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Mechanism

Epigenetic clocks

Tools that predict biological age from the DNA methylation pattern: Horvath (353 CpG sites), Hannum (71), GrimAge, DNAm PhenoAge. Epigenetic acceleration predicts mortality and disease.
Category
Scientific evidence
Very high
Number of findings
3
Sources
Our own summaries · Cited studies

Effect by goal · level of evidence

Longevity

reduces Very high cohorts (AA)
Aging biomarker

Each one-year increase in PhenoAge is associated with +9% all-cause mortality, +10% cardiovascular, and +20% from diabetes.

View the study (cohorts) →

Inflammation

reduces Moderate cohorts (AA)
Exposome marker

Air pollution accelerates the clock by up to 6 years; chronic alcohol, 2.2 years.

View the study (cohorts) →

Dementia and Alzheimer's

reduces Moderate RCT (AA)
Partially reversible

Vitamin D supplementation at 4,000 IU/day reduced epigenetic aging by 1.85 years in a trial of deficient African Americans.

View the study (RCT) →

In depth

*⏱ 4 min read

Epigenetic clocks are biological age prediction tools based on changes in an individual's DNA methylation profile, a type of chemical modification that alters gene activity without changing the gene sequence and that accumulates systematically throughout life, leaving a molecular fingerprint that modern algorithms can read with surprising precision.

DNA methylation is the process by which a methyl group, a chemical structure of three hydrogen atoms and one carbon atom, attaches to one of the four nitrogenous bases of DNA, usually cytosine in regions called CpG islands, changing the geometry of the DNA strand and silencing the expression of adjacent genes, with direct consequences for aging and susceptibility to chronic diseases.

Chronological, epigenetic, and biological age

The science of aging distinguishes three concepts that do not always coincide: chronological age, which is simply the number of years lived; epigenetic age, which reflects the DNA methylation pattern and can differ significantly from the former; and biological or phenotypic age, which measures the real physiological and functional state of the organism by calculating the risk of disease and death in comparison with people of the same chronological age.

Epigenetic age acceleration occurs when epigenetic age exceeds chronological age, and it can be due to intrinsic factors, such as metabolism and genetics, as well as extrinsic factors related to lifestyle and environment, including diet, smoking, exposure to ultraviolet radiation, air pollution, and certain mental disorders such as major depression.

The main epigenetic clocks

The Hannum clock, created by Dr. Gregory Hannum, calculates epigenetic age based on 71 CpG positions in human blood DNA, with high accuracy for predicting life expectancy although limited to adult blood tissue; the original Horvath clock, by Dr. Steven Horvath, uses 353 CpG islands from 51 different types of tissues and cells, identifying epigenetic age with 96% accuracy to within approximately four years of actual age.

GrimAge, creatively named after the Grim Reaper, is a composite clock that predicts life expectancy and health in units of years by combining seven DNA methylation estimators with an estimator of smoking pack-years, being 18% more accurate than chronological age and 14% more accurate than other epigenetic clocks, and able to reliably predict time to death, coronary heart disease, cancer, and menopause.

The DNAm PhenoAge clock, created by Dr. Morgan Levine and Dr. Steve Horvath, calculates an individual's phenotypic age from methylation at 513 CpG islands and from clinical biomarkers of age-related disease, such as albumin, creatinine, glucose, C-reactive protein, and blood components; each year of increase in epigenetic PhenoAge is associated with 9% higher all-cause mortality, 10% higher cardiovascular mortality, and 20% higher mortality from diabetes.

What accelerates and what slows epigenetic aging

Studies that have used the Horvath clock to assess epigenetic acceleration have identified that higher fish consumption, elevated levels of carotenoids as a marker of fruit and vegetable intake, a higher level of education, moderate alcohol consumption, and higher physical activity are associated with slower extrinsic epigenetic aging, whereas a high BMI and metabolic syndrome accelerate both intrinsic and extrinsic aging.

Vitamin D supplementation appears to influence epigenetic age: in a study of 51 African American people with suboptimal vitamin D levels, taking 4,000 IU daily was associated with a 1.85-year reduction in epigenetic aging according to the Horvath clock, while 2,000 IU daily reduced aging according to the Hannum clock by 1.90 years, which suggests that other dietary and lifestyle factors could have similar effects.

Air pollution accelerates the epigenetic clock significantly: a study of more than 2,700 white women exposed to particulate matter found that their epigenetic aging accelerated by up to six years, while high alcohol consumption accelerated phenotypic aging by an average of 2.2 years in people with alcohol use disorder, with a polymorphism in the APOL2 gene further amplifying that effect.

Reprogramming and the future of epigenetic clocks

One of the most fascinating frontiers of this field is the possibility of resetting the epigenetic clock: Dr. Shinya Yamanaka discovered a group of proteins, now known as Yamanaka factors, that can reprogram mature differentiated cells into pluripotent stem cells, and whose short-term expression can rejuvenate cellular markers of aging and extend the lifespan of organisms by partially resetting the innate epigenetic clock without inducing carcinogenesis.

Epigenetic clocks have potential applications ranging from forensic medicine to the early prevention of chronic diseases, and the direction in which research is pointing is clear: instead of asking whether a person's biomarkers are better, the clinical trials of the future will ask whether that person is simply aging better, turning the biology of aging into a science of precision intervention and not only of observation.

