Home Proteomic analysis of diabetic retinopathy identifies potential plasma-protein biomarkers for diagnosis and prognosis
Article
Licensed
Unlicensed Requires Authentication

Proteomic analysis of diabetic retinopathy identifies potential plasma-protein biomarkers for diagnosis and prognosis

  • Bent Honoré EMAIL logo , Javad Nouri Hajari , Tobias Torp Pedersen , Tomas Ilginis , Hajer Ahmad Al-Abaiji , Claes Sepstrup Lønkvist , Jon Peiter Saunte , Dorte Aalund Olsen , Ivan Brandslund , Henrik Vorum and Carina Slidsborg EMAIL logo
Published/Copyright: February 9, 2024

Abstract

Objectives

To identify molecular pathways and prognostic- and diagnostic plasma-protein biomarkers for diabetic retinopathy at various stages.

Methods

This exploratory, cross-sectional proteomics study involved plasma from 68 adults, including 15 healthy controls and 53 diabetes patients for various stages of diabetic retinopathy: non-diabetic retinopathy, non-proliferative diabetic retinopathy, proliferative diabetic retinopathy and diabetic macular edema. Plasma was incubated with peptide library beads and eluted proteins were tryptic digested, analyzed by liquid chromatography-tandem mass-spectrometry followed by bioinformatics.

Results

In the 68 samples, 248 of the 731 identified plasma-proteins were present in all samples. Analysis of variance showed differential expression of 58 proteins across the five disease subgroups. Protein–Protein Interaction network (STRING) showed enrichment of various pathways during the diabetic stages. In addition, stage-specific driver proteins were detected for early and advanced diabetic retinopathy. Hierarchical clustering showed distinct protein profiles according to disease severity and disease type.

Conclusions

Molecular pathways in the cholesterol metabolism, complement system, and coagulation cascade were enriched in patients at various stages of diabetic retinopathy. The peroxisome proliferator-activated receptor signaling pathway and systemic lupus erythematosus pathways were enriched in early diabetic retinopathy. Stage-specific proteins for early – and advanced diabetic retinopathy as determined herein could be ‘key’ players in driving disease development and potential ‘target’ proteins for future therapies. For type 1 and 2 diabetes mellitus, the proteomic profiles were especially distinct during the early disease stage. Validation studies should aim to clarify the role of the detected molecular pathways, potential biomarkers, and potential ‘target’ proteins for future therapies in diabetic retinopathy.


Corresponding authors: Bent Honoré, MD, D.M.Sc., Department of Biomedicine, Aarhus University, Ole Worm Allé 4, 8000 Aarhus C, Denmark; and Department of Clinical Medicine, Aalborg University, Aalborg, Denmark, E-mail: ; and Carina Slidsborg, MD, PhD, Department of Ophthalmology, Copenhagen University Hospital, Rigshospitalet, Valdemar Hansens Vej 1, 2600 Glostrup, Copenhagen, Denmark, E-mail:

Funding source: Dagmar Marshalls Fond

Funding source: Vissing Fonden

Funding source: A. P. Møller og Hustru Chastine Mc-Kinney Møllers Fond til almene Formaal

Funding source: Einar Willumsens Foundation

Funding source: Kong Christian den Tiendes Foundation

Funding source: Aase and Ejnar Danielsens Foundation

Funding source: August Frederik Wedell Erichsens Foundation

Funding source: Synoptik Foundation

Funding source: Fight for Sight Denmark

Acknowledgments

We are grateful to Ahmed Basim Abduljabar and Mona Britt Hansen for expert technical assistance.

  1. Research ethics: The present study was approved by the Danish Ethics Committee (H-17034984) and the Data Protection Agency.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

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

  4. Competing interests: BH has stocks in Novo Nordisk A/S. CS consultancy for Bayer and Regeneron, and former employee of Novo Nordisk A/S. CSL advisory board for Bayer. Otherwise, authors state no conflict of interest.

