It would be just like me to tease you with a clever, ironic headline like this introducing a long, detailed analysis of the British mortality data. But in this case, I’m being serious. This article is about why I think the data are unreliable and unworthy of our credence.

In August, the British Office of National Statistics (ONS) released data on all-cause mortality broken down by age and vaccination status. The ONS data covers months from April, 2021 through May, 2023.

This is exactly what we need to determine if the COVID vaccines have saved lives. Data by “cause of death” are tainted because hospitals have been incentivized to treat deaths “with COVID” differently from other deaths. But total death counts are harder to corrupt. I wish that more countries would release such data. (Are you listening, CDC?)

I’ve written about dangers of the COVID shots, including one analysis estimating deaths in the hundreds of thousands for the US alone. I should be eager to vindicate my prediction with new data, and the aggregated British data appear damning.

On its face, the straightforward, aggregate calculation says that vaccinated people are three times more likely to die than unvaccinated.

The British data is broken down into seven age categories. (Children are not included. The lowest age category is 18-39, and thereafter the age categories are by decade.) Let’s zoom in on the youngest age group:

A small number of subjects had bad reactions to the first shot, and discontinued from there. These people have elevated risk from the vaccine, but among those who went on to get the full sequence + boosters, the shots appear to be protective.

Other age groups show the same pattern. For example,

For every age group, multiple COVID shots appear to be protective against death from any cause.

A statistics lesson

Of course, it’s mathematically impossible that within every age group, the vaccinated are at lower risk of death but when you aggregate all the age groups together, it turns out that vaccinated people are at a higher risk of death.

That’s what I thought. I spent a whole evening checking and rechecking the calculation.

But I might have remembered Simpson’s Paradox.

The data are correct. The graphs are correct. The key to understanding the situation is that the lion’s share of the deaths (the numerator) are in the oldest age groups, but most of the head count (the denominator) is in the younger age groups.

Crucially, the vaccination rate among the young is much lower than among the old.

So what we are seeing in the aggregate chart is that the oldest people are more likely to be vaccinated, and they are also more likely to die. But that doesn’t mean that vaccination is causing the deaths.

So, does this vindicate the vaccines?

I have written and others have written that side effects from the COVID vaccines, including risk of death, are so much higher than vaccines of the past that it’s hard to see them on the same scale.

Furthermore, there is insurance data showing that death rates are up as much as 40% in working people, and anecdotes tell of people having sudden heart attacks and (widely “debunked”) turbo cancers.

How can this be reconciled with the idea that, in each age group, multiple COVID vaccinations are associated with lower rates of death from all causes?

In the 18-39 age group, almost no one is dying from COVID. The most significant causes of death are accidents, suicide, and overdoses. It is not reasonable to think that three COVID shots have the effect of lowering these risks by 20%.

The most plausible explanation is a strong “healthy user” effect. People who are unvaccinated are, on average, poorer and less educated than the rest of the population, and it is well-accepted that death rates are closely correlated with social class, especially at the lower end. More speculative is that the data has been tampered with, deliberately doctored to hide the vaccines’ dangers. See below, “Fraudulent manipulation…”

For these reasons, I think there is not much to be learned from the new British data.

A pattern too stark to ignore

There is one result embedded in the data that is highly significant (p<0.00002) and implies a role in the vaccines for excess mortality in 2021. During the months March – December, the all-cause death rate in the UK rose by 30%, and the rise was tightly correlated with the cumulative number of vaccinations (R=0.96).

This rise did not continue into 2022, but the overall death rate remained elevated. This pattern, again, cannot be explained as simply as a huge death toll from the vaccine. Thirty percent is enormous, and cannot be hidden by any government’s manipulation of the statistics.

I know… Ed Dowd has famously documented a 40% rise in insurance claims against corporate group life policies; but it was only possible to hide that 40% bump because the base rate among these young people employed by large corporations was so low that 40% of that was too small to show up in a graph of whole-population mortality, which is always dominated by the oldest segments.

Since the British mortality increase is in the population as a whole, it must be concentrated in the older population. Part of the 30% rise was seasonal. Death rates increase 25% winter over summer. But part of the increase is likely due to a lingering effect of the vaccines on mortality and morbidity in the oldest group. Evidence for this is in the continuation of the graph into 2022 and 2023. Death rates are more variable, but we see that they reach new highs in the winter of 2023 and never return to baseline.

Fraudulent manipulation of the ONS data?

Last winter, Madhava Setty publicized research by Norman Fenton suggesting that ONS had tampered with their COVID data. The strongest evidence was for the early months, Jan-Mar of 2021, which are unaccountably absent from the latest edition of the ONS data. The current edition begins with April, 2021. The following graphs are derived from the ONS data release in July, 2022.

The charge that is easiest to verify is that deaths from the vaccine rollout have been shifted into the “unvaccinated” column. We can see this in the form of a spike in deaths of the unvaccinated just as the vaccines were being rolled out. This is most prominent in the death rate for the oldest groups.

This spike is hard to miss — three times as many deaths as expected in the small “unvaccinated” group in those first months when the much larger vaccinated group of elders was being given early priority for the vaccine. Nursing homes were first to be vaccinated.

In the spring, the vaccine was distributed to middle-aged citizens, and — remarkably — we see a similar bump in deaths of “unvaccinated” people in that age group. The bump started later and is more spread out, corresponding to the fact that middle-aged people signed up for the vaccine more gradually.

Starting in the summer, the vaccine was recommended for younger people, and we see a bump for mortality in “unvaccinated” adults under 40 begin in June.

The Jan-Mar data for 2021 has been censored in the new ONS release, and with it the evidence for the obvious spike in the older age groups. But what of the humps for younger age groups? Are they visible in the new data?

To answer this, I’ve plotted data from 2022 and 2023 on the same graph for each age range, so we can see where they match and where they don’t. At older ages, the data match nicely.

But for younger ages, the data have been revised upward, making the humps more prominent. I interpret this to mean that more post-vaccine deaths have been moved into the “unvaccinated” group.

