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PLoS One
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10.1371/journal.pone.0307824
PONE-D-23-24980
Research Article
Medicine and Health Sciences
Surgical and Invasive Medical Procedures
Transplantation
Organ Transplantation
Renal Transplantation
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Surgical and Invasive Medical Procedures
Urinary System Procedures
Renal Transplantation
Biology and Life Sciences
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Glomerular Filtration Rate
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Nitric Oxide
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Nitric Oxide
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Cell Biology
Oxidative Stress
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Anatomy
Renal System
Kidneys
Medicine and Health Sciences
Anatomy
Renal System
Kidneys
Biology and Life Sciences
Biochemistry
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Creatinine
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Anatomy
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Living donors kidney transplantation and oxidative stress: Nitric oxide as a predictive marker of graft function
Nitric oxide as a predictive marker of graft function in kidney transplantation
https://orcid.org/0009-0006-5834-6997
Izemrane Djamila Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Software Validation Visualization Writing – original draft Writing – review & editing 1 2 *
https://orcid.org/0000-0002-8566-8602
Benziane Ali Conceptualization Methodology Resources Supervision Validation Visualization Writing – original draft Writing – review & editing 3
Makrelouf Mohamed Conceptualization Funding acquisition Methodology Resources Writing – review & editing 4 ‡
Hamdis Nacim Conceptualization Funding acquisition Methodology Resources Writing – review & editing 5 ‡
Rabia Samia Hadj Conceptualization Methodology Resources Writing – review & editing 1 6 ‡
Boudjellaba Sofiane Conceptualization Data curation Formal analysis Methodology Software Writing – review & editing 2 7 ‡
Baz Ahsene Conceptualization Funding acquisition Methodology Project administration Resources Supervision Validation Visualization Writing – original draft Writing – review & editing 1
Benaziza Djamila Conceptualization Funding acquisition Methodology Project administration Writing – review & editing 1
1 Laboratory of Biology and Animal Physiology, Higher Normal School, Kouba, Algiers, Algeria
2 National Higher Veterinary School, Issad Abbes, Oued Smar, Algiers, Algeria
3 Department of Nephrology-Hemodialysis and Transplantation, Lamine Debaghine University Hospital, Bab El Oued, Algiers, Algeria
4 Central Biology Laboratory, Lamine Debaghine University Hospital, Bab El Oued, Algiers, Algeria
5 Laboratory of Food Technology Research, Faculty of Engineering Sciences-University M’Hamed Bougara, City Frantz Fanon, Boumerdes, Algeria
6 Department of Nuclear Applications, Nuclear Research Center, Sebala, Algiers, Algeria
7 Laboratory of Research Management of Local Animal Resources (GRAL), National Higher Veterinary School, Issad Abbes, Oued Smar, Algiers, Algeria
Lee John Richard Editor
Weill Cornell Medicine, UNITED STATES OF AMERICA
Competing Interests: The authors have declared that no competing interests exist.

‡ MM, NH, SHR and SB also contributed equally to this work.

* E-mail: d.izemrane@ensv.dz
23 9 2024
2024
19 9 e030782418 9 2023
10 7 2024
© 2024 Izemrane et al
2024
Izemrane et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background

Glomerular filtration rate is the best indicator of renal function and a predictor of graft and patient survival after kidney transplantation.

Methods

In a single-centre prospective analysis, we assessed the predictive performances of 4 oxidative stress biomarkers in estimating graft function at 6 months and 1 year after kidney transplantation from living donors. Blood samples were achieved on days (D-1, D1, D2, D3, D6 and D8), months (M1, M3 and M6) and after one year (1Y). For donors, a blood sample was collected on D-1. Malondialdehyde (MDA), nitric oxide (NO), glutathione s-transferase (GST), myeloperoxydase (MPO), and creatinine (Cr) were measured by spectrophotometric essays. The estimated glomerular filtration rate by the modification of diet in renal disease equation (MDRD-eGFR) was used to assess renal function in 32 consecutive donor-recipient pairs. Pearson’s and Spearman’s correlations have been applied to filter out variables and covariables that can be used to build predictive models of graft function at six months and one year. The predictive performances of NO and MPO were tested by multivariable stepwise linear regression to estimate glomerular filtration rate at six months.

Results

Three models with the highest coefficients of determination stand out, combining the two variables nitric oxide at day 6 and an MDRD-eGFR variable at day 6 or MDRD-eGFR at day 21 or MDRD-eGFR at 3 months, associated for the first two models or not for the third model with donor age as a covariable (P = 0.000, r2 = 0.599, r2adj = 0.549; P = 0.000, r2 = 0.548, r2adj = 0.497; P = 0.000, r2 = 0.553, r2adj = 0.517 respectively).

Conclusion

Quantification of nitric oxide at day six could be useful in predicting graft function at six months in association with donor age and the estimated glomerular filtration rate in recipient at day 6, day 21 and 3 months after transplantation.

Ecole Normale Suéprieure Kouba https://orcid.org/0009-0006-5834-6997
Izemrane Djamila This article was written as part of a doctoral thesis. The funding comes from state institutions, namely the laboratory of biology and animal physiology of Higher Normal School of Kouba, Algiers, Algeria and the central biology laboratory of Lamine Debaghine University Hospital, Bab El Oued, Algiers, Algeria and personnal funding from Djamila Izemrane and Nacim Hamdis. Data AvailabilityWe have made our Data on Dryad public. The DOI and URL assigned to us are shown below. DOI: 10.5061/dryad.kkwh70sb3 URL: https://datadryad.org/stash/share/2amXBXayUq0oPVI2xg_IoF9_04VM3u7AFdfjfNtymFE.
Data Availability

We have made our Data on Dryad public. The DOI and URL assigned to us are shown below. DOI: 10.5061/dryad.kkwh70sb3 URL: https://datadryad.org/stash/share/2amXBXayUq0oPVI2xg_IoF9_04VM3u7AFdfjfNtymFE.
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pmcIntroduction

Kidney transplantation is recognized as the best renal replacement therapy for patients with end-stage renal disease (ESRD). It restores kidney function and improves quality and length of life [1]. The expected function after kidney transplantation depends on many variables: donor features (age, gender, kidney size, and associated comorbidity), harvesting conditions (beating vs. non-heart beating donors, cold ischemia time), and recipient characteristics (warm ischemia time, HLA matching, number of transplants, race, and immunosuppresion) [2]. These parameters are better controlled in living-donor kidney transplantation. Glomerular Filtration Rate (GFR) is considered the best indicator of renal function used to quantify the activity of the kidney and the effectiveness of renal replacement therapy. In addition, clinical events and post-transplant renal function in the first year predict long-term graft survival [3]. Assessment of the GFR is thus necessary for the monitoring of patients after receiving a kidney transplant. Serum creatinine remains the most widely used endogenous marker to estimate GFR in practice [4]. However, the accurate estimation of GFR by creatinine depends on factors such as weight, age, gender and/or race. Several formulas have therefore been constructed to correct the influences of these parameters [5–7]. Some of these equations have been evaluated in renal transplant patients, and the most commonly used are the Modification of Diet in Renal Disease (MDRD), Cockcroft-Gault, and (Chronic Kidney Disease Epidemiology Collaboration) CKD-EPI [7–9].

During the transplantation surgery, the kidney is submitted to blood flow arrest, followed at the time of reperfusion by a sudden increase in oxygen supply. This essential clinical protocol causes massive oxidative stress, which causes tissue damage and cell death [10]. Oxidative stress is a complex phenomenon resulting from the imbalance of cell homeostasis between prooxidants and antioxidants. It is directly caused by reactive species mainly reactive oxygen species (ROS). Some of them contain unpaired electrons called free radicals, whereas others do not, but all of them are very reactive and cause the peroxidation of proteins, carbohydrates, lipids and nucleic acids [11]. Oxidative stress and ROS generation in the kidney disrupt the excretory function of each section of the nephron. It impairs water-electrolyte and acid-base balance and affects kidney regulatory mechanisms [12]. Oxidative stress is directly linked to podocyte damage, depressed glomerular filtration rate, proteinuria, and tubulointerstitial fibrosis [13]. Furthermore, oxidative stress is also related to endothelial cell dysfunction and plays a critical role in chronic kidney disease progression [14]. At this stage, nitric oxide (NO), which is involved in several biological processes, including vasodilatation in smooth muscle cells, inflammation, and immune responses, plays a crucial role [15]. It has been shown that microvascular dysfunction in oxidative stress kidney damage is mediated through nitric oxide synthase (NOS). This event can lead to an impairment of the renal afferent arteriole autoregulation [16], increase in perfusion pressure, causing increases in the amount of superoxide radical (O.2) [17]. Previous studies have demonstrated that the level of oxidative stress markers correlates significantly with the level of renal function and increases as chronic kidney disease progresses [18]. Transplanted kidneys are also subject to oxidative stress damage due to pre- and post-transplant conditions that cause reperfusion injury or an imbalance between oxidants and antioxidants [19]. Nevertheless, kidney transplantation seems to restore a nearly normal level of glycoxidative stress markers, but a complete remission is only possible when the renal function is normal [20]. This can be explained by the fact that renal proximal tubules contain many mitochondria which are critical for the energy demanding process of reabsorption of water and solutes. Mitochondria are the largest producers of oxygen radicals, which in turn, increase the susceptibility of kidneys to oxidative stress-induced damage [21]. Thus, the measurement of oxidative stress markers is promising for predicting future risk of graft dysfunction. Despite a significant amount of literature on oxidative stress in relation to renal disease or ischemia/reperfusion in kidney transplant, data regarding the use of oxidative stress biomarkers for prediction of kidney graft function remain limited.

To our knowledge, only two studies have investigated the use of oxidative stress biomarkers for prediction of kidney graft function. In 2014, a study concluded that malondialdehyde level on day 7 might represent a useful predictor of one-year serum creatinine [22]. The second was conducted in 2021 and highlighted the association of higher levels of free thiols at day 1 and day 5 with higher measured GFR at Day 5 as well as with measured GFR at one year [23].

Our study aims to examine, within one year, the variations of systemic levels of four oxidative stress biomarkers, malondialdehyde, nitric oxide, glutathione s-transferase and myeloperoxidase in renal transplant recipients from living donors. It also aims to investigate the association of any of these biomarkers with graft function at six months and one year after transplantation as well as assess their predictive performance in eGFR by MDRD equation at six months and one year post-transplantation.

Materials and methods

Study design and patient population

Between october 2017 and november 2019, patients who received consecutive ABO compatible renal transplants at the Nephrology-Hemodialysis and Transplant department of the Lamine DEBAGHINE University Hospital, were recruited, as well as their living related donors and spouses. Exclusion criteria were surgical complications and patients under the age of 18 years. The ethics committee of the Lamine DEBAGHINE University Hospital has approved the study. All patients of the cohort signed a written informed consent. The left kidney was transplanted and the type of anastomosis is uretero-vesical in most cases. Celsior is used as the organ preservation fluid.

Data collection

At the time of recruitment, demographic, clinical, immunological, serological data, as well as the data related to kidney transplanatation were collected. Data on the evolution of renal function and posttransplant complications were recorded, during the first year.

Sampling and laboratory

For a total of 10 samples per patient, blood samples were processed as follows: 24 hours before transplant surgery (D-1); on the following morning (12–18) hours after graft reperfusion (D1); then on days 2, 3, 6, 8 and 21(D2, D3, D6, D8, D21) and then after months 1, 3, 6 (M1, M3, M6) and finally 1 year (1Y) after transplantation. For the donors, a sample was collected on D-1. The blood samples were taken by conventional procedures. Blood was centrifuged and the plasma/serum was aliquoted and frozen at -20°C until further assay. Concentrations in peripheral blood of four oxidative stress biomarkers: plasma malondialdehyde (pMDA), serum nitric oxide (sNO), plasma glutathiones-transferase (pGST), serum myeloperoxydase (sMPO), and serum creatinine (sCr) were mesured by spectrophotometric essays.

Reagents

For this study, we used high quality analytical chemicals from Sigma (St. Louis, MO).

Plasma malondialdehyde (pMDA) level measurment

Lipid peroxidation level in plasma was estimated by determining the end product of lipid peroxidation, MDA, by using thiobarbituric acid (TBA) test [24]. An aliquot of 100 μL was added to a reaction mixture containing 50 μL of 8.1% sodium dodecyl sulfate, 375 μL of 20.0% acetic acid (pH 3.5), 375 μL of 0.8% thiobarbituric acid. Samples were then boiled for 1h at 95°C and centrifuged at 3000 g for 10 min. The plasma MDA concentration is calculated using the molar extinction coefficient of the MDA-TBA complex at 532 nm of 1.56 x 105 mmol. L-1.cm-1. Values are expressed in micromoles per litre (μmol/L).

Serum nitric oxide (sNO) level determination

The rates of NO were evaluated by the quantification of its stable physiological metabolites (nitrite) [25]. By means of a microplate reader spectrophotometer, the level of nitrite in all serum was determined on the basis of the Griess reaction. A volme of 25 μL of each sample was mixed with 25 μL of Griess reagent (5% sulfanilamide, 0.5% napthylethylenediamine dihydrochloride, and 20% HCl). The samples were incubated at room temperature, protected from light for 20min and the optical density was measured at 543nm. The nitrite concentration was calculated using standard range from a 1 mM sodium nitrite stock solution and expressed in micromoles per litre (μmol/L).

Serum myeloperoxidase (sMPO) activity

The method based on the O-dianisidine in the presence of H2O2 is used to assess plasma myeloperoxidase (MPO) activity. A volume of 100 μL of each sample was mixed with 2900 μL of phosphate buffer (50 mM, pH = 6) containing 0.167 mg/mL O-dianisidine Dihydrochloride and 0.1% hydrogen peroxide (H2O2) [26]. The absorbance of each sample was measured every minute and then for 3 min at λ = 470 nm. One unit of MPO activity corresponds to one micromole of hydrogen peroxide (H2O2) degraded per min and at 25°C [27]. The following formula is used to estimate the activity of MPO.

