Lifespan of Harold Katcher’s Rats

Preliminary results from lifespan studies with E5

Harold Katcher has developed a protocol for lab rats using intravenous injection with a blood plasma fraction he calls “E5”. Three years ago, he announced that treated rats evinced many features of rejuvenation, including improvements in grip strength, endurance, and learning capacity. Two years ago, he announced that treated rats also were epigenetically younger, according to a rodent methylation clock developed by Steve Horvath.

This year, with a grant from Heales Foundation, Harold and his partner Akshay Sanghavi have supervised a trial in which older rats were treated with E5 and then allowed to live out their full lifespans so we might know whether epigenetic and phenotypic rejuvenation translate into increased life expectancy. Just this week, I obtained from them birth and death data for the experimental rats. There were 8 control rats, untreated, all dead, and 8 treated rats, 5 dead and 3 still living.


Executive summary of my findings: At any given age, treated rats are 4x less likely to die; but translated into life expectancy, this is less impressive. The rats are living a little longer, but not nearly so much as their methylation age would have predicted. There is good evidence for compressed morbidity — treated rats are healthier later in life, and their deaths are less spread out in time than control rats. Caveats: All rats in the epigenetic experiment were male, while all rats in the lifespan study were female. Also, the protocol was initiated at a later age in the lifespan study compared to the epigenetic study.


Raw data: Time is the rats’ age in days. “Death” =0 indicates that 3 of the rats are still living. Group 1 is control, Group 2 is rats treated with E5.

Time Death Group
1034 1 1
1064 1 1
1069 1 1
1155 1 1
1158 1 1
1159 1 1
1161 1 2
1173 1 2
1179 0 2
1183 1 2
1193 1 2
1197 1 1
1200 1 1
1209 0 2
1209 0 2
1218 1 2

How this data is analyzed

It is conventional and, IMO, also reasonable that the data on age at death are interpreted as a “probability of mortality”. Of course, a greater time period indicates a lower probability of mortality. Less intuitive, the probability of mortality is based on the number of rats that remain alive at any given time, and not on the total number of rats. Thus, when the first rat dies, its probability of mortality is just ⅛, but when the last rat dies, its probability of mortality is 1.0.

If a rat is still living it may contribute to the denominator only for rats who died earlier, and not for rats who died later.

Using these conventions, I produced the following plot for probability of mortality for the two groups. I have plotted probability of mortality on a log scale because it is an empirical fact that probability of death increases exponentially with age. This is called the “Gompertz rule”. If the Gompertz rule holds, then we expect the plot on a log scale to be a straight line. I have drawn the best straight line through the two sets of points.

The Gompertz distribution is characterized by two numbers. One is the base mortality rate, which is related to how early the animals start dying. The other is the mortality rate doubling time. The probability of death doubles again and again over the life of the animals. A short doubling time indicates that the deaths are all bunched together, and a long doubling time indicates that the deaths are spread out over a broader range of ages.

You can see that, compared to controls, the treated rats started dying later and that their mortality doubling time is shorter, with deaths bunched more closely in age.

There is substantial uncertainty in these conclusions because of the small number of rats, but there is enough data here to give us confidence in the basic conclusions:

  • Treated rats are less likely to die young
  • Once they begin dying, treated rats die faster than controls
  • It is unclear from data so far whether maximum lifespan has been increased. We will have a better handle on this question when we see how long the remaining rats live.

One more concern about the experiment: Rats are social. Treated rats were housed separately from control rats, 2 or 3 to a cage. Just like people, rats are more likely to die after their cage mates die. I don’t have information about which rats were housed with which, but the death dates show some signs of being bunched together. This social effect could amplify the difference in mortality patterns between treated and control rats.

Cox proportional hazard

The most conventional way to analyze contingent survival data is called the “Cox proportional hazard model”, a relatively new statistical innovation introduced by David R. Cox in 1972. Many drug treatments and environmental hazards are reported on the basis of Cox models.

Result of the Cox model is reported as a “hazard ratio”, interpreted to mean that “if you do X you will be Y% more (or less) likely to die at any given time.”

The Cox model has the advantage that it is independent of the Gompertz rule or any other assumption about how mortality risk changes over time. It has the disadvantage that it can be misleading if the two different groups have qualitatively different mortality patterns.

The Cox model assumes that the difference between the two groups can be expressed as a simple ratio. If the Gompertz rule holds, a simple ratio translates (using the mortality rate doubling time) into an age change. For example, for humans in modern Western cultures, mortality doubles every 7 years. A Cox ratio of ½ is thus equivalent to rejuvenation by 7 years.

I’ve done the Cox analysis for Katcher’s rats because it is conventional, but my opinion is that its assumptions are not satisfied in this case. The mortality rate doubling time seems to change in the treated rats, indicated by the fact that the slopes of the two lines are different. So interpret the Cox results with this in mind.

Cox analysis indicates that the hazard ratio for treated rats is 0.24, meaning that treated rats are 4x less likely to die. The p value = 0.02, indicating confidence in the conclusion that treated rats are living longer than untreated. Increase in life expectancy is about 7%, which is 85 days for the treated rats. Again, these numbers can change when we see how the remaining 3 rats fare.

