The Mother of All Clinical Trials, which I announced in April, continues to progress at a charmed pace. This is a project to collect information from people who are already using a variety of measures to extend their life expectancy, and to use a methylation clock and some innovative statistics to tell us which combinations are effective. It is to be an open-source study, with all data, results and analysis freely available to the public and the research community.We have done no fundrasing as yet, but have collected remarkable volunteer talent and a mammoth donation-in-kind from Zymo Research, the only company that presently offers methylation age testing commercially.
Our principal unfilled role is a project director, who will recruit, train and manage 5,000 subjects from within the life extension community, oversee collection of data, and keep them motivated. This could be a volunteer position, or it may involve fundraising your salaray + related expenses.
We are also looking for a lawyer who can advise us on privacy, HIPAA, IRB matters, and related IP issues. Write to Josh Mitteldorf <firstname.lastname@example.org> if you are interested in working with us.</email@example.com>
My Himalayan Experience
I’m going to invoke my prerogative as a blogger and talk a bit about my personal experience. Two weeks ago, I was trekking in the Himalayas. This adventure has been on my bucket list for many years, both because of the grandeur of the landscape and the challenge of exercise at high altitudes. This spring, I finally got around to it.
your blogger, day 3
On the rare occasions when I’ve been above 4,000 meters in the past, I was a little short of breath but didn’t have headaches or nausea that are commonly experienced. A week before my trip, I looked up the Chinese word for altitude sickness and stopped into the herbal pharmacy at the shopping mall near where I was living in Beijing. The pharmacist offered a box of pills, whose ingredients included rhodiola, Goji berry, ginseng and taurine, all of which have some evidence as longevity aids.
I arrived at Lhasa airport at 7 in the evening (3600 meters) but the sun was still high in the sky, due partially to the fact that it was close to summer solstice, but mostly because China’s single time zone should really be 3, and Tibet is in the far west. I had a limo to the city, and didn’t feel bad at all. I went out for a late dinner, and felt the first headache symptoms as I went to bed. In the middle of the night I awoke with a rip-roaring headache, and a sense of déjà vu. Only then did it dawn on me that I had forgotten to ask the waiter at my restaurant to avoid MSG in my meal, a mistake which I had already made twice before in my 8 weeks in Beijing. The headache was gone by mid-morning, and never returned during my week in the Himalayas.
For the trek, I was tacked onto a group from Singapore, all half my age. We were out at 4,000 to 5,000 meters over four days, covering about 20 Km per day of ups and downs. Air at 5,000 meters is just about half the pressure (half the O2) compared to sea level. I never felt sick, but I was out of breath whenever we walked uphill, even a small incline. For the second day out when we first crossed 5,000 meters, I was doing kapalabhati for hours on end (fast, yogic belly breath) — pumping air into my lungs as fast as I could to avoid the lightheadedness that would stop me in my treks.
Apnea – the mind cure
I have had sleep apnea for 20 years. When I’m asleep, my body forgets to breathe, until my brain senses oxygen deprivation, startles me half-awake, I gasp a few breaths, fall back asleep, and the same cycle repeats. I’ve just barely managed the condition by sleeping on my stomach. Sometimes I’m aware of the apnea as it happens, but mostly I’m not; during the day I have bouts of sleepiness, presumably because my nighttime sleep is not deep, and I’m fortunate that usually I have the freedom to take naps as needed.
What I didn’t learn until I got to Lhasa: Altitude makes apnea worse. On the one hand, there’s less oxygen, so we need to be breathing faster; on the other hand, there’s also less CO2, and it’s the buildup of CO2 in the blood that the body senses in order to regulate breathing. I usually take 1 mg melatonin at bedtime, for longevity benefit rather than for sleep. While in Tibet, I suspended melatonin because statistically it exacerbates apnea, and in my experience, melatonin at higher doses seemed to be a major factor.
My first night out on the trail, I really sensed the apnea, much more so than in Lhasa. I repeatedly felt myself startled awake, panicked and panting. I wasn’t sleeping much.
The second night, my difficulty sleeping was more severe, and I was inspired in the middle of the night to try an experiment. I sat erect in a meditation pose and found a rhythm that gave me enough air = 3 heartbeats inhale, 5 heartbeats exhale. (This was about three times faster than my resting breath at home.) I used meditation techniques to keep my mind returning to the breath, aware of the rhythm, and aware when the O2 budget felt insufficient, and I needed to breathe deeper and faster for a bit. After about 20 minutes, I lay down and maintained the same counts, the same breathing rhythm, the same relaxed, meditative mental posture. I deliberately formed the intention to impress the rhythm on my unconscious, so that it might continue to breathe in the same pattern after I dozed off. The technique worked. It was awhile before I dozed off, but the time meditating was fully relaxing, and gave me the feeling that my brain and body were restoring as they might have if asleep. When, eventually, I did fall off to sleep, there was no panicked awakening. I can’t be sure whether the apnea was returning because I was in a tent alone, but as far as I could tell, it was relaxing sleep.
I regard the experience as a breakthrough in my relationship with apnea, and I’ve continued to rhythmically breathe myself to sleep in the 2 weeks since I’ve returned to sea level.
Adaptation to Altitude
Many peoples around the world who are adapted to high altitude living have more red blood cells. This works to carry more oxygen more efficiently to the tissues, but high RBC inclines the blood to clotting, and increases risk of heart disease and stroke. The Himalayan peoples have a better idea. They actually have lower RBC counts than the rest of us, but they have a genetic variant known as EPAS1 that enable their mitochondria to function just fine, to burn sugar efficiently at low oxygen levels.
Until recently, the origin of EPAS1 was a mystery. Then, in 2014, the geneticists traced it to a group called the Denisovans, 40,000 years ago. Denisovans were an offshoot of Neanderthal man, chronicled from a single finger bone of a single young woman, found in a cave in Siberia in 2010. The bone had enough DNA to do a complete sequence, and an entire subspecies known fro this single example. The Denisovans interbred with other human tribes of Asia, and the EPAS1 gene was originally their contribution to humanity. It disappeared in many places, but in Tibet, it was useful, so it stuck.
It may be counterintuitive that more is not better when it comes to red blood cells. P.D. Mangan has been beating the drum to advise us that iron levels on the low side of normal are better not just for cardiovascular risk, but for many other aspects of health as well.
There is indirect evidence linking hypoxia to longevity. Hypoxia shifts gene expression toward a stress response that is known to overlap with longevity genes [ref, ref]. Hypoxia increases lifespan in bees [ref], fruitflies [ref], and lab worms [ref]. A study correlating altitude with life expectancy across the US found tentative evidence for a benefit from living at higher altitude.
I’m not impressed by the arguments that hypoxia is a factor in the longevity of whales, naked mole rats, and other animals whose lifestyles incidentally lead to hypoxia–too many confounding variables.
Evidence on apnea
Apnea is two separate diseases. Obstructive Sleep Apnea (OSA) has a mechanical origin in blockage of the windpipe. It is associated with obesity, but studies find that independent of obesity, apnea is a mortality risk. Central Sleep Apnea (CSA) originates in the central nervous system, but its logic and mechanisms remain obscure.
OSA incidence increases modestly with age. CSA increases dramatically with age.
CSA is much rarer than OSA, but its incidence increases dramatically after age 65. (For CSA, I was unable to find a graph like the above.) CSA is associated with heart disease and stroke, and the direction of causality is unclear. It may be both that heart failure contributes to apnea and apnea contributes to heart failure [ref]. For those of us who suffer from CSA, it would be interesting to know if treating the symptoms (say, with CPAP) lowers cardiovascular risk. Consensus of the medical community is “yes”, but this conclusion may be driven by economic and legal factors. I have been unable to find a definitive answer in the primary literature, because the direction of causality is so hard to discern. This small study (2005) found a major decrease in 5-year CV mortality for those who accepted CPAP treatment compared to those who could not tolerate CPAP. This larger study (2016) found that CPAP effectively alleviated the symptoms of apnea, but had no discernible effect on CV mortality. Of course, better sleep at night and better alertness during the day are sufficient reasons to treat the symptoms of apnea. But some of us aren’t helped by CPAP.
The Bottom Line
Hiking at high altitudes is a great challenge, but not necessarily the best conditioning for long life. Unless you’ve got Denisovan genes, you will adapt with higher red blood counts, which, for most of us, is a net negative.
Sleep apnea is entwined with heart disease, so it is difficult to separate cause and effect. Lowering the risk factors for apnea may be as important as treating the apnea itself. There is but little indication that sleep quality directly affects your mortality risk, but it certainly affects quality of life.
My own experience suggests that it’s possible to use meditation techniques to plant suggestions in the unconscious that alleviate sleep apnea and improve sleep quality. Hypnotism, autosuggestion, and biofeedback might be effective as well. It’s hard to do controlled studies to demonstrate this benefit, and it may be even harder to get them funded. But it’s an approach worth exploring.
Time and again, evolution has learned (after repeated blind alleys) to do what is best for the community in the long term and not always what is best for the individuals in the short term. But such gains are fragile, easily lost if a cheater can gain a short-term advantage and its progeny take over the community.
Human societies have rules that encourage cooperation, and enforcement mechanisms for people who are reluctant to cooperate. Cooperation in biology is very old, and it turns out that evolution thought about enforcement a billion years before Thomas Hobbes. To see what this has to do with theories of aging, you’ll have to be patient.
Story #1: Conjugation and Cell Senescence
Story #2: Sex Required for Reproduction in Plants and Animals
Story #3: Antagonistic Pleiotropy — a Revisionist Theory
To begin, I’m going to ask you to think fresh thoughts about sex. (Have I lost you already?)
Sex and reproduction, reproduction and sex. Go together like a horse and carriage, right? Well, how did it come to be that way? Sex is not a way to reproduce. Sex is a way to share genes. But sex has become so tightly linked to reproduction that it requires mental gymnastics to imagine that it might have been otherwise.
Reproduction without sex—that’s not too hard. It’s cloning. Or it’s mitosis, simple cell division which is how bacteria do it.
But sex without reproduction? What’s that? Remember—sex is the mixing of genomes between different individuals with different genomes. Does anyone do that except as a prelude to reproduction? What would it even look like?
Bacteria share genes willy-nilly. They shed plasmids, which are little loops of DNA, and they pick up plasmids from around them. The plasmid may be from the same kind of bacteria or another kind of bacteria entirely. Sometimes the gene they pick up is useful; sometimes, not so much; sometimes the imported gene kills them. Bacteria can afford this daredevil lifestyle because there are a lot of them, and their credo is experimentation. Change or die. Bacteria are constantly changing, not only because their generations are measured in hours instead of years, but the change from generation to generation is also greater than large animals and plants. Under stress, they mutate and change even faster. Bacteria are artists of change, and their genius is figuring out what it takes to survive in the environment where they happen to be now. For bacteria, sex is spitting out plasmids and picking them up.