MPO (U/L) = (ΔA/min x 3000 μL x 106 μmol/mol) / (11300 L x mol-1 x cm-1 x 1 cm x 100 μL).

= Δ A/min x 2832 μmol/min

= Δ A/min x 2832 U/L.

Δ A/min = A3 –A2 / 2, A3 = absorbance at three min at 470 nm and A2 = absorbance at two min at 470 nm.

11300 L mol-1. cm-1 = molar absorptivity coefficient.

Sample volume: 100μL

Total volume: 3000μL

Plasma glutathione S-transferase (pGST) activity

We mesured the activity of GST using the method of a previous study [28]. Briefly, an aliquot of 100 μL of plasma was diluted into a final volume of 1mL containing 1mM GSH, 1mM CDNB in 0.1M potassium phosphate buffer, pH 6.5. The optical density was read at 30 second intervals for 3 minutes, starting from the 30th second. The enzyme activity was expressed in micromol glutathione oxidation per minute at 25°C and was calculated using a molar extinction coefficient of 9.6 mM- 1 cm- 1 at 340 nm wavelength.

Serum creatinine level (sCr) measurment and estimation of graft function

Serum creatinine was measured with the COBAS INTEGRA® 400. To estimate glomerular filtration rate, a simplified modification of diet in renal disease equation (MDRD-eGFR) was used, based on the measure of serum creatinine [7].

For man MDRDml/min/1,73m2 = 186 x (sCrmg/dl) -1,154 x (ageyrs) -0,203 x 1,212 if African origin

For woman MDRDml/min/1,73m2 = 186 x (sCrmg/dl) -1,154 x (ageyrs) -0,203 x 0.742 x 1,212 if African origin

Statistics

We analysed the normal distribution of continuous variables using the Shapiro-Wilk test. Mean and standard deviation were used to describe the distributions of concentrations and enzymatic activities of serum creatinine (sCr), serum myeloperoxidase (sMPO), serum nitric oxide (sNO), plasma malondialdehyde (pMDA) and plasma glutathione S-transferase (pGST) in the study subjects. The median and the 25th and 75th quartiles (interquartile range [IQR]) were used for variables with skewed distributions. Categorical variables were recoded into binary variables. Depending on the conditions of application, the Student’s test or the Wilcoxon-Mann-Whitney tests were performed, to investigate the association between oxidative stress markers and demographic/clinical, treatment variables and to analyse longitudinal changes in oxidative stress marker levels. Pearson’s and Spearman’s correlations were analysed, respectively, for variables with a normal distribution and for those without, to filter out variables (levels of MDA and NO, activity of MPO and GST, MDRD-eGFR) and covariables (living donor status, recipients and donor’s age, recipient’s and donors’ body mass index (BMI), age and BMI ratio between recipient and donor, recipient and donor gender, pretransplant time on dialysis, warm and cold ischemia, antithymocyte globulin (ATG), primary immunosuppressive treatment, complications, HLA mismatch, delayed graft function (DGF) and acute rejection episodes) that can be used to build a six months and one year predictive model of graft function, as well as to avoid collinearity.

Multivariable stepwise linear regression was performed to assess the predictive performance of NO and MPO in estimating graft function at six months and also to predict graft function at one year.

Statistical analyses were performed using SPSS version 20.0 statistical software. A p-value of < 0.05 was considered significant.

Results

Study cohort

Between October 2017 and November 2019, 46 adult’s recipients were consecutively recruited. Three patients had primary graft failure and had grafts removed, one patient died from a myocardial infarction, and six patients will be excluded due to poor adherence to the sampling schedule. Among the remaining 36 patients, 4 had surgical complications, which limited the study cohort to 32 recipients and their donors. All patients were on standard immunosuppressive therapy consisting of calcineurin inhibitors (cyclosporine or tacrolimus), antiproliferative agents (mycophenolate mofetil) and corticosteroids (prednisone). Calcineurin inhibitors are prescribed according to the number of mismatches. We reserve tacrolimus for patients with more than 4 mismatches and the presence of DSA. Cyclosporine is prescribed for patients with less than 3 mismatches. The therapeutic ranges are codified by the laboratory in accordance with international standards.

Doses are then adjusted according to the therapeutic range after three months. If non-DSA (donor specific antibody) anti-HLA antibodies are present, calineurin inhibitors are started on D-5. Prednisone is given in full doses of 1 mg/kg, and then tapered from D7 post-transplant. The dose of mycophenolate mofetil is 2 g/d. Table 1 describes the demographic, clinical, immunological, transplantation data and therapeutic characteristics of the recipients and their living donors.

10.1371/journal.pone.0307824.t001 Table 1 Demographic, clinical, immunological and therapeutic characteristics in kidney transplant donors and recipients.

Donors 		
    Age (yr)	39.8±10.6	
    Male sex	11/32 (34.4%)	
    BMI (kg/m2)	27.2±3.5	
    Serum creatinine (mg/l)	7.0±1.6	
    MDRD-eGFR (ml/min/1,73m2) (non Africain)	117.8±28.8	
Recepients		
    Age (yr)	35.5±11	
    Male sex	26/32 (81.25%)	
    BMI (kg/m2)	23.7±4.5	
    Serum creatinine (mg/l)	91.8±23.4	
    MDRD-eGFR (ml/min/1,73m2) (non Africain)	7.2±1.9	
    Time on dialysis (mo)	36.7±45	
    Cause of kidney disease		
        Indeterminate	25/32 (78.125%)	
        IgA nephropathy	2/32 (6.25%)	
        Chronic glomerulonephritis	2/32 (6.25%)	
        Tubulointerstitial nephritis	1/32 (3.125%)	
        hypertensive nephropathy	1/32 (3.125%)	
        Lupus nephropathy	1/32 (3.125%)	
    Induction regimen		
        Antithymocyte globulin (ATG)	28/32 (87.5%)	
        Basiliximab	4/32 (12.5%)	
    Immunosuppression at time of discharge		
        Cyclosporine A+ Mycophenolate mofetil+ prednisone	19/32 (59.37%)	
        Tacrolimus + Mycophenolate mofetil+ prednisone	13/32 (40.63%)	
    Complications	18/32	
        Rejection	6/18	
        CMV infection	6/18	
        Recurrence of membrano-proliferative glomerulonephritis	1/18	
        Giardiosis infection	1/18	
        DGF	4/18	
Donors-Recepients 		
    HLA mismatch (A, B, DR)	3 (2–3)	
    Warm ischemia (sd)	128±88.5	
    Cold ischemia (min)	102.2 ±27.6	
    Age Donor/Age Recipient	0.95±0.3	
    BMI Donor/BMI Recipient	0.89±0.2	
    Related living donor	25/32 (78.1%)	
        Brother or Sister	14/32 (43.75%)	
        Parents	7/32 (21.9%)	
        Children	1/32 (3.1%)	
        Aunts and uncles	2/32 (6.25%)	
        Cousins	1/32 (3.1%)	
    Non related living donor (partner)	7/32 (21.9%)	
Note: Values are expressed as mean ±standard deviation, absolute numbers and percentages or median (interquartile range).

HLA, human leucocyte antigen; BMI, body Mass Index; MDRD-eGFR, estimated glomerular filtration rate by modification of diet in ranal disease equation; CMV, cytomegalovirus; DGF, delayed graft function.

Oxidative stress biomarkers

Comparison with healthy subjets

We compared oxidative markers rates measured in 32 kidney transplanted patients at D-1 with those of their 32 living donors considered as healthy subjects (control group).

The patients and the controls were in the same age category. Before kidney transplatataion, the recipients presented a significantly increased mean±SD pMDA levels (15.76±6.93 vs. 10.28±6.78 μmole/L, P = 0.003) compared with controls. Despite high values of mean±SD sNO in pre-transplanted recipients compared to control group, the difference was not significant (65.53±57.96 vs. 42.50±24.14 μmole/L, P = 0.27). No significant differences were detected in pGST and sMPO activities in pre-transplanted recipients compared to the control group (57.49±18.81 vs. 61.65±20.11 μmole/min, P = 0.43 and 59.75±41.01 vs. 42.01±15.01 U/L, P = 0.29 respectively).

Longitudinal change in oxidative stress biomarkers

Table 2 illustrates the evolution of oxidative stress biomarkers and creatinine during the first year posttransplantation. In six months posttransplantation, the activity of the two enzymes sMPO and pGST did not express any significant fluctuations, except for sMPO activity at D1 compared to pre-transplantation which displayed a significant increase (30.96±26.21 vs. 59.75±41.01 U/L, P = 0.017 respectively). Means±SD of pMDA and of sNO significantly decreased on the first day compared to pre-transplantation (15.76±6.97 vs. 10.18±6.02 μmole/L, P = 0.001 and 65.53±57.96 vs. 28.34 ±22.54 μmole/L, P<0.0001 respectively). A reduction of approximately 35% and 32% in pMDA and sNO rates was respectively observed at six-month post-transplantation.

10.1371/journal.pone.0307824.t002 Table 2 Temporary variations of oxidative stress biomarkers and creatinine in plasma and serum of recipients and donors during the first year posttransplantation.

 	pMDA (μmole/L)	sMPO (U/L)	pGST (μmole/min)	sNO (μmole/L)	sCr (mg/L)	
Healthy subjects	10.28±6.78	42.01±15.01	61.65±20.11	42.50±24.14	07.04±01.58	
D-1	15.76±6.97	59.75±41.01	57.50±18.81	65.53±57.96	91.84±23.87	
D1	10.18±6.02	30.96±26.21	59.76±20.67	28.34±22.54	37.75±19.66	
D2	10.92±7.11	40.78±31.83	60.77±22.63	40.06±34.54	24.31±22.17	
D3	11.55±5.64	46.57±35.35	63.23±22.10	43.30±33.57	19.87±20.99	
D6	11.20±7.20	57.66±44.55	61.28±26.28	52.07±38.31	16.53±19.06	
D8	10.89±6.98	57.71±43.59	66.02±28.45	52.75±43.84	15.12±14.04	
D21	12.19±5.07	66.72±44.06	65.65±18.84	62.46±43.64	14.11±05.90	
M1	13.90±7.19	62.57±40.74	66.21±21.47	54.53±30.78	13.58±06.70	
M3	09.96±6.69	50.37±36.70	66.55±22.51	45.45±37.05	13.10±03.75	
M6	10.22±5.96	49.91±27.56	67.84±17.33	44.50±35.83	13.12±05.19	
Y1	ID	ID	ID	ID	16.14±18.43	
Note: Values are expressed as mean ±standard deviation. D, day; M, months; Y, year; pMDA, plasma malondialdehyde; sMPO, serum myeloperoxidase; pGST, plasma glutathione s-transferase; sNO, serum nitric oxide; sCr, serum creatinine; ID: insufficient data.

Construction of prediction models of graft function

Pearson’s and Spearman’s correlations have been applied to filter out variables and covariables that can be used to build predictive models of graft function at six months and one year, as well as to avoid collinearity between them. To assess graft function, MDRD-eGFR based on the measure of serum creatinine was used. Correlations between oxidative stress markers and renal function at six months and one year are represented in Table 3. Out of the four biomarkers, only rates of NO at day 6 and at day 21 as well as MPO activity at one month were correlated to the level of MDRD-eGFR at six months (P = 0.020, ρ = 0.416; P = 0.019, ρ = 0.441 and P = 0.017, ρ = 0.454 respectively). None of the four biomarkers were correlated with MDRD-eGFR at one year. The rates of NO and activity of GST are correlated most of the time with each other, levels of MDA and MPO activity were less correlated. Table 4 shows that most of the MDRD-eGFR variables in recipients are correlated with graft function at six months and one-year post-transplantation. In addition, the MDRD-eGFR variable in donors is correlated with the MDRD-eGFR variable in recipients at six months. Correlations between demographic/clinical characteristics, treatment and data related to kidney transplantation with renal function at six months and one year are illustrated in Table 5.

10.1371/journal.pone.0307824.t003 Table 3 Correlations between oxidative stress markers and renal function at six months posttransplantation.

	 	GST_D-1	GST_D1	GST_D2	GST_D3	GST_D6	GST_D8	GST_D21	GST_1M	GST_3M	GST_6M	GST_Donors	
MDRD-eGFR_6M	r/ ρ	-0.175	-0.174	-0.198	0.109	-0.045	-0.001	-0.126	-0.109	-0.050	0.255	0.158	
P	0.374	0.368	0.285	0.558	0.807	0.995	0.522	0.573	0.788	0.190	0.431	
MDRD-eGFR_1Y	r/ ρ	-0.193	-0.187	-0.048	0.293	0.094	0.102	0.149	0.119	0.001	0.124	-0.234	
P	0.326	0.331	0.799	0.104	0.608	0.584	0.450	0.540	0.994	0.530	0.506	
		MPO_D-1	MPO_D1	MPO_D2	MPO_D3	MPO_D6	MPO_D8	MPO_D21	MPO_1M	MPO_3M	MPO_6M	MPO Donors	
MDRD-eGFR_6M	r/ ρ	0.149	0.329	0.255	0.010	0.262	0.045	0.137	0.454 *	0.077	0.005	-0.133	
P	0.467	0.093	0.166	0.956	0.148	0.810	0.487	0.017	0.687	0.979	0.509	
MDRD-eGFR_1Y	r/ ρ	-0.160	0.178	0.048	-0.008	0.066	-0.227	-0.052	0.025	-0.081	0.017	-0.011	
P	0.435	0.375	0.799	0.965	0.718	0.218	0.793	0.900	0.672	0.928	0.241	
		MDA_D-1	MDA_D1	MDA_D2	MDA_D3	MDA_D6	MDA_D8	MDA_D21	MDA_1M	MDA_3M	MDA_6M	MDA_Donors	
MDRD-eGFR_6M	r/ ρ	-0.306	-0.296	0.109	0.272	0.083	0.118	0.206	0.287	0.179	0.035	0.178	
P	0.113	0.119	0.567	0.153	0.664	0.534	0.302	0.138	0.345	0.860	0.374	
MDRD-eGFR_1Y	r/ ρ	-0.036	-0.249	0.083	0.282	0.067	-0.135	-0.002	0.238	0.099	-0.243	0.019	
P	0.854	0.193	0.664	0.138	0.725	0.478	0.992	0.222	0.604	0.212	0.926	
		NO_D-1	NO_D1	NO_D2	NO_D3	NO_D6	NO_D8	NO_D21	NO_1M	NO_3M	NO_6M	NO_Donors	
MDRD-eGFR_6M	r/ ρ	0.216	0.382	0.235	0.274	0.416 *	0.276	0.441 *	0.444	0.357	0.275	0.214	
P	0.280	0.054	0.212	0.143	0.020	0.133	0.019	0.058	0.053	0.149	0.275	
MDRD-eGFR_1Y	r/ ρ	-0.049	0.093	0.087	0.093	0.324	0.016	0.179	0.134	0.101	0.032	0.006	
P	0.806	0.650	0.647	0.624	0.076	0.932	0.362	0.495	0.594	0.870	0.974	
Note: values shown in bold type indicate a Spearman correlation and values shown in thin type indicate a Pearson correlation. Results of this table represent also those of simple linear regression.

