Literature DB >> 26280308

Improved age determination of blood and teeth samples using a selected set of DNA methylation markers.

Bram Bekaert1,2, Aubeline Kamalandua1, Sara C Zapico3, Wim Van de Voorde1,2, Ronny Decorte1,2.   

Abstract

Age estimation from DNA methylation markers has seen an exponential growth of interest, not in the least from forensic scientists. The current published assays, however, can still be improved by lowering the number of markers in the assay and by providing more accurate models to predict chronological age. From the published literature we selected 4 age-associated genes (ASPA, PDE4C, ELOVL2, and EDARADD) and determined CpG methylation levels from 206 blood samples of both deceased and living individuals (age range: 0-91 years). This data was subsequently used to compare prediction accuracy with both linear and non-linear regression models. A quadratic regression model in which the methylation levels of ELOVL2 were squared showed the highest accuracy with a Mean Absolute Deviation (MAD) between chronological age and predicted age of 3.75 years and an adjusted R(2) of 0.95. No difference in accuracy was observed for samples obtained either from living and deceased individuals or between the 2 genders. In addition, 29 teeth from different individuals (age range: 19-70 years) were analyzed using the same set of markers resulting in a MAD of 4.86 years and an adjusted R(2) of 0.74. Cross validation of the results obtained from blood samples demonstrated the robustness and reproducibility of the assay. In conclusion, the set of 4 CpG DNA methylation markers is capable of producing highly accurate age predictions for blood samples from deceased and living individuals.

Entities:  

Keywords:  CpG marker; DNA methylation; DNA-based age prediction; quadratic regression modeling

Mesh:

Substances:

Year:  2015        PMID: 26280308      PMCID: PMC4844214          DOI: 10.1080/15592294.2015.1080413

Source DB:  PubMed          Journal:  Epigenetics        ISSN: 1559-2294            Impact factor:   4.528


  29 in total

1.  Age related changes in 5-methylcytosine content in human peripheral leukocytes and placentas: an HPLC-based study.

Authors:  C Fuke; M Shimabukuro; A Petronis; J Sugimoto; T Oda; K Miura; T Miyazaki; C Ogura; Y Okazaki; Y Jinno
Journal:  Ann Hum Genet       Date:  2004-05       Impact factor: 1.670

2.  Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science.

Authors:  Renata Zbieć-Piekarska; Magdalena Spólnicka; Tomasz Kupiec; Żanetta Makowska; Anna Spas; Agnieszka Parys-Proszek; Krzysztof Kucharczyk; Rafał Płoski; Wojciech Branicki
Journal:  Forensic Sci Int Genet       Date:  2014-10-14       Impact factor: 4.882

3.  Evaluation of DNA methylation markers and their potential to predict human aging.

Authors:  Deborah Soares Bispo Santos Silva; Joana Antunes; Kuppareddi Balamurugan; George Duncan; Clarice Sampaio Alho; Bruce McCord
Journal:  Electrophoresis       Date:  2015-07-14       Impact factor: 3.535

4.  Methylation of ELOVL2 gene as a new epigenetic marker of age.

Authors:  Paolo Garagnani; Maria G Bacalini; Chiara Pirazzini; Davide Gori; Cristina Giuliani; Daniela Mari; Anna M Di Blasio; Davide Gentilini; Giovanni Vitale; Sebastiano Collino; Serge Rezzi; Gastone Castellani; Miriam Capri; Stefano Salvioli; Claudio Franceschi
Journal:  Aging Cell       Date:  2012-10-14       Impact factor: 9.304

5.  A twin study of mitochondrial DNA polymorphisms shows that heteroplasmy at multiple sites is associated with mtDNA variant 16093 but not with zygosity.

Authors:  Toby Andrew; Cassandra D Calloway; Sarah Stuart; Sang Hoon Lee; Raj Gill; Gail Clement; Philip Chowienczyk; Tim D Spector; Ana M Valdes
Journal:  PLoS One       Date:  2011-08-03       Impact factor: 3.240

6.  Epigenetic predictor of age.

Authors:  Sven Bocklandt; Wen Lin; Mary E Sehl; Francisco J Sánchez; Janet S Sinsheimer; Steve Horvath; Eric Vilain
Journal:  PLoS One       Date:  2011-06-22       Impact factor: 3.240

7.  Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population.