Bacterial plasmid (electron micrograph)
Story #1: Conjugation and Cell Senescence
Protists, or protoctista, are single-cell eukaryotes—far more complex and structured than bacteria, with a cell nucleus and many more organelles, a million times bigger than bacteria but still a single cell. Examples are amoebas and paramecia. Protists share genes by a process called conjugation that challenges our idea of the individual. As promised, sex in protists is not linked to reproduction…well, maybe indirectly linked, as we’ll see.
Amoeba eats two paramecia (Amoeba's lunch) - YouTube
(This movie isn’t conjugation; it’s a hunting expedition.)
In conjugation, two paramecia (Dick and Jane) sidle up to each other and their cell membranes coalesce, forming one big cell. Then the cell nuclei, where the chromosomes live, find each other and the two nuclear membranes open up and merge, just as the cells did. A double size cell with double size nucleus, and two copies of each chromosome. Somehow the chromosomes pair up with the appropriate partner. Like blind people trying to navigate a crowded room, how do the chromosomes arrange a meeting place with their partners? (If chromosomes had telephones, I suppose they would be cell phones. OK, it isn’t funny.) Somehow, Dick’s chromosomes finds Jane’s corresponding chromosome, nearly identical but for the crucial variations that make them individuals. The chromosomes line up in pairs so they can swap genes with one another. Genes cross over until each chromosome contains about half Dick’s genes and half Jane’s. Then—again using their cell phones for coordination—the chromosomes segregate. One from each pair goes north, the other goes south, so that when the nucleus splits in two again, each half has a full complement. Two cells go their separate ways, but the cells that emerge from this process are no longer Dick and Jane. Each one of them is half Dick and half Jane, in its genes, in its cytoplasm, and in its mitochondria.
THIS is conjugation. It only takes place between protozoa of the same species.
Conjugation is sex without reproduction. We started with two cells and ended with two cells. They pooled their genes, but didn’t produce “offspring”. Both Dick/Jane and Jane/Dick will someday undergo mitosis and copy themselves, but Dick and Jane have ceased to be, merged instead into an amalgam.
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Individual Selection and Group Selection, Short-term Advantage and Long-term Welfare
Why do cells do this? Let’s talk about fitness. In the short term, the race is to the swift. Reproduction is everything, especially among microbes which are always in a tight race with billions of others, and the one that reproduces fastest is the victor in Darwin’s lottery. So natural selection at the individual level motivates Dick and Jane to get on with the business of copying themselves as fast aspossible.
Why did they take time out to merge their genes? Dick and Jane individually must have thought they had a good thing going, each having survived a long while, and beaten out the competition. They each had a combination of genes that work well together. Why would they take a flier on the off-chance that their genes might do even better in some other combination? “Survival of the fittest” at its crudest level simply means that those who reproduce fastest crowd out everyone else. Sharing genes takes time and energy. You can’t afford it.
To make this less abstract: Imagine a puddle with cells swimming in it, all the same species. Suppose some of the cells—the Joneses—go straight to work reproducing, doubling their numbers, while others—the Smiths—stop along the way to have sex with other Smiths. They’re all increasing exponentially, but the Joneses grow at a faster rate. More doublings of the Joneses leads to a powerful numerical advantage. Pretty soon, the Joneses have overwhelmed the Smiths and crowded them out. The Smiths are a thing of the past, driven to extinction. We say, “the Joneses have evolved to fixation.”
Short-term individual selection says “Don’t do it! Don’t have sex!” But in the long run, the communal legacy is more robust if they DO share genes. Having many diverse combinations of genes is insurance against changes in the environment, and a high-risk investment that just might yield big dividends if the right opportunity opens up in the future. But there’s a danger that the Joneses will crowd out the Smiths in short order, and they won’t live to see the day when their robust diversity shows to their advantage.
After many, many cycles of losing sex in the short term and missing diversity in the long term, evolution stumbled on an expedient. A counter was built into the chromosomes, counting replications. Everyone is allowed to clone about a hundred times, without sharing genes. After that, without conjugation, the cell slows down and dies, stopped dead in its tracks. Every so often, every cell lineage must take time out for conjugation, or the lineage dies.
The counter is the telomere. To enforce conjugation, nature arranged for telomerase to be locked away (in paramecia and other protists) during mitosis. Each act of reproduction makes the telomeres a little shorter. Only during conjugation is telomerase unlocked, and the counter is reset, so the lineage can continue to clone.
Twenty years ago, William Clark wrote two books on this subject at a level accessible to readers of this column. Sex and the Origins of Death, followed by A Means to an End. I read both as they came out, and they had a profound effect on my thinking about evolution and aging.
Cell senescence is programmed death. Can this be an evolutionary advantage? Can programmed death evolve to protect the community from the fast crowd that doesn’t want to share their genes? Sure, there is a long-term advantage, but how was evolution so clever as to arrange this? How did it happen that telomerase came to be sequestered, available only during conjugation? I’ve looked through the evolutionary literature, and found no explanations, so I have asked this question myself, modeling with a computer simulation. The model works surprisingly well. One important feature of the model is that there is a limited reservoir of the food that cells need in order to grow. This means that the “cheaters” who avoid conjugation and reproduce faster don’t have an advantage for long, because they use up the available food store faster. Another crucial feature is that conjugation sometimes leads to combinations of genes that are more efficient at using food resources.
Story #2: Sex and Reproduction in Plants and Animals
Half a billion years ago, there was an explosion of multicelled life. Gene sharing is not so easily arranged when there are billions or trillions of cells in each fully-grown organism. Sure, all life passes through an embryo stage, starting with a single cell. But embryos are hardly in a position to seek out a partner and share genes. So evolution needed to invent anew both the mechanics of gene sharing and a means to enforce it on individuals whose primary Darwinian motivation was to reproduce as fast as possible.
So nature took the bull by the horns (or perhaps another part of his anatomy). She laid down the law: “From now on, it takes two to tango. Anyone who wants to reproduce is going to have to share genes.”
Sex and reproduction were tied together anatomically, and the connection was so tight that no would-be cheater could get around the barriers. For some (dioecious) species, there were two separate sexes so that no single individual had the tools to reproduce by itself. In other (hermaphroditic) species, each individual could make both eggs and sperm, and there had to be barriers to self-fertilization, custom-designed for each anatomy.
Exactly how this came about is unknown. Meiosis is an operation of baroque complexity, though clearly an outgrowth of both protist conjugation and mitosis. Graham Bell (quoting Emerson) called it the Masterpiece of Nature, but neither he nor anyone proposed an evolutionary pathway that might have created it.
We know that this whole business of separate sexes and all the cellular and metabolic complexity that it entails managed to evolve, and we know that it offers no conventional advantage in terms that neo-Darwinist theory can understand. No one doubts that the link between sex and reproduction femerged from a process of evolution, but the standard mechanisms recognized by conservative evolutionary theory are at a loss to explain it.
How do we understand evolution of sex? What is the accepted explanation?
Classical evolutionary theory (neo-Darwinism) is in a bind. The theory inherited from R. A. Fisher in the early part of the 20th Century insists that there is only one mechanism of evolution, and that is one-mutation-at-a-time. Each incremental change has to provide a benefit that is capable of gradually spreading through the gene pool. In other words, all by itself and immediately it has to offer the bearers (on average) a faster rate of reproduction. On the other hand, there are numerous examples of complex adaptations (like sexual reproduction) that provide no immediate benefit for reproduction, and that require many changes to many genes in order to be functional at all. Classical evolutionary theory just says, “that’s a tough problem that we haven’t solved yet.”
But it’s more than that. The very limited repertoire of mechanisms recognized by classical evolutionary theory quite obviously can never explain the provenance of sex, or of aging, or of countless other common traits. Classical evolutionary theory is going to have to adapt or die.
I haven’t tried to model the evolution of sex because I can’t think how to do it. The problem is just too hard—all the advantage is with the cheaters, who can reproduce twice as fast because they don’t have two different sexes to support. Nevertheless, look around you—somehow nature managed to arrange most plants and animals in two sexes.
Story #3: Antagonistic Pleiotropy — a Revisionist Theory
Like sex, aging is a trait that benefits the community in the long run, but is costly to the individual in the short run. It’s not as extreme as sex—the benefit is not so essential, and the cost is much less than the cost of sex. (Two sexes cuts fitness by half, by the classical definition of “fitness”. Time and energy required for the mechanics of sex only add to the cost.) So the problem is not as severe as Story #2, but once again, nature has a problem: How to make death obligatory, so that there is population turnover and population diversity and (more important) so the population doesn’t explode past sustainable levels, leading to population crashes and extinction.
Nature’s solution was once again to tie together aging with reproduction, but the link isn’t nearly so tight and consistent as in the case of sex. In fact, population can be kept within sustainable limits either by controlling fertility or limiting lifespan, or any combination of the two, so tying longer lifespan to lower fertility (and vice versa) helps to allow for diversity and flexible strategies, while guarding against those deadly population blooms.
The name for nature’s solution is Antagonistic Pleiotropy. Fertility and longevity are coded in the genome in such a way that inheritance of lifespan and fertility are inversely linked. Higher fertility goes with shorter lifespan. Lower fertility goes with longer lifespan. As long as the two vary together in this way, the threat of population explosion can be kept at bay.
You might be thinking: pleiotropy is everywhere. We don’t need an explanation for pleiotropy, because it’s built into the way genomes are organized. Very few genes have just one mission. A web of regulation affects everything at once, so that distinct traits emerges from many genes, and every gene contributes to many traits. This is true of the way that adaptive traits are realized in nature. To think this way, you have to think of aging as an adaptive trait that nature actively wants to protect.
The Classical view of Antagonistic Pleiotropy
Contrast this with the orthodox theory of Antagonistic Pleiotropy, which has become the best-accepted theory for the evolution of aging. In the orthodox theory, genes for fertility and other traits that are highly beneficial to the individual are tightly linked to deterioration that we call “aging”. Out of the box, the genes work this way, and the forces of evolution have been unable, over half a billion years, to tease the two apart. There is a mighty motivation (says classical theory) to separate aging from fertility so that the individual can have the best of both worlds, but there are physical limitations or logical connections that make this impossible. Hence, natural selection has had to swallow the bitter pill of aging in order to get the sweet nectar of faster reproduction.
