Home Medicine Relationship between renal tubulointerstitial fibrosis and serum prolidase enzyme activity
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Relationship between renal tubulointerstitial fibrosis and serum prolidase enzyme activity

  • Baris Eser ORCID logo EMAIL logo , Ibrahim Dogan ORCID logo , Erdal Komut ORCID logo , Sumeyra Koyuncu ORCID logo , Nihal Aydemir ORCID logo , Aysel Colak ORCID logo , Hulya Akgun ORCID logo and Huseyin Kayadibi ORCID logo
Published/Copyright: August 10, 2020
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Turkish Journal of Biochemistry
From the journal Turkish Journal of Biochemistry Volume 45 Issue 5

Abstract

Objectives

The severity of interstitial fibrosis/tubular atrophy (IFTA) is the most important determinant of the irreversible progression of chronic kidney disease (CKD). Prolidase is the key enzyme in collagen turnover and is associated with an extracellular matrix increase. We aimed to evaluate the relationship between the presence and degree of IFTA and serum prolidase enzyme activity (SPEA) in patients undergoing a renal biopsy.

Methods

This cross-sectional study included 56 patients who underwent a renal biopsy (30 males; mean age 45.3 ± 16.8 years) and also 54 healthy volunteers (21 males; mean age 42.7 ± 8.2 years). IFTA scoring was performed on the basis of percentage of IFTA presence in renal biopsy tissues (1=<10%; 2=10–24%; 3=25–50%; 4=>50%). SPEA was measured by spectrophotometric method.

Results

The proteinuria and SPEA levels of the patients were significantly higher than the controls (p<0.001 and p<0.001, respectively). SPEA decreased significantly when the IFTA score increased (p<0.002). In the correlation analysis, the IFTA score was negatively correlated with SPEA (rs=−0.461, p<0.001), and positively correlated with proteinuria (rs=0.274, p=0.041).

Conclusion

These findings suggest that increased collagen turnover decreases over time concerning the progression of renal fibrosis. Monitoring of SPEA level may useful as a biomarker for early determination of CKD progression and severity.

Öz

Giriş

İntersitisyal fibrozis/tubüler atrofi (IFTA) şiddeti kronik böbrek hastalığı (KBH)’nın geri dönüşümsüz ilerlemesinin en önemli belirleyicisidir. Prolidaz, kollajen döngüsünün kilit enzimidir ve ekstraselüler matriks artışı ile ilişkilidir. Çalışmamızda böbrek biyopsisi yapılan hastalarda IFTA varlığı ve derecesi ile serum prolidaz enzim aktivitesi (SPEA) arasındaki ilişkinin değerlendirilmesi amaçlandı.

Yöntem

Bu kesitsel çalışmaya böbrek biyopsisi yapılan 56 hasta (30 erkek; ortalam yaş 45.3 ± 16.8 yıl) ve 54 sağlıklı gönüllü (21 erkek; ortalama yaş 42.7 ± 8.2 yıl) dahil edildi. Renal biyopsi dokularında IFTA yaygınlık yüzdesi temelinde IFTA skorlaması yapıldı (1=<10%; 2=10–24%; 3=25–50%; 4=>50%). SPEA’i spektrofotometrik yöntem ile ölçüldü.

Bulgular

Hastaların kontrollere göre proteinüri ve SPEA düzeyleri istatistiksel olarak anlamlı şekilde daha yüksekti (sırasıyla, p<0.001 ve p<0.001). IFTA skoru artarken SPEA istatistiksel olarak anlamlı şekilde azalmakta idi (p<0.002). Korelasyon analizinde IFTA skoru SPEA ile negatif (rs=−0.461, p<0.001) ve proteinüri ile pozitif korelasyon gösterdi (rs=0.274, p=0.041).

Sonuç

Bu bulgular, renal fibrozisin ilerlemesi ile ilgili olarak artan kollajen döngüsünün azaldığını düşündürmektedir. SPEA seviyesinin izlenmesi, KBH ilerlemesinin ve şiddetinin erken belirlenmesi için bir biyobelirteç olarak yararlı olabilir.

