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Design, cohort profile and comparison of the KTD-Innov study: a prospective multidimensional biomarker validation study in kidney allograft rejection

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Abstract

There is an unmet need for robust and clinically validated biomarkers of kidney allograft rejection. Here we present the KTD-Innov study (ClinicalTrials.gov, NCT03582436), an unselected deeply phenotyped cohort of kidney transplant recipients with a holistic approach to validate the clinical utility of precision diagnostic biomarkers. In 2018–2019, we prospectively enrolled consecutive adult patients who received a kidney allograft at seven French centers and followed them for a year. We performed multimodal phenotyping at follow-up visits, by collecting clinical, biological, immunological, and histological parameters, and analyzing a panel of 147 blood, urinary and kidney tissue biomarkers. The primary outcome was allograft rejection, assessed at each visit according to the international Banff 2019 classification. We evaluated the representativeness of participants by comparing them with patients from French, European, and American transplant programs transplanted during the same period. A total of 733 kidney transplant recipients (64.1% male and 35.9% female) were included during the study. The median follow-up after transplantation was 12.3 months (interquartile range, 11.9–13.1 months). The cumulative incidence of rejection was 9.7% at one year post-transplant. We developed a distributed and secured data repository in compliance with the general data protection regulation. We established a multimodal biomarker biobank of 16,736 samples, including 9331 blood, 4425 urinary and 2980 kidney tissue samples, managed and secured in a collaborative network involving 7 clinical centers, 4 analytical platforms and 2 industrial partners. Patients' characteristics, immune profiles and treatments closely resembled those of 41,238 French, European and American kidney transplant recipients. The KTD-Innov study is a unique holistic and multidimensional biomarker validation cohort of kidney transplant recipients representative of the real-world transplant population. Future findings from this cohort are likely to be robust and generalizable.

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Data availability

A sample of the data is available as an open access resource under the CC BY-NC resource into the synapse public repository (https://www.synapse.org/KTD) and is freely available. A sign-in process is required to access the data. Full source de-identified data will be available from the corresponding author after the end of the study and after examination and approval by the Ethics Committee of the KTD-Innov’s consortium.

Abbreviations

ABM:

Agence de la biomédecine

AMR:

Antibody-mediated rejection

CC BY-NC:

Creative Commons NonCommercial license

CNIL:

Commission nationale de l’informatique et des libertés

eCRF:

Electronic case report form

GDPR:

General data protection regulation

HLA:

Human leukocyte antigen

KTD-Innov:

Kidney transplant diagnostics innovation

TCMR:

T cell-mediated rejection

UNOS:

United network for organ sharing

References

  1. Muduma G, Odeyemi I, Smith-Palmer J, Pollock RF. Review of the clinical and economic burden of antibody-mediated rejection in renal transplant recipients. Adv Ther. 2016;33(3):345–56. https://doi.org/10.1007/s12325-016-0292-y.

    Article  PubMed  Google Scholar 

  2. Loupy A, Mengel M, Haas M. Thirty years of the international banff classification for allograft pathology: the past, present, and future of kidney transplant diagnostics. Kidney Int. 2022;101(4):678–91. https://doi.org/10.1016/j.kint.2021.11.028.

    Article  PubMed  Google Scholar 

  3. Danger R, Le Berre L, Cadoux M, et al. Subclinical rejection-free diagnostic after kidney transplantation using blood gene expression. Kidney Int. 2023;103(6):1167–79. https://doi.org/10.1016/j.kint.2023.03.019.

    Article  CAS  PubMed  Google Scholar 

  4. Lubetzky ML, Salinas T, Schwartz JE, Suthanthiran M. Urinary cell mRNA profiles predictive of human kidney allograft status. Clin J Am Soc Nephrol. 2021;16(10):1565–77. https://doi.org/10.2215/CJN.14010820.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rabant M, Amrouche L, Lebreton X, et al. Urinary C-X-C motif chemokine 10 independently improves the noninvasive diagnosis of antibody–mediated kidney allograft rejection. J Am Soc Nephrol. 2015;26(11):2840–51. https://doi.org/10.1681/ASN.2014080797.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Loupy A, Lefaucheur C, Vernerey D, et al. Complement-binding anti-HLA antibodies and kidney-allograft survival. N Engl J Med. 2013;369(13):1215–26. https://doi.org/10.1056/NEJMoa1302506.

