Comparison of seven different enzymatic methods for serum glycated albumin in pregnant women: a multicenter study
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Dandan Sun
,
Yicong Yin
and
Ling Qiu
Abstract
Objectives
To evaluate the consistency of seven enzymatic glycated albumin (GA) assays in pregnant women based on a multicenter study.
Methods
Samples were collected from pregnant women at three different gestational stages: 4–13 weeks (n=150), 24–28 weeks (n=300, including 150 GDM subjects), and 29–40 weeks (n=300, including 150 GDM subjects), across three hospitals between July 2022 and December 2023 in China. These samples were analyzed using seven enzymatic GA methods (Lucica, Norudia, BSBE, Maccura, Meikang, Reebio, and Zybio assays). Spearman correlation analysis, Passing–Bablok regression, and Bland–Altman plots were used to evaluate the consistency between the Lucica used in our laboratory and the other selected assays. The effects of albumin concentration and gestational stage on the consistency of GA were evaluated through stratified analyses.
Results
The correlation coefficients between Lucica and the other six assays for GA% measurement ranged from 0.741 to 0.906 (p<0.0001), with the mean relative biases ranging from −15.5 to +6.7 %. In trimester-stratified analysis, the highest correlation coefficient was observed in the first trimester for all assays except Maccura, and the bias increased with advancing gestational age for all assays except BSBE. In albumin-stratified analysis (30–45 g/L), the correlation increased with increasing albumin concentration for all assays, while the bias decreased except for BSBE and Maccura assays.
Conclusions
Poor analytical consistency was observed in enzymatic GA assays for pregnant women, with discrepancies varying across gestational stages and albumin concentrations. Reference intervals for pregnant women should be established based on trimester-stratified and manufacturer-specific criteria.
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Research ethics: This study was reviewed and approved by the local Ethics Committee (ID: HS2912, Date: 2021.04.27).
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. SDD, CZ, JMY: conceptualization, investigation, methodology, data curation and analysis, writing – review and editing. These authors have equal contribution to this work. GXZ, GR, HLA: methodology support, technical assistance. MCC: statistical analysis, visualization. ZY: revised it critically. MY, WM: performed experiments, resources and data collection. QL, YYC, YSL: methodology design, data interpretation, writing – review and editing, supervision, manuscript finalization, correspondence.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
1. ACOG Practice Bulletin No. 190: gestational diabetes mellitus. Obstet Gynecol 2018;131:e49-64. https://doi.org/10.1097/AOG.0000000000002501.Search in Google Scholar PubMed
2. International Diabetes Federation. IDF Diabetes Atlas, 10th ed. https://diabetesatlas.org/resources/previous-editions/ [Accessed 10 Mar 2025].Search in Google Scholar
3. International Diabetes Federation. China diabetes report 2000–2045. https://diabetesatlas.org/data/en/country/42/cn.html [Accessed 19 Feb 2025].Search in Google Scholar
4. Liu, Q, Chen, X, Wei, S, Wang, F. Prevalence of gestational diabetes mellitus and associated factors in Shenzhen, China: a retrospective analysis of 70,427 pregnant women. Int J Diabetes Dev Ctries 2023;43:517–22. https://doi.org/10.1007/s13410-022-01126-8.Search in Google Scholar
5. Zhu, H, Zhao, Z, Xu, J, Chen, Y, Zhu, Q, Zhou, L, et al.. The prevalence of gestational diabetes mellitus before and after the implementation of the universal two-child policy in China. Front Endocrinol 2022;13:960877. https://doi.org/10.3389/fendo.2022.960877.Search in Google Scholar PubMed PubMed Central
6. Tian, ML, Du, LY, Ma, GJ, Zhang, T, Ma, XY, Zhang, YK, et al.. Secular increase in the prevalence of gestational diabetes and its associated adverse pregnancy outcomes from 2014 to 2021 in Hebei Province, China. Front Endocrinol 2022;13:1039051. https://doi.org/10.3389/fendo.2022.1039051.Search in Google Scholar PubMed PubMed Central
7. Zhao, Y, Zhao, Y, Fan, K, Jin, L. Serum uric acid in early pregnancy and risk of gestational diabetes mellitus: a cohort study of 85,609 pregnant women. Diabetes Metab 2022;48:101293. https://doi.org/10.1016/j.diabet.2021.101293.Search in Google Scholar PubMed
