Kidney markers are some of the most frequently used laboratory tests in patient care, and correct clinical decision making depends upon knowledge and correct application of biological variation (BV) data. The aim of this study was to review available BV data and to provide updated BV estimates for the following kidney markers in serum and plasma; albumin, creatinine, cystatin C, chloride, potassium, sodium and urea.
Relevant studies were identified from a historical BV database as well as by systematic literature searches. Retrieved publications were appraised by the Biological Variation Data Critical Appraisal Checklist (BIVAC). Meta-analyses of BIVAC compliant studies with similar design were performed to deliver global estimates of within-subject (CVI) and between-subject (CVG) BV estimates. Out of the 61 identified papers, three received a BIVAC grade A, four grade B, 48 grade C, five grade D grade and one was not appraised as it did not report numerical BV estimates. Most studies were identified for creatinine (n=48). BV estimates derived from the meta-analysis were in general lower than previously reported estimates for all analytes except urea. For some measurands, BV estimates may be influenced by age or states of health, but further data are required.
This review provides updated global BV estimates for kidney related measurands. For all measurands except for urea, these estimates were lower than previously reported.
For the measurands analyzed in this review, there are sufficient well-designed studies available to publish a trustworthy estimate of BV. However, for a number of newly appearing kidney markers no suitable data is available and additional studies are required.
The authors acknowledge Thomas Roraas for his invaluable advice and assistance in performing all statistical analysis required for this study.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.
1. Fraser, CG. Biological variation: from principles to practice. Washington: AACC Press; 2001. Search in Google Scholar
2. Fraser, CG, Sandberg, S. Biological variation. In: Rifai, N, Horvath, AR, Wittwer, CT, editors. Tietz textbook of cinical chemistry and molecular biology, 6th ed. St. Louis, Missouri: Elsevier; 2017:157–70 pp. Search in Google Scholar
3. Ricós, C, Baadenhuijsen, H, Libeer, JC, Hyltoft Petersen, P, Stöckl, D, Thienpont, L, et al. Currently used criteria for evaluating performance in EQA in European countries and a proposal for harmonization. Eur J Clin Chem Clin Biochem 1996;34:159–65. Search in Google Scholar
4. Mina, A. A new quality control model using performance goals based on biological variation in external quality assurance schemes. Clin Chem Lab Med 2006;44:86–91. https://doi.org/10.1515/cclm.2006.017. Search in Google Scholar
5. Ricós, C, Álvarez, V, Cava, F, García-Lario, JV, Hernández, A, Jiménez, CV, et al. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest 1999;59:491–500. https://doi.org/10.1080/00365519950185229. Search in Google Scholar
6. Minchinela, J, Ricós, C, Perich, C, Fernández-Calle, P, Álvarez, V, Doménech, MV, et al. Desirable specifications for total error, imprecision, and bias, derived from intra- and inter-individual biologic variation. Available from: https://www.westgard.com/biodatabase1.htm [Accessed 27 Apr 2020]. Search in Google Scholar
7. Aarsand, A, Roraas, T, Fernández-Calle, P, Ricós, C, Diaz-Garzón, J, Jonker, N, et al. The biological variation data critical appraisal checklist (BIVAC): a new standard for evaluating studies on biological variation and results for critical appraisal of 128 biological variation studies. Clin Chem 2018;64:501–14. https://doi.org/10.1373/clinchem.2017.281808. Search in Google Scholar
8. Bartlett, WA, Braga, F, Carobene, A, Coskun, A, Prusa, R, Fernández-Calle, P, et al. A check-list for critical appraisal studies of biological variation. Clin Chem Lab Med 2015;53:879–85. https://doi.org/10.1515/cclm-2014-1127. Search in Google Scholar
9. Aarsand, AK, Fernandez-Calle, P, Webster, C, Coskun, A, Gonzales-Lao, E, Diaz-Garzon, J, et al. The EFLM biological variation database. Available from: https://biologicalvariation.eu/ [Accessed 27 Apr 2020]. Search in Google Scholar
