Diagnostic and prognostic biomarkers for tubulointerstitial fibrosis

Renal fibrosis is the final common pathophysiological pathway in chronic kidney disease (CKD) regardless of the underlying cause of kidney injury. Tubulointerstitial fibrosis (TIF) is considered to be the key pathological predictor of CKD progression. Currently, the gold‐standard tool to identify TIF is kidney biopsy, an invasive method that carries risks. Non‐invasive diagnostics rely on an estimation of glomerular filtration rate and albuminuria to assess kidney function, but these fail to diagnose early CKD accurately or to predict progressive decline in kidney function. In this review, we summarize the current and emerging molecular biomarkers that have been studied in various clinical settings and in animal models of kidney disease and that are correlated with the degree of TIF. We examine the potential of these biomarkers to diagnose TIF non‐invasively and to predict disease progression. We also examine the potential of new technologies and non‐invasive diagnostic approaches in assessing TIF. Limitations of current and potential biomarkers are discussed and knowledge gaps identified.


Introduction
Renal fibrosis is a hallmark of chronic kidney disease (CKD), a growing health issue responsible for a substantial burden of illness and premature mortality. Chronic kidney disease is characterized by progressive loss of kidney function leading to kidney failure, which necessitates dialysis or transplantation or results in premature death. Chronic kidney disease affects >10% of the population worldwide, and its rate is increasing with the increasing rates of obesity and diabetes. Symptoms of CKD may occur when >90% of kidney function is lost, and any opportunity to reverse kidney disease or stabilize kidney function at this point is futile. Evidence of kidney damage relies on biochemical evidence of a reduction in glomerular filtration rate (GFR) [assessed as estimated glomerular filtration rate (eGFR)], with or without albuminuria. However, patients presenting with even minor reductions in eGFR or microalbuminuria already show advanced pathological damage. Despite blockade of the renin-angiotensin system (Sica & Bakris, 2002) and, more recently, the use of sodium-glucose linked transport-2 (SGLT2) inhibitors (Seufert & Laubner, 2019;Nuffield Department of Population Health Renal Studies & Consortium, 2022) and finerenone in patients with diabetic kidney disease (Bakris et al., 2020), in addition to targeted treatments to prevent the complications of renal failure, including blood pressure-lowering therapy, erythropoietin-stimulating agents, cholesterol-lowering agents, phosphate binders, restoration of acid-base balance and calcimimetics, large numbers of people with CKD continue to progress to kidney failure (Aapro et al., 2018;Bover et al., 2016;Chan et al., 2017). The progression rate varies from patient to patient, and prediction of patients at risk of rapid decline in renal function is, at best, imprecise.
Renal fibrosis is characterized by renal interstitial fibroblast proliferation, resulting in aberrant and excessive deposition of extracellular matrix (ECM), which leads to the destruction of normal renal tubules and interstitial structures. Tubulointerstitial fibrosis (TIF) is correlated with the decline in kidney function (Wong & Pollock, 2014) and can alone predict progression to renal failure [area under the receiver operating characteristic curve (AUC) = 0.832] (Menn-Josephy et al., 2016). Hence, it has been recognized as the most potent predictor of progression of CKD (Hewitson, 2009;Humphreys, 2018;Mariani et al., 2018;Nath, 1992;Risdon et al., 1968;Wong & Pollock, 2014). Histological examination of the kidney tissue by kidney biopsy remains the current gold standard for assessing renal fibrosis. However, this is invasive and can cause complications, such as bleeding, pain or infection (Whittier & Korbet, 2004).
Recently, multiple studies have focused on the detection of urinary or blood biomarkers for predicting the risk of development of kidney diseases and to monitor progression (Cao et al., 2015;Liang et al., 2017;Mansour et al., 2017;Wong & Pollock, 2014;Wong et al., 2013). In addition, recent technologies, such as omics (proteomics and transcriptomics) and imaging (hyperspectral autofluorescence imaging, ultrasound and magnetic resonance imaging), have also been used to monitor TIF and develop a non-invasive diagnostic method for diagnosing and monitoring CKD (Chen et al., 2020;Mahbub et al., 2021;Muzamil et al., 2020;Zhang et al., 2021). However, to date, there is no specific serum or urinary marker(s) robust enough to reflect the degree of renal TIF.
In this review, we analyse the current and emerging biomarkers for the identification of TIF in the context of CKD. We describe the association between conventional markers of kidney function (eGFR and albuminuria) with TIF, then present a comprehensive summary of potential biomarkers for determining the degree of TIF in urine and blood. The role of emerging molecular TIF biomarkers and new technologies in predicting future CKD are also summarized. In the last section, we rank the potential biomarkers based on their correlation co-efficiency with TIF and diagnostic power to predict TIF levels and discuss whether those biomarkers outperform the existing biomarkers, eGFR and albuminuria.