In my version, antagonistic pleiotropy is an evolved linkage, after the fact. In the standard version, antagonistic pleiotropy is an inescapable precondition, a given fact about the way genes work that evolution, with all her wiles, has been unable to evade.
How do we know that my interpretation of AP is the right one and all the theorists have it wrong?
Because the classic theory requires that every “aging gene” must have a benefit that more than compensates, and after 30 years of genetic experiments, pleiotropic costs.have been identified for only about half of the known aging genes.
We’ve seen that evolution is capable of some amazing feats. It just doesn’t pass muster that evolution has been trying to find a pleiotropy bypass for half a billion years but doesn’t seem to be able to find one.
Because some of the best-known cases involve quasi-pleiotropic linkages that can be broken in the lab. It’s just not that hard to have your cake and eat it, too. The first example was AGE-1, the first bona fide aging gene to be discovered (in lab worms, 1989).
When you look at the actual mechanisms of pleiotropy, many of them don’t seem to be functionally essential, but involve unexpected connections between unrelated functions. The most recent example is that methylation aging seems to be inversely related to telomerase expression.
Of these 3 stories, the story of evolved Antagonistic Pleiotropy (#3) is the easiest to model and simulate, which is to say that the model requires few assumptions and works to evolve pleiotropy without a lot of adjustment or tinkering. This alone gives me confidence that AP is evolved, and that the usual interpretation for the meaning of AP is upside down.
I have been working to turn my computer model into an academic article, and a draft of the paper, not yet submitted, is posted here.
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 CH3 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. About 60% of the sites lose methylation with age and 40% gain methylation with age. 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.
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 timeqs, 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]
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 vit A, 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.
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.
There are a great number of promising interventions that might have anti-aging benefits, singly and in combination. There is a testing bottleneck, which means that we don’t know what works. By way of contrast, there is a well-documented catalog of life extension interventions in lab worms, but for humans we’re mostly in the dark. To complicate things further, lab worms are clonal populations, while every human is different, and there are growing indications that many if not most medications work for some people and not others.
Horvath’s methylation clock is a disruptive technology that could make human testing of longevity interventions ten times faster and 100 times cheaper than it has been in the past. No one is yet doing this kind of testing, but you and I should be advocating vigorously, and volunteering as subjects to help test whatever it is that we are already doing.
Let me begin with the punchline, and work backward to build a foundation under the idea. I think we might learn a great deal and push the science of anti-aging medicine forward with a study encompassing about 10,000 people like you and me—people who are aware of the long-term consequences of their diet, exercise, supplements, and medications—10,000 people who are trying different combinations of things in a conscious effort to maintain long-term health and extend their lives. We need a standard form for recording our individual habits and a standard measure of progress. Subjects will be required to
keep diaries of what they are doing for long-term health (It would be helpful but not necessary that they keep to the same program for a year or two.)
send in blood or urine samples at the beginning and end of a year for methylation testing
sign up for a database so all their records can compiled
Given a database like this, multivariate statistical techniques can, in principle, separate the effects of different interventions individually, and also their interactions.
The idea is only as good as the Horvath clock. Can we detect differences in aging rate over a time period as short as a year or two? And how sure are we that the Horvath clock really captures the differences that affect aging and long-term health? That’s what next week’s article will be about.
The present cost of methylation testing is several hundred dollars, but a funder would only have to put up a fraction of that. The rest would be covered by participants themselves, and Zymo Research, the only company offering commercial testing of methylation age, would offer bulk discounts because they are investing in their future, and because their costs are likely to drop with volume.
So far, I’ve talked about this with Steve Horvath of UCLA, Brian Delaney of LEF, Larry Jia of Zymo, and Elissa Epel of UCSF. All are enthusiastic about the idea. Though none is yet convinced to throw resources into the project, I believe that this trial or something much like it will begin within a year, as scientists and funders have a chance to recognize its potential and rearrange their plans appropriately. (I will also approach Aubrey de Grey at SENS, but their primary commitment is to a different model, developing new interventions rather than testing what we have already.)
For several years I’ve been talking to anyone who will listen about the importance of testing. (Here’s a 2015 link, and here’s an update from two months ago.) Aging clocks based on DNA methylation are a disruptive technology which will change the way we screen putative longevity treatments. We now have the potential to learn in a very few years what works and what does not.
There are a great number of promising interventions that might have anti-aging benefits, singly and in combination. Some are already approved and safe for use in humans, yet we don’t know what will be most effective. Because human longevity studies are prohibitively slow and expensive, none have ever been funded or conducted. (We know only accidentally that aspirin and metformin lower mortality rates in humans, because these drugs were prescribed to tens of millions of people beginning in the 1960s for cardiovascular disease and diabetes, respectively, with no premonition that they might extend lifespan.)
We have relied on animal tests, biochemical theory, and guesswork because testing in humans has been impractical. Epidemiological studies require treating a very large population and following them over a course of decades. Even very substantial difference is mortality rates can be difficult to detect because the baseline mortality rate is low, because researchers inevitably lose track of some subjects over such long time scales, and because there are so many confounding variables that must be overcome with sheer numbers.
Testing of anti-aging interventions in humans has been so expensive and slow that we have been forced to make inferences from animal tests, supplemented by historic (human) data from drugs that happen to have a large user base going back decades. As it turns out, it is much easier to extend lifespan in worms than in mammals, and even the interventions that work in rodents don’t always work in humans. Conversely, there are drugs that work in humans that don’t work in mice—how are we to find them?
We know so much more about life extension in C. elegans worms than in people because worms live only a few weeks, are easily cloned, and can be grown by the thousands in standard laboratory conditions. Humans are not so easily controlled, they can’t be genetically engineered or cloned, and their lives can’t be manipulated in the interest of science. It takes decades to document the long-term effects of dietary changes, drugs, supplements and exercise routines, and it generally requires thousands of people to separate the effects of one particular intervention from all the differences in genetics and lifestyle that distinguish human individuals.
Just this year, a test is available that is accurate enough to measure anti-aging benefits on short time scales, without waiting for subjects to die. DNAm PhenoAge is a simple blood test developed at the UCLA lab of Steve Horvath. It determines risk of age-related mortality accurate to about 1 year of biological age. Averaging over just a hundred people pinpoints biological age with accuracy of one month. This implies that an anti-aging benefit can be detected with high reliability using a test population of just a few hundred people, followed for two years, tested at the beginning and end of this period. A study that might have required fifteen years and cost hundreds of millions of dollars can now be completed in two years at a cost of less than $1 million. When this new technology is embraced, we will have the means to separate the most effective treatment combinations from a large field of contenders.
1. Testing is Important
We have a program in basic science that will eventually lead to understanding of aging at a molecular level. This will suggest molecular interventions that can alter the course of aging. This approach is a sure bet, and it will yield a great deal of interesting science and clinical applications along the way. The drawback is that it is slow. At least several decades will be required to understand aging from the system level down to the molecular level. What can be done to accelerate progress toward substantial anti-aging remedies?
You might think that the bottleneck is in ideas. What we need is a disruptive idea. Something like CRISPR or the Yamanaka factors, or maybe some engineered molecule that leaves rapamycin in the dust.
I don’t think so. How would we recognize this great idea if we saw it? If it were rather conventional, it’s unlikely it would produce revolutionary results. On the other hand, if the idea were profoundly different and innovative, why would we believe in it without extensive testing? And who would pay for the testing?
I believe that testing is really the bottleneck here. We may well have our powerful anti-aging tonic already in hand, and we don’t know it. And if the breakthrough is yet to come, we will need a way to recognize that it works.
Two years ago, I proposed that the best promise is in combinations of known therapies.
The listed interventions all have been shown to extend lifespan in rats or mice.
We know what they do individually, but we don’t know how they interact among themselves. In reality, of course, we’ll never see that 172% life extension. Almost all interactions are expected to be redundancy—in other words A and B together are a marginal improvement over either A or B separately. But occasionally, we will discover that A and B synergize. A and B administered together yield life extension greater than the sum of what is available from each of them separately.
But there are an enormous number of combinations to test. How are we going to find those combinations that synergize together?
2. Testing in humans is slow and expensive
It’s not just because humans require a level of care and safety that you don’t worry about in animal tests. It’s the length of the human lifespan.
If you’re studying an old drug like metformin or aspirin, then you have a database of people who have used it for decades, and you can look for small differences in their rates of disease or mortality.
But suppose you want to try a new remedy, or a new combination of remedies? Typically, you would choose several thousand people as a test group. You need to wait for a substantial number of them to die, so you want to start as late as possible. On the other hand, it’s easier to maintain the health of a younger person than to restore the health of an older person, so you want subjects as young as possible. So perhaps you compromise with an age around 50 or 60.
Then you administer the drug or combination of drugs in half the subjects and a placebo in the other half. You follow them for a decade and monitor compliance. How many of them are still taking your placebo 10 years later? Out of a sample of 1,000 sixty-year-olds, you expect 120 of them to die before their 70th birthday. Now suppose you had an intervention that would cut the death rate by 10%, so only 108 of them died. The trouble is that statistically, you can’t tell the difference between 108 and 120. The random fluctuations will overwhelm this difference.
How large a sample would you have to start with in order to detect that difference with 95% confidence? For N=6,000 tests + 6,000 controls, you would detect a 10% difference with 95% confidence half the time. If you wanted to be 90% sure that your results would be statistically significant, you would need 15,000 test subjects and 15,000 controls, tracked for 10 years. The cost would be in the hundreds of millions of dollars.
Another way to think about the same example: Imagine that the treatment you are testing does not immediately lower the mortality rate, but it slows the rate of aging by 20%. The result is about the same—a 10% lower mortality over 10 years.
In New York’s Einstein School of Medicine, Nir Barzilai is organizing the first ever clinical trial of an anti-aging drug. Metformin is the drug he chose, based on lower rates of all-cause mortality, cancer, and Alzheimer’s disease among people who have been prescribed metformin to control diabetes. The risk of Alzheimer’s between age 60 and 80 is about 10%. Data from people taking metformin suggest this could reduce this to 7% [Knowler, 2002]. Barzilai is still trying to fund this study with about $50 million. For that, the TAME study hopes to recruit 3,000 subjects (1500+1500) [Sciencemag 2015]. What is the probability that they will have results significant at the (p<0.05) level? Answer: 83%. You may think that’s pretty good. Or you may be horrified that he could spend $50 million and there’s a 1 in 7 chance that, just because of dumb luck, the trial wouldn’t produce significant results. There’s a footnote in the 83% number: The 31% Alzheimer’s risk reduction comes from a study of younger people, but Barzilai is planning to recruit subjects from 65 to 79 years old because the rate of AD is higher.
