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  • Title: Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus.
    Author: Colombo M, McGurnaghan SJ, Bell S, MacKenzie F, Patrick AW, Petrie JR, McKnight JA, MacRury S, Traynor J, Metcalfe W, McKeigue PM, Colhoun HM, Scottish Diabetes Research Network (SDRN) Type 1 Bioresource Investigators and the Scottish Renal Registry.
    Journal: Diabetologia; 2020 Mar; 63(3):636-647. PubMed ID: 31807796.
    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 HbA1c, 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 < 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.
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