Conclusions

I have been committed to the idea that methylation clocks provide a real indication of biological age, and that reduction in methylation age will translate to a longer lifespan. My DataBETA study is premised on this hypothesis. There is good theoretical and indirect experimental support for the idea that epigenetics is a driving force behind aging (last week’s blog).

On their face, these new results suggest the possibility that methylation age might be decoupled from life expectancy. This is worrisome, but there are other possible interpretations of the situation.

We don’t have methylation results for the actual animals in the lifespan study. I’ve heard there was some mixup sending tissue samples to Horvath’s lab for analysis. There are various reasons these animals may not have responded to E5 treatment as well as the previous group.

Katcher’s rats are our best opportunity to answer this urgent question about a causal link between methylation status and lifespan. Fortunately, he is beginning another lifespan study with both male and female rats, which will follow more closely the protocol of the original study, but will extend in time to offer lifespan data. Unfortunately, the composition of E5 is still proprietary, so the minds of other scientists and the resources of other laboratories are not available to study the remarkable effects reported from E5. Wider collaboration is urgently needed to study lifespan and also to optimize dosage, timing, and delivery procedures. A collaboration with Johns Hopkins University has been announced by Katcher’s company (called Yuvan), but we have as yet no details.

Possible theoretical interpretation

I have written in the past about the Achilles heel of methylation clocks. Aging is like a civil war within the body. In youth, all metabolic systems are protective, but with age there are systems that attack and destroy the body. Examples are autoimmunity and inflammation.

Typically, methylation sites (CpG’s) chosen for inclusion in a clock algorithm are correlated with age. There are two possible reasons that an epigenetic change might be correlated with age, depending on which side of the civil war the system is fighting for. A given CpG might be associated with a self-destruction gene, or it might be a protective response to the body sensing higher levels of damage. The training algorithm, based on correlation with chronological age or even with mortality, is generally unable to distinguish between these two possibilities.

I have proposed on theoretical grounds that drivers of aging ought to be more common than responses to damage. Methylation clocks are only useful for evaluating anti-aging interventions to the extent that they are based on genes that drive aging. It’s only through experiments like Katcher’s that we can learn if our methylation clocks have been contaminated with genes that protect from damage.

These preliminary results are a signal of caution and a call for more research, but the evidence is indirect and the results are too thin to change theoretical perspectives now.

Is there a master timekeeper, upstream of the methylation clock?

I have promoted the idea that aging is programmed and that the program is epigenetic. Hence epigenetic age is fundamental. But what is it that imprints epigenetic age on the chromosomes and keeps it updated? Is the “methylation clock” responding to a higher authority, a separate clock which coordinates epigenetic age throughout the body? Do epigenetic clocks in different tissues talk to each other? Such questions are important not just for theoretical understanding, but also because they have two practical consequences. (1) Can we rejuvenate the body with system-wide signaling, or do we have to de-age cell-by-cell?  (2) Can we be confident that if we set back the body’s methylation age the person will feel younger and live longer? 

I have been reading and thinking about these questions for several weeks, and I can report no clear answers.


Is aging a cell-by-cell deterioration, or is it orchestrated at the level of the whole body and managed through signal molecules in the blood? If pressed, I think everyone would have to admit there is some of each, and differences within the community of aging biologists are about the relative importance of the former vs the latter.

One thing I think we ought to be able to agree on is that the system level, including signal molecules in the blood, makes a vastly more accessible target for anti-aging interventions. Repairing the body cell-by-cell is a daunting proposition; whereas modifying levels of signal molecules in the blood is a piece of cake, once we identify those molecules and determine their optimal youthful levels. The words “low-hanging fruit” come to mind, as well as “Pascal’s wager”.

If there are multiple, independent aging clocks, it is probable that the one that registers the oldest age is the one that can kill us, independent of the others. To make the big leaps in life extension that we are looking for, we probably will need to reset all the clocks. How much do the cell-level and system-level clocks talk to each other? How much progress can we expect to make by working at the (more accessible) system level without addressing the (more challenging) cell-level aging?

Why is the preponderance of research devoted to aging at the cellular level? A small part of the explanation comes from scientific inertia; aging was understood in terms of increased cellular entropy for many years, whereas the paradigm of central control remained in a Russian backwater until publication of the Stanford parabiosis experiments in 2005. A larger part of the explanation has been the infusion of venture capital into aging research in the last decade. You can’t patent hormones and you can’t make money from rebalancing blood levels of the body’s native signal molecules. I believe that the profit motive has deeply corrupted aging research, as it corrupted medical research through the previous century.

I am passionate about these issues, but I leave them aside to talk about questions of fundamental interest: Differential gene expression—epigenetics, and methylation in particular— seem able to change the body’s age state. It seems clear that gene expression is the primary way in which the age state of the body is transmitted and coordinated system-wide. But is gene expression the end of the line, the ultimate upstream aging clock? Or is there a “higher authority” that keeps track of time and programs the body’s methylation, etc accordingly? Does the epigenetic state of the body constitute an autonomous time-keeping mechanism, or is there a time-keeping reference clock, perhaps in the hypothalamus, which dictates the body’s age through secretions, and distant cells respond to these secretions by adjusting their methylation patterns?

