Home Medicine Comparative analysis of population-based and personalized reference intervals for biochemical markers in peri-menopausal women: population from the PALM cohort study
Article
Licensed
Unlicensed Requires Authentication

Comparative analysis of population-based and personalized reference intervals for biochemical markers in peri-menopausal women: population from the PALM cohort study

  • Jiaming Wu , Penghui Feng , Jinming Zhang , Xingtong Chen , Rong Chen EMAIL logo , Min Luo EMAIL logo and Falin He EMAIL logo
Published/Copyright: May 30, 2025
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 63 Issue 12

Abstract

Objectives

Significant changes in clinical biochemical markers occur during the peri-menopausal period. Traditional population-based reference intervals (popRIs) may not reflect individual physiological variability, limiting clinical interpretation. This study aimed to establish personalized reference intervals (prRIs) for menopausal women and compare them with popRIs.

Methods

We analyzed 899 healthy women aged 35–64 from the Peking Union Medical College Hospital Aging Longitudinal Cohort of Women in Midlife (PALM) cohort. 13 biochemical markers were evaluated across reproductive, menopausal transition, and postmenopausal stages. Six key biomarkers were selected through Kruskal–Wallis tests and ranked by their importance in menopausal status classification using a Random Forest model. Biological variation (BV) data were used to calculate total variation (TV) and index of individuality (II). The prRIs were constructed based on BV estimates, and the reference interval index (RII) was applied to compare popRIs and prRIs.

Results

ALT, TG, and FSH showed significant differences across menopausal stages and ranked highly in the Random Forest model. These markers also had large BV and differed across three menopausal stages. Most II values ranged from 0.6 to 1.4, and all median RII values were below 1.0, suggesting limited utility of popRIs. Crea in reproductive women had the highest proportion of RII>1.0, while FSH showed RII<0.5 in over 90 % of women in the menopausal transition.

Conclusions

For women in the menopausal transition with high BV estimates, combining popRIs with prRIs improves interpretation. Larger, more diverse cohorts are needed to validate and optimize prRIs for clinical application.

Keywords: menopause; personalized reference intervals; population-based reference intervals; biological variation; index of individuality; reference interval index
Corresponding authors: Falin He, National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing, 100730, China; and Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China, E-mail: ; and Min Luo and Rong Chen, Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China, E-mail: (M. Luo), (R. Chen)
Jiaming Wu, Penghui Feng and Jinming Zhang contributed equally to this work.

Funding source: China Postdoctoral Science Foundation

Award Identifier / Grant number: 2023M740320

Funding source: Special Support Plan for Clinical Research in Central High-level Hospital of PUMCH

Award Identifier / Grant number: Grant 2022-PUMCH-A-116

Acknowledgments

We are grateful to all the staff members for taking part in this study. We thank the participants for their cooperation and sample contributions.

  1. Research ethics: This study was approved by the Ethics Committee of Peking Union Medical College Hospital.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: Large language models (e.g. ChatGPT) were used to assist in language polishing. All scientific content was created and validated by the authors.

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

  6. Research funding: This work was supported by Special Support Plan for Clinical Research in Central High-level Hospital of PUMCH (Grant 2022-PUMCH-A-116), and China Postdoctoral Science Foundation (2023M740320).

  7. Data availability: Not applicable.

References

1. Nelson, HD. Menopause. Lancet 2008;371:760–70. https://doi.org/10.1016/s0140-6736-08-60346-3.Search in Google Scholar

2. Ko, SH, Jung, Y. Energy metabolism changes and dysregulated lipid metabolism in postmenopausal women. Nutrients 2021;13:4556. https://doi.org/10.3390/nu13124556.Search in Google Scholar PubMed PubMed Central

3. Anagnostis, P, Stevenson, JC. Cardiovascular health and the menopause, metabolic health. Best Pract Res Clin Endocrinol Metabol 2024;38:101781. https://doi.org/10.1016/j.beem.2023.101781.Search in Google Scholar PubMed