Keywords: chronic kidney disease; collagen turnover; prolidase enzyme activity; proteinuria; tubulointerstitial fibrosis
Anahtar kelimeler: Kollajen döngüsü; kronik böbrek hastalığı; prolidaz enzim aktivitesi; proteinüri; tubulointerstisyel fibroz

Introduction

The structure of normal kidney tissue is composed of glomerular, tubulointerstitial, and vascular compartments. In response to renal injury, fibrosis may develop in these compartments separately or together. Tubulointerstitial changes play an important role in the pathogenesis of chronic kidney disease (CKD). Regardless of the underlying disease, increased tubulointerstitial volume or degree of tubular atrophy/interstitial fibrosis (IFTA) is the most important determinant of the irreversible progression of CKD. IFTA is characterized by an increase in the number of activated renal fibroblasts–myofibroblasts– and excessive accumulation of extracellular matrix (ECM) components [1].

Prolidase, a member of the matrix metalloproteinase family, is a cytosolic dipeptidase and specifically cleaves proline and hydroxyproline at the C-terminal of imidodipeptides. Prolidase enzyme liberates the proline required for the construction of collagen, which is the major component ECM. Besides, this enzyme acts as a rate-limiting factor in the final step of collagen catabolism. Prolidase enzyme activity has been detected in tissues such as kidney, liver, intestinal mucosa, and cell types such as leukocytes, erythrocytes, fibroblasts as well as plasma [2]. Increased prolidase enzyme activity in active fibroblasts has been shown to increase collagen turnover and ECM production [3]. It has also been suggested that the change in prolidase enzyme activity may play a role in the pathogenesis of various diseases associated with ECM and fibrosis [4], [5], [6], [7]. However, the importance of prolidase enzyme activity in the pathogenesis of diseases has not yet been fully elucidated.

Early detection of tubulointerstitial damage and a better understanding of pathophysiological processes can both may help to slow the progression of CKD, and may manage the renoprotective treatment earlier. Regardless of the cause of renal damage, we think that increased prolidase enzyme activity in activated myofibroblasts may lead to tubulointerstitial damage. Since there is no literature data about our hypothesis, we aimed to evaluate the relationship between the presence and stage of IFTA and serum prolidase enzyme activity (SPEA) in adult patients undergoing a renal biopsy.

Methods

Study participants and data collection

This cross-sectional study included 56 patients (30 males, 26 females; mean age 45.3 ± 16.8 years; range 18–73 years) who underwent renal biopsy at the nephrology clinic and on 54 healthy volunteers (21 males, 33 females; mean age 42.7 ± 8.2 years; range 28–60 years) from March 2018 to May 2019.

The group of patients was composed of those who underwent a kidney biopsy due to the presence of unexplained proteinuria over 1 g/day, with or without elevated serum creatinine (Scr) levels, and rapid incresea of Scr and progression of proteinuria levels in patients with CKD [8]. The controls consisted of age-sex matched healthy volunteers and no history of chronic disease. Demographic and somatometric data of the participants were determined.

Patients with acute or chronic infectious disease, uncontrolled hypertension, endocrine disease (thyroid, parathyroid), acute or chronic organ failure (heart, liver, lung), malignancy, surgical intervention in the last month, autoimmune or inflammatory diseases, pregnancy, presence of renal transplant and those using agents that may affect SPEA (nonsteroid anti-inflammatory drugs, antioxidants) were excluded from the study.

Blood samples were obtained between 08:00 and 11:00 AM after overnight fasting and 24 h urine samples were taken for the measurement of protein excretion from all participants. All blood and urine samples of patients were collected the day before renal biopsy. The blood samples were centrifuged at 1,310 g for 10 min to obtain serum and to use for the analysis of biochemical parameters [blood urea nitrogen (BUN), Scr, glucose, uric acid, albumin, lipids, high sensitivity c-reactive protein (hsCRP), and SPEA]. Complete blood count and erythrocyte sedimentation rate (ESR) were analyzed. Serum samples for SPEA assay were stored at −80 °C until the day of analysis.

