Dataset Information

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

ABSTRACT:

Aims/hypothesis

The aim of this study was to provide data from a contemporary population-representative cohort on rates and predictors of renal decline in type 1 diabetes.

Methods

We used data from a cohort of 5777 people with type 1 diabetes aged 16 and older, diagnosed before the age of 50, and representative of the adult population with type 1 diabetes in Scotland (Scottish Diabetes Research Network Type 1 Bioresource; SDRNT1BIO). We measured serum creatinine and urinary albumin/creatinine ratio (ACR) at recruitment and linked the data to the national electronic healthcare records.

Results

Median age was 44.1 years and diabetes duration 20.9 years. The prevalence of CKD stages G1, G2, G3 and G4 and end-stage renal disease (ESRD) was 64.0%, 29.3%, 5.4%, 0.6%, 0.7%, respectively. Micro/macroalbuminuria prevalence was 8.6% and 3.0%, respectively. The incidence rate of ESRD was 2.5 (95% CI 1.9, 3.2) per 1000 person-years. The majority (59%) of those with chronic kidney disease stages G3-G5 did not have albuminuria on the day of recruitment or previously. Over 11.6 years of observation, the median annual decline in eGFR was modest at -1.3 ml min^-1 [1.73 m]^-2 year^-1 (interquartile range [IQR]: -2.2, -0.4). However, 14% experienced a more significant loss of at least 3 ml min^-1 [1.73 m]^-2. These decliners had more cardiovascular disease (OR 1.9, p?=?5?×?10^-5) and retinopathy (OR 1.3 p?=?0.02). Adding HbA_1c, prior cardiovascular disease, recent mean eGFR and prior trajectory of eGFR to a model with age, sex, diabetes duration, current eGFR and ACR maximised the prediction of final eGFR (r² increment from 0.698 to 0.745, p?-16). Attempting to model nonlinearity in eGFR decline or to detect latent classes of decliners did not improve prediction.

Conclusions

These data show much lower levels of kidney disease than historical estimates. However, early identification of those destined to experience significant decline in eGFR remains challenging.

SUBMITTER: Colombo M

PROVIDER: S-EPMC6997248 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Colombo Marco M McGurnaghan Stuart J SJ Bell Samira S MacKenzie Finlay F Patrick Alan W AW Petrie John R JR McKnight John A JA MacRury Sandra S Traynor Jamie J Metcalfe Wendy W McKeigue Paul M PM Colhoun Helen M HM

Diabetologia 20191205 3

<h4>Aims/hypothesis</h4>The aim of this study was to provide data from a contemporary population-representative cohort on rates and predictors of renal decline in type 1 diabetes.<h4>Methods</h4>We used data from a cohort of 5777 people with type 1 diabetes aged 16 and older, diagnosed before the age of 50, and representative of the adult population with type 1 diabetes in Scotland (Scottish Diabetes Research Network Type 1 Bioresource; SDRNT1BIO). We measured serum creatinine and urinary albumi ...[more]

PMID: 31807796

Similar Datasets

Project description: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.

Dataset Information

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

Aims/hypothesis

Methods

Results

Conclusions

Publications

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

Similar Datasets

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