    Article  CAS  PubMed  Google Scholar 

  7. Hricik DE, Nickerson P, Formica RN, et al. Multicenter validation of urinary CXCL9 as a risk-stratifying biomarker for kidney transplant injury. Am J Transplant. 2013;13(10):2634–44. https://doi.org/10.1111/ajt.12426.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Suthanthiran M, Schwartz JE, Ding R, et al. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N Engl J Med. 2013;369(1):20–31. https://doi.org/10.1056/NEJMoa1215555.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Danger R, Chesneau M, Paul C, et al. A composite score associated with spontaneous operational tolerance in kidney transplant recipients. Kidney Int. 2017;91(6):1473–81. https://doi.org/10.1016/j.kint.2016.12.020.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gavlovsky PJ, Tonnerre P, Guitton C, Charreau B. Expression of MHC class I-related molecules MICA, HLA-E and EPCR shape endothelial cells with unique functions in innate and adaptive immunity. Hum Immunol. 2016;77(11):1084–91. https://doi.org/10.1016/j.humimm.2016.02.007.

    Article  CAS  PubMed  Google Scholar 

  11. Mengel M, Loupy A, Haas M, et al. Banff 2019 meeting report: molecular diagnostics in solid organ transplantation-consensus for the Banff human organ transplant (B-HOT) gene panel and open source multicenter validation. Am J Transplant. 2020;20(9):2305–17. https://doi.org/10.1111/ajt.16059.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Reeve J, Bohmig GA, Eskandary F, et al. Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. JCI Insight. 2017. https://doi.org/10.1172/jci.insight.94197.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Loupy A, Van Duong Huyen JP, Hidalgo L, et al. Gene expression profiling for the identification and classification of antibody-mediated heart rejection. Circulation. 2017;135(10):917–35. https://doi.org/10.1161/CIRCULATIONAHA.116.022907.

    Article  CAS  PubMed  Google Scholar 

  14. Loupy A, Lefaucheur C, Vernerey D, et al. Molecular microscope strategy to improve risk stratification in early antibody-mediated kidney allograft rejection. J Am Soc Nephrol. 2014;25(10):2267–77. https://doi.org/10.1681/ASN.2013111149.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Selleck MJ, Senthil M, Wall NR. Making meaningful clinical use of biomarkers. Biomark Insights. 2017;12:1177271917715236. https://doi.org/10.1177/1177271917715236.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bossuyt PM, Parvin T. Evaluating biomarkers for guiding treatment decisions. EJIFCC. 2015;26(1):63–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Naesens M, Anglicheau D. Precision transplant medicine: biomarkers to the rescue. J Am Soc Nephrol. 2018;29(1):24–34. https://doi.org/10.1681/ASN.2017010004.

    Article  CAS  PubMed  Google Scholar 

  18. Ioannidis JPA, Bossuyt PMM. Waste, leaks, and failures in the biomarker pipeline. Clin Chem. 2017;63(5):963–72. https://doi.org/10.1373/clinchem.2016.254649.

    Article  CAS  PubMed  Google Scholar 

  19. Raynaud M, Al-Awadhi S, Louis K, et al. Prognostic biomarkers in kidney transplantation a systematic review and critical appraisal. J Am Soc Nephrol. 2023. https://doi.org/10.1681/ASN.0000000000000260.

    Article  PubMed  Google Scholar 

  20. Jamshaid F, Froghi S, Di Cocco P, Dor FJ. Novel non-invasive biomarkers diagnostic of acute rejection in renal transplant recipients: a systematic review. Int J Clin Pract. 2018. https://doi.org/10.1111/ijcp.13220.