4. Cucinella, L, Martini, E, Tiranini, L, Molinaro, P, Battista, F, Nappi, RE. Genitourinary syndrome of menopause: should we treat symptoms or signs? Curr Opin Endocr Metab Res 2022;26:100386. https://doi.org/10.1016/j.coemr.2022.100386.Search in Google Scholar

5. Than, S, Moran, C, Beare, R, Vincent, A, Lane, E, Collyer, TA, et al.. Cognitive trajectories during the menopausal transition. Front Dement 2023;2:1098693. https://doi.org/10.3389/frdem.2023.1098693.Search in Google Scholar PubMed PubMed Central

6. Fidecicchi, T, Giannini, A, Chedraui, P, Luisi, S, Battipaglia, C, Genazzani, AR, et al.. Neuroendocrine mechanisms of mood disorders during menopause transition: a narrative review and future perspectives. Maturitas 2024;188:108087. https://doi.org/10.1016/j.maturitas.2024.108087.Search in Google Scholar PubMed

7. Maki, PM, Panay, N, Simon, JA. Sleep disturbance associated with the menopause. Menopause 2024;31:724–33. https://doi.org/10.1097/gme.0000000000002386.Search in Google Scholar PubMed

8. Gatenby, C, Simpson, P. Menopause: physiology, definitions, and symptoms. Best Pract Res Clin Endocrinol Metabol 2024;38:101855. https://doi.org/10.1016/j.beem.2023.101855.Search in Google Scholar PubMed

9. Andrews, R, Lacey, A, Bache, K, Kidd, EJ. The role of menopausal symptoms on future health and longevity: a systematic scoping review of longitudinal evidence. Maturitas 2024;190:108130. https://doi.org/10.1016/j.maturitas.2024.108130.Search in Google Scholar PubMed

10. Doyle, K, Bunch, DR. Reference intervals: past, present, and future. Crit Rev Clin Lab Sci 2023;60:466–82. https://doi.org/10.1080/10408363.2023.2196746.Search in Google Scholar PubMed

11. Ozarda, Y. Reference intervals: current status, recent developments and future considerations. Biochem Med (Zagreb) 2016;26:5–16. https://doi.org/10.11613/bm.2016.001.Search in Google Scholar

12. Coskun, A, Sandberg, S, Unsal, I, Serteser, M, Aarsand, AK. Personalized reference intervals: from theory to practice. Crit Rev Clin Lab Sci 2022;59:501–16. https://doi.org/10.1080/10408363.2022.2070905.Search in Google Scholar PubMed

13. 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

14. Coskun, A, Sandberg, S, Unsal, I, Yavuz, FG, Cavusoglu, C, Serteser, M, et al.. Personalized reference intervals – statistical approaches and considerations. Clin Chem Lab Med 2021;60:629–35. https://doi.org/10.1515/cclm-2021-1066.Search in Google Scholar PubMed

15. Jameson, JL, Longo, DL. Precision medicine-personalized, problematic, and promising. N Engl J Med 2015;372:2229–34. https://doi.org/10.1056/nejmsb1503104.Search in Google Scholar

16. Ashley, EA. Towards precision medicine. Nat Rev Genet 2016;17:507–22. https://doi.org/10.1038/nrg.2016.86.Search in Google Scholar PubMed

17. Ma, C, Yu, Z, Qiu, L. Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration. Crit Rev Clin Lab Sci 2024;61:298–316. https://doi.org/10.1080/10408363.2023.2291379.Search in Google Scholar PubMed

18. Sun, X, Luo, M, Ma, M, Tang, R, Wang, Y, Liu, G, et al.. Ovarian aging: an ongoing prospective community-based cohort study in middle-aged Chinese women. Climacteric 2018;21:404–10. https://doi.org/10.1080/13697137.2018.1458833.Search in Google Scholar PubMed

