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Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing
DNA methylation has been identified as the most promising molecular biomarker for the prediction of age. Several DNA methylation-based models have been proposed for age prediction based on blood samples, using mainly pyrosequencing. These methods present different performances for age prediction and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586942/ https://www.ncbi.nlm.nih.gov/pubmed/31222117 http://dx.doi.org/10.1038/s41598-019-45197-w |
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author | Daunay, Antoine Baudrin, Laura G. Deleuze, Jean-François How-Kit, Alexandre |
author_facet | Daunay, Antoine Baudrin, Laura G. Deleuze, Jean-François How-Kit, Alexandre |
author_sort | Daunay, Antoine |
collection | PubMed |
description | DNA methylation has been identified as the most promising molecular biomarker for the prediction of age. Several DNA methylation-based models have been proposed for age prediction based on blood samples, using mainly pyrosequencing. These methods present different performances for age prediction and have rarely, if ever, been evaluated and intercompared in an independent validation study. Here, for the first time, we evaluate and compare six blood-based age prediction models (Bekaert(1), Park(2), Thong(3), Weidner(4), and the Zbiec-Piekarska 1(5) and Zbiec-Piekarska 2(6)), using DNA methylation analysis by pyrosequencing on 100 blood samples from French individuals aged between 19–65 years. For each model, we perform correlation analysis and evaluate age-prediction performance (mean absolute deviation (MAD) and standard error of the estimate (SEE)). The best age-prediction performances were found with the Bekaert and Thong models (MAD of 4.5–5.2, SEE of 6.8–7.2), followed by the Zbiec-Piekarska 1 model (MAD of 6.8 and SEE of 9.2), while the Park, Weidner and Zbiec-Piekarska 2 models presented lower performances (MAD of 7.2–8.7 and SEE of 9.2–10.3). Given these results, we recommend performing systematic, independent evaluation of all age prediction models on a same cohort to validate the different models and compare their performance. |
format | Online Article Text |
id | pubmed-6586942 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65869422019-06-27 Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing Daunay, Antoine Baudrin, Laura G. Deleuze, Jean-François How-Kit, Alexandre Sci Rep Article DNA methylation has been identified as the most promising molecular biomarker for the prediction of age. Several DNA methylation-based models have been proposed for age prediction based on blood samples, using mainly pyrosequencing. These methods present different performances for age prediction and have rarely, if ever, been evaluated and intercompared in an independent validation study. Here, for the first time, we evaluate and compare six blood-based age prediction models (Bekaert(1), Park(2), Thong(3), Weidner(4), and the Zbiec-Piekarska 1(5) and Zbiec-Piekarska 2(6)), using DNA methylation analysis by pyrosequencing on 100 blood samples from French individuals aged between 19–65 years. For each model, we perform correlation analysis and evaluate age-prediction performance (mean absolute deviation (MAD) and standard error of the estimate (SEE)). The best age-prediction performances were found with the Bekaert and Thong models (MAD of 4.5–5.2, SEE of 6.8–7.2), followed by the Zbiec-Piekarska 1 model (MAD of 6.8 and SEE of 9.2), while the Park, Weidner and Zbiec-Piekarska 2 models presented lower performances (MAD of 7.2–8.7 and SEE of 9.2–10.3). Given these results, we recommend performing systematic, independent evaluation of all age prediction models on a same cohort to validate the different models and compare their performance. Nature Publishing Group UK 2019-06-20 /pmc/articles/PMC6586942/ /pubmed/31222117 http://dx.doi.org/10.1038/s41598-019-45197-w Text en © The Author(s) 2019 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/. |
spellingShingle | Article Daunay, Antoine Baudrin, Laura G. Deleuze, Jean-François How-Kit, Alexandre Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing |
title | Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing |
title_full | Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing |
title_fullStr | Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing |
title_full_unstemmed | Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing |
title_short | Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing |
title_sort | evaluation of six blood-based age prediction models using dna methylation analysis by pyrosequencing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586942/ https://www.ncbi.nlm.nih.gov/pubmed/31222117 http://dx.doi.org/10.1038/s41598-019-45197-w |
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