of kidney damage such as tubulointerstitial disease and hypertensive kidney disease. Therefore, there is now a paradigm shift to novel biomarkers which could identify patients with CKD early enough so that prompt interventions could slow down the progression of the disease.

#### **3.1. Biomarkers of diabetic nephropathy (DN) and other causes of CKD**

The major pathogenic components of DN consist of renal fibrosis, mesangial expansion, glomerular hypertrophy, oxidative stress and tubular inflammation [53]. Myriad novel biomarkers of DN have now been identified. An attempt to categorize them has also been made [50]. *Glomerular biomarkers* include transferrin, immunoglobulin G (IgG), ceruloplasmin, type IV collagen, laminin, glycosaminoglycans (GAGs), lipocalin-type prostaglandin D synthase (L-PGDS), fibronectin, podocytes-podocalyxin, and VEGF. *Tubular biomarkers* include NGAL, α-1-microglobulin, KIM-1, NAG, cystatin C, and L-FABP. *Biomarkers of inflammation* comprise TNF-α, IL-1β, IL-18, IP-10, monocyte chemoattractant protein 1 (MCP-1), granulocyte colony-stimulating factor (G-CSF), eotaxins, RANTES (regulated on activation, normal T cell expressed and secreted) or CCL-5, and orosomucoid. A typical example of *biomarkers of oxidative stress* is 8oHdG, while *miscellaneous biomarkers* include some tubular markers such as urine heart fatty acid-binding protein and urine retinol-binding protein 4 (RBP4), and podocyte biomarkers such as podocalyxin, nephrin, and VEGF, and urine advanced glycation end products (AGEs). Notably, these podocyte biomarkers are also regarded as glomerular markers. Some of the biomarkers of DN are shown in **Table 1**, and will be briefly discussed below.

NAG has the ability to predict the onset and progression of CKD in diabetes mellitus. For instance, baseline urine levels of this tubular biomarker can independently predict microalbuminuria and macroalbuminuria in type 1 diabetes mellitus [54]. Furthermore, it is a sensitive biomarker for the detection of early renal damage in type 2 diabetes mellitus [55], while

Furthermore, high levels of KIM-1 have been observed in DN: a disease characterized by renal fibrosis and tubular inflammation, among other components that have been previously

elevated urine levels can precede microalbuminuria in type 1 diabetes mellitus [56].

**Biomarkers Clinical significance Comments**

• Ig A nephropathy • Polycystic kidney disease

• Neutrophil gelatinase-associated

• Monocyte chemoattractant pro-

• Interleukin-18 • Lupus nephritis

**Table 1.** Novel biomarkers of chronic kidney diseases.

tein-1 (MCP-1)

Among type 1 diabetics.

Among type 2 diabetics.

‡

†

• N-acetyl-β-D-glucosaminidase • Diabetic nephropathy‡,†

lipocalin

• Kidney injury molecule-1 • Diabetic nephropathy‡ • Predicts CKD progression to

• Idiopathic glomerulonephritis (MN, FSGS, and MCN)

• Liver fatty acid-binding protein • Diabetic nephropathy†,‡ • Predicts early CKD and

• Cystatin C • Diabetic nephropathy† • Predicts early CKD in type 2

• α-1-microglobulin • Diabetic nephropathy† • Predicts early CKD in type 2

• Diabetic nephropathy†

• Retinol-binding protein 4 • Tubulointerstitial diseases • Elevated urine levels

MN, membranous nephropathy; FSGS, focal segmental glomerulosclerosis; MCN, minimal change nephropathy.

ESKD

Biomarkers of Common Childhood Renal Diseases http://dx.doi.org/10.5772/intechopen.74016

of disease

2 diabetics

diabetics

diabetics

diabetics

• Elevated urine levels • Same as in MCP-1

• Diabetic nephropathy† • Predicts CKD and cardiovas-

• Predicts CKD progression from early stages to ESKD

131

• Predicts early and late stages of CKD in type 1 diabetics, and early CKD in type 2 diabetics • Predicts proteinuria-induced tubular damage in early stage

> progression to ESKD in type 1 diabetics and early CKD in type

cular disease risk in type 2


MN, membranous nephropathy; FSGS, focal segmental glomerulosclerosis; MCN, minimal change nephropathy. ‡ Among type 1 diabetics.

† Among type 2 diabetics.

with a moderate predictive value [46, 47]. In addition, its combination with another urine biomarker such as L-FABP resulted in early detection of AKI after cardiac surgery in a sample of adult patients before a rise in serum creatinine was noted [48]. Similarly, in murine models of ischemic and toxic AKI, NGAL was identified as one of the most speedily-induced proteins; its level was elevated by several folds in both serum and urine within hours of the insult [49].

