Discovery and Validation of a Biomarker Model (PRESERVE) Predictive of Renal Outcomes After Liver Transplantation.
Journal
Hepatology (Baltimore, Md.)
ISSN: 1527-3350
Titre abrégé: Hepatology
Pays: United States
ID NLM: 8302946
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
23
04
2019
accepted:
05
09
2019
pubmed:
12
9
2019
medline:
21
4
2021
entrez:
12
9
2019
Statut:
ppublish
Résumé
A high proportion of patients develop chronic kidney disease (CKD) after liver transplantation (LT). We aimed to develop clinical/protein models to predict future glomerular filtration rate (GFR) deterioration in this population. In independent multicenter discovery (CTOT14) and single-center validation (BUMC) cohorts, we analyzed kidney injury proteins in serum/plasma samples at month 3 after LT in recipients with preserved GFR who demonstrated subsequent GFR deterioration versus preservation by year 1 and year 5 in the BUMC cohort. In CTOT14, we also examined correlations between serial protein levels and GFR over the first year. A month 3 predictive model was constructed from clinical and protein level variables using the CTOT14 cohort (n = 60). Levels of β-2 microglobulin and CD40 antigen and presence of hepatitis C virus (HCV) infection predicted early (year 1) GFR deterioration (area under the curve [AUC], 0.814). We observed excellent validation of this model (AUC, 0.801) in the BUMC cohort (n = 50) who had both early and late (year 5) GFR deterioration. At an optimal threshold, the model had the following performance characteristics in CTOT14 and BUMC, respectively: accuracy (0.75, 0.8), sensitivity (0.71, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75), and negative predictive value (0.76, 0.82). In the serial CTOT14 analysis, several proteins, including β-2 microglobulin and CD40, correlated with GFR changes over the first year. We have validated a clinical/protein model (PRESERVE) that early after LT can predict future renal deterioration versus preservation with high accuracy. This model may help select recipients at higher risk for subsequent CKD for early, proactive renal sparing strategies.
Sections du résumé
BACKGROUND AND AIMS
A high proportion of patients develop chronic kidney disease (CKD) after liver transplantation (LT). We aimed to develop clinical/protein models to predict future glomerular filtration rate (GFR) deterioration in this population.
APPROACH AND RESULTS
In independent multicenter discovery (CTOT14) and single-center validation (BUMC) cohorts, we analyzed kidney injury proteins in serum/plasma samples at month 3 after LT in recipients with preserved GFR who demonstrated subsequent GFR deterioration versus preservation by year 1 and year 5 in the BUMC cohort. In CTOT14, we also examined correlations between serial protein levels and GFR over the first year. A month 3 predictive model was constructed from clinical and protein level variables using the CTOT14 cohort (n = 60). Levels of β-2 microglobulin and CD40 antigen and presence of hepatitis C virus (HCV) infection predicted early (year 1) GFR deterioration (area under the curve [AUC], 0.814). We observed excellent validation of this model (AUC, 0.801) in the BUMC cohort (n = 50) who had both early and late (year 5) GFR deterioration. At an optimal threshold, the model had the following performance characteristics in CTOT14 and BUMC, respectively: accuracy (0.75, 0.8), sensitivity (0.71, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75), and negative predictive value (0.76, 0.82). In the serial CTOT14 analysis, several proteins, including β-2 microglobulin and CD40, correlated with GFR changes over the first year.
CONCLUSIONS
We have validated a clinical/protein model (PRESERVE) that early after LT can predict future renal deterioration versus preservation with high accuracy. This model may help select recipients at higher risk for subsequent CKD for early, proactive renal sparing strategies.
Identifiants
pubmed: 31509263
doi: 10.1002/hep.30939
pmc: PMC7883482
mid: NIHMS1666345
doi:
Substances chimiques
Biomarkers
0
CD40 Antigens
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1775-1786Subventions
Organisme : NIAID NIH HHS
ID : U01AI084146
Pays : United States
Organisme : NIAID NIH HHS
ID : UM2 AI117870
Pays : United States
Organisme : NIAID NIH HHS
ID : R21 AI113916
Pays : United States
Organisme : NIAID NIH HHS
ID : U01 AI084146
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG049385
Pays : United States
Organisme : NIAID NIH HHS
ID : R21AI113916-01
Pays : United States
Informations de copyright
© 2019 by the American Association for the Study of Liver Diseases.
