Part II: Why We Should Trust the Methylation Clocks to Measure Aging

Last week, I proposed that methylation age could be used to measure the benefits of putative anti-aging interventions. This procedure has the potential to slash the cost and the duration of testing. The reason is that we don’t have to wait for a small percentage of experimental subjects to become sick or die. The vast majority of subjects in a human anti-aging trial give us no information whatever. In contrast, with the aging clock, every experimental subject is a data point, and the effect on his aging might be measured in a year or two.

The proposal depends critically on the assumption that whatever slows aging will slow the methylation clock, and the converse: whatever slows the methylation clock slows aging. Some people will find this hard to believe, because their fundamental conception of aging is an accumulation of damage, so that any association with methylation will be incidental or worse! (What if the changes in methylation that accompany aging tell the story of the body’s increasingly powerful efforts to repair the damage from aging?)

But for those of us who come to the table open to the idea that aging is an epigenetic program, a close (causal) association between methylation and aging seems utterly expected. For decades, developmental biologists have assumed that development in childhood is driven by age-dependent gene expression. The only thing that prevents us from seeing that the same is true about aging is a kind of prejudice from evolutionary theory that I have described in my book and in this blog.

4. Brief History of the Horvath Clock

Of many biochemical markers that cells use for epigenetic control, methylation of CpG sites is best studied. If you know what that is all the better; if you don’t, all you need to know is methylation is a modification of the DNA by adding a CH 3 group to Cytosine residues in a promotor region adjacent to a gene. Regions with heavy methylation tend to suppress expression of the (usually) adjacent gene. Methylation isn’t the only means by which gene expression is controlled — there are many others. But it is far the best-studied and, given present technology, it is the only epigenetic marker that can be routinely measured, for a few hundred dollars in a small sample of blood, urine, or nanogram-scale biopsy of other tissue.

The clock was developed by Steve Horvath at UCLA, and first published in 2013, built on an idea of Teschendorff from a few years earlier.. He identified patient records for methylation measurements of tissue samples from 8,000 individuals, with associated ages. Methylation is recorded as a number between 0 and 1 for each Cytosine, indicating the proportion of that site that is methylated. He scanned the entire genome for sites that changed most with age, and varied least from one tissue type to another. In this way, he identified 353 sites, and optimized a set of 353 multipliers, such that multiplying levels of methylation at each site by each multiplier and adding the products produced a number that could be mapped onto chronological age. About 45% of the multipliers are negative (sites losing methylation with age) and 55% positive (gaining methylation).

The original Horvath clock correlates 0.95 with chronological age. The standard error in predicting any one individual’s age is + 4 years. Averages of N individuals increase the accuracy of the clock by √N, so that the average of 100 individuals is accurate to 0.4 years. (This is a general statistical principle that is useful to remember.) For our purposes, the relevant question is: measuring the same individual at two different times, how accurate is the difference in Horvath age compared to the elapsed time? There is no data on this yet, but we might safely assume that it is well under 4 years, since standard error of 4 years represents mostly individual departures from the average.

Five years after Horvath’s original publication, there are several other clocks based on methylation. Just this spring, Horvath has developed a new clock, not yet published, which, to my knowledge, is the best standard we have. This is the Levine/Horvath clock. It is based on 513 methylation sites and it is calibrated not to chronological age, but to a tighter measure of age-based health, derived from blood lipid profies, inflammatory markers, insulin resistance, etc, which Horvath calls “phenotypic age”. Consequently, it is less well correlated with chronological age than the original, but it is better able to predict mortality than either the classic Horvath clock or chronological age itself. By this measure, the scatter has been greatly reduced.

There is statistical evidence that the Levine clock reliably reports phenotypic age, and there is theoretical reason to believe that what the clock measures is close to the root cause of aging.

5. Statistical evidence that the Levine Clock=PhenoAge reliably measures biological age

What I find most convincing is the meta-analysis based on historic data. Levine and Horvath use old, frozen blood samples to calculate a Horvath and Levine Ages as it was at some past date. These are people who have died since the blood was drawn, and Horvath Age accurately “predicts” the remaining life expectancy of the subjects. [Chen, Aging 2016]. There is less data available for the new Levine clock, but strong indications it performs much better than the Horvath clock for this purpose.

