Machine learning techniques can be used to generate aging clocks from any sufficiently complex set of biological data obtained from individuals of varying chronological ages. The research community is generating new clocks at a fair pace, most of which are doomed to vanish into obscurity, while trying to better understand best use cases and limitations of the small number of very well-studied clocks. As researchers here point out, when omics data is used as the foundation for a clock, it is typically only one type of omics data, most commonly epigenomic data. It is suggested that new clocks should be developed that employ multiple omics sources, not just one. While I’m sympathetic to this view, it seems to me that the priority remains to make better use of the well-studied clocks that exist. It remains the case that no-one has solved the issues that prevent the use of clocks as a low-cost, fast way to assess the effectiveness of novel approaches to rejuvenation.
Biological clocks can be broadly categorized by the outcome used to train them. Early clocks, including Horvath’s clock and Hannum’s clock, were trained to predict chronological age from DNA methylation (DNAm) patterns and demonstrated the ability to track biological aging across tissues and identify accelerated aging in conditions. Other clocks have since been developed, providing enhanced predictive accuracy for age-associated diseases, life expectancy, and personal aging patterns. However, clocks trained on chronological age have generally shown limited ability to predict age-related disease incidence or mortality in the general population. To address this limitation, a newer generation of clocks has been trained directly on hard outcomes such as all-cause mortality and disease incidence, DNAm-based approaches, metabolomics, and routine clinical biomarkers, consistently demonstrating superior performance in predicting health trajectories.
Organ-specific aging clocks differ conceptually from traditional biomarker-based approaches. While individual biomarkers reflect isolated molecular changes associated with aging, organ clocks integrate coordinated molecular patterns within a specific tissue to estimate its biological age relative to chronological age. This distinction is critical as organs do not age uniformly and may exhibit asynchronous aging trajectories that are not captured by systemic or single-biomarker models. By quantifying tissue-level functional decline rather than individual molecular alterations, organ clocks provide a framework for linking molecular aging to organ-specific disease risk and clinical outcomes.
However, single-omic approaches provide partial insights due to the multidimensional nature of biological aging which encompasses genetic, epigenetic, transcriptional, proteomic, and metabolic dimensions that interact across tissues and change dynamically over time. Therefore, multi-omics approaches have gained attention as integrative methods that capture interactions to provide a more holistic assessment of biological aging. Despite these advances, only a few studies have integrated multi-omics clocks. Even in the case of multi-omics, technical variability across different omics platforms and limited availability of longitudinal datasets limit the ability to track changes in biological aging. This review aims to evaluate current biological clock approaches, explore strategies for multi-omics integration, and suggest a respective framework.
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