8. He, LR, Yu, L, Guo, Y. Birth weight and large for gestational age trends in offspring of pregnant women with gestational diabetes mellitus in southern China, 2012–2021. Front Endocrinol 2023;14:1166533. https://doi.org/10.3389/fendo.2023.1166533.Search in Google Scholar PubMed PubMed Central
9. Dong, L, Zhong, W, Qiao, T, Wang, Z, Liang, Y, Zhao, DQ. Investigation and study on the epidemiology of gestational diabetes mellitus in Guizhou. World J Diabetes 2025;16:98438. https://doi.org/10.4239/wjd.v16.i2.98438.Search in Google Scholar PubMed PubMed Central
10. Retnakaran, R, Ye, C, Hanley, AJ, Connelly, PW, Sermer, M, Zinman, B. Treatment of gestational diabetes mellitus and maternal risk of diabetes after pregnancy. Diabetes Care 2023;46:587–92. https://doi.org/10.2337/dc22-1786.Search in Google Scholar PubMed
11. Sweeting, A, Wong, J, Murphy, HR, Ross, GP. A clinical update on gestational diabetes mellitus. Endocr Rev 2022;43:763–93. https://doi.org/10.1210/endrev/bnac003.Search in Google Scholar PubMed PubMed Central
12. Castorino, K, Durnwald, C, Ehrenberg, S, Ehrhardt, N, Isaacs, D, Levy, CJ, et al.. Practical considerations for using continuous glucose monitoring in patients with gestational diabetes mellitus. J Womens Health 2025;34:10–20. https://doi.org/10.1089/jwh.2023.0864.Search in Google Scholar PubMed
13. American Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: standards of care in diabetes-2025. Diabetes Care 2025;48:S27-49. https://doi.org/10.2337/dc25-s002.Search in Google Scholar PubMed PubMed Central
14. Attard, SM, Herring, AH, Wang, H, Howard, AG, Thompson, AL, Adair, LS, et al.. Implications of iron deficiency/anemia on the classification of diabetes using HbA1c. Nutr Diabetes 2015;5:e166. https://doi.org/10.1038/nutd.2015.16.Search in Google Scholar PubMed PubMed Central
15. Rao, LV, Pratt, GW, Bi, C, Kroll, MH. Large-scale retrospective analyses of the effect of iron deficiency anemia on hemoglobin A1c concentrations. Clin Chim Acta 2022;529:21–4. https://doi.org/10.1016/j.cca.2022.02.005.Search in Google Scholar PubMed
16. Xiong, W, Zeng, ZH, Xu, Y, Li, H, Lin, H. Circulating glycated albumin levels and gestational diabetes mellitus. World J Diabetes 2024;15:1802–10. https://doi.org/10.4239/wjd.v15.i8.1802.Search in Google Scholar PubMed PubMed Central
17. Hashimoto, K, Noguchi, S, Morimoto, Y, Hamada, S, Wasada, K, Imai, S, et al.. A1C but not serum glycated albumin is elevated in late pregnancy owing to iron deficiency. Diabetes Care 2008;31:1945–8. https://doi.org/10.2337/dc08-0352.Search in Google Scholar PubMed PubMed Central
18. Kaiafa, G, Veneti, S, Polychronopoulos, G, Pilalas, D, Daios, S, Kanellos, I, et al.. Is HbA1c an ideal biomarker of well-controlled diabetes? Postgrad Med J 2021;97:380–3. https://doi.org/10.1136/postgradmedj-2020-138756.Search in Google Scholar PubMed PubMed Central
19. Paroni, R, Ceriotti, F, Galanello, R, Battista, LG, Panico, A, Scurati, E, et al.. Performance characteristics and clinical utility of an enzymatic method for the measurement of glycated albumin in plasma. Clin Biochem 2007;40:1398–405. https://doi.org/10.1016/j.clinbiochem.2007.08.001.Search in Google Scholar PubMed
20. Tao, X, Koguma, R, Nagai, Y, Kohzuma, T. Analytical performances of a glycated albumin assay that is traceable to standard reference materials and reference range determination. J Clin Lab Anal 2022;36:e24509. https://doi.org/10.1002/jcla.24509.Search in Google Scholar PubMed PubMed Central
21. Paleari, R, Bonetti, G, Calla, C, Carta, M, Ceriotti, F, Di Gaetano, N, et al.. Multicenter evaluation of an enzymatic method for glycated albumin. Clin Chim Acta 2017;469:81–6. https://doi.org/10.1016/j.cca.2017.03.028.Search in Google Scholar PubMed
22. Choe, W, Kim, S, Chang, J, Park, H, Kim, HN, Yoo, SJ. Performance of Norudia glycated albumin assay on multiple analytical platforms and comparison to Lucica assay. Clin Lab 2020;66. https://doi.org/10.7754/clin.lab.2020.200107.Search in Google Scholar PubMed
23. Lenters-Westra, E, Atkin, SL, Kilpatrick, ES, Slingerland, RJ, Sato, A, English, E. Limitations of glycated albumin standardization when applied to the assessment of diabetes patients. Clin Chem Lab Med 2024;62:2526–33. https://doi.org/10.1515/cclm-2024-0591.Search in Google Scholar PubMed
