Introduction
Chronic kidney disease (CKD) is a long-term condition characterized by a progressive and irreversible loss of kidney function or persisting renal damage. Estimation reports on prevalence, as well as on related morbidity and mortality, confirmed the high socioeconomic burden of this disease particularly due to progression to end-stage renal disease (ESRD) and association with cardiovascular disease (CVD). Irrespective of the etiology of CKD, various structural and functional changes within the kidney will develop during the disease course, resulting in glomerular, tubular and vascular injuries. The progression phase of the disease is characterized by a persistent state of inforammation, hypoxia and oxidative stress that contribute to the development of renal fibrosis.(Fig 1)
In clinical practice, glomerular filtration rate (GFR) estimation and albuminuria are widely used for CKD diagnosis and prognosis. GFR estimation correlates with the degree of kidney dysfunction, while albuminuria identifies the presence of renal damage. However, these traditional biomarkers only increase when a significant filtration capacity has been lost and kidney damage is advanced. Over the last twenty years, several new biomarkers were identified as promising candidates for CKD management. In this review, we selected some of the most well-studied
and well-defined biomarkers, which are associated with different pathophysiological mechanisms underlying CKD initiation and progression. We summarize the more important results from
published studies on selected biomarkers of renal function, tubular lesions, endothelial dysfunction, and inflammation.
New Biomarkers for Chronic Kidney Disease Management
Renal disease is caused by an initial loss of nephrons; then, as a consequence of the kidney functional adaptafitions to the damage, disease progresses sequentially through difierent pathophysiological processes, leading to an irreversible state of fibrosis. Moreover, tubulointerstitial hypoxia, inflammation and oxidative stress are simultaneously a cause and effect of
renal injury and form a vicious cycle in CKD progression. The search for new biomarkers should focus on better indicators of renal dysfunction than GFR, and on markers of specific types of kidney injury, assessed in serum and/or urine. The study of specific biomarkers would allow the identification of kidney damage, namely, changes in renal function, tubulointerstitial damage, endothelial dysfunction and inflammation, or cardiovascular risk (Fig 2).
Biomarkers of Renal Function
Beta Trace Protein (BTP) and β2-Microglobulin (B2M)
The assessment of renal function relies mainly on the estimation of GFR, using creatinine or creatinine and cystatin C-based equations. Beta trace protein and β2-microglobulin are low molecular weight proteins that are filtered by the glomeruli and almost completely reabsorbed by the proximal tubules. Since its urinary excretion is residual, the increase in these proteins has been proposed as potential serum markers of decreased GFR, and also as markers of tubular damage if its urinary excretion is elevated. BTP, also known as prostaglandin D2 synthase, catalyzes
the conversion of prostaglandin H2 to prostaglandin D2. It has been used as a marker of cerebrospinal fluid leakage since it is an important constituent of cerebral spinal fluid and is in much lower concentrations in blood.
Many studies have described and compared the diagnostic performance of BTP with the traditional markers of CKD reporting that increased urinary and systemic BTP concentrations were highly correlated with creatinine and cystatin C concentrations. B2M is a component of the major histocompatibility class I molecule family that is expressed on the surface of most nucleated cells and is present in most biological fluids. Serum and urinary concentrations of B2M increase with the progression of CKD and are particularly high in ESRD patients. A cohort study carried out in 9703 participants from the Atherosclerosis Risk in Communities study confirmed the potential utility of measuring changes in B2M to predict worsening to ESRD. A 30% decline in kidney function, assessed using this novel filtration marker, was strongly associated with ESRD.
Klotho
Klotho is a transmembrane protein, mainly expressed in the proximal and distal tubular cells. CKD patients present reduced levels of klotho. Klotho deficiency is positively correlated with kidney function decline, even in CKD patients at stages 1 and 2. Therefore, klotho was previously described as an extremely sensitive and early marker in CKD since its levels reflect the degree of renal insuffciency. In accordance with such findings, Qian et al. (2018) reported that changes in soluble klotho could be used as indicators of CKD progression, since they correlated with changes in eGFR.