The biochemical parameters were measured by Beckman Coulter AU 5800 autoanalyzer, and complete blood count analysis was performed by MindrayBC-6800 hematology analyzer. Estimated glomerular filtration rate (eGFR) values were determined by the use of Chronic Kidney Disease Epidemiology Collaboration equation [9]. Serum hsCRP and ESR levels were measured by nephelometric and Westergren methods, respectively. Urine protein analysis was measured by Beckman Coulter AU 5800 autoanalyzer. In the colorimetric method of Beckman Coulter urinary protein reagent, pyrogallol red is combined with molybdate to form a red complex. This assay is based on the shift in absorbance that occurs when the pyrogallol red-molybdate complex binds basic amino groups of proteins. The absorbance of blue-purple complex which is directly proportional to the protein concentration in the sample is formed with a maximum at 600 nm [10].

Renal biopsy procedure and histopathological evaluation

Renal tissues which are obtained by using semi-automatic Tru-cut 16G 15 cm biopsy needles (Ref No: GSN1615, Geotek Medical Ltd. Turkey) under ultrasound guidance were evaluated. Histopathological evaluations were performed by two pathologists who were unaware of SPEA results. In case of different stage of decision, biopsy specimens were re-evaluated by two pathologists together at the same time. Biopsy material with 10 or more glomeruli was accepted as sufficient. Biopsy materials were fixed in 10% neutral buffered formalin, then dehydrated in graduated alcohols, and embedded in paraffin. For primary diagnosis and histopathological evaluation, 0.4 µm sections were prepared from tissue samples and stained with periodic acid-Schiff (PAS), trichrome, hematoxylin & eosin, and evaluated by light and immunofluorescence microscopy. For the evaluation of IFTA, cortical areas in trichrome and PAS sections were examined sequentially at Nikon microscope with 200x magnification. The extent of IFTA was evaluated by counting the percentage of areas with tubular dilation, interstitial infiltration, and fibrosis per field of the cortex. IFTA score was calculated by using a scoring system based on the percentage of IFTA (1=<10%; 2=10–24%; 3=25–50%; 4=>50%). The evaluation of global glomerulosclerosis was performed with a similar scoring system, and was calculated by taking average values [11], [12].

Measurement of serum prolidase enzyme activity

In the method, 25 μL of serum sample was added to the 75 μL of activation solution (50 mmol/L Tris-HCl buffer at pH 7 containing 1 mmol/L GSH, 50 mmol/L MnCl2), and incubated at 37 °C for 30 min for the optimum stabilization and activation of prolidase enzyme. Hundred microliter of 144 mmol/L Gly-Pro (Sigma-Aldrich, G3002) was added into the mixture, and was incubated at 37 °C for 5 min. After incubation, 1 mL of glacial acetic acid was added to stop the reaction. Three hundred microliter of Tris-HCl buffer (pH 7.8) and 1 mL of ninhydrin solution (3 g/dL ninhydrin was dissolved in 0.5 mol/L orthophosphoric acid) were added into the mixture, and then incubated at 90 °C for 25 min. All samples were chilled with ice and read within 20 min at 515 nm against the reagent blank without delay with the spectrophotometer (Biochrom Ltd. Cambridge CB4 OFJ England, Libra S60). SPEA was defined as proline in μmol/L that forms in 1 min. Within a day and between day % coefficients of variation of the method were found to be less than 10% for high and low serum values [13].

Statistical analysis

All statistical analyses were performed using the SPSS 23.0 package for Windows (IBM Corp.; Armonk, NY, USA). Shapiro Wilks and Kolmogorov Smirnov analyses were used to determine the normal distribution of the groups. The data were expressed as mean ± standard deviation and median (25th–75th interquartile range) for Gaussian and non-Gaussian distributed variables, respectively. Categorical variables were compared using the chi-square test, and were expressed as numbers and percentages. Bonferroni correction performed one way ANOVA and Student’s t-test were used to compare the parameters in the normally distributed groups, while Kruskal Wallis and Mann Whitney U tests were used to compare the parameters in the non-normally distributed groups. Spearman correlation analysis was used to determine the relationship between the parameters. In addition, the independent effect of each variable on the presence of IFTA was assessed by using univariate logistic regression analysis, and then statistically significant parameters were assessed by the multivariate logistic regression analysis. The p values less than 0.05 were considered statistically significant.

Results

When the patients and controls were compared, there was no statistically significant difference between age, gender, body mass index (BMI), systolic blood pressures (SBP), and diastolic blood pressures (DBP) (p>0.05). While eGFR (p<0.001) and serum albumin levels were significantly lower (p<0.001), urinary protein excretion and SPEA levels were significantly higher in patients compared to controls. Clinical characteristics of the participants are represented in Table 1.