3. Suppose we could accurately measure effects on aging without having to wait…
What’s the alternative? I’m so glad you asked. Suppose we could actually measure aging. We don’t have to wait for someone to die or be diagnosed with dementia. We can do a blood test instead and determine that “this subject has aged 1.5 years” or “this subject has been rejuvenated by 0.5 years”.
To reimagine the TAME protocol with an aging clock, we need to add an assumption about what the effect of metformin might be on the Horvath clock (or successor). From reduction in mortality combined with an actuarial table, we might infer an age setback. Lamanna 2010 reports a OR=0.80. Facila 2017 report OR=1.34/2.24=0.60. Bannister 2014 reports OR=0.85 when comparing diabetics on metformin with non-diabetics (yes — metformin in some studies reduces mortality for diabetics lower than it would have been if they didn’t have diabetes in the first place). The logarithmic increase in mortality for a 60-year-old is about 0.075, corresponding to a range for actuarial setback of 2 to 7 years for long-term metformin use.
Let’s say the experiment lasts 2 years and after 2 years on metformin, the subjects might have aged only 1¾ years. Very conservative, I think. Compared to 3000 subjects over 10 years, You could get equivalent results from a Horvath clock over 2 years time with 200 subjects. The total cost of the study could be reduced from $50 million to less than $1 million.
These probablities are not difficult to compute, but their inputs are very uncertain. We don’t know how much scatter there will be in the difference between two Horvath clock readings when repeated for the same person. I’ve assumed 1.414 years. It coud well be better. We don’t know whether metformin will slow the epigenetic clock, and by how much. It may be that we will get that same 3-months benefit in one year instead of two.
The Bottom Line
Numbers are my thing, and I’m sorry if I’ve left your head spinning. The take-home is that by switching from traditional epidemiological studies of mortality to the Horvath clock, we can get the same information five times faster and 100 times cheaper.
For example: Barzilai’s TAME study is projected to cost $50 million, it will take 10 years, and it will teach us the benefits of just one drug. The study I’m proposing will cost less than $10 million and most of this will be covered by Zymo (as discounts) and by subjects themselves. It will take only 2 years, and we will learn about a dozen different interventions and their interactions.
Next week, Part II: Reasons to think that the Horvath Clock will be up to this task
I have been enthusiastic about telomerase therapies for anti-aging since 2003. But if I can’t change my mind as new data appears, what’s the point of being a scientist? I still believe that lengthening telomeres is a net benefit, but the potential for added years is modest, and there are probably risks and tradeoffs. The study that has most influenced me is this one, implying that telomerase affects epigenetics (through methylation) in ways that accelerate aging. My theory is that the unexpected relationship between telomerase and methylation is an example of antagonistic pleiotropy, but pleiotropy in a very different sense from the standard evolutionary theory.
Do people with longer telomeres have longer life expectancy? In 2003, Richard Cawthon of University of Utah first addressed this question experimentally with a study that was clever, innovative and courageous. It was innovative in that he introduced a fast and convenient way to measure telomere length from very small quantities of DNA, using the Polymerase Chain Reaction. It was clever in that, instead of a “prospective study” measuring telomere length in his subjects and then following 20 years to see what would happen to them, he did the experiment retrospectively, using historic samples of blood that had been taken from people twenty years earlier and kept in frozen storage by a local hospital. And it was courageous in that everyone believed at the time that extending life could not be so easy as just lengthening telomeres, or else the body would already be doing it! That is to say, no one would fund the study because they thought they knew how it had to come out.
But they were wrong. Even with Cawthon’s small sample of only 143 subjects, the relationship between telomere length and diseases of old age jumped out of the statistics. The quartile with the shortest telomeres had suffered two times higher mortality and three times greater incidence of heart disease in the intervening 20 years than those with the longest telomeres.
Red blood cells have no DNA, hence no telomeres, but white blood cells are constantly dividing to target specific bacterial, so the telomeres in white blood cells are a sensitive measure of immune health. Cawthon reported that the group with shortest telomeres had suffered 9 times the rate of infectious disease compared to the longest telomere group.
At the time of Cawthon’s study, there was a great deal of skepticism, based purely on theory. The standard hypothesis was that all animals are evolved to live as long as possible, all else being equal, and if telomerase were being held back, there must be a powerful downside associated with it. I was already marching to the beat of a different drummer in 2003, and I didn’t believe that evolution was always going for the longest lifespan available. Because I believe that aging is an evolutionary program, it was easy for me to see telomere shortening as part of the program. The biggest clue in my mind was the evolutionary origin of telomere shortening in single-celled protozoans. In the ciliates (e.g. paramecium), telomerase is not expressed in mitosis (when the cell copies itself), but only when it conjugates (recombining genes with other individuals) with another. Hence, a cell that just goes on reproducing as fast as possible without sharing its genes was doomed to die of cell senescence. A billion years ago, telomeres were already a means of enforcing the communal imperative, Share your genes! It is easy to imagine that the same evolutionary imperative has persisted through the aeons, and that telomere shortening insures death in many higher organisms. Indeed, since Cawthon, it has been demonstrated that short telomeres are a mode of aging in dogs, cats, and horses, (but not cows, pigs or mice).
Three years ago, I reported on a Danish study that replicated Cawthon’s results on a huge scale. In 60,000 subjects, Rode associated short telomeres with all-cause mortality, heart disease, diabetes, and some cancers.
Telomere shortening leads to senescence and higher disease risk by three known mechanisms. First, stem cells with the shortest telomeres stop reproducing, hence the body’s tissues don’t renew as efficiently. Second, senescent cells are not just dead weight, they actually emit chemical signals (cytokines) that increase inflammation. This has been called SASP, for Senescence-Associated Secretory Phenotype.) Third, senescence in the bone marrow that generates new white blood cells is especially damaging to the immune system, because it prevents the body from responding effectively when challenged with new infections.
Telomeres and cancer
If short telomeres cause all these problems, why would the body ever allow its telomeres to become short? It was recognized early in the game that production of telomerase entails no substantial metabolic cost, so the question challenges the conventional theory that individual animals are evolved to live as long as possible. Of course, for us who believe that aging is programmed, there is no problem with this. But the first suggestion of an answer within the conventional paradigm came from Carol Greider, one of the original discoverers of telomerase, and independently from Ruth Sager. Telomerase is needed to make cells immortal. 90% of cancer cells have found ways to bypass the suppression of telomerase in order to continue proliferating unabated. Greider and Sager proposed that keeping telomerase under lock and key constitutes one of the barriers that keeps cells from going rogue as tumors. Telomere shortening helps to prevent cancer.
This argument never made any sense to me. First, what good was it to suppress cancer if the net effect was to shorten lifespan? And second, I believe that the body’s principal defense against cancer is the immune system, and if short telomeres can cripple the immune system, that was likely to do more to promote cancer than to prevent it.
Nevertheless, the idea that telomerase is rationed to protect against cancer persisted in the biomedical community for 20 years based on theory alone, even as it was moderated by the discovery of SASP.
Experimental link between telomeres and cancer
I came into this field very skeptical of the idea that long telomeres could cause cancer. But as the evidence has accumulated, I’m compelled to reconsider. Just last summer, I blogged critically about the largest genetic study to date, linking genetic predisposition for longer telomeres with cancer rates later in life. I noted that the measured effect is actually quite small, but is reported blown up to alarming proportions by exponential extrapolation. But that didn’t mean it was necessarily wrong, only that it was unconvincing. Shortly afterward, I became aware of observational studies, based on measured telomere length rather than the genetic predisposition. These are harder to refute.
In this study from the Moffitt Cancer Center, short telomeres (as measured directly, not imputed from genetic variation) are associated with higher risk of squamous cell skin cancer, but long telomeres are associated with higher risk of melanoma skin cancer. Same methodology, same authors. Why would I believe one and disbelieve the other? Melanoma tends to occur at younger ages than squamous cell carcinoma, this supporting the Greider hypothesis that telomere shortening should be especially important for cancer prevention while we are still in a fertile stage of life. The Moffitt results on melanoma were confirming a finding reported earlier from Harvard Med School.
In this study, people with the longest telomeres had nearly twice the risk of lung cancer compared to people with short telomeres, after adjustment for age and smoking status. There are 25 co-authors, and Cawthon is #2. In this study, short telomeres protect against (devastatingly lethal) pancreatic cancer, and in this one, there is an elevated risk of breast cancer associated with long telomeres.
There are studies contradicting each of these findings. Overall, the field seems to be more of a confused mess even than most areas of epidemiology. But for lung cancer, melanoma, and pancreatic cancer, the predominance of the evidence says that longer telomeres are associated with higher risk.
Longer telomeres uncontroversially protect against heart disease and stroke. There is no contradiction of this finding in sight, and there has been no contradiction of the major finding (by Rode and Cawthon) that short telomeres increase all-cause mortality. Perhaps that’s all we need to know.
Stop the Presses
Just a few weeks ago, I learned of this new study linking telomerase to the epigenetic changes that the methylation clock associates with aging. The implication is that telomerase accelerates aging. It began with an investigation by Steve Horvath’s group (about which I reported last month) asking, what genetic variations are associated with people who age faster or slower than average, according to the Horvath methylation clock? They did a genome-wide search for statistical correlates and the standout association was telomerase. People who have small genetic variations that support greater telomerase expression tend to have longer telomeres, but they also tend to age faster, as measured by the Horvath clock.
It’s been known for a long time that telomerase has other effects in addition to lengthening telomeres. But this is the first time that telomerase has been reported to affect DNA methylation. So it seems we are presented with a tradeoff, or pleiotropy, or Catch-22, or “damned if you do, damned if you don’t.”
The association between telomerase and accelerated aging (measured by methylation) was found in the genetic statistics, and then confirmed in a cell culture. When telomerase was artificially activated in the cell culture, the methylation patterns changed in the cells consistent with older age according to the Horvath clock. In fact (and remarkably in my opinion) they found no Horvath aging at all in the cell cultures that lacked telomerase. Could it be that telomerase is the one and only driver of epigenetic aging at the cellular level?
Telomere length and the Horvath methylation clock are both correlated with age, but they are not otherwise correlated with each other. The Horvath clock is a combination of 353 methylation levels that is optimized to correlate maximally with age. The observed correlation is 0.95. Telomere length is not statistically optimized but measured as nature offers it, and its correlation is much weaker (~0.4 according to my estimate, as I have not found this number in print). Thus Horvath clock is an excellent measure of chronological age, and combining information about telomere length can make it potentially a little more accurate yet. But the telomere clock on its own is a very unreliable measure of age.
