Josh Mitteldorf studies evolutionary theory of aging using computer simulations.
The surprising fact that our bodies are genetically programmed to age and to die
offers an enormous opportunity for medical intervention. It may be that therapies
to slow the progress of aging need not repair or regenerate anything, but only
need to interfere with an existing program of self-destruction.
Mitteldorf has taught a weekly yoga class for thirty years. He is an advocate for
vigorous self care, including exercise, meditation and caloric restriction.
After earning a PhD in astrophysicist, Mitteldorf moved to evolutionary biology as a
primary field in 1996. He has taught at Harvard, Berkeley, Bryn Mawr, LaSalle
and Temple University. He is presently affiliated with MIT as a visiting scholar.
In private life, Mitteldorf is an advocate for election integrity as well as
public health. He is an avid amateur musician, playing piano in chamber groups,
French horn in community orchestras. His two daughters are among the first children
adopted from China in the mid-1980s.
Much to the surprise of evolutionary biologists, genetic experiments indicate
that aging has been selected as an adaptation for its own sake. This poses a
conundrum: the impact of aging on individual fitness is wholly negative, so aging
must be regarded as a kind of evolutionary altruism. Unlike other forms of
evolutionary altruism, aging offers benefits to the community that are weak, and
not well focussed on near kin of the altruist. This makes the mechanism
challenging to understand and to model.
more at http://mathforum.org/~josh
Experience tells us that it is much easier to extend median lifespan than maximum lifespan. Katcher’s trial of E5 in 8 rats breaks this expectation. The last of Harold Katcher’s rats has died, and she outlived her sisters by 7 months. Compared to controls, the average lifespan of treated rats increased 9.6%, while the maximum lifespan increased 22.0%. (For reasons unknown, the control rats lived longer than most Sprague-Dawley rats.)
When I last wrote about Katcher’s rats, there were just two left alive, and the survival curve conformed well to the Gompertz rule, which says that mortality rates increase exponentially with age. (There is no theoretical basis for the Gompertz rule, but it has been found to be a good empirical model for aging in many species.) From a Gompertz fit to the first 6 rats, I was projecting a maximum lifespan of 1250 days. The 7th rat wasn’t far off from that estimate, but the last of the 8 has lived just over 4 years (1464 days), breaking the record for lab rats. “Sima” lived 5% longer than the previous longest-lived Sprague-Dawley rat.
Sima received five infusions during her lifetime at intervals of 3 months. A sixth scheduled treatment was held on the judgment of the experimenters, so that at Sima’s death she was more than six months out from her last infusion. The fact that one rat lived longer than the rest is an invitation to experiment with optimization of the E5 protocol. All the rats were genetically identical and raised in the same lab. All received their first treatment around 2 years of age. Perhaps there are physiological tests that would offer suggestions why Sima responded better to the treatment.
Where does this project need to go?
It has been almost three years since I wrote up (breathlessly) the results of Katcher’s first study. I called it an Age Reduction Breakthrough. I still believe that plasma transfusions are the most promising path toward real, practical rejuvenation in the near-term. It is frustrating how little has happened in the intervening years. This should be a crash project for laboratories around the world, and instead it is being confined to a small group of scientists in Mumbai and Baltimore who are holding the IP.
Edison invented the lightbulb in 1879, and the first commercial units went on sale to the public in 1880.
We all should be demanding of Katcher and Sanghavi and our funding agencies a full-scale research program.
Determining what are the essential ingredients in E5 (Our patent system provides perverse incentives NOT to do this.)
Developing synthetic methods for creating these proteins (perhaps with vats of genetically modified E coli, a method which provides insulin and other human proteins in bulk at extremely low cost).
Experimenting with different schedules and dosages, using Horvath clocks for feedback
Following all this up with lifespan studies in several mammalian species
Simultaneously offering E5 in combination with plasma dilution to human volunteers who are eager to be experimental subjects in exchange for probability of substantial health benefits.
Katcher and Sanghavi have a company called Yuvan Research, and they have connections with a laboratory at Johns Hopkins University in Baltimore. But this program is moving much more slowly than I would like (perhaps you concur) because the resources they have available are limited and Yuvan is jealously guarding its intellectual property. It didn’t help that Yuvan’s working capital was parked with Silicon Valley Bank, which went belly-up on Friday.
I suspect that many partners for Yuvan are available worldwide if suitable legal agreements can be reached.
I begin from the paradigm that aging is not a random process but an adaptation, a programmed self-destruction. This was my entree into the field 27 years ago, and has been the theme of much of my research, including two books. For a summary of the evidence, here’s a blog post from 2015, and here’s the non-technical version of my book.
Once you accept that the body is killing itself on a schedule, you have to ask how the schedule is maintained. Just as growth is programmed in early life and then puberty is initiated by turning on sex hormones, aging happens when inflammation and auto-immunity and insulin resistance are gradually switched on when the body attains a certain age. This implies a master clock, probably the same clock that governs development also controls aging.
Where is that clock and how does it work? If we had an answer to these questions, we could reset the aging clock and an 80-year-old body would act like a 30-year-old body, with all the robust repair and regenerative mechanisms fully engaged.
The aging clock that we seek must have two conflicting properties. Of course, it must keep time reliably to trigger the phenotypes of growth, development, and then senescence on schedule. It must also be homeostatic. Homeostasis is a fundamental property of life. All biological systems tend to restore their state when deranged by the environment. If the clock is perturbed, it must be able to find its way back to a remembered biological age.
Homeostasis is a defining feature of living organisms, perhaps the most fundamental property of life itself. The word refers to the capacity of a system to maintain its state in the face of slings and arrows of outrageous fortune. For example, we can swim in ice water or we can trek through the Sahara and our bodies maintain a core temperature pretty close to 98.6ºF. We can drink Coca-Cola or we can fast for a week, and still the body keeps enough glucose in the blood to feed our cells without poisoning us with excessive blood sugar. We can take powerful proton pump inhibitors that jam with the body’s mechanism for creating stomach acid, but after a few months the body ramps up the acid factory as much as needed to digest our food.The point is that the body has many jobs that it needs to do, and it is exquisitely adapted to accomplish the most important of these no matter what are the outward circumstances. In the long run, the most important component of Darwinian fitness is the ability to keep on keeping on when the unexpected happens. Homeostasis.
But how can a clock be homeostatic? If the body’s clock is knocked far off the biologically-determined age, where is the reference information from which it can be reset?
The existence of a clock is implied by the paradigm in which aging is an adaptive program. The need for homeostasis is a general property of biological systems. The only way that the two conditions can be met simultaneously is if there are several independent time-keeping mechanisms, and they are constantly exchanging information. This condition derives from theoretical considerations. I concede that this is a dangerous way to draw conclusions about biology, which has always been primarily an experimental science. So I am going out on a limb to put forth this hypothesis: There ought to be several independent time-keeping mechanism in the bodies of complex organisms like the human animal, and there is continual cross-talk by which they are able to establish consensus, and reset the readout if one clock should differ substantially from the others.
I have written in this column about a central clock in the neuroendocrine center of the brain, the suprachiasmatic nucleus, as documented (independently) by Claudia Cavadas (Lisbon) and Dongshen Cai (NYC). Steve Horvath has convinced us that there is a decentralized clock in the epigenetic state of dispersed cells around the body. Perhaps this counts as more than one clock, since Horvath has measured differences in aging rates in different organs. Epigenetic changes in stem cells may deserve the status of a separate clock.Telomere length in stem cells may constitute another clock. The immune system appears to have its own aging schedule, which was the subject of Roy Walford’s first theory of aging. Perhaps oxidative status of the dispersed mitochondria constitutes a fifth clock. A new possibility is raised by the research from Michael Levin’s Tufts University laboratory. Levin has demonstrated a role for electrical patterns in morphogenesis, healing, and regeneration. His model for cancer is not genetic; rather he has demonstrate that he can create tumors from normal genomes by disrupting their electrical connection to one another, and he can cure cancer in highly-mutated tumors without killing the cells, merely by restoring the cells’ electrical connectivity. Levin has speculated that electrical patterning is lost with age because many vertebrates are able to regenerate limbs or parts of limbs early in life, and gradually lose this capacity as they mature.
We should not be surprised to discover other, independent timekeepers as well. After all, the body must be coerced into killing itself, despite robust Darwinian adaptations that support individual survival and fecundity. If there were an easy path to mutating aging out of existence, then natural selection operating on a short time frame would have found it (with disastrous consequences for the species and the ecosystem).
If my hypothesis is correct, then all these clocks are exchanging signals, cross-checking to establish a consensus age and resetting accordingly. How do the various clocks exchange information? The obvious place to look is signal molecules in the blood. These may be hormones or other large proteins; they may be short peptides; they may be RNAs or ribozymes; they may be extracellular vesicles containing a package of coordinated signal molecules of various types. Age signaling may be accomplished with a combination of all the above. This reasoning leads to the inference that exchanging young blood plasma for old ought to be a robust anti-aging strategy. Besides erythrocytes and leukocytes, the blood carries many thousands of different signals as dilute molecular species dissolved in plasma. Some of these, I suggest, constitute primary information about the age state of the body. This information must be capable of resetting all the body’s different aging clocks.
Of course, the work has commenced nearly two decades ago. Experiments in heterochronic parabiosis. Irina and Mike Conboy did the first experiments in the modern era while they were graduate students at Stanford. Harold Katcher wrote up theory of why this work ought to be the portal to robust rejuvenation. The Conboys have gone on to promote a perspective in which old age is established affirmatively by molecular species in the blood of old animals. In their model, diluting old blood with neutral saline albumen is a path to rejuvenation. In cooperation with Dobri Kiprov, they have initiated a human trial in rejuvenation based on blood dilution.
Katcher and Tony Wyss-Coray and Amy Wagers have championed the converse perspective, that senescence is linked to a dearth of youthful signals in the blood of older animals. Katcher has done proof-of-concept experiments in rats. Wyss-Coray has built a company based on injecting blood factors to treat Alzheimer’s Disease. The company, called Alkahest, has been acquired by the Spanish giant, Grifols, which has been a major player in plasmapheresis therapies. Dan Stickler has a clinic in Texas offering blood plasma from young donors. David Haase at the Maxwell Center has long experience offering plasmapheresis as a therapy.
I think it likely that the most effective strategies for rejuvenation will involve both removal of pro-aging factors and addition of anti-aging factors to the blood plasma. Discovering what these factors are and how they work ought to be a major target for research in the field of medical gerontology. One easy place to start is with extracellular vesicles. These are larger than molecules, smaller than cells, and they are normally filtered out in plasmapheresis. But they may play a crucial role in plasma exchange as an anti-aging strategy; it will be important to know.
How well can we expect the new plasma transfusion therapies to work? We have some experience that should guide our expectations.
Two summers ago, I was hit by a car while riding my bicycle and lost most of the blood in my body. Heroic surgery kept me from bleeding to death, against all odds. I received blood during that first surgery and more blood was lost and replenished during 7 follow-up surgeries. The median age of blood donors in America is about 47. I was 72. It’s possible that replacement of the majority of my blood with younger donors had a rejuvenating effect, but if there was any benefit at all, it was not dramatic enough to be noticeable to me or my family.
5 million people a year receive blood transfusions. Some of them are very old, and none of them has skipped out of the transfusion clinic with youthful hair color and de-wrinkled foreheads.
A history of blood donation year after year has a substantial benefit for life extension, but it is not dramatic. Giving a pint of blood is equivalent to a 10% dilution, and habitual donors do this a few times per year. (I was donating blood several times a year before my 2021 hospitalization.) Based on standard life tables, 30% decrease in mortality is equivalent to about 3 years of added life.
From this, I would expect that my proposed homeostatic age-resetting mechanism works slowly. We don’t know specifically the lifetime of aging signals in the blood because we don’t know what these signals are; but studies of generic protein turnover in blood plasma suggest ten days or so as a reasonable guess. In Katcher’s rats, the figure is closer to 2 days.
From this, we can say it is likely that resetting the rat’s biological clock takes at least several times 2 days, and resetting the human clock requires at least several times 10 days. It may require repeated transfusions every 10 days over a period of months to see the full effect.