Authors:  Jordana T Bell; Pei-Chien Tsai; Tsun-Po Yang; Ruth Pidsley; James Nisbet; Daniel Glass; Massimo Mangino; Guangju Zhai; Feng Zhang; Ana Valdes; So-Youn Shin; Emma L Dempster; Robin M Murray; Elin Grundberg; Asa K Hedman; Alexandra Nica; Kerrin S Small; Emmanouil T Dermitzakis; Mark I McCarthy; Jonathan Mill; Tim D Spector; Panos Deloukas
Journal:  PLoS Genet       Date:  2012-04-19       Impact factor: 5.917

8.  Increased DNA methylation levels of the insulin-like growth factor binding protein 1 gene are associated with type 2 diabetes in Swedish men.

Authors:  Tianwei Gu; Harvest F Gu; Agneta Hilding; Louise K Sjöholm; Claes-Göran Ostenson; Tomas J Ekström; Kerstin Brismar
Journal:  Clin Epigenetics       Date:  2013-11-19       Impact factor: 6.551

9.  Aging of blood can be tracked by DNA methylation changes at just three CpG sites.

Authors:  Carola Ingrid Weidner; Qiong Lin; Carmen Maike Koch; Lewin Eisele; Fabian Beier; Patrick Ziegler; Dirk Olaf Bauerschlag; Karl-Heinz Jöckel; Raimund Erbel; Thomas Walter Mühleisen; Martin Zenke; Tim Henrik Brümmendorf; Wolfgang Wagner
Journal:  Genome Biol       Date:  2014-02-03       Impact factor: 13.583

10.  Aging effects on DNA methylation modules in human brain and blood tissue.

Authors:  Steve Horvath; Yafeng Zhang; Peter Langfelder; René S Kahn; Marco P M Boks; Kristel van Eijk; Leonard H van den Berg; Roel A Ophoff
Journal:  Genome Biol       Date:  2012-10-03       Impact factor: 13.583

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  42 in total

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Journal:  Mol Genet Genomics       Date:  2016-11-04       Impact factor: 3.291

2.  Epigenetic Associations With Estimated Glomerular Filtration Rate Among Men With Human Immunodeficiency Virus Infection.

Authors:  Junyu Chen; Yunfeng Huang; Qin Hui; Raina Mathur; Marta Gwinn; Kaku So-Armah; Matthew S Freiberg; Amy C Justice; Ke Xu; Vincent C Marconi; Yan V Sun
Journal:  Clin Infect Dis       Date:  2020-02-03       Impact factor: 9.079

3.  Differences in non-enzymatic glycation products in human dentine and clavicle: changes with aging.

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4.  DNA methylation levels and telomere length in human teeth: usefulness for age estimation.

Authors:  Ana Belén Márquez-Ruiz; Lucas González-Herrera; Juan de Dios Luna; Aurora Valenzuela
Journal:  Int J Legal Med       Date:  2020-01-02       Impact factor: 2.686

5.  Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors.

Authors:  Anastasia Aliferi; David Ballard
Journal:  Methods Mol Biol       Date:  2022

6.  Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites.

Authors:  Xueli Han; Chao Xiao; Shaohua Yi; Ya Li; Maomin Chen; Daixin Huang
Journal:  Int J Legal Med       Date:  2022-07-11       Impact factor: 2.791

7.  Application of Aspartic Acid Racemization for Age Estimation in a Spanish Sample.

Authors:  Sara C Zapico; Douglas H Ubelaker
Journal:  Biology (Basel)       Date:  2022-06-03

8.  DNA methylation of decedent blood samples to estimate the chronological age of human remains.

Authors:  Yessenia Anaya; Patrick Yew; Katherine A Roberts; W Reef Hardy
Journal:  Int J Legal Med       Date:  2021-07-10       Impact factor: 2.686

Review 9.  The use of DNA methylation clock in aging research.

Authors:  Xi He; Jiaojiao Liu; Bo Liu; Jingshan Shi
Journal:  Exp Biol Med (Maywood)       Date:  2020-11-11

10.  Pubertal development in healthy children is mirrored by DNA methylation patterns in peripheral blood.

Authors:  Kristian Almstrup; Marie Lindhardt Johansen; Alexander S Busch; Casper P Hagen; John E Nielsen; Jørgen Holm Petersen; Anders Juul
Journal:  Sci Rep       Date:  2016-06-28       Impact factor: 4.379

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