Références
Am J Kidney Dis. 2009 Dec;54(6):1012-24
pubmed: 19850388
Liver Transpl. 2006 Nov;12(11):1667-72
pubmed: 17031831
Transplantation. 2017 May;101(5):945-955
pubmed: 28437387
Hepatology. 2014 Apr;59(4):1522-31
pubmed: 24123197
N Engl J Med. 2016 Nov 24;375(21):2106-2108
pubmed: 27959735
N Engl J Med. 2003 Sep 4;349(10):931-40
pubmed: 12954741
PLoS One. 2014 Oct 20;9(10):e110527
pubmed: 25329716
Gastroenterology. 2015 Sep;149(3):649-59
pubmed: 25985734
J Clin Pathol. 1992 Jul;45(7):561-7
pubmed: 1381383
Am J Transplant. 2012 Nov;12(11):3008-20
pubmed: 22882750
J Hepatol. 2014 Aug;61(2):286-92
pubmed: 24713190
Liver Transpl. 2010 Nov;16(11):1257-66
pubmed: 21031541
J Hypertens. 2018 Jan;36(1):110-118
pubmed: 28858977
Transplantation. 2015 Jul;99(7):1431-5
pubmed: 25654627
Hepatology. 2019 Oct;70(4):1349-1359
pubmed: 31002431
Hepatology. 2014 Dec;60(6):2017-26
pubmed: 25078558
Proteomics Clin Appl. 2008 Sep;2(9):1338-48
pubmed: 21136927
BMC Nephrol. 2013 Jan 17;14:17
pubmed: 23327592
Eur J Clin Invest. 1981 Jun;11(3):239-43
pubmed: 6168470
N Engl J Med. 2014 Dec 18;371(25):2375-82
pubmed: 25386767
Am J Transplant. 2011 Sep;11(9):1972-8
pubmed: 21794091
Am J Transplant. 2016 Sep;16(9):2532-44
pubmed: 26932352
Transplantation. 2004 Feb 27;77(4):553-6
pubmed: 15084934
Transplantation. 2010 Jan 27;89(2):215-21
pubmed: 20098285
J Am Soc Nephrol. 2005 Sep;16(9):2714-23
pubmed: 16033859
Liver Transpl. 2009 Dec;15(12):1852-60
pubmed: 19938135
Nat Biotechnol. 2010 May;28(5):470-7
pubmed: 20458317
N Engl J Med. 2002 Sep 12;347(11):797-805
pubmed: 12226150
Am J Kidney Dis. 2012 May;59(5):653-62
pubmed: 22305758
Kidney Int. 2017 Jan;91(1):196-203
pubmed: 28029431
Clin J Am Soc Nephrol. 2016 Aug 8;11(8):1343-52
pubmed: 27189318
Cell Transplant. 2010;19(6):723-9
pubmed: 20525436
Ann Intern Med. 2009 May 5;150(9):604-12
pubmed: 19414839
Am J Nephrol. 2012;36(1):1-10
pubmed: 22699366
Transplantation. 2001 Dec 27;72(12):1934-9
pubmed: 11773892
Kidney Int. 2015 Oct;88(4):888-96
pubmed: 26200946
Biomarkers. 2012 Jun;17(4):336-42
pubmed: 22455661
Ren Fail. 2006;28(5):419-25
pubmed: 16825092
Clin J Am Soc Nephrol. 2016 Dec 7;11(12):2141-2149
pubmed: 27852662
Am J Transplant. 2010 Sep;10(9):2017-25
pubmed: 20883535
Biochem J. 1994 Feb 15;298 ( Pt 1):181-7
pubmed: 8129718
Aliment Pharmacol Ther. 2013 May;37(10):989-97
pubmed: 23577724
Liver Transpl. 2006 Feb;12(2):285-91
pubmed: 16447198
J Hepatol. 2014 Jul;61(1):35-42
pubmed: 24613364
N Engl J Med. 2013 Sep 5;369(10):932-43
pubmed: 24004120
Medicine (Baltimore). 2016 Mar;95(10):e3011
pubmed: 26962815
Kidney Int. 2011 Jul;80(1):17-28
pubmed: 21150873