Concerning the effectiveness of E5 in Katcher’s rats, we are still learning. The biomarkers, including Horvath age, were very promising. Longevity seemed to be increased only modestly, however — except for Sima, the last remaining rat (out of 8) who has now broken records for rat longevity at 46 months. Harold’s reported rejuvenation of skin on one hand based on a single application of E5 is an intriguing anecdote that encourages us to hope for the best.
I’m going to pose a much more speculative argument that rejuvenation through transfusions of young blood will be effective, but will not change paradigms about human lifespan. I conjecture that the richest and most powerful people in the world have special access to technologies that are unavailable to you and me. Have some of them been getting blood transfusions from young donors? David Rockefeller died six years ago, just short of 102 years old. Queen Elizabeth died at 96. Evelyin de Rothschild died last year at 91. Henry Kissinger is still alive at 99. Jimmy Carter is 98. George Soros and Warren Buffett are each 92 and they look it.
The bottom line
I remain optimistic about Harold Katcher’s work and about plasma exchange in general, but we still have a lot of work to do.
And by the way
There are other ways to die, unrelated to age. Bioweapons research has spawned a worldwide epidemic, killing millions, and next time could be worse. We live in a time when collective threats to our existence may soon become as great as the mortality burden imposed by age.
Aging evolved for the purpose of stabilizing ecosystems. Human beings have shown themselves to be adept at destabilizing ecosystems, and now we want to further expand our numbers and extend our power of nature. Mankind cannot “transcend” nature. Humans cannot live outside the context of Gaia’s ecosystem. If we try, it will be the extinction not of Gaia but of the human race.
I believe that the pursuit of longevity is not only scientifically feasible but also worthwhile. But this grand project must be pursued in tandem with an even grander project, which is to change our relationship to naturally ecology from a miner and exploiter to a cooperative steward.
There is evidence that the native population of America had such an ethos, together with the wisdom and the science to create richer ecosystems for their own sake and for the sake of nature’s abundance and diversity. It is required of humanity that we quickly learn that lost art, on pain of extinction.
For those of us who believe that aging is programmed into the life cycle, gene expression seems the most likely transmission of information about age through the body. Different genes are turned on and off at different stages of development and through the lifetime. This has been, in my opinion, the most fruitful basis for understanding aging and its remediation. For example, methylation patterns affect gene expression, and methylation patterns are the best measure we have of biological age.
The reason that this hypothesis doesn’t lead immediately to a treatment protocol is that evolution has not engineered the body the way a human would design a machine. Human engineering is based on understanding and isolating causes. One mechanism is designed for each desired effect. Biochemistry doesn’t work that way. Every molecule has multiple functions and every function requires many chemical components to make it work. In an engineered system, there is a hierarchy of causes and effects, a few high-level switches and many low-level switches. In a biological system, there is a network of interactions. Chemicals may have a primary (low level) biological function, but the same molecule also serves as a transcription factor, affecting at a high level the output of related chemicals.
The advantage of human engineering is that it is comprehensible. It is relatively easy to fix. If you see something that’s not working, the design specs tell you what component is likely malfunctioning and you can replace it.
The advantage of nature’s way is robustness. When a component fails, alternative pathways open up to take up the slack.
Last year, my left leg was injured so severely after I was hit by a car that the main vein returning blood from the leg was irreparable, and was surgically sealed off. During the first weeks in the hospital, my left leg swelled up to twice the size of my right because the arteries bringing blood down were fully open, but the return pathway was blocked. But over the ensuing months, other veins gradually expanded to accommodate the increased flow, and my left leg now is almost the same size as my right.
The take-home message is that we think that if we could change gene expression in an old person to mimic the gene expression of youth, the body would look and act young again. But there are thousands of genes that are differentially expressed, and the questions are still up in the air:
Is there some small subset of genes that controls the others sufficiently that we can add and subtract some manageable number of components from the blood to recreate a youthful metabolism?
Are these all proteins? Or are there RNAs or other signals that are essential to the process?
How can we determine what is the minimal set of molecular species that needs to be modified?
And if we restore the youthful balance of signal molecules through the body, will this recreate a stable, youthful state, or is it necessary to treat the body frequently to prevent relapse to the old metabolic state?
Leapfrogging ahead of these research institutes with a practical demonstration has been Harold Katcher. Katcher’s method is proprietary. He tells us that it is a “plasma fraction”. When I first heard this several years ago, I thought of the pioneering work of scientists in St Petersburg using peptides, which are very short proteins. I used to think the fraction must be the shortest proteins.
In light of this new paper from Northwestern, I thought it must be the longest proteins. A brief email exchange with Katcher confirmed this guess.
Both Katcher and the Northwestern authors mention the possibility that mRNA splicing might be impaired with age. In all eukaryotes (that’s everything larger than bacteria), genes are not stored contiguously in our chromosomes, but rather in segments that code for modules, or pieces of a protein. The mRNA is copied from the chromosome, and then various pieces of mRNA are spliced together to form a full, functional gene before the reconstituted mRNA is delivered to a ribosome to be read and translated into a protein. Presumably, longer proteins require more splicing, so impaired RNA splicing could account for a deficit of longer proteins as we age.
Katcher’s E5 is based on a process of filtering proteins from pigs’ blood plasma and selecting the largest molecular weights. It seems to work in rats, but the process of ramping up to create sufficient quantities of E5 for human trials is proceeding slowly, dragged down in part by the IP that Katcher and his partner are holding close to their chests.
Meanwhile, the four questions I listed above are not being addressed. Patent law is working against us, since Katcher’s E5 patent is for a process of extraction. If a subset of active ingredients is identified and the minimal set of rejuvenating proteins becomes known, his patent becomes worthless. Naturally occurring proteins cannot be patented.
This is the maddening influence of capitalism and intellectual property law on anti-aging science. The most promising avenue for rejuvenation (IMO) is not attracting research attention because it cannot attract venture capital; it can’t attract venture capital because there is no attractive business model; and there is no business model because of the structure of our patent law.
Can methylation clocks be relied on in this context? The earliest clocks were trained on chronological age only, and yet they predicted morbidity and mortality better than chronological age. I’ve been enthusiastic about the technology since 2013. But recently there have been substantial challenges to the validity of all the existing clocks.
An article published last week fulfills a wish I’ve expressed in previous columns. I’ve believed that methylation clocks are the best tool that we have for evaluating anti-aging interventions, and there is potential to accelerate anti-aging trials enormously. But it’s not proven that setting back any given methylation clock will result in added longevity.
Methylation clocks are a surrogate for gene expression. The problem is that two things happen to gene expression with age.
 Programmed aging turns on chronic inflammation and shuts down repair mechanisms with age.
 The body is increasingly damaged, and repair mechanisms are triggered in response to that damage.
Yes, the body is fighting with itself as we get older. Some of the epigenetic changes that happen are suicidal, but the fundamental self-protective responses are still in play.
Suppose you take an anti-aging pill. If the pill makes you score younger according to  then you’re destined to live longer; but if you score younger according to  the chances are your life will be further shortened.
The way most methylation clocks are derived gives you no indication what mix of  and  goes into the age algorithm. To complicate the situation further, a strong type  response might be an indication of more damage, which would be bad, or an effective hormetic response, which would be good.
(I have argued that the PhenoAge clock is probably biased toward , and I have worried that the GrimAge clock may have a lot of .)
This preprint by Ying et al, a collaboration between the Gladyshev lab at Harvard and the Horvath lab at UCLA, uses bioinformatic methods to confront the problem head-on.
“It is unclear whether the DNA methylation changes that are used to predict age are causal to aging-related phenotypes or are simply byproducts of the aging process that does not influence aging themselves.”
I would add: Or worse — some methylation changes with age actually mitigate damage , and “resetting” those would be damaging to health, probably shortening lifespan.
The experiment we want to do would be to change methylation at thousands of CpGs in a sample of people and see how the change affects their health. This is way impractical, both because we cannot yet manipulate cytosine methylation directly, and because the number of subjects involved would be enormous. A practical approach would be to calibrate an aging clock against historic data using remaining lifespan as a target (see below, “A Simpler Alternative”). This procedure has not been followed, to my knowledge, but PhenoAge, GrimAge, and Dunedin Pace all have some features of this procedure.
The new article that I’m reviewing here takes a different approach, deploying GWAS.
GWAS (Genome-Wide Association Studies) have been used in the past to correlate various health outcomes with common genetic variants. For example, one could look at 23-and-me data for people who have had heart attacks and compare to a matched sample of people who have not had heart attacks; then look for genome variations (SNPs) that tend to be different in the two groups. Fast computers can do millions of correlation calculations and report back the ones that show the strongest results.
How might this be applied to methylation sites? Methylation affects which genes are turned on in an individual, rather than which allele of the gene that individual happens to have. The trick is to look for 3-way correlations. MeQTL stands for “methylation quantitative trait loci”. There are SNPs that are associated both with methylation changes at particular CpGs and also with health outcomes. Maybe it is through the CpG that the methylation change is causing the health effect. How can they determine if this is the case? A proposal was put forward by Richardson et al 
We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39 1008). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections (P < 3.91 1004). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. … Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritizing candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease. [Richardson 2018]
The method is to look for 3-way correlations between (A) the trait in question, (B) methylation of a particular CpG site and (C) a point genetic variation (SNP). It’s a dangerous game for a number of reasons. First, scanning over a huge number of possibilities assures that some of them come up positive just by chance. If you run an experiment 20 times, you’ll probably come up with some result (in one run) that shows up as statistically significant, with odds against chance of p<0.05. Similarly, if you search a million CpG sites to see if any of them are associated with your favorite trait, then one of them is bound to show up with an association so strong that you report p<10^-6. It sounds impressive to say p<0.000001, but in this case it’s just the laws of chance operating on a huge number of possible associations.
The people who do these analyses are not incompetent. They know this, and they compensate by setting the threshold 100 times low, in this case p<10^-8. But these probabilities are always computed based on a number of unverifiable assumptions, and it’s always possible that some chance association slips in.
There’s a second problem with these 3-way associations. Remember that what we’re trying to establish is that a particular CpG causes a particular trait.
CpG ⇒ trait
Lacking a way to directly correlate the CpG with the trait, this method relies on the fact that both the trait and the CpG are associated with the same SNP. The “causal” implication is that
SNP ⇒ CpG ⇒ trait
But the authors gloss over the possibility that
SNP ⇒ trait and SNP ⇒ CpG
In words: Suppose the SNP has two actions (pleiotropy is the rule rather than the exception) so that it independently causes the trait and the CpG methylation. In this case, there is no indication that the CpG actually causes the trait. An even worse possibility is this:
SNP ⇒ trait ⇒ CpG
This is particularly worrisome. For example if the trait is inflammation, you would like to establish the fact that this particular methylation site causes inflammation. But if the CpG is associated with a quenching response to inflammation, then you’d expect that CpG activation might be part of the body’s solution to perceived inflammation.
The fact that you tend to see fire trucks and fires in the same places around a city doesn’t mean that the fire trucks cause fires.
I am all for using fancy statistics to learn about the metabolism. But the headier the statistics you use, the more you have to worry about things that could go wrong.
This is all in the context of my limited sophistication with advanced statistical methods. The authors of this study recognize all the hazards that I have catalogued, and they present arguments why they have effectively compensated for them. I don’t have the background to pass judgment on those arguments.
“Here, we leveraged large-scale genetic data and performed epigenome-wide Mendelian Randomization (EWMR) on 420,509 CpG sites to identify CpG sites that are causal to twelve aging-relatedtraits. We found that none of the existing clocks are enriched for causal CpG sites.”
I take this as a red flag. How can it be that there is negligible overlap between the CpGs found by this new GWAS methodology and any of the Horvath clocks of the past? I believe that aging is driven at a deep level by epigenetic changes, and this gives credibility to the idea behind methylation clocks. I have acknowledged the risk that these clocks may be polluted with some CpGs that are not drivers of aging  but responses to aging . But if these new results are to believed, we have to throw out GrimAge and PhenoAge as worse than useless.
