Abstract
Aims/hypothesisWe aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes.MethodsWe 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.ResultsFor 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.Conclusions/interpretationAmong 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.
Highlights
Diabetic kidney disease (DKD) is the cause of most renal failure and impaired renal function in type 1 diabetes mellitus
We evaluated the same set of biomarkers in two different cohorts, the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) [4] and the Finnish Diabetic Nephropathy study (FinnDiane) [5], to assess reproducibility and generalisability of the results across a range of characteristics
We report median and interquartile range (IQR) for continuous variables, and frequency for categorical variables a p value is for the difference in means or proportions between the two cohorts b For the albumin/ creatinine ratio (ACR) category we compared normoalbuminuric to all others
Summary
Diabetic kidney disease (DKD) is the cause of most renal failure and impaired renal function in type 1 diabetes mellitus. As such it is a major contributor to the reduced life span in type 1 diabetes [1]. There is wide variation in the rate of renal function decline among those with type 1 diabetes with some people being much more susceptible than others. This makes conducting clinical trials of drugs challenging because, over the typical trial follow-up time, the average loss in renal function is modest [2]. An improved ability to predict which people with diabetes will progress most rapidly would facilitate oversampling of such people into clinical trials, thereby improving trial power
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