19. Tang, R, Fan, Y, Luo, M, Zhang, D, Xie, Z, Huang, F, et al.. General and central obesity are associated with increased severity of the vms and sexual symptoms of menopause among Chinese women: a longitudinal study. Front Endocrinol 2022;13:814872. https://doi.org/10.3389/fendo.2022.814872.Search in Google Scholar PubMed PubMed Central

20. Harlow, SD, Gass, M, Hall, JE, Lobo, R, Maki, P, Rebar, RW, et al.. Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging. J Clin Endocrinol Metab 2012;97:1159–68. https://doi.org/10.1016/j.fertnstert.2012.01.128.Search in Google Scholar PubMed PubMed Central

21. Breiman, L. Random forests. Mach Learn 2001;45:5–32. https://doi.org/10.1023/a:1010933404324.10.1023/A:1010933404324Search in Google Scholar

22. Hu, J, Szymczak, S. A review on longitudinal data analysis with random forest. Briefings Bioinf 2023;24:bbad002. https://doi.org/10.1093/bib/bbad002.Search in Google Scholar PubMed PubMed Central

23. 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

24. Carobene, A, Banfi, G, Locatelli, M, Vidali, M. Within-person biological variation estimates from the European Biological Variation Study (EuBIVAS) for serum potassium and creatinine used to obtain personalized reference intervals. Clin Chim Acta 2021;523:205–7. https://doi.org/10.1016/j.cca.2021.09.018.Search in Google Scholar PubMed

25. Fokkema, MR, Herrmann, Z, Muskiet, FA, Moecks, J. Reference change values for brain natriuretic peptides revisited. Clin Chem 2006;52:1602–3. https://doi.org/10.1373/clinchem.2006.069369.Search in Google Scholar PubMed

26. Coşkun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized and population-based reference intervals for 48 common clinical chemistry and hematology measurands: a comparative study. Clin Chem 2023;69:1009–30. https://doi.org/10.1093/clinchem/hvad113.Search in Google Scholar PubMed

27. Wang, S, Zhao, M, Su, Z, Mu, R. Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes. Clin Chem Lab Med 2022;60:606–17. https://doi.org/10.1515/cclm-2021-0479.Search in Google Scholar PubMed

28. EFLM. 2025. Available from: https://biologicalvariation.eu/.Search in Google Scholar

29. Sandberg, S, Carobene, A, Bartlett, B, Coskun, A, Fernandez-Calle, P, Jonker, N, et al.. Biological variation: recent development and future challenges. Clin Chem Lab Med 2022;61:741–50. https://doi.org/10.1515/cclm-2022-1255.Search in Google Scholar PubMed

30. Bartlett, WA, Sandberg, S, Carobene, A, Fernandez-Calle, P, Diaz-Garzon, J, Coskun, A, et al.. A standard to report biological variation data studies - based on an expert opinion. Clin Chem Lab Med 2024;63:52–9. https://doi.org/10.1515/cclm-2024-0489.Search in Google Scholar PubMed

31. Jonker, N, Aslan, B, Boned, B, Marqués-García, F, Ricós, C, Alvarez, V, et al.. Critical appraisal and meta-analysis of biological variation estimates for kidney related analytes. Clin Chem Lab Med 2022;60:469–78. https://doi.org/10.1515/cclm-2020-1168.Search in Google Scholar PubMed

32. Casarini, L, Crepieux, P. Molecular mechanisms of action of FSH. Front Endocrinol 2019;10:305. https://doi.org/10.3389/fendo.2019.00305.Search in Google Scholar PubMed PubMed Central

33. Fraser, CG, Harris, EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409–37. https://doi.org/10.3109/10408368909106595.Search in Google Scholar PubMed

34. Wang, S, Zhao, M, Su, Z, Yang, D, Mu, R. Annual biological variation and personalized reference intervals of 8 serum liver enzymes and 3 noninvasive tests in fatty liver patients. Clin Chim Acta 2025;569:120156. https://doi.org/10.1016/j.cca.2025.120156.Search in Google Scholar PubMed