D, day; M, months; GST, glutathione s-transferase; MDA, malondialdehyde; NO, nitric oxide; MPO, myeloperoxidase; MDRD-eGFR, estimated glomerular filtration rate by the modification of diet in renal disease equation.

P ≤0.05; ** P ≤ 0.01; P*** ≤0.001.

10.1371/journal.pone.0307824.t004 Table 4 Correlations of estimated Glomerular Filtration Rate by Modification of Diet in Renal Disease equation variables with graft function at six months and one year.

		MDRD-eGFR_D-1	MDRD-eGFR_D1	MDRD-eGFR_D2	MDRD-eGFR_D3	MDRD-eGFR_D6	MDRD-eGFR_D8	MDRD-eGFR_D21	MDRD-eGFR_1M	MDRD-eGFR_3M	MDRD-eGFR_6M	MDRD-eGFR_1Y	MDRD-eGFR Donors_	
MDRD-eGFR_6M	r/ ρ	0.149	0.318	0.528**	0.451**	0.478**	0.531 **	0.563**	0.459**	0.649 **	1	0.648**	0.486**	
P	0.415	0.076	0.002	0.010	0.006	0.002	0.001	0.008	0.000		0.000	0.006	
MDRD-eGFR_1Y	r/ ρ	0.161	0.471 **	0.440*	0.482**	0.416*	0.469 **	0.415*	0.283	0.567 **	0.648**	1	0.109	
P	0.379	0.006	0.012	0.005	0.018	0.007	0.018	0.117	0.001	0.000		0.560	
Note: values shown in bold type indicate a Spearman correlation and values shown in thin type indicate a Pearson correlation. Results of this table represent also those of simple linear regression.

D, day; M, months; Y, year; MDRD-eGFR, estimated glomerular filtration rate by the modification of diet in renal disease equation.

*P ≤0.05

** P ≤ 0.01

P*** ≤0.001.

10.1371/journal.pone.0307824.t005 Table 5 Correlations between demographic/clinical characteristics, treatment and data related to kidney transplantation with renal function at six months and one year.

	MDRD-eGFR_6M	MDRD-eGFR_1Y	
	r/ ρ	P	r/ ρ	P	
Donor age	-0.351*	0.049	-0.229	0.207	
Recipient age	-0.292	0.105	0.047	0.800	
Recipient gender	0.165	0.367	-0.208	0.253	
Donor gender	0.146	0.425	0.267	0.139	
Donor type	-0.020	0.911	-0.098	0.593	
BMI recipient	-0.239	0.188	-0.178	0.330	
BMI donor	-0.278	0.123	-0.436 *	0.013	
Time of dialysis	-0.178	0.328	0.117	0.525	
Cold ischemia	-0.307	0.106	-0.273	0.152	
Warm ischemia	-0.030	0.875	0.431 *	0.017	
ATG	-0.251	0.166	0.056	0.760	
Primary immunosuppressive treatment	0.010	0.955	0.300	0.095	
Complications	-0.256	0.158	-0.450 **	0.010	
DGF	-0.194	0.286	-0.138	0.451	
Rejection	-0.165	0.367	-0.304	0.091	
Recipient age/Donor age	0.075	0.682	0.210	0.248	
Recipient BMI/Donor BMI	0.102	0.579	0.076	0.679	
HLA mismatch (A, B, DR)	-0.111	0.559	-0.016	0.935	
Note: values shown in bold type indicate a Spearman correlation and values shown in thin type indicate a Pearson correlation. Results of this table represent also those of simple linear regression. D, day; M, months; Y, year; MDRD-eGFR, estimated glomerular filtration rate by the modification of diet in renal disease equation; ATG, antithymocyte globulin; DGF, delayed graft function; BMI, body mass index.

*P ≤0.05

** P ≤ 0.01

P*** ≤0.001.

Donor age was correlated with MDRD-eGFR at six months (P = 0.049, r = -0.351), while donor BMI, complication and warm ischemia were correlated with MDRD-eGFR at one year (P = 0.013, ρ = -0.436; P = 0.010, ρ = -0.450 and P = 0.017, ρ = 0.431 respectively). The results (P and r) of the bivariate correlations shown in Tables 3–5 represent also the results of univariate linear regressions. No collinearity was observed between the variables that were used to build the predictive models.

Prognosis of graft function at six months

We compared the predictive performances of NO levels at day 6, at day 21 and MPO activity at one month in assessing MDRD-pGFR (predicted glomerular filtration rate by the Modification of diet in renal disease) equation at six months, to the predictive performances of all MDRD-eGFR variables at all times potentially predictive, by applying multivariable stepwise linear regression. Tables 6, 7 shows results of multivariable stepwise linear regression for prognosis of graft function at six months. For each model, the variables introduced and excluded are listed above. In the first step, the variables NO at day 6, NO at day 21, MPO at one month and all of MDRD-eGFR variables were tested separately associated to donor age as covariable (Table 6 models 1–12). In the second step, the combination of one of these variables with one variable of MDRD-eGFR, in addition to donor age were tested. The best combinations with the highest values of regression coefficients "r", coefficients of determination "r2", adjusted coefficients of determination "adjusted r2" are indicated in Tables 6, 7 (Table 7 models 13–15). Both models 2 and 3 show that the NO variables at day 21 and MPO at one month lose their statistical significance and were thus removed from the final model. There is therefore no interest in assessing these variables in the second step. The mathematical equations of the predictive models are indicated in the Table 8 and are of the following form:

Ŷ = β0 + β1 X1 +β2 X2 + β3X3 +…

Ŷ: Predicted variable.

β0: Constant.

β1, β2, β3: Regression coeifficient of each predictor.

X1, X2, X3: Significant predictor variable.

10.1371/journal.pone.0307824.t006 Table 6 Results of multivariable stepwise linear regression for prognosis of graft function at six months.

Model	Significant predictor	Regression coeifficient	P	95% CI	r	r2	r2 adjusted	P	
1	NO D6	0.248	0.007	0.074 ˗ 0.422	0.621	0.386	0.336	0.002	
Donor age	- 0.826	0.012	-1.456 - -0.196	
2	Donor age	- 0.980	0.024	-1.819- -0.141	0.426	0.181	0.150	0.024	
3	Donor age	- 0.934	0.029	-1.764- -0.103	0.420	0.177	0.144	0.029	
4	MDRD-eGFR D1	0.813	0.004	0.278–1.347	0.493	0.243	0.218	0.004	
5	MDRD-eGFR D2	0.494	0.002	0.198–0.791	0.528	0.279	0.254	0.002	
6	MDRD-eGFR D3	0.366	0.004	0.126–0.605	0.586	0.343	0.298	0.002	
Donor age	- 0.882	0.019	-1.607- -0.158	
7	MDRD-eGFR D6	0.329	0.003	0.122–0.535	0.598	0.358	0.314	0.002	
Donor age	- 0.846	0.022	-1.562- -0.131	
8	MDRD-eGFR D8	0.392	0.001	0.175–0.609	0.559	0.313	0.290	0.001	
9	MDRD-eGFR D21	0.671	0.001	0.304–1.037	0.639	0.409	0.368	0.000	
Donor age	- 0.716	0.042	-1.405- -0.026	
10	MDRD-eGFR M1	0.575	0.008	0.160–0.989	0.459	0.211	0.185	0.008	
11	MDRD-eGFR M3	0.662	0.000	0.413–0.911	0.704	0.496	0.479	0.000	
12	MDRD-eGFR donor	0.426	0.006	0.135–0.718	0.486	0.236	0.210	0.006	
Variables introduced in the model:

 1 NO D6, donor age and MDRD-eGFR M6 as dependent variable.

 2 NO D21, donor age and MDRD-eGFR M6 as dependent variable. NO D21 loses its significant and was removed from the final model statistical

 3 MPO M1, donor age and MDRD-eGFR M6 as dependent variable. MPO M1 loses its statistically significant and was removed from the final model.

 4 MDRD-eGFR D1, donor age and MDRD-eGFR M6 as dependent variable. Donor age loses its statistically significant and was removed from the final model.

 5 MDRD-eGFR D2, donor age and MDRD-eGFR M6 as dependent variable. Donor age loses its statistically significant and was removed from the final model.

 6 MDRD-eGFR D3, donor age and MDRD-eGFR M6 as dependent variable.

 7 MDRD-eGFR D6, donor age and MDRD-eGFR M6 as dependent variable.

 8 MDRD-eGFR D8, donor age and MDRD-eGFR M6 as dependent variable. Donor age loses its statistically significant and was removed from the final model.

 9 MDRD-eGFR D21, donor age and MDRD-eGFR M6 as dependent variable.

 10 MDRD-eGFR M1, donor age and MDRD-eGFR M6 as dependent variable. Donor age loses its statistically significant and was removed from the final model.

 11 MDRD-eGFR M3, donor age and MDRD-eGFR M6 as dependent variable. Donor age loses its statistically significant and was removed from the final model.

 12 MDRD-eGFR donor, donor age and MDRD-eGFR M6 as dependent variable. Donor age loses its statistically significant and was removed from the final model.

10.1371/journal.pone.0307824.t007 Table 7 Results of multivariable stepwise linear regression for prognosis of graft function at six months post-transplantation.

Model	Significant Predictor	Regression coeifficient	P	95% CI	r	r2	r2 adjusted	P	
13	NO D6	0.226	0.002	0.092–0.360	0.774	0.599	0.549	0.000	
MDRD-eGFR D6	0.339	0.000	0.169–0.509	
Donor age	-0.691	0.033	-1.322- -0.061	
14	NO D6	0.227	0.011	0.056–0.398	0.740	0.548	0.497	0.000	
MDRD-eGFR D21	0.591	0.001	0.259–0.923	
Donor age	-0.862	0.009	-1.492- -0.231	
15	NO D6	0.190	0.000	0.438–1.101	0.743	0.553	0.517	0.000	
MDRD-eGFR M3	0.770	0.010	0.050–0.331	
Variables introduced in the model:

 1 NO D6, MDRD-eGFR D6, donor age and MDRD-eGFR M6 as dependent variable.

 2 NO D6, MDRD-eGFR D21, donor age and MDRD-eGFR M6 as dependent variable.

 3 NO D6, MDRD-eGFR M3, donor age and MDRD-eGFR M6 as dependent variable. Donor age loses its statistical significant and was removed from the final model.

NO, levels of serum oxide nitric; D, day; M, month; MDRD-eGFR, estimated glomerular filtration rate by modification of diet in renal disease equation.

10.1371/journal.pone.0307824.t008 Table 8 Mathematical equations of the predictive models (Predicted variable; Ŷ = MDRD-pGFR M6 (ml/min/1.73m2).

Models	β0	β1	x1	β2	x2	β3	x3	
Model 1	90.378	+ 0.248	NO D6	- 0.826	Donor age	/	/	
Model 2	110.934	- 0.980	Donor age	/	/	/	/	
Model 3	105.881	- 0.924	Donor age	/	/	/	/	
Model 4	51.244	+ 0.813	MDRD-eGFR D1	/	/	/	/	
Model 5	46.764	+ 0.494	MDRD-eGFR D2	/	/	/	/	
Model 6	82.413	+ 0.366	MDRD-eGFR D3	- 0.882	Donor age	/	/	
Model 7	78.219	+ 0.329	MDRD-eGFR D6	- 0.846	Donor age	/	/	
Model 8	40.492	+ 0.392	MDRD-eGFR D8	/	/	/	/	
Model 9	55.542	+ 0.671	MDRD-eGFR D21	- 0.716	Donor age	/	/	
Model 10	32.824	+ 0.575	MDRD-eGFR M1	/	/	/	/	
Model 11	24.823	+ 0.662	MDRD-eGFR M3	/	/	/	/	
Model 12	21.271	+ 0.426	MDRD-eGFR donor	/	/	/	/	
Model 13	59.414	+ 0.226	NO D6	+ 0.339	MDRD-eGFR D6	- 0.691	Donor age	
Model 14	53.818	+ 0.227	NO D6	+ 0.591	MDRD-eGFR D21	- 0.862	Donor age	
Model 15	8.032	+ 0.190	NO D6	+ 0.770	MDRD-eGFR M3	/	/	
NO, oxide nitric (μmole/L); D, day; M, month; MDRD-eGFR, estimated glomerular filtration rate by modification of diet in renal disease (ml/min/1.73m2).

Prognosis of graft function at one year

To predict renal function at one year, we included in this model all covariables correlated with one-year MDRD-eGFR rate (donor BMI, warm ischemia and complication), with the MDRD-eGFR at six months variable, that expresses the highest value of the correlation coefficients (r = 0.648 and P <0.01) among the MDRD-eGFR variables potentially predictive (Table 4). Results of multivariable stepwise linear regression for prognosis of graft function at one-year post-transplantation are indicated in Table 9. The introduced and excluded variables and covariables are listed at the bottom of the table. No variable is transformed.