Table 1:

Demographic and laboratory characteristics of the patient and control groups.

ParametersPatients (n=56)Controls (n=54)p-Value
Age, years45.3 ± 16.842.7 ± 8.20.299
Male, n (%)30 (53.6)21 (38.9)0.123
BMI, kg/m227.1 ± 4.326.7 ± 4.00.696
SBP, mm/Hg120 (120–130)121 (112–126)0.195
DBP, mm/Hg80 (70–80)78 (74–83)0.291
DM, n (%)7 (12.5)
HT, n (%)17(30.4)
Smoking, n (%)6 (10.7)6 (11)0.947
Histopathologic diagnosis
 MCD, n (%)12 (21.4)
 IgAN, n (%)11 (19.6)
 FSGS, n (%)8 (14.3)
 MGN, n (%)8 (14.3)
 Amyloidosis, n (%)5 (8.9)
 DNP, n (%)4 (7.1)
 Others, n (%)8 (14.3)
RAAS blockers, n (%)13 (23.3)
Hemoglobin, g/dL12.7 ± 2.013.9 ± 1.40.001
Glucose, mg/dL94 (85–104)92 (86–101)0.566
BUN, mg/dL21 (14–30)12 (10–14)<0.001
Creatinine, mg/dL1.1 (0.6–1.7)0.7 (0.6–0.8)<0.001
eGFR (mL/min/1.73 m2)65 (47–105)111 (104–114)<0.001
Albumin, g/L32 ± 1044 ± 4<0.001
Uric acid, mg/dL6.2 ± 1.44.9 ± 1.2<0.001
HDL-C, mg/dL52 ± 1850 ± 130.457
LDL-C, mg/dL142 (103–197)109 (93–137)0.002
NLR2.23(1.60–2.88)1.75 (1.29–2.07)0.002
ESR, mm/h31 (16–59)8 (5–18)<0.001
hsCRP, mg/L3.4 (1.2–9.6)0.8 (0.4–1.2)<0.001
Proteinuria, mg/day3,458 (2,000–6,612)66 (48–88)<0.001
Prolidase, U/L1,311 (1,016–1,927)660 (553–851)<0.001

  1. Categorical data are presented as frequencies and percentages; continuous variables are presented as mean ± standard deviations or median and interquartile ranges (IQR: 25th – 75th) depending on their distributions.

  2. Abbreviations: BMI, Body mass index; BUN, Blood urea nitrogen, DBP, Diastolic blood pressure; DM, Diabetes mellitus; DNP, Diabetic nephropathy; eGFR, Estimated glomerular filtration rate, ESR, Erythrocyte sedimentation rate; FSGS, Focal segmental glomerulosclerosis; HDL-C, high density lipoprotein cholesterol; hsCRP, High sensitivity c-reactive protein; HT, Hypertension; IgAN, IgA nephropathy; LDL-C, Low density lipoprotein cholesterol; MGN, membranous glomerulonephritis; MCD, Minimal change diseas; NLR, Neutrophil to lymphocyte Ratio; RAAS, Renin angiotensin aldosterone system; SBP, Systolic blood pressure.

Distributions of patients based on IFTA 1, 2, 3, and 4 scoring were 26 (46.4%), 18 (32.1%), 7 (12.6%), and 5 (8.9%), respectively. Due to the number of patients with IFTA scores of 3 and 4 was low, both of these groups were combined as one group for the statistical analyses. No statistically significant difference was observed among age, gender, BMI, SBP, and DBP when IFTA score 1, 2, and 3 + 4 were compared (p>0.05).While the IFTA score increased, it was found that eGFR and SPEA levels significantly decreased (p<0.001 and p<0.002, respectively). SPEA levels of patients with IFTA score 3 + 4 were significantly lower than those with IFTA score 1 and score 2 (p<0.001 and p=0.031, respectively, Figure 1). Proteinuria was increasing with the IFTA score increase, but was not statistically significant (p=0.127). Demographic and laboratory characteristics of patients, according to IFTA scores are detailed in Table 2.