Several novel biomarkers have also been identified in the evaluation of childhood CKD: especially CKD secondary to idiopathic nephrotic syndrome and diabetic nephropathy (DN) [2, 50]. Remarkably, an overlap exists between some of the biomarkers of CKD and those of AKI and acute pyelonephritis. For instance, urine NAG, NGAL, cystatin C and L-FABP all play a role in the evaluation of both CKD and these acute kidney diseases. Similar to the grouping of biomarkers of AKI, biomarkers of CKD can be broadly classified into *biomarkers of kidney function*

GFR is the most important marker of kidney function in CKD, although it is poorly estimated in most clinical and research settings. Thus, equations for its estimation are predicated upon filtration biomarkers such as serum creatinine and serum cystatin C [51]. However, equations for eGFR based on cystatin C appear more reliable because this biomarker is not affected by muscle mass and gives a better representation of GFR, in addition to having a more stable rate

Although albuminuria represents the traditional biomarker of kidney damage, it may only be present after significant damage has occurred, or may be absent in other types of kidney damage such as tubulointerstitial disease and hypertensive kidney disease. Therefore, there is now a paradigm shift to novel biomarkers which could identify patients with CKD early

The major pathogenic components of DN consist of renal fibrosis, mesangial expansion, glomerular hypertrophy, oxidative stress and tubular inflammation [53]. Myriad novel biomarkers of DN have now been identified. An attempt to categorize them has also been made [50]. *Glomerular biomarkers* include transferrin, immunoglobulin G (IgG), ceruloplasmin, type IV collagen, laminin, glycosaminoglycans (GAGs), lipocalin-type prostaglandin D synthase (L-PGDS), fibronectin, podocytes-podocalyxin, and VEGF. *Tubular biomarkers* include NGAL, α-1-microglobulin, KIM-1, NAG, cystatin C, and L-FABP. *Biomarkers of inflammation* comprise TNF-α, IL-1β, IL-18, IP-10, monocyte chemoattractant protein 1 (MCP-1), granulocyte colony-stimulating factor (G-CSF), eotaxins, RANTES (regulated on activation, normal T cell expressed and secreted) or CCL-5, and orosomucoid. A typical example of *biomarkers of oxidative stress* is 8oHdG, while *miscellaneous biomarkers* include some tubular markers such as urine heart fatty acid-binding protein and urine retinol-binding protein 4 (RBP4), and podocyte biomarkers such as podocalyxin, nephrin, and VEGF, and urine advanced glycation end products (AGEs). Notably, these podocyte biomarkers are also regarded as glomerular markers. Some of the biomarkers of DN are shown in **Table 1**, and will be briefly discussed below.

enough so that prompt interventions could slow down the progression of the disease.

**3.1. Biomarkers of diabetic nephropathy (DN) and other causes of CKD**

**3. Chronic kidney disease (CKD) and biomarkers**

and *biomarkers of kidney damage*.

130 Biomarker - Indicator of Abnormal Physiological Process

of production compared to creatinine [52].

**Table 1.** Novel biomarkers of chronic kidney diseases.

NAG has the ability to predict the onset and progression of CKD in diabetes mellitus. For instance, baseline urine levels of this tubular biomarker can independently predict microalbuminuria and macroalbuminuria in type 1 diabetes mellitus [54]. Furthermore, it is a sensitive biomarker for the detection of early renal damage in type 2 diabetes mellitus [55], while elevated urine levels can precede microalbuminuria in type 1 diabetes mellitus [56].

Furthermore, high levels of KIM-1 have been observed in DN: a disease characterized by renal fibrosis and tubular inflammation, among other components that have been previously mentioned. For instance, majority of type 1 diabetic subjects with DN (stage 1 to 3 CKD) who had higher plasma KIM-1 levels reportedly ended up with end-stage kidney disease (ESKD); only a few of their counterparts who had lower plasma KIM-1 levels developed ESKD [57]. Baseline plasma KIM-1 levels also correlated with the rate of eGFR decline after adjusting for baseline urine albumin-to-creatinine ratio, eGFR, and glycated hemoglobin (Hb1Ac).

Another biomarker of DN is L-FABP, whose baseline urine levels in newly-diagnosed type 1 diabetics not only predicted the development of microalbuminuria but also the progression of microalbuminuria to macroalbuminuria [58]. In type 2 diabetes mellitus, elevated urine level of this biomarker also plays a role in predicting early CKD [59], and also serves as an independent predictor of CKD progression in type 1 diabetics [60]. Thus, L-FABP has the advantage of predicting kidney injury before albuminuria, especially in type 1 diabetics. Serum and urine cystatin C levels are useful biomarkers for early prediction of nephropathy in type 2 diabetes mellitus [61], and for progression of diabetic kidney disease [62]. Furthermore, α-1 microglobulin has been identified as an inexpensive biomarker for the early prediction of DN [63]. Elevated urine levels occur in patients with normoalbuminuric type 2 diabetes mellitus: preceding the onset of microalbuminuria and confirming this tubular biomarker as a more sensitive urine biomarker of CKD [64].