“Contradicting the popular notion that most age-related changes are bad for the organism, our findings revealed that, in terms of the number of CpGs, there was no enrichment for either protective or damaging methylation changes during aging.”
The first two papers to propose that methylation is a primary driver of aging appeared in 2012 [one, two]. I was an enthusiastic supporter in 2013. A decade later, these papers have seeded an entire field of study. But the above captioned statement suggests that this was all in vain, that methylation changes with age are a correlation only, and they don’t really have to do with either drivers of aging  or protection from damage .
“Our results suggest that the known lifespan-related effect [of APOE] may be mediated by DNA methylation.”
This in itself is a stunning conclusion, and should lead to methylation-based therapies for the millions of people who are at elevated risk of AD and CVD based on APOE4 in their genomes.
Three new clocks
Ying et al announce the creation of three new methylation clocks based on the science described above. CausAge was developed using traditional methods with an additional boost for sites that are identified through GWAS as associated with causes of age-related decline. DamAge was designed to include only sites that are associated with increasing damage to the body; while AdaptAge was designed to be based on the opposite — sites associated with protective adaptations. Scoring older on the DamAge clock is presumably a bad thing because it indicates more damage, while scoring older on the AdaptAge clock should be associated, paradoxically, with a longer life expectancy. “Therefore, we hypothesized that DamAge acceleration may be harmful and shorten life expectancy, whereas AdaptAge acceleration would be protective or neutral, which may indicate healthy longevity.” I had to read this sentence several times because it was the reverse of what I expected from the text. Based on this sentence, I’m guessing that DamAge corresponds to what I called  above, and AdaptAge is .
“We found that short-term treatment with cigarette smoke condensate in bronchialepithelial cells significantly accelerated DamAge but did not affect other tested clocks (Fig. 6c). Additionally, a 6-week omega-3 fatty acid supplementation in overweight subjects, which has been shown to be protective against age-related cardiovascular diseases, significantly increased AdaptAge and reduced DamAge (Fig. 6c).”
This is another question mark for me. Cigaratte smoke makes DamAge look older. I have always imagined that the direct effect of smoke is to damage lung tissue, and that some kind of epigenetic response to this is the body’s attempt to protect itself. That would be a type  (hormetic) response. But according to the above captioned sentence, cigarette smoke has a direct and detrimental effect on methylation, a type  response. I can’t say this is impossible, just unexpected.
A Simpler Alternative
The ideal methylation clock would be rooted in a deep understanding of biochemistry. We would know which genes are associated with inflammation, with immune senescence, with sex hormones, with apoptosis, with blood lipids—and also which CpGs turn these genes on and off. This kind of detail is not available at present, but it is a reasonable long-range goal.
Here’s my proposal for creating an aging clock that measures what we are most interested in: is a given person’s biological age older or younger than others of the same chronological age?
Start with a historic sample of methylation profiles from biobanked samples of people who have died in the intervening years. Train a new methylation clock on the number of years that each subject survived since the sample was drawn, and include in the target a penalty for chronological age. In other words, the training target is the number of years the person actually lived minus the number of years he would have been expected to live given his chronological age.
(After juggling in my mind the gender of pronouns in the previous sentence, I realize that, given what we know about different aging mechanisms by sex, we really should do this analysis so as to create separate methylation clocks for men and women.)
There is a lot more data available now than when Steve Horvath did his pioneering work in 2012-13. His clocks have been constrained to be linear in each methylation site for adults. But we have reason to believe that epigenetic changes come in waves, with different methylation sites being most important at different ages. So let’s calibrate two different clocks for each decade of life, and for men and women.
While I’m making a wish list, I would add that my ideal methylation clock should avoid being too sensitive to any one CpG. This is for the practical reason that there are quality control issues that come from lab technique and from manufacture of bead chips.
The problem comes from having large positive and negative contributions in the age algorithm, which cancel sensitively in the final result. The way to avoid this problem is to derive two separate clocks, one based only on CpGs that increase methylation with age and the other based only on those that decrease methylation with age; then average the two calculations for the plus and minus clocks.
I believe this project is more than feasible, it should be a lot less work than the present analysis by Ying et al. And for me, it would have the advantage of being transparent, not dependent on unnecessary assumptions, and avoiding indirect references from 3-way correlations. If anyone reading this has access to the data, I would love to do the statistics.
And why should we have faith that the construction I outline should lead predominantly to sites that have a causal relationship to senescence ? It’s not a slam dunk, but the fact that a person dies later than expected is a good indication that protective responses are up and programmed aging is down. Our understanding of the situation is confounded by hormesis.
The Bottom Line
This new work should cause re-thinking of all the anti-aging work of the last several years that has relied on methylation clocks to gauge success. The message is that the clocks that have been developed so far are at best orthogonal to the real causes of aging, and at worst they could be encouraging interventions that actually shut off the body’s natural protections. If we believe the new results, DamAge would be a far better choice than anything previously available for any anti-aging trial, but the difference DamAge minus AdaptAge might be even better.
As the DataBETA project is launched this fall, I have a direct professional interest in choosing the right technology to evaluate a wide range of interventions and combinations that are commonly used by people who are trying to live longer.
I have been enthusiastic about methylation clocks from the beginning on the basis that (A) for theoretical reasons, I find it credible that epigenetics drives aging, and (B) the original Horvath clock, derived only from chronological age, predicts mortality better than chronological age. I have a hard time throwing that reasoning out based on one new preprint. But there has also been Katcher’s lifespan experiment, and Levine’s caution based on theoretical grounds, both of which have shaken my faith in methylation clocks earlier this year.
Frankly, if Horvath’s name hadn’t been on this ms, I would not have been inclined to take it seriously. Should we believe the new results, which seem to discredit all the analysis that he and others have done in the first decade of epigenetic clocks?
I don’t have the expertise to answer this question. I can hope that this work receives a deep and respectful challenge during the peer review process, and that people with more statistical chops than I have will debate both sides of this question over the coming months.
The work of George Church combines a broad knowledge of science with an ambitious imagination. Our world needs visionaries, and Church is one of a kind. His Harvard laboratory is at the cutting edge of several key areas of biochemistry. Please construe the following criticism narrowly. There’s just one of his ideas that I think is dangerous enough that I am moved to speak out against it. Changing the genetic code is a really, really bad idea.
I’ve heard Dr Church speak three times in the last month, and each time he has mentioned a technology to make humans resistant to all viruses. He has already done a proof-of-concept experiment, creating a strain of E coli that uses a different genetic code from all other life on earth (including, of course, all viruses). Since viruses use the host cell’s machinery for translating their own genome, the virus won’t be able to replicate in a host with a modified genetic code.
Changing the genetic code is not the same as “gene editing” or “gene therapy”. Gene therapy involves changing one gene (perhaps throughout the cells or a particular organ, perhaps in every cell in the body). Changing the genetic code is a global search-and-replace in the entire 3 billion base pairs of the human genome. It’s a hundred million changes, all of which have to go just right, together with a change of instructions about how to interpret the new code.
What is the Genetic Code?
Roughly speaking, DNA is an information molecule and proteins are service molecules. Most of the functions of a cell are performed by proteins, including signaling, energy transduction, locomotion, filtering, promoting beneficial chemical reactions and inhibiting others — essentially all the functions of metabolism are accomplished by proteins.
A protein molecule is a chain of (usually) thousands of amino acid units strung together in a particular order. There are 20 amino acids to choose from, and they have differently shaped fields of electric charge around them that make them attract or repel other amino acids. So a string of amino acids that is a protein wants to fold into a unique, characteristic shape, and the shape determines its function in the metabolism.
Example: This is what the insulin protein looks like after it is folded up.
DNA lives in the cell nucleus and is copied and passed from mother to daughter when a cell divides in order to clone itself. One of the functions of DNA is to carry information about how to build proteins. (In fact, this was the first and primary function of DNA that was discovered in the 1950s.) DNA is constructed of millions of four nucleic acid subunits strung together in a particular order. (Note that a nucleic acid is not an amino acid.) DNA is built from only 4 nucleic acid subunits (usually abbreviated A, C, T, and G) compared to 20 amino acids subunits used to construct proteins.
The “genetic code” is a mapping between DNA and proteins. Each sets of 3 nucleic acids specifies a particular amino acid. A “gene” is a stretch of DNA that contains instructions for making a particular protein. So, for example, a stretch of DNA that is 30,000 base pairs long may contain instructions for creating a protein of length 10,000 amino acids. Only about 3% of our DNA consists of genes. To “read” these instructions is the function of a ribosome. Ribosomes are organelles, little chemical factories, millions in every cell, where proteins are made.
The genetic code constitutes a language through which DNA (mediated by messenger RNA) tells the ribosome what amino acids to string together in what order. The messenger RNA copies a gene and then leaves the nucleus to look for a ribosome. The ribosome reads the message. Every time it sees the 3 nucleic acids GCT, it adds an amino acid called Alanine. If it sees GGA, it adds a Glycine, and so on. A combination of 3 DNA units specifies each protein unit, with some redundancy. Redundancy means, for example, that TCT, TCC, TCA, and TCG all correspond to the single amino acid called Serine.
(U is another way of saying T in this language. DNA uses T, while RNA uses U for the same meaning.)
The association of a particular triple with a particular amino acid is thought to be mostly arbitrary. It’s just a language that DNA uses to talk to the protein factory.
One of the deepest truths about biology is that this arbitrary language, this same language, is shared by all living things. All living things, from the toadstool to the bowhead whale, use the same language to communicate between the DNA and the ribosome.
This means that all life can exchange DNA with all other life. From bacteria to baboons, we all speak the same language
This is also powerful evidence that all life is related. Life arose once, and differentiated on a vast tree over the course of 4 billion years. You and I and the toadstool and the bowhead whale are all cousins.
This is a doodle from Darwin’s notebook in which he first conceived the Tree of Life.
We might imagine that we have to reprogram millions of ribosomes in each of trillions of cells in the body to change the genetic code. That would be correct about reaching trillions of cells, but maybe incorrect about the ribosomes. As it turns out, all the ribosomes are reading from the same hymn sheet, and we can change the hymn sheet with standard genetic engineering.. The genetic code is itself encoded as proteins, and the proteins are made from a nuclear DNA template. So for example, there’s a particular protein that has a slot for the GCA combination and another slot that attracts Leucine. This one protein is responsible for one pairing in the genetic code. If this one protein is deleted from the genome, then no ribosome in that cell will be able to read GCA. The ribosome will choke when it gets to GCA and abandon work on the protein it started to make.
Part of what makes this feasible is that, besides GCA, there are other combinations that code for Alanine. So the plan might be to eliminate this one protein that translates GCT into Alanine, and then to go through the entire genome — billions of bases — and do a “global find and replace” operation. Every time there is a GCT, replace it with GCA, which also codes for Alanine. These two changes would put the cell aright again, so Alanine would appear in all the same proteins as before. But an invading virus wouldn’t know that the code had been changed. The virus might sometimes use GCT for Alanine, and it wouldn’t work. The ribosomes would be able to decode native DNA, but not the virus’s DNA.
Of course, while we’re genetic engineering, we could change that one protein in a way that makes CGA translate into Tyrosine instead. That change would be global. We would change the genetic code for all of the DNA in that cell that is engineered in this particular way.
The global find and replace function is called multiplex base editing, and it is already (the last decade) a developing lab technique. There are several ways to do it. For example,
A base editor is a fusion of catalytically inactive CRISPR–Cas9 domain (Cas9 variants) and cytosine or adenosine deaminase domain that introduces desired point mutations in the target region enabling precise editing of genomes. [ref]
In other words, CRISPR is used to locate particular sequences wherever they occur on the genome, and an enzyme called a deaminase is used to modify all of the copies of one particular nucleic acid base in that stretch of DNA. For example, deamination of T turns it into U, and the next step is to add a reagent that will change the U into something else, perhaps A in the example above.
Suppose humans were engineered with a different genetic code
We would still be able to eat food, because our bodies ignore the DNA in the food we eat. The DNA is de-activated but not digested. The proteins in the food we eat would be available just as before, because the body recognizes those proteins, irrespective of what genetic code was used to ake them.