The Horvath group designed an experiment to separate the direct effect of telomerase on methylation from an indirect effect (telomerase ⇒ telomere length ⇒ methylation age). They found no indirect effect. Telomerase itself affects methylation aging, but telomere length does not.
This raises (what is for me) an uncomfortable question. Many “good” life habits have been associated with telomerase expression, including exercise, meditation, and social integration. Could it be that these habits are simultaneously slowing our telomere aging, while hastening our epigenetic aging?
“While the paradoxical finding cannot be disputed on scientific grounds, its biological interpretation remains to be elucidated.” [Lu et. al, 2018, the same study I’ve been talking about]
(Another finding of this same study: Earlier menopause is associated with epigenetic age acceleration in women, but this is mitigated by hormone replacement therapy. HRT modestly slows aging, as measured by the Horvath clock.)
Antagonistic Pleiotropy turned Upside Down
So, what’s going on? My inclination is always to think in evolutionary terms.
Antagonistic Pleiotropy is the standard explanation for aging, though I have long argued that it doesn’t fit the data. The theory says that some genes enhance fertility and survival early in life, but have detrimental effects late in life. These genes are selected in a Darwinian process because their benefits outweigh their costs. Even though they die younger, those individuals carrying the pleiotropic genes leave more offspring, and that’s what counts for evolution. The crux of the theory is that nature is caught between Scylla and Charybdis, forced by limitations of the available genes to choose either high fertility with short lifespan or low fertility with longer lifespan. Crucial to the theory is the assumption that it is biologically impossible to separate the benefits of these pleiotropic genes (fertility) from their costs, so that there is no way evolution could engineer higher fertility without triggering later senescence.
This theory was formulated by George Williams in 1957, long before anyone had heard of epigenetics. He assumed that if you have a gene, you’re stuck with it for life. We can’t blame Williams for the frame of mind that he brought to the evolutionary question, but we now know that this is very much not the case. The body turns genes on and off in individual tissues and at specific times with exquisite precision. In fact, most of the euklaryotic genome is devoted not to genes, but to epigenetic controls of one kind or another.
The fact is that genes are turned on that dial up fertility and promote robust replacement cell growth early in life, and aging at that time occurs quite slowly. Later in life, these growth and fertility genes are dialed way back, and that is the era in which aging comes at us with a vengeance. This, to me, is a direct refutation of Antagonistic Pleiotropy as a theory.
Nevertheless, many examples pleiotropic genes have been found in studies of aging. The above story of telomerase seems to be a conspicuous example. Telomerase promotes epigenetic aging, while lack of telomerase promotes cellular senescence. “If the ’skeeters don’t getcha then the gaitors will.”[ref]
My interpretation of pleiotropy is in my book and some of my academic papers. It is this: Aging has been built into our genomes by natural selection for the sake of the community. Fixed lifespan, (especially when modified conditions of food stress) is helpful in preventing population overshoot that can lead to famines, epidemics, and extinction. But whenever a trait is good for the community and bad for the individual, there is a temptation for the individual to cheat (“cheating” is actually the term used by evolutionary theorists). In this case, cheating would mean evolving a longer lifespan via selfish genes that spread rapidly through the population, because they are more successful at the lowest level of Darwin’s competition.
Individual competition would erase aging if left unchecked. The results would be great for individual fitness, but soon would be disastrous for the population. Overpopulation would ensue, followed by the famines and epidemics mentioned above. Evolution has learned (over a very long expanse of time) to protect the communal interest, placing barriers in the way of individual selection for ever longer lifespan. This is the evolutionary significance of pleiotropy. It provides that no simple mutation can substantially extend any aspect of lifespan without adversely affecting another aspect of lifespan or of fertility. The aging clock has been “purposely” configured so as to be spread out over several different mechanisms, tied not just to other pro-aging mechanisms but to fertility as well. Aging is hard to get rid of “by design”.
In the standard theory that I don’t believe, antagonistic pleiotropy is a precondition, and evolution has had to make the best of a bad deal. In my version, antagonistic pleiotropy has been crafted by natural selection in its long-term mode. Limiting lifespan has been so important to the viability of the population that evolution has arranged to protect it from leaking away due to cheating, and antagonistic pleiotropy is one of the ways in which this is arranged. I have modeled this process in numerical simulations of evolution.
My guess is that the connection between telomerase and epigenetic aging is an example of antagonistic pleiotropy in this latter sense–certainly not in the sense of Williams, because on their face telomerase and methylation have little to do with one another.
Bad news for life extension strategies
But whatever the theoretical origins, the pleiotropic connection between telomerase and epigenetic aging complicates any strategy we might devise for slowing the progression of human aging.
I believe that the preponderance of evidence still indicates that activating telomerase has a net benefit for lifespan, but that probably we can add at most a few years by this route. I think that epigenetics is much closer to the core, the origin of aging, and that interventions to modify epigenetic aging will eventually be our holy grail. The caveat is that telomeres are simple, but methylation is complicated, and methylation is just one of many epigenetic mechanisms.
Methylation of DNA is the best-known mode of epigenetic regulation (turning genes on and off). Methylation patterns are stable unless they are actively changed, and can persist over decades, even across generations.
Four years ago, biostatistician Steve Horvath of UCLA identified a set of 353 methylation sites that are best-correlated with human (chronological) age. These are sites where genes are turned on and off at particular stages of life. A computer analysis of a gene sample (from blood or skin or even urine) can determine a person’s age within about two years.
Two reasons the Horvath Clock is important. First, it is the best measure we have of a person’s biological age, so it provides an objective measure of whether our anti-aging interventions are working. Say you’re excited about a new drug and you want to know whether it really makes people younger. Before the Horvath clock, you had to give it to thousands of people and wait a long time to see if fewer of them were dying, compared to people who did not get the drug. The Horvath clock is a huge shortcut. You can give the drug to just a few people and measure their Horvath (methylation) age before and after. With just a few dozen people over a two-year period, you can get a very good idea whether your drug is working.
Second, there is evidence and theory to support the idea that the methylation sites that Horvath identified are not just markers of aging but causes of aging. That means that if we can figure out how to get inside the cell nucleus and re-configure the methylation patterns on the chromosomes, we should be able to address a root cause of aging. (Before we get too excited: “Gene therapy” has been around 20 years but is still in a developmental stage; “epigenetic therapy” is what we need, and it does not yet exist, but is technically feasible using genetically engineered viruses and CRISPR.)
In 2012-2013, three papers appeared proposing the idea that the deep cause of aging (in humans and many other higher animals) is an epigenetic program [Johnson, Mitteldorf, Rando]. Genes are turned on and off at various stages of life, producing growth, development and aging in seamless sequence. (A fourth paper by Blagosklonny proposed a similar idea, but focused on the role of a single transcription factor controlling gene expression (mTOR) and shied away from the conclusion that natural selection might have preferred aging affirmatively. Here’s an earlier presentiment by Blagosklonny.)
It’s a powerful hypothesis that proposes to resolve evolutionary and metabolic questions alike. It contains a seed of a prescription for anti-aging research—although epigenetics has proved to be so complicated that practical modification of the body’s gene expression schedule may require a lot more groundwork.
Unbeknownst to any of us working on these theoretical papers, Steve Horvath was already working on calibration and measurement of the epigenetic aging clock, and he published his basic result by the end of 2013.
One remarkable property of the Horvath clock is that it is more accurate than chronological age for predicting who will contract aging diseases and who will die. Even though the clock was derived with an algorithm that matched the output clock age as closely as possible to chronological age, the result proved to contain more information than chronological age. “In deriving the clock, chronological age was used as a proxy for biological age.” People whose “methylation age” is greater than their chronological age are likely to suffer health deterioration and to die sooner than people whose methylation age is less than their chronological age.
Horvath has openly shared his methodology and his computer program. Based on the Horvath clock, a California company began last year to offer a commercial test for methylation age. You can send a blood or urine sample to Zymo Research.
Candidate aging clocks
Horvath describes how he came up with the idea of a methylation clock by a process of elimination, beginning with four candidate clocks:
Gene expression profile
Telomere length – This had been measured easily and cheaply for more than a decade, but its correlation with chronological age (and with mortality) is not strong enough to be useful as a biological clock.
Gene expression profile: Which genes are being transcribed into RNA at a given time? This can be measured by extracting RNA, and turns out to be highly tissue-specific. In other words, it varies according to which part of the body you’re looking at.
Proteomic data: Genes, once transcribed, are translated into proteins. Some of these proteins stay in the cell while others circulate through the body. Gene CHIP technology measures levels of different proteins reliably and inexpensively.
DNA Methylation: Easier to measure than (2) or (3). Methylation is only one of many mechanisms controlling gene expression, but it is one of the most persistent. Horvath found that a subset of DNA methylation sites seems to be characteristic of age no matter where in the body they are measured.
What is DNA methylation?
Adjacent to many genes is a promoter site, a location on the same chromosome which stores temporary information about whether the gene is turned on or off. Promoter sites contain the base sequence C-G-C-G-C-G-C repeated. This is called a CpG island (where the “p” just tells you that the C is linked to G on the same strand, rather than being linked across strands, in which C is paired with G.)
C stands for “Cytosine”, and the Cytosine molecule can be modified by adding an extra methyl group (CH3) to form 5-methyl Cytosine.
The cell has molecular workers that are deployed to go around specifically adding methyl groups in some parts of the DNA or removing them in others. The bottom line is that methylated Cytosine is a sign that says “don’t transcribe the adjacent gene.” When the methyl groups are removed, it is a signal that the gene are to be transcribed once more.
Enzymes called methyl transferases are deployed to precise regions of the genome to turn genes on and off. Methylation can be transient. There is evidence for circadian cycles of methylation. Or it can be quite long-lasting. Methylation patterns can persist for decades, and are copied when cells replicate, so that methylation patterns can be passed to offspring as part of one’s epigenetic legacy. Inherited methylation sites are the exception however; most of the genome is programmed fresh with age-zero, pluripotent methylation patterns when egg and sperm cells are generated.
How the methylation clock works
Using a standard statistical algorithm, Horvath identified 353 CpG sites that were most strongly correlated with chronological age, no matter where in the body he looked. The same algorithm provided 353 numbers to be multiplied by methylation levels at each site, then added up to produce a number. The number is not directly a measure of age, but in the last step a table is used (an empirically-derived curve) to associate the number with an age.
