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Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus
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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997248/ https://www.ncbi.nlm.nih.gov/pubmed/31807796 http://dx.doi.org/10.1007/s00125-019-05052-z |
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author | Colombo, Marco McGurnaghan, Stuart J. Bell, Samira MacKenzie, Finlay Patrick, Alan W. Petrie, John R. McKnight, John A. MacRury, Sandra Traynor, Jamie Metcalfe, Wendy McKeigue, Paul M. Colhoun, Helen M. |
author_facet | Colombo, Marco McGurnaghan, Stuart J. Bell, Samira MacKenzie, Finlay Patrick, Alan W. Petrie, John R. McKnight, John A. MacRury, Sandra Traynor, Jamie Metcalfe, Wendy McKeigue, Paul M. Colhoun, Helen M. |
author_sort | Colombo, Marco |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-019-05052-z) contains peer-reviewed but unedited supplementary material, which is available to authorised users. |
format | Online Article Text |
id | pubmed-6997248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-69972482020-02-19 Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus Colombo, Marco McGurnaghan, Stuart J. Bell, Samira MacKenzie, Finlay Patrick, Alan W. Petrie, John R. McKnight, John A. MacRury, Sandra Traynor, Jamie Metcalfe, Wendy McKeigue, Paul M. Colhoun, Helen M. Diabetologia Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-019-05052-z) contains peer-reviewed but unedited supplementary material, which is available to authorised users. Springer Berlin Heidelberg 2019-12-05 2020 /pmc/articles/PMC6997248/ /pubmed/31807796 http://dx.doi.org/10.1007/s00125-019-05052-z Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Colombo, Marco McGurnaghan, Stuart J. Bell, Samira MacKenzie, Finlay Patrick, Alan W. Petrie, John R. McKnight, John A. MacRury, Sandra Traynor, Jamie Metcalfe, Wendy McKeigue, Paul M. Colhoun, Helen M. Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus |
title | Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus |
title_full | Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus |
title_fullStr | Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus |
title_full_unstemmed | Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus |
title_short | Predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus |
title_sort | predicting renal disease progression in a large contemporary cohort with type 1 diabetes mellitus |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997248/ https://www.ncbi.nlm.nih.gov/pubmed/31807796 http://dx.doi.org/10.1007/s00125-019-05052-z |
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