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Predicting the probability of death using proteomics
Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortalit...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213855/ https://www.ncbi.nlm.nih.gov/pubmed/34145379 http://dx.doi.org/10.1038/s42003-021-02289-6 |
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author | Eiriksdottir, Thjodbjorg Ardal, Steinthor Jonsson, Benedikt A. Lund, Sigrun H. Ivarsdottir, Erna V. Norland, Kristjan Ferkingstad, Egil Stefansson, Hreinn Jonsdottir, Ingileif Holm, Hilma Rafnar, Thorunn Saemundsdottir, Jona Norddahl, Gudmundur L. Thorgeirsson, Gudmundur Gudbjartsson, Daniel F. Sulem, Patrick Thorsteinsdottir, Unnur Stefansson, Kari Ulfarsson, Magnus O. |
author_facet | Eiriksdottir, Thjodbjorg Ardal, Steinthor Jonsson, Benedikt A. Lund, Sigrun H. Ivarsdottir, Erna V. Norland, Kristjan Ferkingstad, Egil Stefansson, Hreinn Jonsdottir, Ingileif Holm, Hilma Rafnar, Thorunn Saemundsdottir, Jona Norddahl, Gudmundur L. Thorgeirsson, Gudmundur Gudbjartsson, Daniel F. Sulem, Patrick Thorsteinsdottir, Unnur Stefansson, Kari Ulfarsson, Magnus O. |
author_sort | Eiriksdottir, Thjodbjorg |
collection | PubMed |
description | Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death. |
format | Online Article Text |
id | pubmed-8213855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82138552021-07-01 Predicting the probability of death using proteomics Eiriksdottir, Thjodbjorg Ardal, Steinthor Jonsson, Benedikt A. Lund, Sigrun H. Ivarsdottir, Erna V. Norland, Kristjan Ferkingstad, Egil Stefansson, Hreinn Jonsdottir, Ingileif Holm, Hilma Rafnar, Thorunn Saemundsdottir, Jona Norddahl, Gudmundur L. Thorgeirsson, Gudmundur Gudbjartsson, Daniel F. Sulem, Patrick Thorsteinsdottir, Unnur Stefansson, Kari Ulfarsson, Magnus O. Commun Biol Article Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death. Nature Publishing Group UK 2021-06-18 /pmc/articles/PMC8213855/ /pubmed/34145379 http://dx.doi.org/10.1038/s42003-021-02289-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Eiriksdottir, Thjodbjorg Ardal, Steinthor Jonsson, Benedikt A. Lund, Sigrun H. Ivarsdottir, Erna V. Norland, Kristjan Ferkingstad, Egil Stefansson, Hreinn Jonsdottir, Ingileif Holm, Hilma Rafnar, Thorunn Saemundsdottir, Jona Norddahl, Gudmundur L. Thorgeirsson, Gudmundur Gudbjartsson, Daniel F. Sulem, Patrick Thorsteinsdottir, Unnur Stefansson, Kari Ulfarsson, Magnus O. Predicting the probability of death using proteomics |
title | Predicting the probability of death using proteomics |
title_full | Predicting the probability of death using proteomics |
title_fullStr | Predicting the probability of death using proteomics |
title_full_unstemmed | Predicting the probability of death using proteomics |
title_short | Predicting the probability of death using proteomics |
title_sort | predicting the probability of death using proteomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213855/ https://www.ncbi.nlm.nih.gov/pubmed/34145379 http://dx.doi.org/10.1038/s42003-021-02289-6 |
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