Home Medicine Comparison of seven different enzymatic methods for serum glycated albumin in pregnant women: a multicenter study
Article
Licensed
Unlicensed Requires Authentication

Comparison of seven different enzymatic methods for serum glycated albumin in pregnant women: a multicenter study

  • Dandan Sun , Zheng Cao , Mingyuan Jiao , Xiuzhi Guo , Ran Gao , Chaochao Ma , Ying Zhu , Lian Hou , Ying Meng , Meng Wang , Songlin Yu EMAIL logo , Yicong Yin EMAIL logo and Ling Qiu EMAIL logo
Published/Copyright: September 10, 2025
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 64 Issue 1

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.

Keywords: glycated albumin; pregnant women; glucose monitoring; enzymatic assay; consistency
Corresponding authors: Ling Qiu, Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Science, Shuaifu Yuan No. 1, Dongcheng District, Beijing, 100730, China; and State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China, E-mail: ; Yicong Yin and Songlin Yu, Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Science, Shuaifu Yuan No. 1, Dongcheng District, Beijing, 100730, China, E-mail: (Y. Yin), (S. Yu)
Dandan Sun, Zheng Cao and Mingyuan Jiao contributed equally to this work.

  1. Research ethics: This study was reviewed and approved by the local Ethics Committee (ID: HS2912, Date: 2021.04.27).

  2. Informed consent: Not applicable.

  3. 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.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. 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).

Received: 2025-05-02
Accepted: 2025-08-26
Published Online: 2025-09-10
Published in Print: 2026-01-27

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Keeping pace with patient safety by developing and qualifying higher-order reference measurement procedures for laboratory measurement standardization
  4. Review
  5. The role of AI in pre-analytical phase – use cases
  6. Opinion Paper
  7. Total laboratory automation: fit for its intended purposes?
  8. Guidelines and Recommendations
  9. EFLM checklist for the assessment of AI/ML studies in laboratory medicine: enhancing general medical AI frameworks for laboratory-specific applications
  10. Candidate Reference Measurement Procedures and Materials
  11. An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedure for the quantification of cortisol in human serum and plasma
  12. Isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedures for the quantification of 24(R),25-dihydroxyvitamin D2 and 24(R),25-dihydroxyvitamin D3 in human serum and plasma
  13. An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedure for the quantification of cortisone in human serum and plasma
  14. Candidate reference measurement procedure based on isotope dilution-two dimensional-liquid chromatography-tandem mass spectrometry for the quantification of androstenedione in human serum and plasma
  15. An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedure for the quantification of 17β-estradiol in human serum and plasma
  16. Isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedures for the quantification of total and free phenytoin in human serum and plasma
  17. An isotope dilution-liquid chromatography-tandem mass spectrometry based candidate reference measurement procedure for the simultaneous quantification of 25-hydroxyvitamin D3 and 25-hydroxyvitamin D2 in human serum and plasma
  18. General Clinical Chemistry and Laboratory Medicine
  19. Quality assurance using patient split samples: recommendations for primary healthcare laboratories
  20. Age distorts the interpretation of FIB-4
  21. Not all anti-parietal cell antibody tests are equal for diagnosing pernicious anemia
  22. Impact of renal and hepatic function on dihydropyrimidine dehydrogenase phenotype assessed by enzyme activity in peripheral blood mononuclear cells and uracilemia
  23. Fecal leukocyte esterase levels predict endoscopic severity as an alternative biomarker in inflammatory bowel disease
  24. Cancer Diagnostics
  25. CA-125 glycovariant assays enhance diagnostic sensitivity in the detection of epithelial ovarian cancer
  26. Cardiovascular Diseases
  27. Defining the analytical characteristics of a novel high-sensitivity point-of-care troponin I assay in its intended clinical environment
  28. An automatic chemiluminescence immunoassay for a novel biomarker NT-IGFBP-4: analytical performance and clinical relevance in heart failure
  29. Analysis of total cholesterol results measured in the initial period of the Croatian screening program for familial hypercholesterolemia: a pilot study
  30. Diabetes
  31. Comparison of seven different enzymatic methods for serum glycated albumin in pregnant women: a multicenter study
  32. Infectious Diseases
  33. Comparative analysis of monocyte distribution width alterations in Escherichia coli sepsis: insights from in vivo and ex vivo models
  34. Proadrenomedullin for prediction of early and mid-term mortality in patients hospitalized for community-acquired pneumonia
  35. Annual Reviewer Acknowledgment
  36. Reviewer Acknowledgment
  37. Letters to the Editor
  38. Biological variation of serum Golgi protein 73 concentrations
  39. Are vitamins A and E results truly traceable and clinically useful? A practical and critical inquiry
  40. Tafasitamab interference in immunofixation electrophoresis
  41. Improvement in the turnaround time of PTH(1–84) as part of the intraoperative PTH monitoring for parathyroidectomy
  42. Rethinking the use of “one-way ANOVA” in CLSI EP15-A3 – a call for terminological precision and methodological clarity
  43. Toxic beauty: acute kidney injury triggered by hair-straightening treatment
  44. Congress Abstracts
  45. 57th National Congress of the Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC – Laboratory Medicine)
https://doi.org/10.1515/cclm-2025-0530
Keywords for this article
glycated albumin; pregnant women; glucose monitoring; enzymatic assay; consistency

