11/14/2022 0 Comments Biological age vs chronological age![]() Epigenetic clocks that can quantify a “biological age” that is distinct from chronological age, could have a profound impact on aging research. Since these original reports on mortality risk, studies have increasingly used epigenetic clocks to evaluate interventions that may extend lifespan in both mice(Wang et al., 2017) and humans(Fahy et al., 2019). The first molecular predictors of chronological age included telomere length and p16INK4A levels(Tsygankov et al., 2009) but, recently, “epigenetic clocks” have supplanted them in accuracy and precision(Horvath, 2013), as well as their ability to predict all-cause mortality risk (Perna et al., 2016). By extension, if molecular changes associated with age-related diseases can be identified, hypotheses about potentially effective interventions that extend biological life and healthspan can be made. Predicting age from molecular data has been a long-standing interest in aging research because it implies that we can identify the correlative/causal factors behind aging. However, if purpose-built clocks are necessary for meaningful predictions, then the utility of clocks and their application in the field needs to be considered in that context. Purpose-built clocks for specific tissues age ranges or phenotypes may perform better for their specific purpose. Despite the reproducible and accurate age predictions from DNA methylation data, these findings suggest they may have limited utility as currently designed in understanding the molecular biology of aging and may not be suitable as surrogate endpoints in studies of anti-aging interventions. This is critical to applying clocks both to new sample sets in basic research, as well as understanding if clinically available tissues will be feasible samples to evaluate “epigenetic aging” in unavailable tissues (e.g., brain). We also compare tissue-specific and pan-tissue clock performance. There was only a weak association between “accelerated” epigenetic aging and disease. Of these predictive sites, the average methylation change over a lifetime was small (~1.5%) and these sites were under-represented in canonical regions of epigenetic regulation. We found ~20% of the measured genomic cytosines can be used to make many different epigenetic clocks whose age prediction performance surpasses that of telomere length. To do this, we examined over 450,000 methylation sites from 9,699 samples. Thus, it is important to understand the relationship between predictive clock sites and aging biology. Computational models using DNA methylation data can create “epigenetic clocks” which are proposed to reflect “biological” aging. Epigenetic alterations are a hallmark of aging and age-related diseases. ![]()
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