Methylation clocks promise to revolutionize testing of anti-aging interventions in humans, by measuring their effectiveness in a few months instead of having to wait years to see if they affect mortality statistics. But there are signs that the clocks we have can be deceptive. I think this is because the clocks capture aspects of aging that are defensive and aspects that are programmed, and there has been no effort to tease them apart. To the extent that the body is defending itself against perceived damage, the clocks are measuring the wrong thing. (This summary probably won’t make sense until you read the details below — please be patient.)
I recently submitted an academic study on this subject.
We have a lot of life extension treatments that work in lab animals. Do any of these work in humans? In practice, we need aging clocks to test them, because testing them directly requires tens of thousands of people to be followed over decades for each intervention. I’ve been enthusiastic about aging clocks since Steve Horvath published his groundbreaking analysis in 2013. But recently I’ve realized that the field needs a course correction.
Epigenetic clocks are based on patterns of methylation in our DNA. Genes are turned on and off at different places in the body, at different times of day, and, crucially, at different stages of life. Methylation is the most accessible and easiest to measure of many methods by which the body turns genes on and off. By focusing on the methylation patterns that change consistently over a lifetime, Hannum, Horvath and others who came after them have created computer algorithms that can calculate a “biological age”.
The idea of “biological age” doesn’t depend on your theory of aging. But the utility of epigenetic clocks in assessing the benefits of putative anti-aging measures certainly does depend on fundamental concepts about aging, about which experts are still divided.
Why does gene expression change in old age?
“Nothing in biology makes sense except in the light of evolution.”
— T. Dobzhansky
1) If you believe in programmed aging, then the directed changes in gene expression are means of self-destruction. Genes are turned on that increase inflammation, destroying arteries and neurons. Apoptosis is up-regulated to the point where healthy muscle and brain cells are dying. Protective anti-oxidants, DNA repair, and autophagy are down-regulated. All this destruction is accomplished via turning genes on and off. If any intervention sets back the methylation clock, then there is less self-destruction, more repair and maintenance. We expect that the body will live longer if the methylation clock reads a lower age.
2) If you believe the neo-Darwinist theory that the body cannot be purposely destroying itself, then aging is an accumulation of incidental damage at the cellular and molecular levels. If there are associated epigenetic changes, these cannot be causing the destruction, so they must be a response to the damage. Changes in gene expression as captured in the methylation clocks must be the body’s effort to protect itself with increased immune function, increased autophagy, increased antioxidants, increased DNA repair. If any intervention sets back the methylation clock, then there is less repair and maintenance. We expect that setting the aging clock back to a younger age will actually decrease life expectancy. This insight is counter-intuitive, but, if correct, it changes the logic of methylation clocks.
For people who don’t believe that aging is an evolved program, the whole idea of a methylation clock is a non-starter. No matter how accurate the clock is, setting it back is counter-productive. Even if the clock is calibrated to markers of health (like the PhenoAge clock) or calibrated to actual mortality (GrimAge), it is still based on the body’s response to damage, and not on the damage itself. Setting back the clock is counter-productive, because it means dialing down the body’s repair and maintenance system.
Since 2013, there has been a kind of double-think in the world of anti-aging research. Most researchers, at least in public, continue to embrace perspective (2), even as they adopt methylation clocks to evaluate the interventions they develop.
All this is assuming perspective (2). But I’m notorious for being a proponent of perspective (1). From perspective (1), turning back the methylation clock is a good thing. It means that the body’s program of self-destruction is dialed back. So where’s the problem?
In recent years, I have become convinced that epigenetic changes of both types (1) and (2) are taking place simultaneously as the body ages. The body is at war with itself. The self-destructive adaptations listed above are real: dialing down repair and maintenance, promoting systemic inflammation, apoptosis of healthy cells, derangement of the immune system. But the body retains its protective responses, and there are also changes in gene expression that ramp up the repair processes. All the present clocks include a mixture of (1) and (2); this is why we do not yet have a reliable metric for the efficacy of anti-aging technologies.
What is the evidence that changes of types (1) and (2) are both components of all extant aging clocks?
Some of the best-established interventions for extending lifespan do not affect the major algorithmic clocks, or do so modestly compared to what might be expected from their observed effects on lifespan. Rapamycin extends lifespan of male mice without affecting their methylation age in the Horvath rodent clock. Participants in the CALERIE study who have adopted extreme CR diets showed no significant benefit according to either the GrimAge or PhenoAge clocks.
Conversely, Katcher’s intravenous infusion of exosomes (E5) has a dramatic effect on the Horvath rodent/human clock, reducing epigenetic age by half, but thus far seems to extend lifespan less than the clock setback would imply. The Conboys recently published a withering criticism of the utility of current methylation clocks, and of the machine learning algorithms from which they are created. They report that clocks in common use do not respond as expected to known life-shortening conditions, such as Down Syndrome, inflammaging associated with arthritis, and Parkinson Disease.