And, if the answer is that methylation constitutes an independent clock, does that clock advance cell-by-cell independently, or does gene expression at the cell level export proteins that coordinate methylation age across the body?

I don’t have answers, but several experiments bear on these questions, and offer a nuanced outlook.

The practical question

We need measures of biological age in order to efficiently tell us when we are on the right track with an anti-aging intervention. Methylation clocks are presently the best technology we have for measuring biological age. So, can we be confident that if an intervention sets back the methylation clock that the intervention really is making a person (or animal) younger?

Reasons to think yes:

  • Methylation clocks track chronological age better than any other biomarker
  • Some of the difference between methylation age and chronological age is meaningful. It correlates with mortality. In other words, each of the major methylation clocks is a better predictor of life expectancy than chronological age. Remarkably, this is true even for the clock algorithms that were trained only on chronological age.
  • There are theoretical reasons for believing that epigenetics is the primary driver of aging, so that methylation changes may actually be close to the causal nexus of biological age. (This conclusion is especially cogent for theorists like me who believe that aging is an evolutionary program; however, there are also prominent scientists in the field who reject programmed aging but embrace epigenetics as a primary driver of aging.)

Two things that could go wrong:

  • There could be a higher authority, a centralized clock that sets up the methylation state. If this is the case, then setting back the body’s methylation age may be temporary, and the methylation state will revert to the age programmed by a central clock. (Cavadas and Cai have adduced evidence that aging signals are transmitted from the hypothalamus.)
  • In the worst case, methylation changes with age could be an adaptive response when the body senses the accumulation of damage. In other words, the body changes its gene expression when damaged because it is working overtime to repair that damage. In this case, resetting methylation state to a younger age just makes the body less able to cope with the consequences of aging and actually shortens lifespan.

Evidence from parabiosis

In parabiosis, a young mouse is surgically joined to an older mouse of the same genotype. Tissues of the old mouse respond by becoming functionally younger. Since this 19th-century finding was brought to the modern scientific community, the search has been on for chemical factors in the blood that either promote aging or promote youth. [read more].

The parabiosis phenomenon and related findings in rejuvenation through blood plasma transfusions has led to a paradigm that says aging is coordinated throughout the body by signals in the blood. To the extent that single cells age, this is happening under central control, and the process can even be reversed if the cell is exposed to the right signals.

BUT

Evidence from bone marrow transplants

Bone marrow transplants are the most powerful available treatment for leukemia, and are also applied for some rarer diseases. The bone marrow comes with the epigenetic age of the donor, and thus the (white) blood cells subsequently generated by the transplanted bone marrow also carry age information. Several different studies [ref, ref, ref, ref] have found consistently that the white blood cells (and presumably the bone marrow from whence they came) retain the age signature of the donor. The donor may be younger or older than the patient. In either case, the methylation age of the patient’s white blood cells—post-op and for years afterward—remains keyed to the donor and does not correlate significantly with the patient’s age.

The lesson of parabiosis experiments was supposed to be that cell aging is not cell-autonomous, but rather a response to signals in the blood that instruct the cells what age to be. Young somatic cells could be aged rapidly in an old blood plasma environment, and — more impressively — old somatic cells could be made younger in a young environment.

Now we have a series of bone marrow transplant studies where the methylation age of the donor is the determining factor, not the patient into which the marrow was transplanted. Bone marrow contains the stem cells from which blood cells grow. Blood cells turn over every few months and they represent an accessible tissue sample which reflects the age state of the bone marrow in approximately real time.

“We found that the DNAm age of the reconstituted blood was not influenced by the recipient’s age, even 17 years after HSCT, in individuals without relapse of their hematologic disorder.” Soraas et al (2019)

This seems on its face to contradict our paradigm from parabiosis that says cell age is not cell-autonomous, but is programmed by the environment. How can we interpret the two results together? Some possibilities…

  • Maybe only differentiated somatic cells are susceptible to age programming by plasma proteins, and not stem cells.
  • Maybe these stem cells are providing the biochemical environment in the plasma. Maybe the stem cells and the white blood cells that they generate are the agents that secrete the plasma proteins responsible for sending age signals.
  • Please think creatively about other possibilities.

Another result from these bone marrow studies

Consistently, the blood cells get older after transplant, whether they are transplanted from young-to-old or from old-to-young. This says two things. First, the point of comparison is the donor age, i.e., the age of the cells pre-transplant, and not the age of the patient who is associated with the systemic environment. Second, the cells seem to age rapidly after transplant, as measured by the methylation age. From there, the age of the cells may (in some studies) revert slowly to their original age trajectory over a period of several years.

Why the rapid methylation aging? It seems like a good guess that the rapid aging initially comes from high rates of reproduction in these transplanted cells that are generating a whole new source of much-needed blood. Could this be a link between telomeres and methylation age (which previously were found to be inversely correlated? Or is there another mechanism by which stem cells keep track of the number of times they have divided asymmetrically?

Am I the only one asking these questions?