This is the raw output of the function before it is transformed into an age. Notice that methylation changes very rapidly during the first 5 years of life, gradually slowing during the growth phase and straightening out to constant slope after about age 18.
Even though the Horvath clock was designed to be independent of what part of the body DNA was drawn from, some variations appear. Most noticeable is female breast tissue, which ages faster than the rest of the body, and brain tissue, which ages more slowly. Blood and bone tissue tend to age a little faster. (Sperm and egg cells are “age zero” no matter the age of the person from whom the germ cells were drawn. Placentas from women of all ages are age zero.) Similarly, induced stem cells (using the 4 Yamanaka factors) have zero age. In contrast, a similar treatment can change one differentiated cell type into another, for example, turning a skin cell into a neuron. This does not affect epigentic age.
Liver cells tend to be older than the rest of the body in people who are overweight, and younger than the rest of the body in people who are underweight. Other tissues don’t seem to show this relationship. For example, fat cells do not have older methylation ages in people who are obese. And, perhaps surprisingly, weight loss does not reverse the accelerated methylation age of the liver (at least, not within the 9-month time frame of the one study looking at this).
Studies have been done correlating methylation age with various diseases and, of course, mortality. Corrections are made for every kind of environmental factor, including smoking, obesity, exercise, workplace hazards, etc, called collectively the “extrinsic factors”. The result is that methylation age rises with extrinsic factors, and independently methylation age is also correlated with intrinsic (genetic) factors that affect lifespan. Horvath estimates that genetics controls 40% of the variation in methylation age (as it differs from chronological age).
Men are slightly older than women in methylation age. This is already evident by age 2. Delayed menopause is associated with lower epigenetic age. Cognitive function correlates inversely with methylation age of the brain.
Speaking before Horvath at the same conference, Jim Watson claims there are many supplements and medications that can slow the Horvath clock. The one he focuses on is metformin, which, he says, has epigenetic effects via an entirely different pathway from lowering blood sugar (the purpose for which it has been prescribed to tens of millions of diabetics).
Here’s a curious clue: There is a tiny number of children who never develop or grow, and continue to look like babies through age 20 and perhaps beyond. These children have normal methylation age. Whatever it is that blocks their growth, it is not the methylation changes in their DNA. Does this mean that there are other epigenetic controls, more powerful than methylation, that control growth and development? Or does it mean that children with this syndrome have normal epigenetic development, but something downstream from gene expression is blocking their growth? Conversely, Hutchinson-Gilford progeria is caused by a defect in the LMNA gene which causes children to age and die before they even grow up. Hutchinson-Gilford children have normal methylation ages by the Horvath clock.
Radiation, like smoking and exposure to environmental oxidation, tends to age the body faster. This is independent of methylation age—which is unaffected by radiation. Neither smoking nor radiation exposure affect epigenetic age. HIV also accelerates aging, and HIV does affect methylation age.
Methylation age and telomere age are both correlated with chronological age, and they both predict mortality and morbidity independent of chronological age. But the two measures are not correlated with each other. In other words, the information contained in the methylation clock and in measures of telomere length complement one another to offer a better predictor of future aging decline than either of them separately.
Diet has a weak effect on methylation age. Very high carbohydrate, very low protein diets are noticeably terrible. Beyond this, there seem to be two sweet spots: one for the Ornish-style protein-restricted diet and one for the Zone/Atkins style diet. Weak evidence to be sure, but suggestive that they both work.
“The epigenetic clock is broken in cancer tissue.” [ref]
Building on the original clock
The original clock was optimized to track chronological age, and yet it fortuitously provided more information than chronological age. In a second iteration, Horvath set out explicitly to track biological age. He used historic blood samples from the 1990s, and paired them with hospital records and death certificates to search for methylation sites that correlate best with aging-related health outcomes. The result was the phenotypic clock, DNAm phenoAge. This uses 513 methylation sites to predict
(loss of) physical strength
(loss of) cognitive ability
On the drawing board: An epigenetic clock specialized to work well with skin and blood cells, (which are the most accessible). (Enough skin cells can be scraped painlessly from the inside of your mouth (buccal epithelial cells) to do a DNAm test.)
Connection to Parabiosis and Plasma Transfusions
Several groups have begun to experiment with transfusions of blood plasma from a young donor as a possible path to rejuvenation. Horvath reports an encouraging finding: Sometimes older people contract a form of leukemia that requires a blood and marrow transfusion (including the stem cells that give rise to new blood) from a donor. The finding is that after this treatment, the blood of the patient continues to show the methylation age of the donor, not the patient.
Epigenetic Aging and Telomere Aging Bound to a See-Saw Relationship
(This was the most exciting new result for me personally, because it relates to an idea I have held dear for more than a decade.)
Methylation age is older or younger than chronological age in different people, generally by about +2 years. 40% of the variation is due to genetics. Some common genetic variants can make the clock run faster or slower. The most prominent genetic variants link telomere aging to methylation aging. The faster your epigenetic clock runs, the longer your telomeres. The slower your epigenetic clock runs, the shorter your telomeres. [preprint]
There’s a word for this in the genetic theory of aging. It’s called Antagonistic Pleiotropy. Back in 1957, George Williams theorized that the genes causing aging ought to have simultaneous beneficial and detrimental effects. That would explain why natural selection has permitted aging to occur, despite the fact that it cuts off fitness. Williams said: Nature had no choice but to accept the genes that cause aging because there was no other way to get the benefits of these same genes (which he surmised ought to enhance fertility).
My theory of Antagonistic Pleiotropy is that it is not a situation of “forced choice”; rather, aging is important for the health of the community, and mother nature has been faced with the dilemma: how to keep aging in place despite efficient natural selection against it on the individual level. Aging is so important to the community that evolution has been motivated to find ways to keep it in place, despite the short-term temptation for natural selection to favor those with longer lives (thus greater opportunities to leave offspring). In my hypothesis, evolution invented pleiotropy to address this problem. The telomerase-epigenetic clock connection is an example. There is no physically necessary connection between telomerase and epigenetic aging, but the two have evolved a see-saw link so that it is more difficult to mutate aging away.
This also relates to my coverage last fall of the telomerase-cancer connection. At the time, I was scratching my head, why should genetic variants that lengthen telomeres be associated with higher rates of some cancers? Here is a clue: The same genetic variants that lengthen telomeres also accelerate the epigenetic aging program. The specific example of a cancer that is most closely tied to higher telomerase levels is melanoma, which is a cancer that is less sensitive to age than other cancers. People tend to get melanoma earlier in life than other skin cancers. Therefore, I predict that other pleiotropic links will be found between these genetic variants that promote longer telomeres and other mechanisms linked specifically to melanoma.
The Bottom Line
All these data in a field so new is a tribute to Horvath’s industriousness and to the promise and fruitfulness of a new methodology.
The data so far suggest that methylation programming is a big part of the driver of aging, but not the whole story. Smoking affects life expectancy, but it doesn’t affect methylation age. Weight loss benefits life expectancy, but it is invisible to methylation age. Most curious are those children who fail to develop, or age prematurely, even though their methylation age is progressing on schedule.
What does it mean that radiation ages the body without advancing the methylation clock? Perhaps that accumulation of damage is part of the phenotype of aging, though I remain hopeful that the body remains capable of undoing that damage even late in life, if it is re-programmed to want to do so. What does it mean that AIDS advances the aging clock? Perhaps that the immune system is a central signaling mechanism in the aging process.
So, it’s “methylation plus”. Plus what? Not just methylation plus damage”; though we can certainly shorten our lifespan with radiation or smoking, we can’t increase our lifespan by avoiding toxins. “Methylation plus other epigenetic programs”—this would be my first guess. “Methylation plus mitochondrial state” would be a close second. Methylation is all in the nucleus, and the cytoplasm of the cell seems to store independent information, and can even re-program the state of the nucleus, as suggested by parabiosis experiments. There is also evidence for“Methylation plus telomere shortening”.
The Most Promising Way Forward for Anti-Aging Science Today
We now have many effective interventions (mostly of small effect) for longevity and preventive care. Most readers of this blog take more than one supplement each day; some of us (I confess) take very many. We make an unthinking assumption that “more is better”, or rather that if A is beneficial and B is beneficial then if we take A and B we can get the benefits of both. We don’t think to question or deconstruct this reasoning. It is rooted in a reductionism that works pretty well in the physical sciences, much less well in biology.
We know that the benefits of all these interventions don’t just add up like numbers in a spreadsheet, but we continue to act as though this were our reality.
The truth is that we know almost nothing about the cross-talk among different health interventions. The reasons so few experiments have been done are plain enough, but the situation has become untenable. There is an urgent need to understand the interactions among treatments. We might begin with those that are individually most promising, but expect surprises. The combinations that offer the greatest longevity benefits may turn out to be pieced together from components that individually have little or no effect.
I might have said ‘the most promising way forward for medical research today, because I believe that anti-aging science is the most productive area of medical research. If you are reading this page, you probably know this already, but we all take comfort in confirmation of what we already know. So:
Prevention is more cost-effective than treatment. The root cause of most disease in the developed world is aging. This point has been made decisively [for example], most eloquently by Aubrey de Grey. (The root cause of most disease in the third world is poverty, and ending poverty is also an essential imperative, but it is not a subject for medical research.)
The problem of interactions has been neglected for a number of reasons:
Unconscious linear thinking
The dizzying number of combinations that need to be studied
The want of a guiding paradigm that would provide context for individual studies
Scientific inertia: researchers are more likely to study (and funders are more likely to support) research programs that are established and proven
But the problem is potentially of great import. We expect a great deal of redundancy among the mechanisms of action of various interventions we know about. Taking two or three or four drugs that address the same biological pathway is likely to be a costly waste. More rarely, longevity drugs may interact in ways that actually interfere and reduce overall effectiveness.
But we have good reason to hope that in rare cases there are combinations that are more than the sum of their parts. These fortuitous combinations synergize to offer greater benefits than they provide separately. Finding a few such combinations would be a jackpot that justifies many, many expected null results.
The huge number of possibilities to be covered
If we begin with 30 individual interventions, there are 435 pairs of interactions and 4060 combinations of three and 27,405 combinations of four. If we think traditionally, each one of these combinations is a research program in itself, requiring at least several person-years of professional effort plus overhead. This is the daunting reality that confronts anyone who is intent on beginning to address the problem of interactions. 27,405 experiments of any kind is a labor of Hercules, even for a well-funded, fully roboticized biomedical lab.
