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Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden

Robert F. Hillary, Anna J. Stevenson, Daniel L. McCartney, Archie Campbell, View ORCID ProfileRosie M. Walker, View ORCID ProfileDavid M. Howard, Craig W. Ritchie, Steve Horvath, Caroline Hayward, View ORCID ProfileAndrew M. McIntosh, David J. Porteous, Ian J. Deary, Kathryn L. Evans, Riccardo E. Marioni
doi: https://doi.org/10.1101/2020.01.31.928648
Robert F. Hillary
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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Anna J. Stevenson
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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Daniel L. McCartney
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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Archie Campbell
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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Rosie M. Walker
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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  • ORCID record for Rosie M. Walker
David M. Howard
2Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
3Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4UX, UK
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Craig W. Ritchie
4Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4UX, UK
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Steve Horvath
5Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90095-7088, USA
6Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, 90095-1772, USA
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Caroline Hayward
7MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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Andrew M. McIntosh
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
3Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4UX, UK
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  • ORCID record for Andrew M. McIntosh
David J. Porteous
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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Ian J. Deary
8Lothian Birth Cohorts, University of Edinburgh, Edinburgh, EH8 9JZ, UK
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Kathryn L. Evans
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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Riccardo E. Marioni
1Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
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  • For correspondence: riccardo.marioni@ed.ac.uk
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Abstract

Individuals of the same chronological age display different rates of biological ageing. A number of measures of biological age have been proposed which harness age-related changes in DNA methylation profiles. These include methylation-based predictors of chronological age (HorvathAge, HannumAge), all-cause mortality (DNAm PhenoAge, DNAm GrimAge) and telomere length (DNAm Telomere Length). In this study, we test the association between these epigenetic markers of ageing and the prevalence and incidence of the leading causes of disease burden and mortality in high-income countries. Furthermore, we test the clocks’ relationships with phenotypic measures associated with these conditions, including spirometric and biochemical traits. We carry out these analyses in 9,537 individuals from the Generation Scotland: Scottish Family Health Study. We find that DNAm GrimAge outperforms other epigenetic clocks in its associations with self-report disease prevalence and related clinical traits. DNAm GrimAge associates with chronic obstructive pulmonary disease (COPD) prevalence (Odds Ratio = 3.29, P = 3.0 × 10-4) and pulmonary spirometry tests (β = [-0.10 to −0.15], P = [1.4 × 10-4 to 1.4 × 10-6]) at study baseline after adjusting for possibly confounding risk factors including alcohol, body mass index, deprivation, education and smoking. After adjusting for these confounding risk factors, DNAm GrimAge, DNAm PhenoAge and DNAm Telomere Length, measured at study baseline, predict incidence of ICD-10-coded disease states including COPD, type 2 diabetes and cardiovascular disease after thirteen years of follow-up (Hazard Ratios = [0.80 (telomere length) to 2.19 (GrimAge)], P = [9.9 × 10-4, 1.9 × 10-14]). Our data show that despite accounting for several possible confounding variables, epigenetic markers of ageing predict incidence of common disease. This may have significant implications for their potential utility in clinical settings to complement gold-standard methods of clinical assessment and management.

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Posted February 01, 2020.
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Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden
Robert F. Hillary, Anna J. Stevenson, Daniel L. McCartney, Archie Campbell, Rosie M. Walker, David M. Howard, Craig W. Ritchie, Steve Horvath, Caroline Hayward, Andrew M. McIntosh, David J. Porteous, Ian J. Deary, Kathryn L. Evans, Riccardo E. Marioni
bioRxiv 2020.01.31.928648; doi: https://doi.org/10.1101/2020.01.31.928648
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Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden
Robert F. Hillary, Anna J. Stevenson, Daniel L. McCartney, Archie Campbell, Rosie M. Walker, David M. Howard, Craig W. Ritchie, Steve Horvath, Caroline Hayward, Andrew M. McIntosh, David J. Porteous, Ian J. Deary, Kathryn L. Evans, Riccardo E. Marioni
bioRxiv 2020.01.31.928648; doi: https://doi.org/10.1101/2020.01.31.928648

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