Already a decade ago I was thinking about the question How Does the Body Know How Old It Is? Questions about time-keeping mechanisms and coordination of age information through the body go hand-in-hand with conceptions of aging as a programmed phenomenon, and perhaps the prejudice against programmed aging helps to explain the fact that few aging researchers are thinking in this way. A welcome exception is this article by Argentine gerontologists, which I was delighted to discover just yesterday. Lehmann et al:  Hierarchical Model for the Control of Epigenetic Aging.

Although there is evidence suggesting that the cellular epigenetic clock possesses an intrinsic ticking rate [ref, ref, ref] multiple observations at organismal level in humans and other mammals lead to the inference that in vivo, the ticking rate of the clock in tissues is synchronized by a master pacemaker.

Lehmann cites as prima facie evidence for this

For a given chronological age, it was found that in DNA samples taken from whole blood, peripheral blood mononuclear cells, buccal epithelium, colon, adipose, liver, lung, saliva, and uterine cervix, Horvath’s algorithm read essentially the same epigenetic age, the only exceptions being some brain regions and very few other organs.

In addition, she cites Katcher’s success in rejuvenating rats (and their diverse organs) using only a set of intravenous signals. The article goes on to propose a model in which there are four time-keepers in the body, coordinated by signals in the circulatory systems. The four are:

  1. Methylation
  2. Light-sensing and neural processing
  3. Neuroendocrine signaling (esp the suprachiasmatic Nucleus of the Hypothalamus)
  4. The Immune system, including thymic involution

Curiously, she does not include the replication counter implicit in telomere shortening, which Fossel, Blasco and other luminaries have adduced as the primary source of aging. I also would add that the hypothalamus is the best candidate we have, not just for one aging clock among several, but as a central, coordinating organ.

Fig. 1. Proposed organismal regulatory network in mammals. The diagram includes the autonomic nervous system (ANS, acting via neurotransmitters), the neuroendocrine system (NES, acting via blood-borne hormones), the immune system (acting via blood-borne cytokines and thymic hormones), the circadian clocks (acting via blood-borne hormones and neurotransmitters) and a putative pathway connecting the neuroendocrine network to the DNAm clock in organs and cells. All networks act on peripheral organs. Inset- Bidirectional interactions among all networks including (in red) the hypothetical DNAm network.

 

In addition to Lehmann, there is a 2021 review by Raj and Horvath, speculation from the horse’s mouth. They note that all the Horvath clocks are based on small differences in % of cells methylated at a given site (conventionally notated as β).

Increase in epigenetic age is contributed by changes of methylation profiles in a very small percent of cells in a population.

One way to interpret this fact (my speculation, not R&H) is that immune sensitivity, (anti-) oxidation, and inflammation are all under tight homeostasis in the body, because these are sensitive functions, balanced on a knife edge between insufficient protection and self-destruction. It is easy to tip the balance over toward self-destruction with small changes in the set point for a few signal molecules in blood plasma.

Another way to interpret this (again, my speculation) is that it is only a handful of cells at the tail end of the distribution that go over an edge into a state where they cause all the damage of aging. This hypothesis is consonant with the story about short telomeres, cell senescence, SASP, and the powerful benefit of senolytics. However, a big hole in the narrative is that it requires a set of CpG’s that would be capable of precipitously tipping the cell over into a toxic state. We know that critically short telomeres can do this, but there is no study yet of methylation-induced cell senescence. R&H speculate about such a mechanism connected with PCR=Polychrome Repressive Complex.

Raj and Horvath also stress the continuity between epigenetic changes that begin in utero, associated with development, and the changes that lead to senescence late in life. Blagosklonny as well has emphasized this point.

“Collectively, these five features of DNA methylation allow one to summarize with some degree of certainty that epigenetic ageing is a measure of change of epigenetic heterogeneity, contributed by a relatively small percentage of cells, seemingly in line with developmental processes that are conserved across species and begins very soon after conception. This seemingly inescapable deduction provides us with a reference point against which models and hypotheses can be measured.”

If I may carry the logic of these two experts one step further, I would emphasize the role that methylation has in determining what hormones and enzymes are secreted into the blood. Therein lies the possibility that intracell methylation clocks are coordinating, both with other cells and with other clocks, via signal molecules in blood plasma.

Other provocative findings that we might hope to integrate into a theory of aging

The methylomes of naked mole rats age at a normal rate, but the phenotypes of the rats themselves show no signs of age [ref]. Males and females age epigenetically in somewhat different ways [ref].  Methyl donor molecules in the diet can lead to a younger methylome, with benefits both for hyper- and hyomethylated regions (validated for MTHFR snps only) [ref]. When human fibroblasts are reprogrammed (with RNA) to turn them into neurons, they remember their Horvath age even after forgetting their identity [ref]. BMI is associated with accelerated methylation aging [ref]. Mice challenged with a high-fat diet can be brought back to normal weight with a normal diet, but accelerated methylation aging persists [ref]. Cessation of smoking decreases Hannum and Horvath DNAmAge [href]. The methylation shadow cast by years of smoking is a better predictor of subsequent morbidity and mortality than the smoking history itself [ref]. Methylation image of telomere length is a better prediction of age and mortality than is telomere length itself [ref]. Pregnancy increases Hannum Age, DNAmAge, and PhenoAge [ref].
(Apologies to Rafil Kroll-Zaiti)

Bulletin

Katcher has been conducting a longevity trial for rats treated with E5 (background story here). Partial results suggest that treated rats are living statistically longer than untreated, but not as much as you would expect if the greatly reduced methylation age indicated full rejuvenation. The results are preliminary, and I will publish a full analysis in this space as soon as I can get the detailed dataset.