In addition, many of the life styles that promote long life have been confirmed to slow the Levine clock, while, conversely, obesity and high blood pressure and insulin resistance have been found to accelerate aging as measured by the Levine clock.

Epigenetic age correlates with progression of Alzheimer’s and Parkinson’s Disease [ Levine 2016 ]

Same for Arthritis [ Horvath 2015 ]

Menopause moves the methylation clock forward. Early menopause is associated with accelerated methylation aging, and late menopause with younger methylation age.

Epigenetic age is accelerated by obesity, blood sugar, insulin, and inflammation

Epigenetic age is retarded by carotenoids*, exercise, education (!), and by diets high in vegetables, fruits and nuts.

Stem cell transplants lower epigenetic age more dramatically than anything (from a study of leukemia patients [ Stolzel 2017 ]). Epigenetic age is set back ~8 years for a short period, but then accelerates to a set-forward a few years after treatment.

6. Theoretical foundation of the Horvath Clock

The original Horvath clock was developed by a statistical process that took into account only chronological age. But Horvath age turns out to be a better predictor than chronological age for risk of all the diseases of old age. This is powerful evidence that methylation is measuring something fundamental about the aging process. If an individual’s methylation age is higher or lower than his chronologial age, the difference is a powerful predictor of his disease risk and how long he will live. This can only be true if methylation is associated with a fundamental cause of age-related decline.

An emerging theory the last 7 years is that aging procedes under epigenetic control. De Magalhaes, Rando, Blagosklonny, Johnson and Mitteldorf—all have independently proposed an epigenetic basis for aging. The root cause of aging—the reason our bodies are different at age 70 compared to age 20—is that different sets of genes are expressed at different times of life. This priniciple is already well-accepted for growth and development [ref, ref]. During formation of the body in utero, gene expression rapidly changes, and in early childhood, the growth and mathuration of the body are widely agreed to occur under epigenetic control. But now we know that much of the change in methylation is continuous, from development through aging [ref]. I call this programmed aging. Blagosklonny hedges and says “quasi-programmed”. The difference is about evolutionary purpose and whether function is related to natural selection. My view is that we are programmed for a fixed lifespan for the stability of the community. Blagosklonny’s is that the epigenetic changes that start in development continue afterward through a kind of inertia because there isn’t enough natural selection to turn these changes around.

But for the sake of the reliability of the methylation clock in evaluation of anti-aging interventions, these two perspectives converge: they both support the expectation that methylation age will be an excellent criterion for trying and judging new ideas and combinations of old ideas.

Parabiosis experiments support the idea that factors circulating in the blood have a deep effect on the age of the body. This is indirect support for the epigenetic foundations of aging, because these blood factors come from gene expression in cells–especially but not exclusively endocrine cells.

7. Counter-arguments

A) ‘Epigenetic drift’ — Many authors still write about changes in methylation during aging as “epigenetic drift”. For those who cannot accept the idea that aging is programmed, it is much more palatable to imagine a loss of order in gene expression, a randomization of gene expression. Indeed, this is true. It is part of the story that gene expression does become more random with age. But it is also true that there are specific gene expression changes associated with aging–the methylation clock is based on such programmed changes.

B) Perhaps gene expression changes are a response to damage, the body’s attempt to mitigate aging. This is the suspicion that haunts the aging clock. If this is the case, interventions that thwart the mitigation would come out looking like age reversal, but in fact they’d have the opposite effect, increasing risk of disease and mortality. Support for this idea comes from the prejudice that says “the body would never purpposefully destroy itself.” But there is no evidence for this idea, and in fact many of the programmed changes have been shown to be detrimental. For example, signals for inflammation are increased, DNA repair is slowed down, and the anti-oxidant metabolism is suppressed.

