Send to

Choose Destination
Diabetologia. 2019 Sep;62(9):1616-1627. doi: 10.1007/s00125-019-4915-0. Epub 2019 Jun 20.

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

Author information

Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK.
WellChild Laboratory, Evelina London Children's Hospital, Guy's and St Thomas' National Health Service Foundation Trust, London, UK.
Department of Paediatrics, University of Cambridge, Cambridge, UK.
Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, Australia.
MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK.
Public Health, NHS Fife, Kirkcaldy, UK.



We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes.


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[1.73 m]-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 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[1.73 m]-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 with using clinical record data alone.


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 < 10-4). 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 r2 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 r2 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.


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.


Clinical science; Epidemiology; Metabolomics; Nephropathy; Proteomics

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center