The finding, if validated, suggests that multiple clocks in the body are not completely synchronized, and the “fastest clock wins”, meaning that it kills the animal no matter what the other clocks may say.

Conclusions

I am disappointed as you are in not being able to provide fundamental answers, but I hope that (together with Lehmann, Goya, Raj, and Horvath) we have provided a framework and a set of questions that can guide fundamental research. Very few other researchers are addressing these questions, and the answers will be crucial both for devising effective interventions and also for measuring the effectiveness of interventions that we already have.

I recommend this book for every life extensionist

Surviving Death: a journalist investigates evidence for an afterlife
by Leslie Kean

 

Readers of this blog are interested in life extension. We relish the experience of being alive, and we struggle with dread of death, and there is diversity among us how much relish and how much dread we harbor.

We believe in the methodology of science, and we look to biological science for solutions that will preserve our bodies from the ravages of age. Most of us subscribe to the scientific consensus that our bodies support our experience, our brains engender our consciousness, and without our brains, we would be nothing.

How do we respond when we are presented with scientific evidence that the brain is not the source of consciousness; that experience can exist in the absence of neural activity; that death of the body will change but will not necessarily end the experience that we relish?

If we believe in Science with a capital S, if we have faith in the community of scientists and the conclusions in which a great majority of scientists concur, we say, “This is not worth my time. I know it must be wrong. I’m not going to think about it.”

If, on the other hand, we believe in science with a small s—the scientific method, the gathering of evidence and the testing of hypotheses against all the available evidence—then we read Leslie Kean’s book, and our mouths hang agape, and we wonder how we can ever reconcile what she reports with the picture of the world that has served us so well all our lives.

The material in this book is so radical that if we accept that even some portion of it is reliable, and if we are honest and courageous enough to explore the consequences, then we must rethink our relationship to life extension and then begin to overhaul our relationship to life.

Reincarnation

Kean begins with a story that is stunning enough in its own right, but previously well-established by other researchers. Carol Bowman first interviewed the Leininger family and documented the story of their son, who called himself “James the Third” because he had previously lived the life of James Houston, Jr, a World War II fighter pilot who was shot down over Japan in the Battle of Iwo Jima (1945). As a three-year-old (in 2001), James the Third recognized and name parts of the plane that Houston piloted and the aircraft carrier from which he was deployed. From a period photo, he was able to identify by name other members of Houston’s flight crew as well as his two sisters.

The Leininger case is particularly compelling because it is well-confirmed and includes dramatic detail. But in other respects, it is representative of thousands of stories that have been collected at Univ of Virginia. Most of them involve a sudden, violent death in a previous life, leaving a lingering sense of incompleteness. Frequently, children have knowledge of details from their past lives, and occasionally, children will speak in languages that they were not exposed to in their present reincarnation. It’s called xenoglossy

It is natural to take thees stories as support for a traditional (Buddhist or Hindu) account of reincarnation. In that narrative, each of us is an immortal soul, and we evolve through a series of excursions when we assume physical form for the purpose of education via broadened experience. For the most part, we forget our past during each incarnation, but sometimes memories leak through the veil. Leinginer’s story validates part of this, but is subject to other interpretations as well. Memories may be transferred without any continuity of personality across incarnations. Children may spontaneously experience remote viewing or clairvoyance. If reincarnation is a thing, it may be rare or common, and not necessary universal. The story cracks open our dogmatic commitment to a materialistic perspective, but it does not compel a particular alternative.

Can such stories be consistent with the “conservative” view that consciousness is generated by the brain, with all knowledge and experience completely dependent on the physical brain? Only if we postulate new physics that transmits information, not attenuated by time or space, and that our nerves are evolved to take advantage of this yet-to-be-discovered effect. In my mind, this is more of a stretch than simply to adopt William James’s view that the brain is a transducer, not a generator of consciousness.

Near death Experiences

For Kean’s book, reincarnation is just an opener, and as her accounts stretch the limits of our reality to the breaking point, her voice becomes increasingly familiar and convincing. In the last chapters, she relates accounts in the first person, and, fantastic though they are, we find it hard to dismiss her because she has used 300 pages to earn our trust.

Near death experiences are another well-plowed regime for anyone who is open to reading the literature. People in extremis have memories of experience that took place while their EEG plots (electrical activity in the brain) were flatlined and they were technically dead. These often include tunnels with a white light at the end, meetings with deceased relatives, and spirit guides. On the one hand, the cases are more specific in what they can tell us about what it’s like to be dead. On the other hand, they are easier to dismiss as illusions or false memories or hallucinations of an oxygen-starved brain. Kean reminds us of the occasional cases where people with brains that are technically dead remember details of their resuscitation, the doctors or nurses in their hospital room. More occasionally, people report visiting distant relatives during this time. And there is just one case where a woman on an operating table experienced floating up from her body and seeing a sneaker on the roof which could not be seen from any point inside the hospital or from the ground. Her description of the sneaker was later verified.

