Volume 27, Issue 122 (May & June 2019)                   J Adv Med Biomed Res 2019, 27(122): 29-34 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Masaebi F, Looha M A, Nasiri M, Kazeruni F, Zayeri F, Gharishvandi F. Assessing the Prediction Power of Plasma Neutrophil Gelatinase-Associated Lipocalin and Serum Cystatin C for Diagnosis Kidney Damage. J Adv Med Biomed Res 2019; 27 (122) :29-34
URL: http://journal.zums.ac.ir/article-1-5391-en.html
Assessing the Prediction Power of Plasma Neutrophil Gelatinase-Associated Lipocalin and Serum Cystatin C for Diagnosis Kidney Damage
Fatemeh Masaebi1 , Mehdi Azizimohammad Looha1 , Malihe Nasiri2 , Faranak Kazeruni3 , Farid Zayeri * 4, Fatemeh Gharishvandi5
1- Dept. of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3- Dept. of Laboratory Medicine, School of Allied Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- Proteomics Research Center and Dept. of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran , fzayeri@gmail.com
5- Dept. of Clinical Biochemistry, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Abstract:   (146946 Views)

Background & Objective: Chronic Kidney Disease (CKD) has been recognized as a serious public health threat. The early detection of kidney damage in CKD is a useful way to reduce the disease burden. This study aimed to determine the power of Neutrophil gelatinase-associated lipocalin (NGAL) and cystatin C (Cys-C) to predict the kidney damage in Iranian patients.
Materials & Methods: This study was conducted at Shohadaye Tajrish Hospital on 72 renal patients. The estimated glomerular filtration rate (GFR) was assumed as the gold standard method. The NGAL and Cys-C were used as predictors and estimated GFR was used as a response variable. Three logistic regression models were fitted to investigate the impact of single and multiple markers for the prediction of GFR status.
Results: The regression models with NGAL and Cys-C as single predictors, and with both of them as multivariate predictors, were fitted to the data. The markers except for Cys-C were significantly related to the renal damage in all models (P<0.05). The obtained odds ratio for the model with NGAL, Cys-Cand both NGAL and Cys-C were 1.142, 1.004 and 1.125, respectively. The sensitivity and specificity of the models with NGAL, Cys-C and both of them were 96.00 and 100.00; 64.00 and 97.87; and 96.00 and 100, respectively. 
Conclusion: Our findings revealed that the NGAL biomarker as a single predictor could result in high predictor power for classifying the patients with and without kidney damage. Thus, the clinicians can use this marker for the early prediction of this renal problem.

Keywords: Kidney damage, NGAL, Cystatin C, AUC, Iranian population
Full-Text [PDF 651 kb]   (157060 Downloads) |   |   Full-Text (HTML)  (2808 Views)  

✅ Our findings revealed that the NGAL biomarker as a single predictor could result in high predictor power for classifying the patients with and without kidney damage. Thus, the clinicians can use this marker for the early prediction of this renal problem.