10.1371/journal.pone.0307824.t009 Table 9 Result of multivariable stepwise linear regression for prognosis of graft function at one year post-trasplantation.

Model	Significant predictor	Regression coeifficient	P	95% CI	r	r2	r2 adjusted	P	
Complication	-12.471	0.027	- 23.435- -1.507	0.831	0.690	0.655	0.000	
MDRD-eGFR M6	0.676	0.000	0.419–0.934	
Warm ischemia	0.101	0.002	0.040–0.161	
Variables introduced in the model: MDRD-eGFR M6, donor BMI, complications, warm ischemia and MDRD-eGFR at Y1 as dependent variable. Donor BMI loses its statistical significance and was removed from the final model prediction.

MDRD-pGFR 1Y(ml/min/1.73m2) = 18.386 + 0.676 x MDRD-eGFR M6(ml/min/1.73m2)− 12.471 x complication + + 0.101 x warm ischemia(sd).

A complication = 1; no complication = 0; MDRD-eGFR M6, estimated glomerular filtration rate by modification of diet in renal disease equation at six months; MDRD-pGFR, predicted glomerular filtration rate by the modification of diet in renal disease equation.

Discussion

In our prospective longitudinal study, we set the hypothesis that oxidative stress biomarkers may be predictive of graft function at six months and at one year in living donor transplant recipients. For this purpose, we first measured in recipients the rates of sCr, pMDA, sNO and activities of pGST and sMPO on days (D-1, D1, D2, D3, D6 and D8), months (M1, M3 and M6) and after one year (1Y) post transplantation. We also measured these biomarkers in donors. MDRD-eGFR, based on serum creatinine, was used to assess the renal function of recipients and their donors. Most studies agree that oxidative stress increases progressively with the advanced stages of chronic renal failure. The systemic concentration of oxidising molecules is high among patients with chronic renal failure, and enzymatic activities and the level of antioxidant molecules in the blood are low, leading to oxidative stress [29–31].

The present results show signifiantly elevated levels of MDA, in pre-transplant patients compared to donors, considered to be healthy subjects. This result was in accordance with previous studies [22, 32]. No significant difference between recipients at D-1 (ESRD patients) and controls was noted in NO level, despite a 50% increase over controls in ESRD patients. This can be explained by small sample size, small effect size and large variation in the sample [33]. Our results are similar to previous studies [34, 35]. No significant differences were detected in GST and MPO activities in pre-transplanted recipients compared to the control group. In addition, both enzymes show no significant change during the first year of post-transplant follow-up.

Our results are consistent with a study where there were no significant differences in GST-α levels between donors, which were considered as controls, and recipients before living-donor liver tranplantation [36]. Similarly, plasma MPO protein concentration, measured by ELISA, did not significantly differ between predialysis and control subjects. In contrast, MPO concentration was markedly increased in hemodialysis patients compared to control subjects or predialysis patients [37].

We observed a reduction of approximately 35% and 32% in pMDA and sNO levels respectively six months after transplantation. Our data suggest that the improvement in MDA and NO biomarkers begins on the first day of kidney transplantation and continues throughout the study period. These results are consistent with those of previous studies carried out on cohorts of patients transplanted from living donors, in which plasma levels of MDA and nitrate were analyzed before and for 28 days after transplantation for MDA and before and for 14 days after transplantation for nitrate, respectively [32, 38, 39]. Renal transplantation from living donors rapidly normalised creatinine, urea, GFR, citruline and nitrate. However, despite increased net protein catabolism in peripheral tissues, indicated by increased phenylalanine/tyrosine molar ratios, low arginine and high asymmetric dimethylarginine concentrations persisted throughout the period examined. Alterations in other amino acids also suggest a similar disturbance in arginine metabolism in recipients after renal transplantation [38]. Furthermore, rates of proinflamatory proteins interleukin 6 (IL-6), tumor necrosis factor alpha (TNF- α) and C-reactive protein (CRP)), as well as those of plasma protein carbonyls and F2-isoprostanes known to be markers of oxidative stress have been reported as being significantly elevated in ESRD, with significant decreases two months after renal transplantation from living donors [40]. On the other hand, a previous study reports that there was no significant change in antioxidant enzyme activities, glutathione peroxidase, catalase and superoxide dismutase during the monitored period of three months in deceased donors [39].

Our results indicate that there are no significant differences in the levels of the four biomarkers between male and female patients, who have had complications and those who have not.

Secondly, we applied Spearman and Pearson bivariate correlations to filter out variables (pMDA level, sNO level, pGST activity, sMPO activity, MDRD-eGFR) and covariables (demographic/clinical characteristics, treatment and renal transplant data related to kidney transplantation) that can be used to build predictive models of graft function (MDRD-pGFR) at 6M and 1Y, as well as to avoid collinearities between all variables. We compared the predictive performances of NO levels at D6, at D21 and MPO activity at 1M in assessing MDRD-pGFR equation at 6M, to the predictive performances of all MDRD-eGFR variables at all times potentially predictive. Both models 2 and 3 show that the NO variables at D 21 and MPO at 1M lose their statistical significances and were removed from the final model. Models of MDRD-pGFR are based on multivariable stepwise linear regression. None of the oxidative stress biomarkers predicted graft function at 1Y.

Our bivariate correlation results, which as a reminder also represent the results of univariate linear regressions at 6M, shows that NO at D6 has a predictive performance close to that of the MDRD-eGFR variable at D3 post-transplantation (ρ = 0.416 and r = 451 respectively) and an increase of the predictive performances of the MDRD-eGFR variables as the sixth month approaches (rD2 = 0.528, rD3 = 0.451, rD6 = 0.478, ρD8 = 0.531, rM1 = 0.563, ρM3 = 0,649 respectively). On the other hand, we found that NO levels on day six are also predictive of creatinine levels at six months.

We also found that living donor parameters independently predicted MDRD-pGFR, with donor age predicting graft function at six months (P = 0.049, r = -0.351), while donor BMI predicted graft function at one year (P = 0.013, ρ = -0.436). Among kidney transplant data, warm ischaemia and complications were predictive of graft function at one year (P = 0.017, ρ = 0.431 and P = 0.010, ρ = -0.450 respectively). Among the fifteen models, three models with the highest coefficients of determination stand out. They were models 13, 14 and 15 (Table 7: P = 0.000, r2 = 0.599, r2adj = 0.549; P = 0.000, r2 = 0.548, r2adj = 0.497; P = 0.000, r2 = 0.553, r2adj = 0.517 respectively).

To put these findings in perspective, in many areas of the social and biological sciences, an r2 of about 0.50 or 0.60 is considered high [41], and the number of 10 observations per predictor is required, i.e. 30 observations for the 3 predictors (eGFR-MDRD D6 or D21 or M3, NO D6 and age donor).

In addition, the value of the adjusted r2 differs slightly from that of the r2, which indicates the reliability of our models. The adjusted r2 shows the interest of the cumulative effect of adding a variable to a model. In multiple linear regression, the addition of a variable to a model increases systematically r2 without systematically increasing the value of the adjusted r2.

The p-values of these three models are highly significant P = 0.000 and the application conditions are met (normality of residuals, homoscedasticity of residuals and absence of multicollinearity). Also, the p-values for the variable NO D6 were also significant in models 13, 14 and 15 (P = 0.002, P = 0.011, P = 0.000, respectively).

In addition, model 13, which represents the best model, can predict graft function at 6 months on the basis of quantification of creatininemia levels (allowing calculation of eGFR-MDRD) and nitric oxide levels in the blood on day 6. One unit of eGFR-MDRD D6 increases eGFR-MDRD M6 by 0.339 (coefficient of regression), similarly one unit of NO increases eGFR-MDRD M6 by 0.226 (coefficient of regression). The coefficients of the two serum biomarkers are relatively similar.

This study shows for the first time that NO measurement on day six post-transplant can be a predictive marker of eGFR at six months. Despite these encouraging results, there is a need for evaluation and validation in a larger cohort and by other centers. To our knowledge, only two studies have investigated the use of oxidative stress biomarkers for prediction of kidney graft function. In 2014, a study concluded that malondialdehyde level on day 7 might represent a useful predictor of one year serum creatinine [22]. The second was conducted in 2021 and highlighted the association of higher levels of free thiols at day 1 and day 5 with higher measured GFR at day 5 as well as with measured GFR at one year [23]. In addition, increased MDA levels on day 1 after kidney transplantation might be an early prognostic indicator of DGF and plasma levels of free thiols at 30 minutes and 90 minutes post-transplantation, were significantly higher among patients experiencing DGF [22, 23].

NO is a labile radical gas generated endogenously by a large number of cells throughout the body via three different NOS isoenzymes. Neuronal NOS (nNOS; also known as NOS1) and endothelial NOS (eNOS; also known as NOS3) are constitutively expressed, while inducible NOS (iNOS; also known as NOS2) is mainly associated with inflammatory conditions, which explains the duality between the beneficial and deleterious effects of NO [42]. NO is involved in the kidney’s autoregulatory mechanisms, which aim to keep blood flow and GFR relatively constant despite variations in renal perfusion pressure over a wide range (80–180 mmHg). These mechanisms are essential for preventing barotrauma via the myogenic response, tubuloglomerular feedback derived from the macula densa and their interactions, as well as making a substantial contribution to renal sodium and water management by inhibiting tubular sodium reabsorption along the nephron in response to the renin-angiotensin-aldosterone system [43]. L-Arginine, molecular oxygen, NADPH and tetrahydrobiopterin (BH4) are equally important substrates or cofactors that lead to the equimolar generation of NO and L-citrulline [43]. It is known that decreased bioavailability of NO or deficiency of NO production due to decreased availability of the substrate, L-arginine or an increased asymmetric dimethylarginine (ADMA), a potent inhibitor of endothelial NO synthase, causes development of cardiovascular diseases and chronic kidney diseases, and is highly associated with aging [44]. In an 11-year follow-up of a cohort of 1407 healthy participants of average age (58) and Northern European origin, the association of serum levels of endogenous NO inhibitors ADMA and symmetric dimethylarginine (SDMA), as well as the NO precursors (arginine, citrulline and ornithine) with a decline in mGFR and the development of CKD (mGFR < 60 ml/min per 1.73 m2) were studied. Higher levels of SDMA were associated with a slower annual decline in GFR, while higher levels of citrulline and ornithine were associated with an accelerated decline in GFR. Higher levels of citrulline were associated with the development of chronic kidney disease [45]. In a small cohort study of 25 patients with different stages of CKD and 25 healthy subjects, NO showed a significant positive correlation with eGFR (r = 0.476, P = 0.016) in patients with stage 3 and 4 CKD; and citrulline showed a significant positive correlation with creatinine in patients with stage 1 and 2 CKD (r = 0.415, P = 0.044) [46]. In addition, the authors of a study investigating the effects of chronic dietary supplementation with L-arginine on kidney aging do not recommend long-term dietary supplementation with L-arginine, particularly among the elderly. In fact, such supplementation accelerates the functional decline of the kidneys and vascular system as people age. The study was conducted on young mice (4 months old) and elderly mice (18–24 months old), given either a standard diet containing 0.65% L-arginine or a diet supplemented with 2.46% L-arginine for 16 weeks. L-arginine supplementation further increased age-associated albuminuria and mortality, particularly in females, and was accompanied by elevated levels of renal arginase-II (Arg-II). L-arginine supplementation increases ROS and decreases NO production in the aortas of aged mice [47]. On the other hand, a prospective study done on 50 recipients of renal allografts revealed a significant increase of serum nitrate and episodes of acute rejection compared with other causes of renal dysfunction (delayed graft function, urinary tract infection and tacrolimus toxicity). The authors suggest that NO is a useful marker to aid in the diagnosis of rejection [35]. Serum NO level is also a prognostic parameter for chronic rejection, as shown by the authors of a study who suggested that continuously elevated serum NO levels could predict graft loss 6 to 12 months earlier. Threshold values of 150 mmol/L nitric oxide products and 3 mg/dL serum creatinine were found to be positively predictive of graft loss within one year [48].

As for the influence of donor’s parameters, a number of studies have been carried out. A study conducted in 2018 on donor-recipient function correlation concluded that, excluding unpredictable complications in the post-transplant period, the donor’s pre-donation eGFR, eGFR in donors at hospital discharge and age were the best predictors of recipient and donor eGFR after one year and can be used as a tool to manage expectations for the post-transplant period [49]. In 2020, a retrospective study of a cohort of 290 pairs of donors and recipients who had undergone a kidney transplant from living donors revealed by means of univariate linear regression analysis, that the donor kidney weight/recipient body weight, donor kidney weight/recipient body surface area, donor kidney weight/recipient body mass index, donor kidney volume/recipient body weight, donor kidney volume/recipient body surface area, donor kidney volume/recipient body mass index, and donor body weight/recipient body weight were significantly correlated with eGFR and serum creatinine in recipients within two years of transplantation. In multivariate linear regression analysis, the donor kidney weight/recipient body weight ratio and donor age were significantly correlated with eGFR at 6, 12, 18 and 24 months post-transplant, with the donor kidney weight/recipient body weight ratio performing better in predicting good renal allograft function at 12 months post-transplant [50]. Moreover, a model for predicting early graft function one month after kidney transplantation from living donors has been developed and validated in 2022. The model includes the ratio of cortex weight to recipient weight and the donor’s eGFR as preoperative predictors [49, 51]. Cortical volume predicts graft function at one year in living donors and the amount of interstitial fibrosis predicts graft function at one year in recipients. This is the conclusion of a study carried out in 2023 on 49 living kidney donors and 51 recipients, in which the associations between GFR at one-year post-transplant in donors and recipients with cortical volume and histomorphometric parameters (total number of glomeruli, glomerular volume, glomerular sclerosis, renal fibrosis and arteriolar dimensions) has been explored [52]. In 2019, a study developed and validated a new model for predicting graft function using pre-operative marginal factors in living donor kidney transplantation, using four preoperative variables as predictors, namely donor age, donor eGFR, donor hypertension and donor-recipient body weight ratio [53].