    Article  PubMed  Google Scholar 

  21. Menon MC, Murphy B, Heeger PS. Moving biomarkers toward clinical implementation in kidney transplantation. J Am Soc Nephrol. 2017;28(3):735–47. https://doi.org/10.1681/ASN.2016080858.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Divard G, Goutaudier V. Global perspective on kidney transplantation: France. Kidney360. 2021;2(10):1637–40. https://doi.org/10.34067/KID.0002402021.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Agence de la Biomédecine. The French National Report 2021. France, Saint-Denis La Plaine, 2022

  24. Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Epidemiology. 2007;18(6):805–35. https://doi.org/10.1097/EDE.0b013e3181577511.

    Article  PubMed  Google Scholar 

  25. Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11): e012799. https://doi.org/10.1136/bmjopen-2016-012799.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Yoo D, Goutaudier V, Divard G, et al. An automated histological classification system for precision diagnostics of kidney allografts. Nat Med. 2023;29(5):1211–20. https://doi.org/10.1038/s41591-023-02323-6.

    Article  CAS  PubMed  Google Scholar 

  27. Loupy A, Haas M, Roufosse C, et al. The Banff 2019 kidney meeting report (I): updates on and clarification of criteria for T cell- and antibody-mediated rejection. Am J Transplant. 2020;20(9):2318–31. https://doi.org/10.1111/ajt.15898.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Braudeau C, Ashton-Chess J, Giral M, et al. Contrasted blood and intragraft toll-like receptor 4 mRNA profiles in operational tolerance versus chronic rejection in kidney transplant recipients. Transplantation. 2008;86(1):130–6. https://doi.org/10.1097/TP.0b013e31817b8dc5.

    Article  CAS  PubMed  Google Scholar 

  29. Ashton-Chess J, Giral M, Mengel M, et al. Tribbles-1 as a novel biomarker of chronic antibody-mediated rejection. J Am Soc Nephrol. 2008;19(6):1116–27. https://doi.org/10.1681/ASN.2007101056.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sayadi S GE, Südholt M, Vince N, Gourraud PA. Distributed contextualization of biomedical data: a case study in precision medicine. In: IEEE/ACS 17th International conference on computer systems and applications (AICCSA), Antalya, Turkey, 2020. 2020, pp. 1–6. https://doi.org/10.1109/AICCSA50499.2020.9316502

  31. Guillaudeux M, Rousseau O, Petot J, et al. Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis. NPJ Digit Med. 2023;6(1):37. https://doi.org/10.1038/s41746-023-00771-5.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3: 160018. https://doi.org/10.1038/sdata.2016.18.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tang F, Ishwaran H. Random forest missing data algorithms. Stat Anal Data Min. 2017;10(6):363–77. https://doi.org/10.1002/sam.11348.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Waljee AK, Mukherjee A, Singal AG, et al. Comparison of imputation methods for missing laboratory data in medicine. BMJ Open. 2013. https://doi.org/10.1136/bmjopen-2013-002847.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Little RJ, D’Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367(14):1355–60. https://doi.org/10.1056/NEJMsr1203730.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Locher C, Le Goff G, Le Louarn A, Mansmann U, Naudet F. Making data sharing the norm in medical research. BMJ. 2023;382:1434. https://doi.org/10.1136/bmj.p1434.

    Article  PubMed  Google Scholar 

  37. UNESCO. Recommendation on Open Science. https://www.unesco.org/en/open-science/about

  38. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55–63. https://doi.org/10.7326/M14-0697.

    Article  PubMed  Google Scholar 

  39. Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform. 2014;48:193–204. https://doi.org/10.1016/j.jbi.2014.02.013.

    Article  PubMed  Google Scholar 

  40. Oweira H, Ramouz A, Ghamarnejad O, et al. Risk factors of rejection in renal transplant recipients: a narrative review. J Clin Med. 2022. https://doi.org/10.3390/jcm11051392.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hart A, Singh D, Brown SJ, Wang JH, Kasiske BL. Incidence, risk factors, treatment, and consequences of antibody-mediated kidney transplant rejection: a systematic review. Clin Transplant. 2021;35(7): e14320. https://doi.org/10.1111/ctr.14320.