For the youngest ages, it looks as though the data were adjusted more, proportionally, but the scale is much smaller because the death counts are very low for the young.

The bottom line

I’ll end where I began this entry. I’m not going to analyze the British ONS data because I don’t think the numbers are reliable.

The big news: (1) the active ingredient in E5 is exosomes. (2) Young exosomes retain their ability to rejuvenate across mammalian species lines. Work that remains: (1) calibrate a new generation of methylation clocks. (2) Combine removal of old exosomes with addition of young exosomes. Optimize dosage and timing of treatments.

---

What is in E5, Katcher’s fountain of youth? From the beginning, it was described as a “plasma fraction”, leaving us guessing. Blood plasma contains thousands of chemical species in tiny quantities that nevertheless have powerful, systemic signaling effects. I thought once the patent was applied for, there would be no more need for secrecy, and the patent would reveal the formula; but the patent application was for a process, not a substance. Katcher and Sanghavi claimed rights for a broad array of substances extracted from the blood in different “embodiments” of the patented technique.

Then, in last month’s preprint posted on BioRxiv, the authors casually revealed the secret, almost parenthetically. “the exosome fraction of the plasma, which we term as E5”. So E5 is not large proteins or small proteins, it’s not non-coding RNAs — according to this document, E5 is made of exosomes and exosomes alone. Independently, a group from the Smidt Heart Institute published in July their finding that exosomes from embryonic plasma had a profound rejuvenating effect, while plasma without exosomes had no effect. (Akshay alerted me to that paper when it came out.)

This new preprint reports on experiments using E5 from piglets to rejuvenate a total of 14 rats. Epigenetic markers, cognitive, physical, and metabolic markers all improved impressively. Some of the limitations of Katcher’s experiments remain to be resolved. Because of small numbers and killing of test rats to obtain tissue samples, we still do not have a strong indication to what extent the restoration of all these markers of age translates into increased longevity.

A major finding is that exosomes from a young pig can rejuvenate an old rat. It is remarkable, very lucky, and certainly not to be taken for granted that such specific signals can cross species lines. What a horrible situation we would have been in if it turned out that the best anti-aging treatment available depended on harvesting the blood of babies.

Results

There were only 14 treated rats in this round of experimentation. 6 males in round 1 were sacrificed so that methylation of their organs could be tested. 6 female and 2 male rats in round 2 were kept alive long enough for their blood to be drawn for methylation testing, but not long enough to determine their lifespans.

Methylation clocks: For 6 treated rats, age 109 weeks, these were the average epigenetic ages by tissue:

Tissue	Epigenetic age
liver	28 weeks
blood	39
heart	58
hypothalamus	82

The preprint does not provide error bars for these average epigenetic ages, but I expect (based on the published minimum and maximum, and the small sample size) that the 95% confidence intervals are broad, corresponding to rejuvenation by anything between 30% and 70%.

The fact that various tissues are rejuvenated by exosomes indicates that exosomes are a medium for transmission of age information through the body, and that young exosomes in the blood are able to induce system-wide changes in gene expression.

It’s common for medical gerontologists to frame their work in terms of their favorite evolutionary theory of aging, which is always the 70-year-old, conservative theory that won’t raise any hackles. I’m grateful that Dr Katcher is unabashed about framing his medical research in evolutionary terms, and that his evolutionary theory agrees with mine. Aging is a timed program of self-destruction, coordinated through the body by chemical signals, and evolved because ecological communities are more stable when individuals have predictable lifespans.

The hypothalamus is a neuroendocrine region of the the brain that has been associated with circadian timekeeping (day-night) and also with tracking biological age. The fact that gene expression in the hypothalamus was rejuvenated to a far lesser extent than the other three tissues suggests that hypothalamus plays a special time-keeping role, and that perhaps the hypothalamus is a source for these exosomes in vivo. It may be that the hypothalamus gradually erases half the epigenetic gains from the E5 treatment over time. From this and previous tests, we still have limited data on long-term benefits of E5 treatment.

Blood markers:

“we measured the levels of the following biomarkers on 30, 60, 90, 120 and 155 days from the start of the experiment: bilirubin, serum glutamic-pyruvic transaminase (SGPT) and serum glutamic-oxaloacetic transaminase (SGOT) to monitor liver function; triglycerides (TG), HDL and cholesterol to monitor risk of atherosclerosis and heart disease, and liver function as well; glucose to monitor the pancreas and diabetes; and creatinine and blood urea nitrogen for kidney function. The levels of all these biomarkers in the treated old rats were altered towards the values of young rats, without exception.”

(Note: Blood sugar and triglycerides in treated old rats was reduced to levels indistinguishable from untreated young rats. In humans, Type 2 Diabetes is a primary means by which the body destroys itself. — JJM)

Cognitive recovery: Treated rats learned to escape from a Barnes maze faster than old rats.

Oxidative stress: ROS markers were restored close to a young state. Antioxidant enzymes GSH, SOD, and catalase were increased close to youthful levels.

Inflammation: TNF-α and IL-6 were reduced close to the levels of young rats. Nrf-2 was increased close to the level of young rats.

Sarcopenia and muscular fitness: Grip strength of treated rats rose within a week to levels comparable to young controls, and remained strong for at least 30 days.

A new/old biological clock based on glycation of antibodies

IgG is a blood component, another name for “antibodies”. These bind specifically to viruses and bacteria and tag the cells for attack by phagocytes, another immune component.  Antibodies are not cells or globules but individual Y-shaped protein molecules with both arms of the Y able to bind to the (same) antigen, and the stem of the Y modified to attract a phagocyte only when the other end signals that it’s “got one”.

Immune aging is a big deal, as it was identified as a prime cause of aging by Roy Walford already in the 1960s. Greg Fahy pushed the theory into a therapeutic concept four years ago with his TRIIM program. With TRIIM, the idea was validated that a rejuvenated immune system can signal rejuvenation to epigenetic age and, presumably, other aspects of aging.

