Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing

• Published in: Scientific reports Vol. 9; no. 1; pp. 8862 - 10
• Main Authors: Daunay, Antoine, Baudrin, Laura G, Deleuze, Jean-François, How-Kit, Alexandre
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 20.06.2019; Nature Publishing Group UK
• Subjects: Adult; Age; Aged; Aging - genetics; Biochemistry, Molecular Biology; Correlation analysis; Deoxyribonucleic acid; DNA; DNA - blood; DNA Methylation; Female; Genetic Markers; Genetics; Genomics; High-Throughput Nucleotide Sequencing; Humans; Life Sciences; Male; Middle Aged; Models, Statistical; Prediction models; Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-45197-w

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 , Park , Thong , Weidner , and the Zbiec-Piekarska 1 and Zbiec-Piekarska 2 ), 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.