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

Marco Colombo, Stuart J. McGurnaghan, Samira Bell, Finlay MacKenzie, Alan W. Patrick, John R. Petrie, John A. McKnight, Sandra MacRury, Jamie Traynor, Wendy Metcalfe, Paul M. McKeigue, Helen M. Colhoun (Lead / Corresponding author), Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO), The Scottish Renal Registry (SRR)

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
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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 (r 2 increment from 0.698 to 0.745, p < 10 −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.

Original languageEnglish
Pages (from-to)636-647
Number of pages12
Issue number3
Early online date5 Dec 2019
Publication statusPublished - Mar 2020


  • Clinical science
  • Epidemiology
  • Nephropathy

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism


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