However, the human body with changed genetic code could not be infected by viruses that use the old, standard genetic code. Viruses don’t have ribosomes of their own, but count on the host’s ribosomes to translate their genes into the proteins they need. Humans that were engineered to have a different genetic code would play a trick on the virus, translating its DNA (or RNA) into the wrong protein, or failing to translate it at all.
What could go wrong?
Our bodies contain hundreds of trillions of viruses. Western science has not begun to study these relationships, and we don’t know which are helpful, which are harmful, which are neutral. We cut off all symbiosis with viruses at our peril.
We do know that our gut microbiomes and skin microbiomes harbor thousands of species of bacteria, most of which are beneficial. Would they still be able to work with us if we used a different genetic code? (The known interactions all involve proteins, so that, presumably would be unaffected if the change in code went off without a hitch.)
A man and woman who have genomes that are coded differently could never have children together. Does Dr Church imagine switching over everyone’s genetic code in the same year so that such couples would never occur?
Horizontal Gene Transfer = exchange of genes between organisms that have no parent-child connection. We know little about it, except that HGT plays a major role in evolution, in the long run. Is HGT important in the space of a single lifetime? No study has asked that question.
We know that all of us are exhaling exosomes all the time, and that they send signals that are picked up by other humans and other animals and plants in the vicinity. Exosomes contain snippets of DNA. How would a person with a modified genetic code read these signals? How dependent are we on these signals? We don’t know.
DNA has other functions than coding for proteins. In fact, more than 97% our our DNA does not code for proteins. (This was once called junk DNA by people who theorized that it was just leftover debris from the past and silenced viral infections.) We have some ideas about what this DNA does for us. An obvious role is in epigenetics. It determines how DNA folds and unfolds, and so the 97% is important for gene expression. Would a change in the genetic code affect gene expression? We don’t know. What other roles are there for DNA? We don’t know. How would a global modification of DNA affect a living human? We don’t know.
The whole purpose of this exercise is to confer viral resistance. What if the viruses are on to us, and they evolve to adapt to our new genetic code? The rate of viral evolution ranges over 5 orders of magnitude and is a poorly understood. Viral adaptation to a new genetic code might never happen, or it might happen very quickly.
Changing the genetic code must be done in most cells of the body to stop a viral infection. Must the change reach every last cell? If the body becomes a chimera with the new genetic code in 90% of cells but 10% retaining the old code, will conflicts arise and the cells begin a civil war? In theory, mRNA does not pass between cells, so this should not be a problem, but we really don’t know.
The big picture
I have advocated approaches to aging based on signaling. Aging is centrally orchestrated at the system level. We don’t have to reach into every last cell and fix the damage; we need only to restore the body to a young epigenetic state, and all the innate mechanisms of repair and renewal will be once again available.
Repair at the cellular level is more invasive. There is much opportunity for things to go wrong. I believe that this kind of approach is unnecessarily exacting and difficult.
Changing the genetic code is a cell-level therapy, which, in my calculus, is a strike against it. Furthermore, it goes beyond restoring the cells to a youthful state; it creates an artificial state with unknown consequences.
And why? People in the prime of life with young immune systems almost never die of viral infections. If we can achieve rejuvenation of the immune system via any of the means now on the drawing board, then changing the genetic code will be unnecessary.
George Church is a giant in the field of biochemistry. His visionary ideas and research projects are moving science and technology in many good directions. But he doesn’t think in terms of ecology. It’s true in general that biochemical science has advanced explosively in recent decades, leaving the study of ecology in the dust. Biochemistry has benefited from computer analysis. Ecology requires intensive field observations. But ecology is just as important to the understanding of life as biochemistry.
We can be confident that humans are intimately connected to the ecosystem that spawned us. There has been exactly one experiment in which a small group of humans tried to live in an artificial ecosystem for a year. It ended disastrously in just a few weeks. No one has begun to catalog the ways that we are dependent on the earth’s biosphere, or to ask how those connections would be changed if we used a different genetic code from every other plant, animal, fungus, microbe, and virus on the planet.
For the foreseeable future, changing the genetic code in the human body — even if it proves to be feasible — would be a reckless step into unknown territory for our species.
Rebalancing proteins in the blood is the single most promising strategy for age reversal in the present environment. There are two competing schools for how to approach this. I’m calling on both to put their heads together and develop a strategy that combines their insights.
<rant> Please forgive me while I rant for a paragraph before beginning this column in earnest. Len Hayflick demonstrated that senescence in many animal species, probably including humans, is promoted by lack of a simple, cheap enzyme (telomerase) that every cell knows how to produce. To anyone who hasn’t been indoctrinated into the selfish gene dogma, this would be a sure indication that the body is trying to kill itself. But fifty years on, Len is still saying that aging = entropy catching up to a body’s chemistry. An equally powerful discovery came from Irina and Mike Conboy, who have been at the forefront of experiments demonstrating that aging is centrally coordinated through signal molecules in the blood. In every context but this one, the Conboys will acknowledge that these molecules are subject to directional selection and are tightly regulated in the metabolism. But when the blood plasma fills up with pro-inflammatory cytokines during aging, the Conboys insist that this is an accident. The body made a mistake. They call it “deregulation”. And in case anyone misses the point, they add in parentheses, “(noise)”. These are exactly analogous to the directed changes that cause growth, puberty, cessation of growth, onset of menopause, etc. In those other context, the change in balance of plasma proteins are signals, but in the context of aging, they must be “noise”.
And even more incomprehensibly, the “noise” to which they refer always goes in one direction, and that is producing too muchof some signal molecules, and the “noise” always manages to emphasize exactly those signals that bring the body down in a hailstorm of inflammation.
Evolution is a many-splendored thing, and natural selection is perfectly capable of producing well-regulated, interdependent communities. This has meant selection for Goldilocks rates of reproduction balanced artfully against death rates that are also well regulated under evolutionary control.
And YES, it does matter whether you think of aging as signal or noise. (I apologize again as my rant spills into its fourth paragraph.) It matters because if aging comes from a set of signals, we know well how to block those signals, e.g., with drugs that jam their receptors. But if it’s noise, the task is so much more difficult because it unfolds differently in every individual.
It’s no secret to readers of this column that I think altering the balance of signal molecules in blood plasma is the most promising road to anti-aging in humans. There are now two competing approaches to this project. The Katcher school says that there are youthful factors missing in the blood of old animals, while the Conboy school says that there is an excess of pro-aging factors. Both are quick to say that yes, it is a balance of pro-aging and anti-aging factors in the blood that ultimately determines the animal’s fate. But Katcher says that if you deliver the right combination of youthful factors, they will reprogram the epigenetics so that the pro-aging factors retreat as a side-effect; while the Conboys claim that if you dilute the blood, removing equal proportions of pro-aging and anti-aging factors, that dilution is sufficient to reset the aging clock, and stimulate new production of the youthful factors.
Problems with the Katcher protocol
Until last week, Katcher had the more compelling data (IMHO), because he demonstrated dramatic epigenetic age reversal in rats. But last spring, the disappointing results in a small lifespan trial (8 rats) makes us wonder if his protocol needs a lot of fine tuning before it’s ready for prime time. And another weakness in his protocol is that he doesn’t know what is in the blood-derived E5 elixir that does its magic. He tells me there are efforts underway to identify the active components of E5. I think this determination is a high priority with global implications for health, so, by my lights, the analytic work on E5 should be a top priority. But there is a financial incentive not to know what are the active components of E5. This is because Katcher’s Yuvan Research has a patent on the process of extraction, but the components themselves are natural proteins, and thus they cannot be patented. So as soon as the information about the active components of E5 become public, his process patent risks becoming worthless. Other, larger laboratories than Yuvan will be able to synthesize the chemicals and sell them. I fear that research is being held back, and for what? I don’t even believe that the strategy of secrecy can secure the patent rights for Yuvan, because the knowledge will inevitably leak out, and Yuvan doesn’t have the resources to pursue multi-million dollar court battles over patent rights.
Human trials of plasma dilution
Now there is a new article from the Conboys analyzing results of plasma dilution in three human subjects. They show improvements suggestive of rejuvenation in several biomarkers. They do not report methylation age. They do begin the analysis process, and offer suggestions about what may be the most important pro-aging components of blood plasma that must be removed or inactivated.
Why don’t they measure methylation age using any of the available clock algorithms? There is a short statement why they don’t believe in methylation clocks, and they express the opinion that another biomarker of aging, one not based on “machine learning or large data sets” is urgently needed by the community. I believe that methylation clocks are the best means we have at present to evaluate the effects of anti-aging interventions, and in this one respect I find myself (for a change) aligned with the majority view in the field. The Conboys owe us a better explanation why they have gone to such great lengths to report other biomarkers of aging, but they don’t offer us the simple one that most researchers rely on.
Accumulated DNA damage triggers genetic aberrations, senescence , and loss of cell function and leads to age-related diseases .
It’s a popular theory aging that DNA damage is an important driver of aging , but I don’t believe it.
Interestingly, the procedure of small animal plasma exchange to dilute the circulating factors in plasma effectively reset the age-elevated systemic proteome and restored youthful healthy maintenance and repair of muscle, liver, and brain, without any added young blood, young plasma, or young factors [15–17].
This is a crucial point. How strong is the evidence? The three references are all previous publications from the Conboy lab. Ref 15 describes results of delivering young blood into old mice, an experiment which cannot tell us whether dilution alone rejuvenates gene expression. Ref 16 is about plasma dilution in mice and humans. This study establishes that something in old blood inhibits satellite (stem cell) growth, necessary for healing and repair, and that dilution is sufficient to restore youthful activity of these cells. Some evidence is noted of changes in the global proteome toward a more youthful state. Ref 17 establishes that plasma dilution is sufficient to enhance cognitive performance and reduce inflammation in old mice.
There is a section of the paper documenting “proteome noise”, which the Conboys propose as an important biomarker of aging. I disagree, of course. I see the directed changes in gene expression as the important drivers of aging, and the random changes are secondary. Much of the Conboys’ paper is devoted to analyzing noise in the proteome of subjects, and interpreting this as an aging biomarker which moves in the direction of youthfulness after plasma dilution. I admit much of the biochemistry is above my pay grade. I can’t comment on the merits of their proposed components of a new proteomic clock. But from the vantage of scientific methodology, developing appropriate biomarkers of aging should be a separate endeavor, done in advance. Criteria for successful rejuvenation should be established ahead of time, and not developed on the fly with results of the experiment already in hand.
I would have liked to see methylation age before and after treatment. I understand that the Conboys have reasons for not giving credence to the methylation algorithms. But how about A1C or CRP? These are measures of insulin resistance and inflammation, respectively, that are standard blood tests, but are not mentioned in the Conboy paper. How about any measures of cognitive or physical performance? There are no phenotypic aging markers in the Conboy paper.
Breaking new ground
The Conboys identify two proteins, TDP43 and TLR4, that were previously unfamiliar to me, but are markers of an aging proteome. The former is associated with cancer, the latter with dementia, and both increase with age. Both are attenuated with the Conboys’ plasma dilution protocol. I recognize that it is labor-intensive work to identify specific protein targets and test them individually, but this is the kind of work I think is most valuable going forward.
How I think about aging
My (tentative) model: “Old” and “young” are always in the body’s repertoire of behaviors, and the body will choose according to the signals it receives. The age state of the body is stored in the epigenetic state of cells, and communicated through hormones and other signal molecules in the blood. Some of these molecules also act as transcription factors, and they can feed back to affect the epigenetic state of dispersed cells. This is the reason for hope that a younger environment in the blood can effect long-lasting rejuvenation.
The great task before the Conboys and Katcher and other researchers in plasma rejuvenation is to identify which of the hundreds of proteins that change with age are the few transcription factors that are capable of reprogramming expression of the rest.
There is no guarantee that a small subset of proteins exists that can do the job, but we won’t know until we look.
And there remains the possibility that a central clock in the hypothalamus is able to override records of biological age in the epigenetics of dispersed cells. If this turns out to be the case, then we have to find ways to breach the blood-brain barrier and reprogram the hypothalamus.