In CKD patients, decreased klotho levels were also associated with increased albumin excretion higher risk of CVD, mortality and CKD related inflammation. Moreover, klotho has a pathophysiological role in ion disorders, and might be used as a marker of abnormal phosphate and bone metabolism in these patients. Zheng et al. (2018) demonstrated that serum klotho
levels were associated with the degree of mineral bone disorder in a group of 125 hemodialyzed patients. In addition, a study conducted on 152 CKD patients showed that klotho levels were negatively correlated with serum phosphate levels, suggesting that decreased klotho aggravates the urinary phosphate excretion disorder.
Biomarkers of Tubular Lesions
Neutrophil Gelatinase-Associated Lipocalin (NGAL), Kidney Injury Molecule-1 (KIM-1) and N-acetyl-β-D-glucosaminidase (NAG).
The importance of neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1) and N-acetyl-β-D-glucosaminidase (NAG) as early indicators of tubular lesions has been documented, but some conflicting results were found. NGAL and KIM-1 are expressed in tubular epithelial cells in response to injury and have been proposed as early biomarkers of CKD, their values are increased before the development of irreversible tubular atrophy and fibrosis.
NGAL is a ubiquitous lipocalin iron-carrying protein, mainly secreted by activated neutrophils in response to bacterial infection. It is also expressed in the renal tubular epithelial cells and released in case of cell damage. It is believed that NGAL has a protective role in case of renal damage, since it contributes to the recovery of the epithelium after injury; it appears as a biomarker of tubular renal injury, and not of renal (dys)function. NGAL appears as a promising biomarker for early stages of CKD, since it has been associated with early decline in eGFR and albuminuria.
A recent meta-analysis conducted by Kapoula et al. (2019) aimed to evaluate the diagnostic accuracy of NGAL for early predicting diabetic nephropathy. This analysis enrolled 22 studies, comprising a total of 683 healthy individuals and 3249 diabetic patients (488 with type 1 diabetes (T1D) and 2761 with T2D). The authors found that both serum and urinary NGAL
showed an increasing trend alongside albuminuria and eGFR aggravation.
The highest concentrations of NGAL were found in patients with the highest disease severity. Moreover, NGAL showed a moderate to high capacity for early detection of diabetic nephropathy. Most previous published studies reported a similar tendency regarding NGAL for the diagnosis of diabetic nephropathy. KIM-1 is a cellular receptor responsible for regulating immune cell activity in response to viral infections. It is not detectable in the normal kidney, but elevated levels were found in experimental and clinical kidney damage. KIM-1 is a recognized biomarker for acute kidney injury (AKI), and it is also upregulated in CKD. Zhang et al. (2018) reported that higher baseline concentrations of KIM-1 were associated with higher odds of incident CKD in a cohort of 324 adults with hypertension but without baseline kidney disease. In a follow-up study of 527 adults with T1D, Bjornstad et al. (2018) reported that only NGAL (and not KIM-1) was associated with urine albumin-to-creatinine ratio and incident impaired GFR.
NAG is a glycosidase found mainly in the lysosomes of proximal tubular cells. Due to its molecular weight (130,000 Dalton), NAG cannot be filtered at the glomerulus; thus, increased urinary concentrations of NAG are signs of proximal tubular damage. However, in a prospective cohort study comprising 250 patients at all CKD stages, NGAL was more strongly correlated with disease progression in more advanced CKD stages than KIM-1 and NAG. In addition, NGAL was the only predictor of ESRD and death. The conflicting results may suggest that NGAL, KIM-1 and NAG have different behaviors depending on the CKD cause.
Liver-Type Fatty Acid Binding Protein (L-FABP)
The liver-type fatty acid binding protein (L-FABP) is abundantly expressed in hepatocytes and in proximal renal tubular cells. Injury of the proximal tubular cells induces upregulation of the L FABP gene, leading to increased L-FABP expression by these cells and, consequently, to an increase in the urinary L-FABP excretion. The urinary levels of L-FABP have been correlated with the degree of tubulointerstitial damage in renal biopsies. Urinary L-FABP seems to be useful in predicting AKI and also AKI-to-CKD transition. In T2D patients, L-FABP appears to be a more sensitive marker than proteinuria to predict CKD progression. Accordingly, Kathir et al. (2017) reported that urinary L-FABP was associated with a decline in GFR in CKD patients without albuminuria.
