Biomarker panels associated with progression of renal disease in type 1 diabetes.
Blackbourn, Luke AK
Dalton, R Neil
McKeigue, Paul M
FinnDiane Study Group and the Scottish Diabetes Research Network (SDRN) Type 1 Bioresource Collaboration,
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Colombo, M., Valo, E., McGurnaghan, S. J., Sandholm, N., Blackbourn, L. A., Dalton, R. N., Dunger, D., et al. (2019). Biomarker panels associated with progression of renal disease in type 1 diabetes.. Diabetologia, 62 (9), 1616-1627. https://doi.org/10.1007/s00125-019-4915-0
Aims We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. Methods We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 〖ml min〗^(-1) [1.73m]^(-2), with those from FinnDiane being oversampled for albuminuria. A total of 297 circulating biomarkers (30 proteins, 121 metabolites, 146 tryptic peptides) were measured in non-fasting serum samples using the Luminex platform and LC-MSMS. We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 〖ml min〗^(-1) [1.73m]^(-2) year^(-1) ) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared to using clinical record data alone. Results For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO and 9 proteins and 5 metabolites or tryptic peptides in FinnDiane beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p<〖10〗^(-4)). The strongest associations were with CD27 antigen, Kidney Injury Molecule-1 and Alpha-1-Microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r^2 for prediction of final eGFR from 0.47 to 0.58 in SDRNTBIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r^2 was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MSMS platform performed less well. Conclusions Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling rapid progressors into clinical trials, especially where there is little information on prior eGFR trajectories.
FinnDiane Study Group and the Scottish Diabetes Research Network (SDRN) Type 1 Bioresource Collaboration, Humans, Diabetic Nephropathies, Diabetes Mellitus, Type 1, Disease Progression, Glomerular Filtration Rate, Chromatography, Liquid, Logistic Models, Bayes Theorem, Adult, Middle Aged, Female, Male, Tandem Mass Spectrometry, Biomarkers
This study was supported by funding from Juvenile Diabetes Research Foundation (Ref. 1-SRA-2016-333-M-R); Chief Scientist Office (Ref. ETM/47); Diabetes UK (Ref. 10/0004010); Folkhälsan Research Foundation; the Wilhelm and Else Stockmann Foundation; the Liv och Hälsa Society; the Novo Nordisk Foundation; the Helsinki University Hospital Research Funds; and the Academy of Finland (134379 and 275614). In-kind contribution from Scottish Diabetes Research Network.
Juvenile Diabetes Research Foundation Ltd (JDRF) (via University of Edinburgh) (1-SRA-2016-333-M-R)
External DOI: https://doi.org/10.1007/s00125-019-4915-0
This record's URL: https://www.repository.cam.ac.uk/handle/1810/292400
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