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Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria
BACKGROUND: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. METHODS: Urine samples were obtained from 155 pat...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5889591/ https://www.ncbi.nlm.nih.gov/pubmed/29625564 http://dx.doi.org/10.1186/s12933-018-0697-9 |
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author | Currie, Gemma E. von Scholten, Bernt Johan Mary, Sheon Flores Guerrero, Jose-Luis Lindhardt, Morten Reinhard, Henrik Jacobsen, Peter K. Mullen, William Parving, Hans-Henrik Mischak, Harald Rossing, Peter Delles, Christian |
author_facet | Currie, Gemma E. von Scholten, Bernt Johan Mary, Sheon Flores Guerrero, Jose-Luis Lindhardt, Morten Reinhard, Henrik Jacobsen, Peter K. Mullen, William Parving, Hans-Henrik Mischak, Harald Rossing, Peter Delles, Christian |
author_sort | Currie, Gemma E. |
collection | PubMed |
description | BACKGROUND: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. METHODS: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. RESULTS: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. CONCLUSION: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5889591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58895912018-04-10 Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria Currie, Gemma E. von Scholten, Bernt Johan Mary, Sheon Flores Guerrero, Jose-Luis Lindhardt, Morten Reinhard, Henrik Jacobsen, Peter K. Mullen, William Parving, Hans-Henrik Mischak, Harald Rossing, Peter Delles, Christian Cardiovasc Diabetol Original Investigation BACKGROUND: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. METHODS: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. RESULTS: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. CONCLUSION: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users. BioMed Central 2018-04-06 /pmc/articles/PMC5889591/ /pubmed/29625564 http://dx.doi.org/10.1186/s12933-018-0697-9 Text en © The Author(s) 2018 Open AccessThis 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Original Investigation Currie, Gemma E. von Scholten, Bernt Johan Mary, Sheon Flores Guerrero, Jose-Luis Lindhardt, Morten Reinhard, Henrik Jacobsen, Peter K. Mullen, William Parving, Hans-Henrik Mischak, Harald Rossing, Peter Delles, Christian Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria |
title | Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria |
title_full | Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria |
title_fullStr | Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria |
title_full_unstemmed | Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria |
title_short | Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria |
title_sort | urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria |
topic | Original Investigation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5889591/ https://www.ncbi.nlm.nih.gov/pubmed/29625564 http://dx.doi.org/10.1186/s12933-018-0697-9 |
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