Correlation of conventional markers of kidney function with TIF
Current measurement of kidney function and classification of the grading of CKD are done using eGFR and albuminuria (Levin & Stevens, 2014). In clinical practice, eGFR is considered as the best measure for renal function that can be measured over time, while the presence of albumin (albuminuria) or proteins (proteinuria) in urine is considered as a reflection of dysfunction in glomerular filtration, because proteins cannot pass the filtration barrier in normally functioning kidneys (Butt et al., 2020). Although these conventional biomarkers are used in clinical practice to assess kidney function, various studies have shown that they cannot predict TIF levels (Table 1; Gonzalez et al., 2022;Magalhaes et al., 2017;Sparding et al., 2021;L.-T. Zhou et al., 2017).
A recent study reported a negative correlation between eGFR and the percentage of TIF at the time of biopsy in patients with immunoglobulin A nephropathy (IgAN; r = −0.53, P < 0.0001), whereas proteinuria was not correlated with TIF (r = 0.12, P > 0.05) (Sparding et al., 2021). Another study presented weak and insignificant correlation between TIF and eGFR (r = −0.22, P = 0.16), between TIF and albuminuria (r = −0.13, P = 0.39) and between TIF and proteinuria (r = −0.07, P = 0.66) in patients with CKD attributable to different aetiologies (Magalhaes et al., 2017). This was consistent with a study by Gonzalez et al. (2022), which also showed that eGFR is not correlated with TIF (r = −0.17, P = 0.29), whereas proteinuria is weakly correlated with TIF (r = 0.29, P = 0.017) in patients with diabetic nephropathy. These contradictory results in human studies are most probably attributable to different aetiologies in different patients and to possible biopsy sampling and analysis errors (Berchtold et al., 2017). Hence, more comprehensive studies with large cohorts using standardized biopsy sampling methods should be performed for more consistent results.
The eGFR can discriminate between patients with no/mild kidney fibrosis (≤25% fibrosis in the tissue) and patients with moderate-to-severe fibrosis (>25%) with a discrimination accuracy (AUC) of 0.83 (Cao et al., 2015). Another study demonstrated that eGFR could discriminate between the levels of fibrosis in biopsy-proven CKD patients with an AUC of 0.76. However, proteinuria had no discriminatory effect when assessing TIF levels (AUC = 0.59) in patients with various CKD aetiologies [glomerulonephritis, IgAN, non-IgA membranoproliferative glomerulonephritis, minimal change disease, membranous nephropathy, focal segmental glomerulosclerosis, IgM and C1q nephropathy, lupus nephritis, diabetic nephropathy, Alport's syndrome and anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis] (L.-T. .

Molecular biomarkers of TIF in urine and blood
In this section, we summarize studies of different molecular biomarkers in urine or blood that have previously been associated with CKD and independently correlated with TIF.
Transforming growth factor-β1. Transforming growth factor-β1 (TGF-β1) is one of the main cytokines that play an important role in the pathogenesis of renal inflammation and fibrosis (Isaka, 2018;Tang et al., 2019). It is secreted by many cell types, including inflammatory cells, kidney tubular epithelial cells, fibroblasts and pericytes (Fuchs et al., 2021;Ito et al., 2010;Koesters et al., 2010;Meng et al., 2015). Newly synthesized TGF-β1 is released into the ECM and binds to the latency-associated peptide (LAP) via TGF-β binding protein (LTBP) to form a latent complex (Gu et al., 2020). Transforming growth factor-β1 in the latent ECM-bound form is inactive but is converted to an active form following injury, but during the injury the active form is released by reactive oxygen species (ROS), proteolysis by plasmin or metalloproteinases, interacting with thrombospondin and integrins and acidosis (Annes et al., 2003;Jobling et al., 2006;Murphy-Ullrich & Suto, 2018;Wipff & Hinz, 2008). Transforming growth factor-β1 plays a role in epithelial-to-mesenchymal transition and fibroblast activation during renal tubular injury that contribute to the development of fibrosis in CKD (Lovisa et al., 2015). The level of TGF-β1 is normally reduced at the completion of normal wound healing processes (Penn et al., 2012), but expression of TGF-β1 mRNA is correlated with kidney fibrosis in many different models of CKD (Fuchs et al., 2021;Gifford et al., 2021;Koesters et al., 2010;J Physiol 601.14  Abbreviations: ANCA, anti-neutrophil cytoplasmic antibody; AUC, area under the curve; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; NR, not reported; TIF, tubulointerstitial fibrosis. Wang et al., 2014;Yang et al., 2021). Hence, the detection of active TGF-β1 in urine or blood (Table 2) is considered as a predictor of kidney fibrosis. Several studies have shown a positive correlation of urinary TGF-β1 (uTGF-β1) with TIF in CKD (Table 2). In patients with membranous glomerulonephritis, total uTGF-β1 (latent and active) in the samples collected 1 year before the biopsy from patients with eGFR > 60 ml/min/1.73 m 2 was strongly correlated with TIF scores at the time of biopsy (r = 0.86, P = 0.01), while total uTGF-β1 measured at the time of biopsy had a non-significant correlation with fibrosis (Honkanen et al., 1997). It should be considered that fibrosis is a dynamic process of production and degradation of ECM and cellular components whose levels can fluctuate during development of kidney fibrosis (Humphreys, 2018). We, thus, hypothesize that levels of uTGF-β1 increase at the time of development of fibrosis, but then become quiescent in established fibrosis. Alternatively, the filtration of TGF-β1 could be reduced when GFR decreased. Nonetheless, this study suggested that the total uTGF-β1 reflects ongoing fibrosis and could be an early biomarker to predict the development of fibrosis (Honkanen et al., 1997).