35. Johnson, PR, Shahangian, S, Astles, JR. Managing biological variation data: modern approaches for study design and clinical application. Crit Rev Clin Lab Sci 2021;58:493–512. https://doi.org/10.1080/10408363.2021.1932718.Search in Google Scholar PubMed

36. Nikniaz, L, Nikniaz, Z, Tabrizi, JS, Sadeghi-Bazargani, H, Farahbakhsh, M. Is within-normal range liver enzymes associated with metabolic syndrome in adults? Clin Res Hepatol Gastroenterol 2018;42:92–8. https://doi.org/10.1016/j.clinre.2017.06.006.Search in Google Scholar PubMed

37. Polyzos, SA, Goulis, DG. Menopause and metabolic dysfunction-associated steatotic liver disease. Maturitas 2024;186:108024. https://doi.org/10.1016/j.maturitas.2024.108024.Search in Google Scholar PubMed

38. Yang, J, Xu, M, Wang, Z, He, M, Zhang, G, Jin, L, et al.. Unraveling estrogen and PCSK9’s roles in lipid metabolism disorders among ovariectomized mice. Reprod Sci 2025;32:316–25. https://doi.org/10.1007/s43032-024-01614-8.Search in Google Scholar PubMed

39. Zhang, X, Chang, KM, Yu, J, Loomba, R. Unraveling mechanisms of genetic risks in metabolic dysfunction-associated steatotic liver diseases: a pathway to precision medicine. Annu Rev Pathol 2025;20:375–403. https://doi.org/10.1146/annurev-pathmechdis-111523-023430.Search in Google Scholar PubMed

40. Martínez-Espartosa, D, Alegre, E, Casero-Ramírez, H, Díaz-Garzón, J, Fernández-Calle, P, Fuentes-Bullejos, P, et al.. Clinical utility of personalized reference intervals for CEA in the early detection of oncologic disease. Clin Chem Lab Med 2025;63:376–86. https://doi.org/10.1515/cclm-2024-0546.Search in Google Scholar PubMed

41. Foy, BH, Petherbridge, R, Roth, MT, Zhang, C, De Souza, DC, Mow, C, et al.. Haematological setpoints are a stable and patient-specific deep phenotype. Nature 2025;637:430–8. https://doi.org/10.1038/s41586-024-08264-5.Search in Google Scholar PubMed PubMed Central

42. Greene, DN, Ahmed, SB, Daccarett, S, Kling, JM, Lorey, TS, Rytz, CL, et al.. A comprehensive review of estradiol, progesterone, luteinizing hormone, and follicle-stimulating hormone in the context of laboratory medicine to support women’s health. Clin Chem 2025;71:842–57. https://doi.org/10.1093/clinchem/hvaf039.Search in Google Scholar PubMed

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0658).

