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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
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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.
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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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