A modest proposal
Harold Katcher, Mike and Irina Conboy are at the forefront of anti-aging technologies today. Both labs are very close to having an effective treatment for humans, close in the sense that there remain no conceptual hurdles, but only the predictable quotidien work of expert lab biochemists. In other words, a lot of work remains to be done, but the map is drawn.
Aging is not a cell-autonomous function, but happens under system-level control, with information about the body’s age communicated by signal molecules in the blood. This is the key insight on which Katcher and the Conboys agree.
To those of us watching from the outside, it is clear that a rebalancing of young and old plasma components will have a dramatic effect on health and lifespan. The remaining task is to identify a minimal set of those factors that must be removed (or neutralized) and those that must be added to the blood of an old person in order to trigger a global resetting of the epigenome toward full youthful gene expression.
We, the consuming public, would benefit greatly if the Conboys would hire Katcher to come work in their lab. Their two conceptions need not be antithetical. Let’s call on them to work together to identify that minimal set of blood factors, resetting of which can accomplish robust rejuvenation.
The importance to humanity of this research agenda must override the personality differences, the philosophical differences, the legal and IP problems that must be overcome to make this collaboration possible.
Sickening by John Abramson, MD. Mariner Books, 2022, ISBN 978-1328957818
This book describes the top-to-bottom control over medical research and its dissemination exerted by large drug manufacturers. Of course, they have their own research staff, paid to test their patented drugs and make sure those tests come out favorably. In addition, they are the largest sponsors of research at all the major medical schools, their reprint purchases from medical journals supply 40% of total journal revenue, and they are the largest advertiser in mainstream media, newspapers and TV, assuring that the most influential purveyors of science news know where their bread is buttered.
Thus “science-based medicine” has become tilted toward science that is curated and supported by the companies that profit from a particular approach to medicine, and toward exaggerating the benefits and minimizing the risks from the most recent and most expensive medicines.
Sickening, yes. But the actual situation is even worse than the bleak picture Dr Abramson paints. Big Pharma is not just cheating us, their approach to medicine has had disastrous effects on public health in the West, especially the USA.
For example, why would anyone believe that the same companies that defrauded the FDA and paid billion dollar fines would be telling us the truth about safety trials of their vaccines?
This book describes the scandalous state of healthcare in America, and traces the problems to financial domination by the pharmaceutical industry. The story is told with numbers. It’s a book only a statistician could love.
I’m a statistician. What’s my problem?
Limited hangoutdef : A piece of journalism that covers a scandal, often with breathless intensity, but focuses on lesser crimes, and thereby diverts readers’ attention from the worst excesses. See also controlled opposition.
We call it “healthcare”, but of course this word ranks with “Ministry of Truth” and “Re-education center” as an Orwellian deception. So much can be done to protect our health, starting with exercise, social support, organic vegetables, and clean air. But “healthcare” in America has nothing to do with these things, and it’s all about waiting until you develop symptoms, then suppressing those symptoms in the most expensive way possible. The US ranks first in the world in high-tech medicine, first by a long shot in medical expense per capita, and 68th in healthy longevity.
“Only about 20% of a population’s health is determined by medical care; most of the rest is determined by these external factors [including] interaction between individuals and their social, cultural, and physical environment.”
40% comes from social factors and wealth disparities; 30% from diet, exercise, and individual behaviors; 10% from pollution and other environmental factors. A compelling theme of this book is that we spend an outsized portion of our national income on the 20% and have utterly neglected the other 80%.
A generation ago, Dr Abramson was a pioneer in muckraking from the inner bowels of the Pharma industry. To a large extent, this book is based on research he did in the early 2000s when he served as consultant to plaintiffs and expert witness in court cases against drug companies. Missing is the story of how much worse the situation has become since then, with an explosive rise in deception during the COVID pandemic.
In the intervening decades, Big Pharma has captured the regulators at FDA and CDC and has solidified its control over the mainstream media. Without these two institutions reliably in their camp, the great deceptions of 2020-22 could never have been accomplished.
Abramson barely mentions vaccines, but when he does so it is with the understanding that, of course, vaccines are safe, the best thing Western medicine has to offer, also the best tools we have for preventive medicine. This bias is my largest beef, and I will say more about it after describing what the book does well.
“The uniquely high revenue generated by prescription drugs in the United States creates a self-generating cycle. High prices create surplus funds, which are used, in part, for advertising and lobbying, which maintain the manufacturers’ control of the knowledge and pricing, which increases profits to fund the next round of even more expensive new drugs, and so on.”
There you have it. The Pharma industry is so profitable that they can pay off the research institutions, the doctors, the regulatory agencies, and the Congress to maintain their profitability. Controlling the flow of medical knowledge is the linchpin of their business strategy, and they do it so well that most health professionals have no idea that what they read in the medical journals is high-falutin advertising copy.
“Under our current system, it is more profitable for large pharmaceutical companies to commit crimes and pay the fines than to obey the law.”
In case after case, Abramson describes how companies have been convicted of defrauding the FDA and other corporate crimes. Humans are jailed if they are convicted of such crimes; but for companies the equivalent of jail would be putting the company in receivership or forbidding the company to do business for a period of years. This never happens. Instead the companies pay fines which are always a fraction of the profits they reap from the very fraud they have committed. Thus “crime pays” if you’re a drug company.
“We can’t be in the business of policing every piece of data we put out.”
— Editor of the New England Journal
The most prestigious medical journals have become the least reliable. Precisely because of the respect they command, they have been targeted by the drug companies for capture. When an article is published that demonstrates the benefits of a new drug, the manufacturer will buy hundreds of reprints, which salesmen then distribute to doctors in their offices. Journals have become addicted to sales of reprints. Britain’s best-respected journal, The Lancet, gets 40% of gross revenue from selling reprints to drug companies, while the Journal of the American Medical Association and New England Journal of Medicine (JAMA and NEJM) refuse to reveal their revenue from reprints.
Chapter 1 revisits Abramson’s old haunting ground and reminds us of medicine’s most famous scandal: Vioxx. Merck had been one of the most highly-regarded companies in America before its executives made an economic decision to (statistically) kill tens of thousands of its customers, so long as the lawsuits were costing less than the profit margin.
Chapter 2 is about the epilepsy drug Neurontin, which was repurposed and illegally marketed for pain control. (It is legal for physicians to prescribe any drug off-label, but it is not legal for the manufacturers to talk to doctors about off-label applications.) “There is no other drug being used to treat so many different conditions with so little benefit.”
In the older cases of Merck’s Vioxx and Pfizer’s Neurontin, Abramson did the right thing, and patiently pored through the data from company trials of the drugs, demonstrating that the data told a different story from the companies’ summaries to the FDA. But in the case of Pfizer and Moderna’s clinical trials for their mRNA vaccines, the data were even more damning, yet Dr Abramson does not review them. In fact, he scolds the companies for charging so much for their vaccines that they are unaffordable in Third World countries. But there is good evidence that the mRNA vaccines are doing more harm than good.
For example, when you read that the Pfizer trials showed they are 95% effective, you might be excused for thinking that 20 people died in the placebo group for every 1 person who died in the group that got the vaccine. The truth, from Pfizer’s own FDA submission, is that more people died in the vaccinated group than the placebo group [FDA doc, p 23]. Wouldn’t you think that this vaccine should be dead in the water the moment that this information was known? But FDA found excuses to ignore this most significant of all indicators, and sleep-walked to fast-track emergency authorization of the vaccine.
The theme of Chapter 3 is that statin drugs lower risk of heart attacks for those who have already had one, but are being marketed to a great many more people who are judged to be “at risk”. Certainly, statin drugs are among the most over-prescribed in history, but I would like to see Abramson address the deeper controversy about their mode of action. Usually, they are prescribed to lower cholesterol levels in the blood, but many medical researchers today believe that the link between cholesterol an CV risk has been discredited. The alternative view is that the benefit of statins comes exclusively from their anti-inflammatory effect. If this is true, then inflammation can be lowered far more safely and with fewer side effects by natural herbs (curcumin), omega 3s, and NSAIDs.
Chapter 4 is about insulin. Until reading it, I didn’t know that most insulin sold today is not natural insulin but a synthetic protein, slightly modified from human insulin, that arguably provides improved performance for some Type 1 diabetes, and inarguably costs hundreds of times as much. Many diabetics can’t afford the more expensive drug and don’t know about the less expensive version, so they scale back dosage to save money and they pay with their health. I wonder if synthetic insulin doesn’t cause other long-term health problems as well. The corporate motive to “improve” on human insulin is that natural hormones cannot be patented, so they must compete on price. But insulin is literally billions of years old and has multiple metabolic functions, including regulation of lifespan in yeast cells and lab worms and humans, too. Yes, insulin has a direct impact on aging itself. Before we modify a hormone that has co-evolved with diverse aspects of our metabolism, we should be doing whole-life studies to establish long-term benefits. Synthetic insulin has been improved based on short-term, narrowly-focused studies that demonstrate marginal improvement in control of blood sugar only.
Chapter 5 is mostly about income disparities being the deep cause of bad health in America. Amen. I wish he had fingered our dysfunctional economy as being the reason for most antidepressant prescriptions.
In Chapter 6, he tells the story of a meta-analysis review of relevant data on the antiviral drug Tamiflu, signed by the prestigious and once independent Cochrane Collaboration. The review was modified after discovery that the most compelling evidence in its database was provided by the manufacturer, Hoffman-Laroche, data which had never been peer reviewed and never made public before the “review article” was written. The revised article found that Tamiflu had minimal benefit, and no impact on severe outcomes or deaths, but this revision came only after US Homeland Security had purchased a $1.3 billion stockpile of the drug for emergency use.
Since 1975, the Federal office of Health Technology Assessment provided independent evaluation of what was working and what was cost effective. Under the Clinton Administration, HTA was defunded. To save money. There was, at one time, a Federal Clearing House, a project of the Agency for Healthcare Research and Quality that vetted healthcare information. AHRQ was shuttered during the Trump Administration. To save money.
In Chapter 7, Abramson lays out the story of Prilosec’s replacement by Nexium, which is in fact no more effective than Prilosec, but which enabled Pfizer to effectively extend patent rights on a bestselling drug. What he doesn’t say is that both Prilosec and Nexium are deeply flawed strategies for countering stomach acid reflux (GERD). Both products are Proton Pump Inhibitors, which act by interfering with the body’s acid-generating mechanism. But the stomach requires acid to digest food, and the body chemistry quickly learns to compensate. People taking a PPI drug adapt to it by upregulating the enzymes that produce acid; the result is that PPIs are highly addictive, as discontinuation of use results in a painful surge of excess stomach acid. Older and cheaper antacid strategies don’t have this problem.
Chapter 8 is theoretical: Why we can’t rely on the free market to fix our problems. Why aren’t honest drug companies producing better products able to crowd out the parasites? The reason involves control of information that doctors and consumers need to make medical decisions.
Chapter 9 is about Obamacare. He describes how the bill faced stalwart opposition in Congress, and in the end it achieved an increase in percentage of insured Americans from 80% to 88%. But passage was possible only because both Big Pharma and Big Insurance were solidly behind the bill; and this, in turn, was because the Obamacare plan posed no public competition for private healthcare, regulation of the industry was not included, and bargaining for price breaks on drugs was explicitly forbidden. Obamacare added to the profitability of both the insurance industry and for-profit hospitals. In the story that Abramson tells, the President fought valiantly for the “public option” that might have held down costs, but in the end the combined political clout of the drug and insurance industries defeated the reforms he had promised during his campaign. I’m not so sure that Obama was not in on the fix from the beginning.
Chapters 10 and 11 describe the political conditions that maintain our state of high prices and low quality research. I think Abramson is correct in focusing on data transparency as the key reform that is needed. The central cause of dysfunction in our medical system is that we rely on for-profit corporations to summarize for us the science that supports the benefits of their own products, while raw data remains proprietary. This is not madness, it is fraud. Abramson rallies us into the coalition that will be necessary to break the stranglehold that Pharma has on government. He has more faith in America’s democracy than I have, but I hope he’s right.
Quibbles and suggestions:
He mentions in passing the advantage of polyunsaturated fats for lowering cholesterol. This is badly out of date. I assume that the reference slipped through Abramson’s careful editorial pen because it was part of a quote that was intended for other purposes. Nevertheless, it should be flagged for readers so they don’t take it as a recommendation to increase intake of polyunsaturated fats. Recently Dr Mercola has inveighed against polyunsaturated fats.
Is it right that Ivermectin needs only one dose a year to prevent river blindness? This study from 2014 that twice yearly works much better. Still, this is impressive testimony to a Nobel prizewinning drug that has had enormous benefits for people whose environments routinely expose them to parasites. Abramson doesn’t mention the propaganda war that disparaged of Ivermectin as “horse paste” during the COVID pandemic. In reality, dozens of clinical trials and observational studies have indicated that IVM is the best preventive and probably the best treatment we know for COVID (see also). The story of Uttar Pradesh in India is eye-opening.
Abramson notes several times that life expectancy in the US has improved in recent decades. He fails to mention that the increase is almost entirely for men. In 1980, men’s lifespans were 8 years shorter than womens’. Now, men have nearly caught up, while women’s lifespans have barely improved. Surely, neglect of research on women’s health is a scandal in its own right.
Why doesn’t he explore the relationship of insurance companies to the problem? Insurance companies’ financial interest is opposite to the interest of drug companies, and they have used their market power to demand discounted drug prices. But they have rarely gone to bat for the health of their patients, doing their own research to determine which drugs are actually benefiting their customers’ health and reimbursing those at a higher rate than more questionable drugs.
He might have told the story of antidepressants, sold based symptoms that reflect patients’ despair about the state of American culture and the economic pressures that come from wealth disparity. Two generations of Americans have grown up numbing themselves in response to problems that are far larger than their individual depression. He might have told how frequently side effects of drugs are treated with more drugs, which have their own side effects in an escalating, profitable spiral that is devastating to patients’ health and lucrative for the medical establishment.
Why are vaccines a sacred cow, untouchable in the press?
It is a remarkable public relations coup. The Pharma industry has surpassed Big Tobacco as the #1 industry hated by the American public. But this same public believes that vaccines are life-saving preventatives, and never questions their safety. The vaccines, of course, are sold and tested largely by the same companies that they hate, and the public never connects the dots. Why should we think that companies that have repeatedly been convicted of criminal fraud would honestly report the benefits and the risks of their vaccines?
The truth is that some vaccines have saved hundreds of millions of lives, while others are actually doing more harm than good. Vaccines have general, long-term effects on the immune system, and these can either increase or decrease risk of diseases other than the one for which the vaccine was targeted. (Refer to the work of Christine Stabell Benn.) Vaccines have communal as well as individual benefits, but they also have communal costs; so discussion of any particular vaccine must be nuanced, taking account of wide-ranging social factors. This analysis is never, never done — not by the press nor the epidemiologists nor the journals, certainly not by the FDA. Instead, the world is divided into pro-vax and anti-vax. The latter are disparaged as “enemies of science”. One of the Orwellian triumphs of the vaccine industry is that anyone who asks for vaccines to submit to the same testing regimen as every other drug category is tarred as an “anti-vaxxer”.
There are five reasons why Big Pharma is so jealously protecting its fiefdom, marketing vaccines without public or regulatory opposition.
Childhood vaccines are actually mandated by most states for school children, guaranteeing a market among people who don’t even have any present health problems.
Vaccine manufacturers enjoy legal immunity and cannot be sued in America when their products cause harm in vaccinated people.
Vaccines bypass most tests for safety and efficacy. In “placebo-controlled” trials for vaccines on the childhood schedule, the “placebo” is usually a previously approved vaccine, rather than a harmless saline solution. This practice has masked an escalating spectrum of side effects, growing as the number of vaccines expands.
Childhood vaccine injuries, mostly unrecognized as such, feed a pipeline of lifelong customers for other drugs, especially stimulants and antidepressants.
As a result, vaccines were already the most profitable sector of any drug company’s portfolio before vaccine profits went through the roof in 2021.
I won’t pretend to any comprehensive history of vaccines and their discontents, but I want to tell two stories with which I have some personal familiarity.
The childhood vaccine schedule
Dr Paul Thomas is a pediatrician in Portland Oregon. His practice is rooted in standard Western medicine, but he believes in informed consent. So every time a child comes up for a scheduled vaccine, he explains in detail to the parent the trial results, the pros and the cons, the benefits and the risks as far as they can be known from the medical literature. As a result, his patients have become a diverse sample of vaccination status, with some accepting the full vaccine schedule and some others having no vaccines at all, and most of his patients selective about which vaccines they accept. A few years ago, he worked with statistician James Lyons-Weiler to study the histories of these patients over 10 years of follow-up. How did the fully vaccinated, the less vaccinated, and the unvaccinated fare in the subsequent years of their childhood?
They wrote up their study and published it (2020) in the International Journal of Environmental Research and Public Health. In every measure of health, the unvaccinated children did better. One sixth as many allergies and anemias. One fourth as many asthmas and colds. Zero cases of ADHD among 561 unvaccinated children; nationally, the rate of ADHD is 9.4%. The article presents a graph demonstrating a positive relationship between the number of vaccinations a child receives and the number of future medical problems s/he experiences.
Fig caption: The horizontal axis divides Dr Thomas’s juvenile patients into 20 groups from the least to the most vaccinations received. The vertical axis is the normalized number of office visits. Green bars show that the number of routine check-ups was about the same across the board. Red bars show that showing up in the office to deal with fevers of any kind was much more common among the heavily vaccinated.
It is well-known that vaccines have long-lasting effects on the immune system, affecting susceptibility to multiple diseases, but the mix of benefits and risks is impossible to predict without a study of this kind. Remarkably, this is the first such study ever attempted published. Had no one ever asked the question, “are vaccinated children better off overall?” Or had they asked the question and didn’t like the answer they found?
Immediately after publication, the Oregon Medical Board suspended Dr Thomas’s license on an emergency basis, without a hearing. The journal received complaints. The publication was “irresponsible” because it would encourage vaccine hesitancy. The study was “misleading” in unspecified ways. A year later, the journal retracted the article without explanation to the authors.
And so we don’t know whether the extensive vaccine schedule now recommended (often mandated) for children is doing more good than harm. We don’t know because someone doesn’t want us to know.
Suppression of COVID treatments to clear the way for vaccines
Hydroxychloroquine (HCQ) is a drug with multiple uses and a well-established safety profile. It is taken daily by millions of people who have Lupus, and hundreds of millions more who live in areas where malaria is prevalent. HCQ was used successfully in China to treat the first SARS outbreak in 2003. It works by opening cell walls to allow zinc ions to enter; zinc strongly suppresses the replication of respiratory viruses, including SARS and SARS-CoV-2.
During the spring of 2020, many small studies were being conducted around the world to see if early treatment with HCQ and zinc could keep patients out of the hospital. Then, in May, a major study appeared in the British journal The Lancet. (A companion article appeared in New England Journal of Medicine.) The authors compiled hospital records from six continents, with 100,000 patients who received HCQ and those who did not. In this huge sample of COVID patients, those treated with HCQ were dying at twice the rate of those who did not. The results were so compelling that dozens of smaller studies around the world were discontinued. It would not be ethical to expose COVID patients to HCQ under the circumstances.
But this was the opposite of what had been found previously in smaller studies. Other medical researchers, reading the article, wanted to check the calculations. They asked for the database of patients and outcomes. Weeks passed, and the authors of the Lancet study could not produce the database. Quietly, without announcement or apology, the study was retracted, along with the companion in NEJM.
The story came out: the database had been presented to high-profile academic doctors at Harvard and Stanford by a small Chicago company called Surgisphere. The doctors were excited to have such an extensive database to work from, and they failed to ask even the most obvious questions. Surgisphere had no relationship with dozens of hospitals around the world. The patients in the database were not real people. The data had been fabricated from whole cloth.
But the damage had been done. HCQ studies had been shut down, and the drug had been tainted as dangerous and ineffective, a reputation which has survived to this day.
It gets worse. Subsequent studies of HCQ were designed to fail. They were limited to hospitalized patients, in late stages of the disease when the virus is already gone. Toxic dosages were given to test subjects, causing heart complications and deaths that were completely avoidable. The low dose of HCQ would have worked just fine in early stage COVID.
HCQ was deliberately discredited. In most of the 50 states, pharmacists are forbidden from dispensing it for COVID, or else they think they are forbidden, which has the same effect. The measure of this crime is that HCQ and zinc, used early, would likely have saved millions of COVID patients from hospitalization and death worldwide, extrapolating from some of the honest studies.
The reason for suppressing HCQ and Ivermectin was not just that they are cheap, out-of-patent alternatives. FDA’s rules for emergency authorization say that a vaccine can only be considered for emergency use if no available treatments exist. HCQ (and later IVM) threatened the vaccine strategy that had been determined in advance, with tens of billions of dollars invested.
Abramson recounts the late-breaking story of FDA approval of the Alzheimer’s drug Aduhelm, despite the fact that its own advisors had found the drug had no clinical benefit and serious side effects. There is no longer any daylight between FDA and the industries that it was created to regulate. Exactly the same dynamic is underway with approval of the mRNA vaccines. The vaccines’ efficacy plummets after a few months, while the number of deaths reported after mRNA vaccination has been 90 times greater than the worst previous vaccine (Shingrix). These vaccines were based on an entirely new, speculative technology, and rushed to market with zero long-term testing, yet the FDA ignored all precedents, ignored the company’s own data, made no pretense of risk/benefit analysis, and approved the vaccines for every age group. What does Abramson have to say about this? The vaccines “saved countless lives and prevented enormous suffering.”
The bottom line
Dr Abramson does a good job describing the inflation of drug costs and the importance of data transparency. I think he seriously underestimates the harm that has been done to America’s health by a medical system centered on patented drugs.
I can end this review on an upbeat note by retelling my own experience with hospitals, doctors, and the medical establishment. I had almost no such experience until this time last year, partly because I distrusted what Western medicine had to offer, partly because I was both lucky and careful, and had no chronic health issues into my 70s. But last July, the front end of my bicycle had a date with a speeding truck, and I immediately had an opportunity to sample the best that Western medicine has to offer, namely trauma medicine.
My ambulance hit the ER mid-afternoon and I was given a 20% chance of living through the night. Not only did the surgeons save my life with intensive, simultaneous attention to a dozen places where I was bleeding (internally and externally) faster than they could transfuse blood into me; they also were preparing to rebuild my shattered and lacerated left leg, even as they gave me low odds of ever living to enjoy it.
They did a lot of things right, stopping the bleeding, putting rods and screws in both legs, repairing my shattered pelvis with a rod and a second titanium pubic bone, tying off a vein that was too badly damaged to repair.
I was flat on my back, unable to roll over in bed for three months. One year later, I am swimming and bicycling almost at the level of a year ago. My yoga practice is coming back, and I am hiking in the woods more comfortably each day, so far without the balance and dexterity that I used to have. I can’t jump or run, but I am building in that direction and I haven’t given up. Given my age and the severity of my injuries, I am an outlier in the rate and extent of my recovery. Given the kind of collision that sent me to the hospital, it is a miracle that I lived, let alone that my brain and spine were not injured.
Perhaps of interest, I refused all pain medication in the hospital, with the exception of ibuprofen on a handful of nights. I believe, but can’t prove, that pain medication slows recovery.
“How many people have to receive a given drug in order to save one life?” This number is called the “number needed to treat,” or NNT. For metformin and the best drugs we have that treat chronic disease, this number is in the range NNT=30. In other words, 29 people will have all the side effects of the drug and no benefit so that we can save 1 life. For preventive statins and the most questionable drugs in common use, NNT can be over 1,000.
Harold Katcher’s patent was unveiled last week, and it’s not what I thought it would be.
I thought it would be a list of several molecular forms, together with recipes for how to make them and how to administer them intravenously for increased longevity.
I hoped it would inspire laboratories around the world to replicate Harold’s results and to vary the formula with the intent of optimizing results and streamlining delivery. I imagined a quantum advance in parabiosis-derived experimentation.
Instead, the patent seeks to cover a broad range of techniques for extracting proteins and entire exosomes from blood plasma. It may be designed to obfuscate. I am unfamiliar with patent law, and this may be entirely conventional; instead of giving explicit instructions that another researcher can follow, there are several alternatives at every step, with the claim that they are all variations on the basic technique, and the patent covers them all. I presume that Harold knows which of these options at each step are the ones used to create E5; but no one reading the patent could recreate Harold’s work without some inspired guesses.
Ever since the Stanford parabiosis experiments of 2005, there has been evidence that aging is centrally coordinated and that the blood transmits information telling the body how old it is. Young tissues quickly deteriorate when exposed to the blood plasma of an old animal, and old tissues are rejuvenated in the presence of young blood plasma.
So the pressing question is: what is it in the plasma that transmits these signals? Is it predominantly pro-aging signals that need to be removed, or predominantly anti-aging signals that need to be enhanced? How many such chemicals are there? Are they proteins or active RNAs or something else?
These are difficult questions because blood plasma contains thousands of molecular signals in trace amounts. The quantities vary with activity and time of day, and many of them vary with age. We would dearly love to have a recipe for a handful of transcription factors that need to be added or removed, with the result that they would trigger readjustments in the rest.
I had assumed until last week that Harold has this information, and that he has held it back from the public while his business partner secures patent rights and builds a distribution network for humans.
But now it seems that Harold has general knowledge of the class of chemicals signals that is most effective, but that he does not know specific molecular formulas. Indication is that it is a class of proteins.
Harold has told us that he has been building facilities for synthesizing E5. The patent seems to say that he has some techniques for extracting from plasma a cocktail of many substances that remain incompletely characterized. Akshay has told me that they get plasma from pig’s blood, discarded by butchers.
There are large proteins and short peptides and everything in between. A “plasma fraction” may contain a specific range of molecular weights. But in the patent, several different ranges are listed, so we don’t have the crucial information, “which range is the effective one?” I presume that Harold knows.
Perhaps among readers of this blog there are people well-versed in biotech patent law and others who know more about the biochemistry of blood-derived proteins. If so, please contact me and respond to this patent from a more informed perspective than i can derive.
Imperative for the near future
We know that the active ingredients are proteins, and Harold knows the range of molecular weights. Several different ranges are listed in the patent, and several fractioning techniques are specified for specifying them. I presume that one of these leads to successful rejuvenation and the others are decoys.
So, the next step will require Harold’s cooperation, because even after publication of the patent, no one else will be able to replicate his formula. If he and Akshay are willing to subject E5 to laboratory analysis, then the protein constituents can be individually characterized. I personally don’t know how this is done, but I do know it is possible because biochemists generate pictures like this one routinely.
The number of chemicals in a given range of molecular weights is probably small, perhaps a few dozen; and of these, the active ingredients necessary for the formula to work constitute a smaller set, perhaps less than a dozen. Once we have the chemical formulas for all the constituents of E5, we can test different combinations of them and within a year of trial and error, we should be able to identify the minimal effective set. Then these can be synthesized in a modern factory and we won’t need a river of pig’s blood to rejuvenate humanity.
Harold is not the only or even the first to conduct research with blood-derived proteins inspired by parabiosis experiments. There is ongoing research at Stanford, Berkeley, Harvard, Alkahest and now Altos Labs.
The next steps are crucial, and they will require more investment than Harold and Akshay’s Yuvan Research has available. I hope Yuvan will partner with a laboratory that has resources to analyze E5 and then test constituent ingredients to optimize rejuvenation effects with a minimal set of injected proteins.
Harold Katcher has developed a protocol for lab rats using intravenous injection with a blood plasma fraction he calls “E5”. Three years ago, he announced that treated rats evinced many features of rejuvenation, including improvements in grip strength, endurance, and learning capacity. Two years ago, he announced that treated rats also were epigenetically younger, according to a rodent methylation clock developed by Steve Horvath.
This year, with a grant from Heales Foundation, Harold and his partner Akshay Sanghavi have supervised a trial in which older rats were treated with E5 and then allowed to live out their full lifespans so we might know whether epigenetic and phenotypic rejuvenation translate into increased life expectancy. Just this week, I obtained from them birth and death data for the experimental rats. There were 8 control rats, untreated, all dead, and 8 treated rats, 5 dead and 3 still living.
Executive summary of my findings: At any given age, treated rats are 4x less likely to die; but translated into life expectancy, this is less impressive. The rats are living a little longer, but not nearly so much as their methylation age would have predicted. There is good evidence for compressed morbidity — treated rats are healthier later in life, and their deaths are less spread out in time than control rats. Caveats: All rats in the epigenetic experiment were male, while all rats in the lifespan study were female. Also, the protocol was initiated at a later age in the lifespan study compared to the epigenetic study.
Raw data: Time is the rats’ age in days. “Death” =0 indicates that 3 of the rats are still living. Group 1 is control, Group 2 is rats treated with E5.
How this data is analyzed
It is conventional and, IMO, also reasonable that the data on age at death are interpreted as a “probability of mortality”. Of course, deaths spread over a greater time period indicates a lower rate of mortality. Less intuitive, the rate of mortality is based on the number of rats that remain alive at any given time, and not on the total number of rats. Thus, when the first rat dies, its probability of mortality is just ⅛, but when the last rat dies, its probability of mortality is 1.0.
If a rat is still living it may contribute to the denominator only for rats who died earlier, and not for rats who died later.
Using these conventions, I produced the following plot for probability of mortality for the two groups. I have plotted probability of mortality on a log scale because it is an empirical fact that probability of death increases exponentially with age. This is called the “Gompertz rule”. If the Gompertz rule holds, then we expect the plot on a log scale to be a straight line. I have drawn the best straight line through the two sets of points.
The Gompertz distribution is characterized by two numbers. One is the base mortality rate, which is related to how early the animals start dying. The other is the mortality rate doubling time. The probability of death doubles again and again over the life of the animals. A short doubling time indicates that the deaths are all bunched together, and a long doubling time indicates that the deaths are spread out over a broader range of ages.
You can see that, compared to controls, the treated rats started dying later and that their mortality doubling time is shorter, with deaths bunched more closely in age.
There is substantial uncertainty in these conclusions because of the small number of rats, but there is enough data here to give us confidence in the basic conclusions:
Treated rats are less likely to die young
Once they begin dying, treated rats die faster than controls
It is unclear from data so far whether maximum lifespan has been increased. We will have a better handle on this question when we see how long the remaining rats live.
One more concern about the experiment: Rats are social. Treated rats were housed separately from control rats, 2 or 3 to a cage. Just like people, rats are more likely to die after their cage mates die. I don’t have information about which rats were housed with which, but the death dates show some signs of being bunched together. This social effect could amplify the difference in mortality patterns between treated and control rats.
Cox proportional hazard
The most conventional way to analyze contingent survival data is called the “Cox proportional hazard model”, a relatively new statistical innovation introduced by David R. Cox in 1972. Many drug treatments and environmental hazards are reported on the basis of Cox models.
Result of the Cox model is reported as a “hazard ratio”, interpreted to mean that “if you do X you will be Y% more (or less) likely to die at any given time.”
The Cox model has the advantage that it is independent of the Gompertz rule or any other assumption about how mortality risk changes over time. It has the disadvantage that it can be misleading if the two different groups have qualitatively different mortality patterns.
The Cox model assumes that the difference between the two groups can be expressed as a simple ratio. If the Gompertz rule holds, a simple ratio translates (using the mortality rate doubling time) into an age change. For example, for humans in modern Western cultures, mortality doubles every 7 years. A Cox ratio of ½ is thus equivalent to rejuvenation by 7 years.
I’ve done the Cox analysis for Katcher’s rats because it is conventional, but my opinion is that its assumptions are not satisfied in this case. The mortality rate doubling time seems to change in the treated rats, indicated by the fact that the slopes of the two lines are different. So interpret the Cox results with this in mind.
Cox analysis indicates that the hazard ratio for treated rats is 0.24, meaning that treated rats are 4x less likely to die. The p value = 0.02, indicating confidence in the conclusion that treated rats are living longer than untreated. Increase in life expectancy is about 7%, which is 85 days for the treated rats. Again, these numbers can change when we see how the remaining 3 rats fare.
I have been committed to the idea that methylation clocks provide a real indication of biological age, and that reduction in methylation age will translate to a longer lifespan. My DataBETA study is premised on this hypothesis. There is good theoretical and indirect experimental support for the idea that epigenetics is a driving force behind aging (last week’s blog).
On their face, these new results suggest the possibility that methylation age might be decoupled from life expectancy. This is worrisome, but there are other possible interpretations of the situation.
We don’t have methylation results for the actual animals in the lifespan study. I’ve heard there was some mixup sending tissue samples to Horvath’s lab for analysis. There are various reasons these animals may not have responded to E5 treatment as well as the previous group.
Katcher’s rats are our best opportunity to answer this urgent question about a causal link between methylation status and lifespan. Fortunately, he is beginning another lifespan study with both male and female rats, which will follow more closely the protocol of the original study, but will extend in time to offer lifespan data. Unfortunately, the composition of E5 is still proprietary, so the minds of other scientists and the resources of other laboratories are not available to study the remarkable effects reported from E5. Wider collaboration is urgently needed to study lifespan and also to optimize dosage, timing, and delivery procedures. A collaboration with Johns Hopkins University has been announced by Katcher’s company (called Yuvan), but we have as yet no details.
Possible theoretical interpretation
I have written in the past about the Achilles heel of methylation clocks. Aging is like a civil war within the body. In youth, all metabolic systems are protective, but with age there are systems that attack and destroy the body. Examples are autoimmunity and inflammation.
Typically, methylation sites (CpG’s) chosen for inclusion in a clock algorithm are correlated with age. There are two possible reasons that an epigenetic change might be correlated with age, depending on which side of the civil war the system is fighting for. A given CpG might be associated with a self-destruction gene, or it might be a protective response to the body sensing higher levels of damage. The training algorithm, based on correlation with chronological age or even with mortality, is generally unable to distinguish between these two possibilities.
I have proposed on theoretical grounds that drivers of aging ought to be more common than responses to damage. Methylation clocks are only useful for evaluating anti-aging interventions to the extent that they are based on genes that drive aging. It’s only through experiments like Katcher’s that we can learn if our methylation clocks have been contaminated with genes that protect from damage.
These preliminary results are a signal of caution and a call for more research, but the evidence is indirect and the results are too thin to change theoretical perspectives now.
I have promoted the idea that aging is programmed and that the program is epigenetic. Hence epigenetic age is fundamental. But what is it that imprints epigenetic age on the chromosomes and keeps it updated? Is the “methylation clock” responding to a higher authority, a separate clock which coordinates epigenetic age throughout the body? Do epigenetic clocks in different tissues talk to each other? Such questions are important not just for theoretical understanding, but also because they have two practical consequences. (1) Can we rejuvenate the body with system-wide signaling, or do we have to de-age cell-by-cell? (2) Can we be confident that if we set back the body’s methylation age the person will feel younger and live longer?
I have been reading and thinking about these questions for several weeks, and I can report no clear answers.
Is aging a cell-by-cell deterioration, or is it orchestrated at the level of the whole body and managed through signal molecules in the blood? If pressed, I think everyone would have to admit there is some of each, and differences within the community of aging biologists are about the relative importance of the former vs the latter.
One thing I think we ought to be able to agree on is that the system level, including signal molecules in the blood, makes a vastly more accessible target for anti-aging interventions. Repairing the body cell-by-cell is a daunting proposition; whereas modifying levels of signal molecules in the blood is a piece of cake, once we identify those molecules and determine their optimal youthful levels. The words “low-hanging fruit” come to mind, as well as “Pascal’s wager”.
If there are multiple, independent aging clocks, it is probable that the one that registers the oldest age is the one that can kill us, independent of the others. To make the big leaps in life extension that we are looking for, we probably will need to reset all the clocks. How much do the cell-level and system-level clocks talk to each other? How much progress can we expect to make by working at the (more accessible) system level without addressing the (more challenging) cell-level aging?
Why is the preponderance of research devoted to aging at the cellular level? A small part of the explanation comes from scientific inertia; aging was understood in terms of increased cellular entropy for many years, whereas the paradigm of central control remained in a Russian backwater until publication of the Stanford parabiosis experiments in 2005. A larger part of the explanation has been the infusion of venture capital into aging research in the last decade. You can’t patent hormones and you can’t make money from rebalancing blood levels of the body’s native signal molecules. I believe that the profit motive has deeply corrupted aging research, as it corrupted medical research through the previous century.
I am passionate about these issues, but I leave them aside to talk about questions of fundamental interest: Differential gene expression—epigenetics, and methylation in particular— seem able to change the body’s age state. It seems clear that gene expression is the primary way in which the age state of the body is transmitted and coordinated system-wide. But is gene expression the end of the line, the ultimate upstream aging clock? Or is there a “higher authority” that keeps track of time and programs the body’s methylation, etc accordingly? Does the epigenetic state of the body constitute an autonomous time-keeping mechanism, or is there a time-keeping reference clock, perhaps in the hypothalamus, which dictates the body’s age through secretions, and distant cells respond to these secretions by adjusting their methylation patterns?
And, if the answer is that methylation constitutes an independent clock, does that clock advance cell-by-cell independently, or does gene expression at the cell level export proteins that coordinate methylation age across the body?
I don’t have answers, but several experiments bear on these questions, and offer a nuanced outlook.
The practical question
We need measures of biological age in order to efficiently tell us when we are on the right track with an anti-aging intervention. Methylation clocks are presently the best technology we have for measuring biological age. So, can we be confident that if an intervention sets back the methylation clock that the intervention really is making a person (or animal) younger?
Reasons to think yes:
Methylation clocks track chronological age better than any other biomarker
Some of the difference between methylation age and chronological age is meaningful. It correlates with mortality. In other words, each of the major methylation clocks is a better predictor of life expectancy than chronological age. Remarkably, this is true even for the clock algorithms that were trained only on chronological age.
There are theoretical reasons for believing that epigenetics is the primary driver of aging, so that methylation changes may actually be close to the causal nexus of biological age. (This conclusion is especially cogent for theorists like me who believe that aging is an evolutionary program; however, there are also prominent scientists in the field who reject programmed aging but embrace epigenetics as a primary driver of aging.)
Two things that could go wrong:
There could be a higher authority, a centralized clock that sets up the methylation state. If this is the case, then setting back the body’s methylation age may be temporary, and the methylation state will revert to the age programmed by a central clock. (Cavadas and Cai have adduced evidence that aging signals are transmitted from the hypothalamus.)
In the worst case, methylation changes with age could be an adaptive response when the body senses the accumulation of damage. In other words, the body changes its gene expression when damaged because it is working overtime to repair that damage. In this case, resetting methylation state to a younger age just makes the body less able to cope with the consequences of aging and actually shortens lifespan.
Evidence from parabiosis
In parabiosis, a young mouse is surgically joined to an older mouse of the same genotype. Tissues of the old mouse respond by becoming functionally younger. Since this 19th-century finding was brought to the modern scientific community, the search has been on for chemical factors in the blood that either promote aging or promote youth. [read more].
The parabiosis phenomenon and related findings in rejuvenation through blood plasma transfusions has led to a paradigm that says aging is coordinated throughout the body by signals in the blood. To the extent that single cells age, this is happening under central control, and the process can even be reversed if the cell is exposed to the right signals.
Evidence from bone marrow transplants
Bone marrow transplants are the most powerful available treatment for leukemia, and are also applied for some rarer diseases. The bone marrow comes with the epigenetic age of the donor, and thus the (white) blood cells subsequently generated by the transplanted bone marrow also carry age information. Several different studies [ref, ref, ref, ref] have found consistently that the white blood cells (and presumably the bone marrow from whence they came) retain the age signature of the donor. The donor may be younger or older than the patient. In either case, the methylation age of the patient’s white blood cells—post-op and for years afterward—remains keyed to the donor and does not correlate significantly with the patient’s age.
The lesson of parabiosis experiments was supposed to be that cell aging is not cell-autonomous, but rather a response to signals in the blood that instruct the cells what age to be. Young somatic cells could be aged rapidly in an old blood plasma environment, and — more impressively — old somatic cells could be made younger in a young environment.
Now we have a series of bone marrow transplant studies where the methylation age of the donor is the determining factor, not the patient into which the marrow was transplanted. Bone marrow contains the stem cells from which blood cells grow. Blood cells turn over every few months and they represent an accessible tissue sample which reflects the age state of the bone marrow in approximately real time.
“We found that the DNAm age of the reconstituted blood was not influenced by the recipient’s age, even 17 years after HSCT, in individuals without relapse of their hematologic disorder.” Soraas et al (2019)
This seems on its face to contradict our paradigm from parabiosis that says cell age is not cell-autonomous, but is programmed by the environment. How can we interpret the two results together? Some possibilities…
Maybe only differentiated somatic cells are susceptible to age programming by plasma proteins, and not stem cells.
Maybe these stem cells are providing the biochemical environment in the plasma. Maybe the stem cells and the white blood cells that they generate are the agents that secrete the plasma proteins responsible for sending age signals.
Please think creatively about other possibilities.
Another result from these bone marrow studies
Consistently, the blood cells get older after transplant, whether they are transplanted from young-to-old or from old-to-young. This says two things. First, the point of comparison is the donor age, i.e., the age of the cells pre-transplant, and not the age of the patient who is associated with the systemic environment. Second, the cells seem to age rapidly after transplant, as measured by the methylation age. From there, the age of the cells may (in some studies) revert slowly to their original age trajectory over a period of several years.
Why the rapid methylation aging? It seems like a good guess that the rapid aging initially comes from high rates of reproduction in these transplanted cells that are generating a whole new source of much-needed blood. Could this be a link between telomeres and methylation age (which previously were found to be inversely correlated? Or is there another mechanism by which stem cells keep track of the number of times they have divided asymmetrically?
Am I the only one asking these questions?
Already a decade ago I was thinking about the question How Does the Body Know How Old It Is? Questions about time-keeping mechanisms and coordination of age information through the body go hand-in-hand with conceptions of aging as a programmed phenomenon, and perhaps the prejudice against programmed aging helps to explain the fact that few aging researchers are thinking in this way. A welcome exception is this article by Argentine gerontologists, which I was delighted to discover just yesterday. Lehmann et al: Hierarchical Model for the Control of Epigenetic Aging.
Although there is evidence suggesting that the cellular epigenetic clock possesses an intrinsic ticking rate [ref, ref, ref] multiple observations at organismal level in humans and other mammals lead to the inference that in vivo, the ticking rate of the clock in tissues is synchronized by a master pacemaker.
Lehmann cites as prima facie evidence for this
For a given chronological age, it was found that in DNA samples taken from whole blood, peripheral blood mononuclear cells, buccal epithelium, colon, adipose, liver, lung, saliva, and uterine cervix, Horvath’s algorithm read essentially the same epigenetic age, the only exceptions being some brain regions and very few other organs.
In addition, she cites Katcher’s success in rejuvenating rats (and their diverse organs) using only a set of intravenous signals. The article goes on to propose a model in which there are four time-keepers in the body, coordinated by signals in the circulatory systems. The four are:
Light-sensing and neural processing
Neuroendocrine signaling (esp the suprachiasmatic Nucleus of the Hypothalamus)
The Immune system, including thymic involution
Curiously, she does not include the replication counter implicit in telomere shortening, which Fossel, Blasco and other luminaries have adduced as the primary source of aging. I also would add that the hypothalamus is the best candidate we have, not just for one aging clock among several, but as a central, coordinating organ.
Fig. 1. Proposed organismal regulatory network in mammals. The diagram includes the autonomic nervous system (ANS, acting via neurotransmitters), the neuroendocrine system (NES, acting via blood-borne hormones), the immune system (acting via blood-borne cytokines and thymic hormones), the circadian clocks (acting via blood-borne hormones and neurotransmitters) and a putative pathway connecting the neuroendocrine network to the DNAm clock in organs and cells. All networks act on peripheral organs. Inset- Bidirectional interactions among all networks including (in red) the hypothetical DNAm network.
In addition to Lehmann, there is a 2021 review by Raj and Horvath, speculation from the horse’s mouth. They note that all the Horvath clocks are based on small differences in % of cells methylated at a given site (conventionally notated as β).
Increase in epigenetic age is contributed by changes of methylation profiles in a very small percent of cells in a population.
One way to interpret this fact (my speculation, not R&H) is that immune sensitivity, (anti-) oxidation, and inflammation are all under tight homeostasis in the body, because these are sensitive functions, balanced on a knife edge between insufficient protection and self-destruction. It is easy to tip the balance over toward self-destruction with small changes in the set point for a few signal molecules in blood plasma.
Another way to interpret this (again, my speculation) is that it is only a handful of cells at the tail end of the distribution that go over an edge into a state where they cause all the damage of aging. This hypothesis is consonant with the story about short telomeres, cell senescence, SASP, and the powerful benefit of senolytics. However, a big hole in the narrative is that it requires a set of CpG’s that would be capable of precipitously tipping the cell over into a toxic state. We know that critically short telomeres can do this, but there is no study yet of methylation-induced cell senescence. R&H speculate about such a mechanism connected with PCR=Polychrome Repressive Complex.
Raj and Horvath also stress the continuity between epigenetic changes that begin in utero, associated with development, and the changes that lead to senescence late in life. Blagosklonny as well has emphasized this point.
“Collectively, these five features of DNA methylation allow one to summarize with some degree of certainty that epigenetic ageing is a measure of change of epigenetic heterogeneity, contributed by a relatively small percentage of cells, seemingly in line with developmental processes that are conserved across species and begins very soon after conception. This seemingly inescapable deduction provides us with a reference point against which models and hypotheses can be measured.”
If I may carry the logic of these two experts one step further, I would emphasize the role that methylation has in determining what hormones and enzymes are secreted into the blood. Therein lies the possibility that intracell methylation clocks are coordinating, both with other cells and with other clocks, via signal molecules in blood plasma.
Other provocative findings that we might hope to integrate into a theory of aging
The methylomes of naked mole rats age at a normal rate, but the phenotypes of the rats themselves show no signs of age [ref]. Males and females age epigenetically in somewhat different ways [ref]. Methyl donor molecules in the diet can lead to a younger methylome, with benefits both for hyper- and hyomethylated regions (validated for MTHFR snps only) [ref]. When human fibroblasts are reprogrammed (with RNA) to turn them into neurons, they remember their Horvath age even after forgetting their identity [ref]. BMI is associated with accelerated methylation aging [ref]. Mice challenged with a high-fat diet can be brought back to normal weight with a normal diet, but accelerated methylation aging persists [ref]. Cessation of smoking decreases Hannum and Horvath DNAmAge [href]. The methylation shadow cast by years of smoking is a better predictor of subsequent morbidity and mortality than the smoking history itself [ref]. Methylation image of telomere length is a better prediction of age and mortality than is telomere length itself [ref]. Pregnancy increases Hannum Age, DNAmAge, and PhenoAge [ref].
(Apologies to Rafil Kroll-Zaiti)
Katcher has been conducting a longevity trial for rats treated with E5 (background story here). Partial results suggest that treated rats are living statistically longer than untreated, but not as much as you would expect if the greatly reduced methylation age indicated full rejuvenation. The results are preliminary, and I will publish a full analysis in this space as soon as I can get the detailed dataset.
The finding, if validated, suggests that multiple clocks in the body are not completely synchronized, and the “fastest clock wins”, meaning that it kills the animal no matter what the other clocks may say.
I am disappointed as you are in not being able to provide fundamental answers, but I hope that (together with Lehmann, Goya, Raj, and Horvath) we have provided a framework and a set of questions that can guide fundamental research. Very few other researchers are addressing these questions, and the answers will be crucial both for devising effective interventions and also for measuring the effectiveness of interventions that we already have.