Dr Fahy also subscribes to a programmed theory of aging, and he personally offered me my first opportunity for a wide audience in the anti-aging community in 2010.   Mitteldorf_Evolutionary-Origins-of-Aging.pdf

Hence, “immune age” is a promising target for measuring success of anti-aging interventions, independent of methylation age. This idea has been realized with a Glycan-Age clock. Antibodies in the blood are coated in complex carbohydrate (sugar) molecules, or glycans, and the structure of the glycans carry information, analogously to the way that DNA or proteins carry information via their sequencing. The particular sugars attached to antibodies change with age, and they become important signals promoting inflammation later in life. Clocks based on glycation of antibodies are as old as methylation clocks, though they have been developed to a lesser extent.

It is surprising, perhaps, that glycan age was never measured in the TRIIM study, but Katcher’s group added glycan age to the tests on his treated rats.

“a significant (p < 0.05) reduction in the relative abundance of the pro-inflammatory agalactosylated IgG2a glycoform (G0) was recorded. This was accompanied by a simultaneous upsurge in the antiinflammatory digalactosylated glycoform (G2)”

Is there such a thing as “biological age”?

The preprint includes the statement,

“However, in the context of aging and rejuvenation, it is crucial to differentiate between improved health or organ function, which could be achieved via medication or surgery, and genuine molecular age reversal.”

Intuitively, we all feel that this must be a legitimate concept. In our experience, we can look at most people and form a pretty good idea how old they are, and this translates into reliable expectations about their stamina, resistance to disease, and ability to cope with a fundamentally new environment.

But this is because we’ve mostly encountered natural humans. We know, for example, that movie stars can have expensive surgeries and skin treatments that make them look decades younger. We don’t expect that people who have had a face lift will live longer or have more youthful vitality.

It is perfectly possible to imagine anti-aging treatments that rejuvenate the liver but not the nervous system, or that slash the risk of heart disease but increase the risk of cancer. Hence, it is advisable to report a variety of functional and metabolic tests rather than relying on any one measure of “biological age”.

What is measured by the methylation clocks?

For more than a decade, I have been committed to the paradigm that the body’s gene expression changes with age, and that changing gene expression is a driver of aging. Methylation, as a convenient surrogate for gene expression, would then seem to be a reliable measure of biological age.

But in the last four years, I’ve entertained questions about this paradigm, and I am no longer absolutely committed. The crucial question is: Does gene expression change as a driver of aging, the body destroying itself with inflammation and by scaling back autophagy and repair? Or does gene expression change in response to damage accumulated with age, scaling up repair functions to mitigate damage?

As recently as four years ago, I wrote confidently that the former greatly predominated. Epigenetic changes drive aging. On this basis, I was confident that if you change the methylation clock, you must be changing the driver of aging, and this was bound to lead to longer lifespan.

I have since become convinced that both kinds of changes in gene expression accrue with age, and that there is no easy way to tell the drivers from the responses. I first blogged about this in response to the GrimAge clock. GrimAge is the best predictor we have of remaining life expectancy, but it is based not just on drivers of aging but also on the body’s distressed response to toxins and cell-level damage.

The bottom line is that epigenetic clocks are the best measure of life expectancy that we have, and that they are improving every year, but while uncertainty remains, it is well to supplement methylation clocks with specific tests of metabolic function, stamina, inflammation, and cognitive performance.

Six new methylation clocks

Prof Horvath (presumably it was Horvath) trained four new clocks on rat tissues plus two new clocks that were cross-trained on humans and rats. Since rats have a maximum lifespan of 3.8 years and humans 122.5 years, one logical way to train a clock that works for both species would be to scale the rat ages and human ages so that one rat year is about 32 human years. This is just what Horvath did.

But in addition, he trained a second combination clock (rat and human) to read in absolute years. This presumes there are changes in epigenetics of a very old rat (say, 3 years) that are parallel to epigenetic changes taking place in a very young human (also 3 years). A strange concept, indeed! What kinds of changes would we expect to take place in a 3-year-old rat and simultaneously in a 3-year-young human? My guess is that the training program is selecting those sites that behave very differently in rat and human, and that results derived for rats with this clock will not translate.

But I would suggest that even the time-scaled clock (based on 32 years of human life = 1 year of rat life) is suboptimal. This is because different events in development and aging may occur at different points in the life cycle. A male rat reaches sexual maturity at about 8 weeks. Using a 32x multiplier, this would correspond to a 5-year-old boy. But in fact, boys don’t reach sexual maturity until well into their teens.

Prof Horvath has been developing methylation clocks for more than a decade, and his work has had a deep impact on the science of aging. To my knowledge, all his clocks are based on straight-line changes in methylation over a lifetime, and I have counseled that this is an unnecessary limitation on the clocks’ accuracy. More realistic clocks can be constructed based on parabolic curves, or even the simple expedient of grafting two straight lines together to create a “spline”. He has been creating cross-species clocks for the last few years, and to my knowledge these have all been based on a single scaling factor for each species. My recommendation is that here it is even more important to project the life cycle of one species onto another using a flexible mapping, pegged at several intermediate time points which are determined in a mathematical optimization process. Cross-species clocks will be of increasing importance as we translate mouse studies to develop experimental protocols for human medicine.

Unanswered questions

A few paragraphs up, I referred to a major issue in methylation clocks: A given intervention affects a set of methylation sites that are all associated with younger age. Which of these are actually beneficial, leading to life extension; and which represent reversion of the body to a younger state when it had less damage to deal with? For the latter, the younger state corresponds to repair mechanisms that are less active (presumably because they are less needed), and “rejuvenation” to this state could actually lead to shorter life expectancy.

To date, the best methylation clocks have been calibrated by how well they predict future life expectancy. This is, of course, very useful information, but it is all based on naturally-aged individuals. Our interventions that reduce methylation age may not increase lifespan.

