Title |
DNA methylation age of blood predicts all-cause mortality in later life
|
---|---|
Published in |
Genome Biology, January 2015
|
DOI | 10.1186/s13059-015-0584-6 |
Pubmed ID | |
Authors |
Riccardo E Marioni, Sonia Shah, Allan F McRae, Brian H Chen, Elena Colicino, Sarah E Harris, Jude Gibson, Anjali K Henders, Paul Redmond, Simon R Cox, Alison Pattie, Janie Corley, Lee Murphy, Nicholas G Martin, Grant W Montgomery, Andrew P Feinberg, M Daniele Fallin, Michael L Multhaup, Andrew E Jaffe, Roby Joehanes, Joel Schwartz, Allan C Just, Kathryn L Lunetta, Joanne M Murabito, John M Starr, Steve Horvath, Andrea A Baccarelli, Daniel Levy, Peter M Visscher, Naomi R Wray, Ian J Deary |
Abstract |
BackgroundDNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age.ResultsHere we test whether differences between people¿s chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age, (¿age), was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between ¿age and mortality. A 5-year higher ¿age is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher ¿age. A pedigree-based heritability analysis of ¿age was conducted in a separate cohort. The heritability of ¿age was 0.43.ConclusionsDNA methylation-derived measures of accelerated ageing are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 26 | 25% |
United Kingdom | 12 | 11% |
Spain | 8 | 8% |
Australia | 5 | 5% |
France | 2 | 2% |
Italy | 2 | 2% |
India | 2 | 2% |
Canada | 2 | 2% |
South Africa | 1 | <1% |
Other | 7 | 7% |
Unknown | 38 | 36% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 56 | 53% |
Scientists | 41 | 39% |
Practitioners (doctors, other healthcare professionals) | 6 | 6% |
Science communicators (journalists, bloggers, editors) | 2 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | <1% |
Germany | 3 | <1% |
United Kingdom | 3 | <1% |
Spain | 2 | <1% |
Sweden | 2 | <1% |
Uruguay | 1 | <1% |
Austria | 1 | <1% |
Czechia | 1 | <1% |
France | 1 | <1% |
Other | 5 | <1% |
Unknown | 874 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 184 | 20% |
Student > Ph. D. Student | 177 | 20% |
Student > Bachelor | 85 | 9% |
Student > Master | 81 | 9% |
Professor | 49 | 5% |
Other | 166 | 18% |
Unknown | 160 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 214 | 24% |
Agricultural and Biological Sciences | 187 | 21% |
Medicine and Dentistry | 107 | 12% |
Neuroscience | 39 | 4% |
Psychology | 37 | 4% |
Other | 116 | 13% |
Unknown | 202 | 22% |