The results of all these studies agree that the donor’s age is a predictor of graft function. Twelve-month eGFR is a strong predictor of long-term graft failure when taken into account for clinical events occurring from discharge to one year. These findings may improve patient management and clinical evaluation of further interventions [3]. In our study, the best model for predicting graft function one year after transplantation (P = 0.000, r2 = 0.690, r2adj = 0.655) included the following parameters: complication, warm ischaemia and recipient GFR estimated at six months, the latter representing the best predictor among all the MDRD-eGFR variables. Early prediction of GFR one year after transplantation was carried out in a large multi-centre cohort of 376 patients. A panel of biomarkers including gene expression, cytokine, metabolomic and antibody reactivity profiles, in the pre- and early post-transplant period were analysed. The pre-transplant data yielded a Pearson correlation coefficient of r = 0.39 between measured and predicted GFR at one year. Two weeks after transplantation, the correlation increased to r = 0.63, and at three months, to r = 0.76. The authors point out that eGFR showed remarkable stability, achieving similar results on its own to the full subset of clinical markers. They investigated whether demographic factors added value to the prediction of 1-year GFR compared to eGFR alone. Multiparametric regression revealed that recipient age, donor age and donor CMV serostatus at 2 weeks were independently associated with eGFR at 1 year (recipient age: P = 0.045; donor age: P<0.001; donor CMV serostatus: P = 0.004; eGFR at 2 weeks: P<0.001). Similarly, the association between donor age and BMI at 3 months with eGFR at 1 year was independent of eGFR at 3 months (donor age: P<0.001; BMI: P = 0.008; eGFR at 3 months: P<0.001). They concluded that these variables are considered to be prognostic factors [54]. On the other hand, the performance of equations for estimating GFR based on serum creatinine was compared with that of GFR measured during 20 years of longitudinal follow-up in a single-study centre of 417 transplant patients. All eGFR equations showed similar trends toward measured GFR, following a decline in GFR, no significant differences were observed in the individual changes (slopes) of measured GFR or estimated GFR in predicting graft loss in the coming months or years. However, the percentage of transplant patients with a >30% decline in GFR in the last period before graft loss was significantly lower for the estimated GFR than for the measured GFR, with discordant measured GFR results in ~25% of cases [55]. In 2023, a race-free estimated glomerular filtration rate equation specific to kidney transplant recipients and based on creatinine measurement, was developed and validated in a multiple large international cohorts of recipients from Europe, United States, South America, Canada, Asia, Africa and Oceania. It showed significantly improved performance compared to the race-free 2021 CKD-EPI equation (developed in individuals with native kidneys) and performed well in the external validation cohorts [56]. The P30 values (P30 being the proportion of eGFR within 30% of measured GFR) ranged from 73.0% to 91.3% [56].

In conclusion, the results of our study suggest that nitric oxide quantification at day six will be useful in predicting graft function at six months in living donor kidney transplants. Furthermore, among the demographic, clinical, induction/primary immunossupressive treatment, complications, rejection, and transplant-related parameters of the living donor-recipient pair, only donor age is predictive of graft function at six months. The combination of factors, complications, warm ischaemia and MDRD-eGFR at six months offers a better prediction model of glomerular filtration rate one year after transplantation.

The authors are extremely appreciative for the commitment and crucial help of the nursing staff for sample collection. The authors are extremely grateful to Dr. Safia Zenia statistician for her help in statistical analysis. The authors are deeply grateful to Mr. Kamel Babouche for his linguistic corrections.

10.1371/journal.pone.0307824.r001
Decision Letter 0
Sabbah Belal Nedal Academic Editor
© 2024 Belal Nedal Sabbah
2024
Belal Nedal Sabbah
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
2 Nov 2023

PONE-D-23-24980Living donors kidney transplantation and oxidative stress: nitric oxide as a predictive marker of graft functionPLOS ONE

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: The authors have evaluated the relationship between blood levels of oxidative stress markers and eGFR after living donor kidney transplantation.

The topic was interesting and meaningful.

However, poor English made it difficult to understand.

Probably due to the small sample size, multivariate analysis has shown that oxidative stress markers cannot predict eGFR. It is inappropriate to describe as if they have predictive ability despite these findings.

In the large number of multiple comparisons made in the first half of the study, it is not surprising that some of them were falsely “statistically significant” due to alpha errors.

What makes it different and novel from similar reports already published?

I think it is not suitable for publication as is.

Reviewer #2: Dear authors,

This is an excellent idea and well written manuscript. However some points:

1- the manuscript is too lengthy and it should be rewritten concisely.

2- Some of the tables are very complicated and need to be edited.

3- More detailed information in methods, regarding donor specification, surgeons and surgical techniques.

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Reviewer #1: No

Reviewer #2: Yes: Seyed Reza Yahyazadeh

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10.1371/journal.pone.0307824.r002
Author response to Decision Letter 0
Submission Version1
14 Dec 2023

Dear Academic Editors and Peer Reviewers

We thank you so much for the time you have devoted to reading and understanding this manuscript, as well as for your valuable comments and relevant remarks.

Further to your observations about the language of the article, I submitted the latter to Mr. Kamel Babouche, who holds a long experience in English-language teaching and training as a:

- Former high-school teacher of English, (1978-1990).

- Former English-language teacher trainer at a teaching school, (1990-1996).

- Former general inspector for English, (1996-2014).

- Former consultant at British Council Algeria, (2014-2018).

Mr. Babouche read through the article to see how best he could improve it in terms of language quality. Though he admitted there is much redundancy, his view is that the nature of the text, as well as the jargon in relation to the discipline allow for a certain amount of redundancy as regards the syntax. He reckons it is difficult to bring syntactic changes without running the risk of altering the meaning and comprehension of the article, and so limited himself to correcting a few grammatical and lexical mistakes. All mistakes are corrected in green in the manuscript titled "Revised Manuscript with Track Changes".

I also wish to draw your attention to the fact that English language -as a tool of teaching and studying at university- has been introduced only recently in the Algerian universities and is gradually replacing French. This means that we have to make more effort to reach good standards of English and we are doing our best for that purpose. Therefore I hope to rely on your understanding and indulgence as regards the linguistic quality of the article.

In addition, we have responded to the 6 points required by PLOS ONE, as well as to the reviewers' comments.

PLOS ONE requirements

1- The formatting of the unmarked version complies with the recommendations of the link https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf

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2- Response to Emily Chenette, Editor-in-Chief of PLOS ONE, and Iain Hrynaszkiewicz, Director of Open Research Solutions at PLOS: We created the doi of our raw data according to the registrations on the https://plos.org/dryad-data/ website.

3- We have specified in the cover letter that funding from the two laboratories (Animal Biology and Physiology laboratory at the Ecole Normale Supérieure in Kouba, Algiers, Algeria and the Central Biology Laboratory at the CHU Lamine Debaghine, Bab El Oued, Algiers, Algeria) is used to finance our analyses.

4- We have mentioned in the cover letter and in green in the statistics section of the manuscript titled "Revised Manuscript with Track Changes" that our raw data are available to all scientific readers via https://datadryad.org/stash/share/2amXBXayUq0oPVI2xg_IoF9_04VM3u7AFdfjfNtymFE , lines 705 and 706.

5- The words "data not shown" have been removed from the text and appear in red in the "Results section/ Construction of prediction models of graft function" of the manuscript titled "Revised Manuscript with Track Changes", line 323.

6- We have included our full ethics statement in the "Methods" section of the manuscript titled "Revised Manuscript with Track Changes", lines 138,139 and 140.

Response to reviewer 1 Anonymous

1- As mentioned above, grammatical and lexical errors have been corrected by Mr. Kamel Babouche.

2- We would like to make a small clarification: we claim that the quantification of nitric oxide at day 6 in association of MDRD-eGFR D6, or MDRD-eGFR D21, or MDRD-eGFR M3 and/or donor age makes it possible to predict the glomerular filtration rate at 6 months, and consequently, the functioning of the kidney transplant in patients transplanted from living donors (see models 13, 14 and 15 table 6b) (Respectively, P= 0.000, r2 = 0.599, r2adj= 0.549 ; P= 0.000, r2= 0.548, r2adj = 0.497 ; P= 0.000, r2= 0.553, r2adj = 0.517). The other three biomarkers (malondialdehyde, myeloperoxidase and gluthathione s transferase) were not predictive in this study.

In many areas of the social and biological sciences, an r2 of about 0.50 or 0.60 is considered high (R.D. Cook and S. Weisberg (1999), Applied Regression Including Computing and Graphics, Wiley, p. 281), and the number of 10 observations per predictor is required, i.e. 30 observations for the 3 predictor variables (eGFR-MDRD D6 or D21 or M3, NO D6, age donor), so my sample of 32 patients is correct.

In addition, the value of the adjusted r2 differs slightly from that of the r2, which indicates the reliability of our models. The adjusted r2 shows the interest of the cumulative effect of adding a variable to a model. In multiple linear regression, the addition of a variable to a model increases systematically r2 without systematically increasing the value of the adjusted r2.

On another note, the P values of these three models are highly significant P=0.000 and the application conditions are met (normality of residuals, homoscedasticity of residuals and absence of multicollinearity). Also, the P values for the variable NO D6 were also significant in models 13, 14 and 15 (P= 0.002, P=0.011, P=0.000, respectively).

In addition, in model 13, which represents the best model, we can predict graft function at 6 months on the basis of quantification of creatininemia levels (allowing calculation of eGFR-MDRD) and nitric oxide levels in the blood on day 6. 1 unit of eGFR-MDRD D6 increases eGFR-MDRD M6 by 0.339 (coefficient of regression), similarly 1 unit of NO increases eGFR-MDRD M6 by 0.226 (coefficient of regression). The coefficients of the two serum biomarkers are relatively similar.

All these clarifications are provided from line 550 to line 565 and colored in green, in the document entitled "Revised Manuscript with Track Changes".

3- The first part of the results has been pruned to target the objectives. It is clear that some of the P values may be debatable.

4- The originality of this article is that, for the first time, modified MDRD equations have been developed to predict renal graft function at 6 months. These equations take into account two serum biomarkers nitric oxide (a biomarker associated with GFR according to references (36, 50, 51 and 52) and creatininemia, in addition to donor age. Several studies on donor parameters agree that donor age predicts graft function (references 55, 56, 57, 58 and 59).

However, this work deserves to be completed by validating these equations on a large cohort of kidney transplant recipients from living donors, or even testing these equations on a cadaveric cohort.

Response to reviewer 2 Mr. Seyed Reza Yahazadeh

1- Indeed, some readers found the manuscript long. Some parts of the text have been trimmed to make the results easier. All the parts concerned are marked in red (Results and discussion) in the document entitled "Revised Manuscript with Track Changes", from line 268 to line 310 and from line 496 to line 500. Also, two of the bibliographic references, namely 46 and 47, are no longer listed.

2- Some readers also found the tables difficult to read. A change has been made to the layout of tables 6a and 6b. The variables introduced and significant for each of the models are listed at the bottom of the table and not in the table itself. Table 7 has been completely modified to make the equations of the 15 models used to predict graft function at 6 months easier to read. These changes are marked in green in the document entitled "Revised Manuscript with Track Changes".

3- We have added a few details concerning surgery and donors as requested in the "Methods" section, namely ABO-compatible transplantation, spouses as unrelated living donors, uretero-vesical anastomosis in most cases, Celcior preservation fluid used and left kidney transplant, lines 135, 140 and 141. These details are marked in green in the document named "Revised Manuscript with Track Changes".

Sincerely yours,

Djamila IZEMRANE, Ali BENZIANE and Ahcène BAZ on behalf of the authors.

Corresponding author: Djamila IZEMRANE.

- Laboratory of Biology and Animal Physiology, Higher Normal School, Kouba, Algiers, Algeria, 16308.

- National Higher Veterinary School, Issad Abbes, Oued Smar, Algiers, Algeria, 16059.

Email: d.izemrane@ensv.dz. izemranedjamila@gmail.com. Phone number: +213 669 20 82 01 / +213 772 21 67 32.

Attachment Submitted filename: Rebuttal letter IZEMRANE DJAMILA.docx

10.1371/journal.pone.0307824.r003
Decision Letter 1
Sabbah Belal Nedal Academic Editor
© 2024 Belal Nedal Sabbah
2024
Belal Nedal Sabbah
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
4 Jan 2024

PONE-D-23-24980R1

Living donors kidney transplantation and oxidative stress: nitric oxide as a predictive marker of graft function

PLOS ONE

Dear Dr. IZEMRANE,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision.

Kind regards,

Belal Nedal Sabbah

Academic Editor

PLOS ONE

Additional Editor Comments:

The reviewers have indicated that the manuscript in its current state is not suitable for publication. Please find the comments below for your own reference.

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Comments to the Author

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Reviewer #1: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have submitted a revised manuscript, but it isn't easy to understand how the paper has changed. It would be better to include the reviewer's remarks and a point-by-point response to each of them in the response letter.

There are still typographical errors in the revised manuscript and the problems I pointed out regarding multiple comparisons have not been resolved. There are numerous multivariable stepwise linear regression analyses in which only a few variables are selected for inclusion. Some of these analyses will likely produce good results by chance. In addition, the choice of variables seems somewhat arbitrary and raises the question of why all the information in Table 4 was not included in the analysis.

**********

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10.1371/journal.pone.0307824.r004
Author response to Decision Letter 1
Submission Version2
24 Jan 2024

We have responded to the 6 points required by PLOS ONE, as well as to the reviewers' comments.