In two previous rounds of Katcher’s experiments, males experienced more epigenetic rejuvenation, but they were killed before their lifespans could be measured; while females experienced less epigenetic rejuvenation, and their lifespans were measured to be only modestly extended relative to controls. Unexplained is the fact that the controls lived far longer than Sprague-Dawley rats are usually expected to live, and that a single treated rat lived far longer than 7 others that received the same treatment. These are all curious observations that warrant further investigation.

The failure of Katcher’s rats last year to live as long as their methylation age predicted should be a motivator for future research, studying not just the interventions but also the clocks.

We can try to classify different methylation sites based on theory; but if we want to address the question head-on, we need different experimental interventions that affect different CpG sites. Then we can see which CpG sites, when artificially methylated or demethylated, lead to longer lifespan. Hence, Katcher’s experiments provide a unique opportunity to determine in practice: which methylation sites, when reverted to a younger age state, actually extend lifespan? This is an experimental solution to the stickiest problem in aging clocks: which epigenetic changes correspond to drivers of aging and which correspond to responses to damage?

Do we have methylation for Sima, Katcher’s record-setting long-lived rat? If so, we might compare methylation profiles for Sima to his cage-mates that were treated the same way, yet died many months earlier. This is a start, but what we really need is an experiment in which about a hundred rats (M and F) are treated with different E5 protocols, their methylation profiles are monitored over time, and they are also permitted to live until their natural end. Manipulated methylation profiles can then be correlated with life expectancy, and from this a uniquely useful methylation clock can be constructed.

---

For me, the bottom line is the same thing I’ve been saying since the beginning: Katcher’s research is the most promising line of investigation in the field of anti-aging medicine, and it is a travesty that it is taking place under a single investigator’s direction, and with very limited resources. It will take extensive lab resources to optimize E5 dosage and timing, and to try combining addition of young exosomes with removal of old exosomes. This same research can be the basis for the first methylation clock that is known to predict lifespan for rejuvenated animals.

I would like to see the abundant money that is now in anti-aging research re-directed into rejuvenation via exosomes, and I would like to see Katcher sharing his techniques openly with the research community.

Extracellular Vesicles (EVs) have only been studied in the 21st century. Think of them as natural lipid nanoparticles, or endogenous viruses. They transmit information around the body and they’re small enough to be exhaled and carried in the air to communicate with other individuals and even other species. EVs are encapsulated in fats that facilitate entry into cells, and inside they contain proteins, RNA, DNA, lipids — all of which carry information. EVs are a universal biological language, a barely-explored medium of communication.

Last week, an extraordinary paper was published demonstrating the rejuvenation potential of EVs from very young animals injected into old animals. The authors come from Smidt Heart Institute in Los Angeles, and their primary field is cardiology, not gerontology, so they focus on heart-derived EVs and benefits for rejuvenating the heart. But the benefits they observe go well beyond the heart and include lifespan in rodents and rejuvenation of human cells from rat-derived EVs.

This research should be understood in the light of parabiosis experiments by the Conboys, Rando, and others and in particular to the rejuvenation technology of Harold Katcher. I am grateful to Harold’s partner, Akshay Sanghavi, for alerting me to this publication. Katcher’s blood-derived rejuvenation serum is called E5, and Akshay has told me that E5 includes EVs as well as a wide range of proteins. The current article suggests the possibility that it is wholly the EVs that are responsible for the benefits of E5. Indeed, the authors repeated their experiments with (1) whole blood plasma, (2) plasma with the EVs removed, and (3) just EVs. They found rejuvenation effects associated with whole plasma and with EVs alone, but not with plasma minus EVs. If this holds up, it is the study’s most important finding. Akshay told me that EVs are easier and cheaper to isolate than the molecular constituents of E5 (proteins).

For all those involved in parabiosis and plasma exchange research, I suggest that it should be an immediate priority to replicate the Smidt findings that all of the rejuvenating power of young blood is contained in EVs. The Conboys might be interested in asking whether the pro-aging effect of plasma infusions from old to young animals is also an effect of EVs.

The state-of-the-art EV separation technique for large throughput is called an acoustic nanofilter. Ultrasound pressure can be tuned to separate a particular size of particle in a specially-designed medium. If I were advising Yuvan, I would suggest that they develop an expertise in acoustic nanofiltration ASAP as a next generation replacement for their plasma fractioning technology.

Exosomes

Exosomes are the most common type of EV, and probably the most relevant to aging applications. They seem to be a general vehicle for inter-cellular and inter-individual communication. The study of exosomes is in its infancy, but it is already known that exosomes are tagged in a way that recipient cells can distinguish and choose which exosomes to pick up and “read the message”.

One area of exosome activity that has been studied is the communication of antigens to the immune system. A particular type of exosome includes foreign proteins that are potential invaders, and they are shared not just within the body but through the air. When you think of herd immunity, consider not just the contagion of people who carry disease but also the information about what diseases are in the air that is transmitted in social interactions.

When communities and whole countries were locked down during COVID, one consequence was to prevent uninfected individuals from learning and preparing an immune response to the virus through the sharing of exosomes.

“In the nervous system, exosomes have been found to help in myelin formation, neurite growth, and neuronal survival, thus playing a role in tissue repair and regeneration.” [ref, ref, ref, ref, ref, ref] “It has been demonstrated that the mesenchymal stem cell exosomes themselves can act as a therapeutic agent to help [repair] tissue injury.” [ref, ref, ref, ref, ref, ref]

“Microvesicles” are, as far as I can tell, a kind of fat exosome. They are as large as bacteria, whereas exosomes are closer to the size of viruses, but the range of contents and hypothesized functions is the same.

Apoptotic Bodies — another potential target for EV therapies

There is also a specific EV type that apoptotic cells send to induce apoptosis in other cells. Apoptosis is programmed cell death, playing an important role in self-destruction of diseased and cancerous cells. However, aging involves a cascade of apoptosis in healthy cells that leads to loss of muscle tissue (sarcopenia) and brain cells (neurodegeneration). Apoptotic bodies are the type of EV that triggers the apoptosis cascade, and I speculate that removing apoptotic bodies from the blood will have future potential as an anti-aging therapy. Apopototic bodies are even larger than “jumbo” microvesicles, so it should not be difficult to separate them in medical applications. It is a reasonable guess that the effectiveness of senolytic therapies is directly related to a reduction in apoptotic bodies, which are secreted by senescent cells. (These are my own speculations, not mentioned in the paper that I’m reviewing here.)