References

  1. Muntean, Andrew G.; Hess, Jay L. (2009). Epigenetic Dysregulation In Cancer. The American Journal Of Pathology 175, 4.
  2. He, Yu; et al. (2019). Epigenetic Dysregulation Of Enhancers In Neurons Is Associated With Alzheimers Disease Pathology And Cognitive Symptoms. Nature Communications 10, 1.
  3. Alberti, Savenio; et al. (2015). Epigenetic Inheritance And The Missing Heritability. Human Genomics 9, 1.
  4. Helin, Kristian; Rasmussen, Kasper Dindler (2016). Role Of TET Enzymes In DNA Methylation, Development, And Cancer. Genes & Development 30, 7.
  5. Kusui, Chika; et al. (2001). DNA Methylation Of The Human Oxytocin Receptor Gene Promoter Regulates Tissue-Specific Gene Suppression. Biochemical And Biophysical Research Communications 289, 3.
  6. Wang, Tina; Havas, Aaron; Ideker, Trey (2014). DNA Methylation Aging Clocks: Theory And Practice. Nature Reviews Genetics.
  7. Yuen, Rachel K. C.; et al. (2011). Systematic Changes In Diel Variation In CpG Methylation In Murine Peripheral Tissues. Genome Biology And Evolution 3.
  8. Horvath, Steve (2013). DNA Methylation Age Of Human Tissues And Cell Types. Genome Biology 14, 10.
  9. Jung, Minjung; Pfeifer, Gerd P. (2015). Aging And DNA Methylation. BMC Biology 13, 1.
  10. Ehrlich, Melanie (2002). DNA Methylation In Cancer: Too Much, But Also Too Little. Oncogene 21, 35.
  11. Kulis, Miriam; Esteller, Manel (2010). DNA Methylation And Cancer. Advances In Genetics 70.
  12. Weinhouse, Caren; et al. (2016). An Epigenome-Wide Association Study Of Early-Life Tobaccosmoke Exposure In Preschool-Aged Children. Environmental Health Perspectives 124, 12.
  13. Dolinoy, Dana C. (2008). The Agouti Mouse Model: An Epigenetic Biosensor For Nutritional And Environmental Alterations On The Fetal Epigenome. Nutrition Reviews 66.
  14. Markides, Kyriakos S.; Coreil, Jeannine (1986). The Health Of Hispanics In The Southwestern United States: An Epidemiologic Paradox. Public Health Reports 101, 3.
  15. Dato, Serena; et al. (2018). Contribution Of Epigenetic Variation To Phenotypic Variability Of Human Aging: Population Differences. The Journals Of Gerontology 73, 11.
  16. Levine, Morgan E.; et al. (2018). An Epigenetic Biomarker Of Aging For Lifespan And Healthspan. Aging 10, 4.
  17. Lavigne, Guillaume; et al. (2018). Epigenetic Clock Analysis Of Diet, Exercise, Education, And Lifestyle Factors. Aging 10, 2.
  18. Hannum, Gregory; et al. (2013). Genome-Wide Methylation Profiles Reveal Quantitative Views Of Human Aging Rates. Molecular Cell 49, 2.
  19. Simons, Robert L.; et al. (2016). Discrimination, Segregation, And Chronic Inflammation: Testing The Weathering Explanation For The Poor Health Of Black Americans. Developmental Psychology 52, 11.
  20. Smith, Amy K.; et al. (2011). Differential White Matter Microstructure And Trajectory In Schizophrenia And Bipolar Disorder. Journal Of Psychiatric Research 45, 5.
  21. Gao, Xu; et al. (2016). DNA Methylation Changes Of Whole Blood Cells In Response To Active Smoking Exposure In Adults: A Systematic Review Of DNA Methylation Studies. Clinical Epigenetics 8, 1.
  22. Horvath, Steve; et al. (2014). Accelerated Epigenetic Aging In Down Syndrome. Aging Cell 14, 3.
  23. Horvath, Steve; et al. (2014). Obesity Accelerates Epigenetic Aging Of Human Liver. Proceedings Of The National Academy Of Sciences 111, 43.
  24. Fahy, Greg M.; et al. (2019). Reversal Of Epigenetic Aging And Immunosenescent Trends In Humans. Aging Cell 18, 6.
  25. Lu, Ake T.; et al. (2019). DNA Methylation GrimAge Strongly Predicts Lifespan And Healthspan. Aging 11, 2.
  26. Kresovich, Jacob K.; et al. (2019). Methylation-Based Biological Age And Breast Cancer Risk. Journal Of The National Cancer Institute 111, 10.
  27. Lind, Mats J.; et al. (2018). Epigenetic Aging In Major Depressive Disorder. American Journal Of Psychiatry 175, 8.
  28. Peng, Cheng; et al. (2016). Epigenome-Wide Signature Of Exposure To Particulate Matter Air Pollution In Multiple Cohorts. Environmental Research 151.
  29. Takahashi, Kazutoshi; Yamanaka, Shinya (2006). Induction Of Pluripotent Stem Cells From Mouse Embryonic And Adult Fibroblast Cultures By Defined Factors. Cell 126, 4.
  30. Ocampo, Alejandro; et al. (2016). In Vivo Amelioration Of Age-Associated Hallmarks By Partial Reprogramming. Cell 167, 7.
  31. Tajima, Shingo; Suetake, Isao; Takeshita, Kohei; Nakagawa, Atsushi; Kimura, Hironobu (2016). Domain Structure Of The Dnmt1, Dnmt3a, And Dnmt3b DNA Methyltransferases. Advances In Experimental Medicine And Biology 945.
  32. Maegawa, Shinji; et al. (2017). Caloric Restriction Delays Age-Related Methylation Drift. Nature Communications 8, 1.

Information, not medical advice. Scientific evidence reflects the quality of the studies.

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