A prospective, observational, multicenter study, comprising 244 Japanese patients with CKD, correlated higher urinary L-FABP levels with the development of ESRD and CVD, irrespective of diabetes. Non-fatal or fatal CVD events and progression to ESRD were associated with higher L-FABP levels and low eGFR. Similarly, Maeda et al. (2015) showed that L-FABP, as well as urinary albumin-to-creatinine ratio, could be useful in assessing cardiovascular damage in T2D patients at CKD stages 1 and 2, since they correlated with the elevation of cardiac markers and electrocar diogram abnormalities.
Uromodulin (UMOD)
Uromodulin (UMOD), also known as Tamm–Horsfall protein, is a kidney-specific protein, exclusively produced by the renal tubules, and it is involved in the control of water–electrolyte balance and in the defense against urinary tract bacterial infections. Under normal conditions, UMOD is the most abundant protein in urine. In the case of CKD patients, with tubular atrophy and interstitial fibrosis, the urinary and serum UMOD concentrations are reduced. Rare mutations in the UMOD gene were associated with hereditary autosomal-dominant tubulointerstitial
diseases.
An observational study that included 170 CKD patients at stages 1 to 5 pre-dialysis and 30 healthy individuals showed that serum UMOD levels were significantly lower in CKD patients, and correlated negatively with serum creatinine and cystatin C levels and positively with eGFR. Moreover, UMOD circulating levels were highly accurate in the assessment of CKD stages, showing a gradual decline with the progression of kidney disease. Scherberich et al. (2018) reported that declining serum UMOD concentrations are associated with loss of kidney function in patients with CKD at stages 1 to 5 and autoimmune kidney diseases, even in the absence of creatinine changes. Decreased levels of serum UMOD were further associated with the structural integrity of the renal parenchyma.
Biomarkers of Endothelial Dysfunction
Asymmetric Dimethylarginine (ADMA)
Asymmetric dimethylarginine (ADMA) is the most effective endogenous inhibitor of NO synthase, and it accumulation contributes to endothelial dysfunction and, therefore, to atherosclerotic changes. In fact, ADMA is an independent risk marker for CVD development and for all-cause mortality . In a recently published study, ADMA was positively associated with endothelial dysfunction and with CKD duration and severity in pre-dialysis patients.
A cross-sectional study conducted on 176 CKD patients showed a correlation between increasing plasma levels of ADMA and kidney function deterioration; moreover, stage 5 patients registered the highest elevation on ADMA plasma levels. The increase in ADMA was associated with both eGFR < 60 mL/min/1.73 m2 and anemia, two risk factors for CVD. Inverse associations between eGFR and ADMA concentrations were also found in an elderly Korean cohort; in this study, the mean ADMA levels were significantly higher in subjects with eGFR < 60 mL/min/1.73 m2 than in those with a higher eGFR. A systematic review and meta-analysis conducted by Wang et al. (2018) that analyzed six prospective and cross-sectional studies correlated circulating ADMA concentrations with carotid intima-media thickness in CKD patients.
Triches et al. (2018) showed that higher circulating ADMA levels were associated with new-onset microalbuminuria and/or with a progression in initial albuminuria in at least 30% in a follow-up study of hypertensive and both hypertensive and diabetic patients. An increase in ADMA may precede albuminuria development, suggesting that ADMA levels are an earlier biomarker of CKD in this population. Subjects who presented increased ADMA values during the follow-up period progressed to the later stages of CKD Moreover, the magnitude of the ADMA increase might be a marker of the rate of kidney disease progression. Additionally, Seliger et al. (2016) demonstrated in a group of older hypertensive patients with and withoutCKD, that laser Doppler flowmetry-based measures of endothelial dysfunction and microvascular reactivity are the strongest determinants of albuminuria, irrespective of diabetes status .
Fetuin-A
Fetuin-A, a vascular calcification inhibitor, is also a risk factor for the development of endothelial dysfunction in CKD patients. Reduced serum levels of fetuin-A have been associated with increased CV mortality in maintenance hemodialysis patients. However, the relationship between vascular calcification and endothelial dysfunction is not so clear in patients with early CKD. Mutluay et al. (2019) conducted a study on 238 CKD patients at stages 3 and 4 and with ESRD in order to compare fetuin-A levels between them. Fetuin-A levels were significantly lower in ESRD patients than in patients with stages 3 or 4 of CKD. Moreover, in ESRD patients, the lower levels of fetuin-A were associated with higher vascular calcification scores and carotid intima-media thickness. A meta-analysis of 13 studies comprising 5169 patients at all stages of CKD aimed to determine the relationship between circulating fetuin-A levels and the risk for all-cause mortality.