Figure 1: Comparison of serum prolidase enzyme activity (SPEA) levels according to interstitial fibrosis/tubular atrophy (IFTA) scores.
Figure 1:

Comparison of serum prolidase enzyme activity (SPEA) levels according to interstitial fibrosis/tubular atrophy (IFTA) scores.

Table 2:

Comparison demographic and biochemical data of patients according to IFTA scores.

ParametersScore 1 (n=26)Score 2 (n=18)Score 3 + 4 (n=12)p-Value
Age, years43.3 ± 19.546.4 ± 16.347.8 ± 110.700
Male, n (%)14 (53.8)9 (50.0)7 (58.3)0.904
BMI, kg/m226.8 ± 4.426.6 ± 4.328.2 ± 4.30.598
SBP, mm/Hg120 (120–130)120 (120–130)130 (120–138)0.224
DBP, mm/Hg80 (70–80)80 (70–80)80 (73–80)0.344
DM, n (%)2 (7.7)2 (11.1)3 (25)0.341
HT, n (%)8 (30.8)4 (22.2)5 (41.7)0.566
Smoking, n (%)3 (11.5)0 (0)3 (25)0.063
RAAS blockers, n (%)7 (26.9)2 (11.1)4 (33.3)0.275
Hemoglobin, g/dL13.4 ± 1.912.1 ± 2.412.2 ± 1.30.069
Glucose, mg/dL94 (84–105)92 (85–97)95 (89–117)0.515
BUN, mg/dL17 (11–21)a,b23 (15–43)29 (23–45)0.001
Creatinine, mg/dL0.7 (0.6–1.0)c,d1.2 (0.9–1.6)e1.9 (1.6–3.0)<0.001
eGFR (mL/min/1.73 m2)102 ± 32f,g65 ± 26h37 ± 17<0.001
Albumin, g/L33 ± 1.032 ± 1131 ± 060.783
Uric acid, mg/dL5.9 ± 1.96.7 ± 1.56.4 ± 1.20.222
HDL-C, mg/dL53 ± 1951 ± 1853 ± 170.971
LDL-C, mg/dL166 ± 80151 ± 59116 ± 1090.818
NLR2.00 (1.40–2.85)2.57 (2.23–3.42)2.09 (1.42–2.76)0.499
ESR, mm/h25 (19–45)i18 (12–60)j48 (37–63)0.016
hsCRP, mg/L2.7 (0.4–3.7)k,l4.8 (2.5–10)7.5 (3.5–21)0.002
Proteinuria, mg/day3,000 (1,425–4,925)3,503 (2,000–6,863)5,426 (3,009–10,957)0.127
Prolidase, U/L1,572 (1,172–2,137)m1,255 (990–1,741)n939 (860–1,281)0.002

  1. Categorical data are presented as frequencies and percentages; continuous variables are presented as mean ± standard deviations or median and interquartile ranges (IQR: 25th–75th) depending on their distributions.

  2. Abbreviations: BMI, Body mass index; BUN, Blood urea nitrogen; DBP, Diastolic blood pressure; DM, Diabetes mellitus; eGFR, Estimated glomerular filtration rate; ESR, Erythrocyte sedimentation rate; HDL-C, High density lipoprotein cholesterol; hsCRP, High sensitivity c-reactive protein; HT, Hypertension; IFTA, Intestinal fibrosis tubular atrophy; LDL-C, Low density lipoprotein cholesterol; NLR, Neutrophil to lymphocyte ratio; RAAS, Renin angiotensin aldosterone system; SBP, Systolic blood pressure.

  3. ap=0.026 compared with the score 2.

  4. bp < 0.001compared with the score 3 + 4.

  5. cp=0.01 compared with the score 2.

  6. dp < 0.001compared with the score 3 + 4.

  7. ep=0.008 compared with the score 3 + 4.

  8. fp < 0.001 compared with the score 2.

  9. gp < 0.001 compared with the score 3 + 4.

  10. hp=0.024 compared with the score 3 + 4.

  11. ip=0.002 compared with the score 3 + 4.

  12. jp=0.039 compared with the score 3 + 4.

  13. kp=0.010 compared with the score 2.

  14. lp=0.002 compared with the score 3 + 4.

  15. mp < 0.001 compared with the score 3 + 4.

  16. np=0.031 compared with the score 3 + 4.