  5. Research funding: This work was supported by grants from Aase and Ejnar Danielsens Fond, Fight for Sight Denmark, Dagmar Marshalls Fond, Einar Willumsens Fond, August Frederik Wedell Erichsens Fond, Kong Christian den Tiendes Fond, The Synoptikfonden and Vissing Fonden. The mass spectrometry instrumentation was kindly donated by A. P. Møller og Hustru Chastine Mc-Kinney Møllers Fond til almene Formaal. The sponsors or funding organizations had no role in the design or conduct of this research.

  6. Data availability: The raw data can be obtained on request from the corresponding author.

References

1. Saeedi, P, Petersohn, I, Salpea, P, Malanda, B, Karuranga, S, Unwin, N, et al.. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019;157:105843. https://doi.org/10.1016/j.diabres.2019.107843.Search in Google Scholar PubMed

2. Safi, SZ, Qvist, R, Kumar, S, Batumalaie, K, Ismail, ISB. Molecular mechanisms of diabetic retinopathy, general preventive strategies, and novel therapeutic targets. BioMed Res Int 2014;2014:801269.10.1155/2014/801269Search in Google Scholar PubMed PubMed Central

3. Ciulla, TA, Amador, AG, Zinman, B. Diabetic retinopathy and diabetic macular edema. Diabetes Care 2003;26:2653–64. https://doi.org/10.2337/diacare.26.9.2653.Search in Google Scholar PubMed

4. Marcovecchio, ML, Lucantoni, M, Chiarelli, F. Role of chronic and acute hyperglycemia in the development of diabetes complications. Diabetes Technol Therapeut 2011;13:389–94. https://doi.org/10.1089/dia.2010.0146.Search in Google Scholar PubMed

5. Kusuhara, S, Fukushima, Y, Ogura, S, Inoue, N, Uemura, A. Pathophysiology of diabetic retinopathy: the old and the new. Diabetes Metab J 2018;42:364–76. https://doi.org/10.4093/dmj.2018.0182.Search in Google Scholar PubMed PubMed Central

6. Stitt, AW, Curtis, TM, Chen, M, Medina, RJ, McKay, GJ, Jenkins, A, et al.. The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res 2016;51:156–68. https://doi.org/10.1016/j.preteyeres.2015.08.001.Search in Google Scholar PubMed

7. Dou, X, Duerfeldt, AS. Small-molecule modulation of ppars for the treatment of prevalent vascular retinal diseases. Int J Mol Sci 2020;21:9251. https://doi.org/10.3390/ijms21239251.Search in Google Scholar PubMed PubMed Central

8. Velez, G, Tang, PH, Cabral, T, Cho, GY, Machlab, DA, Tsang, SH, et al.. Personalized proteomics for precision health: identifying biomarkers of vitreoretinal disease. Transl Vis Sci Technol 2018;7:12. https://doi.org/10.1167/tvst.7.5.12.Search in Google Scholar PubMed PubMed Central

9. Cehofski, LJ, Honoré, B, Vorum, H. A review: proteomics in retinal artery occlusion, retinal vein occlusion, diabetic retinopathy and acquired macular disorders. Int J Mol Sci 2017;18:907. https://doi.org/10.3390/ijms18050907.Search in Google Scholar PubMed PubMed Central

10. Amorim, M, Martins, B, Caramelo, F, Gonçalves, C, Trindade, G, Simão, J, et al.. Putative biomarkers in tears for diabetic retinopathy diagnosis. Front Med 2022;9:873483. https://doi.org/10.3389/fmed.2022.873483.Search in Google Scholar PubMed PubMed Central

11. Winiarczyk, D, Winiarczyk, M, Balicki, I, Szadkowski, M, Michalak, K, Winiarczyk, S, et al.. Proteomic analysis of tear film in canine diabetic patients with and without retinopathy. J Vet Res 2022;66:629–35. https://doi.org/10.2478/jvetres-2022-0053.Search in Google Scholar PubMed PubMed Central