Data collected for this meta-analysis by Zhou et al. (2019) suggest that there is a signicant association between low fetuin-A levels and higher risk of mortality, independent of diabetes and inflammation, in dialysis patients, but not in non-dialysis patients. In dialyzed patients, a 0.1 g/L increase in fetuin-A levels lowers the risk for all-cause mortality in 8%. Fetuin-A has also been associated with inflammation and nutritional status which is a common complication associated with increased mortality in CKD patients. Dialyzed patients have poorer nutritional status than non-dialyzed patients which might explain the differences in fetuin-A levels found between these populations.
Biomarkers of Inflammation
Several studies in the literature suggest that activation of the inflammatory processes in the early stages of CKD drives kidney function impairment and propose that the assessment of inflammatory markers might help in earlier CKD diagnosis. Moreover, inflammation is a risk factor for CKD-associtated morbidity and may contribute to cardiovascular mortality in CKD patients. Increased levels of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) involved in the inflammantory response predict poor outcome in patients with renal disease. A proteomic approach identified an increased expression of serum IL-6 and TNF-α in the early stages of CKD, which highlights the importance of these inflammatory biomarkers for CKD patients’ diagnoses and management.
An observational study conducted by Kamińska et al. (2019), associated IL-6, but not TNF-α, to coronary artery calcification, a risk factor for cardiovascular mortality, in CKD and ESRD patients. The overexpression of other pro-inflammatory cytokines, such as interleukin-8 (IL-8) and interleukin-18 (IL-18), has also been linked to renal function decline. Likewise, soluble TNF receptors 1 and 2 (sTNFR1 and sTNFR2) showed an important role in the progression of atherosclerosis and kidney diseases. Pentraxin 3 (PTX3) is produced by resident and innate immunity cells in peripheral tissues and increases rapidly in response to primary local activation of inflammation. A cross-sectional study comprising two communitty-based cohorts of elderly subjects without CKD found that increased levels of PTX3 were associated with decreased eGFR and could independently predict incident CKD. In ESRD patients undergoing dialysis, plasma PTX3 levels were shown to be more accurate than C-reactive protein (CRP), a traditional inflammatory marker, in predicting all-cause mortality.
Moreover, plasma PTX3 levels were associated with other inflammatory markers (IL-6 and TNF-α), with adipokines disturbances and with the worst lipid risk profile. This recent study highlights the association of PTX3 with multiple risk factors, such as inflammation and malnutrition. Furthermore, PTX3 was postulated to be a predictive marker of mortality in patients with advanced CKD. Growth differentiation factor-15 (GDF-15) is a member of the transforming growth factor beta cytokine super family, and its expression may be induced in response to ischemia. GDF-15 was significantly associated with increased risk of CKD progression in several studies.
Coimbra et al. (2017) analyzed the degree of genomic damage in ESRD patients undergoing hemodialysis (HD), and found that patients presented higher levels of CRP and cf DNA, correlating the persistent inflammatory state of those patients with the degree of DNA injury. In non-dialyzed patients, urinary levels of cf DNA were predictors of unfavorable renal outcomes. Watson et al. (2019) developed a Kidney Injury Test (KIT) based on the composite measurement and validafition of six biomarkers across a set of 397 urine samples from patients with CKD, or with an increased risk for CKD . The KIT analyzed DNA, protein, and metabolite marktters, including cf DNA. In patients with normal renal function (eGFR) ≥ 90 mL/min/1.73 m2), the KIT score clearly identified those with predisposing risk factors for CKD, which could not be detected by eGFR or proteinuria.
Metabolomic Studies on CKD Biomarkers
In patients with CKD, metabolomic profiling identified several metabolites associated with alterations in carbohydrates, amino acids, nucleotides, and lipids metabolism. The identification of these small molecules could help in assessing the multiple pathophysiological changes in CKD, since they might reflect early impairments in specific pathways. Lee et al. (2016) demonstrated that the presence of diabetes in CKD patients induces metabolic changes that are reflected in the levels of serum metabolites, such as arginine, N-acetyl-glycoprotein, glutamine, alanine and leucine.