There is a hierarchy of experimental models for studying anti-aging interventions:
Human cell cultures are the cheapest and fastest, but we learn the least
Complementing human cells are yeast cells, which actually have a life expectancy and some biology that overlaps our own
Studies of thousands of C. elegans worms can be done efficiently with robotic controls and worm counters.
Fruitflies are a great deal “more like us” than worms and they can be raised in large numbers, live just a few weeks.
Lab rats and mice are expensive, but they are mammals with biology that is much like our own. Experiments in rodent longevity last 2 to 3 years.
Human trials require extensive safety measures and typically take decades to see subtle changes in health and mortality statistics; but this is the most direct indication of what we want to know.
So, how might we begin?
We have no idea what we will find. Maybe there will be a few spectacular combinations. Maybe the interactions will turn out to be small, mostly negative, and boringly expected. (My guess is that both of these will turn out to be true.) We should not try to define the second stage of the program until we have results from the first.
The first step is to choose the most promising interventions to combine. A great number of drugs and supplements are known that extend lifespan in rodents and/or lower mortality in human epidemiology. Magalhaes and Kaeberlein have put together a large database of animal studies that seems to be off-line at present. Here is a list I proposed in this column two years ago:
Beta Lapachone (Pao d’Arco)
Dinh lang (Policias fruticosum)
Gynostemma pentaphyllum (jiao-gu-lan)
N-Acetyl Cysteine (NAC) / Glutathione and precursors
Oxytocin (not oral)
J147 (a promising new Alzheimer’s drug)
NR, NMN and NAD precursors
We might add
Polyphenols from tea
Flavinoids from blueberries
Cardarine / GW501516 / PPAR agonists
Dasatinib / Quercetin
Momordica charantia (bitter melon)
Gotu kola / Bacopa
Pine bark extract
Interventions not in pill form include
Intermittent fasting (various schedules)
Plasma transfusions from younger individuals
Transplanted young thymus
Transplanted young suprachiasmatic nucleus
How to prioritize and explore the huge number of combinations? Here are four ways we might begin to sort through the possibilities:
Use theory: Look for biochemical mechanisms that seem complementary
Traditional Chinese Medicine, Ayurvedic medicine and other ancient traditions suggest combinations of herbs that long experience says function together.
Broad screens for especially potent combinations
Statistical mining of an on-line registry of what people are taking presently
Let’s look at these one at a time.
1. Biochemical Theory
We know a few biochemical pathways that are linked to longevity. They all overlap and talk to each other. Nevertheless, we expect that treatments that address the same pathway are likely to be redundant, whereas treatments that address distinctive pathways have a better chance of synergizing. For example, insulin resistance is a robust hallmark of aging. The insulin pathway is most plastic and most accessible to intervention. Fasting and caloric restriction address the insulin pathway, as do metformin berberine, jiaogulan and bitter melon. Exercise has many benefits, some of which work through the insulin pathway.
We might continue classifying interventions that address other pathways. Here are some longevity pathways of which I am aware:
Immune senescence / thymic involution
Epigenetic reprogramming / transcription factors
Anabolism / Catabolism imbalance
P53 / Apoptosis
Someone who knows more biochemistry than I do might be willing to classify the interventions I list (and others) according to these 10 pathways (and others). Here is a template in Google Sheets, which I establish as an open Wiki. http://tinyurl.com/longevity-pathways
2. Eastern and Indigenous Medical Traditions
Many useful modern medicines are derived from ancient folk wisdom. But this work has proceeded with a deductive logic, isolating active chemicals from whole plants (as aspirin from willow bark, cycloastragenol from astragalus, and curcumin from turmeric). Many folk traditions, especially Traditional Chinese Medicine, are based on not just whole herbs but combinations of herbs that have been found over the ages to work together. Ideas may be taken from these traditions to prioritize combinations for testing. For example, the best known Chinese longevity formula is Shou-wu-chi (首乌汁;), which is compounded of (list from Wikipedia):
The Ayurvedic tradition is less contains fewer formulas, but combinations that are said to contribute to longevity include these (which I found, just for illustration, at Banyan Botanicals)
Haritaki (Terminalia chebula)
Guduchi (Tinospora cordifolia)
Amalaki (Embelica officinalis)
Kumari (Aloe barbadensis, or aloe vera)
Guggulu (Commiphora mukul)
Brahmi or gotu Kola (Centella asiatia) or closely-related Bacopa
Ashwagandha (Withania somnifera)
3. Broad screens for particularly effective combinations
Two years ago in this space, I proposed a screening protocol in which all combinations of 3 interventions from a universe of 15 would be tried on just 3 mice each. I showed with a computational model that if these included at least one lucky combination that increased longevity by more than 50%, then, despite the small number of mice, it would be identified with at least 80% confidence. Combinations of three from a universe of fifteen is a kind of sweet spot for this particular experimental design, and much less is learned if the numbers are scaled back. This means it is not feasible to test the concept on a small scale. The full proposal requires 1365 mice in cages of three, followed for at least two years. Cost estimate is about $2 million in the US or Europe, perhaps as low as $500,000 to do the same experiment in China. I would be eager to work with any lab that has the expertise and the facilities to implement this protocol. The experimental design and simulated analysis was recently published in English in a Russian journal.
4. Data-mining of an online registry where people record what supplements they are taking and commit to reporting their health history
It would be a great public service if someone were to establish a web-based registry where individuals could share information about what supplements they are taking and what results they are getting. Over years, this could turn into a data miner’s heaven for information about individual drugs and lifestyles and their interactions. The subject is too big for a controlled experiment, but enlisting the public would be a great and greatly-rewarding project.
I know there are web sites such as Longecity that are excellent resources for anecdotal accounts of others’ experiences. But the data is not in a format that lends to statistical summaries. If you know of an existing online database of this sort, please reach out and share the web address with me.
I have preliminary plans to create such a web site in conjunction with a forthcoming book project.
There may already be a viable plan for major life extension hiding in plain sight. There is no extant research program to explore the relationships and interactions among life extension measures. Eventually, some large, well-funded agency (perhaps NEA or the Buck Institute) will take on this project in a systematic way. But the large organizations are conservative, and are unlikely to begin until the ice is broken. Thus, even the first shards of information in this area are likely to be valuable indications of a new research direction.
If you have a research lab, or if you know are connected to someone who might be interested in this project, or if you have a funding source, please let us work together.
My book (with Dorion Sagan) has just been released as a Kindle edition in the UK, Australia and New Zealand, Hong Kong, Russia, Singapore and various European countries. The book is sold in America by Flatiron/Macmillan as Cracking the Aging Code. The British edition is called What Good is Death?
Life Extension Foundation has just announced that next week they are going to announce a partnership with the Young Blood Institute for what is perhaps the most ambitious human trial of anti-aging medicine ever. It’s a daring project, with what is IMO a most promising target. But I find details of their protocol puzzling, and haven’t been able to get satisfying answers from LEF or from YBI about why they’ve made the choices they have, and how they will be able to learn from the project.
The principal treatment consists in 6 plasma transfusions scheduled over 4 weeks.
Extensive testing is planned, including telomere age and methylation age in addition to a full battery of standard blood tests like lipids and inflammation markers.
The program is self-funded by research subjects, with projected cost ~ $50,000 per participant.
In each transfusion procedure, red and white blood cells will be separated and cycled back into the subject. Blood plasma with dissolved blood chemicals will be removed. It will be replaced not by full plasma from a donor but by albumin and gamma globulin only.
“Rescue Elders” project of LEF
Last year, Life Extension Foundation announced a new and ambitious program of human experimentation at the edge of medical science, sponsoring high-risk trials to prospect for anti-aging breakthroughs in the near term. (The project’s name, Society for the Rescue of our Elders, was taken from an 18th Century group in Amsterdam, Society for Recovery of Drowned Persons, that was formed after the efficacy of artificial respiration was first discovered.) Their first project was a clinical trial of rapamycin, now ongoing. This present program of plasma transfusions is their second project.
It’s my belief that the body’s primary aging clock is epigenetic. That is to say, different combinations of genes are expressed at different times in life, and in old age the constellation of genes that is turned on causes inflammation, auto-immunity, and a preponderance of anabolism over catabolism. The master’s tools are deployed in old age to dismantle the master’s house.
As a general concept, I think this is the best working hypothesis we have. But if it is correct, it doesn’t offer an immediate key to rejuvenating the body. The problem is that epigenetics is enormously complicated. (The genetic code, in contrast, is as simple as it can be—a code of correspondence between triples of nucleic bases in the DNA with the 20 amino acids that are linked together, then folded to form proteins.)
Methylation of chromosomes is the best-known and first-discovered mechnism by which genes are turned on and off. In addition to methylation, there are dozens of other epigenetic markers and signals that are applied directly to DNA or indirectly to the histone spools, beads of protein that around which DNA is coiled.
Different genes are turned on in different parts of the body. This is the primary way that the body differentiates one kind of cell from another—they all have the same genes, but different combinations of genes are turned on in a nerve cell or a muscle cell or a skin cell. Overlayed on these differences from one cell type to another, genes are turned on and off with age. This effect is reliable and consistent enough that Steve Horvath was able to construct a methylation clock based on 353 methylation sites that change consistently with age across all cell types in the body.
The connection to blood signals was supplied by research from Stanford, Berkeley and Harvard, in which blood from a young mouse is introduced into an old mouse, and is shown to rejuvenate its tissues, stimulate new growth, and promote healing. With a small conceptual leap, I imagine that there is a self-regulating epigenetic clock distributed through the body. On the one hand, epigenetic markers in each cell give each cell its characteristic age. On the other hand, these same cells are sending signals though the blood (transcription factors) that are continually updating the epigenetic program and keeping it in sync throughout the body. The hope is that (even if we don’t understand in detail how the epigenetics is programmed) the substitution of a young blood environment for an older blood environment will reprogram epigenetics in the distributed cells, and after a few cycles it will be self-sustaining. That is, once the cells are reprogrammed to be younger, they will themselves send signals into the blood that maintain the younger state.
Criticism of the protocol
Here is a description of the proposed YBI protocol. Six times over a period of 4-6 weeks, patients will be hooked up to a plasmapheresis machine. Whole blood is removed from one arm, and a mixture is returned to a vein in the other arm. The mixture that is returned will include all the patient’s own red and white blood cells. But the blood plasma, clear liquid with all the dissolved signal molecules, will be removed. The plasma will not be replaced by blood plasma from a younger patient, as in a standard plasma transfusion. Instead, the return side will contain only albumin and gamma globulin. These are the hydrostatic and immune components of the plasma (antibodies). The theory is that auto-immune aspects of aging will be addressed in this way…but the antibodies are generated continually by white blood cells, so that the treatment will not last long. Hence the rationale for frequent repetitions of the treatment, less than a week between treatments.