The new research

Rejuvenating effects of young extracellular vesicles in aged rats and in cellular models of human senescence by Grigorian-Shamagian et al, in Nature Scientific Reports. Senior author = Eduardo Marbán.

---

The authors extracted EVs from neonatal rat hearts, and applied them to cell cultures and injected them into older live rats (22 months). They used whole plasma and plasma minus EVs as controls, establishing that it is the EVs that carry the benefits. They extracted corresponding EVs from heart cells of neonatal humans, and applied them to cultures of human cells.

They found EKG evidence that hearts were functioning better in treated animals. Insulin resistance was blood sugar were reduced.

Exercise endurance increased in treated animals, while declining progressively in controls.

They found that pliability of tissue was restored toward young levels. Hearts, lungs, muscles, and kidneys all improved their function in treated rats.

Tissue samples (muscle and heart) look better in ways that I don’t pretend to understand.

Old rats were treated with four monthly infusions, then kept for 16 weeks more before sacrificing all animals. During those 16 weeks, 6 of 14 control rats died and only 3 of 36 treated rats. My calculation (Fisher’s exact test) indicates life extension with a 99% confidence level.

Tests were conducted on two human cell cultures: fibroblasts from middle-aged donors treated with human EVs, and cardiac cells treated with rat EVs. Fibroblasts increased reproductive capacity, decreased apoptosis, and a more youthful transcriptome. Similarly, cardiac cells exhibited better self-assembly and a more youthful transcriptome.

The bottom line

In case you haven’t yet read between the lines, I’m excited about this work. I would have liked to see results of some Horvath clocks and a hat tip to the Katcher preprint, but in other respects, I find the research quite thorough and convincing. A major question in plasma exchange research has been the identification of the active component, out of thousands of protein species. This is only one study, but it suggests that researchers look at the activity of EVs rather than proteins or RNAs.

 

A crucial question is whether EVs from other mammals can be used to rejuvenate human tissues. This study suggests yes, but the demonstration was limited to cell cultures and no direct comparison was made between treatment with human-derived and rat-derived EVs. The author has not responded to my email requesting any data relevant to this question. If animal EVs work in humans, the therapeutic market may soon open; EVs should be easy to extract from the blood of young animals that are being slaughtered for meat. If not, we face a major ethical dilemma, as aborted and stillborn infants will support only a tiny fraction of the potential demand for rejuvenating EVs. EVs from young human donors can probably be extracted but it’s a lot to ask of our children. The authors suggest that EVs could be manufactured from human cell cultures. “Given that allogeneic CDCs [cardiosphere-derived cells] are already in advanced testing and have proven safe to date, such cells can be used as manufacturing platforms for EVs, enabling rapid progress to clinical testing in a variety of aging-related disorders.”

The Yamanaka factors (four proteins, abbreviated OSKM) are a treatment that can completely reset any ordinary, functional cell into a pluripotent stem cell, able to regenerate tissues of any type. In the process, the cell loses epigenetic markers of age and reverts to an embryonic state. But differentiated cells are differentiated so they can be functional. Taking them all the way back to stem cells destroys their function. Remarkably, differentiated cells can be taken part-way back, becoming younger but not losing its functional identity. This has been done with pulsed doses of OSKD or with just three of the four factors, OSK. Last year, the Harvard laboratory of David Sinclair reported success rejuvenating the aged retinas of mice with this process.

Rejuvenating a whole body in this way is impractical. Precisely controlling the dosage and the timing of OSK to each cell cannot be achieved outside a Petri dish. To make matters worse, some of the treated cells have a tendency to grow into tumors. 

Sinclair has been searching for other chemicals (besides the Yamanaka factors) that might have a rejuvenating effect at the cellular level without risking cancer and without problematic sensitivity to dosage and timing. With modern, computerized laboratories, it is practical to test thousands of chemicals shotgun-style in separate cell cultures, automating both the delivery of chemicals and the measurement of outcomes.

Recently, Sinclair reported the results of this process: Of the many combinations assayed in his shotgun experiments, six were identified that successfully rejuvenated cells in culture without the cells losing their identities and without causing cancer. “Thus, rejuvenation by age reversal can be achieved, not only by genetic, but also chemical means.”

This sounds like breakthrough science. Let’s look more closely.

What is the evidence that the cells are rejuvenated?

The new Sinclair article cites two lines of evidence

• Nucleocytoplasmic compartmentalization (NCC)
• An aging clock based on which genes are being transcribed

 

NCC was a new idea for me. There are certain proteins that belong in the nucleus, and they tend to leak into the surrounding cytoplasm as one sign of aging in the cel. 

Aging clocks based on transcription are a promising idea but not as well developed as methylation clocks.

Maybe I’m too suspicious, but I’m inclined to ask: Why didn’t Sinclair choose one of the well-established, validated aging clocks as a target, for example the robust Horvath multi-tissue or multi-species clocks? If he was going to use a transcriptomic clock, why did he have to invent a new one, rather than use Lehalier’s clock? If he was going to introduce a new clock, why wouldn’t he validate it by reference to established aging clocks?

I wonder if there is an untold history of this research, in which the Sinclair lab tried and failed to rejuvenate cells as measured by the more conventional aging clocks. 

Aging is not cell-autonomous

In different laboratories, aging research has been pursued via two pathways: top-down and bottom-up. Top-down researchers are looking for central coordination of the age state of the body, broadcast probably via signal molecules in the blood. (Michael Levin thinks that it may also occur through persistent patterns of electrical voltage.) Bottom-up researchers are looking for ways that individual cells lose focus and function with age.

The deep problem with Sinclair’s approach is that it derives from an overly reductionist perspective. Aging of the body is reduced to aging of individual cells. Aging of cells is understood as a blurring in the precision chemistry that the cell relies on for optimum health. 