12. Anderson, NL, Anderson, NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteom: MCP 2002;1:845–67. https://doi.org/10.1074/mcp.r200007-mcp200.Search in Google Scholar PubMed

13. Wilkinson, CP, Ferris, FL, Klein, RE, Lee, PP, Agardh, CD, Davis, M, et al.. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 2003;110:1677–82. https://doi.org/10.1016/s0161-6420(03)00475-5.Search in Google Scholar PubMed

14. Zougman, A, Selby, PJ, Banks, RE. Suspension trapping (STrap) sample preparation method for bottom-up proteomics analysis. Proteomics 2014;14:1000–6. https://doi.org/10.1002/pmic.201300553.Search in Google Scholar PubMed

15. Cehofski, LJ, Kojima, K, Terao, N, Kitazawa, K, Thineshkumar, S, Grauslund, J, et al.. Aqueous fibronectin correlates with severity of macular edema and visual acuity in patients with branch retinal vein occlusion: a proteome study. Invest Ophthalmol Vis Sci 2020;61:6. https://doi.org/10.1167/iovs.61.14.6.Search in Google Scholar PubMed PubMed Central

16. Honoré, B. Proteomic protocols for differential protein expression analyses. In: Methods in molecular biology. New York, NY: Humana; 2020.10.1007/978-1-0716-0255-3_3Search in Google Scholar PubMed

17. Ludvigsen, M, Thorlacius-Ussing, L, Vorum, H, Moyer, MP, Stender, MT, Thorlacius-Ussing, O, et al.. Proteomic characterization of colorectal cancer cells versus normal-derived colon mucosa cells: approaching identification of novel diagnostic protein biomarkers in colorectal cancer. Int J Mol Sci 2020;21:3466. https://doi.org/10.3390/ijms21103466.Search in Google Scholar PubMed PubMed Central

18. Tyanova, S, Temu, T, Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 2016;11:2301–19. https://doi.org/10.1038/nprot.2016.136.Search in Google Scholar PubMed

19. Bateman, A, Martin, MJ, Orchard, S, Magrane, M, Ahmad, S, Alpi, E, et al.. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res 2023;51:D523–31. https://doi.org/10.1093/nar/gkac1052.Search in Google Scholar PubMed PubMed Central

20. Tyanova, S, Temu, T, Sinitcyn, P, Carlson, A, Hein, MY, Geiger, T, et al.. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016;13:731–40. https://doi.org/10.1038/nmeth.3901.Search in Google Scholar PubMed

21. Tusher, VG, Tibshirani, R, Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98:5116–21. https://doi.org/10.1073/pnas.091062498.Search in Google Scholar PubMed PubMed Central

22. Szklarczyk, D, Kirsch, R, Koutrouli, M, Nastou, K, Mehryary, F, Hachilif, R, et al.. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 2023;51:D638–46. https://doi.org/10.1093/nar/gkac1000.Search in Google Scholar PubMed PubMed Central

23. Martens, M, Ammar, A, Riutta, A, Waagmeester, A, Slenter, DN, Hanspers, K, et al.. WikiPathways: connecting communities. Nucleic Acids Res 2021;49:D613–21. https://doi.org/10.1093/nar/gkaa1024.Search in Google Scholar PubMed PubMed Central

24. Liberzon, A, Birger, C, Thorvaldsdóttir, H, Ghandi, M, Mesirov, JP, Tamayo, P. The molecular signatures database hallmark gene set collection. Cell Syst 2015;1:417–25. https://doi.org/10.1016/j.cels.2015.12.004.Search in Google Scholar PubMed PubMed Central

25. Liberzon, A, Subramanian, A, Pinchback, R, Thorvaldsdóttir, H, Tamayo, P, Mesirov, JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011;27:1739–40. https://doi.org/10.1093/bioinformatics/btr260.Search in Google Scholar PubMed PubMed Central

26. Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, et al.. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles [Internet]; 2005. Available from: www.pnas.orgcgidoi10.1073pnas.0506580102.Search in Google Scholar

27. Mootha, VK, Lindgren, CM, Eriksson, KF, Subramanian, A, Sihag, S, Lehar, J, et al.. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003;34:267–73. https://doi.org/10.1038/ng1180.Search in Google Scholar PubMed

28. Shannon, P, Markiel, A, Ozier, O, Baliga, NS, Wang, JT, Ramage, D, et al.. Cytoscape: a software Environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498–504. https://doi.org/10.1101/gr.1239303.Search in Google Scholar PubMed PubMed Central

29. Olansky, L. Advances in diabetes for the millennium: chronic microvascular complications of diabetes CME. MedGenMed 2004;6(3 Suppl):14.Search in Google Scholar

30. Luc, K, Schramm-Luc, A, Guzik, TJ, Mikolajczyk, TP. Oxidative stress and inflammatory markers in prediabetes and diabetes. J Physiol Pharmacol 2019;70:809–24. https://doi.org/10.26402/jpp.2019.6.01.Search in Google Scholar PubMed

31. Goldin, A, Beckman, JA, Schmidt, AM, Creager, MA. Advanced glycation end products: sparking the development of diabetic vascular injury. Circulation 2006;114:597–605. https://doi.org/10.1161/circulationaha.106.621854.Search in Google Scholar PubMed

32. Zhang, P, Gao, J, Pu, C, Zhang, Y. Apolipoprotein status in type 2 diabetes mellitus and its complications (review). Mol Med Rep 2017;16:9279–86. https://doi.org/10.3892/mmr.2017.7831.Search in Google Scholar PubMed

33. Hansen, MS, Rasmussen, M, Grauslund, J, Subhi, Y, Cehofski, LJ. Proteomic analysis of vitreous humour of eyes with diabetic macular oedema: a systematic review. Acta Ophthalmol 2022;100:E1043–51.10.1111/aos.15168Search in Google Scholar PubMed

34. Freitas Lima, LC, Braga, VDA, do Socorro de França Silva, M, Cruz, JC, Sousa Santos, SH, de Oliveira Monteiro, MM, et al.. Adipokines, diabetes and atherosclerosis: an inflammatory association. Front Physiol 2015;6:304. https://doi.org/10.3389/fphys.2015.00304.Search in Google Scholar PubMed PubMed Central

35. Stefan, N, Häring, HU. The role of hepatokines in metabolism. Nat Rev Endocrinol 2013;9:144–52. https://doi.org/10.1038/nrendo.2012.258.Search in Google Scholar PubMed

36. Sun, HH, Chai, XL, Li, HL, Tian, JY, Jiang, KX, Song, XZ, et al.. Fufang Xueshuantong alleviates diabetic retinopathy by activating the PPAR signalling pathway and complement and coagulation cascades. J Ethnopharmacol 2021;265:113324. https://doi.org/10.1016/j.jep.2020.113324.Search in Google Scholar PubMed

37. Xiao, H, Xin, W, Sun, LM, Li, SS, Zhang, T, Ding, XY. Comprehensive proteomic profiling of aqueous humor proteins in proliferative diabetic retinopathy. Transl Vis Sci Technol 2021;10:3. https://doi.org/10.1167/tvst.10.6.3.Search in Google Scholar PubMed PubMed Central

38. Deng, G, Moran, EP, Cheng, R, Matlock, G, Zhou, K, Moran, D, et al.. Therapeutic effects of a novel agonist of peroxisome proliferator-activated receptor alpha for the treatment of diabetic retinopathy. Invest Ophthalmol Vis Sci 2017;58:5030–42. https://doi.org/10.1167/iovs.16-21402.Search in Google Scholar PubMed PubMed Central

39. Sharma, N, Ooi, JL, Ong, J, Newman, D. The use of fenofibrate in the management of patients with diabetic retinopathy: an evidence-based review. Aust Fam Physician 2015;44:367–70.Search in Google Scholar