Expression of MCP-1 is upregulated in kidney diseases which present with a continued inflammatory response, such as in DN and lupus nephritis. Some reports indeed indicate that elevated levels of urine MCP-1 were observed in DN [65], as well as in active lupus nephritis [66]. Serum and urine levels of IL-18 were positively correlated with albumin excretion rate, whereas its serum levels were positively correlated with the development of carotid intima-media thickness in type 2 diabetics, and may therefore be a predictor of DN progression and cardiovascular diseases [67].

Finally, urine RBP4 is elevated in patients with tubulointerstitial disease, and may constitute a risk factor for long-term allograft loss, independent of the histology of renal biopsy, as well as for albuminuria [68].

SRNS patients with the active disease when compared to those in remission and the controls [70]. The diagnostic utility of neopterin for active idiopathic nephrotic syndrome was thus highlighted, but its poor discriminatory ability for SSNS and SRNS were also noted in the report. Third, uVDBP was reported to have a high discriminatory ability in distinguishing SRNS from SSNS [72]. For instance, levels of uVDBP were significantly higher in patients with SRNS than in patients with SSNS and in the controls. Despite the direct correlation between microalbuminuria and uVDBP, the latter exhibited a higher discriminatory ability for differ-

**Novel biomarkers (body fluids) Reported role of biomarkers (references)**

• Neopterin (serum) • Diagnostic\*\* (Bakr et al. [70])

(Bakkalŏglu et al. [69])

Biomarkers of Common Childhood Renal Diseases http://dx.doi.org/10.5772/intechopen.74016 133

(Bakr et al. [70])

(Bennett et al. [72])

(Calişkan et al. [71])

(Piyaphanee et al. [73])

(Fede et al. [74])

(Calişkan et al. [71])

• Prognostic\*\*\* (Fede et al. [74])

• Discriminatory†

• Prognostic‡

• Diagnostic¥

• Adiponectin (serum) • Diagnostic\*

• Vitamin D binding protein (urine) • Discriminatory†

• N-acetyl-beta-D glucosaminidase (urine) • Discriminatory†

• α 1-β glycoprotein (13.8 kDa fragment) (urine) • Discriminatory†

Raised serum levels in steroid-resistant nephrotic syndrome (SRNS) relapse.

**Table 2.** Role of some novel biomarkers in childhood idiopathic nephrotic syndrome.

Differentiates SRNS from steroid-sensitive nephrotic syndrome (SSNS).

\*\*Raised serum levels in primary active nephrotic syndrome.

\*\*\*Predicts tubular injury and dysfunction in SRNS.

Many biomarkers have now been identified for the diagnostic and prognostic evaluation of acute and chronic diseases of the kidney in children. However, more evidence-based studies are still required to validate some of the novel biomarkers. Remarkably, a biomarker-panel comprising several of the markers potentially improves their sensitivity and specificity in disease evaluation. Inequities in the availability and accessibility of the laboratory tools between the developed and developing world still remain a challenge. Biotechnology firms should urgently prioritize the

mass production of tools for identifying these biomarkers in order to bridge this gap.

entiating SRNS from SSNS than the former.

**4. Conclusion**

• beta2-microglobulin (urine)

• beta2-microglobulin (urine)

Predicts steroid-responsiveness.

Elevated levels in SRNS and SSNS.

\*

†

‡

¥

• N-acetyl-beta-D glucosaminidase (urine)

• N-acetyl-beta-D glucosaminidase (urine)

#### **3.2. Biomarkers of idiopathic nephrotic syndrome**

The use of biomarkers in childhood idiopathic nephrotic syndrome has been well documented [2]. It represents a non-invasive approach in diagnostic nephrology, as these markers can be used in the prediction and prognostic evaluation of the disease, as well as in differentiating steroid-resistant nephrotic syndrome (SRNS) from steroid-sensitive nephrotic syndrome (SSNS). **Table 2** summarizes the list of identified biomarkers reported for childhood idiopathic nephrotic syndrome. Adiponectin (ADPN) – one of the adipokines, neopterin, β2-microglobulin, and NAG were reported to be diagnostic markers [69–71]. In addition to neopterin and NAG, urine vitamin D-binding protein (u VDBP) and α1β-glycoprotein were able to differentiate SRNS from SSNS [72, 73] while NAG and β2-microglobulin could also predict steroid responsiveness and renal outcome in SRNS [74]. Some of these biomarkers are further discussed as follows. First, elevated serum ADPN levels were documented in SRNS patients in relapse compared to those in remission [69]. Specifically, strong positive correlations were observed between serum ADPN levels and lipid parameters/proteinuria, whereas negative correlations were noted between ADPN levels and serum protein/albumin levels. Second, serum neopterin levels were found to be significantly elevated among SSNS and


