Biomarker panels associated with progression of renal disease in type 1 diabetes.

Diabetologia 2019 Sep 20;62(9):1616-1627. Epub 2019 Jun 20.

MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK.

Aims/hypothesis: 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[1.73 m], 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 electrospray tandem MS (LC-MS/MS). We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 ml min[1.73 m] year) 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 with using clinical record data alone.

Results: For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p < 10). The strongest associations were with CD27 antigen (CD27), kidney injury molecule 1 (KIM-1) and α1-microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r for prediction of final eGFR from 0.47 to 0.58 in SDRNT1BIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well.

Conclusions/interpretation: 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 individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00125-019-4915-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677704PMC
September 2019
8 Reads

Publication Analysis

Top Keywords

tryptic peptides
12
renal disease
8
final egfr
8
metabolites tryptic
8
proteins metabolites
8
egfr
5
investigated associations
4
platform electrospray
4
electrospray tandem
4
associations final
4
lc-ms/ms investigated
4
tandem lc-ms/ms
4
egfr adjusted
4
progression loss
4
loss 3 ml min[173 m] year
4
3 ml min[173 m] year linear
4
rapid progression
4
egfr rapid
4
adjusted baseline
4
baseline egfr
4

Altmetric Statistics

References

(Supplied by CrossRef)

SJ Livingstone et al.
JAMA 2015

J Skupien et al.
Diabetes Care 2016

HM Colhoun et al.
Diabetologia 2018

T Akbar et al.
Int J Epidemiol 2017

LM Thorn et al.
Diabetes Care 2005

AS Levey et al.
Ann Intern Med 2009

CM Carvalho et al.
Biometrika 2010

B Carpenter et al.
J Stat Softw 2017

Similar Publications