My principal fear is that the planned YBI protocol may be able to do only half the job. My conjecture is that it is the signal molecules that actually maintain the epigenetic program. The proposed protocol will remove the bad ones, and that’s half the job. It may be that there are transcription factors from young blood that are deficient in the old and need to be replenished. Full plasma transfusions from young donors would do both, fully replacing the blood environment of an old person with the blood environment of a young person. But it is expensive and requires many donors for each patient. It is to control expense that YBI has chosen to do do the removal, but not replacement of blood signal molecules.
Just last year, Tony Wyss-Coray headed a Stanford trial for AD, through a for-profit spinoff called Alkahest. Alzheimer’s patients were given four doses of young blood plasma. But the dose was small, a total of 1.5 liters of plasma, and the bad actors weren’t being removed. Results were disappointing, but perhaps this is because the procedure was not bold enough.
Beginning in 1924, a Soviet Bolshevik named Alexander Bogdanov experimented on himself, receiving a series of 10 blood transfusions from younger donors. He was 51 years old at the start of the experiment, and contemporaries report that he appeared physically ten years younger in the course of the procedures. He self-reported prodigious health benefits and return of youthful vigor. The experiment ended tragically in 1928, when he received blood from a student who had been infected with malaria, and died of the infection.
Harold Katcher has been thinking about the rejuvenation potential of plasma transfusions for a long while, and here is the protocol he suggested five years ago. He does not speculate about what schedule would be ideal, and he cautions us that extensive experimentation with mice and even in cell cultures would be useful before beginning human trials.
Two years ago, I spoke via skype with Jesse Karmazin (Stanford University and Ambrosia). He told me that as a med student he had done an analysis of historic data from transfusions performed at Stanford University Hospital, and found that those who had received blood from young donors had better outcomes and better long-term survival rates than those whose blood had come from older donors. I was very interested in this claim, and asked him for the data that supported it. He told me it could not be released for reasons of patient privacy. I never did get to see that data, and he never published his analysis.
Last year, a published study claimed the opposite: that in a large database of Swedish and Danish patients transfused between 1995 and 2012, they were unable to detect any survival difference between those who received blood from young donors and a matched group of patients whose transfused blood came froun old donors.
Ideally we would like to learn many details from a trial of HPE (heterochronic plasma exchange). Fundamentally, we would test the basic question whether circulating factors in the blood are indeed able to reprogram the epigenetics of cells throughout the body, and whether this will have a salubrious effect on vitality, appearance, metabolism and the immune system. A well-designed trial might also teach us more
How long does the young plasma profile remain in the bloodstream before the body’s old cells take over and drag the proportions back down to where they were? (At this point, the next infusion would be appropriate.)
How many transfusions are required before the body’s cells are reprogrammed, and the young plasma profile becomes self-sustaining?
Transfusions from young donors are a good place to start, but obviously not a practical solution for rejuvenating large numbers of people in the long term. But if we can learn which chemical constituents need to be removed and which need to be added, it is possible that a core handful of such factors might be discovered. Those that need to be added can be manufactured in bulk by vats of genetically modified E coli. Those that need to be removed can be targeted with antibodies and removed in a simplified blood filtering procedure. This is a promising research path—perhaps the most promising that is visible from where we are now. But we’ll never know if it can work until we do an expensive and time-consuming series of experiments.
How many transcription factors need to be regulated in order to the job? This is the biggest unknown. When I spoke with Irina Conboy four years ago, she was optimistic that the number may be less than ten, but last year, she was less optimistic. I take heart from the fact that just four Yamanaka factors can turn a differentiated cell into a zero-age stem cell.
Toward the future
Plasma transfusions are a safe, approved medical procedure, used for decades as treatment for (especially) auto-immune diseases. No FDA approval would be needed for a clinical trial, using transfusions “off-label” to test rejuvenation potential. However this is not a project likely to be picked up by venture capitalists looking to make a quick buck. The first reason is that the process will be expensive and time-consuming, with a great deal of trial and error. The second reason is that when it is all over, everyone will know what are the best schedules and procedures, and the most important transcription factors in our blood—but it is doubtful that this will be patentable intellectual property, or that the investors would be able to maintain a trade secret. What we need is a substantial public investment or a middle-aged billionaire angel investor who is thinking clearly about his own destiny a decade or two down the road.
Like you, dear Readers, I tend to be focused on the biochemistry, and have to remind myself again and again that the mind and body are intertwined. I came out last week with my core belief about biology: Mechanistic physics explains only half of what we are. Life has its own laws which we will discover only if we admit they exist.
In fact, the most powerful thing we can do to prolong life expectancy is to have robust connections to other humans. The best-documented effects are for empowering relationships with community (especially cooperative action for change) and intimate relations of love. Together, these factors contribute more to life expectancy than any diet or exercise program, or any supplements you can take. The difference is comparable to life expectancy difference between heavy smokers and non-smokers. (I wrote about these topics 2 years ago: [1. Social status and depression, activism vs powerlessness, 2. Family]
Elissa Epel is famous for having elucidated the connection between stress and eroding telomeres. But she has also brought us positive messages: Meditation is associated with telomerase expression and longer telomeres. Altruism breeds telomerase. Loving-kindness is associated with longer telomeres. In a publication last summer, she and co-authors documented the benefits of sex. Women (all subjects were partnered females) who had sex at least once in the week surveyed had longer telomeres than women who did not.
The result added to evidence that goes back at least 20 years. The Caerphilly study showed that frequency of sex correlated with lower all-cause mortality in men. The conclusion extends to women. The tendency of medical professionals to interpret the result in terms of the biochemistry of orgasm has been tempered, as it became clear that sex with a partner, with or without orgasm, has benefits above masturbation [ref]. Intimacy without sex has its health rewards, as does the strength of one’s community fabric.
So, in this context, the headline result from the newest study is no surprise. The puzzle is that, even though powerful connections between social relations and health are confirmed again and again, the details keep changing, and consistency is elusive.
For example, the study just cited found that subjects who reported more sexual activity had longer telomeres, but they didn’t have more telomerase activity. In fact, they had (almost statistically significant) less telomerase activity. This was a short-term study. Telomerase activity is a short-term variable, and telomere length is supposed to respond in the longer term to telomerase activity. We should not have been surprised if an increase in telomerase had been observed, without a significant difference in telomerelength. The opposite finding suggests a missing link in the causal chain. (The Discussion text in the article is very open about this mystery.)
The study included only women. Women have been found to be more sensitive to the quality of loving attention and the depth of their connections in love, while men tend to respond to the cruder quantitative variable of sexual activity [ref]. But for women in this study, telomere length was related only to the frequency of sex, and not to the quality of relationship, or to relationship satisfaction. In fact, they found no significant association with any of the subjective questions asked concerning satisfaction with the relationship, or feelings of closeness. Again, the investigators themselves were surprised.
Paradoxical results from other studies: Men (>57yo) who had frequent sex (more than once per week) and men who self-reported that sex was “extremely satisfying” had twice as many heart attacks in the ensuing five years [ref]. In the same study, results for women were not strong enough to be statistically significant, but were strange enough to be puzzling. Women (>57yo) who reported sexual relations that were highly satisfying had higher risk of cardiovascular disease, but women who reported most intense pleasure from sex had lower risk. “These findings challenge the assumption that sex brings uniform health benefits to everyone.”
This classic study found that marriage offers substantial benefits in life expectancy for both men and women, but that the benfits for men are far larger. The relative risk in mortality rate, unmarried vs married, is 1.5 for women but 3.5 for men. The large disparity has not held up in more recent studies.
This is the most comprehensive recent review of the relationship between social variables and all-cause mortality, but it is confusingly written (I believe the verbal interpretation of statistics is incorrect). The message comes through loud and strong, that social integration accounts for a large benefit in decreased all-cause mortality, accounting for 5 to 10 years of life expectancy. But even more than in other fields of social science, there are contradictory results and inconsistencies that thwart anyone trying to tell a neat story.
Why is social connection so important to health
“Two main types of models have been proposed to explain how social support influences physical health. In main-effect models, high levels of social integration are health promoting, regardless of whether one is under stress [ref, ref]. Greater integration into one’s social network gives an individual identity, purpose, and control, a perceived sense of security and embeddedness, and a source of reinforcement for health-promoting behaviors or punishment for health-compromising behaviors, all of which can promote health [ref]. In the stress-buffering model [ref], the negative effects of stress occurring outside of one’s social relationships (e.g., at work) are diminished by the presence of strong social support, which can mitigate stressful events directly (e.g., intervening on a friend’s behalf) or through reducing stress appraisals [ref].” [quoted from Robles, 2004]
Bert Uchino distinguishes between “perceived support” and “received support”. The correlation of the former with health and mortality variables is robust. But the latter is sometimes found to be inversely correlated with health. This seems to say that if people are helping you and you don’t appreciate it, you’re worse off than if you had been on your own. If you think you’re embedded in a caring and supportive community, you’ll live longer. If you’re actually embedded in a caring community, but you devalue what you’ve got or if you isolate yourself because you’re more comfortable that way, your life expectancy is shortened. This is a morality tale if I ever heard one.
Conflictual interactions in the context of marriage (as in Western culture generally) contribute to higher levels of systemic inflammation [ref]. But this study found no relationship between job stress in men and measures of chronic inflammation. Maybe it depends on what is meant by “stress”. This study suggests that feeling out of control (powerlessness, low status) is associated with markers of inflammation.
Why do we care about this? Many of us are fanatical about following the best evidence when designing exercise and supplement regimens for ourselves. But is there anyone out there who is waiting for the latest correlation with telomere length before deciding whether to fall in love? (I didn’t think so.)
No, the reason we care about this subject is that it reminds us that aging is a social process almost as much as it is a biological process, even if the social correlates of longevity confound our best intuitions about how to live well.
And perhaps it reminds us, indirectly, that in the “rationalization” of our health care system, we have made a bad bargain. Over the course of my lifetime, medical practice in America has gone from a model of individualized care by family doctors to impersonal care by specialists. Medical care has become more evidence-based, and there is a much better chance that the doctor who treats your condition has a deep knowledge and experience of that condition. But what we’ve lost along the way is the doctor-patient relationship—both because you see a different specialist for each condition, and also because as doctors’ time is squeezed to optimize profit, the time for listening and empathizing has been eliminated. Despite the accumulation of studies showing that doctor-patient relationship has an outsized effect on prognosis, our present health care system is systemically deficient in human caring.