Disciplining herself to remain objective, Kean acknowledges that reports from people who have had NDEs (and actual deathbed experiences) cannot prove that consciousness outlives the body. But she notes a general similarity between what NDEers report and the accounts of children when they talk about the time between incarnations.

Communicating with the dead

To appreciate mediumship, Kean opines, you have to be there. She incorporates a chapter by a credentialed researcher about rigorously controlled studies, but only after she relates in detail the experiences she had contacting her departed brother and another lost friend through three separate mediums. Some 80% to 90% of the details they report are accurate, including names and recall of specific conversations. But (says Kean), this can’t begin to convey the emotional intimacy of feeling a departed person’s personality coming through in the style and language of the communication. For each of the two deceased persons, Kean reports personal details known only to the deceased and herself, which the medium accurately references.

The medium who makes sceptics pant and tremble like nervous horses |  Europaranormal

Mrs. Piper, 1857-1950

Is this evidence that the medium is in touch with a still-existing spirit of the deceased? Kean and her academic expert both admit that this is a difficult question. If the information is known to the sitter, then the medium could have obtained it through telepathy with the living (and if it is not known to the sitter, how can it be verified?) But mediums themselves report that the way information comes to them feels very different from telepathy, and EEGs of the same person doing psychic readings and mediumship seem to corroborate this.

Finally, Kean reports details of the compelling story of a man whose great uncle died on a battlefield of the Great War contacted him through a medium forty years later and related the exact coordinates of the unmarked grave site in which he was buried.

Physical appearance of the dead in seances

For some reason, it is easier for me personally to accept non-physical transfer of information than to believe in the physical incarnation of ghosts or specters. But by this point in the book, Kean has established herself as such a credible witness that these fantastical tales of her personal experience leave me baffled and perplexed.

The science that we understand gives us the technology for transportation and communication, for comfort and convenience. But the science that we don’t understand imparts to us a sense of awe and wonder, and motivation to continue our investigations in new and creative ways.

I am less concerned than Kean and her experts with distinguishing between explanations from telepathy and from post-mortem survival for the phenomena they describe. The big message for me is one of non-local mind. Once it has been established that mind has an existence that cannot be explained by functions of the brain—that, indeed, a part of the mind’s awareness appears to be untethered to any spatial location—for me, there is no longer any reason to suppose that the mind dies with the brain.

I carry with me from early childhood the memory of repeating the phrase, “I am Josh” and savoring an intuitive conviction of its absurdity. A part of me that was deeper than experience seemed to know that “I” am an abstract observer of this physical universe, and not a piece of matter within it. Today, this is just an intellectual curiosity, as I have long ago lost the cosmic expansiveness of the child’s experience.

I plan to continue pursuing life extension as a celebration of life rather than the hopeful forestalling of a dread event. And the sense of mystery and wonder that these anomalous phenomena provide continues to enhance the time I have on earth.

Anne Jeffreys, Glamorous Ghost of '50s TV, Is Dead at 94 - The New York  Times

Topper

GlyNAC improves biomarkers in humans and extends lifespan in rodents

Antioxidants proved a bust for life extension almost 25 years ago, but glutathione stands out as an exception. We lose glutathione as we age, and supplementing to increase glutathione levels has multiple benefits, possibly on lifespan.

Glutathione is manufactured in the body via an ancient mechanism taking as input cysteine, glutamic acid, and glycine. Supplementing N-Acetyl Cysteine (NAC) and glycine are independently associated with health benefits, and possibly increased lifespan. Glutamine seems to be in adequate supply for most of us.

Each cell manufactures its own glutathione. (GSH is an abbreviation for the reduced form of glutathione.) Concentrations of GSH within a cell a typically 1,000-fold higher than in blood plasma. When we look for glutathione deficiency, we measure the blood level, because that is convenient. It is much harder to measure intracellular levels of GSH. These two studies [20112013] demonstrated that intracellular levels decline with age more consistently and more severely than blood levels. People in their 70s have less than ¼ the glutathione (in red blood cells) that they had when they were in their 20s. The same study also found that intracellular levels of cysteine and glycine but not glutamate decline with age.

Supplementing with NAC is already known to boost glutathione levels. But here is a motivation to try a combination of glycine and NAC, dubbed “GlyNAC” to see if we can do even better. This work has been spearheaded by Rajagopal Sekhar.

In humans, “Supplementing GlyNAC for a short duration of 2 wk corrected the intracellular deficiency of glycine and cysteine, restored intracellular GSH synthesis, corrected intracellular GSH deficiency, lowered OxS, improved MFO, and lowered insulin resistance.” [Sekhar] Most of these benefits are theoretical. Lowering oxidation levels is a double-edged sword. MFO=mitochondrial fatty acid oxidation, and this benefit is on firmer footing. Membranes are made of fatty acids, and mitochondrial efficiency, like most everything in the body, depends on highly selective membranes. The crowning benefit is improved insulin sensitivity, and we can be fairly confident this leads to longer healthspan.

The two recent studies, in humans and mice, are indeed impressive.