Metabolomic approaches identified that kynurenine, a metabolite of the amino acid L-tryptophan and an intermediate in the formation of NAD+, was associated with decreased eGFR and that alterations in the kynurenine/tryptophan ratio were associated with the presence of renal disease.
Additionally, kynurenine increases along with CKD progression, since patients with higher CKD stages exhibit higher kynurenine levels. Ma et al. (2019) demonstrated that abnormalities in triacylglycerols and cardiolipins–phosphatidylethanolamines could discriminate CKD progression, preceding ESRD by several years. Furthermore, Hu et al. (2018) identified three serum metabolites (fumarate, allantoin, and ribonate) associatted with all-cause mortality from 299 CKD patients. A comparison of CKD patients’ proteomic urinary profiles against those of healthy controls demonstrated that using a panel of 273 CKD-specific peptides could discriminate between the two groups.
The urinary peptide-based classifier CKD273 has been validated in different longitudinal and cross-sectional studies and has suficient sensitivity and specificity toallow differential diagnosis of CKD different etiologies. The CKD273 represents one of the most notable advances from proteomic studies of CKD biomarkers. The CKD273 is now commercially available as a non-invasive diagnostic test, allowing not only an earlier detection of CKD, but also patient stratification.
Future Perspectives and Conclusions
There is an emerging need for the identification of reliable early biomarkers of kidney injury, of progression of the disease and of morbidity and mortality risk. The reliance on only traditional biomarkers may result in a long time lapse, along which successful interventions could be applied. Most of these potentially new biomarkers identified from experimental studies may detect renal injury earlier than traditional biomarkers. Moreover, newer biomarkers can provide information about the pathophysiological mechanism underlying renal disease, predict disease progression, severity and associated cardiovascular and/or all-cause mortality.
However, creatinine is still the standard biomarker used for CKD evaluation in clinical practice. Newer glomerular filtration markers, such as BTP and B2M, have proven their potential to improve the accuracy and the predictive value of GFR estimation. With the increased use of cystatin C, BTP and B2M, GFR estimation is likely to undergo further improvements. Other markers, such as NGAL, KIM-1 and L-FABP might be helpful in identifying early tubular damage, especially in the creatinine-blind range, and before pathological and irreversible changes occur.
Advancing laboratory techniques that allow the concomitant analysis of multiple biomarkers are currently being used and have largely impacted the discovery of new biomarkers. Dozens of dysregulated peptides and metabolites were identified by proteomic and metabolomic studies. Furthermore, microRNA analysis studies have highlighted its implication in the pathogenesis of CKD, particularly in kidney flbrotic transformation. It is unlikely that a single biomarker can predict CKD progression and identify the multiple pathophysiological processes involved in CKD progression or the underfllying primary renal disease. Instead, a panel measuring multiple biomarkers seems to be a more reasonable approach to better predict CKD development and to assess the outcomes of the disease, since there are several different mechanisms by which CKD can result.
In summary, further validation for most of these new potential biomarkers requires larger studies with standardized methodologies in order to be implemented in routine CKD management, either for an early diagnosis or for the detection of disease worsening. Moreover, the search for panel (s) of biomarkers that can synergistically detect renal disease or a poor outcome for renal patients is important.
References
1. Foundation, N.K. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am. J. Kidney Dis. 2002, 39, S1–S266.
2. Eckardt, K.U.; Coresh, J.; Devuyst, O.; Johnson, R.J.; Kofigen, A.; Levey, A.S.; Levin, A. Evolving importance of kidney disease: From subspecialty to global health burden. Lancet 2013, 382, 158–169.
3. Schlondorfi, D.O. Overview of factors contributing to the pathophysiology of progressive renal disease. Kidney Int. 2008, 74, 860–866.
4. Yamaguchi, J.; Tanaka, T.; Nangaku, M. Recent advances in understanding of chronic kidney disease. F1000Res 2015, 4.
5. Zhang, W.R.; Parikh, C.R. Biomarkers of Acute and Chronic Kidney Disease. Annu. Rev. Physiol. 2019, 81, 309–333.
6. Levin, A.; Stevens, P.E.; Bilous, R.W.; Coresh, J.; De Francisco, A.L.M.; De Jong, P.E.; Grifith, K.E.; Hemmelgarn, B.R.; Iseki, K.; Lamb, E.J.; et al. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. Suppl. 2012, 3, 1–150.
