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Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes
AIMS/HYPOTHESIS: Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. METHODS: We combi...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061158/ https://www.ncbi.nlm.nih.gov/pubmed/29796748 http://dx.doi.org/10.1007/s00125-018-4641-z |
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author | Nowak, Christoph Carlsson, Axel C. Östgren, Carl Johan Nyström, Fredrik H. Alam, Moudud Feldreich, Tobias Sundström, Johan Carrero, Juan-Jesus Leppert, Jerzy Hedberg, Pär Henriksen, Egil Cordeiro, Antonio C. Giedraitis, Vilmantas Lind, Lars Ingelsson, Erik Fall, Tove Ärnlöv, Johan |
author_facet | Nowak, Christoph Carlsson, Axel C. Östgren, Carl Johan Nyström, Fredrik H. Alam, Moudud Feldreich, Tobias Sundström, Johan Carrero, Juan-Jesus Leppert, Jerzy Hedberg, Pär Henriksen, Egil Cordeiro, Antonio C. Giedraitis, Vilmantas Lind, Lars Ingelsson, Erik Fall, Tove Ärnlöv, Johan |
author_sort | Nowak, Christoph |
collection | PubMed |
description | AIMS/HYPOTHESIS: Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. METHODS: We combined data from six prospective epidemiological studies of 30–77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay. Multivariable-adjusted Cox regression was used in a discovery/replication design to identify biomarkers for incident MACE. We used gradient-boosted machine learning and lasso regularised Cox regression in a random 75% training subsample to assess whether adding proteins to risk factors included in the Swedish National Diabetes Register risk model would improve the prediction of MACE in the separate 25% test subsample. RESULTS: Of 1211 adults with type 2 diabetes (32% women), 211 experienced a MACE over a mean (±SD) of 6.4 ± 2.3 years. We replicated associations (<5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)-12, IL-27 subunit α (IL-27a), kidney injury molecule (KIM)-1, fibroblast growth factor (FGF)-23, protein S100-A12, TNF receptor (TNFR)-1, TNFR-2 and TNF-related apoptosis-inducing ligand receptor (TRAIL-R)2. Addition of the 80-protein assay to established risk factors improved discrimination in the separate test sample from 0.686 (95% CI 0.682, 0.689) to 0.748 (95% CI 0.746, 0.751). A sparse model of 20 added proteins achieved a C statistic of 0.747 (95% CI 0.653, 0.842) in the test sample. CONCLUSIONS/INTERPRETATION: We identified eight protein biomarkers, four of which are novel, for risk of MACE in community residents with type 2 diabetes, and found improved risk prediction by combining multiplex proteomics with an established risk model. Multiprotein arrays could be useful in identifying individuals with type 2 diabetes who are at highest risk of a cardiovascular event. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-018-4641-z) contains peer-reviewed but unedited supplementary material, which is available to authorised users. |
format | Online Article Text |
id | pubmed-6061158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-60611582018-08-09 Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes Nowak, Christoph Carlsson, Axel C. Östgren, Carl Johan Nyström, Fredrik H. Alam, Moudud Feldreich, Tobias Sundström, Johan Carrero, Juan-Jesus Leppert, Jerzy Hedberg, Pär Henriksen, Egil Cordeiro, Antonio C. Giedraitis, Vilmantas Lind, Lars Ingelsson, Erik Fall, Tove Ärnlöv, Johan Diabetologia Article AIMS/HYPOTHESIS: Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. METHODS: We combined data from six prospective epidemiological studies of 30–77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay. Multivariable-adjusted Cox regression was used in a discovery/replication design to identify biomarkers for incident MACE. We used gradient-boosted machine learning and lasso regularised Cox regression in a random 75% training subsample to assess whether adding proteins to risk factors included in the Swedish National Diabetes Register risk model would improve the prediction of MACE in the separate 25% test subsample. RESULTS: Of 1211 adults with type 2 diabetes (32% women), 211 experienced a MACE over a mean (±SD) of 6.4 ± 2.3 years. We replicated associations (<5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)-12, IL-27 subunit α (IL-27a), kidney injury molecule (KIM)-1, fibroblast growth factor (FGF)-23, protein S100-A12, TNF receptor (TNFR)-1, TNFR-2 and TNF-related apoptosis-inducing ligand receptor (TRAIL-R)2. Addition of the 80-protein assay to established risk factors improved discrimination in the separate test sample from 0.686 (95% CI 0.682, 0.689) to 0.748 (95% CI 0.746, 0.751). A sparse model of 20 added proteins achieved a C statistic of 0.747 (95% CI 0.653, 0.842) in the test sample. CONCLUSIONS/INTERPRETATION: We identified eight protein biomarkers, four of which are novel, for risk of MACE in community residents with type 2 diabetes, and found improved risk prediction by combining multiplex proteomics with an established risk model. Multiprotein arrays could be useful in identifying individuals with type 2 diabetes who are at highest risk of a cardiovascular event. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-018-4641-z) contains peer-reviewed but unedited supplementary material, which is available to authorised users. Springer Berlin Heidelberg 2018-05-24 2018 /pmc/articles/PMC6061158/ /pubmed/29796748 http://dx.doi.org/10.1007/s00125-018-4641-z Text en © The Author(s) 2018 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 Nowak, Christoph Carlsson, Axel C. Östgren, Carl Johan Nyström, Fredrik H. Alam, Moudud Feldreich, Tobias Sundström, Johan Carrero, Juan-Jesus Leppert, Jerzy Hedberg, Pär Henriksen, Egil Cordeiro, Antonio C. Giedraitis, Vilmantas Lind, Lars Ingelsson, Erik Fall, Tove Ärnlöv, Johan Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes |
title | Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes |
title_full | Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes |
title_fullStr | Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes |
title_full_unstemmed | Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes |
title_short | Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes |
title_sort | multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061158/ https://www.ncbi.nlm.nih.gov/pubmed/29796748 http://dx.doi.org/10.1007/s00125-018-4641-z |
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