10. Burdick, RK, Graybill, FA. Confidence intervals on variance components. Statistics: textbooks and mono-graphs, Vol. 127 New York (NY): Marcel Dekker; 1992:78–115 pp. Search in Google Scholar
11. Sahai, H, Ojeda, MM. Analysis of variance for random models. Boston: Birkhäuser; 2004. Search in Google Scholar
12. Tu, D, Shao, J. The jackknife and bootstrap, 1st ed. New York: Springer Series in Statistics; 1995. Search in Google Scholar
13. Carobene, A, Marino, I, Coskun, A, Serteser, M, Unsal, I, Guerra, E, et al. The EuBIVAS project:within- and between-subject biological variation data for serum creatinine using enzymatic and alkaline picrate methods and implications for monitoring. Clin Chem 2017;63:1527–36. https://doi.org/10.1373/clinchem.2017.275115. Search in Google Scholar
14. Aarsand, A, Diaz-Garzón, J, Fernandez-Calle, P, Guerra, E, Locatelli, M, Bartlett, WA, et al. The EuBIVAS: within- and between-subject biological variation data for electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin and glucose. Clin Chem 2018;64:1380–93. https://doi.org/10.1373/clinchem.2018.288415. Search in Google Scholar
15. Carobene, A, Aarsand, AK, Guerra, E, Bartlett, WA, Coskun, A, Díaz-Garzón, J, et al. Deuropean biological variation study (EuBIVAS): within- and between-subject biological variation data for 15 frequently measured proteins. Clin Chem 2019;65:1031–41. https://doi.org/10.1373/clinchem.2019.304618. Search in Google Scholar
16. Pineda-Tenor, D, Laserna-Mendieta, EJ, Timon-Zapata, J, Rodelgo-Jimenez, L, Ramos-Corral, R, Recio-Montealegre, A, et al. Biological variation and reference change values of common clinical chemistry and haematologic laboratory analytes in the elderly population. Clin Chem Lab Med 2013;51:851–62. https://doi.org/10.1515/cclm-2012-0701. Search in Google Scholar
17. Nunes, LA, Brenzikofer, R, De Macedo, DV. Reference change values of blood analytes from physically active subjects. Eur J Appl Physiol 2010;110:191–8. https://doi.org/10.1007/s00421-010-1493-8. Search in Google Scholar
18. Carter, JL, Parker, CT, Stevens, PE, Eaglestone, G, Knight, S, Farmer, CT, et al. Biological variation of plasma and urinary markers of acute kidney injury in patients with chronic kidney disease. Clin Chem 2016;62:876–83. https://doi.org/10.1373/clinchem.2015.250993. Search in Google Scholar
19. Rivara, MB, Zelnick, LR, Hoofnagle, AN, Newitt, R, Tracy, RP, Kratz, M, et al. Diurnal and long-term variation in plasma concentrations and renal clearances of circulating markers of kidney proximal tubular secretion. Clin Chem 2017;63:915–23. https://doi.org/10.1373/clinchem.2016.260117. Search in Google Scholar
20. Perich, C, Minchinela, J, Ricós, C, Fernández-Calle, P, Álvarez, V, Doménech, MV, et al. Biological variation database: structure and criterion used for generation and update. Clin Chem Lab Med 2015;53:299–305. https://doi.org/10.1515/cclm-2014-0739. Search in Google Scholar
21. Fraser, CG, Harris, EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Lab Sci 1989;27:409–37. https://doi.org/10.3109/10408368909106595. Search in Google Scholar
22. Carobene, A, Marino, I, Coskun, A, Serteser, M, Unsal, I, Guerra, E, et al. The EuBIVAS project: within-and between-subject biological variation data for serum creatinine using enzymatic and alkaline picrate methods and implications for monitoring. Clin Chem 2017;63:1527–36. https://doi.org/10.1373/clinchem.2017.275115. Search in Google Scholar
23. National Kidney Function. KDOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Part 5. NKF. 2002. Available from: https://kidneyfoundation.cachefly.net/professionals/KDOQI/guidelines_ckd/index.htm [Accessed on 27 Apr 2020]. Search in Google Scholar
24. Young, DS, Harris, EK, Cotlove, E. Biological and analytic components of variation in long-term studies of serum constituents in normal subjects. Clin Chem 1971;17:403–10. https://doi.org/10.1093/clinchem/17.5.403. Search in Google Scholar
25. Kashani, K, Cheungpasitporn, W, Ronco, C. Biomarkers of acute kidney injury: the pathway from discovery to clinical adoption. Clin Chem Lab Med 2017;55:1074–89. https://doi.org/10.1515/cclm-2016-0973. Search in Google Scholar
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2020-1168).
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