Received: 2025-05-30
Accepted: 2025-08-04
Published Online: 2025-05-30
Published in Print: 2025-11-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorials
  3. Challenging the dogma: why reviewers should be allowed to use AI tools
  4. Multivariate approaches to improve the interpretation of laboratory data
  5. Review
  6. Interference of therapeutic monoclonal antibodies with electrophoresis and immunofixation of serum proteins: state of knowledge and systematic review
  7. Opinion Papers
  8. Urgent call to the European Commission to simplify and contextualize IVDR Article 5.5 for tailored and precision diagnostics
  9. The importance of laboratory medicine in the management of CKD-MBD: insights from the KDIGO 2023 controversies conference
  10. Supplementation of pyridoxal-5′-phosphate in aminotransferase reagents: a matter of patient safety
  11. HCV serology: an unfinished agenda
  12. From metabolic profiles to clinical interpretation: multivariate approaches to population-based and personalized reference intervals and reference change values
  13. Genetics and Molecular Diagnostics
  14. A multiplex allele specific PCR capillary electrophoresis (mASPCR-CE) assay for simultaneously analysis of SMN1/SMN2/NAIP copy number and SMN1 loss-of-function variants
  15. General Clinical Chemistry and Laboratory Medicine
  16. From assessment to action: experience from a quality improvement initiative integrating indicator evaluation and adverse event analysis in a clinical laboratory
  17. Evaluation of measurement uncertainty of 11 serum proteins measured by immunoturbidimetric methods according to ISO/TS 20914: a 1-year laboratory data analysis
  18. Assessing the harmonization of current total vitamin B12 measurement methods: relevance and implications
  19. The current status of serum insulin measurements and the need for standardization
  20. Method comparison of plasma and CSF GFAP immunoassays across multiple platforms
  21. Cerebrospinal fluid leptin in Alzheimer’s disease: relationship to plasma levels and to cerebrospinal amyloid
  22. Verification of the T50 Calciprotein Crystallization test: bias estimation and interferences
  23. An innovative immunoassay for accurate aldosterone quantification: overcoming low-level inaccuracy and renal dysfunction-associated interference
  24. Oral salt loading combined with postural stimulation tests for confirming and subtyping primary aldosteronism
  25. Evaluating the performance of a multiparametric IgA assay for celiac disease diagnosis
  26. Clinical significance of anti-mitochondrial antibodies and PBC-specific anti-nuclear antibodies in evaluating atypical primary biliary cholangitis with normal alkaline phosphatase levels
  27. Reference Values and Biological Variations
  28. Establishment of region-, age- and sex-specific reference intervals for aldosterone and renin with sandwich chemiluminescence immunoassays
  29. Validation of a plasma GFAP immunoassay and establishment of age-related reference values: bridging analytical performance and routine implementation
  30. Comparative analysis of population-based and personalized reference intervals for biochemical markers in peri-menopausal women: population from the PALM cohort study
  31. Hematology and Coagulation
  32. Evaluation of stability and potential interference on the α-thalassaemia early eluting peak and immunochromatographic strip test for α-thalassaemia --SEA carrier screening
  33. Cardiovascular Diseases
  34. Analytical and clinical evaluation of an automated high-sensitivity cardiac troponin I assay for whole blood
  35. Diabetes
  36. Method comparison of diabetes mellitus associated autoantibodies in serum specimens
  37. Letters to the Editor
  38. Permitting disclosed AI assistance in peer review: parity, confidentiality, and recognition
  39. Response to the editorial by Karl Lackner
  40. Hemolysis detection using the GEM 7000 at the point of care in a pediatric hospital setting: does it affect outcomes?
  41. Estimation of measurement uncertainty for free drug concentrations using ultrafiltration
  42. Cryoglobulin pre-analysis over the weekend
  43. Accelerating time from result to clinical action: impact of an automated critical results reporting system
  44. Recent decline in patient serum folate test levels using Roche Diagnostics Folate III assay
  45. Kidney stones consisting of 1-methyluric acid
  46. Congress Abstracts
  47. 7th EFLM Conference on Preanalytical Phase
  48. Association of Clinical Biochemists in Ireland Annual Conference
  49. Association of Clinical Biochemists in Ireland Annual Conference
  50. 17th Congress of the Portuguese Society of Clinical Chemistry, Genetics and Laboratory Medicine
https://doi.org/10.1515/cclm-2025-0658
Keywords for this article
menopause; personalized reference intervals; population-based reference intervals; biological variation; index of individuality; reference interval index