    Article  PubMed  Google Scholar 

  42. Foroutan F, Friesen EL, Clark KE, et al. Risk factors for 1-year graft loss after kidney transplantation: systematic review and meta-analysis. Clin J Am Soc Nephrol. 2019;14(11):1642–50. https://doi.org/10.2215/CJN.05560519.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lemoine M, Titeca Beauport D, Lobbedez T, et al. Risk factors for early graft failure and death after kidney transplantation in recipients older than 70 years. Kidney Int Rep. 2019;4(5):656–66. https://doi.org/10.1016/j.ekir.2019.01.014.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Clayton PA, McDonald SP, Russ GR, Chadban SJ. Long-term outcomes after acute rejection in kidney transplant recipients: an ANZDATA analysis. J Am Soc Nephrol. 2019;30(9):1697–707. https://doi.org/10.1681/ASN.2018111101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. McDonald SP. Australia and New Zealand dialysis and transplant registry. Kidney Int Suppl. 2015;5(1):39–44. https://doi.org/10.1038/kisup.2015.8.

    Article  Google Scholar 

  46. Matas AJ, Smith JM, Skeans MA, et al. OPTN/SRTR 2011 annual data report: kidney. Am J Transplant. 2013;13(Suppl 1):11–46. https://doi.org/10.1111/ajt.12019.

    Article  PubMed  Google Scholar 

  47. Loupy A, Vernerey D, Tinel C, et al. Subclinical rejection phenotypes at 1 year post-transplant and outcome of kidney allografts. J Am Soc Nephrol. 2015;26(7):1721–31. https://doi.org/10.1681/ASN.2014040399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Rush D, Nickerson P, Gough J, et al. Beneficial effects of treatment of early subclinical rejection: a randomized study. J Am Soc Nephrol. 1998;9(11):2129–34. https://doi.org/10.1681/ASN.V9112129.

    Article  CAS  PubMed  Google Scholar 

  49. Sawinski D, Trofe-Clark J, Leas B, et al. Calcineurin inhibitor minimization, conversion, withdrawal, and avoidance strategies in renal transplantation: a systematic review and meta-analysis. Am J Transplant. 2016;16(7):2117–38. https://doi.org/10.1111/ajt.13710.

    Article  CAS  PubMed  Google Scholar 

  50. Mayer AD, Dmitrewski J, Squifflet JP, et al. Multicenter randomized trial comparing tacrolimus (FK506) and cyclosporine in the prevention of renal allograft rejection: a report of the European Tacrolimus Multicenter Renal Study Group. Transplantation. 1997;64(3):436–43. https://doi.org/10.1097/00007890-199708150-00012.

    Article  CAS  PubMed  Google Scholar 

  51. Sharif A. Deceased donor characteristics and kidney transplant outcomes. Transpl Int. 2022;35:10482. https://doi.org/10.3389/ti.2022.10482.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Aubert O, Reese PP, Audry B, et al. Disparities in acceptance of deceased donor kidneys between the United States and France and estimated effects of increased US acceptance. JAMA Intern Med. 2019;179(10):1365–74. https://doi.org/10.1001/jamainternmed.2019.2322.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Truchot A, Raynaud M, Loupy A. Excess mortality after kidney transplantation: Does sex matter? Kidney Int. 2023;103(6):1023–4. https://doi.org/10.1016/j.kint.2023.03.011.

    Article  PubMed  Google Scholar 

  54. Massie AB, Kucirka LM, Segev DL. Big data in organ transplantation: registries and administrative claims. Am J Transplant. 2014;14(8):1723–30. https://doi.org/10.1111/ajt.12777.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Yu M, King KL, Husain SA, et al. Discrepant outcomes between national kidney transplant data registries in the united states. J Am Soc Nephrol. 2023;34(11):1863–74. https://doi.org/10.1681/ASN.0000000000000194.