PLOS ONE requirements

1- The formatting of the unmarked version complies with the recommendations of the link https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2- Response to Emily Chenette, Editor-in-Chief of PLOS ONE, and Iain Hrynaszkiewicz, Director of Open Research Solutions at PLOS: We created the doi of our raw data according to the registrations on the https://plos.org/dryad-data/ website.

3- We have specified in the cover letter that funding from the two laboratories (Animal Biology and Physiology laboratory at the Ecole Normale Supérieure in Kouba, Algiers, Algeria and the Central Biology Laboratory at the CHU Lamine Debaghine, Bab El Oued, Algiers, Algeria) is used to finance our analyses.

4- We have mentioned in the cover letter and in green in the statistics section of the manuscript titled "Revised Manuscript with Track Changes" that our raw data are available to all scientific readers via https://datadryad.org/stash/share/2amXBXayUq0oPVI2xg_IoF9_04VM3u7AFdfjfNtymFE , lines 705 and 706.

5- The words "data not shown" have been removed from the text and appear in red in the "Results section/ Construction of prediction models of graft function" of the manuscript titled "Revised Manuscript with Track Changes", line 323.

6- We have included our full ethics statement in the "Methods" section of the manuscript titled "Revised Manuscript with Track Changes", lines 138,139 and 140.

Response to reviewer 1 Anonymous

1- As mentioned above, grammatical and lexical errors have been corrected by Mr. Kamel Babouche.

2- We would like to make a small clarification: we claim that the quantification of nitric oxide at day 6 in association of MDRD-eGFR D6, or MDRD-eGFR D21, or MDRD-eGFR M3 and/or donor age makes it possible to predict the glomerular filtration rate at 6 months, and consequently, the functioning of the kidney transplant in patients transplanted from living donors (see models 13, 14 and 15 table 6b) (Respectively, P= 0.000, r2 = 0.599, r2adj= 0.549 ; P= 0.000, r2= 0.548, r2adj = 0.497 ; P= 0.000, r2= 0.553, r2adj = 0.517). The other three biomarkers (malondialdehyde, myeloperoxidase and gluthathione s transferase) were not predictive in this study.

In many areas of the social and biological sciences, an r2 of about 0.50 or 0.60 is considered high (R.D. Cook and S. Weisberg (1999), Applied Regression Including Computing and Graphics, Wiley, p. 281), and the number of 10 observations per predictor is required, i.e. 30 observations for the 3 predictor variables (eGFR-MDRD D6 or D21 or M3, NO D6, age donor), so my sample of 32 patients is correct.

In addition, the value of the adjusted r2 differs slightly from that of the r2, which indicates the reliability of our models. The adjusted r2 shows the interest of the cumulative effect of adding a variable to a model. In multiple linear regression, the addition of a variable to a model increases systematically r2 without systematically increasing the value of the adjusted r2.

On another note, the P values of these three models are highly significant P=0.000 and the application conditions are met (normality of residuals, homoscedasticity of residuals and absence of multicollinearity). Also, the P values for the variable NO D6 were also significant in models 13, 14 and 15 (P= 0.002, P=0.011, P=0.000, respectively).

In addition, in model 13, which represents the best model, we can predict graft function at 6 months on the basis of quantification of creatininemia levels (allowing calculation of eGFR-MDRD) and nitric oxide levels in the blood on day 6. 1 unit of eGFR-MDRD D6 increases eGFR-MDRD M6 by 0.339 (coefficient of regression), similarly 1 unit of NO increases eGFR-MDRD M6 by 0.226 (coefficient of regression). The coefficients of the two serum biomarkers are relatively similar.

All these clarifications are provided from line 550 to line 565 and colored in green, in the document entitled "Revised Manuscript with Track Changes".

3- The first part of the results, from line 270 to line 310 and colored in red in the document entitled "Revised Manuscript with Track Changes" has been pruned to target the objectives. It is clear that some of the P values may be debatable.

4- The originality of this article is that, for the first time, modified MDRD equations have been developed to predict renal graft function at 6 months. These equations take into account two serum biomarkers nitric oxide (a biomarker associated with GFR according to references (36, 50, 51 and 52) and creatininemia, in addition to donor age. Several studies on donor parameters agree that donor age predicts graft function (references 55, 56, 57, 58 and 59).

However, this work deserves to be completed by validating these equations on a large cohort of kidney transplant recipients from living donors, or even testing these equations on a cadaveric cohort.

Response to reviewer 2 Mr. Seyed Reza Yahazadeh

1- Indeed, some readers found the manuscript long. Some parts of the text have been trimmed to make the results easier. All the parts concerned are marked in red (Results and discussion) in the document entitled "Revised Manuscript with Track Changes", from line 268 to line 310 and from line 496 to line 500. Also, two of the bibliographic references, namely 46 and 47, are no longer listed.

2- Some readers also found the tables difficult to read. A change has been made to the layout of tables 6a and 6b. The variables introduced and significant for each of the models are listed at the bottom of the table and not in the table itself. Table 7 has been completely modified to make the equations of the 15 models used to predict graft function at 6 months easier to read. These changes are marked in green in the document entitled "Revised Manuscript with Track Changes".

3- We have added a few details concerning surgery and donors as requested in the "Methods" section, namely ABO-compatible transplantation, spouses as unrelated living donors, uretero-vesical anastomosis in most cases, Celcior preservation fluid used and left kidney transplant, lines 135, 140 and 141. These details are marked in green in the document named "Revised Manuscript with Track Changes".

Attachment Submitted filename: Response to reviewers IZEMRANE DJAMILA.docx

10.1371/journal.pone.0307824.r005
Decision Letter 2
Lee John Richard Academic Editor
© 2024 John Richard Lee
2024
John Richard Lee
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
18 Apr 2024

PONE-D-23-24980R2

Living donors kidney transplantation and oxidative stress: nitric oxide as a predictive marker of graft function

PLOS ONE

Dear Dr. IZEMRANE,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

John Richard Lee, M.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

Reviewer #5: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: While the authors have attempted to generate a model to predict renal allograft graft function at one year using markers of oxidative stress in a living kidney donation model, there is no validation cohort to test the model.

Reviewer #4: The previous reviewers comments have been reviewed and edits to the current manuscript as well. The paper is improved in its current form, however, I would consider adjusting the following:

1. The paper continues to be quite lengthy. The introduction and discussion should be limited to to only background and details that relate to findings of the paper. For example in the first paragraph the discussion about the MDRD and measuring of eGFR does not need to be as detailed as it is and the authors can focus only on the limitations of the current testing which is why they proceeded with their study. Similarly in the discussion, the detailed discussion of the biomarkers studies in the paper and the evidence for their usage is likely not needed and the focus of the discussion should be on the specific findings of the paper and the review of other references that support their findings should be more succinct

2. Were there different findings in terms of the biomarkers in terms of DGF? Were levels of sNO or the other biomarkers higher in the patients that had DGF as compared with those whose grafts functioned immediately?

Reviewer #5: The paper is an interesting examination however there a multiple typos and grammatical errors throughout the manuscript. Ones that I saw were:

Line 58: recognized; Line 86: oxygen; Line 105: hemodialysis; Line 110: energy demanding, mitochondria; Line 117: In 2014, a; Line 119: measured; Line 123: living donors. It also; Line 124: transplantation as well; Line 129: October 2017 and November; Line 131: living donors.; Line 133: study. All; Line 134: The left kidney was transplanted and the type; Line 169: A volume of 100 ul, 2900 ul; Line 222: therapy consisting of calcineurin; Line 226: Table 1; Line 433 correlation results, which; Line 434: at 6M, shows; Line 501: aging; Line 576: race-free; Table 1: Rejection, Brother, Cousins; Table 5: Rejection

Regarding formatting their data, I felt it was confusing at times. For example, in Table 1, the authors displays Age (yr) then the data is 39.8 (10.6). The convention is that what is in the parenthesis matches in the data. However regarding the data I believe the authors wanted to represent it as 39.8±10.6 showing the standard deviation of the number. If that is so, I would covert all the data were appropriate to the ± format.

In the methods; it would be useful to describe the centers' calcineurin inhibitor trough goals over the first year. Moreover, this maybe beyond the scope of their analysis, but would they able to see if there is a correlation between average calcineurin levels and GFR and average calcineurin levels and the the levels of their biomarkers. For example, higher levels of calcineurin inhibitors would constrict the afferent arterioles and perhaps increase NO production in the the efferent arterioles in the kidney. Maybe this effect is not only happening in the glomerulus but also systemically and can be can be seen in the blood. If they can perform that analysis perhaps they can comment on the possible effects of calcineurin inhibitors on their biomarkers.

Regarding Table 1; since the authors are using the MDRD equation it would be interesting in the demographic data to see the percentage of patients that fall into the African versus non-African calculations to get more information regarding the examined population.

If possible, Table 3 should be converted into a figure showing the dot plots with correlations of the 3 significant findings MDRD 6M/MPO 1M, MDRD 6M/NO D6 and MDRD 6M/NO D21 which would highlight their findings. The table can be moved to a supplemental section if readers want to view it.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

**********

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10.1371/journal.pone.0307824.r006
Author response to Decision Letter 2
Submission Version3
9 May 2024

Algiers, May 9th 2024.

Dear Academic Editor and Peer Reviewers

We thank you so much for the time you have devoted to reading and understanding this manuscript. We would also like to express our gratitude for the interest you have shown in our work, which is reflected in your pertinent comments and recommendations for improving the manuscript.

We have responded to the 2 points required by PLOS ONE, as well as to the reviewers' comments.

PLOS ONE requirements

1- Please note that the content of our financial disclosure was changed in the cover letter. We have removed the reference to personal contributions by Djamila IZEMRANE and Nacim HAMDIS.

2- As far as depositing our laboratory protocols in protocols.io is concerned, we feel that there is no need to make an additional contribution, given that these protocols are referenced (see methods section) and that the analysis methods are common methods with few modifications.

Response to reviewers

Reviewer#3

Despite these encouraging results, there is a need for evaluation and validation in a larger cohort and by other centers. This is mentioned in the lines 474, 475 of the document entitled "Revised Manuscript with Track Changes".

Cohorts for the creation and validation of predictive models are generated using the 20-80 or 30-70 method. Given that the number of patients transplanted to date at the University Hospital of Beb El Oued, with a follow-up of one year after the last patient in our cohort, is 58 patients. We cannot therefore validate a posteriori our predictive model of graft function at one year.

In addition, it is impossible to obtain D6 blood samples from these patients in order to repeat the NO assays for validation of the models predicting graft function at six months.

Reviewer#4

1- We have followed your recommendations by deleting some paragraphs from the introduction (lines 69-73, lines 79-83, lines 99-101 and lines 103-106) and from the discussion (lines 386,387, lines 389-395 and lines 400-407). All of these paragraphs are highlighted in red in the document entitled "Revised Manuscript with Track Changes". This has resulted in the deletion of references (10, 11, 22, 41, 42 and 43) and a change in numbering from 12th reference.

2- For patients with DGF compared to patients with prompt graft function, the activity of sMPO was higher for patients with DGF. The significant differences were observed at D6 and D8 (respectively, 112.04±44.32 vs. 49.89±39.49 U/L, P=0.023 and 126.81±59.29 vs. 47.48±30.72 U/L, P=0.01).

In addition, levels of sNO at D8 posttransplantation (99.86±44.22 vs. 45.77±40.0 µmole/L, P=0.027), higher in favour of the DGF group.

Reviewer#5

1- Typos and grammatical errors in the docuement entitled "Revised Manuscript with Track Changes" have been corrected : Line 59: recognized; Line 87: oxygen; Line 106: hemodialysis; Line 111: energy demanding, mitochondria; Line 118: In 2014, a; Line 120: measured; Line 124: living donors. It also; Line 125: transplantation as well; Line 130: October 2017 and November; Line 132: living donors.; Line 134: study. All; Line 135: The left kidney was transplanted and the type; Line 170: A volume of 100 ul, 2900 ul; Line 223: therapy consisting of calcineurin; Line 227: Table 1; Line 434 correlation results, which; Line 435: at 6M, shows; Line 502: aging; Line 577: race-free; Table 1: Rejection, Brother, Cousins; Table 5: Rejection. All these corrections are hilighted in green in the document entiteled "Revised Manuscript with Track Changes"

2- The data format has been corrected and hilighted in green, in tables 1 and 2, as well as in the paragraphs mentioning the t-test results, from line 246 to line 251 and from line 256 to line 259 in the document entitled "Revised Manuscript with Track Changes"

3- We have clarified the immunosuppressive therapeutic strategy of our transplant centre in the method section of the manuscript entitled "Revised Manuscript with Track Changes", from line 225 to line 232

4- The results of the bivariate correlations between mean and individual NO values and mean and individual calcineurin inhibitor values did not reveal any significance, on any of the follow-up days. The same was true for the correlations between mean and individual glomerular filtration rate values and mean and individual calcineurin inhibitor values. Similarly, we mentioned in Table 5 that there is no correlation between glomerular filtration rate and the choice of calcineurin inhibitor.

5- The MDRD equation used is for patients of non-African origin, see table 1.

6- The authors prefer to keep table 3, which expresses the values of the Pearson and Spearman coefficients "r" and "ρ", rather than the scatterplot, which shows the shape or trend of the correlation, especially for a small sample size with a wide dispersion of values in relation to the mean. The significant values of these correlations are highlighted in grey in table 3.

Sincerely yours,

Djamila IZEMRANE, Ali BENZIANE and Ahcène BAZ on behalf of the authors.

Corresponding author: Djamila IZEMRANE.

- Laboratory of Biology and Animal Physiology, Higher Normal School, Kouba, Algiers, Algeria, 16308.

- National Higher Veterinary School, Issad Abbes, Oued Smar, Algiers, Algeria, 16059.