In the correlation analysis, IFTA score was negatively correlated with SPEA (rs=−0.461, p<0.001), eGFR (rs=−0.685, p<0.001) and hemoglobin (rs=−0.307, p=0.022), and positively correlated with ESR (rs=0.281, p=0.038), hsCRP (rs=0.475, p<0.001) and proteinuria (rs=0.274, p=0.041). Additionally, global glomerulosclerosis scores and IFTA scores were positively correlated (rs=0.757, p<0.001). However, while SPEA was significantly correlated with eGFR (rs=0.403, p=0.002), it was not correlated significantly with proteinuria (rs=−0.118, p=0.398).

Logistic regression analyses were performed to determine the independent factors associated with the presence of IFTA. Univariate and multivariate logistic regression analysis demonstrated that SPEA was associated with the presence of IFTA (p<0.001, p=0.046, respectively) (Table 3).

Table 3:

Univariate and multivariate logistic regression analysis of variables associated with the presence of IFTA.

VariablesUnivariate analysisMultivariate analysis
OR (95% CI)Waldp-ValueOR (95% CI)Waldp-Value
Prolidase1.006 (1.004–1.008)25.552<0.0011.13 (1.000 –1.026)3.9910.046
eGFR0.960 (0.942–0.977)20.230<0.0010.863 (0.731–1.019)3.0360.081
Albumin0.041 (0.011–0.150)23.202<0.0010.00 (0.00–1.074)3.7890.052
Uric acid2.056 (1.451–2.913)16.442<0.0016.963 (0.884–54.838)3.3970.065
LDL-C1.014 (1.005–1.023)10.1730.0010.969 (0.922–1.019)1.4660.226
ESR1.091 (1.049–1.135)18.818<0.0010.898 (0.749–1.077)1.3490.246

  1. Abbreviations: eGFR, Estimated glomerular filtration rate; ESR, Erythrocyte sedimentation rate; IFTA, Intestinal fibrosis tubular atrophy; LDL-C, Low density lipoprotein cholesterol.

Discussion

To the best of our knowledge, this is the first study evaluating the association between IFTA and SPEA levels in adult patients with persistent proteinuria. SPEA levels were significantly higher in patients compared to healthy controls. While SPEA and eGFR levels were negatively correlated with IFTA stage, the level of ESR and hsCRP which are markers of inflammation, and proteinuria was positively correlated. It has also been shown that SPEA can be independently associated with the presence of IFTA.

IFTA and glomerulosclerosis are the final common pathways observed in CKD progression regardless of etiology. However, it is known that the tubulointerstitial area of renal tissue causes fewer misconceptions in determining the degree of kidney damage compared to the glomeruli [14]. Consistent with the literature data, the IFTA score, used to determine the severity of chronic kidney injury, was negatively correlated with eGFR and positively correlated with global glomerulosclerosis severity.

Chronic kidney damage is manifested by various structural changes, including the accumulation of ECM. All of the structural changes considered as an accumulation of ECM are characterized by both tubular degeneration, including tubular cell death and tubular atrophy, and interstitial fibrosis (IF) [15]. Cell culture studies have shown that increased prolidase enzyme activity in activated fibroblasts increases ECM production, and prolidase enzyme activity in fibroblasts is regulated through ECM interaction with cell surface integrin receptors [3]. In our study SPEA levels were significantly higher in patients with proteinuria than healthy controls, suggesting that increased collagen turnover and prolidase enzyme activity may have an effect on CKD progression in these patients.

The most widely accepted view of the underlying mechanism of CKD progression is tubulointerstitial damage caused by the direct toxic effect of proteinuria [16]. Proteinuria affects the regulation of the signaling pathways of tubule cells, resulting in changes in tubule cell growth, apoptosis, and gene transcription. This results in the production of pro-inflammatory factors that cause inflammation and fibrosis [17]. To our knowledge, in a single study evaluating the relationship between proteinuria and SPEA, increased SPEA was associated with microalbuminuria in patients with type 2 diabetes mellitus [18]. In our study, it was found that IFTA score was positively correlated with proteinuria levels and negatively correlated with SPEA levels. We consider that changes in SPEA may have effect in the complex pathophysiological process associated with the proteinuria-induced tubular injury.