Other researchers (including the Conboys, Harold Katcher, and Izpisua Belmonte) are pursuing the top-down approach to rejuvenation, based on a paradigm in which aging is coordinated systemically, and not controlled at the cellular level. Interventions are in the bloodstream, through which biochemical signals transmit information about the age state of the body. 

Sinclair repeats without qualification his prejudice that “aging is a loss of epigenetic information.” He cites his previous experiments as support for “the idea that a loss of epigenetic information, resulting in changes in gene expression, leads to the loss of cellular identity.” But we have learned that methylation changes and other epigenetic changes with age are a redirection as much as a loss of information. Some of the changes are predictable and programmed, and, indeed, the idea of a methylation clock depends on the regularity of methylation changes. 

For the future

I like Sinclair’s idea for high-throughput screening of rejuvenation molecules. I would add that combinations should be tried after the first step of identifying promising single candidates. The example of OSKM demonstrates that combinations are able to achieve what no single chemical can do alone. In the discussion section, Sinclair indicates that his lab is already doing this, and that the work is guided by an investigation of the mechanisms of action of the chemicals that his screening procedure discovered. 

The present work was done with human fibroblasts (cells from connective tissue) and the discussion section mentions that ongoing work to repeat the test with cells from other tissues. The screening procedure requires the extensive laboratory resources that Sinclair is able to command. 

A consistent theme in my work is that aging is evolved, programmed, and centrally coordinated. I’ve cited diverse evidence for this, based on observation, not theory. As a paradigm to guide anti-aging research, there is an additional reason to research central control rather than cell-autonomous interventions. That is that engineering the rejuvenation of every cell in the body is a daunting task. In contrast, if aging is centrally controlled and communicated via biochemical signaling, we can expect that modification of the signaling environment ought to be far more accessible goal.

There is still confusion about what happened during the Pandemic. The public health response was innovative, defying historic precedent. Was it effective? One reason it is hard to know is that the health records are administered by the same agency (CDC) that managed the pandemic. Death certificates are subjective, and the listed cause of death is prone to error and outright manipulation. Death counts are often reliable, even when cause of death is debatable.

The CDC has recently released death counts from 2021, and they are even higher than 2020. In addition to the deaths attributed to COVID, there were 155,000 excess deaths in 2020-21 among younger Americans. It is a reasonable conjecture that these deaths were related to the circumstances of the pandemic. Below, I lay out the numbers and explore some interpretations.

---

The CDC keeps track of how many people die each year, their ages, the place, and the cause of death. Data is released to the public 18 months after year end through a searchable resource at https://wonder.cdc.gov/. All-cause mortality data for 2021 was recently released, Unexplained high death rates in 2020 continued and accelerated in 2021. On top of the death burden of COVID, there were 155,000 deaths in people under 65. (I focus on people age 15-64, because COVID deaths are relatively rare. Only about one fifth of COVID deaths were under 65.) 

Deaths in this younger age group were up 19% in 2020 compared to the average of the previous five years. In 2021, deaths were even higher, up 30% compared to the same baseline. Most of this is not accounted by COVID. Excluding COVID deaths, there is an excess of 17% in 2020 and 23% in 2021. That’s 155,000 unexplained deaths. COVID deaths in this age group were 220,000 over two years, so the unexplained deaths are two thirds of the official COVID death count. Excluding the oldest group (55-64 years), the unexplained deaths are actually greater than the COVID deaths. 

Word on the Hill is that official counts of COVID deaths were overstated, so the difference becomes more dramatic and the cause more mysterious. These numbers are far outside year-to-year fluctuations (~20,000) or statistical uncertainties. 155,000 Americans is almost three Vietnam Wars. The last time younger Americans were dying at the 2021-22 rate was World War II.

Proportionally, the excess deaths were concentrated in the 25-44 age group. But the highest absolute numbers were in the oldest age group (55-64), as is almost always the case. Excess deaths among males and females were skewed 64:36, but this is actually normal; in this age range, men are dying at a much higher rate than women. 

The biggest wave of excess deaths occurred in August through October of 2021. This was not a period of heavy vaccination and it is off-season for respiratory infections. The second biggest wave was December, 2020 through February, 2021. This was a time when COVID was reaching its second wave, but remember that COVID deaths are excluded in this tabulation. A speculation is that the initial release of the Pfizer vaccine was so toxic that the formula was changed within a few weeks. This hypothesis is supported by VAERS data, which were surging during these same months. 

What caused 155,000 excess deaths?

This count is in addition to deaths coded COVID-19. The increase began before the vaccine rollout, so at least the 2020 portion cannot be blamed on vaccine deaths. An obvious place to look is in unintended consequences of the closures and lockdowns ordered by Federal health officials as an emergency response.

CDC categorizes deaths by cause, (but then doesn’t break this down by age or month). The top ten causes of death from 2019-21 were, in order,

1. Heart disease
2. Cancer
3. COVID-19
4. Accidents
5. Strokes
6. Lung diseases including emphysema and COPD 
7. Alzheimer’s disease
8. Diabetes
9. Chronic liver disease and cirrhosis
10. Kidney failure

Some of these categories increased significantly in 2020 and 2021 compared to 2019 as a baseline. There were 75,000 excess heart attack deaths over the two years. Some of this may be attributed to heart attack patients forgoing emergency care because they feared COVID in the hospitals. Deaths classed as “accident” were up by 28,000 in 2020 and a striking 52,000 in 2021. Deaths listed as “suicide” seem to be intentionally undercounted, but (surprisingly) they did not increase in 2020 or 2021.

Drug overdose deaths might be expected to increase during lockdowns. Drug overdose deaths are not listed as a separate category by CDC, but from this CDC announcement it is possible to estimate 15,000 extra deaths per year in 2020 and 2021. According to CDC policy, some of these may have been listed as “accidents”. 

	2020 XS	2021 XS
1. Heart disease	37,921	36,506
2. Cancer	–	–
3. COVID-19	–	–
4. Accidents	27,915	51,895
5. Strokes	10,259	12,885
6. Lung diseases	–	–
7. Alzheimer disease	12,743	–
8. Diabetes	14,541	15,647
9. Liver disease	7,284	12,227
10. Kidney failure	–	–
Total accounted for	110,663	129,160

Excess deaths in the above table are differences compared to a 2019 baseline. Empty cells connote counts that were negative or not significantly positive.

Interpretation

A classical public health response to a pandemic was advocated by Dr David Katz, by Dr John Ioannidis, by Dr Harvey Risch, by the Great Barrington coalition, and several other prominent epidemiologists in the spring of 2020. Their proposal would have been to isolate and protect the most vulnerable, while allowing the virus to spread among young, healthy people who would almost certainly survive the disease, and would earn the herd immunity needed to end spread of the virus. 

Instead, US public health agencies tried a radically new approach, constricting commerce, shuttering churches, concert halls, restaurants, and cultural institutions, encouraging millions of people to stay in their homes, masking the healthy as well as the sick, discouraging experimentation with early treatments that seemed promising while rushing vaccines through testing into mass distribution.

We cannot know what events might have unfolded had these mainstream public health experts prevailed, but the results do not reflect well on choices made by our CDC and NIH and the White House task force. COVID deaths were high in 2020 despite the restrictions, and twice as high in 2021 despite the vaccines. Consequences of the lockdowns and closures were not limited to the huge economic loss ($14 trillion according to this USC study), but included indirect deaths that were higher in young people than deaths from COVID itself.

Let us encourage our public health officials to learn from these mistakes.

---

After writing this, I was referred by a reader to the work of Denis Rancourt, a Canadian researcher. In this video he does a much more thorough job that I have done, starting with US all-cause mortality data but digging deeper by locale and interpreting through a perspective he has gained from international data.

Data tables

Aspirin is an old staple for life extension. Lately it has received bad press. For those who wish to replace aspirin, there are two actions that must be considered: anti-inflammatory and blood thinning. There are many good anti-inflammatories, all of which act through similar pathways, so that in the end we don’t know have optimal control over increasing inflammation. There are many prescription blood thinners, but no evidence that they increase life expectancy. Nattokinase and lumbrokinase are natural products, in a class by themselves. They show great promise, but there are no good studies.

---

Years ago, I wrote that aspirin and vitamin D were an easy, cheap way to buy a modicum of life extension. I still think that’s true.

It has been 4 years since the last time I wrote about aspirin. I’ve become more suspicious of ulterior motives in the medical literature during these years. Not only do they rig the trials of the drugs that they own and hope to profit from, they also fund research to try to discredit anything out-of-patent that might compete with their newest drugs. Witness the suppression two years ago of hydroxychloroquine and ivermectin, and chronic efforts against vitamin D.

There is an ongoing campaign to discredit daily low-dose aspirin as a generalized preventive, a lot of coverage in major media based on differences that are barely statistically significant.

This study was shut down after 3 years, based on a marginally significant indication of increased mortality in the aspirin arm. It has long been recognized that some studies must be discontinued if, along the way, it becomes overwhelmingly clear that one treatment branch is experiencing harm; but when ethics is invoked in alignment with the interests of pharmaceutical capital, I get suspicious, maybe cynical. Some of the benefits of aspirin become active only after 5 years of accumulated anti-inflammatory effects. Shutting down the trial assured that these benefits would never be reported.

Dual benefit

The benefits of aspirin fall into two categories: As a blood thinner, aspirin prevents heart attacks and strokes. As an anti-inflammatory, aspirin lowers the risk of all the diseases of old age, including AD, CVD, and cancer.

We know a great deal about aspirin because it has been in use since 1897, and daily low-dose aspirin has been prescribed to tens of millions of people since 1980. With 125 years of safety data, whatever risks there are must be very subtle indeed. The primary risk is for ulcers and bleeding of the upper GI tract. It is real, but affects a small percentage of people.

The anti-clotting benefits of aspirin are immediate, but the anti-inflammatory benefits unfold over many years. Short-term studies risk short-changing aspirin.

For those who experience GI bleeding or those who wish to avoid the risk, other NSAIDs are not an improvement. Better to replace aspirin with two separate strategies, one for blood thinning and one for inflammation.

Blood clotting is a balancing act

Blood must flow smoothly through capillaries that are no bigger than the red blood cells themselves. But when the body is injured, blood must be able to clot quickly and reliably to stem the loss of blood. The chemistry of blood clots is delicately balanced and tightly regulated, a marvelously adaptive system that we take for granted.

All heart attacks are exacerbated by blood clotting, and for most, blood clots are the proximate cause. 87% of strokes are ischemic, caused by blood clots, with the remaining 13% hemorrhagic,  caused by the opposite — uncontrolled bleeding in the brain. Thinning the blood can increase the risk of hemorrhagic stroke, while decreasing risk of heart attacks and ischemic strokes.

How does aspirin work

Platelets are the smallest cells in your blood, and there are about 1 for every 20 or 30 red blood cells. When platelets are activated, they clump together to form blood clots. Platelet activation is a self-reinforcing cascade, as activated platelets produce the enzyme thromboxane (or TxA2) which causes more activation, both within the same platelet and nearby. Producing TxA2 requires an intermediate step involving another enzyme, cyclooxygenase (COX2). Aspirin works by binding to COX2, effectively pulling it out of commission. Less COX2 means less TxA2, and less platelet activation.

Other blood thinners

P2Y₁₂ antagonists are a class of prescription drugs that keep activated platelets from clumping together. The glue that binds platelets is adenosine diphosphate, or ADP, and P2Y₁₂ antagonists bind to the same receptors on the surface of platelets where ADP normally goes, so they block the station and displace the ADP glue. Clopidogrel, ticlopidine, ticagrelor, prasugrel, and cangrelor are all P2Y₁₂ antagonists.