Email: d.izemrane@ensv.dz. izemranedjamila@gmail.com. Phone number: +213 669 20 82 01

Attachment Submitted filename: Response to reviewers IZEMRANE 9-5-24 PLOS ONE.docx

10.1371/journal.pone.0307824.r007
Decision Letter 3
Lee John Richard Academic Editor
© 2024 John Richard Lee
2024
John Richard Lee
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version3
26 Jun 2024

PONE-D-23-24980R3Living donors kidney transplantation and oxidative stress: nitric oxide as a predictive marker of graft functionPLOS ONE

Dear Dr. IZEMRANE,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 10 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

John Richard Lee, M.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #5: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: Djamila et al present a study looking into using nitric oxide levels in recipients from living kidney donors as one of the predictors of graft function. Thank you for the revising the manuscript. I have no further critiques

Reviewer #5: Based on the most recent revisions I have no new analysis for the author. It is mostly gramatical and formating issues. Comments will have line numbers before them from the most recent revision:

62- kidney size (remove renal), 94- endothelial cell dysfunction, 125- remove "and ;"; remove "to", 126- post-transplantation, 142- blood samples were, 154- using the thiobarbituric, remove "_" before An, 155- change to 375 µL, 163- change to 25 µL; change to 25 µL, 180- change to 100 µL, 181- change to 3000 µL, 183- change to 100 µL, 184- change to 1 mL, 201 the Student's T or, 209- remove "throughout the first year", 214- A p-value of < 0.05, 220- remove "have been detransplanted" put "had grafts removed", 225- prednisone, 227- Cyclosporine, 259- respectively were, 260- remove "respectively", 269- remove "a simplified equation for glomerular filtration rate estimated by modification of diet in renal disease" you defined MDRD-eGFR earlier, 270- measure (typo), 282-308- In these tables you present the numbers as either ",0", ".0" or "0.0". They should all be "0.0" as that is what you are reporting in the text, 326- (Table 6a models), 327- were tested, 329- Table 6 (Table 6b , 332- remove "No variable is transformed.", 339- Table 6a, 354- Table 6b, 354-360- MDRD-pGFR is not in either of these tables. Either insert the equation like you do in Table 8 or remove them from the legend, 365- (Table 4), 366- Table 8, 378- (1Y) post, 379- remove "A simplified equation for glomerular filtration rate estimated by modification of diet in renal disease" you defined MDRD-eGFR prior, 387- significantly, 398- Our results are similar to previous studies, 409- change to "donors, which were considered as controls, and recipients", 417- analyzed, 420- peripheral tissues, indicated , 421- arginine , 423- in arginine metabolism, 424- C-reactive protein, 431- change to "male and female patients who have had complications and those who have not.", 442- results, which , 444- r=0.451, (ρ=0.416 and r=0.451 respectively), 446- ρM3=0.649 , 451- (P=0.017, ρ=0.431 and P=0.010, ρ=-0.450 respectively), 453-456- change to "out. They were models 13, 14 and 15 (Table 6b: P= 0.000, r2 = 0.599, r2adj= 0.549; P= 0.000, r2 455 =0.548, r2adj = 0.497; P= 0.000, r2= 0.553, r2adj = 0.517 respectively)." , 457- Add: "To put these findings in perspective, in many areas", 464- Remove "On another note,". The p-values of these three models are highly significant (P=0.000) and , 466- Also, the p-values for , 468- In addition, model 13, which , 470- One unit , 476- In 2014, a study , 479- measured (typo) GFR at day (typo) 5, 481- indicator of DGF and plasma levels of free thiols at 30 minutes and 90 minutes post-transplantation, 488- blood flow and GFR relatively , 493- Comment: What is the difference between NADPH and molecular NADPH?, 496- an increased asymmetric dimethylarginine (ADMA) , 500- endogenous NO inhibitors, ADMA and , 501- as well as the NO precursors (arginine, citrulline and ornithine) with , 502- were studied. , 506- 25 healthy subjects, NO showed , 516- decreases NO production , 517- of renal allografts, 518- serum nitrate and episodes of acute rejection , 525-526- Remove "The results of the most important of these are reported below." 532-534: Remove the spaces before and after "/" , 535- Remove "estimated glomerular filtration rate", 542- donor's eGFR as , 550- donor eGFR, , 551-554- Remove "This is a simple but useful guide for estimating graft function one year after renal transplantation, particularly in marginal donors (elderly patients, patients with hypertension and atherosclerotic disease), in the clinical setting. , 560- recipient GFR estimated at six , 561- Add: One study attempted to predict the GFR one year , 562- transplantation was carried out in a large , 564- period were (change) analyzed (typo) , 573- They concluded that these variable are considered , 576- trends towards measured GFR , 577- no significant differences were observed, 583- validated in multiple large international , 585- the race-free (typo) 2021 (moved) CKD-EPI equation (developed in individuals with native kidneys) and , 586-587- validation cohorts [62]. The P30 values (P30 being the proportion of eGFR within 30% of measured GFR) ranged from 73.0% to 91.3% [62]. 590- clinical, induction (typo)/primary immunosupressive (typo) treatment, 592- 593- change to: warm ischaemia and MDRD-eGFR at six , References- You removed some citations. Make sure the final number is correct and reflected in the text. On line 587 you have 62 citations. Should be less.

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10.1371/journal.pone.0307824.r008
Author response to Decision Letter 3
Submission Version4
7 Jul 2024

Algiers, July 7th 2024.

Dear Academic Editor and Peer Reviewers,

The authors would like to thank you for the time taking to reread and improve this manuscript.

We have responded to the three requirements of PLOS ONE and to the requests for corrections from Reviewer n#5.

PLOS ONE requirements

1- Please note that the content of our financial disclosure was changed in the cover letter (May 9th, 2024). We have removed the reference to personal contributions by Djamila IZEMRANE and Nacim HAMDIS.

2- As far as depositing our laboratory protocols in protocols.io is concerned, we feel that there is no need to make an additional contribution, given that these protocols are referenced (see methods section) and that the analysis methods are common methods with few modifications.

3- We have checked our original list of 56 references in the "Retraction Watch Database" Version 1.0.8.0. No references have been retracted. The final number of references is correct and in accordance with the text.

Response to reviewers

Reviewer#3

The authors thank you for your comment.

Reviewer#5

The authors thank you for your scrupulous examination of language, words and grammar, as well as for all your recommendations.

1- Typos and grammatical errors in the docuement entitled "Revised Manuscript with Track Changes" have been corrected and hilighted in green or in red :

line 62- kidney size (we have removed renal), line 85- endothelial cell dysfunction (we have added cell), line 112- we have removed "and ;" and "to", line 113- post-transplantation, line 130- blood samples were, line 142- using the thiobarbituric, we have removed "_" before An, lines 143, 150, 167, 168, 170, and 171 - we have respectively changed 375 µl, 25 µl, 100 µl, 3000 µl, 100 µl and 1 ml to 375 µL, 25 µL, 100 µL, 3000 µL, 100 µL and 1 mL, line 188- the Student's test, line 196- we have removed "throughout the first year", line 201- A p-value of < 0.05, line 207- we have removed "have been detransplanted" and we have put "had grafts removed", line 212- prednisone, line 214- Cyclosporine, line 244 and line 246- respectively were, line 248- we have removed "respectively", line 258- we have removed "a simplified equation for glomerular filtration rate estimated by modification of diet in renal disease", line 259- measure (typo), lines 271-298- In these tables all numbers are presented as "0.0" as indicated in the text, line 315- (Table 6a models), line 316- were tested, line 318- in Table 6 (Table 6b), line 321- we have removed "No variable is transformed.", line 328- Table 6a, line 343- Table 6b, line 344-349- we have removed MDRD-pGFR from the legend, line 354- (Table 4), line 355- Table 8, line 367- (1Y) post, lines 368 and 369- we have removed "A simplified equation for glomerular filtration rate estimated by modification of diet in renal disease", line 375- significantly, line 379- Our results are similar to previous studies, line 384- change to "donors, which were considered as controls, and recipients", line 393- analyzed, line 396- peripheral tissues, indicated , line 397- arginine , line 399- in arginine metabolism, line 400- C-reactive protein, line 407- "male and female patients who have had complications and those who have not.", line 418- results, which , line 420- r=0.451, (ρ=0.416 and r=0.451 respectively), line 422- (rD2=0.528, rD3=0.451, rD6=0.478, ρD8=0.531, rM1=0.563, ρM3=0,649 respectively), line 427- (P=0.017, ρ=0.431 and P=0.010, ρ=-0.450 respectively), line 429-432- change to "out. They were models 13, 14 and 15 (Table 6b: P= 0.000, r2 = 0.599, r2adj= 0.549; P= 0.000, r2 455 =0.548, r2adj = 0.497; P= 0.000, r2= 0.553, r2adj = 0.517 respectively)." , line 433- we have added : "To put these findings in perspective, in many areas", line 440-we have removed "On another note," The p-values, line 442- Also, the p-values for, line 444- In addition, model 13, which, lines 446 and 447- One unit, line 452- In 2014, a study, line 455- measured (typo) GFR at day (typo) 5, lines 457 and 458- indicator of DGF and plasma levels of free thiols at 30 minutes and 90 minutes post-transplantation, line 464- blood flow and GFR relatively, lines 472 and 473- an increased asymmetric dimethylarginine (ADMA), line 476- endogenous NO inhibitors, ADMA and , line 477- as well as the NO precursors (arginine, citrulline and ornithine) with , line 478- were studied, line 482- 25 healthy subjects, NO showed, line 493- decreases NO production, line 494- of renal allografts revealed a significant increase of serum nitrate and episodes of acute rejection , lines 501 and 502- we have removed "The results of the most important of these are reported below." lines 508-510- we have removed the spaces before and after "/", line 511- we have removed "estimated glomerular filtration rate", line 518- donor's eGFR, line 526- donor eGFR, line 527-529- we have removed "This is a simple but useful guide for estimating graft function one year after renal transplantation, particularly in marginal donors (elderly patients, patients with hypertension and atherosclerotic disease), in the clinical setting", line 536- recipient GFR estimated at six months, line 537- Early prediction of GFR one year, line 538- transplantation was carried out in a large , line 540- period were analyzed (typo), line 549- variables, lines 552 and 553- trends towards measured GFR, line 553- no significant differences were observed, line 559- validated in multiple large international, line 561- the race-free 2021 CKD-EPI equation developed in individuals with native kidneys, lines 562 and 563- The P30 values (P30 being the proportion of eGFR within 30% of measured GFR) ranged from 73.0% to 91.3%, line 566- clinical, induction (typo)/primary immunosupressive (typo) treatment, lines 568 and 569- warm ischaemia and MDRD-eGFR at six months.

2- On line 469, there is no difference between NADPH and molecular NADPH. We have deleted "and molecular NADPH" from the sentence. We made a mistake in paraphrasing the authors of reference 43.

3- The final number of references is correct (56) and in line with the text.

Sincerely yours,

Djamila IZEMRANE, Ali BENZIANE and Ahcène BAZ on behalf of the authors.

Corresponding author: Djamila IZEMRANE.

- Laboratory of Biology and Animal Physiology, Higher Normal School, Kouba, Algiers, Algeria, 16308.

- National Higher Veterinary School, Issad Abbes, Oued Smar, Algiers, Algeria, 16059.

Email: d.izemrane@ensv.dz. izemranedjamila@gmail.com. Phone number: +213 669 20 82 01

Attachment Submitted filename: Response to reviewers IZEMRANE 7-7-24 PLOS ONE.docx

10.1371/journal.pone.0307824.r009
Decision Letter 4
Lee John Richard Academic Editor
© 2024 John Richard Lee
2024
John Richard Lee
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version4
12 Jul 2024

Living donors kidney transplantation and oxidative stress: nitric oxide as a predictive marker of graft function

PONE-D-23-24980R4

Dear Dr. IZEMRANE,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Additional Editor Comments (optional):

Reviewers' comments:

10.1371/journal.pone.0307824.r010
Acceptance letter
Lee John Richard Academic Editor
© 2024 John Richard Lee
2024
John Richard Lee
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
23 Jul 2024

