Fight Aging!

Cellular biochemistry changes in characteristic ways with age. Aging is a stochastic process of damage accumulation, followed by diverse consequences, but there are considerable similarities under the hood for all that the final dysfunctions are so varied and individual. An intricate iron structure left unprotected in the rain will collapse in any one of a hundred different ways, but underlying all of those possible breakages is the one common process of rust. Thus any sufficiently large body of data derived from individuals of various ages, whether omics or clinical chemistry or functional tests, can be used to produce algorithm combinations of values that reflect biological age. These algorithms are known as aging clocks.

The first, and still most widely used clocks are based on epigenetic data. Specifically they make use of the methylation status of CpG sites on the genome, decorations to nuclear DNA that change its structure to expose or hide specific regions, and thus change patterns of gene expression - which proteins are produced, and in what amount. A cell is in constant feedback with itself and its environment, DNA methylation constantly changing. But some of those changes are characteristic of damage and damaged environment of aged tissues.

The challenge with DNA methylation clocks, or any other aging clock, lies in understanding how the measurements made connect to underlying processes of aging and age-related diseases. Since the clocks are produced by machine learning approaches operating on data, that understanding doesn't exist yet. It is hard to take a clock measurement at face value without either knowing how its data relates to mechanisms of aging, or without a great deal of validation using real world data. It seems plausible that the real world validation approach will beat out the slow path to sufficient understanding, at least when it comes to justifying the use of some forms of aging clock for some forms of intervention in the matter of aging.

As today's open access paper notes, DNA methylation clocks have been used in a fair number of clinical trials for interventions that might be expected to modestly adjust the pace of aging or state of aging. There is enough data to start talking about when and how we should trust these DNA methylation clock measures. Nonetheless, this is still only the first step along a much longer road. In a world in which people continue to debate the conclusions of extensive clinical data for common therapies decades after their introduction - consider aspirin use for example - rapid consensus should not be the expected outcome for any tool or treatments.

DNAm aging biomarkers are responsive: Insights from 51 longevity interventional studies in humans

Aging biomarkers can potentially allow researchers to rapidly monitor the impact of an aging intervention, without the need for decade-spanning trials, by acting as surrogate endpoints. Prior to testing whether aging biomarkers may be useful as surrogate endpoints, it is first necessary to determine whether they are responsive to interventions that target aging. Epigenetic clocks are aging biomarkers based on DNA methylation (DNAm) with prognostic value for many aging outcomes. Many individual studies are beginning to explore whether epigenetic clocks are responsive to interventions. However, the diversity of both interventions and epigenetic clocks in different studies make them difficult to compare systematically.

Here, we curate TranslAGE-Response, a harmonized database of 51 public and private longitudinal interventional studies and calculate a consistent set of 16 prominent epigenetic clocks for each study, along with 95 other DNAm biomarkers that help explain changes in each clock. With this database, we discover patterns of responsiveness across a variety of interventions and DNAm biomarkers. For example, clocks trained to predict mortality or pace of aging have the strongest response across all interventions and show consistent agreement with each other, pharmacological and lifestyle interventions drive the strongest response from DNAm biomarkers, and study population and study duration are key factors in driving responsiveness of DNAm biomarkers in an intervention. Some classes of interventions such as TNF-alpha inhibitors have strong, consistent effects across multiple studies, while others such as senolytic drugs have inconsistent effects. Clocks with multiple sub-scores (i.e. "explainable clocks") provide specificity and greater mechanistic insight into responsiveness of interventions than single-score clocks.

Our work can help the geroscience field design future clinical trials, by guiding the choice of interventions, specific subsets of epigenetic clocks to minimize multiple testing, study duration, study population, and sample size, with the eventual aim of determining whether epigenetic clocks can be used as surrogate endpoints.