Prospects of using cystatin c as an early predictor of diabetic nephropathy

Cover Page


Cite item

Full Text

Abstract

Hypothesis/aims of study. Using early non-invasive markers of diabetic nephropathy (DN) in clinical practice is important to early start of nephroprotective therapy and leads to improving the quality of life, while decreasing disability and mortality of diabetic patients. The aim of the study was to estimate the potential of using serum cystatin C and glomerular filtration rate (GFR) calculated by CKD-EPIcys and CKD-EPIcr-cys equations for an early diagnosis of DN in type 1 diabetic (T1D) women who were planning pregnancy or were in the I trimester of pregnancy.

Study design, materials, and methods. 47 T1D women were examined, of whom 25 individuals were pregnant and 22 ones were planning pregnancy. In all patients, glycated hemoglobin and serum cystatin C levels were determined, GFR was estimated by the creatinine clearance test, MDRD, CKD-EPIcr, CKD-EPIcys, and CKD-EPIcr-cys equations, with diabetes training done.

Results. The pregnant group and the planning pregnancy group were distinguished by glycated hemoglobin (p = 0.001), serum creatinine (p = 0.001), and GFR estimated by the creatinine clearance test (р = 0.017), CKD-EPIcr (р = 0.005), and CKD-EPIcr-cys (р = 0.046) equations. There was no difference in urinary creatinine, serum cystatin C, and GFR estimated by CKD-EPIcys equation and daily urinary protein excretion between the study groups. Most pregnant women (87.5%) were in stage C1 and only 12,5% in stage C2 as determined by estimated GFR using the CKD-EPIcr formula, which was significantly different compared to the planning pregnancy group, where the percentage of women in stages C1 and C2 was comparable (р = 0.002). In addition, most pregnant patients were in stage C1, while most of the patients planning pregnancy were referred to stage C2 by GFR estimated by CKD-EPIcysequation. Stage C3a was diagnosed in the both study groups only when CKD-EPIcys equation for GFR estimation was used. Most women from both groups were in stage C1 when GFR was estimated by the creatinine clearance test, the percentage ratio of patients in stages C1 and C2 in both groups being comparable.

Conclusion. Our results demonstrated that serum cystatin C and GFR estimation by CKD-EPIcys equation could improve nephropathy diagnostic accuracy among T1D patients with a normal serum creatinine level and intact GFR based on creatinine level.

About the authors

Natalia V. Borovik

The Research Institute of Obstetrics, Gynecology, and Reproductology named after D.O. Ott

Author for correspondence.
Email: borovik1970@yandex.ru
ORCID iD: 0000-0003-0835-6741
SPIN-code: 9010-7276

MD, PhD, Senior Researcher. The Department of Endocrinology of Reproduction

Russian Federation, Saint Petersburg

Maria I. Yarmolinskaya

The Research Institute of Obstetrics, Gynecology, and Reproductology named after D.O. Ott; orth-Western State Medical University named after I.I. Mechnikov

Email: m.yarmolinskaya@gmail.com
ORCID iD: 0000-0002-6551-4147
SPIN-code: 3686-3605

MD, PhD, DSci (Medicine), Professor of the Russian Academy of Sciences, the Head of the Department of Endocrinology of Reproduction, the Head of the Diagnostics and Treatment of Endometriosis Center

Russian Federation, Saint Petersburg

Olga B. Glavnova

The Research Institute of Obstetrics, Gynecology, and Reproductology named after D.O. Ott

Email: o.glavnova@mail.ru

MD. The Department of Endocrinology of Reproduction

Russian Federation, Saint Petersburg

Alyona V. Tiselko

The Research Institute of Obstetrics, Gynecology, and Reproductology named after D.O. Ott

Email: alenadoc@mail.ru
ORCID iD: 0000-0002-2512-833X
SPIN-code: 5644-9891

MD, PhD, Senior Researcher. The Department of Endocrinology of Reproduction

Russian Federation, Saint Petersburg

Svetlana V. Suslova

The Research Institute of Obstetrics, Gynecology, and Reproductology named after D.O. Ott

Email: sv07s@mail.ru

MD. The Department of Endocrinology of Reproduction

Russian Federation, Saint Petersburg

Ekaterina S. Shilova

The Research Institute of Obstetrics, Gynecology, and Reproductology named after D.O. Ott; Almazov National Medical Research Center

Email: katia.shilova@gmail.com
ORCID iD: 0000-0002-5225-6054
SPIN-code: 9703-5970

