Every year, if we are fortunate enough, we get to spend our birthday with our loved ones. Although we become one year older, our bodies tell a different story. Our chronological age is simply the elapsed time since birth, while our biological age depends on our biological condition. The question is, how can we accurately measure biological age, and hence aging?
To measure functional capability better than chronological age, in 1988, Baker and Sprott proposed identifying biomarkers of aging, and the U.S. National Institute on Aging (NIA) led the 10-year Biomarker Program to identify them (Baker, 1988; Sprott, 1999). Sprott’s conclusion was that, “although progress is being made in developing biomarkers of aging, it is too early to constitute a definitive panel of biomarkers for either animal models or humans” (Sprott, 1999).
Fifteen years later, Steve Horvath published the first multi-tissue biomarker age estimator that applies to all sources of DNA, except sperm, and to the entire lifespan—from prenatal samples to centenarians (Horvath, 2013; Horvath et al., 2018). Horvath credits the success of his epigenetic clock to scientific breakthroughs such as the Human Genome Project, microarray technology, and the advancement of biostatistics as well as open access to big data, like the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), through which he collected thousands of DNA methylation (DNAm) samples (Horvath, 2013; Horvath et al., 2018).
Increasing evidence suggests that a myriad of aging manifestations are epigenetic—reversible changes to gene expression without altering the DNA sequence (Horvath, 2013; Oberdoerffer, 2007; Tsurumi & Li, 2012). DNAm is a type of epigenetic control and chronological age has a profound effect on genome-wide DNAm levels, thereby causing genes to get hyper or hypomethylated (Horvath, 2013). Epigenetic clocks, or age estimators, are sets of CpGs incorporated in a mathematical algorithm to estimate years of age of a DNA source—such as cells, tissues, or organs (Horvath, 2013; Horvath et al., 2018). These estimators are made by regressing chronological age on CpGs using a supervised machine learning method, such as a penalized regression model like lasso or elastic net. The model then selects the most informative CpGs for estimating age.
Horvath’s clock automatically selected 353 CpGs from 8,000 that were used to train and test the model (Horvath, 2013; Horvath et al., 2018). The 193 positively and 160 negatively correlated CpGs get respectively hypermethylated and hypomethylated with age (Horvath, 2013; Horvath et al., 2018). The clock’s high accuracy has been validated in hundreds of independent data sets (Horvath et al., 2018). And, it has been leveraged along blood-based age estimators such as Hannum’s clock and Levine’s DNAm PhenoAge to experimentally study biological age and potential longevity interventions in cells, non-human animals, and humans (Horvath et al., 2018). While vast research still needs to be done, a small clinical trial (TRIIM) conducted by Fahy, Horvath, and a team of researchers suggests that biological age can be indeed reversed (Abbott, 2019; Fahy, 2019).
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Abbott, Alison. “First Hint That Body's 'Biological Age' Can Be Reversed.” Nature, vol. 573, no. 7773, 2019, p. 173, https://doi.org/10.1038/d41586-019-02638-w.
Fahy, Gregory M, et al. “Reversal of Epigenetic Aging and Immunosenescent Trends in Humans.” Aging Cell, vol. 18, no. 6, 2019, pp. e13028-n/a, https://doi.org/10.1111/acel.13028.