Abstract
Objectives
Urine samples are frequently used in the clinical practice. In our study, we aimed to calculate the biological variations (BV) of analytes and analyte/creatinine ratios measured in spot urine.
Methods
Second-morning spot urine samples were collected from 33 (16 female, 17 male) healthy volunteers once weekly for 10 weeks and analyzed in the Roche Cobas 6,000 instrument. Statistical analyzes were performed using BioVar, an online BV calculation software. The data were evaluated in terms of normality, outliers, steady state, homogeneity of the data, and BV values were obtained by analysis of variance (ANOVA). A strict protocol was established for within-subject (CVI) and between-subject (CVG) estimates for both genders.
Results
There was a significant difference between female/male CVI estimates of all analytes except potassium, calcium and magnesium. No difference was found in CVG estimates. When the analytes that had a significant difference in CVI estimates in spot urine analytes were compared to creatinine, it was observed that the significant difference between the genders disappeared. There was no significant difference between female/male CVI and CVG estimates in all spot urine analyte/creatinine ratios.
Conclusions
Since the CVI estimates of analyte/creatinine ratios are lower, it would be more reasonable to use them in result reporting. Reference ranges should be used with caution, since II values of almost all parameters are between 0.6 and 1.4. The CVI detection power of our study is 1, which is the highest value.
Acknowledgments
We would like to thank all the volunteers included in 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.
-
Ethical approval: The study was approved by the Ethics Committee of University of Health Sciences Ankara Training and Research Hospital (degree no: 743–2021) and in line with the Declaration of Helsinki.
References
1. Braga, F, Panteghini, M. Generation of data on within-subject biological variation in laboratory medicine: an update. Crit Rev Clin Lab Sci 2016;53:313–25. https://doi.org/10.3109/10408363.2016.1150252.Search in Google Scholar PubMed
2. Ricos, C, Perich, C, Minchinela, J, Alvarez, V, Simon, M, Biosca, C, et al.. Application of biological variation – a review. Biochem Med 2009;19:250–9. https://doi.org/10.11613/bm.2009.023.Search in Google Scholar
3. Fraser, C. Biological variation: from principles to practice. Washington, DC: AACC Press; 2001.Search in Google Scholar
4. Sandberg, S, Fraser, CG, Horvath, AR, Jansen, R, Jones, G, Oosterhuis, W, et al.. Defining analytical performance specifications: consensus statement from the 1st strategic conference of the European federation of clinical chemistry and laboratory medicine. Clin Chem Lab Med 2015;53:833–5. https://doi.org/10.1515/cclm-2015-0067.Search in Google Scholar PubMed
5. Ceriotti, F, Fernandez-Calle, P, Klee, GG, Nordin, G, Sandberg, S, Streichert, T, et al.. Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM strategic conference. Clin Chem Lab Med 2017;55:189–94. https://doi.org/10.1515/cclm-2016-0091.Search in Google Scholar PubMed
6. Ricós, C, Alvarez, 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 PubMed
7. Minchinella, J, Ricos, C, Perich, C, Fernandez-Calle, P, Alvarez, V, Domenech, M, et al.. Biological variation database and quality specifications for imprecision, bias and total error (desirable and minimum) the 2014 update. Available from: https://www.westgard.com/biodatabase-2014-update.htm [Accessed 19 Nov 2022].Search in Google Scholar
8. Aarsand, AK, Røraas, T, Fernandez-Calle, P, Ricos, C, Díaz-Garzón, J, Jonker, N, et al.. The biological variation data critical appraisal checklist: a standard for evaluating studies on biological variation. Clin Chem 2018;64:501–14. https://doi.org/10.1373/clinchem.2017.281808.Search in Google Scholar PubMed
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 1 Dec 2022].Search in Google Scholar
10. Carobene, A, Strollo, M, Jonker, N, Barla, G, Bartlett, WA, Sandberg, S, et al.. Sample collections from healthy volunteers for biological variation estimates’ update: a new project undertaken by the working group on biological variation established by the European federation of clinical chemistry and laboratory medicine. Clin Chem Lab Med 2016;54:1599–608. https://doi.org/10.1515/cclm-2016-0035.Search in Google Scholar PubMed