Prolidase activity has been shown to correlate positively with collagen turnover and increased SPEA in some diseases characterized by increased collagen turnover [4], [19], [20]. However, it has been found that SPEA is lower in chronic inflammatory musculoskeletal diseases characterized by excessive collagen accumulation and tissue fibrosis [5], [21]. Besides, SPEA was found to be low in patients undergoing dialysis for end-stage renal failure as compared with the healthy volunteers [22]. It has been emphasized that low SPEA in various diseases may be associated with advanced tissue fibrosis and decrease in collagen turnover [6], [7], and the progression of fibrosis from mild-moderate to severity may lead to decrease in SPEA levels [4], [23]. Our study shows that the negative correlation between the IFTA score and eGFR and SPEA supports a gradual slowdown in collagen turnover due to chronic progressive renal fibrosis consistent with the findings of previous studies.

There are not enough studies on the importance of SPEA and factors affecting its activity in the pathogenesis of renal injury. In a clinical study of patients with diabetic nephropathy, it was emphasized that SPEA was positively correlated with oxidative stress (OS) [24]. It has been shown that decreased levels of nitric oxide (NO) in renal tissue, which plays a key role in the pathogenesis of OS mediated renal damage, cause antioxidant gene expression that protects endothelial and mesangial cells from apoptosis and fibrosis [25], [26]. Additionally, it has been shown that NO stimulates collagen synthesis and prolidase activity in fibroblasts. NO acts on this enzyme by increasing serine/threonine phosphorylation [27]. There have been conflicting results in studies evaluated NO level and prolidase enzyme activity. It was shown that both NO level and prolidase enzyme activity in bladder tissue samples were higher in patients with bladder tumors than in those without bladder tumors [28]. In another study, it was emphasized that decreased SPEA and high serum NO levels may be related in patients with diabetic neuropathy [29]. In our patient, we think that the decreased NO level associated with CKD progression may be effective in decreasing SPEA.

There are some limitations in this study. Due to the cross-sectional design of the study, interpretations of causality according to data should be made with caution. Another limitation is that the heterogeneous etiologies of primary renal injury and the relatively low number of patients. Finally, it will be appropriate to investigate the relationship between NO synthetase and prolidase expressions in renal tissues obtained from same patient’s population or in experimental CKD animal models, and their results can support our hypothesis.

According to the data of this study in which the relationship between renal fibrosis and SPEA was evaluated, SPEA levels were higher in patients with persistent proteinuria than the healthy controls, whereas IFTA scores were correlated negatively with SPEA levels. These findings suggest the decrease of increased collagen turnover over time due to the progression of renal fibrosis. We consider that these results may contribute to the understanding of the pathophysiological mechanisms associated with the prolidase activity related renal fibrosis. Monitoring of SPEA level may useful as a biomarker for early determination of CKD progression and severity.

Corresponding author: Assistant Prof. Baris Eser, M.D., Hitit University Faculty of Medicine, Department of Nephrology, 19200 Corum, Turkey, Phone: +90 506 264 78 56, Fax: +90 364 223 30 31, E-mail:

Acknowledgments

No funding received.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: The authors report no conflicts of interest.

  4. Ethical considerations: The study was approved by the local ethics committee (The protocol date/number; 27.02.2018/2018–19), and was conducted in accordance with Helsinki 2018 Declaration. Written informed consent was obtained from all participants.

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Received: 2020-01-17
Accepted: 2020-06-13
Published Online: 2020-08-10