PONE-D-23-24980R4

PLOS ONE

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==== Refs
References

1 Hariharan S , Johnson CP , Bresnahan BA , Taranto SE , McIntosh MJ , Stablein D . Improved graft survival after renal transplantation in the United States 1988 to 1996. N Engl J Med. 2000; 342 : 605–612. doi: 10.1056/NEJM200003023420901 .10699159
2 Alvarez S , Boltansky A , Ursu M , Carvajal D , Innocenti G , Vukusich, et al . Prediction of Renal Function After Living Donor Kidney Transplantation. Transplant. Proc. 2010; 42 : 260–261. doi: 10.1016/j.transproceed.2009.11.035 .20172324
3 Schold JD , Nordyke RJ , Wu Z , Corvino F , Wang W , Mohan S . Clinical Events and Renal Function in the First Year Predict Long-Term Kidney Transplant Survival. Kidney 360. 2022; 3 : 714–727. doi: 10.34067/KID.0007342021 .35721618
4 Santos J , Martins LS . Estimating glomerular filtration rate in kidney transplantation: Still searching for the best marker. World J Nephrol. 2015; 4 : 345–353. doi: 10.5527/wjn.v4.i3.345 .26167457
5 Rule AD , Larson TS , Bergstralh EJ , Slezak JM , Jacobsen SJ , Cosio FG . Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med. 2004; 141 : 929–937. doi: 10.7326/0003-4819-141-12-200412210-00009 .15611490
6 Walser M , Drew HH , Guldan JL . Prediction of glomerular filtration rate from serum creatinine concentration in advanced chronic renal failure. Kidney Int. 1993; 44 : 1145–1148. doi: 10.1038/ki.1993.361 .8264148
7 Levey AS , Greene T , Kusek J , Beck GJ . A simplified equation to predict glomerular filtration rate from serum creatinine. J Am Soc Nephrol. 2000; 11 : 155A.
8 Levey AS , Coresh J , Greene T , Marsh J , Stevens LA , Kusek JW , et al . Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem. 2007; 53 : 766–772. doi: 10.1373/clinchem.2006.077180 .17332152
9 Cockcroft DW , Gault MH . Prediction of creatinine clearance from serum creatinine. Nephron. 1976; 16 : 31–41. doi: 10.1159/000180580 .1244564
10 Carcy R , Cougnon M , Poet M , Durandy M , Sicard A , Counillon L , et al . Targeting oxidative stress, a crucial challenge in renal transplantation outcome. Free Rad Bio Med. 2021; 169 : 258–270. doi: 10.1016/j.freeradbiomed.2021.04.023 .33892115
11 Tejchman K , Kotfis K , Sie´nko J . Biomarkers and Mechanisms of Oxidative Stress-Last 20 Years of Research with an Emphasis on Kidney Damage and Renal Transplantation. Int J Mol Sci. 2021; 22 : 26. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347360/. doi: 10.3390/ijms22158010 34360776
12 Podkowinska A , Formanowicz D. Chronic Kidney Disease as Oxidative Stress- and Inflammatory- Mediated Cardiovascular Disease. Antioxid. 2020; 9 : 54. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463588/ doi: 10.3390/antiox9080752 32823917
13 Duni A , Liakopoulos V , Roumeliotis S , Peschos D , Dounousi E . Oxidative Stress in the Pathogenesis and Evolution of Chronic Kidney Disease: Untangling Ariadne’s Thread. Int J Mol Sci. 2019; 20 : 17. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695865/
14 Yilmaz MI , Saglam M , Caglar K , Cakir E , Sonmez A , Ozgurtas T , et al . The determinants of endothelial dysfunction in CKD: Oxidative stress and asymmetric dimethylarginine. Am J Kidney Dis. 2006; 47 :42–50. doi: 10.1053/j.ajkd.2005.09.029 .16377384
15 Modlinger PS , Wilcox CS , Aslam S . Nitric oxide, oxidative stress, and progression of chronic renal failure. Semin Nephrol. 2004; 24 , 354–365. doi: 10.1016/j.semnephrol.2004.04.007 .15252775
16 Lai E.Y , Wellstein A , Welch WJ , Wilcox CS . Superoxide modulates myogenic contractions of mouse afferent arterioles. Hypertens. 2011; 58 : 650–656. doi: 10.1161/HYPERTENSIONAHA.111.170472 21859962
17 Li L , Lai EY , Wellstein A , Welch WJ , Wilcox CS . Differential effects of superoxide and hydrogen peroxide on myogenic signaling, membrane potential, and contractions of mouse renal afferent arterioles. Am J Physiol Renal Physiol. 2016; 310 : 1197–1205. doi: 10.1152/ajprenal.00575.2015 .27053691
18 Tabriziani H 1, Lipkowitz MS , Vuong N . Chronic kidney disease, kidney transplantation and oxidative stress: a new look to successful kidney transplantation. Clinical Kidney Journal. 2018; 11 : 130–135. doi: 10.1093/ckj/sfx091 29423212
19 Nafar M , Sahraei Z , Salamzadeh J , Samavat S , Vaziri ND . Oxidative Stress in Kidney Transplantation Causes, Consequences, and Potential Treatment. Iran J Kidney Dis. 2011; 5 : 357–72. .22057066
20 Antolini F , Valente F , Ricciardi D , Fagugli RM . Normalization of oxidative stress parameters after kidney transplant is secondary to full recovery of renal function. Clin Nephrol. 2004; 62 : 131–137. doi: 10.5414/cnp62131 .15356970
21 Gyurászová M , Radana Gurecká R , Bábíčková J , Tóthová L . Oxidative Stress in the Pathophysiology of Kidney Disease: Implications for Noninvasive Monitoring and Identification of Biomarkers. Oxid Med Cell Longev. 2020: 2020, 11 . Available from: https://pubmed.ncbi.nlm.nih.gov/32082479/. doi: 10.1155/2020/5478708 32082479
22 Fonseca I , Reguengo H , Almeida M , Dias L , Martins LS , Pedroso S , et al . Oxidative Stress in Kidney Transplantation: Malondialdehyde Is an Early Predictive Marker of Graft Dysfunction. Transplant. 2014; 97 : 1058–1065. doi: 10.1097/01.TP.0000438626.91095.50 .24406454
23 Nielsen MB , Jespersen B , Birn H , Krogstrup NV , Bourgonje AR , Leuvenink HGD , et al . Elevated plasma free thiols are associated with early and one-year graft function in renal transplant recipients. PLoS ONE. 2021; 6 :11. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255930. doi: 10.1371/journal.pone.0255930 34379701
24 Jentzsch AM , Bachmann H , Furst P , Biesalki HE . Improved analysis of malondialdehyde in human body fluids. Free Radic Biol Med. 1996; 20 : 251–356. doi: 10.1016/0891-5849(95)02043-8 .8746446
25 Jie S , Zhang X , Broderick M , Fein H . Measurement of Nitric Oxide Production in Biological Systems by Using Griess Reaction Assay. Sensors. 2003; 3 : 276–284. doi: 10.3390/s30800276
26 Bradley PP , Priebat DA , Christensen RD , Rothstein G . Measurement of cutaneous inflammation: Estimation of neutrophil content with an enzyme marker. J Invest Dermatol. 1982; 78 : 206–209. doi: 10.1111/1523-1747.ep12506462 .6276474
27 Krueger AJ , Yang JJ , Roy TA , Robbins DJ , Mackerer CR . An Automated Myeloperoxidase Assay. Clin Chem. 1990; 36 : 158. doi: 10.1093/clinchem/36.1.158a .2153483
28 Habig WH , Pabst MJ , Jakoby WB . Glutathione S-transferases: The first enzymatic step in mercapturic acid formation. J Biol Chem. 1974; 249 : 7130–7139. doi: 10.1042/bj2150617 .4436300
29 Dounousi E , Papavasiliou E , Makedou A , Ioannou K , Katopodis KP , Tselepis A , et al . Oxidative stress is progressively enhanced with advancing stages of CKD. Am J Kidney Dis. 2006; 48 : 752–760. doi: 10.1053/j.ajkd.2006.08.015 .17059994
30 Karamouzis I , Sarafidis PA , Karamouzis M , Iliadis S , Haidich AN , Sioulis A , et al . Increase in oxidative stress but not in antioxidant capacity with advancing stages of chronic kidney disease. Am J Nephrol. 2008; 28 : 397–404. doi: 10.1159/000112413 .18063858
31 Tbahriti HF , Kaddous A , Bouchenak M , Mekki K . Effect of Different Stages of Chronic Kidney Disease and Renal Replacement Therapies on Oxidant-Antioxidant Balance in Uremic Patients. Biochem Res Int. 2013; 2013 : 9. Aviable from: doi: 10.1155/2013/358985 24416590
32 Vural A , Yilmaz MI , Caglar K , Aydin A , Sonmez A , Eyileten T , et al . Assessment of oxidative stress in the early posttransplant period: comparisan of cyclosporine A and tacrolimus-based regimens. Am J Nephrol. 2005; 25 :250–255. doi: 10.1159/000086079 .15925859
33 McClure PW . Evaluating Research When "no Significant Differences Were Found": The Issue of Statistical Power. J Hand Ther. 1998; 11 : 212–213. doi: 10.1016/S0894-1130(98)80041-0 .9730099
34 Schmidt RJ , Baylis C . Total nitric oxide production is low in patients with chronic renal disease. Kidney Int. 2000; 58 : 1261–1266. doi: 10.1046/j.1523-1755.2000.00281.x .10972689
35 Khanafer A , Ilham MA , Namagondlu GS , Janzic A , Sikas N , Smith, et al . Increased Nitric Oxide Production During Acute Rejection in Kidney Transplantation: A Useful Marker to Aid in the Diagnosis of Rejection. Transplant. 2007; 84 :580–586. doi: 10.1097/01.tp.0000278120.55796.42 .17876269
36 Jochum C , Beste M , Sowa JP , Farahani MS . Glutathione-S-transferase subtypes α and π as a tool to predict and monitor graft failure or regeneration in a pilot study of living donor liver transplantation. Eur J Med Res. 2011; 16 : 34–40. doi: 10.1186/2047-783x-16-1-34 .21345768
37 Capeillère-Blandin C , Gausson V , Nguyen AT , Descamps-Latscha B , Drüeke T , Witko-Sarsat V . Respective role of uraemic toxins and myeloperoxidase in the uraemic state. Nephrol Dial Trans, 2006; 21 : 1555–1563. doi: 10.1093/ndt/gfl007 .16476719
38 Žunić G , Vučević D , Tomić A , Drašković-Pavlović B , Majstorović I , Spasić S . Renal transplantation promptly restores excretory function but disturbed L-arginine metabolism persists in patients during the early period after surgery. Nitric Oxide. 2015; 44 :18–23. doi: 10.1016/j.niox.2014.11.004 .25460326
39 Vostálová J , Galandáková A , Svobodová AR , Orolinová E , Kajabová M , Schneiderka P , et al . Time-course evaluation of oxidative stress-related biomarkers after renal transplantation. Ren Fail. 2012; 34 : 413–419. doi: 10.3109/0886022X.2011.649658 .22263958
40 Simmons EM , Langone A , Sezer MT , Vella JP , Recupero P , Morrow JD , et al . Effect of renal transplantation on biomarkers of inflammation and oxidative stress in end-stage renal disease patients. Transplant. 2005; 79 : 914–919. doi: 10.1097/01.tp.0000157773.96534.29 .15849543
41 Frost J. Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. Statistics By Jim Publishing; 2020.
42 Kone BC . Nitric oxide in renal health and disease. Am J Kidney Dis. 1997; 11 : 311–333. doi: 10.1016/s0272-6386(97)90275-4 .9292559
43 Carlstrom M. Nitric oxide signalling in kidney regulation and cardiometabolic health. Nat Rev Nephrol. 2021; 17 :575–590. doi: 10.1038/s41581-021-00429-z .34075241
44 Donato A J , Machin DR , Lesniewski LA . Mechanisms of dysfunction in the aging vasculature and role in age-related disease. Circ.Res. 2018; 123 :825–848. doi: 10.1161/CIRCRESAHA.118.312563 .30355078
45 Rinde NB , Enoksen IT , Melsom T , Fuskevag OM , Eriksen BO , Norvik JV . Nitric Oxide Precursors and Dimethylarginines as Risk Markers for Accelerated Measured GFR Decline in the General Population. Kidney Int. Rep. 2023; 8 : 818–826. doi: 10.1016/j.ekir.2023.01.015 .37069987
46 Reddy YS , Kiranmayi VS , Bitla AR , Krishna GS , Srinivasa Rao PVLN, Sivakumar V. Nitric oxide status in patients with chronic kidney disease. Indian J Nephrol. 2015; 25 : 287–291. doi: 10.4103/0971-4065.147376 26628794
47 Huang J , Ladeiras D , Yu Y , Ming XF , Yang Z . Detrimental Effects of Chronic L-Argenine Rich Food on Aging Kidney. Front. Pharmacol. 2021; 11 :13. Available from: 10.3389/fphar.2020.582155.
48 Koyama I , Nemoto K , Watanabe T , Shinozuka N , Ogawa N , Nagashima N , et al . Serum Nitric Oxide Level as a Prognostic Parameter for Chronic Rejection After Renal Transplantation. Transplant. Proc. 2000; 32 : 1789–1790. doi: 10.1016/s0041-1345(00)01372-5 .11119938
49 Godinho I , Guerra J , Melo MJ , Neves M , Gonçalves J , Santana MA , et al . Living-Donor Kidney Transplantation: Donor-Recipient Function Correlation. Transplant. Proc. 2018; 50 : 719–722. doi: 10.1016/j.transproceed.2018.02.003 .29661423
50 Qiu Y , Liu J , Jiang Y , Song T , Huang Z , Fan Y , et al . Effect of donor kidney morphology parameters on the prognosis in living kidney transplantation recipients. Transl Androl Urol. 2020; 9 : 1957–1966. doi: 10.21037/tau-20-680 .33209660
51 Takahashi K , Furuya K , Gosho M , Usui J , Kimura T , Hoshi A , et al . Prediction of early graft function after living donor kidney transplantation by quantifying the “nephron mass” using CT-volumetric software. Front Med. 2022; 9 :10. Available from: https://www.frontiersin.org/articles/10.3389/fmed.2022.1007175/full. doi: 10.3389/fmed.2022.1007175 36388906
52 Buus NH , Nielsen CM , Skov K , Ibsen L , Krag S , Nyengaard JR . Prediction of Renal Function in Living Kidney Donors and Recipients of Living Donor Kidneys Using Quantitative Histology. Transplant. 2023; 107 : 264–273. doi: 10.1097/TP.0000000000004266 .35883240
53 Matsukuma Y , Masutani K , Tanaka S , Tsuchimoto A , Nakano T , Okabe Y , et al . Development and validation of a new prediction model for graft function using preoperative marginal factors in living‑donor kidney transplantation. Clin Exp Nephrol. 2019; 23 : 1331–1340. doi: 10.1007/s10157-019-01774-x .31444656
54 Blazquez-Navarro A , Bauer C , Wittenbrink N , Wolk K , Sabat R , Dang-Heine C , et al . Early prediction of renal graft function: Analysis of a multi-centre, multi-level data set. Curr. Res. Transl. Med. 2021; 70 : 103334. doi: 10.1016/j.retram.2022.103334
55 Pottel H , Delay A , Maillard N , Mariat C , Delanaye P . 20-year longitudinal follow-up of measured and estimated glomerular filtration rate in kidney transplant patients. Clin Kidney J. 2021; 14 : 909–916. doi: 10.1093/ckj/sfaa034 .33777374
56 Raynaud M , Al-Awadhi S , Juric I , Divard G , Lombardi Y , Basic-Jukic N , et al . Race-free estimated glomerular filtration rate equation in kidney transplant recipients: development and validation study. Brit Med J. 2023; 381 : e07365. Available from: doi: 10.1136/bmj-2022-073654 37257905
