A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking

Albert T. Higgins-Chen*, Kyra L. Thrush, Yunzhang Wang, Christopher J. Minteer, Pei-Lun Kuo, Meng Wang, Peter Niimi, Gabriel Sturm, Jue Lin, Ann Zenobia Moore, Stefania Bandinelli, Christiaan H. Vinkers, Eric Vermetten, Bart P. F. Rutten, Elbert Geuze, Cynthia Okhuijsen-Pfeifer, Marte Z. van der Horst, Stefanie Schreiter, Stefan Gutwinski, Jurjen J. LuykxMartin Picard, Luigi Ferrucci, Eileen M. Crimmins, Marco P. Boks, Sara Hägg, Tina T. Hu-Seliger, Morgan E. Levine*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show that technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components (PCs) from CpG-level data as input for biological age prediction. Our retrained PC versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or previous knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of PC-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies and clinical trials of aging interventions.
Original languageEnglish
Pages (from-to)644-661
Number of pages18
JournalNature Aging
Issue number7
Publication statusPublished - 1 Jul 2022

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