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Keeping pace with patient safety by developing and qualifying higher-order reference measurement procedures for laboratory measurement standardization
  4. Review
  5. The role of AI in pre-analytical phase – use cases
  6. Opinion Paper
  7. Total laboratory automation: fit for its intended purposes?
  8. Guidelines and Recommendations
  9. EFLM checklist for the assessment of AI/ML studies in laboratory medicine: enhancing general medical AI frameworks for laboratory-specific applications
  10. Candidate Reference Measurement Procedures and Materials
  11. An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedure for the quantification of cortisol in human serum and plasma
  12. Isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedures for the quantification of 24(R),25-dihydroxyvitamin D2 and 24(R),25-dihydroxyvitamin D3 in human serum and plasma
  13. An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedure for the quantification of cortisone in human serum and plasma
  14. Candidate reference measurement procedure based on isotope dilution-two dimensional-liquid chromatography-tandem mass spectrometry for the quantification of androstenedione in human serum and plasma
  15. An isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedure for the quantification of 17β-estradiol in human serum and plasma
  16. Isotope dilution-liquid chromatography-tandem mass spectrometry-based candidate reference measurement procedures for the quantification of total and free phenytoin in human serum and plasma
  17. An isotope dilution-liquid chromatography-tandem mass spectrometry based candidate reference measurement procedure for the simultaneous quantification of 25-hydroxyvitamin D3 and 25-hydroxyvitamin D2 in human serum and plasma
  18. General Clinical Chemistry and Laboratory Medicine
  19. Quality assurance using patient split samples: recommendations for primary healthcare laboratories
  20. Age distorts the interpretation of FIB-4
  21. Not all anti-parietal cell antibody tests are equal for diagnosing pernicious anemia
  22. Impact of renal and hepatic function on dihydropyrimidine dehydrogenase phenotype assessed by enzyme activity in peripheral blood mononuclear cells and uracilemia
  23. Fecal leukocyte esterase levels predict endoscopic severity as an alternative biomarker in inflammatory bowel disease
  24. Cancer Diagnostics
  25. CA-125 glycovariant assays enhance diagnostic sensitivity in the detection of epithelial ovarian cancer
  26. Cardiovascular Diseases
  27. Defining the analytical characteristics of a novel high-sensitivity point-of-care troponin I assay in its intended clinical environment
  28. An automatic chemiluminescence immunoassay for a novel biomarker NT-IGFBP-4: analytical performance and clinical relevance in heart failure
  29. Analysis of total cholesterol results measured in the initial period of the Croatian screening program for familial hypercholesterolemia: a pilot study
  30. Diabetes
  31. Comparison of seven different enzymatic methods for serum glycated albumin in pregnant women: a multicenter study
  32. Infectious Diseases
  33. Comparative analysis of monocyte distribution width alterations in Escherichia coli sepsis: insights from in vivo and ex vivo models
  34. Proadrenomedullin for prediction of early and mid-term mortality in patients hospitalized for community-acquired pneumonia
  35. Annual Reviewer Acknowledgment
  36. Reviewer Acknowledgment
  37. Letters to the Editor
  38. Biological variation of serum Golgi protein 73 concentrations
  39. Are vitamins A and E results truly traceable and clinically useful? A practical and critical inquiry
  40. Tafasitamab interference in immunofixation electrophoresis
  41. Improvement in the turnaround time of PTH(1–84) as part of the intraoperative PTH monitoring for parathyroidectomy
  42. Rethinking the use of “one-way ANOVA” in CLSI EP15-A3 – a call for terminological precision and methodological clarity
  43. Toxic beauty: acute kidney injury triggered by hair-straightening treatment
  44. Congress Abstracts
  45. 57th National Congress of the Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC – Laboratory Medicine)
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2026 De Gruyter Brill
Downloaded on 15.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2025-0530/html
Scroll to top button