# Where are we in the implementation of tissue-specific epigenetic clocks?

## TL;DR

This paper evaluates how tissue-specific DNA methylation clocks perform compared to general ones, finding that tissue-specific clocks may be more effective for disease prediction.

## Contribution

The study identifies a novel set of genes potentially relevant to epigenetic clocks and evaluates performance factors like model type and dataset size.

## Key findings

- Elastic-net penalization performs best for epigenetic clocks, especially with larger datasets.
- Tissue-specific clocks outperform generic blood-based clocks.
- A novel set of genes (CPT1A, MMP15, SHROOM3, SLIT3, SYNGR) was identified for further investigation.

## Abstract

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense.

Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set.

Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when—unsurprisingly—the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR.

Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.