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Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus

BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response...

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Autores principales: Pena, Michelle J., Heinzel, Andreas, Rossing, Peter, Parving, Hans-Henrik, Dallmann, Guido, Rossing, Kasper, Andersen, Steen, Mayer, Bernd, Heerspink, Hiddo J. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4932762/
https://www.ncbi.nlm.nih.gov/pubmed/27378474
http://dx.doi.org/10.1186/s12967-016-0960-3
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author Pena, Michelle J.
Heinzel, Andreas
Rossing, Peter
Parving, Hans-Henrik
Dallmann, Guido
Rossing, Kasper
Andersen, Steen
Mayer, Bernd
Heerspink, Hiddo J. L.
author_facet Pena, Michelle J.
Heinzel, Andreas
Rossing, Peter
Parving, Hans-Henrik
Dallmann, Guido
Rossing, Kasper
Andersen, Steen
Mayer, Bernd
Heerspink, Hiddo J. L.
author_sort Pena, Michelle J.
collection PubMed
description BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria. METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response. RESULTS: In discovery, median change in urinary albumin excretion (UAE) was −42 % [Q1–Q3: −69 to −8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was −43 % [Q1–Q35: −63 to −23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response. CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-016-0960-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-49327622016-07-06 Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus Pena, Michelle J. Heinzel, Andreas Rossing, Peter Parving, Hans-Henrik Dallmann, Guido Rossing, Kasper Andersen, Steen Mayer, Bernd Heerspink, Hiddo J. L. J Transl Med Research BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria. METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response. RESULTS: In discovery, median change in urinary albumin excretion (UAE) was −42 % [Q1–Q3: −69 to −8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was −43 % [Q1–Q35: −63 to −23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response. CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-016-0960-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-07-05 /pmc/articles/PMC4932762/ /pubmed/27378474 http://dx.doi.org/10.1186/s12967-016-0960-3 Text en © The Author(s) 2016 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 Research
Pena, Michelle J.
Heinzel, Andreas
Rossing, Peter
Parving, Hans-Henrik
Dallmann, Guido
Rossing, Kasper
Andersen, Steen
Mayer, Bernd
Heerspink, Hiddo J. L.
Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
title Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
title_full Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
title_fullStr Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
title_full_unstemmed Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
title_short Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
title_sort serum metabolites predict response to angiotensin ii receptor blockers in patients with diabetes mellitus
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4932762/
https://www.ncbi.nlm.nih.gov/pubmed/27378474
http://dx.doi.org/10.1186/s12967-016-0960-3
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