Biological age as a concept is a measure of the burden of cell and tissue damage, and consequent dysfunction, that causes risk of mortality and disease. Over the past twenty years researchers have developed a range of approaches, starting with epigenetic clocks, that are attempts to produce a useful measure of biological age. There is considerable debate over the degree to which any of these approaches have succeeded, a debate that will only be settled by the accumulation of a great deal of human data. Ultimately, the real utility of a measure of biological age is the rapid assessment of potential rejuvenation therapies, to steer development towards better approaches that produce larger effects. At present it is unclear as to whether any of the approaches can be trusted to produce useful data given an entirely novel approach to the treatment of aging.
Numerous studies have analysed different aspects of biological age and developed clocks and models to assess biological age and measure the molecular changes due to biological ageing. Not only are there several generations of epigenetic clocks used to estimate biological age, but proteome-based clocks were developed, and metabolome- and microbiome-based clocks are being developed as well. Genomic studies have uncovered several genetic mechanisms that promote longevity, with a focus on protective mechanisms such as protective genetic variants and effective DNA repair systems.
Epigenomic changes that influence biological age are modified by diet and exercise and influenced by early life events. Age-related changes in blood proteome were identified, revealing non-linear and organ-specific alterations. Metabolomic profiles in blood plasma have identified age-related shifts in lipid metabolism and redox balance and demonstrated their application as biomarkers for ageing processes and health outcomes. Microbiomics has shown that the uniqueness and diversity of the gut microbiome reflect biological age and that this can also be measured by microbiome derived metabolites in plasma. In addition, multi-omics approaches have uncovered potential biomarkers that not only reflect the ageing processes but can also serve as targets for personalised interventions.
There are several limitations in selecting reliable biomarkers of ageing. First, there is a lack of consistently identified biomarkers, low methodological standardisation, and limited numbers of cohorts in ageing studies. Currently, ageing appears to be a non-linear process that does not progress at the same rate across all biological functions and organs. Comparisons of different clocks and omics data have shown poor correlation, suggesting that each clock or omics may represent a distinct ageing process. There is limited translation of DNA methylation and other biomarkers into clinical practice.
Furthermore, the definition of biological ageing is not yet clearly established within the community. Therefore, relying on only one type of data is unlikely to provide precise, specific, and reliable biomarkers. Ageing is a systems-level biological process, and only systems-level approaches are likely to lead to the development of reliable and interpretable predictions of biological age. Comparison of different omics data has also shown poor correlation between different molecular domains, indicating that each domain may reflect a different ageing process or organ. Moreover, it is clear that individuals and their organs age differently and at different rates.
Link: https://doi.org/10.1016/j.arr.2025.102988
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