MskAge—An Epigenetic Biomarker of Musculoskeletal Age Derived From a Genetic Algorithm Islands Model

ABSTRACT Age is a significant risk factor for functional decline and disease of the musculoskeletal system, yet few biomarkers exist to facilitate ageing research in musculoskeletal tissues. Multivariate models based on DNA methylation, termed epigenetic clocks, have shown promise as markers of biol...

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Bibliographic Details
Published inAging cell Vol. 24; no. 9; pp. e70149 - n/a
Main Authors Green, Daniel C., Reynard, Louise N., Henstock, James R., Reppe, Sjur, Gautvik, Kaare, Peffers, Mandy J., Shanley, Daryl P., Clegg, Peter D., Canty‐Laird, Elizabeth G.
Format Journal Article
LanguageEnglish
Published England John Wiley & Sons, Inc 01.09.2025
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ISSN1474-9718
1474-9726
1474-9728
1474-9726
DOI10.1111/acel.70149

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Summary:ABSTRACT Age is a significant risk factor for functional decline and disease of the musculoskeletal system, yet few biomarkers exist to facilitate ageing research in musculoskeletal tissues. Multivariate models based on DNA methylation, termed epigenetic clocks, have shown promise as markers of biological age. However, the accuracy of existing epigenetic clocks in musculoskeletal tissues are no more, and often less accurate than a randomly sampled baseline model. We developed a highly accurate epigenetic clock, MskAge, that is specific to tissues and cells of the musculoskeletal system. MskAge was built using a penalised genetic algorithm islands model that addresses multi‐tissue clock bias. The final model was trained on the transformed principal components of CpGs selected by the genetic algorithm. We show that MskAge tracks epigenetic ageing ex vivo and in vitro. Epigenetic age estimates are rejuvenated with cellular reprogramming and are accelerated at a rate of 0.45 years per population doubling. MskAge explains more variance associated with in vitro ageing of fibroblasts than the purpose‐developed skin and blood clock. The precision of MskAge and its ability to capture perturbations in biological ageing make it a promising research tool for musculoskeletal and ageing biologists. Created in BioRender. Laird, E. (2025) https://BioRender.com/bqfajrn.
Bibliography:Funding
This work was funded by the Medical Research Council Versus Arthritis Centre for Integrated Research into Musculoskeletal Ageing (CIMA) [MR/R502182/1].
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ISSN:1474-9718
1474-9726
1474-9728
1474-9726
DOI:10.1111/acel.70149