The small human study found that “GlyNAC supplementation for 24 weeks in OA corrected RBC-GSH deficiency, OxS, and mitochondrial dysfunction; and improved inflammation, endothelial dysfunction, insulin-resistance, genomic-damage, cognition, strength, gait-speed, and exercise capacity; and lowered body-fat and waist-circumference.” Though they didn’t measure methylation age, this constellation of improvements gives us confidence that people were looking and acting younger.

In older (71-80 yo) subjects 24 weeks of GlyNAC supplementation raised intracellular GSH levels from 0.4 mmol to 1.2, compared to 1.8 in young adults. (Levels were measured in red blood cells.)

Two central players in aging are inflammation and insulin resistance; both showed excellent response.

Inflammation decreased markedly: Average C-reactive protein (CRP) dropped from 4.9 to 3.2 (compared to 2.4 for young people). IL-6 dropped from 4.8 to 1.1 (ref 0.5 for young). TNFα dropped from 98 to 59 (ref 45).

Insulin resistance fell just as dramatically, along with fasting glucose and plasma insulin.

Cognitive performance improved markedly! as did grip strength, endurance, and gait speed.

GlyNAC subjects lost a lot of weight — 9% of body weight in 24 weeks. This is both very good news and a hint that some of the benefits of GlyNAC may be caloric restriction mimetic effects, indirectly due to suppression of appetite or of food absorption.

Is all this evidence of a decrease in biological age?

But the effects faded weeks after the treatment stopped. This, I believe, is different from resetting methylation age. There is not a lot of data yet to test this, but I believe that methylation is close to the source of aging; in other words, the body senses its age by its epigenetic state, and adjusts repair and protection levels accordingly. Thus changing epigenetics to a younger state, IMO, effectively induces an age change in the body.

If this is correct, then my guess is that GlyNAC does not set back methylation age, based on the fact that the effects must be continually renewed by daily doses of glycine and NAC. On the other hand, mitochondria are such a central player in expressing multiple symptoms of aging that it may well be that continuous treatment with GlyNAC leads to longer lifespan.

…and indeed that is what was just reported in a mouse study. 16 mice lived 24% longer with GlyNAC supplementation, compared to 16 controls. 24% is impressive (see table below). For example, rapamycin made headlines a decade ago with an average lifespan increase of 14%. (In other studies, rapamycin was associated with even greater life extension.) The winner in this table is a Russian pineal peptide, which claims 31% increase in lifespan. I have previously bemoaned the fact that this eye-popping work from the St Petersburg laboratories of Anisimov and Khavinson has not been replicated in the West (though Russian peptides are now commercialized in he West). 

Table source: https://genomics.senescence.info/drugs/browse.php#details-549
(This is a sample — not a complete list.)

Treatment Lifespan increase
Epithalamin 31%
Thymus Peptide 28%
Rapamycin 26%
N-Acetylcysteine 24%
GlyNAC 2022 24%
Spermidine 24%
Acarbose 22%
Phenformin 21%
Ethoxyquin 20%
Vanadyl sulfate 12%
Aspirin 8%

An asterisk must be placed next to the new 24% life extension from GlyNAC. Eleven years ago, Flurkey, found the same 24% life extension with NAC alone. NAC supplementation without glycine is known to increase glutathione production. Do we need glycine in addition, or is cysteine the bottleneck? Levels of both free glycine and cysteine decline with age. This would suggest that supplementation of both should be more effective than supplementing NAC alone. But I was unable to find any study that asked whether GSH levels are raised to a greater extent by GlyNAC than by NAC alone.

Glycine supplementation in large amounts mimics methionine restriction, which is a known but impractical life extension strategy.

If you decide to take glycine, it should be at bedtime, and in large amounts, a teaspoon or two. (I did this for awhile using glycine as a sweetener in hot chocolate soymilk, until I decided it ruined the taste of the chocolate drink. Whether this is a sound reason for tailoring an anti-aging agenda I’ll leave you to decide.)

All this work comes out of the laboratory of Rajagopal V. Sekhar at Baylor College of Medicine in Texas. It’s time that a broader life extension community joined in the action. I’m grateful to Dr Sekhar for commenting on earlier drafts of this article.

Can we trust methylation clocks?

A methylation clock is an empirical construct. There is no understanding of physiology or metabolism built into the process. The clock is engineered to do the best job predicting (in the case of the GRIM-Age clock, for example) future mortality and morbidity based on methylation patterns. The whole process is agnostic about biological mechanism.

It is a legitimate question whether a drug or diet that sets back the methylation clock has actually increased life expectancy. Maybe methylation is a downstream consequence of aging, like grey hair or wrinkled skin. We would hardly expect a skin cream or hair dye to increase life expectancy.

For me, personally, this is an easy question. I have devoted much of my professional career since 1996 to opposing the “selfish gene” version of evolution and promoting multilevel selection. I have collected evidence that aging is a systemic phenomenon, centrally controlled, and that epigenetics (including methylation) is the primary way in which aging is enforced on the body. I was poised to believe that methylation clocks measure something real and important even before the first clocks appeared [2013].

Many other scientists looking at anti-aging interventions have been happy to take a practical approach, not invoking theory at all, but accepting the impressive correlations of aging clocks with other measures of biological age as good enough reason to trust that any intervention able to sett back the methylation clocks is probably setting back biological age.