24. Yun, SG, Kim, SW, Shin, GH, Lee, CK, Ko, SY, Kim, DW, et al.. Analytical performance of two enzymatic methods for glycated albumin. Clin Lab 2020;66. https://doi.org/10.7754/clin.lab.2020.200213.Search in Google Scholar
25. Freitas, PAC, Ehlert, LR, Camargo, JL. Comparison between two enzymatic methods for glycated albumin. Anal Methods 2016;8:8173–8. https://doi.org/10.1039/c6ay02350a.Search in Google Scholar
26. Yin, YC, Zhang, F, Hou, LA, Yu, SL, Li, HL, Guo, XZ, et al.. Performance verification of six enzymatic glycated albumin reagents. Chin J Lab Med 2017;40:436–42.Search in Google Scholar
27. Morton, A, Teasdale, S. Physiological changes in pregnancy and their influence on the endocrine investigation. Clin Endocrinol 2022;96:3–11. https://doi.org/10.1111/cen.14624.Search in Google Scholar PubMed
28. Metzger, BE, Gabbe, SG, Persson, B, Buchanan, TA, Catalano, PA, Damm, P, et al.. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 2010;33:676–82. https://doi.org/10.2337/dc09-1848.Search in Google Scholar PubMed PubMed Central
29. Takei, I, Hoshino, T, Tominaga, M, Ishibashi, M, Kuwa, K, Umemoto, M, et al.. Committee on Diabetes Mellitus Indices of the Japan Society of Clinical Chemistry-recommended reference measurement procedure and reference materials for glycated albumin determination. Ann Clin Biochem 2016;53:124–32. https://doi.org/10.1177/0004563215599178.Search in Google Scholar PubMed
30. Package insert of JCCRM611-1 reference material. https://www.reccs.or.jp/pdf/JCCRM611.pdf [Accessed 13 Feb 2025].Search in Google Scholar
31. EP15 A3 user verification of precision and bias estimation; 2019 [Accessed 13 Feb 2025].Search in Google Scholar
32. Ricos, C, Alvarez, V, Cava, F, Garcia-Lario, JV, Hernandez, A, Jimenez, 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
33. Aarsand, AK, Webster, C, Fernandez-Calle, P, Jonker, N, Diaz-Garzon, J, Coskun, A, et al.. The EFLM biological variation database. https://biologicalvariation.eu/ [Accessed 23 Jun 2025].Search in Google Scholar
34. National Center for Clinical Laboratories 2024 External Quality Assessment Programs in Laboratory Medicine. https://caivd-org.cn/article.asp?id=15690 [Accessed 13 Feb 2025].Search in Google Scholar
35. Hiramatsu, Y, Shimizu, I, Omori, Y, Nakabayashi, M. Determination of reference intervals of glycated albumin and hemoglobin A1c in healthy pregnant Japanese women and analysis of their time courses and influencing factors during pregnancy. Endocr J 2012;59:145–51. https://doi.org/10.1507/endocrj.k10e-410.Search in Google Scholar PubMed
36. Dong, Y, Zhai, Y, Wang, J, Chen, Y, Xie, X, Zhang, C, et al.. Glycated albumin in pregnancy: reference intervals establishment and its predictive value in adverse pregnancy outcomes. BMC Pregnancy Childbirth 2020;20:12. https://doi.org/10.1186/s12884-019-2704-x.Search in Google Scholar PubMed PubMed Central
37. Paleari, R, Succurro, E, Angotti, E, Torlone, E, Caroli, A, Alessi, E, et al.. Why glycated albumin decreases in pregnancy? Evidences from a prospective study on physiological pregnancies of Caucasian women. Clin Chim Acta 2021;520:217–8. https://doi.org/10.1016/j.cca.2021.05.035.Search in Google Scholar PubMed
38. Kumar, D, Banerjee, D. Methods of albumin estimation in clinical biochemistry: past, present, and future. Clin Chim Acta 2017;469:150–60. https://doi.org/10.1016/j.cca.2017.04.007.Search in Google Scholar PubMed
39. Kouzuma, T, Uemastu, Y, Usami, T, Imamura, S. Study of glycated amino acid elimination reaction for an improved enzymatic glycated albumin measurement method. Clin Chim Acta 2004;346:135–43. https://doi.org/10.1016/j.cccn.2004.02.019.Search in Google Scholar PubMed
40. Garcia, MV, Beridze, VN, Martinez, GM, Laborda, GB, Garcia, AS, Fernandez, RE. Overestimation of albumin measured by bromocresol green vs bromocresol purple method: influence of acute-phase globulins. Lab Med 2018;49:355–61. https://doi.org/10.1093/labmed/lmy020.Search in Google Scholar PubMed
41. van Schrojenstein Lantman, M, van de Logt, AE, Prudon-Rosmulder, E, Langelaan, M, Demir, AY, Kurstjens, S, et al.. Albumin determined by bromocresol green leads to erroneous results in routine evaluation of patients with chronic kidney disease. Clin Chem Lab Med 2023;61:2167–77. https://doi.org/10.1515/cclm-2023-0463.Search in Google Scholar PubMed
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0530).
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