Here’s what first clued me in
The GrimAge clock of Lu and Horvath was trained on actual mortality data, using historic blood samples for which the future history of the donors was known. This was a major advance from previous clocks. But one element of the GrimAge development alerted me to the issue concerning type (2) changes, as described above.
Part of the training of GrimAge involved a methylation image of the subject’s smoking history. Smoking is known to accelerate aging and shorten life expectancy. Certain patterns of methylation are associated with smoking, and are also valuable predictors of time until death. These were included in the GrimAge algorithm.
My assumption was that smoking decreases longevity by damaging tissue of the lungs, not by turning on the program of self-destruction. Therefore, if there are methylation changes associated with smoking, they are probably of type (2). In other words, the methylation signature of a smoker who scores as “older” in GrimAge is likely to include activation of more protective pathways than a non-smoker who scores “younger”.
This is an important clue. The methylation profile of a smoker is useful in constructing a GrimAge clock, but it should be counted in reverse. Methylation changes associated with smoking are statistically associated with shorter lifespan, but mechanistically with protection. These changes should have been included in algorithmic clocks with negative coefficients, signaling a younger biological age. This was not how the GrimAge clock was constructed in fact. Methylation changes associated with smoking were included in the GrimAge clock with positive coefficients (simply because they are statistically associated with a shorter life expectancy).
In general, the methylation image of smokers is an example of type (2). All type (2) changes should be counted with negative coefficients in methylation clocks, even though they are statistically associated with older ages and shorter remaining life expectancy.
So it is crucial to distinguish epigenetic changes of type (1) from type (2)
The story of GrimAge carries a message that suggests ways that methylation changes of types (1) and (2) might be teased apart in algorithmic clocks. Present clocks don’t distinguish between (1) and (2) so presumably the two types of methylation changes are combined in a way we might connote as (1) + (2). The goal would be to create a clock built on type (1) changes alone, or, more speculatively, penalize the clock for type (2) changes, so with the result that the algorithm measures (1) – (2) rather than (1) + (2).
The long-term goal would be to understand the metabolic consequences of each CpG change, separately and in combination, so that a clock could be constructed with full confidence that it scores beneficial and detrimental methylation changes appropriately.
Lacking this understanding in the interim, we might make progress toward distinguishing (1) and (2), by learning from the smoking example. One way to acquire a database of type (2) changes is that animal models might be injected with pro-inflammatory cytokines, and their epigenetic consequences mapped. Since the inflammation is imposed externally, we should presume that the response is all type (2).
Similarly, the animals’ immune systems might be challenged, or they might be subjected to laceration or small doses of radiation, again to chart the epigenetic response to compile a list of candidates for type (2) changes. These experiments could not ethically be performed on humans, however there are humans whose aging is accelerated by non-epigenetic factors, including alcohol and drug abuse. Such people might be tested as part of the quest for type (2) changes. People healing from physical and emotional trauma might also be presumed to have epigenomes modified in the direction of type (2).
Hormesis is the body’s overcompensation to challenges. The body is damaged by something we do or we eat or suffer from, but the body overcompensates to the damage such that we live longer.
Caloric restriction (CR) is the best-established example of hormesis. We might have most confidence in the epigenomes of people and animals subjected to caloric restriction]. Across the animal kingdom, CR is the most robust anti-aging strategy known at present, and we can be confident in subtracting CR-associated epigenetic changes from any algorithmic measure of biological age. These changes can be observed in humans, as in the CALERIE study mentioned above; or they can be observed in rodents, which have enough commonality to human metabolism that some of the same methylation sites have common functions in both. Our methylation clocks should be calibrated to be sure that the changes associated with CR are scored toward a younger biological age.
In addition to CR, there are dozens of interventions known experimentally to extend lifespan in rodents, including juvenile exosomes, rapamycin, certain peptides, vitamin D, NAC, certain anti-inflammatories and angiotensin inhibitors. Recently, some of these have been tested for their effect on algorithmic clocks; and this has been interpreted as evidence for or against the intervention. We might reverse the logic and interpret the same data as training to calibrate clocks, assuming that these changes must be beneficial, and the clock algorithm should reward them with a younger age. If an intervention is known to increase lifespan, then we may presume that epigenetic changes observed in response to that intervention are beneficial.
Before 2013, biological age was estimated with measures of performance and appearance: grip strength, gait speed, athletic endurance, memory, exhalation volume (FEV), skin wrinkles, arterial inflammation, cartilage integrity. In the age of epigenetics, these physical characteristics retain their value as predictors of mortality, and a hybrid clock might be devised, combining physical and epigenetic factors.
For the future
I know of projects at Stanford (the Biomarkers of Aging Consortium), at Tally Health, and at Tru Diagnostic to develop the next generation of clocks to evaluate anti-aging interventions. There are probably other, parallel efforts that I don’t know about, but I will hear about them in your comments. I hope that the theory and suggestions in this blog may be useful to them.