Type of Study: Original Article | Subject: Clinical medicine
Received: 2018/10/30 | Accepted: 2019/03/7 | Published: 2019/05/10
References
1. Nogueira A, Pires MJ, Oliveira PA. Pathophysiological mechanisms of renal fibrosis: a review of animal models and therapeutic strategies. In Vivo. 2017; 31(1): 1-22. [DOI:10.21873/invivo.11019] [PMID] [PMCID]
2. Clyne N, Hellberg M, Kouidi E, Deligiannis A, Höglund P. Relationship between declining glomerular filtration rate and measures of cardiac and vascular autonomic neuropathy. Nephrol. 2016. 21(12): [DOI:10.1111/nep.12706] [PMID]
3. Mikolasevic I, Žutelija M, Mavrinac V, Orlic L. Dyslipidemia in patients with chronic kidney disease: etiology and management. Int J Nephrol Ren Dis. 2017; 10: 35. [DOI:10.2147/IJNRD.S101808] [PMID] [PMCID]
4. Boissier R, Sichez PC, Tran S, Delaporte V, Karsenty G, Lechevallier E. Comparison of glomerular filtration rate (GFR) loss after nephrectomy in 3 populations: Living donor nephrectomy, radical nephrectomy and partial nephrectomy for cancer. Eur Urol (Supplements). 2018; 17(2): e765. [DOI:10.1016/S1569-9056(18)31364-2]
5. Shirazian S, Aina O, Park Y, et al. Chronic kidney disease-associated pruritus: impact on quality of life and current management challenges. Int J Nep Ren Dis. 2017; 10: 11. [DOI:10.2147/IJNRD.S108045] [PMID] [PMCID]
6. Webster AC, Nagler EV, Morton RL, Masson Ph. Chronic kidney disease. Lancet. 2017; 389(10075): 1238-1252. [DOI:10.1016/S0140-6736(16)32064-5]
7. Pezeshgi A, Ghodrati S, Kiafar M, Kamali K, Asadi-Khiavi M. Study of neutrophil gelatinase-associated lipocalin in patients with cardiovascular shock. J Renal Inj Prev. 2018; 7(3): 144-147. [DOI:10.15171/jrip.2018.36]
8. Ku E, Glidden DV, Johansen KL, et al. Association between strict blood pressure control during chronic kidney disease and lower mortality after onset of end-stage renal disease. Kidney Int. 2015; 87(5): 1055-1060. [DOI:10.1038/ki.2014.376] [PMID] [PMCID]
9. Chan T-C, Zhang Z, Lin BCh, et al. Long-term exposure to ambient fine particulate matter and chronic kidney disease: a cohort study. Environ Health Perspect. 2018; 126(10): 107002. [DOI:10.1289/EHP3304] [PMID] [PMCID]
10. Li PKT, Lui SL, Ng JKCh, et al. Addressing the burden of dialysis around the world: A summary of the roundtable discussion on dialysis economics at the first international congress of chinese nephrologists. Nephrol. 2017; 22: 3-8. [DOI:10.1111/nep.13143] [PMID]
11. Coca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int. 2012; 81(5): 442-448. [DOI:10.1038/ki.2011.379] [PMID] [PMCID]
12. Jha V, Garcia G, Iseki KSH, et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013; 382(9888): 260-272. [DOI:10.1016/S0140-6736(13)60687-X]
13. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012; 380(9859): 2095-2128. [DOI:10.1016/S0140-6736(12)61728-0]
14. Luyckx VA, Tonelli M, J.W.J.B.o.t.W.H.O. Stanifer. The global burden of kidney disease and the sustainable development goals. Bull World Health Organ. 2018; 96(6): 414. [DOI:10.2471/BLT.17.206441] [PMID] [PMCID]
15. Saber A, Naghibzadeh Tahami A, Najafipour H, Azmandian J. Assessment of prevalence of chronic kidney disease and its predisposing factors in Kerman city. Nephro-Urology Monthly. 2017; 9(2): [DOI:10.5812/numonthly.41794]
16. Nafar M, Mousavi SM, Mahdavi M, et al. Burden of chronic kidney disease in iran a screening program is of essential need. Iran J Kidney Dis. 2008; 2(4): 183-192.
17. De Nicola L, Minutolo RJKI. Worldwide growing epidemic of CKD: fact or fiction. Kidney Int. 2016; 90(3): 482-484. [DOI:10.1016/j.kint.2016.05.001] [PMID]
18. Levey AS, LAJAJoKD Inke. GFR as the "gold standard": estimated, measured, and true. Am J Kidney Dis. 2016; 67(1): 9-12. [DOI:10.1053/j.ajkd.2015.09.014] [PMID]
19. Alkandari O, Hebert D, Langlois V, Robinson L A, Parekh RS. Validation of serum creatinine-based formulae in pediatric renal transplant recipients. Pediatr Res. 2017; 82(6): 1000. [DOI:10.1038/pr.2017.209] [PMID]
20. Bretagne M, Jouinot A, Durand JP, et al. Estimation of glomerular filtration rate in cancer patients with abnormal body composition and relation with carboplatin toxicity. Cancer Chem Pharm. 2017; 80(1): 45-53. [DOI:10.1007/s00280-017-3326-5] [PMID]
21. Qiu X, Liu CH , Ye Y, et al. The diagnostic value of serum creatinine and cystatin c in evaluating glomerular filtration rate in patients with chronic kidney disease: a systematic literature review and meta-analysis. Oncotarget. 2017; 8(42): 72985. [DOI:10.18632/oncotarget.20271]
22. Kirsch AH, Rosenkranz AR. Pre-chronic Kidney Disease (CKD)? Is It Time for a New Staging?, in Prehypertension and Cardiometabolic Syndrome. 2019; 231-240. [DOI:10.1007/978-3-319-75310-2_16]
23. Antonucci E, Lippi G, Ticinesi A, et al. Neutrophil gelatinase-associated lipocalin (NGAL): a promising biomarker for the early diagnosis of acute kidney injury (AKI). Acta Biomed. 2014; 85(3): 289-294.
24. Devarajan PJSJOC, investigation L. Neutrophil gelatinase‐associated lipocalin (NGAL): a new marker of kidney disease. Scan J Clin Lab Inv. 2008. 68: 89-94. [DOI:10.1080/00365510802150158] [PMID] [PMCID]
25. Helanova K, Spinar J, Parenica J. Diagnostic and prognostic utility of neutrophil gelatinase-associated lipocalin (NGAL) in patients with cardiovascular diseases-review. Kidney Blood Pres Res. 2014; 39(6): 623-629. [DOI:10.1159/000368474] [PMID]
26. Munilakshmi U, Shashidhar KN, Muninarayana C, Reddy M, Lakshmaiah V. Neutrophil gelatinase associated lipocalin (ngal), an early marker for urinary tract infection and acute kidney injury. Asian J Biochem. 2018; 13(1): 15-21. [DOI:10.3923/ajb.2018.15.21]
27. Gharishvandi F, Kazerouni F, Rahimipour A, Nasiri M. Evaluation of some factors affecting the risk of kidney damage in patients with hypertension. Arch Adv Biosci. 2014; 5(4).
28. Gharishvandi F, Kazerouni F, Rahimipour A, Nasiri M. Comparative assessment of neutrophil gelatinase-associated lipocalin (NGAL) and cystatin cas early biomarkers for early detection of renal failure in patients with hypertension. Iran Biomed J. 2015; 19(2): 76.
29. Basturk T, Sari O, Koc Y, et al. Prognostic significance of NGAL in early stage chronic kidney disease. Min Urol Nefrol. 2017; 69(3): 307-312.
30. Newman DJ, Thakkar H, Edwards RG, et al. Serum cystatin cmeasured by automated immunoassay: a more sensitive marker of changes in GFR than serum creatinine. Kidney Int. 1995; 47(1): 312-318. [DOI:10.1038/ki.1995.40] [PMID]
31. Martin MV, Barroso S, Herráez O, Sande FD, Caravaca F. Cystatin cas a renal function estimator in advanced chronic renal failure stages. Nefrología. 2006; 26(4): 433-438.
32. Pezeshgi A, Abedi Azar S, Ghasemi H, Kamali K, Esmaeilzadeh A. Role of plasma neutrophil gelatinase-associated lipocalin as an emerging biomarker of acute renal failure following kidney transplantation and its correlation with plasma creatinine. J Renal Inj Prev. 2016; 5(2): 98. [DOI:10.15171/jrip.2016.21] [PMID] [PMCID]
33. Bolignano D, Lacquaniti A, Coppolino G, Donato V, Campo S, Fazio MR. Neutrophil gelatinase-associated lipocalin (NGAL) and progression of chronic kidney disease. Clin J Am Soc Nephrol. 2009; 4(2): 337-344. [DOI:10.2215/CJN.03530708] [PMID] [PMCID]
34. Ghonemy TA, Amro GM. Plasma neutrophil gelatinase-associated lipocalin (NGAL) and plasma cystatin C (CysC) as biomarker of acute kidney injury after cardiac surgery. Saudi J Kidney Dis Transpl. 2014; 25(3): 582. [DOI:10.4103/1319-2442.132194] [PMID]
35. Mitsnefes MM, Kathman TS, Mishra J, et al. Serum neutrophil gelatinase-associated lipocalin as a marker of renal function in children with chronic kidney disease. Kidney Blood Pre Res. 2007; 22(1): 101. [DOI:10.1007/s00467-006-0244-x] [PMID]
36. Fisher MA, Taylor GWJJOP. A prediction model for chronic kidney disease includes periodontal disease. J Periodontol. 2009; 80(1): 16-23. [DOI:10.1902/jop.2009.080226] [PMID] [PMCID]
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Advances in Medical and Biomedical Research

Designed & Developed by : Yektaweb