Research Paper|Volume 8, Issue 5|pp 1034—1044

Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures

Monika Eipel^1,², Felix Mayer³, Tanja Arent³, Marcelo R. P. R.P. Ferreira^4,⁵, Carina Birkhofer⁶, Uwe Gerstenmaier⁶, Ivan G. G. Costa^2,⁵, Stefanie Ritz-Timme³, Wolfgang Wagner^1,²

¹Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University, Aachen, Germany
²Institute for Biomedical Engineering - Cell Biology, University Hospital of RWTH Aachen, Aachen, Germany
³Institute for Legal Medicine, Heinrich Heine University, Düsseldorf, Germany
⁴Department of Statistics, Centre for Natural and Exact Sciences, Federal University of Paraiba, 58051-900, João Pessoa, Brazil
⁵IZKF Computational Biology Research Group, University Hospital of RWTH Aachen, Aachen, Germany
⁶Varionostic GmbH, 89081 Ulm, Germany

Received: February 18, 2016Accepted: May 18, 2016Published: May 31, 2016

https://doi.org/10.18632/aging.100972

Copyright: © 2016 Eipel et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Aging is reflected by highly reproducible DNA methylation (DNAm) changes that open new perspectives for estimation of chronological age in legal medicine. DNA can be harvested non-invasively from cells at the inside of a person's cheek using buccal swabs – but these specimens resemble heterogeneous mixtures of buccal epithelial cells and leukocytes with different epigenetic makeup. In this study, we have trained an age predictor based on three age-associated CpG sites (associated with the genes PDE4C, ASPA, and ITGA2B) for swab samples to reach a mean absolute deviation (MAD) between predicted and chronological age of 4.3 years in a training set and of 7.03 years in a validation set. Subsequently, the composition of buccal epithelial cells versus leukocytes was estimated by two additional CpGs (associated with the genes CD6 and SERPINB5). Results of this “Buccal-Cell-Signature” correlated with cell counts in cytological stains (R² = 0.94). Combination of cell type-specific and age-associated CpGs into one multivariate model enabled age predictions with MADs of 5.09 years and 5.12 years in two independent validation sets. Our results demonstrate that the cellular composition in buccal swab samples can be determined by DNAm at two cell type-specific CpGs to improve epigenetic age predictions.