Articles in the same Issue

  1. Frontmatter
  2. Editorials
  3. Challenging the dogma: why reviewers should be allowed to use AI tools
  4. Multivariate approaches to improve the interpretation of laboratory data
  5. Review
  6. Interference of therapeutic monoclonal antibodies with electrophoresis and immunofixation of serum proteins: state of knowledge and systematic review
  7. Opinion Papers
  8. Urgent call to the European Commission to simplify and contextualize IVDR Article 5.5 for tailored and precision diagnostics
  9. The importance of laboratory medicine in the management of CKD-MBD: insights from the KDIGO 2023 controversies conference
  10. Supplementation of pyridoxal-5′-phosphate in aminotransferase reagents: a matter of patient safety
  11. HCV serology: an unfinished agenda
  12. From metabolic profiles to clinical interpretation: multivariate approaches to population-based and personalized reference intervals and reference change values
  13. Genetics and Molecular Diagnostics
  14. A multiplex allele specific PCR capillary electrophoresis (mASPCR-CE) assay for simultaneously analysis of SMN1/SMN2/NAIP copy number and SMN1 loss-of-function variants
  15. General Clinical Chemistry and Laboratory Medicine
  16. From assessment to action: experience from a quality improvement initiative integrating indicator evaluation and adverse event analysis in a clinical laboratory
  17. Evaluation of measurement uncertainty of 11 serum proteins measured by immunoturbidimetric methods according to ISO/TS 20914: a 1-year laboratory data analysis
  18. Assessing the harmonization of current total vitamin B12 measurement methods: relevance and implications
  19. The current status of serum insulin measurements and the need for standardization
  20. Method comparison of plasma and CSF GFAP immunoassays across multiple platforms
  21. Cerebrospinal fluid leptin in Alzheimer’s disease: relationship to plasma levels and to cerebrospinal amyloid
  22. Verification of the T50 Calciprotein Crystallization test: bias estimation and interferences
  23. An innovative immunoassay for accurate aldosterone quantification: overcoming low-level inaccuracy and renal dysfunction-associated interference
  24. Oral salt loading combined with postural stimulation tests for confirming and subtyping primary aldosteronism
  25. Evaluating the performance of a multiparametric IgA assay for celiac disease diagnosis
  26. Clinical significance of anti-mitochondrial antibodies and PBC-specific anti-nuclear antibodies in evaluating atypical primary biliary cholangitis with normal alkaline phosphatase levels
  27. Reference Values and Biological Variations
  28. Establishment of region-, age- and sex-specific reference intervals for aldosterone and renin with sandwich chemiluminescence immunoassays
  29. Validation of a plasma GFAP immunoassay and establishment of age-related reference values: bridging analytical performance and routine implementation
  30. Comparative analysis of population-based and personalized reference intervals for biochemical markers in peri-menopausal women: population from the PALM cohort study
  31. Hematology and Coagulation
  32. Evaluation of stability and potential interference on the α-thalassaemia early eluting peak and immunochromatographic strip test for α-thalassaemia --SEA carrier screening
  33. Cardiovascular Diseases
  34. Analytical and clinical evaluation of an automated high-sensitivity cardiac troponin I assay for whole blood
  35. Diabetes
  36. Method comparison of diabetes mellitus associated autoantibodies in serum specimens
  37. Letters to the Editor
  38. Permitting disclosed AI assistance in peer review: parity, confidentiality, and recognition
  39. Response to the editorial by Karl Lackner
  40. Hemolysis detection using the GEM 7000 at the point of care in a pediatric hospital setting: does it affect outcomes?
  41. Estimation of measurement uncertainty for free drug concentrations using ultrafiltration
  42. Cryoglobulin pre-analysis over the weekend
  43. Accelerating time from result to clinical action: impact of an automated critical results reporting system
  44. Recent decline in patient serum folate test levels using Roche Diagnostics Folate III assay
  45. Kidney stones consisting of 1-methyluric acid
  46. Congress Abstracts
  47. 7th EFLM Conference on Preanalytical Phase
  48. Association of Clinical Biochemists in Ireland Annual Conference
  49. Association of Clinical Biochemists in Ireland Annual Conference
  50. 17th Congress of the Portuguese Society of Clinical Chemistry, Genetics and 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.
© 2025 De Gruyter Brill
Downloaded on 29.12.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2025-0658/html
Scroll to top button