“DNA PhenoAge acceleration was found to be associated with increased activation of pro-inflammatory and interferon pathways and decreased activation of the transcriptional and translational machineries, the DNA damage response and nuclear mitochondrial signatures” [quote from Horvath 2018; footnote is to Levine 2018

C) Not all anti-aging interventions affect the Levine or Horvath clocks. This is a substantial problem if it turns out that there are real anti-aging strategies that work, and yet the Levine clock won’t tell us that they work. But we don’t really know this yet, because we don’t really know what works. “For example, within a 9-month follow-up period, the substantial weight loss resulting from bariatric surgery was not associated with a reduction in epigenetic age of human liver tissue samples” [quote from Horvath 2018; footnote is to Horvath 2014] To the extent we think that bariatric surgery is a legitimate anti-aging strategy, this is a problem

8. Improvements and adaptations of the Horvath clock

The “clocks” we’re talking about are really mathematical operations. Given the output of a blood (or urine) test that reports what percentage of the DNA is methylated at each of hundreds of thousands of different CpG sites, the “clock” is a computer program that distills this information down to a single number, the predicted age.

The Levine clock is a substantial improvement on the original Horvath clock, attained by calibrating it against health indicators and not just chronological age. For prediction, it leaves its predecessors in the dust.

There are three more ways in which the methylation age test can be improved, and I have begun working with the Horvath lab to do the number crunching in support of these changes.

A) The original clock and all its successors have thus far been based on combining information from 353 different methylation sites in the simplest possible way. They simply have 353 different multipliers. It is these 353 (positive and negative) numbers that have been optimized by the statisticians, so that each multiplier can be multiplied by each methylation, and the 353 products are added up to make a single number that indicates age. My suggestion is to combine the 353 sites in a more flexible way. Some change rapidly during youth and then remain constant. Some change continually over a lifetime. Some don’t change much at all until aging sets in. There is no reason that all the 353 sites have to be treated the same way. Using non-linear math that’s just a little more complicated, the 353 sites can be tracked in a way that corresponds to their peculiar lifetime trajectories. This will improve the clock’s accuracy for any application.

B) The clock might be specialized to the application of testing anti-aging effects on individual humans, i.e., comparing biological age for the same individual at two different times. Some of the scatter in the plot of DNAmAge is due to variation from one individual to another, and some is due to other random factors that don’t depend on the individual. In the past, there was little data available for the same individual at two different times, but this is changing, and now it is feasible to separate the two kinds of scatter. The clock can then be specialized to report age differences even more accurately.

C) Again, for the particular application proposed, there is no need for a clock that works generally on any age, from pre-birth to centennarian. If all of the people in the study are between the ages of 50 and 70, then the clock might be specialized to be more accurate in this age range, at the expense of losing accuracy for younger and older subjects–who aren’t part of the study. It may be worthwhile to take this idea even further and have four sub-specialized clocks, calibrated for ages 50-55, 55-60, 60-65 and 65-70.

In my brief experimentation with the data, I was able to raise the correlation from 95% to 96% using technique #1. I’m guessing that with further work it can be raised to 98%. The reason that it pays to do this is that the cost of a human study depends on (A) how many people are studied and (B) how long a time they are followed. As the scatter in the data is reduced by better statistical techniques, we can find out what we need to know with fewer subjects and a shorter study time. Raising the correlation from 96% to 98% will reduce the number of subjects needed for the experiment by a factor of 4. Alternatively, for the same effort and expense, we wil be able to derive more information.

If we can indeed construct a clock with 98% accuracy, a new benefit will be available: It will be accurate enough to distinguish changes for a single individual with no statistical averaging necessary. This will be a gateway to individualized medicine. There will always be treatments that work for some people but not others, and the future of medicine is connected to knowing what works for you as an individual. Each of us will be able to use the methylation clock to know how we are doing. You can try a new supplement for a year and if it doesn’t work for you as an individual, you’ll know it and switch to trying something else next year.

_____________

*carotenoids are molecules related to vitamin A, but vitamin A itself does not slow the aging clock.