¥ Elevated levels in SRNS and SSNS.

mentioned. For instance, majority of type 1 diabetic subjects with DN (stage 1 to 3 CKD) who had higher plasma KIM-1 levels reportedly ended up with end-stage kidney disease (ESKD); only a few of their counterparts who had lower plasma KIM-1 levels developed ESKD [57]. Baseline plasma KIM-1 levels also correlated with the rate of eGFR decline after adjusting for

Another biomarker of DN is L-FABP, whose baseline urine levels in newly-diagnosed type 1 diabetics not only predicted the development of microalbuminuria but also the progression of microalbuminuria to macroalbuminuria [58]. In type 2 diabetes mellitus, elevated urine level of this biomarker also plays a role in predicting early CKD [59], and also serves as an independent predictor of CKD progression in type 1 diabetics [60]. Thus, L-FABP has the advantage of predicting kidney injury before albuminuria, especially in type 1 diabetics. Serum and urine cystatin C levels are useful biomarkers for early prediction of nephropathy in type 2 diabetes mellitus [61], and for progression of diabetic kidney disease [62]. Furthermore, α-1 microglobulin has been identified as an inexpensive biomarker for the early prediction of DN [63]. Elevated urine levels occur in patients with normoalbuminuric type 2 diabetes mellitus: preceding the onset of microalbuminuria and confirming this tubular biomarker as a more

Expression of MCP-1 is upregulated in kidney diseases which present with a continued inflammatory response, such as in DN and lupus nephritis. Some reports indeed indicate that elevated levels of urine MCP-1 were observed in DN [65], as well as in active lupus nephritis [66]. Serum and urine levels of IL-18 were positively correlated with albumin excretion rate, whereas its serum levels were positively correlated with the development of carotid intima-media thickness in type 2 diabetics, and may therefore be a predictor of DN progression and cardiovascular diseases [67].

Finally, urine RBP4 is elevated in patients with tubulointerstitial disease, and may constitute a risk factor for long-term allograft loss, independent of the histology of renal biopsy, as well

The use of biomarkers in childhood idiopathic nephrotic syndrome has been well documented [2]. It represents a non-invasive approach in diagnostic nephrology, as these markers can be used in the prediction and prognostic evaluation of the disease, as well as in differentiating steroid-resistant nephrotic syndrome (SRNS) from steroid-sensitive nephrotic syndrome (SSNS). **Table 2** summarizes the list of identified biomarkers reported for childhood idiopathic nephrotic syndrome. Adiponectin (ADPN) – one of the adipokines, neopterin, β2-microglobulin, and NAG were reported to be diagnostic markers [69–71]. In addition to neopterin and NAG, urine vitamin D-binding protein (u VDBP) and α1β-glycoprotein were able to differentiate SRNS from SSNS [72, 73] while NAG and β2-microglobulin could also predict steroid responsiveness and renal outcome in SRNS [74]. Some of these biomarkers are further discussed as follows. First, elevated serum ADPN levels were documented in SRNS patients in relapse compared to those in remission [69]. Specifically, strong positive correlations were observed between serum ADPN levels and lipid parameters/proteinuria, whereas negative correlations were noted between ADPN levels and serum protein/albumin levels. Second, serum neopterin levels were found to be significantly elevated among SSNS and

baseline urine albumin-to-creatinine ratio, eGFR, and glycated hemoglobin (Hb1Ac).

sensitive urine biomarker of CKD [64].

132 Biomarker - Indicator of Abnormal Physiological Process

**3.2. Biomarkers of idiopathic nephrotic syndrome**

as for albuminuria [68].

**Table 2.** Role of some novel biomarkers in childhood idiopathic nephrotic syndrome.

SRNS patients with the active disease when compared to those in remission and the controls [70]. The diagnostic utility of neopterin for active idiopathic nephrotic syndrome was thus highlighted, but its poor discriminatory ability for SSNS and SRNS were also noted in the report. Third, uVDBP was reported to have a high discriminatory ability in distinguishing SRNS from SSNS [72]. For instance, levels of uVDBP were significantly higher in patients with SRNS than in patients with SSNS and in the controls. Despite the direct correlation between microalbuminuria and uVDBP, the latter exhibited a higher discriminatory ability for differentiating SRNS from SSNS than the former.