40. Ankit, B, Mathur, G, Agrawal, R, Mathur, K. Stronger relationship of serum apolipoprotein A-1 and B with diabetic retinopathy than traditional lipids. Indian J Endocrinol Metab 2017;21:102–5. https://doi.org/10.4103/2230-8210.196030.Search in Google Scholar PubMed PubMed Central

41. Goldstein, JL, Brown, MS. A century of cholesterol and coronaries: from plaques to genes to statins. Cell 2015;161:161–72. https://doi.org/10.1016/j.cell.2015.01.036.Search in Google Scholar PubMed PubMed Central

42. Schmidt, AM. Diabetes mellitus and cardiovascular disease. Arterioscler Thromb Vasc Biol 2019;39:558–68. https://doi.org/10.1161/atvbaha.119.310961.Search in Google Scholar

43. Lu, F, Liu, Y, Guo, Y, Gao, Y, Piao, Y, Tan, S, et al.. Metabolomic changes of blood plasma associated with two phases of rat OIR. Exp Eye Res 2020;190:107855. https://doi.org/10.1016/j.exer.2019.107855.Search in Google Scholar PubMed

44. Calvet, J, Berenguer-Llergo, A, Gay, M, Massanella, M, Domingo, P, Llop, M, et al.. Biomarker candidates for progression and clinical management of COVID-19 associated pneumonia at time of admission. Sci Rep 2022;12:107855. https://doi.org/10.1038/s41598-021-04683-w.Search in Google Scholar PubMed PubMed Central

45. Di, B, Jia, H, Luo, OJ, Lin, F, Li, K, Zhang, Y, et al.. Identification and validation of predictive factors for progression to severe COVID-19 pneumonia by proteomics. Signal Transduct Targeted Ther 2020;5:217. https://doi.org/10.1038/s41392-020-00333-1.Search in Google Scholar PubMed PubMed Central

46. Adki, KM, Kulkarni, YA. Potential biomarkers in diabetic retinopathy. Curr Diabetes Rev 2020;16:971–83. https://doi.org/10.2174/18756417mta0untmf5.Search in Google Scholar

47. Muhammad, IF, Borné, Y, Hedblad, B, Nilsson, PM, Persson, M, Engström, G. Acute-phase proteins and incidence of diabetes: a population-based cohort study. Acta Diabetol 2016;53:981–9. https://doi.org/10.1007/s00592-016-0903-8.Search in Google Scholar PubMed PubMed Central

48. Thompson, JC, Wilson, PG, Shridas, P, Ji, A, de Beer, M, de Beer, FC, et al.. Serum amyloid A3 is pro-atherogenic. Atherosclerosis 2018;268:32–5. https://doi.org/10.1016/j.atherosclerosis.2017.11.011.Search in Google Scholar PubMed PubMed Central

49. Shahulhameed, S, Vishwakarma, S, Chhablani, J, Tyagi, M, Pappuru, RR, Jakati, S, et al.. A systematic investigation on complement pathway activation in diabetic retinopathy. Front Immunol 2020;11:154. https://doi.org/10.3389/fimmu.2020.00154.Search in Google Scholar PubMed PubMed Central

50. Fujita, T, Hemmi, S, Kajiwara, M, Yabuki, M, Fuke, Y, Satomura, A, et al.. Complement-mediated chronic inflammation is associated with diabetic microvascular complication. Diabetes Metab Res Rev 2013;29:220–6. https://doi.org/10.1002/dmrr.2380.Search in Google Scholar PubMed

51. Giusti, C, Schiaffini, R, Brufani, C, Pantaleo, A, Vingolo, EM, Gargiulo, P. Coagulation pathways and diabetic retinopathy: abnormal modulation in a selected group of insulin dependent diabetic patients. Br J Ophthalmol 2000;84:591–5. https://doi.org/10.1136/bjo.84.6.591.Search in Google Scholar PubMed PubMed Central

