Biological age reflects the functional and molecular state of the body, providing a measure of how 'aged' it is, as opposed to chronological age, which counts the years since birth.

Since 2013, scientists have developed various 'aging clocks' that predict biological age by analyzing different biomarkers, including DNA methylation, protein profiles, and immune function.

The earliest of these, epigenetic clocks, utilize DNA methylation patterns to estimate biological age, with recent advances incorporating multi-generational models that include health, mortality data, and causality analyses to better understand aging mechanisms.

Most aging clocks rely on machine learning models trained on biomarker data from blood samples and other sources, analyzing patterns to estimate biological age and associated health risks.

In addition to epigenetic clocks, other types measure biomarkers such as proteins (proteomic clocks), metabolites (metabolomic clocks), and gene activity (transcriptomic clocks), with some being organ-specific or combined into multiomic clocks.

Currently, these aging clocks are primarily used in research to evaluate how treatments impact biological aging, with potential future applications in clinical settings once they can reliably predict health outcomes and respond to interventions.