Serum N-glycan profiling can predict biopsy-proven graft rejection after living kidney transplantation.
Acute rejection
Biomarkers
Glycobiology
Kidney transplantation
N-Glycan
Journal
Clinical and experimental nephrology
ISSN: 1437-7799
Titre abrégé: Clin Exp Nephrol
Pays: Japan
ID NLM: 9709923
Informations de publication
Date de publication:
Feb 2020
Feb 2020
Historique:
received:
27
08
2019
accepted:
12
11
2019
pubmed:
27
11
2019
medline:
25
11
2020
entrez:
27
11
2019
Statut:
ppublish
Résumé
To evaluate whether serum N-glycan profile can be used as a diagnostic marker of graft rejection after living-donor kidney transplants (KT). We retrospectively examined 174 KT recipients at five medical centers. N-Glycan levels were analyzed in postoperative serum samples using glycoblotting combined with mass spectrometry. We developed an integrated score to predict graft rejection based on a combination of age, gender, immunological risk factors, and serum N-glycan levels at post-KT day D1 and D7. Rejection-free survival rates stratified by the sum of integrated scores (D1 + D7) were evaluated using Kaplan-Meier curves. Of 174, 52 showed graft rejection (Rejection-pos. group) and 122 recipients did not show graft rejection (Rejection-neg. group). The integrated scores were significantly higher in the Rejection-pos. group than those of the Rejection-neg. group. Area-under-curve (AUC) value of integrated scores at post-KT D1, and D7 were 0.84 and 0.84, respectively. The sum of integrated scores (D1 + D7) ≥ 0.50 identified graft rejection with 81% sensitivity and 80% specificity; with an AUC value of 0.87. Recipients with higher sum of integrated scores (D1 + D7 ≥ 0.5) had significantly shorter rejection-free survival than those with lower scores. Evaluation of serum N-glycosylation profiles can identify recipients who are prone to rejection.
Sections du résumé
BACKGROUND
BACKGROUND
To evaluate whether serum N-glycan profile can be used as a diagnostic marker of graft rejection after living-donor kidney transplants (KT).
METHODS
METHODS
We retrospectively examined 174 KT recipients at five medical centers. N-Glycan levels were analyzed in postoperative serum samples using glycoblotting combined with mass spectrometry. We developed an integrated score to predict graft rejection based on a combination of age, gender, immunological risk factors, and serum N-glycan levels at post-KT day D1 and D7. Rejection-free survival rates stratified by the sum of integrated scores (D1 + D7) were evaluated using Kaplan-Meier curves.
RESULTS
RESULTS
Of 174, 52 showed graft rejection (Rejection-pos. group) and 122 recipients did not show graft rejection (Rejection-neg. group). The integrated scores were significantly higher in the Rejection-pos. group than those of the Rejection-neg. group. Area-under-curve (AUC) value of integrated scores at post-KT D1, and D7 were 0.84 and 0.84, respectively. The sum of integrated scores (D1 + D7) ≥ 0.50 identified graft rejection with 81% sensitivity and 80% specificity; with an AUC value of 0.87. Recipients with higher sum of integrated scores (D1 + D7 ≥ 0.5) had significantly shorter rejection-free survival than those with lower scores.
CONCLUSION
CONCLUSIONS
Evaluation of serum N-glycosylation profiles can identify recipients who are prone to rejection.
Identifiants
pubmed: 31768865
doi: 10.1007/s10157-019-01820-8
pii: 10.1007/s10157-019-01820-8
doi:
Substances chimiques
Biomarkers
0
Polysaccharides
0
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
174-184Subventions
Organisme : Japan Society for the Promotion of Science
ID : 15H02563 and 19H05556
Organisme : Japan Society for the Promotion of Science
ID : 17K11119
Références
Int J Urol. 2019 Feb;26(2):247-252
pubmed: 30460731
J Clin Invest. 2015 Nov 2;125(11):4160-70
pubmed: 26436649
Int J Urol. 2019 Dec;26(12):1128-1137
pubmed: 31587389
Int J Mol Sci. 2017 Dec 06;18(12):null
pubmed: 29210993
EMBO J. 1999 Mar 15;18(6):1516-25
pubmed: 10075923
Int J Urol. 2018 Feb;25(2):141-145
pubmed: 29068092
ScientificWorldJournal. 2013 Dec 23;2013:268407
pubmed: 24453820
Biochem Biophys Res Commun. 2014 Jun 13;448(4):390-6
pubmed: 24814705
J Urol. 2014 Mar;191(3):805-13
pubmed: 24140550
Int J Mol Sci. 2017 Feb 22;18(2):
pubmed: 28241428
Clin Exp Nephrol. 2019 Jun;23(6):807-813
pubmed: 30809748
Int J Urol. 2018 May;25(5):450-455
pubmed: 29444550
Int J Mol Sci. 2017 Aug 08;18(8):null
pubmed: 28786963
Int J Urol. 2018 Feb;25(2):112-120
pubmed: 29105189
Cancer Med. 2017 Apr;6(4):739-748
pubmed: 28317343
J Clin Invest. 2013 Sep;123(9):3788-96
pubmed: 23979161
Hum Immunol. 2016 Nov;77(11):1076-1083
pubmed: 26546874
Int J Urol. 2019 Apr;26(4):499-505
pubmed: 30818421
Int J Urol. 2019 Dec;26(12):1114-1120
pubmed: 31522467
Int J Urol. 2019 Feb;26(2):309-311
pubmed: 30430663
Clin Kidney J. 2017 Feb;10(1):106-115
pubmed: 28643819
Mol Cell Proteomics. 2010 Mar;9(3):523-37
pubmed: 20008832
Am J Transplant. 2016 May;16(5):1352-7
pubmed: 26696524
Prostate. 2014 Nov;74(15):1521-9
pubmed: 25154914
Trends Mol Med. 2012 Apr;18(4):224-32
pubmed: 22425488
Int J Urol. 2017 Dec;24(12):833-840
pubmed: 28913939
Int J Urol. 2020 Jan;27(1):30-38
pubmed: 31522462
Transplant Proc. 2014;46(2):445-8
pubmed: 24655984
Am J Nephrol. 2017;46(3):187-194
pubmed: 28848141
Glycoconj J. 1995 Jun;12(3):227-33
pubmed: 7496136
Eur Urol Focus. 2018 Apr;4(3):405-411
pubmed: 28753809
EMBO J. 2011 Jun 28;30(15):3173-85
pubmed: 21712812