MD, Junior Researcher; Junior Researcher

Russian Federation, Saint Petersburg

References

  1. Дедов И.И., Шестакова М.В., Майоров А.Ю., и др. Алгоритмы специализированной медицинской помощи больным сахарным диабетом: клинические рекомендации / Под ред. И.И. Дедова, М.В. Шестаковой, А.Ю. Майорова. — 8-й выпуск // Сахарный диабет. — 2017. — Т. 20. — № 1S. — С. 1–112. [Dedov II, Shestakova MV, Mayorov AY, et al. Standards of specialized diabetes care. Ed. by I.I. Dedov, M.V. Shestakova, A.Y. Mayorov. 8th ed. Diabetes mellitus. 2017;20(1S):1-112. (In Russ.)]. https://doi.org/10.14341/DM20171S8.
  2. Donnelly R, Emslie-Smith AM, Gardner ID, Morris AD. ABC of arterial and venous disease: vascular complications of diabetes. BMJ. 2000;320(7241):1062-1066. https://doi.org/10.1136/bmj.320.7241.1062.
  3. Шамхалова М.Ш., Викулова О.К., Железнякова А.В., и др. Эпидемиология хронической болезни почек в Российской Федерации по данным Федерального регистра взрослых пациентов с сахарным диабетом (2013–2016) // Сахарный диабет. — 2018. — Т. 21. — № 3. — С. 160–169. [Shamkhalova MSh, Vikulova OK, Zheleznyakova AV, et al. Trends in the epidemiology of chronic kidney disease in Russian Federation according to the Federal Diabetes Register (2013-2016). Diabetes mellitus. 2018;21(3):160-169. (In Russ.)]. https://doi.org/10.14341/DM9687.
  4. Hovind P, Tarnow L, Rossing P, et al. Predictors for the development of microalbuminuria and macroalbuminuria in patients with type 1 diabetes: inception cohort study. BMJ. 2004;328(7448):1105. https://doi.org/10.1136/bmj.38070.450891.FE.
  5. Centers for Disease Control and Prevention (CDC). Incidence of endstage renal disease among persons with diabetes — United States, 1990-2002. MMWR Morb Wkly Rep. 2005;54(43):1097-1100.
  6. Abboud O, Adler S, Bertram K. KDIGO 2012 Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2013;3(1):1-150.
  7. Смирнов А.В., Шилов Е.М., Добронравов В.А., и др. Национальные рекомендации. Хроническая болезнь почек: основные принципы скрининга, диагностики, профилактики и подходы к лечению // Нефрология. — 2012. — Т. 16. — № 1. — С. 89–115. [Smirnov AV, Shilov EM, Dobronravov VA, et al. Natsional’nye rekomendatsii. khronicheskaya bolezn’ pochek: osnovnye printsipy skrininga, diagnostiki, profilaktiki i podkhody k lecheniyu. Nephrology. 2012;16(1):89-115. (In Russ.)]
  8. Проба Реберга – Тареева // Клиническая нефрология — 2010. — № 6. — С. 78–79. [Proba Reberga-Tareeva. Clinical Nephrology. 2010;(6):78-79. (In Russ.)]
  9. Арутюнов Г.П., Оганезова Л.Г. Часто задаваемые вопросы о скорости клубочковой фильтрации // Клиническая нефрология. — 2009. — № 3. — С. 35–42. [Arutyunov GP, Oganezova LG. Frequently asked questions about glomerular filtration rate. Clinical Nephrology. 2009;(3):35-42. (In Russ.)]
  10. Tangri N, Stevens LA, Schmid CH, et al. Changes in dietary protein intake has no effect on serum cystatin C levels independent of the glomerular filtration rate. Kidney Int. 2011;79(4):471-477. https://doi.org/10.1038/ki.2010.431.
  11. Fabbian F, Pala M, Monesi M, et al. The estimation of glomerular filtration rate in type 2 diabetic patients may depend on the equation used. Eur Rev Med Pharmacol Sci. 2013;17(20):2791-2797.
  12. Delanaye P, Cavalier E, Cristol JP, Delanghe JR. Calibration and precision of serum creatinine and plasma cystatin C measurement: impact on the estimation of glomerular filtration rate. J Nephrol. 2014;27(5):467-475. https://doi.org/10.1007/s40620-014-0087-7.
  13. Stevens LA, Schmid CH, Greene T, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 2009;75(6):652-660. https://doi.org/10.1038/ki.2008.638.
  14. Baxmann AC, Ahmed MS, Marques NC, et al. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol. 2008;3(2):348-354. https://doi.org/10.2215/CJN.02870707.
  15. Peralta CA, Shlipak MG, Judd S, et al. Detection of chronic kidney disease with creatinine, cystatin C, and urine albumin-to-creatinine ratio and association with progression to end-stage renal disease and mortality. JAMA. 2011;305(15):1545-1552. https://doi.org/10.1001/jama.2011.468.
  16. Manetti L, Pardini E, Genovesi M, et al. Thyroid function differently affects serum cystatin C and creatinine concentrations. J Endocrinol Invest. 2014;28(6):346-349. https://doi.org/10.1007/bf03347201.
