Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study.
Chronic kidney disease
Diabetes mellitus
Prognosis
Proteomics
Journal
Cardiovascular diabetology
ISSN: 1475-2840
Titre abrégé: Cardiovasc Diabetol
Pays: England
ID NLM: 101147637
Informations de publication
Date de publication:
29 03 2023
29 03 2023
Historique:
received:
21
10
2022
accepted:
19
03
2023
medline:
31
3
2023
entrez:
29
3
2023
pubmed:
30
3
2023
Statut:
epublish
Résumé
Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory. We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation. The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline. Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.
Sections du résumé
BACKGROUND
Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory.
METHODS
We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation.
RESULTS
The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline.
CONCLUSIONS
Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.
Identifiants
pubmed: 36991445
doi: 10.1186/s12933-023-01808-5
pii: 10.1186/s12933-023-01808-5
pmc: PMC10061741
doi:
Substances chimiques
Receptor for Advanced Glycation End Products
0
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
74Informations de copyright
© 2023. The Author(s).
Références
Lancet. 2011 Jul 2;378(9785):31-40
pubmed: 21705069
JAMA. 2015 Sep 8;314(10):1021-9
pubmed: 26348752
Eur Heart J. 2022 Feb 10;43(6):474-484
pubmed: 35023547
Am J Kidney Dis. 2022 Jun;79(6):849-857.e1
pubmed: 34752914
Diabetologia. 2020 Apr;63(4):788-798
pubmed: 31915892
Kidney Int. 2019 Dec;96(6):1381-1388
pubmed: 31679767
Nephrol Dial Transplant. 2015 Aug;30(8):1237-43
pubmed: 25326471
Lancet Diabetes Endocrinol. 2021 Nov;9(11):743-754
pubmed: 34619108
Ann Intern Med. 2009 May 5;150(9):604-12
pubmed: 19414839
Diabetologia. 2021 Jul;64(7):1504-1515
pubmed: 33797560
Diabetes Care. 2017 Feb;40(2):280-283
pubmed: 27974345
Diabetes Care. 2020 Feb;43(2):433-439
pubmed: 31727687
Diabetes Care. 2018 Sep;41(9):1947-1954
pubmed: 29980527
Clin J Am Soc Nephrol. 2015 Aug 7;10(8):1371-9
pubmed: 26175542
Diabetes Care. 2017 Mar;40(3):367-374
pubmed: 27998909
Diabetes Care. 2017 Mar;40(3):391-397
pubmed: 28077457
Nephrol Dial Transplant. 2012 Apr;27(4):1454-60
pubmed: 21862458
Clin J Am Soc Nephrol. 2022 Feb;17(2):251-259
pubmed: 34876454
Diabetologia. 2019 Jan;62(1):156-168
pubmed: 30288572
Kidney Int. 2015 Oct;88(4):888-96
pubmed: 26200946
Diabetologia. 2021 Oct;64(10):2147-2158
pubmed: 34415356
Am J Nephrol. 2022;53(1):21-31
pubmed: 35016188
Nat Med. 2019 May;25(5):805-813
pubmed: 31011203
JAMA. 2021 Dec 28;326(24):2498-2506
pubmed: 34962526
Lancet Child Adolesc Health. 2022 Mar;6(3):158-170
pubmed: 35051409
Sci Rep. 2020 Nov 12;10(1):19743
pubmed: 33184434
N Engl J Med. 2021 Sep 2;385(10):896-907
pubmed: 34215025