    Article  PubMed  Google Scholar 

  56. Eikmans M, Gielis EM, Ledeganck KJ, Yang J, Abramowicz D, Claas FFJ. Non-invasive biomarkers of acute rejection in kidney transplantation: novel targets and strategies. Front Med (Lausanne). 2018;5:358. https://doi.org/10.3389/fmed.2018.00358.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors thank Dahlia Tsakiropoulos and Solène Janique for their contribution in the management of the project, and Harold Juillet for their help in setting up and developing the project. The authors thank the clinical research assistants, Marie Bouclier, Francine Tacafred, Julie Obert, Aliaume Le Pape, Maxime Bentoumi-Loaec, Marie Mattera, Fatiha M’Raiagh, and Coralie Champion, as well as Magali Giral for their contribution in the data acquisition. The authors also thank Blaise Robin, Jessy Dagobert, Fariza Mezine for their technical assistance. The authors also thank the Biological Resource Centre for Biobanking of Nantes (CHU Nantes, Nantes Université, Centre de ressources biologiques [BRIF: BB-0033-00040]). The authors thank the Organ Procurement and Transplantation Network for providing the data. The data reported here have been supplied by the United Network for Organ Sharing as the contractor for the Organ Procurement and Transplantation Network. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy of or interpretation by the OPTN or the U.S. Government.

Funding

The KTD-Innov study was funded by the French government, with financial support managed by the National Research Agency (ANR) under the program “Investissements d’avenir”, with the Grant Agreement No. ANR-17-RHUS-0010. INSERM-Action thématique incitative sur programme Avenir (ATIP-Avenir) provided financial support. VG received grants from the French-Speaking Society of Transplantation and the French Foundation for Medical Research. MS received a grant from Université Paris Cité. OA received a grant from the Fondation Bettencourt Schueller. RD, HLM and TN were supported by the ANR project KTD-Innov and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 754995.

Author information

Authors and Affiliations

  1. Paris Institute for Transplantation and Organ Regeneration (PITOR), INSERM U970, Université Paris Cité, 56 rue Leblanc, 75015, Paris, France

    Valentin Goutaudier, Marta Sablik, Maud Racapé, Marc Raynaud, Olivier Aubert, Christophe Legendre, Carmen Lefaucheur & Alexandre Loupy

  2. Department of Kidney Transplantation, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France

    Valentin Goutaudier, Olivier Aubert, Christophe Legendre, Dany Anglicheau & Alexandre Loupy

  3. INSERM UMR 1064, Center for Research in Transplantation and Translational Immunology, ITUN, Nantes Université, CHU Nantes, Nantes, France

    Olivia Rousseau, Béatrice Charreau, Emmanuelle Papuchon, Richard Danger, Laurence Letertre, Hoa Le Mai, Thi-Van-Ha Nguyen, Pierre-Antoine Gourraud & Sophie Brouard

  4. Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des Données, INSERM, CIC 1413, Nantes Université, CHU Nantes, 44000, Nantes, France

    Olivia Rousseau & Pierre-Antoine Gourraud

  5. Agence de la Biomédecine, Saint Denis la Plaine, France

    Benoit Audry & Christian Jacquelinet

  6. Department of Nephrology-Dialysis-Transplantation, Centre Hospitalier Universitaire de Toulouse, Toulouse, France

    Nassim Kamar

  7. Department of Nephrology, Transplantation, Dialysis and Apheresis, CHU Bordeaux, Bordeaux, France

    Lionel Couzi

  8. Department of Transplantation, Edouard Herriot University Hospital, Hospices Civils de Lyon, University Lyon, University of Lyon I, Lyon, France

    Emmanuel Morelon

  9. Department of Nephrology, Centre Hospitalier Universitaire Montpellier, Montpellier, France

    Moglie Le Quintrec

  10. Immunology and Histocompatibility Laboratory, Medical Biology Department, Saint-Louis Hospital, Paris, France

    Jean-Luc Taupin

  11. Clinical Trial Unit Hospital, Lariboisière Saint-Louis AP-HP, Paris Cité University, Paris, France

    Eric Vicaut

  12. Department of Biostatistics, Epidemiology and Bioinformatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Vishnu Potluri & Peter Reese