  17. Fricker M, Wiesli P, Brandle M, et al. Impact of thyroid dysfunction on serum cystatin C. Kidney Int. 2003;63(5):1944-1947. https://doi.org/10.1046/j.1523-1755.2003.00925.x.
  18. Poge U, Gerhardt T, Bokenkamp A, et al. Time course of low molecular weight proteins in the early kidney transplantation period — influence of corticosteroids. Nephrol Dial Transplant. 2004;19(11):2858-2863. https://doi.org/10.1093/ndt/gfh341.
  19. Bjarnadottir M, Grubb A, Olafsson I. Promoter-mediated, dexamethasone-induced increase in cystatin C production by HeLa cells. Scand J Clin Lab Invest. 1995;55(7):617-623. https://doi.org/10.3109/00365519509110261.
  20. Holden SH, Barnett AH, Peters JR, et al. The incidence of type 2 diabetes in the United Kingdom from 1991 to 2010. Diabetes Obes Metab. 2013;15(9):844-852. https://doi.org/10.1111/dom.12123.
  21. Peng TY, Ehrlich SF, Crites Y, et al. Trends and racial and ethnic disparities in the prevalence of pregestational type 1 and type 2 diabetes in Northern California: 1996-2014. Am J Obstet Gynecol. 2017;216(2):177 e171-177 e178. https://doi.org/10.1016/j.ajog.2016.10.007.
  22. Peralta CA, Katz R, Sarnak MJ, et al. Cystatin C identifies chronic kidney disease patients at higher risk for complications. J Am Soc Nephrol. 2011;22(1):147-155. https://doi.org/10.1681/ASN.2010050483.
  23. Blankenberg S, Zeller T, Saarela O, et al. Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: the MONICA, risk, genetics, archiving, and monograph (MORGAM) biomarker project. Circulation. 2010;121(22):2388-2397. https://doi.org/10.1161/CIRCULATIONAHA.109.901413.
  24. Barr EL, Reutens A, Magliano DJ, et al. Cystatin C estimated glomerular filtration rate and all-cause and cardiovascular disease mortality risk in the general population: AusDiab study. Nephrology (Carlton). 2017;22(3):243-250. https://doi.org/10.1111/nep.12759.
  25. Liu X, Foster MC, Tighiouart H, et al. Non-GFR determinants of low-molecular-weight serum protein filtration markers in CKD. Am J Kidney Dis. 2016;68(6):892-900. https://doi.org/10.1053/j.ajkd.2016.07.021.
  26. Oberbauer R, Nenov V, Weidekamm C, et al. Reduction in mean glomerular pore size coincides with the development of large shunt pores in patients with diabetic nephropathy. Exp Nephrol. 2001;9(1):49-53. https://doi.org/10.1159/000020698.
  27. Le Bricon T, Thervet E, Froissart M, et al. Plasma cystatin C is superior to 24-h creatinine clearance and plasma creatinine for estimation of glomerular filtration rate 3 months after kidney transplantation. Clin Chem. 2000;46(8 Pt 1):1206-1207.
  28. Pucci L, Triscornia S, Lucchesi D, et al. Cystatin C and estimates of renal function: searching for a better measure of kidney function in diabetic patients. Clin Chem. 2007;53(3):480-488. https://doi.org/10.1373/clinchem.2006.076042.
  29. Premaratne E, MacIsaac RJ, Finch S, et al. Serial measurements of cystatin C are more accurate than creatinine-based methods in detecting declining renal function in type 1 diabetes. Diabetes Care. 2008;31(5):971-973. https://doi.org/10.2337/dc07-1588.
  30. Perkins BA, Ficociello LH, Ostrander BE, et al. Microalbuminuria and the risk for early progressive renal function decline in type 1 diabetes. J Am Soc Nephrol. 2007;18(4):1353-1361. https://doi.org/10.1681/ASN.2006080872.
  31. Domingueti CP, Foscolo RB, Simoes ESAC, et al. Evaluation of creatinine-based and cystatin C-based equations for estimation of glomerular filtration rate in type 1 diabetic patients. Arch Endocrinol Metab. 2016;60(2):108-116. https://doi.org/10.1590/2359-3997000000151.

Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. Frequency of detection of chronic kidney disease stages when estimating the glomerular filtration rate by CKD-EPIcr equation in the study groups

Download (57KB)
3. Fig. 2. Frequency of detection of chronic kidney disease stages when estimating the glomerular filtration rate by CKD-EPIcys equation in the study groups

Download (62KB)
4. Fig. 3. Frequency of detection of chronic kidney disease stages when estimating the glomerular filtration rate by the creatinine clearance test in the study groups

Download (57KB)

Copyright (c) 2019 Borovik N.V., Yarmolinskaya M.I., Glavnova O.B., Tiselko A.V., Suslova S.V., Shilova E.S.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies