Any sufficiently complex set of biological data can be used to build an aging clock via machine learning techniques, finding combinations of parameters that correlate with biological age, mortality, disease risk, and other outcomes. Phenotypic clocks use measures such as physical performance and clinical chemistry, while epigenetic clocks use DNA methylation or other epigenetic marks. New clocks of all sorts are being produced at a fair pace these days, while some groups are pushing for standardization to some of the better explored epigenetic clocks. Here find a review of the present landscape of phenotypic and epigenetic clocks, while noting that there are many other forms of clock beyond just these: transcriptomic, proteomic, and so forth.
Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process.
This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.