52. Tang, X, Zhang, Z, Fang, M, Han, Y, Wang, G, Wang, S, et al.. Transferrin plays a central role in coagulation balance by interacting with clotting factors. Cell Res 2020;30:119–32. https://doi.org/10.1038/s41422-019-0260-6.Search in Google Scholar PubMed PubMed Central

53. Roy, S, Bae, E, Amin, S, Kim, D. Extracellular matrix, gap junctions, and retinal vascular homeostasis in diabetic retinopathy. Exp Eye Res 2015;133:58–68. https://doi.org/10.1016/j.exer.2014.08.011.Search in Google Scholar PubMed

54. Ozaki, H, Hayashi, H, Oshima, K. Angiogenin levels in the vitreous from patients with proliferative diabetic retinopathy. Ophthalmic Res 1996;28:356–60. https://doi.org/10.1159/000267929.Search in Google Scholar PubMed

55. Gopalakrishnan, V, Purushothaman, P, Bhaskar, A. Proteomic analysis of plasma proteins in diabetic retinopathy patients by two dimensional electrophoresis and MALDI-Tof-MS. J Diabetes Complicat 2015;29:928–36. https://doi.org/10.1016/j.jdiacomp.2015.05.021.Search in Google Scholar PubMed

56. Sennels, L, Salek, M, Lomas, L, Boschetti, E, Righetti, PG, Rappsilber, J. Proteomic analysis of human blood serum using peptide library beads. J Proteome Res 2007;6:4055–62. https://doi.org/10.1021/pr070339l.Search in Google Scholar PubMed

57. Tu, C, Rudnick, PA, Martinez, MY, Cheek, KL, Stein, SE, Slebos, RJC, et al.. Depletion of abundant plasma proteins and limitations of plasma proteomics. J Proteome Res 2010;9:4982–91. https://doi.org/10.1021/pr100646w.Search in Google Scholar PubMed PubMed Central

58. Blume, JE, Manning, WC, Troiano, G, Hornburg, D, Figa, M, Hesterberg, L, et al.. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat Commun 2020;11:3652. https://doi.org/10.1038/s41467-020-17033-7.Search in Google Scholar PubMed PubMed Central

59. Palstrøm, NB, Rasmussen, LM, Beck, HC. Affinity capture enrichment versus affinity depletion: a comparison of strategies for increasing coverage of low-abundant human plasma proteins. Int J Mol Sci 2020;21:5903. https://doi.org/10.3390/ijms21165903.Search in Google Scholar PubMed PubMed Central

60. Mörtstedt, H, Makower, Å, Edlund, PO, Sjöberg, K, Tjernberg, A. Improved identification of host cell proteins in a protein biopharmaceutical by LC–MS/MS using the ProteoMinerTM Enrichment Kit. J Pharm Biomed Anal 2020;185:113256. https://doi.org/10.1016/j.jpba.2020.113256.Search in Google Scholar PubMed

61. Miljanovic, B, Glynn, RJ, Nathan, DM, Manson, JE, Schaumberg, DA. A prospective study of serum lipids and risk of diabetic macular edema in type 1 diabetes [Internet]; 2004. Available from: http://diabetesjournals.org/diabetes/article-pdf/53/11/2883/376033/zdb01104002883.pdf.10.2337/diabetes.53.11.2883Search in Google Scholar PubMed

Received: 2023-04-26
Accepted: 2024-01-16
Published Online: 2024-02-09
Published in Print: 2024-05-27

