Despite success in prolonging the human lifespan, there has been hardly any reduction in chronic disease and illnesses afflicting the elderly. Consequently, many of the years spent during this extended lifespan are in ill health, placing an immense strain on healthcare systems.

Aiming to reduce levels of disease affliction and maximize good health throughout the human lifespan, biologists are creating methods that utilize artificial intelligence to measure biological age and understand how aging heightens the risk of disease. One recent area of development is in epigenetics (a field of science that investigates the activation and deactivation of genes through chemical modifications of the DNA molecule) and the invention and evolution of DNA methylation aging clocks, also referred to as epigenetic clocks.

In order to understand how epigenetic clocks work, one has to understand that there are two types of age: chronological age and biological age. Chronological age simply refers to the number of years one has lived; however, chronological age doesn’t always accurately represent the state of one’s health. Biological age indicates physiological health using measurements of various biomarkers and often reflects a person’s genetics and lifestyle habits. Biological age serves as an important and relevant measurement of someone’s health as it closely relates to mortality and disease rates. For example, an elevated biological age relative to chronological age usually indicates a higher risk of death and morbidity.

In 2011, Steve Horvath, a professor of human genetics and biostatistics at UCLA, discovered a way to estimate biological age using DNA methylation levels (a biological process where methyl groups are added to the DNA molecule) from saliva samples. In 2013, Horvath invented the first multi-tissue epigenetic clock applicable to all nucleated cells, tissues, and organs. The epigenetic clock incorporates an algorithmic combination of DNA methylation marks (genomic regions with certain methylation patterns) associated with chronological age to accurately calculate an age-related phenotype or outcome. In many cases, this phenotype or outcome represents disease, mortality, or debilities, and can be used to determine biological age.

Since 2013, Horvath and other biologists have continued to devise variations of the epigenetic clock. The latest development of the epigenetic clock is called the GrimAge clock, which predicts mortality and morbidity risks. Data collected from the GrimAge clock revealed that the mortality risk for the top 5% of fastest aging people is twice that of the average person.

Furthermore, biologists have begun to use these epigenetic clocks to identify and develop anti-aging treatments and interventions. Recent studies have tested the effects of drugs and compounds on the aging of cells, specifically whether they reverse or slow the aging of cells.

In 2019, Horvath conducted a small phase-1 human clinical trial in which nine healthy participants took three common drugs for one year. The results of the study revealed a biological age reversal effect of 2.5 years. While Horvath and his team continue to follow up with larger and more controlled validation studies, the current results appear very promising. With continued research and experimentation in the field of epigenetics, biologists will likely continue to discover novel interventions and treatments to decelerate or reverse biological aging, ultimately lowering morbidity and mortality rates.