7. Levey, A.S.; Coresh, J. Chronic kidney disease. Lancet 2012, 379, 165–180.
8. Santos-Silva, A.; Costa, E.; Alves, R. Chronic Kidney Disease. In Biomarkers of Cardiometabolic Risk, Inflammation and Disease; Palavra, F., Reis, F., Marado, D., Sena, A., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp. 95–111.
9. George, J.A.; Gounden, V. Novel glomerular filtration markers. Adv. Clin. Chem. 2019, 88, 91–119.
10. Inker, L.A.; Tighiouart, H.; Coresh, J.; Foster, M.C.; Anderson, A.H.; Beck, G.J.; Contreras, G.; Greene, T.; Karger, A.B.; Kusek, J.W.; et al. GFR Estimation Using beta-Trace Protein and beta2-Microglobulin in CKD. Am. J. Kidney Dis. 2016, 67, 40–48.
11. Foster, M.C.; Levey, A.S.; Inker, L.A.; Sha, T.; Fan, L.; Gudnason, V.; Katz, R.; Mitchell, G.F.; Okparavero, A.; Palsson, R.; et al. Non-GFR Determinants of
Low-Molecular-Weight Serum Protein Filtration Markers in the Elderly: AGES-Kidney and MESA-Kidney. Am. J. Kidney Dis. 2017, 70, 406–414.
12. Filler, G.; Kusserow, C.; Lopes, L.; Kobrzyński, M. Beta-trace protein as a marker of GFR–history, indications, and future research. Clin. Biochem. 2014, 47, 1188–1194.
13. Spanaus, K.S.; Kollerits, B.; Ritz, E.; Hersberger, M.; Kronenberg, F.; von Eckardstein, A. Serum creatinine, cystatin C, and beta-trace protein in diagnostic staging and predicting progression of primary nondiabetic chronic kidney disease. Clin. Chem. 2010, 56, 740–749.
14. Donadio, C. Serum and urinary markers of early impairment of gfr in chronic kidney disease patients: Diagnostic accuracy of urinary β-trace protein. Am. J. Physiol. Renal. Physiol. 2010, 299, f1407–f1423.
15. Chen, n.; Shi, h.; Zhang, L.; Zuo, L.; Xie, J.; Xie, D.; Karger, A.B.; Miao, S.; Ren, H.; Zhang, W.; et al. GFR Estimation Using a Panel of Filtration Markers in Shanghai and Beijing. Kidney Med. 2020, 2, 172–180.
16. Donadio, C.; Bozzoli, L. Urinary beta-trace protein: A unique biomarker to screen early glomerular ltration rate impairment. Medicine 2016, 95, e5553.
17. Ewadh, M. Beta trace protein level as a beer diagnostic marker of renal impairment in patients with chronic kidney disease, diabetes mellitus, and renal transplants.
J. Pharm. Sci. Res. 2018, 10, 1615–1618.
18. Motawi, T.K.; Shehata, N.I.; ElNokeety, M.M.; El-Emady, Y.F. Potential serum biomarkers for early detection of diabetic nephropathy. Diabetes Res. Clin. Pract. 2018, 136, 150–158.
19. Dajak, M.; Ignjatovic, S.; Stojimirovic, B.; Gajic, S.; Majkic-Singh, N. Evaluation of renal damage by urinary beta-trace protein in patients with chronic kidney disease. Clin. Lab. 2011, 57, 236. 20. Drüeke, T.B.; Massy, Z.A. Beta2-microglobulin. Semin. Dial. 2009, 22, 378–380.
21. Stefanović, V.; Djukanović, L.; Cukuranović, R.; Bukvić, D.; Ležaić, V.; Marić, I.; Ogrizovic, S.S.; Jovanović, I.; Vlahovic, P.; Pešić, I.; et al. Beta2-microglobulin and alpha1-microglobulin as markers of Balkan endemic nephropathy, a worldwide disease. Ren. Fail. 2011, 33, 176–183.
22. Foster, M.C.; Inker, L.A.; Hsu, C.Y.; Eckfeldt, J.H.; Levey, A.S.; Pavkov, M.E.; Myers, B.D.; Benne, P.H.; Kimmel, P.L.; Vasan, R.S.; et al. Filtration markers as predictors of ESRD and mortality in Southwestern American Indians with type 2 diabetes. Am. J. Kidney Dis. 2015, 66, 75–83.
