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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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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. |
format | Online Article Text |
id | pubmed-4932762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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