11. Bartlett, WA, Braga, F, Carobene, A, Coşkun, A, Prusa, R, Fernandez-Calle, P, et al.. A checklist for critical appraisal of studies of biological variation. Clin Chem Lab Med 2015;53:879–85. https://doi.org/10.1515/cclm-2014-1127.Search in Google Scholar PubMed
12. Winter, SD, Gabow, PA. Measurement of urine electrolytes: clinical significance and methods. CRC Crit Rev Clin Lab Sci 1981;14:163–87. https://doi.org/10.3109/10408368109105863.Search in Google Scholar PubMed
13. Nakamoto, I, Uiji, S, Okata, R, Endo, H, Tohyama, S, Nitta, R, et al.. Diurnal rhythms of urine volume and electrolyte excretion in healthy young men under differing intensities of daytime light exposure. Sci Rep 2021;11:13097. https://doi.org/10.1038/s41598-021-92595-0.Search in Google Scholar PubMed PubMed Central
14. Delanghe, J, Speeckaert, M. Preanalytical requirements of urinalysis. Biochem Med 2014;24:89–104. https://doi.org/10.11613/bm.2014.011.Search in Google Scholar PubMed PubMed Central
15. Yilmaz, G, Yilmaz, FM, Hakligör, A, Yücel, D. Are preservatives necessary in 24-hour urine measurements? Clin Biochem 2008;41:899–901. https://doi.org/10.1016/j.clinbiochem.2008.03.002.Search in Google Scholar PubMed
16. Remer, T, Montenegro-Bethancourt, G, Shi, L. Long-term urine biobanking: storage stability of clinical chemical parameters under moderate freezing conditions without use of preservatives. Clin Biochem 2014;47:307–11. https://doi.org/10.1016/j.clinbiochem.2014.09.009.Search in Google Scholar PubMed
17. Korkmaz, S, Zarasız, G, Göksülük, D, Senes, M, Sönmez, C, Yucel, D. BioVar: an online biological variation analysis tool. Turk J Biochem 2020;45:479–89. https://doi.org/10.1515/tjb-2019-0437.Search in Google Scholar
18. Shapiro, SS, Wilk, MB. An analysis of variance test for normality (complete samples). Biometrika 1965;52:591–611. https://doi.org/10.2307/2333709.Search in Google Scholar
19. Cochran, WG. The distribution of the largest of a set of estimated variances as a fraction of their total. Ann Eugen 1941;11:47–52. https://doi.org/10.1111/j.1469-1809.1941.tb02271.x.Search in Google Scholar
20. Dixon, WJ. Processing data for outliers. Biometrics 1953;9:74–89. https://doi.org/10.2307/3001634.Search in Google Scholar
21. Carobene, A, Røraas, T, Sølvik, UØ, Sylte, MS, Sandberg, S, Guerra, E, et al.. Biological variation estimates obtained from 91 healthy study participants for 9 enzymes in serum. Clin Chem 2017;63:1141–50. https://doi.org/10.1373/clinchem.2016.269811.Search in Google Scholar PubMed
22. Røraas, T, Støve, B, Petersen, PH, Sandberg, S. Biological variation: the effect of different distributions on estimated within-person variation and reference change values. Clin Chem 2016;62:725–36. https://doi.org/10.1373/clinchem.2015.252296.Search in Google Scholar PubMed
23. Walton, RM. Subject-based reference values: biological variation, individuality, and reference change values. Vet Clin Pathol 2012;41:175–81. https://doi.org/10.1111/j.1939-165X.2012.00414.x.Search in Google Scholar PubMed
24. Røraas, T, Petersen, PH, Sandberg, S. Confidence intervals and power calculations for within-person biological variation: effect of analytical imprecision, number of replicates, number of samples, and number of individuals. Clin Chem 2012;58:1306–13. https://doi.org/10.1373/clinchem.2012.187781.Search in Google Scholar PubMed
25. Mason, HJ, Stevenson, AJ, Williams, N, Morgan, M. Intra-individual variability in markers of proteinuria for normal subjects and those with cadmium-induced renal dysfunction: interpretation of results from untimed, random urine samples. Biom J 1999;4:118–28. https://doi.org/10.1080/135475099230921.Search in Google Scholar PubMed
26. Howey, JE, Browning, MC, Fraser, CG. Selecting the optimum specimen for assessing slight albuminuria, and a strategy for clinical investigation: novel uses of data on biological variation. Clin Chem 1987;33:2034–8. https://doi.org/10.1093/clinchem/33.11.2034.Search in Google Scholar
27. Newman, DJ, Pugia, MJ, Lott, JA, Wallace, JF, Hiar, AM. Urinary protein and albumin excretion corrected by creatinine and specific gravity. Clin Chim Acta 2000;294:139–55. https://doi.org/10.1016/s0009-8981(00)00181-9.Search in Google Scholar PubMed
28. Jensen, JS. Intra-individual variation of overnight urinary albumin excretion in clinically healthy middle-aged individuals. Clin Chim Acta 1995;243:95–9. https://doi.org/10.1016/0009-8981(95)06155-x.Search in Google Scholar PubMed