Likewise, a positive correlation (r = 0.60, P < 0.001) between total uTGF-β1 and the percentage of TIF (%TIF) was found at the time of biopsy in patients with lupus nephritis (Susianti et al., 2015). The calculated AUC = 0.9 in this study could discriminate patients with low levels of fibrosis (<5% TIF) from those with a higher degree of fibrosis (≥5% TIF). A positive correlation (r = 0.38, P < 0.01) was also shown between total uTGF-β1 and %TIF in patients with IgAN (Segarra-Medrano et al., 2017).
In the case of plasma TGF-β1, no correlation was found with TIF in patients with heavy proteinuria caused by different glomerular diseases (Goumenos et al., 2002). However, an association was observed between baseline plasma TGF-β1 (both total and active) and end-stage kidney failure in patients with type 2 diabetes who developed end-stage kidney failure during a 5-year follow-up period (Wong et al., 2013). In addition, a high AUC value was observed (AUC = 0.96) if total and active TGF-β1 and bone morphogenic protein-7 (an antagonist of TGF-β1) were used to predict renal end points in patients with type 2 diabetes (Wong et al., 2013).
Collagens. Serum and urinary levels of ECM components, such as collagens, have been examined extensively for their role as biomarkers in predicting kidney TIF (Table 3). Collagen type I, III and VI degradation fragments (C1M, C3M and C6M, respectively), which are generated after degradation of the corresponding collagens by metalloproteinases during ECM remodelling, have been shown to be promising biomarkers for TIF (Hijmans et al., 2017;Papasotiriou et al., 2015;Sparding et al., 2018). Urinary and plasma C1M and C3M (u/pC1M and u/pC3M, respectively) were significantly correlated with renal immunohistochemical expression of collagen type III, a marker of kidney fibrosis in the kidney of rats with adenine-induced nephropathy, 5/6 nephrectomy or chronic anti-Thy1 (the data from those models were pooled for correlation analysis) (uC1M r = 0.56, P = 0.004; pC1M r = 0.55, P < 0.006; and uC3M r = 0.63, P = 0.001; pC3M r = 0.593, P = 0.002; Papasotiriou et al., 2015). A longitudinal analysis of urinary uC1M/creatinine (Cr) and uC3M/Cr levels collected at week 6 and 12 from adriamycin-induced fibrotic rats showed a significant correlation with TIF histological scores obtained at week 12 (r = 0.68, P = 0.06 and r = 0.831, P = 0.00 for uC3M/Cr; and r = 0.67, P = 0.067 and r = 0.694, P = 0.003 for uC1M/Cr at week 6 and 12, respectively; Hijmans et al., 2017). This suggested a potential role of uC1M and uC3M not only in diagnosing TIF, but also in predicting the future development of fibrosis. On the contrary, clinical studies revealed a significant negative correlation between TIF and uC3M. At the time of biopsy, uC3M/Cr was negatively correlated with the histological score for TIF (R 2 = −0.62, P < 0.0001) in patients with IgAN . A similar observation of negative correlation between uC3M/Cr and TIF was reported in patients with lupus nephritis (r = −0.43, P < 0.05; Genovese et al., 2021). The contrary correlations between uC3M and renal fibrosis in animal models and human patients could be attributable to differences in the process of ECM turnover. The difference in the severity of injury and the rapid onset and fast progression of fibrosis in animals could cause more degradation and collagen production in the urine compared with human disease, where fibrosis is developed slowly over time and collagen expression is more abundant in kidney tissue with advanced stages. However, there have been no studies assessing uC1M and uC3M longitudinally in humans with CKD to examine whether they can predict TIF.