  13. Sebia, Lisses, France

    Marie-Eliane Azoury, Alice Pinheiro, Georges Nouadje & Pierre Sonigo

  14. Université Paris Cité, Inserm U1151, Necker Enfants-Malades Institute, Paris, France

    Dany Anglicheau

  15. Eurotransplant International Foundation, Leiden, the Netherlands

    Ineke Tieken & Serge Vogelaar

  16. Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique – Hôpitaux de Paris, Paris, France

    Carmen Lefaucheur

Authors
  1. Valentin Goutaudier
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  2. Marta Sablik
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  3. Maud Racapé
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  4. Olivia Rousseau
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  5. Benoit Audry
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  6. Nassim Kamar
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  7. Marc Raynaud
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  8. Olivier Aubert
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  9. Béatrice Charreau
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  10. Emmanuelle Papuchon
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  11. Richard Danger
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  12. Laurence Letertre
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  13. Lionel Couzi
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  14. Emmanuel Morelon
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  15. Moglie Le Quintrec
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  16. Jean-Luc Taupin
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  17. Eric Vicaut
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  18. Christophe Legendre
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  19. Hoa Le Mai
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  20. Vishnu Potluri
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  21. Thi-Van-Ha Nguyen
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  22. Marie-Eliane Azoury
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  23. Alice Pinheiro
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  24. Georges Nouadje
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  25. Pierre Sonigo
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  26. Dany Anglicheau
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  27. Ineke Tieken
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  28. Serge Vogelaar
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  29. Christian Jacquelinet
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  30. Peter Reese
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  31. Pierre-Antoine Gourraud
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  32. Sophie Brouard
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  33. Carmen Lefaucheur
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  34. Alexandre Loupy
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Consortia

the KTD-Innov Consortium

Contributions

AL and SB are leaders of the KTD-Innov study. SB, AL and PAG designed the KTD-Innov study. VG, MS, MRacapé, OR, MG, PAG, SB, CLefaucheur and AL designed the present study. VG, MS, MRacapé, OR, BA, NK, BC, EP, RD, LL, LC, EM, MLQ, JLT, EV, CLegendre, HLM, VP, TN, HJ, MEA, AP, GN, PS, DA, IT, SV, CJ, PR, PAG, SB, CLefaucheur, AL participated in the building of the KTD-Innov cohort and contributed to data acquisition. VG, MS, BA, IT, PR, CLefaucheur and AL performed data analysis. VG, MS, MRacapé, OR, BA, IT, MRaynaud, OA, IT, PAG, SB, CLefaucheur and AL performed data interpretation. VG, MS, MRacapé, CLefaucheur and AL wrote the first draft of the manuscript. All authors revised and critically reviewed the manuscript.

Corresponding author

Correspondence to Alexandre Loupy.

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Conflict of interest

RD and SB have ownership interests in the BioMAdvanced Diagnostics company not involved in the present study. MEA, AP, GN and PS are employees of Sebia. AL holds shares in Predict4Health, a software company that is not involved in the present research. PAG is the founder of Methodomics (2008) and the co-founder of Big data Santé (2018). He consults for major pharmaceutical companies and start-ups, all of which are handled through academic pipelines (AstraZeneca, Biogen, Boston Scientific, Cook, Docaposte, Edimark, Ellipses, Elsevier, Janssen, Lek, Methodomics, Merck, Mérieux, Octopize, Sanofi-Genzyme). PAG is a volunteer board member at AXA not-for-profit mutual insurance company (2021). He has no prescription activity with either drugs or devices. The other authors declare no competing interests.

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Goutaudier, V., Sablik, M., Racapé, M. et al. Design, cohort profile and comparison of the KTD-Innov study: a prospective multidimensional biomarker validation study in kidney allograft rejection. Eur J Epidemiol (2024). https://doi.org/10.1007/s10654-024-01112-w

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