Warfarin=Coumadin blocks the effect of vitamin K. Vitamin K, in turn, is a precursor of four enzymes which are necessary for blood clotting, including prothrombin and osteocalcin. Warfarin is the most dangerous of the prescription blood thinners.

Calcium, clotting and vitamin K

Vitamin K is not dangerous. Should you ingest too much vitamin K, it will not be turned into the clot-promoting enzymes.

Calcium is a structural component in bones, and calcium works with magnesium to create the signal that makes muscles contract. There is a dynamic and tightly-regulated steady state between calcium circulating in the blood and calcium bound in bones. Vitamin K tends to move calcium out of blood, into bones, and vitamin D does the opposite, moving calcium out of bones. This is the reason for the recommendation that vitamins D and K be taken together.

Excess calcium in the blood can lead to calcium deposits on artery walls, “hardening of the arteries”. Vitamin K is protective. The relevant form of K is vitamin K₂, which is found in fermented vegetables and full fat yoghurts, and is also produced in the gut by a healthy microbiome.

Factor X is another pro-clotting enzyme that is downstream from vitamin K. A newer class of anti-clotting drugs targets factor X. Eliquis, Xarelto, Savaysa, and Betrixaban all work in this way. Inhibitionof factor X appears to be a potent path to dissolving persistent blood clots (deep vein thrombosis), but lower factor X does not correlate with long-term health benefits.

Nattokinase for preventing and dissolving blood clots

Nattokinase (NK) is an enzyme derived from fermented soy (Japanese natto), which thins blood by a different mechanism than aspirin. It is absorbed well when taken orally. There is reason to believe it does not cause ulcers or increase risk of hemorrhagic stroke. It may be superior in every way to expensive, patented blood thinners like Xarelto and Eliquis, but it is not patentable so drug companies are not motivated to do direct comparison studies (and NIH has not stepped up to fund such studies in their stead).

Nattokinase was misnamed by its discoverer (Hiroyuki Sumi) in 1980. It is not a kinase (energy enzyme) but a protease, an enzyme that breaks up proteins by dissolving peptide bonds. In particular, it works by degrading fibrinogen and serine.

Nattokinase works directly on blood clots, dissolving the fibrin bonds that hold platelets together. It does not work by tipping the scales of the body’s innate system that balances clotting and anti-clotting factors. In this respect, it is different from all the anti-clotting drugs mentioned above. It is short-acting and gets out of the system quickly.

Lumbrokinase, derived from earthworms, is a similar compound, and it has a long tradition in Oriental medicine. Gram for gram, lumbrokinase may be even more potent than nattokinase. Lumbrokinase has been used to treat Lyme disease.

For aspirin and other NSAIDs we have long-term data from millions of people who take the product daily. For nattokinase and lumbrokinase, there have been no such studies. Here is a trial currently underway that might begin to close the gap.

Bonus: For people with long COVID or ongoing damage from the mRNA vaccines, NK has a dual benefit. First, NK can dissolve blood clots, the most common problem associated with the virus’s spike protein. And, second, NK actually binds to the spike protein itself and causes it to degrade. Some percentage of vaccinated individuals retain the mRNA or integrate it into the genome, so that the toxic spike protein is still being produced in the body months after injection. NK can be part of a detox program for those with ongoing COVID vaccine injuries. There’s in vitro evidence that NK can inhibit viral growth, including SARS-CoV-2.

Anti-inflammatory drugs and supplements

There are many good options for dialing down inflammaging, though nothing we know can completely block the increase in systemic inflammation with age.

I’ve written in the past about omega 3 oils, curcumin, berberine, resveratrol, ashwagandha, and boswellia. These are all good alternatives if for any reason you are disinclined to take NSAIDs.

Starchy and sugary diets are pro-inflammatory. Exercise is the best anti-inflammatory of all.

Leafy greens, berries, and mushrooms are anti-inflammatory foods. Green tea, ginger, cloves, and rosemary can also be helpful.

The Bottom Line

As always, we wish we had more data, especially concerning the natural supplements and cheap, out-of-patent drugs for which there is insufficient financial incentive to perform expensive, long-term studies.

We don’t have evidence to support the role of blood thinning drugs as protection against long-term risk of heart disease or stroke, or for decreasing all-cause mortality. Nattokinase may be an exception, but we need better studies to know for sure.

Anti-inflammatories, however, are well-supported by animal studies, by longitudinal studies, and by theory. If we can dampen inflammaging, we can decrease risk of heart disease, stroke, cancer, and AD.

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.

Write-up in The Guardian

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).
• Adding plasma dilution to the protocol of infusions
• 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.

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.

Reality check

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.

It seems too simple to explain much, but according to a study out of Northwestern University, large proteins are more prevalent in young animals compared to old.

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:

1. 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?
2. Are these all proteins? Or are there RNAs or other signals that are essential to the process?
3. How can we determine what is the minimal set of molecular species that needs to be modified? 
4. 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?

There are presently several laboratories working with this paradigm from different angles, for example at Berkeley, Stanford, the Salk Inst, Mt Sinai and Einstein Hospital of New York. 

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.

[1] Programmed aging turns on chronic inflammation and shuts down repair mechanisms with age.

[2] 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 [1] then you’re destined to live longer; but if you score younger according to [2] the chances are your life will be further shortened. 

The way most methylation clocks are derived gives you no indication what mix of [1] and [2] goes into the age algorithm. To complicate the situation further, a strong type [2] 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 [1], and I have worried that the GrimAge clock may have a lot of [2].)

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 [2], 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 [2018]

Abstract
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 Random ization (EWMR) on 420,509 CpG sites to identify CpG sites that are causal to twelve aging-related  traits. 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 [1] but responses to aging [2]. 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 [1] or protection from damage [2].

“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 [1] above, and AdaptAge is [2]. 

“We found that short-term treatment with cigarette smoke condensate in bronchial epithelial 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 [2] (hormetic) response. But according to the above captioned sentence, cigarette smoke has a direct and detrimental effect on methylation, a type [1] 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 [1]? 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.