These data suggest that collagen degradation products, such as C1M and C3M, in urine and blood have more promising roles as biomarkers for TIF than the molecules associated with collagen synthesis, such as PRO-C3, PRO-C6 and PIIINP. It should be emphasized that although urinary C3M had a positive correlation in animal models but negative a correlation in humans, both C1M and C3M are still promising biomarkers owing to the strength of correlation with TIF, which is higher than that of other collagen related biomarkers.

Metalloproteinases. Matrix metalloproteinases (MMPs)
are expressed by all glomerular cells, tubular epithelial cells and macrophages in kidneys (Zakiyanov et al., 2019). These enzymes have a role in degradation and remodelling of ECM components. Among the MMPs, MMP-2, MMP-7 and MMP-9 have been investigated to find associations with TIF (Table 4; Zakiyanov et al., 2019).
Epidermal growth factor. Epidermal growth factor (EGF) is detected in tubular epithelial cells of the loop of Henle and distal convoluted tubules. It has roles in renal tubulogenesis and in tubular regeneration after tubulointerstitial injury Grandaliano et al., 2000).
A decrease in urinary EGF (uEGF) was shown to be correlated with TIF, hence it was suggested as a potential biomarker of TIF (Table 4; Ranieri et al., 1996). A significant decrease in the concentration of uEGF in longitudinally collected urine samples from adult male rats with diabetes mellitus and hypertension was correlated with the presence of TIF, determined by Picrosirius staining of the cortex (Betz et al., 2016). In the same study, the clinical assessment of uEGF in patients with type 2 diabetes showed a strong correlation (r = 0.71, P < 0.001) with eGFR (Betz et al., 2016). In another study, uEGF/creatinine (Cr) was inversely correlated with TIF scores (r = −0.75, P < 0.001) obtained from biopsy of patients with CKD and positively correlated with eGFR levels (r = 0.77, P < 0.0001; Ju et al., 2015). This study measured AUC = 0.84 for the base model (eGFR and albuminuria), but the addition of uEGF to the base model increased AUC to 0.90 (Ju et al., 2015). Urinary EGF levels were also inversely correlated with TIF in biopsy samples obtained from patients with primary glomerulonephritis (IgAN, focal segmental glomerulosclerosis, minimal change disease and membranous nephropathy; r = −0.51, P < 0.001) with the AUC = 0.83. Hence this could discriminate normal patients from patients with moderate to severe TIF (Worawichawong et al., 2016) and was negatively Tumour necrosis factor receptors. Tumour necrosis factor receptors (TNFRs), TNFR1 and TNFR2, are receptors of tumour necrosis factor-α (TNF-α), which is an inflammatory cytokine that has a role in the regulation of inflammation, necrosis or apoptosis (Idriss & Naismith, 2000;Murakoshi et al., 2020). TNFR1 is produced in glomerular and tubular endothelial cells, whereas TNFR2 is not produced in healthy kidneys Speeckaert et al., 2012). However, in the presence of inflammation, they are produced in endothelial, mesangial and epithelial cells of glomeruli and in tubular cells (Baud & Ardaillou, 1995). The association of circulating TNFRs with eGFR and albuminuria in patients with diabetes has been studied extensively (Forsblom et al., 2014;Gohda et al., 2012;Kamei et al., 2018;Niewczas et al., 2012;Saulnier et al., 2014). The association of TNFRs with TIF has been examined recently (Table 4). Serum TNFR1 and TNFR2 (sTNFR1 and sTNFR2) were significantly correlated with renal fibrosis in patients with IgAN (r = 0.58, P < 0.0001 and r = 0.56, P < 0.0001), while urinary TNFR1 (uTNFR1) had a weak correlation (r = 0.21, P < 0.05; Sonoda et al., 2015). This was supported by another study, in which high levels of sTNFR1 and sTNFR2 were observed at the time of biopsy in patients with CKDs who had moderate to severe TIF, and sTNFR1 and sTNFR2 levels were associated with kidney disease progression (Hazard ratio = 1.63 and hazard ratio = 1.75, respectively; Srivastava et al., 2021).

Monocyte chemoattractant protein.