© 2020 Baris Eser et al., published by de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Review Article
  3. Newly developed diagnostic methods for SARS-CoV-2 detection
  4. Short Communication
  5. Effect of hemolysis on prealbumin assay
  6. Research Articles
  7. BioVar: an online biological variation analysis tool
  8. High dose ascorbic acid treatment in COVID-19 patients raised some problems in clinical chemistry testing
  9. Immunoassay biomarkers of first and second trimesters: a comparison between pregnant Syrian refugees and Turkish women
  10. Association of maternal serum trace elements with newborn screening-thyroid stimulating hormone
  11. PIK3CA and TP53 MUTATIONS and SALL4, PTEN and PIK3R1 GENE EXPRESSION LEVELS in BREAST CANCER
  12. Evaluation of E2F3 and survivin expression in peripheral blood as potential diagnostic markers of prostate cancer
  13. Age, gender and season dependent 25(OH)D levels in children and adults living in Istanbul
  14. Original Article
  15. Fractional excretion of magnesium as an early indicator of renal tubular damage in normotensive diabetic nephropathy
  16. Research Articles
  17. Diagnostic value of laboratory results in children with acute appendicitis
  18. Evaluation of thiol disulphide levels in patients with pulmonary embolism
  19. Relationship between renal tubulointerstitial fibrosis and serum prolidase enzyme activity
  20. Comparison of test results obtained from lithium heparin gel tubes and serum gel tubes
  21. MHC Class I related chain A (MICA), Human Leukocyte Antigen (HLA)-DRB1, HLA-DQB1 genotypes in Turkish patients with ulcerative colitis
  22. An overview of procalcitonin in Crimean-Congo hemorrhagic fever: clinical diagnosis, follow-up, prognosis and survival rates
  23. Comparison of different equations for estimation of low-density lipoprotein (LDL) – cholesterol
  24. Case-Report
  25. A rare case of fructose-1,6-bisphosphatase deficiency: a delayed diagnosis story
  26. Research Articles
  27. Atypical cells in sysmex UN automated urine particle analyzer: a case report and pitfalls for future studies
  28. Investigation of the relationship cellular and physiological degeneration in the mandible with AQP1 and AQP3 membrane proteins
  29. In vitro assessment of food-derived-glucose bioaccessibility and bioavailability in bicameral cell culture system
  30. Letter to the Editor
  31. The weighting factor of exponentially weighted moving average chart
https://doi.org/10.1515/tjb-2020-0026
Keywords for this article
chronic kidney disease; collagen turnover; prolidase enzyme activity; proteinuria; tubulointerstitial fibrosis

Articles in the same Issue

  1. Frontmatter
  2. Review Article
  3. Newly developed diagnostic methods for SARS-CoV-2 detection
  4. Short Communication
  5. Effect of hemolysis on prealbumin assay
  6. Research Articles
  7. BioVar: an online biological variation analysis tool
  8. High dose ascorbic acid treatment in COVID-19 patients raised some problems in clinical chemistry testing
  9. Immunoassay biomarkers of first and second trimesters: a comparison between pregnant Syrian refugees and Turkish women
  10. Association of maternal serum trace elements with newborn screening-thyroid stimulating hormone
  11. PIK3CA and TP53 MUTATIONS and SALL4, PTEN and PIK3R1 GENE EXPRESSION LEVELS in BREAST CANCER
  12. Evaluation of E2F3 and survivin expression in peripheral blood as potential diagnostic markers of prostate cancer
  13. Age, gender and season dependent 25(OH)D levels in children and adults living in Istanbul
  14. Original Article
  15. Fractional excretion of magnesium as an early indicator of renal tubular damage in normotensive diabetic nephropathy
  16. Research Articles
  17. Diagnostic value of laboratory results in children with acute appendicitis
  18. Evaluation of thiol disulphide levels in patients with pulmonary embolism
  19. Relationship between renal tubulointerstitial fibrosis and serum prolidase enzyme activity
  20. Comparison of test results obtained from lithium heparin gel tubes and serum gel tubes
  21. MHC Class I related chain A (MICA), Human Leukocyte Antigen (HLA)-DRB1, HLA-DQB1 genotypes in Turkish patients with ulcerative colitis
  22. An overview of procalcitonin in Crimean-Congo hemorrhagic fever: clinical diagnosis, follow-up, prognosis and survival rates
  23. Comparison of different equations for estimation of low-density lipoprotein (LDL) – cholesterol
  24. Case-Report
  25. A rare case of fructose-1,6-bisphosphatase deficiency: a delayed diagnosis story
  26. Research Articles
  27. Atypical cells in sysmex UN automated urine particle analyzer: a case report and pitfalls for future studies
  28. Investigation of the relationship cellular and physiological degeneration in the mandible with AQP1 and AQP3 membrane proteins
  29. In vitro assessment of food-derived-glucose bioaccessibility and bioavailability in bicameral cell culture system
  30. Letter to the Editor
  31. The weighting factor of exponentially weighted moving average chart
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