DNA methylation marker identification and poly-methylation risk score in prediction of healthspan termination

Abstract

Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13–1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6, CTSK). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.

Tweetable abstract

The concept of the ‘healthspan’, the time an individual remains morbidity-free, requires more attention. Poly-methylation risk scores have a promising application in discriminating subpopulations at risk of healthspan termination to facilitate early prevention.

Keywords: :

• DNA methylation
• epigenetic effects
• epigenetic mechanisms
• epigenetics and disease
• healthspan termination
• methylation risk scores

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: wwww.tandfonline.com/doi/suppl/10.2217/epi-2023-0343

Author contributions

MQ Yang: data analysis, data interpretation and manuscript preparation. M Wang and XY Zhao: data collation, methodology and supervision. FF Xu, S Liang: code review and supervision. YF Wang and NX Wang: chart collation and verification. ML Sambou and Y Jiang: language proofreading. JC Dai: conceptualization, methodology, study design and supervision. All authors critically reviewed and revised the paper.

Acknowledgments

The authors thank Yaohua Yang and Jirong Long of Vanderbilt University Medical Center for their technical support in the construction of predicted models in this research.

Financial disclosure

This work was supported by the Major Projects of the National Natural Science Foundation of China (82192904, 82192903) and the Science Fund for Distinguished Young Scholars of Jiangsu Province (BK20211533). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

Each study has been approved by the Ethical Committees of the original studies.

Data sharing statement

The data used in model building and refining (FHS and WHI) are publicly available via dbGaP (dbGaP accession numbers: phs000342 and phs000724 for FHS; phs000315, phs000675 and phs001335 for WHI). The ADNI dataset analyzed in the current study is available in the ADNI repository, www.adni-info.org. This research was conducted using the UKB resource under application IDs: 64689 and 79151. Further information is available from the corresponding author upon request.