Monocyte chemoattractant protein (MCP-1) is a chemokine that recruits monocytes and macrophages. In kidneys, it is expressed in tubular cells, glomerular mesangial cells, podocytes and infiltrating leucocytes during interstitial inflammation and fibrosis (Tam & Ong, 2020). Urinary MCP-1/creatinine (uMCP-1/Cr) was shown to be associated with TIF in several studies (Table 4). For example, a significant increase in uMCP-1 levels was found in patients with lupus nephritis who had TIF evident on biopsy (Urrego-Callejas et al., 2021). Longitudinal urine (0, 1, 2, 3, 6 and 24 months) and biopsy (6 and 24 months) collections after renal transplantation from patients with chronic allograft injury exhibited a significant positive association between uMCP-1/Cr levels at the first month and TIF/TA (tubular atrophy) analysed at the 6-month biopsy (Ho et al., 2010). In the same study, an elevated uMCP-1/Cr level 6 months after renal transplantation was associated with severe TIF/TA at the 24-month biopsy, with odds ratio 1.045, P = 0.028 (Ho et al., 2010), which shows the potential of uMCP-1 to predict TIF. In another study, increased uMCP-1 levels were weakly but significantly associated with increased fibrosis in the kidneys of living donors (r = 0.24, P = 0.01; Wang et al., 2016). However, the performance of uMCP-1 was calculated as AUC = 0.66, and uMCP-1 levels alone could distinguish lupus nephritis patients with moderate (26-50%) to severe (>50%) TIF from those with no (<5%) to mild (6-25%) TIF (Zhang et al., 2012). Kidney inflammation, as either a primary or a secondary mechanism, is known to occur during CKD (Kurts et al., 2013). Mononuclear cell infiltration into the tubulointerstitium can cause progressive tissue remodelling and chronic inflammation that can lead to interstitial fibrosis and tubular atrophy (Niedermeier et al., 2009). Although TNFRs and MCP-1 are inflammatory markers (Khanijou et al., 2022), their correlation with TIF during CKD suggests potential use as biomarkers.

Newly emerging TIF biomarkers and technologies for diagnosis and prognosis of TIF
In addition to the biomarkers mentioned above, recent studies have recognized some further molecules as potential biomarkers of TIF, and newly developed imaging technologies have also been applied to diagnose TIF (Table 5).
Vimentin (VIM) is a protein of the intermediate filaments. It is expressed in fibroblasts, smooth muscle cells, endothelial and epithelial cells to provide structural support to cells and to regulate gene expression, cell division, differentiation and signalling (Eriksson et al., 2009). In kidneys, it has been shown that VIM is highly expressed in all compartments of unilateral ureteral obstructed (UUO) rat kidneys (Xiong et al., 2021;Yamashita et al., 2005), and the absence of VIM in knockout mice (Vim −/− ) decreased epithelial-to-mesenchymal transition and TIF in a UUO mouse model (Wang et al., 2018). Those studies support the role of VIM in the development of renal fibrosis. Proteomic analysis of urine samples from the UUO rat model demonstrated increased levels of urinary VIM (uVIM) at week 3, but not at week 1 after initiating kidney injury (Yuan et al., 2015). The urinary VIM mRNA levels were elevated in patients with different forms of CKD (IgAN, non-IgA mesangioproliferative glomerulopathy, membranous nephropathy, lupus nephritis, minimal change disease, focal segmental glomerulosclerosis, diabetic nephropathy and hypertensive nephropathy), and its levels were correlated with the severity of renal fibrosis (r = 0.468, P < 0.001, Cao et al., 2015). Urinary VIM mRNA levels could differentiate patients with no or mild fibrosis (fibrotic area ≤ 25%) from patients with moderate to severe fibrosis (fibrotic area > 25%) with an AUC of 0.86, while eGFR alone had an AUC of 0.83 (Cao et al., 2015). Another study demonstrated that VIM J Physiol 601.14 J Physiol 601.14 mRNA expression in urine of patients with CKD was significantly correlated with the percentage of interstitial fibrosis (r = 0.40, P = 0.001 and AUC = 0.74; L.-T. . Although these data supported the role of uVIM in diagnosing TIF, a long-term follow-up study and larger group studies, in both animal models and human patients, are necessary to confirm whether uVIM alone or in combination with eGFR can be a reliable non-invasive biomarker for renal fibrosis. Uromodulin (UMOD), also known as Tamm-Horsfall protein, is a glycoprotein that is synthesized specifically in the thick ascending limb and distal convoluted tubules of the kidney (Rindler et al., 1990). Its function is unknown, but is proposed to inhibit stone formation (Wolf et al., 2013), regulate sodium and potassium transport, control blood pressure (Trudu et al., 2013) and regulate the innate immune response (Liu et al., 2016). In UUO mouse models, was shown that UMOD protein levels in the kidney tissue and urine were increased 7 days post-UUO (Maydan et al., 2018). In contrast, urinary UMOD (uUMOD) levels were decreased in patients with IgAN compared with control subjects, and this was associated with %TIF and eGFR decline (Zhou et al., 2013). Likewise, low levels of uUMOD were observed in patients with chronic gouty nephropathy, in comparison to gout patients without CKD, although the correlation of uUMOD with TIF was not determined in this study (Wu et al., 2019). A very recent study showed a negative correlation between UMOD with percentages of interstitial fibrosis/tubular atrophy (r = −0.31, P < 0.001) in patients with acute interstitial nephritis (Melchinger et al., 2022). In the same study, the expression of UMOD in the kidney tissue was also negatively correlated with the percentage of fibrosis (r = −0.65, P < 0.05) in mice with unilateral ischaemia-reperfusion injury.
