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

Abstract

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 [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. 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 −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 r 2 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 2 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. Electronic supplementary material The online version of this article (10.1007/s00125-019-4915-0) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Comparison of serum and urinary biomarker panels with albumin/creatinine ratio in the prediction of renal function decline in type 1 diabetes

“…The key findings from this study across the range of CKD stages were that serum biomarkers improve the prediction of future eGFR and progression to <30 ml min −1 [1.73 m] −2 beyond baseline eGFR. As in our past studies [11,21,22], a large number of the biomarkers evaluated showed highly significant associations with eGFR and its decline. However, almost all of the predictive information could be obtained using just a few of these intercorrelated biomarkers.…”

“…Construction of parsimonious panels of biomarkers Urine and serum biomarker sets were modelled independently from each other. As previously described [11], we adopted a Bayesian modelling approach based on hierarchical shrinkage priors, in which the clinical covariates used to control for confounding in the models were assigned a weakly informative Gaussian prior (which induces some regularisation as in ridge regression), while biomarkers were penalised through the regularised horseshoe prior (which heavily shrinks regression coefficients toward zero unless they are informative) to promote sparsity [14]. A similar approach was also adopted elsewhere for biomarker selection in the context of type 2 diabetes mellitus [15].…”

“…An important question is therefore whether any other prognostic biomarkers can improve on ACR or even replace it for predicting renal function decline. In a recent paper [11] we reported a large-scale discovery experiment where we assessed 297 biomarkers in two type 1 diabetes cohorts with starting eGFRs of 75 ml min −1 [1.73 m] −2 or less. We found many highly statistically significant biomarker associations with future eGFR, and that prediction of future eGFR using ACR and baseline eGFR could be improved with biomarkers.…”

Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus

“…Our data further support the use of measures of prior rate of change in eGFR and not just a single baseline eGFR reading when selecting people for entry into randomised trials, such as was recently done in the Preventing Early Renal Loss in Diabetes (PERL) trial [24]. However, prediction of future eGFR remains far from perfect even with the best model, justifying ongoing attempts to identify predictive biomarker panels [25].…”

Protective factors as biomarkers and targets for prevention and treatment of diabetic nephropathy: From current human evidence to future possibilities

“…Uromodulin (UMOD), a most abundant urine protein with a long history in DN, was examined as a predictor for progressive kidney disease in five studies of type 1 diabetes; however, the results of these studies are conflicting [39][40][41][42][43] . The authors of the studies in non-diabetic CKD hypothesized that the observed differences in the magnitude and the direction of the observed association might depend on the timing of the injury and CKD stage, as well as the population being studied and its related comorbid conditions.…”