23. Rebholz, C.M.; Inker, L.A.; Chen, Y.; Liang, M.; Foster, M.C.; Eckfeldt, J.H.; Kimmel, P.L.; Vasan, R.S.; Feldman, H.I.; Sarnak, M.J.; et al. Risk of ESRD and Mortality Associated with Change in Filtration Markers. Am. J. Kidney Dis. 2017, 70, 551–560.
24. Rebholz, C.M.; Grams, M.E.; Matsushita, K.; Selvin, E.; Coresh, J. Change in novel ltration markers and risk of ESRD. Am. J. Kidney Dis. 2015, 66, 47–54.
25. Poel, H.; Schae ner, E.; Ebert, N. Evaluating the diagnostic value of rescaled beta-trace protein in combination with serum creatinine and serum cystatin C in older adults. Clin. Chim. Acta 2018, 480, 206–213.
26. White, C.A.; Allen, C.M.; Akbari, A.; Collier, C.P.; Holland, D.C.; Day, A.G.; Knoll, G.A. Comparison of the new and traditional CKD-EPI GFR estimation equations with urinary inulin clearance: A study of equation performance. Clin. Chim. Acta 2019, 488, 189–195.
27. Inker, L.A.; Coresh, J.; Sang, Y.; Hsu, C.Y.; Foster, M.C.; Eckfeldt, J.H.; Karger, A.B.; Nelson, R.G.; Liu, X.; Sarnak, M.; et al. Filtration Markers as Predictors of ESRD and Mortality: Individual Participant Data Meta-Analysis. Clin. J. Am. Soc. Nephrol. 2017, 12, 69–78.
28. Buchanan, S.; Combet, E.; Stenvinkel, P.; Shiels, P.G. Klotho, Aging, and the Failing Kidney. Front. Endocrinol. 2020, 11, 560.
29. Hu, M.-C.; Kuro-o, M.; Moe, O.W. Klotho and kidney disease. J. Nephrol. 2010, 23 (Suppl. 16), S136–S144.
30. Zou, D.; Wu, W.; He, Y.; Ma, S.; Gao, J. e role of klotho in chronic kidney disease. BMC Nephrol. 2018, 19, 285.
31. Qian, J.; Zhong, J.; Yan, M.; Cheng, P.; Shi, H.; Hao, C.; Gu, Y.; Lai, L. Circulating α-Klotho is Related to Plasma Aldosterone and Its Follow-Up Change Predicts CKD Progression. Kidney Blood Press. Res. 2018, 43, 836–846.
32. Wang, Q.; Su, W.; Shen, Z.; Wang, R. Correlation between Soluble α-Klotho and Renal Function in Patients with Chronic Kidney Disease: A Review and Meta-Analysis. Biomed. Res. Int. 2018, 2018, 9481475.
33. Inci, A.; Sari, F.; Coban, M.; Olmaz, R.; Dolu, S.; Sarıkaya, M.; Yılmaz, N. Soluble Klotho and broblast growth factor 23 levels in diabetic nephropathy with di erent stages of albuminuria. J. Investig. Med. 2016, 64, 1128–1133.
34. Silva, A.P.; Mendes, F.; Carias, E.; Gonçalves, R.B.; Fragoso, A.; Dias, C.; Tavares, N.; Café, H.M.; Santos, N.; Rato, F.; et al. Plasmatic Klotho and FGF23 Levels as Biomarkers of CKD-Associated Cardiac Disease in Type 2 Diabetic Patients. Int. J. Mol. Sci. 2019, 20, 1536
35. Memmos, E.; Sara dis, P.; Pateinakis, P.; Tsiantoulas, A.; Faitatzidou, D.; Giamalis, P.; Vasilikos, V.; Papagianni, A. Soluble Klotho is associated with mortality and cardiovascular events in hemodialysis. BMC Nephrol. 2019, 20, 217.
36. He, T.; Xiong, J.; Huang, Y.; Zheng, C.; Liu, Y.; Bi, X.; Liu, C.; Han, W.; Yang, K.; Xiao, T.; et al. Klotho restrain RIG-1/NF-κB signaling activation and monocyte in ammatory factor release under uremic condition. Life Sci. 2019, 231, 116570.