One of the limitations of UMOD analysis in urine samples is the formation of an insoluble form of UMOD in both fresh and freeze-thawed samples. The insoluble UMOD is needed for measurement along with soluble UMOD to determine the correct amount in disease conditions (Maydan et al., 2018). This is not considered in the majority of studies and is potentially the reason for the high variation in the results across different studies. Hence, more detailed and better-controlled studies on urinary UMOD and its correlation with TIF are needed to define the role of uUMOD as a renal fibrosis biomarker.
One of the newly emerging approaches is to identify fibrotic biomarkers in urinary extracellular vesicles (uEVs). The uEVs carry molecular cargo, such as nucleic acids, lipids, metabolites and proteins, from parental cells (Svenningsen et al., 2020). Hence, they are considered as mirrors of parental cells and can carry cellular biomarkers that reflect the conditions of parental cells, e.g. kidney tubular epithelial cells (Erdbrugger et al., 2021). The uEV-based biomarkers have been investigated in patients with diabetic nephropathy (Abe et al., 2018), glomerulonephritis (Morikawa et al., 2019) and lupus nephritis (Tangtanatakul et al., 2019). Interestingly the mRNAs and microRNAs of different uEVs were shown to be associated with TIF. Relatively higher levels of miR-21, but lower levels of miR-29c in uEVs were observed in CKD patients with TIF compared with CKD patients with non-TIF (diabetic nephropathy, focal segmental sclerosis, IgAN, membranous nephropathy, mesangial proliferative nephropathy, minimal change nephropathy or amyloidosis; Chun-Yan et al., 2018). miR-29c, but not miR21, could discriminate significantly between patients with TIF and those without TIF, with a calculated AUC of 0.86. In addition, the miR-21 level in urinary sediments of patients with IgAN and TIF was significantly higher than in healthy control subjects, whereas miR-205 was significantly lower (Liang et al., 2017). Furthermore, was miR-21 negatively correlated and miR-205 positively correlated with eGFR (r = −0.48, P < 0.001 and r = 0.316, P = 0.025, respectively) although both could distinguish patients with T1 (26-50% TIF on biopsy) and T2 (>50% TIF on biopsy) TIF/TA from patients with T0 (<26% TIF on biopsy) with an AUC of 0.74 (Liang et al., 2017).

Non-invasive tools: imaging and data science approaches
Recently, analysis of tubular epithelial cells in urine by microscopy has been revolutionized by powerful imaging technology combined with an artificial intelligence algorithm. We have recently demonstrated that autofluorescent signals from urinary exfoliated renal proximal tubule cells using multispectral imaging can potentially be used to differentiate between patients with different levels of TIF, assessed by kidney biopsy (Mahbub et al., 2021). Using this technique, we could differentiate patients with TIF from normal control subjects (AUC = 0.90) and distiguish diabetic patients with low eGFR (<60 ml/min/1.73 m 2 ) from diabetic patients with a preserved eGFR (>60 ml/min/1.73 m 2 ; AUC = 0.99; Mahbub et al., 2021). Although this technology has some limitations owing to the influence of cell autofluorescence by metabolic changes and requires further validation in a large cohort, it has the unique potential to establish a standardized urine 'biopsy' method to detect TIF and could be automated by digital analysis to improve reliability.
Other studies have examined the potential of imaging technologies in the diagnosis of kidney fibrosis and dysfunction. Using ultrasound super-resolution imaging to identify and associate renal microvasculature rarefaction with a subsequent chronic impairment, Chen et al. (2020) recently demonstrated an association between renal fibrosis and reduction in kidney size, cortical thickness, relative blood volume and microvascular density in a mouse model of ischaemia-reperfusion injury. Zhang et al. (2021) also recently demonstrated that the kidney stiffness values derived from magnetic resonance elastography imaging were negatively correlated with TIF in patients with CKD (IgAN, minimal change disease/focal segmental glomerulosclerosis, diabetic nephropathy, primary glomerulonephritis, lupus nephritis and chronic interstitial nephritis; r = −0.39, P < 0.001).
In addition, signs of retinopathy revealed by imaging of retinal microvasculature were positively associated with diabetic kidney disease in several studies (Liew et al., 2012;Mottl et al., 2012;Nusinovici et al., 2021;Pedro et al., 2010;Penno et al., 2012). A recent study also showed an association between changes in the iris and CKD (Muzamil et al., 2020). They analysed images of irises obtained from healthy individuals and patients diagnosed with CKD stages 4 and 5 (eGFR < 30 ml/min/1.73 m 2 ) using a deep neural network model on a graphics processing unit-based supercomputing machine that applied the intelligent iris-based chronic kidney identification system algorithm. This revealed an accuracy of 96.8% in the assessment of CKD. Although this study did not report the correlation between iridology and kidney fibrosis, this technology is promising as a non-invasive method for the assessment of kidney fibrosis. Previous studies have demonstrated that cytokines and chemokines in tears are associated with cancer and neurological disorders (Hagan et al., 2016), suggesting that assessment of the eye can be another non-invasive approach to assess disease development. The association of tear composition with TIF/CKD has not yet been reported.