29. Gowans, EM, Fraser, CG. Biological variation in analyte concentrations in urine of apparently healthy men and women. Clin Chem 1987;33:847–50. https://doi.org/10.1093/clinchem/33.6.847.Search in Google Scholar
30. Gowans, EM, Fraser, CG. Do biological-variation data shed light on the demise of some historical indices used to assess calcium homeostasis? Clin Chem 1987;33:1267–8. https://doi.org/10.1093/clinchem/33.7.1267a.Search in Google Scholar
31. Ricós, C, Jiménez, CV, Hernández, A, Simón, M, Perich, C, Alvarez, V, et al.. Biological variation in urine samples used for analyte measurements. Clin Chem 1994;40:472–7. https://doi.org/10.1093/clinchem/40.3.472.Search in Google Scholar
32. Jensen, JEB, Sørensen, HA, Kollerup, G, Jensen, LB, Sørensen, OH. Biological variation of biochemical bone markers. Scand J Clin Lab Invest 1994;54:36–9. https://doi.org/10.3109/00365519409088575.Search in Google Scholar PubMed
33. Nicoll, GW, Struthers, AD, Fraser, CG. Biological variation of urinary magnesium. Clin Chem 1991;37:1794–5. https://doi.org/10.1093/clinchem/37.10.1794.Search in Google Scholar
34. Djurhuus, MS, Gram, J, Petersen, PH, Klitgaard, NAH, Bollerslev, J, Beck-Nielsen, H. Biological variation of serum and urinary magnesium in apparently healthy males. Scand J Clin Lab Invest 1995;55:549–58. https://doi.org/10.1080/00365519509075394.Search in Google Scholar PubMed
35. Lamb, EJ, Jones, GR. Kidney function tests. In: Rifai, N, editor. Tietz textbook of laboratory medicine, 7th ed. St. Louis: Elsevier Inc.; 2023:352–e60 pp.Search in Google Scholar
36. Choi, ST, Song, JS, Kim, SJ, Kim, CH, Moon, SJ. The utility of the random urine uric acid-to-creatinine ratio for patients with gout who need uricosuric agents: retrospective cross-sectional study. J Kor Med Sci 2020;35:e95. https://doi.org/10.3346/jkms.2020.35.e95.Search in Google Scholar PubMed PubMed Central
37. Yamamoto, T, Moriwaki, Y, Takahashi, S, Tsutsumi, Z, Ka, T, Fukuchi, M, et al.. A simple method of selecting gout patients for treatment with uricosuric agents, using spot urine and blood samples. J Rheumatol 2002;29:1937–41.Search in Google Scholar
38. Kawasaki, T, Itoh, K, Uezono, K, Sasaki, H. A simple method for estimating 24 h urinary sodium and potassium excretion from second morning voiding urine specimen in adults. Clin Exp Pharmacol Physiol 1993;20:7–14. https://doi.org/10.1111/j.1440-1681.1993.tb01496.x.Search in Google Scholar PubMed
39. Tanaka, T, Okamura, T, Miura, K, Kadowaki, T, Ueshima, H, Nakagawa, H, et al.. A simple method to estimate populational 24-h urinary sodium and potassium excretion using a casual urine specimen. J Hum Hypertens 2002;16:97–103. https://doi.org/10.1038/sj.jhh.1001307.Search in Google Scholar PubMed
40. Gökçe, Ç, Gökçe, Ö, Baydinç, C, Īlhan, N, Alaşehirli, E, Özküçük, F, et al.. Use of random urine samples to estimate total urinary calcium and phosphate excretion. Arch Intern Med 1991;151:1587–8. https://doi.org/10.1001/archinte.151.8.1587.Search in Google Scholar
41. Paccaud, Y, Rios-Leyvraz, M, Bochud, M, Tabin, R, Genin, B, Russo, M, et al.. Spot urine samples to estimate 24-hour urinary calcium excretion in school-age children. Eur J Pediatr 2020;179:1673–81. https://doi.org/10.1007/s00431-020-03662-z.Search in Google Scholar PubMed
42. Robinson-Cohen, C, Ix, JH, Smits, G, Persky, M, Chertow, GM, Block, GA, et al.. Estimation of 24-hour urine phosphate excretion from spot urine collection: development of a predictive equation. J Ren Nutr 2014;24:194–9. https://doi.org/10.1053/j.jrn.2014.02.001.Search in Google Scholar PubMed
43. Coşkun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized reference intervals in laboratory medicine: a new model based on within-subject biological variation. Clin Chem 2021;67:374–84. https://doi.org/10.1093/clinchem/hvaa233.Search in Google Scholar PubMed
44. Carobene, A, Banfi, G, Locatelli, M, Vidali, M. Personalized reference intervals: from the statistical significance to the clinical usefulness. Clin Chim Acta 2022;524:203–4. https://doi.org/10.1016/j.cca.2021.10.036.Search in Google Scholar PubMed
45. Haeckel, R, Wosniok, W, Kratochvila, J, Carobene, A. A pragmatic proposal for permissible limits in external quality assessment schemes with a compromise between biological variation and the state of the art. Clin Chem Lab Med 2012;50:833–9. https://doi.org/10.1515/cclm-2011-0862.Search in Google Scholar PubMed
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2022-1290).
© 2023 Walter de Gruyter GmbH, Berlin/Boston