37. Zheng, S.; Chen, Y.; Zheng, Y.; Zhou, Z.; Li, Z. Correlation of serum levels of broblast growth factor 23 and Klotho protein levels with bone mineral density in maintenance hemodialysis patients. Eur. J. Med. Res. 2018, 23, 18.
38. Liu, Z.; Zhou, H.; Chen, X.; Chen, H.; Wang, Y.; Wang, T.; Cai, L.; Hong, Y.; Ke, H.; Zheng, J. Relationship between cFGF23/Klotho ratio and phosphate levels in patients with chronic kidney disease. Int. Urol. Nephrol. 2019, 51, 503–507.
39. Hong, Y.A.; Lim, J.H.; Kim, M.Y.; Kim, Y.; Yang, K.S.; Chung, B.H.; Chung, S.; Choi, B.S.; Yang, C.W.; Kim, Y.S.; et al. Assessment of tubular reabsorption of phosphate as a surrogate marker for phosphate regulation in chronic kidney disease. Clin. Exp. Nephrol. 2015, 19, 208–215.
40. Foster, M.C.; Coresh, J.; Hsu, C.Y.; Xie, D.; Levey, A.S.; Nelson, R.G.; Eckfeldt, J.H.; Vasan, R.S.; Kimmel, P.L.; Schelling, J.; et al. Serum beta-Trace Protein and
beta2-Microglobulin as Predictors of ESRD, Mortality, and Cardiovascular Disease in Adults With CKD in the Chronic Renal Insuciency Cohort (CRIC) Study. Am. J. Kidney Dis. 2016, 68, 68–76.
41. Drew, D.A.; Katz, R.; Kritchevsky, S.; Ix, J.; Shlipak, M.; Gutiérrez, O.M.; Newman, A.; Hoofnagle, A.; Fried, L.; Semba, R.D.; et al. Association between Soluble Klotho and Change in Kidney Function: The Health Aging and Body Composition Study. J. Am. Soc. Nephrol. 2017, 28, 1859–1866.
42. Ebert, N.; Koep, C.; Schwarz, K.; Martus, P.; Mielke, N.; Bartel, J.; Kuhlmann, M.; Gaedeke, J.; Toelle, M.; van der Giet, M.; et al. Beta Trace Protein does not outperform Creatinine and Cystatin C in estimating Glomerular Filtration Rate in Older Adults. Sci. Rep. 2017, 7, 12656.
43. Khosravi, N.; Asgari, M.; Khalessi, N.; Hoseini, R.; Khosravi, N. Serum Beta-Trace Protein for Assessment of Kidney Function in Neonates. Iran. J. Kidney Dis. 2018, 12, 11–13.
44. Liu, Q.F.; Yu, L.X.; Feng, J.H.; Sun, Q.; Li, S.S.; Ye, J.M. The Prognostic Role of Klotho in Patients with Chronic Kidney Disease: A Systematic Review and Meta-analysis. Dis. Markers 2019, 2019, 6468729.
45. Yue, L.; Pan, B.; Shi, X.; Du, X. Comparison between the Beta-2 Microglobulin-Based Equation and the CKD-EPI Equation for Estimating GFR in CKD Patients in China: ES-CKD Study. Kidney Dis. 2020, 6, 204–214.
46. Jung, J.Y.; Ro, H.; Chang, J.H.; Kim, A.J.; Lee, H.H.; Han, S.H.; Yoo, T.H.; Lee, K.B.; Kim, Y.H.; Kim, S.W.; et al. Mediation of the relationship between proteinuria and serum phosphate: Insight from the KNOW-CKD study. PLoS ONE 2020, 15, e0235077.
47. Devarajan, P. Neutrophil gelatinase-associated lipocalin (NGAL): A new marker of kidney disease. Scand J. Clin. Lab. Invest. Suppl. 2008, 241, 89–94.
48. Huo, W.; Zhang, K.; Nie, Z.; Li, Q.; Jin, F. Kidney injury molecule-1 (KIM-1): A novel kidney-specific injury molecule playing potential double-edged functions in kidney injury. Transplant. Rev. 2010, 24, 143–146.
49. Ning, M.; Mao, X.; Niu, Y.; Tang, B.; Shen, H. Usefulness and limitations of neutrophil gelatinase-associated lipocalin in the assessment of kidney diseases. J. Lab. Precis. Med. 2018, 3, 1.