Summary of TIF biomarkers and diagnostic approaches
Current standard biomarkers for CKD (eGFR and albuminuria) are used to predict the risk of end-stage kidney disease and identify patients who already have renal impairment likely to progress to end-stage kidney disease (Astor et al., 2011;Wong & Pollock, 2014).
In this context, the challenge is to identify those who are likely to have progressive CKD at early stages of disease. An ideal biomarker for CKD should have three characteristics: (1) early detection of kidney disease before a significant reduction in eGFR or the presence of albuminuria/proteinuria; (2) identification of those patients at greatest risk and who will require more active management (for prognosis); and (3) ability to assess treatment responses non-invasively (Babrak et al., 2019). However, to date there is no available study identifying a biomarker that possesses all these desired biomarker characteristics.
As explained in the Introduction, the degree and extent of TIF accurately predict CKD. Hence, in this review, we aimed to explore and analyse the molecular biomarkers for TIF available in urine and blood. The current and emerging biomarkers for TIF discussed in this paper are summarized in Fig. 1. Studies that independently and directly assessed the association of potential biomarkers with TIF levels (following a kidney biopsy) kidney biopsy were used.
Different studies reported varying correlation coefficients for the biomarkers of interest. This is probably attributable to the use of different patient cohorts or animal models, differences in individual numbers and/or use of different techniques to analyse biomarkers levels (Tables 1-5). Nonetheless, to rank biomarkers, we considered the highest correlation coefficient value reported. In medical research, correlation coefficient values, whether positive or negative, can be interpreted as 0.90-1.0 = very high, 0.70-0.89 = high, 0.50-0.69 = moderate, 0.30-0.49 = low and 0.00-0.29 = no correlation (Mukaka, 2012). Among all biomarkers, uTGF-β1 had the highest correlation coefficient (r = 0.86) with TIF. The ranking of biomarkers was followed by uC3M/Cr (high), uEGF (high), uC1M/Cr (moderate), sPRO-C6 (moderate), uMMP-7 (moderate), pC3M (moderate), sTNFR1 (moderate), sTNFR2 (moderate) and pC1M (moderate), for which r values were >0.55 (Table 6). These biomarkers were classified as potential biomarkers for TIF in this review. The comparison of potential biomarkers with the highest correlation coefficient for eGFR (r = −0.53, moderate) showed that all potential biomarkers outperform the strength of correlation of eGFR with TIF. The rest of the biomarkers, including emerging biomarkers such as urinary VIM mRNA,uPIIINP/Cr,UMOD,and J Physiol 601.14   Another aspect we wanted to analyse is the diagnostic and prognostic properties of biomarkers. A diagnostic biomarker reveals the presence of the disease or condition, whereas a prognostic biomarker identifies the likelihood of disease progression of a clinical event (Califf, 2018). For a biomarker to have both diagnostic and prognostic features, a longitudinal study should be performed, in which urine or blood samples are collected before or in parallel to verified fibrosis by histological analysis. Among all biomarkers (potential and emerging) we presented in this review, only three biomarkers, namely uTGF-β1, uC1M/Cr and uC3M/Cr, have been studied longitudinally and significantly correlated with TIF (Tables 2 and 3). Urinary TGF-β1 can be considered as early diagnostic biomarker and can predict future TIF. Both uC1M/Cr and uC3M/Cr were studied longitudinally in adriamycin-induced renal fibrosis in rats (Hijmans et al., 2017). In contrast to uTGF-β1, the positive correlation of uC1M/Cr and uC3M/Cr levels at 6 weeks before sacrifice and at sacrifice were moderate and high, respectively. This indicated that uC1M/Cr and uC3M/Cr in rats could be used to predict future TIF and for early and late diagnosis of TIF. The rest of the molecular biomarkers were obtained at the time of biopsy and significantly correlated with TIF levels at the time of biopsy. Hence, with the currently available information, these biomarkers should be considered as late diagnostic biomarkers. To assign them as early and/or prognostic biomarkers for TIF, a longitudinal study in patients or animal models is necessary.