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. SARS-CoV-2 is here to stay: do not lower our guard
  4. Reviews
  5. SARS-CoV-2 subgenomic RNA: formation process and rapid molecular diagnostic methods
  6. Prognostic value of anti-SARS-CoV-2 antibodies: a systematic review
  7. Presence of SARS-CoV-2 RNA in COVID-19 survivors with post-COVID symptoms: a systematic review of the literature
  8. Opinion Papers
  9. Harmonizing the post-analytical phase: focus on the laboratory report
  10. Blood-based biomarkers in Alzheimer’s disease – moving towards a new era of diagnostics
  11. A comprehensive review on PFAS including survey results from the EFLM Member Societies
  12. General Clinical Chemistry and Laboratory Medicine
  13. Report from the HarmoSter study: different LC-MS/MS androstenedione, DHEAS and testosterone methods compare well; however, unifying calibration is a double-edged sword
  14. An LC–MS/MS method for serum cystatin C quantification and its comparison with two commercial immunoassays
  15. CX3CL1/Fractalkine as a biomarker for early pregnancy prediction of preterm premature rupture of membranes
  16. Elevated S100B urine levels predict seizures in infants complicated by perinatal asphyxia and undergoing therapeutic hypothermia
  17. The correlation of urea and creatinine concentrations in sweat and saliva with plasma during hemodialysis: an observational cohort study
  18. Tubular phosphate transport: a comparison between different methods of urine sample collection in FGF23-dependent hypophosphatemic syndromes
  19. Reference Values and Biological Variations
  20. Monocyte distribution width (MDW): study of reference values in blood donors
  21. Data mining of reference intervals for serum creatinine: an improvement in glomerular filtration rate estimating equations based on Q-values
  22. Hematology and Coagulation
  23. MALDI-MS in first-line screening of newborns for sickle cell disease: results from a prospective study in comparison to HPLC
  24. Cardiovascular Diseases
  25. To rule-in, or not to falsely rule-out, that is the question: evaluation of hs-cTnT EQA performance in light of the ESC-2020 guideline
  26. Temporal biomarker concentration patterns during the early course of acute coronary syndrome
  27. Diabetes
  28. Proteomic analysis of diabetic retinopathy identifies potential plasma-protein biomarkers for diagnosis and prognosis
  29. Infectious Diseases
  30. Serum biomarkers of inflammation and vascular damage upon SARS-Cov-2 mRNA vaccine in patients with thymic epithelial tumors
  31. A high throughput immuno-affinity mass spectrometry method for detection and quantitation of SARS-CoV-2 nucleoprotein in human saliva and its comparison with RT-PCR, RT-LAMP, and lateral flow rapid antigen test
  32. Evaluation of inflammatory biomarkers and vitamins in hospitalized patients with SARS-CoV-2 infection and post-COVID syndrome
  33. The CoLab score is associated with SARS-CoV-2 viral load during admission in individuals admitted to the intensive care unit: the CoLaIC cohort study
  34. Development and evaluation of a CRISPR-Cas13a system-based diagnostic for hepatitis E virus
  35. Letters to the Editor
  36. Crioplast® is a reliable device to ensure pre-analytical stability of adrenocorticotrophin (ACTH)
  37. Falsely decreased Abbott Alinity-c gamma-glutamyl transferase-2 result from paraprotein and heparin interference: case report and subsequent laboratory experiments
  38. Impact of hemolysis on uracilemia in the context of dihydropyrimidine dehydrogenase deficiency testing
  39. Value of plasma neurofilament light chain for monitoring efficacy in children with later-onset spinal muscular atrophy under nusinersen treatment
  40. Analytical evaluation of the Snibe β-isomerized C-terminal telopeptide of type I collagen (β-CTX-I) automated method
  41. Acute myeloid leukemia with blue-green neutrophilic inclusions have different outcomes: two cases and review of the literature
  42. Congress Abstracts
  43. The 10+1 Santorini Conference
  44. 14th National Congress of the Portuguese Society of Clinical Chemistry, Genetics and Laboratory Medicine
  45. 15th National Congress of the Portuguese Society of Clinical Chemistry, Genetics and Laboratory Medicine
  46. ISMD2024 Thirteenth International Symposium on Molecular Diagnostics
Downloaded on 13.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2023-1128/html
Scroll to top button