In principle one could develop rejuvenation therapies without a way to measure the overall state of biological age. Each of the underlying causes of aging is measurable today: senescent cell burden, mitochondrial dysfunction, presence of amyloids, and so forth. Therapies can be assessed for efficacy in terms of the degree to which they repair the specific forms of cell and tissue damage that they are intended to repair. That doesn't tell us how much of an effect any given therapy will have on life span, however. It remains the case that while the causes of aging are well discussed, their importance relative to one another remains unknown.
It seems plausible that any approach to rejuvenation therapy will receive comparatively little support in today's environment without some measure of success beyond repair of a form of damage, meaning a measure of the degree to which the future risk of age-related disease and mortality is reduced. Unfortunately, that measure is expensive and slow to achieve via the old-fashioned approach of waiting to see what happens following treatment. Hence the strong focus on the development of aging clocks, technologies that may lead to a consensus method of quickly measuring biological age, and thus the effects of a potential rejuvenation therapy.
Critical review of aging clocks and factors that may influence the pace of aging
Aging research has delineated the aging process by classifying two separate but interconnected mechanisms: intrinsic and extrinsic aging. Intrinsic aging describes changes in biological hallmarks including cellular and molecular changes, genetics, and hormonal changes that have been described to occur naturally over time. Extrinsic aging, however, is regulated by exposure to environmental stressors, dietary habits, oxidative stress, and other factors that accelerate physiologic aging. Traditionally, aging has been quantified by chronological age, which is the exact number of years an individual has lived. However, chronological age does not fully capture the heterogeneity of the aging process, excluding many extrinsic factors that contribute to aging.
Subsequently, the calculation of biological age, which aims to account for interindividual variations in aging rate, has become a topic of interest in aging research. Aging clock models are tools that utilize various modeling approaches to estimate chronological or biological age. Moreover, aging clock models can estimate the rate of aging (ΔAge), otherwise known as the difference between model-predicted biological age and chronological age. Positive differences between model-predicted biological age and chronological age indicate accelerated aging whereas a negative difference indicates decelerated aging. If the calculated ΔAge exceeds the mean absolute error (MAE) of the aging rate estimation, these individuals can be determined to be fast or slow agers.
Aging clocks models may utilize any hallmark changes that occur because of aging, and these may include epigenetic changes, telomere length, genomic stability, altered intercellular communication, chronic inflammation, and gut microbiome dysbiosis, among others. Notably, some of the first aging clock models include the Horvath clock and Hannum clock, which are both epigenetic clocks modeled after changes in DNA methylation patterns and varying cytosine phosphate guanine (CpG) sites across the genome. Several aging clock models have emerged since then, varying from microbiome-based clocks to proteomic clocks. Recent advancements in the development of large databases, omics technologies, and deep learning models have accelerated the creation of aging clock predictions. Thus, this review aims to summarize the currently available aging clock models, with the goal of identifying existing and potential clinical applications.
View the full article at FightAging