Accumulation of ECM proteins is the underlying phenomenon in TIF (Bulow & Boor, 2019;Tan & Liu, 2012;Zeisberg & Neilson, 2010). However, surprisingly, information on the use of ECM proteins as urinary or plasma biomarkers for renal fibrosis is very limited. As we have shown in this review, only MMPs (MMP-2, MMP-7 and MMP-9), which are ECM remodelling enzymes, and collagen-related products (PIIINP, PRO-C3, PRO-C6, C1M and C3M), which are formed during ECM turnover, reflect TIF directly (Genovese et al., 2014). The rest of the biomarkers examined here are involved in glomerular function, tubular injury or function, inflammation or repair and can indirectly predict TIF, although their correlation with TIF varies from moderate to high. Therefore, pro-and antifibrotic enzymes and biomarkers depicting early modifications in ECM could help in early TIF diagnosis and prognosis and contribute to the monitoring of early interventions to reduce fibrosis. Additional studies with large cohorts of patients should be performed to validate the data and facilitate the use of these biomarkers in clinics.
In this review article, we focused mostly on biomarkers in blood and urine. The reason is that obtaining serum and urine is non-invasive, easy and low cost, and samples can be obtained longitudinally from a patient. However, one of the limitations of biomarkers in blood and urine is specificity, owing to the potential role of these in other diseases. For example, the expression of TGF-β1 is elevated not only in renal fibrosis, but also during inflammation, cancer and fibrosis of other organs (Kubiczkova et al., 2012;Penn et al., 2012). The levels of soluble collagen I and III in blood and urine were also associated with osteoarthritis (Bay-Jensen et al., 2020), and elevated levels of serum MMP-7 in chronic liver and lung injuries were observed (Irvine et al., 2021). Special care and validation studies are required to ensure the specificity of biomarkers for CKD.
In addition to molecular biomarkers, we have shown that new technologies might have the potential to diagnose TIF. The most promising approach was the analysis of autofluorescent signals from urinary exfoliated renal proximal tubule cells using multispectral imaging (Mahbub et al., 2021). Other imaging approaches, such as ultrasound super-resolution imaging, magnetic resonance elastography imaging or the intelligent iris-based chronic kidney identification system, have gained recent interest and shown promising capabilities in diagnosing CKD (Chen et al., 2020;Mahbub et al., 2021;Muzamil et al., 2020;Zhang et al., 2021). Although more extensive studies are required in different patient cohorts, these recent developments in imaging technologies and the possibility of combining such technologies with machine learning and artificial intelligence open a new approach to non-invasive assessment of early TIF and monitoring of progressive fibrosis.
Biomarkers of TIF have limitations, as mentioned above. Some markers were not studied in larger cohorts of patients with different CKDs, had weak correlations with TIF, or the accuracy of the biomarker (sensitivity, specificity and AUC analysis) was not assessed. Hence, as an alternative to TIF biomarkers, tubular injury biomarkers can be considered as a predictor of TIF, because persistent tubular damage, inflammation and failure in the repair process contribute to fibrosis and the transition from acute kidney injury to CKD (E & Humphreys, 2017). Some of tubular injury biomarkers that were associated with renal adverse events, eGFR and CKD include neutrophil gelatinase-associated lipocalin (NGAL) (Lumlertgul et al., 2020), kidney injury marker-1 (KIM-1) , a combination of tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) , N-acetyl-β-d-glucosaminidase (NAG) and β2-microbrobulin (β2-MG) (Mise et al., 2016), and retinol-binding protein (RBP) (Cai et al., 2022). However, a significant correlation of NAG, β2-MG and RBP with TIF/TA was shown only in diabetic nephropathy patients (Cai et al., 2022;Mise et al., 2016). Although recent studies showed a significant correlation with TIF in diabetic nephropathy, their correlation with TIF in different CKDs is still lacking. Hence, a comprehensive study linking those biomarkers to fibrosis in patients with CKDs of different aetiology is necessary to assess or confirm their potential use as biomarkers for TIF.

Conclusion
In this review article, we identify and rank 12 potential molecular biomarkers of TIF. We also point out limitations and knowledge gaps, propose directions for future studies and clarify the necessity for further studies to determine fibrotic biomarkers and markers that might predict future TIF development of utility in clinical settings.
In summary, the performance (AUCs) of biomarkers for TIF can be ranked in descending order as follows: uTGF-β1, uEGF, sPRO-C6, urinary VIM mRNA, uPRO-C6, uMCP-1, uMMP-7, urinary miR-21, and sTNFR1 and sTNFR2. However, more validation studies are required in sufficiently powered patient cohorts with various forms of CKDs to compare the performance of those markers directly, assess their performance in combination or with eGFR, and determine whether they have a diagnostic or prognostic effect above and beyond conventional clinical markers. A comprehensive approach and more validation studies for direct biomarkers of fibrosis, such as collagen fragments, and matrix turnover enzymes, such as MMPs or lysl oxidases, involving recent technologies (omics, imaging techniques and artificial intelligence) is likely to be required to assess and monitor TIF non-invasively.