Morgan Levine is a biostatistician par excellence. As a post-doc working with Steve Horvath, she developed what I consider to be the best, most usefull methylation clock..With her own research group at Yale, she has continued to innovate, with a promising approach based on the mathematics of principal component analysis [my write-up last September]

In a recent preprint, Morgan Levine has deeply questioned whether methylation clocks can be trusted in the way that so many of us have trusted them. Although young and just at the beginning of her career, she has done more than anyone except Horvath himself to advance methylation clock technology. For her to question the foundational value of her own work is a gesture of courage and deep intellectual honesty.

Before the post-doctoral work with Horvath that created the PhenoAge clock, Levine studied evolutionary biology of aging with the incomparable authority, Caleb Finch. She has her own ideas about the evolutionary origins of aging, and they are rooted in classical evolutionary theory. She sees the cause of aging as somatic evolution and accumulation of damage. She is deeply influenced by Peter Medawar’s [1952] hypothesis that what happens late in life is outside the influence of natural selection.

And so she raises the deep question: how much of epigenetic change associated with age is a driver of aging, and how much is a response to the body’s increasingly damaged state?

“Though the connection between risk and time may appear probabilistic on the surface, the emerging pathology is rooted in the molecular and cellular remodeling of the organismal system over its lifetime. Such changes likely result from accumulated damage, selection pressures at the level of cells, compensatory mechanisms, and/or the unintended consequences of a biological program. However, alterations to a complex system must abide by a hierarchical structure2, initiating at lower levels of biological organization (e.g. molecules) prior to manifesting at the higher levels in which they are typically observed (e.g. tissue and organ dysregulation and failure, and eventually death)3. Thus, to delay, prevent, or even reverse the maladies currently awaiting us in late life, we must discover how to decipher and remodel the molecular fingerprint of aging.”

Here, Levine is raising the most basic questions about the origin and meaning of methylation changes with age. Levine proceeds from her strongest skill—She is master of a wide array of sophisticated statistical tools.

Part of the classification has to do with Yamanaka programming, which is about stem cell vs differentiated cell. Another part comes from Framingham Heart study patients, and which CpGs change with age in FHS subjects. She distinguishes sites that increase methylation with age from others that decrease methylation with age. She associates some CpGs with cancer.

She maps Shores, Islands, and Open Seas. CpG islands are promoter regions of the genome that have lots of CpGs in close proximity. Shores are regions on the boundary of CpG islands, and Open Seas are regions in which CpGs exist as isolated, disconnected units.

Having divided 4779 CpGs into 12 groups, she can ask, How much of each group is represented in each of the most commonly used methylation clocks?

And which modules are performing best and most consistently across clocks?

Conclusions

Levine entertains the idea of “epigenetic drift” as part of the story, however she recognizes that the changes that underpin the most reliable clocks are not “drift” but clearly directional. She asks, to what extent do methylation changes cause aging and to what extent are they responses to various, incidental results of the aging process?

“If DNAm changes were purely reflecting entropic alterations or epigenetic drift, we would expect to see a bias against changes in CpGs that start around 0.5 (corresponding to random chance of methylation at a given site) . However, what we observe is actually a regression away from the mean, in which these heterogeneous populations of cells are systematically losing DNAm with time. This suggests that the green-yellow module’s notable pattern of epigenetic aging is unlikely to stem from noise or aberrant DNAm changes with age. Instead, DNAm changes may reflect cellular selection pressure or clonal expansion in which the cells without DNAm at these CpGs are able to outcompete (proliferate more than) the ones with DNAm . Alternatively, it could reflect a regulated compensatory mechanism that gets initiated with aging, or a continuation of a developmental program that is not turned-off . These scenarios have different implications for our understanding of epigenetic changes. The first would suggest that individual cells are not changing DNAm patterns with age, but rather the changes that are observed in bulk data are happening at the level of cell populations, shifting prevalence of cells with heterogenous states. The second and third scenarios, on the other hand, would suggest within cell DNAm changes, perhaps as a response to extracellular environment or signaling changes with aging (e.g. integrated stress response (ISR) ), or as an extended developmental program that fails to be extinguished—somewhat aligned with the hyper-function theory of aging . In moving forward, single-cell DNAm data may help distinguish individual vs. population changes.”

Here she references “Integrated Stress Response” as a theory of the aging metabolism. She also refers to Mikhail Blagosklonny’s idea that developmental programs have a momentum that spills over into aging phenotypes.

Morgan Levine is a brilliant scientist, facing the harshest possible self-criticism of her work. Her conclusions are tentative and open-ended.

A more definitive, empirical approach

The big, interesting question may not require theoretical analysis. What we want to know is whether we have been justified in using methylation clocks to indicate whether aging has been slowed or reversed. The most direct answer to that question comes from Harold Katcher’s rats, — the most successful example of setting back methylation age in a whole animal. Currently, Katcher has two sets of 8 rats that are the same chronological age; one group has been treated with E5 and has a much lower methylation age. He is waiting to see how long each group lives. So far, 3 of the untreated rats have died, and 1 of the 8 treated rats. Of course, the treated rats look and act much younger, and have physiological characteristics of younger rats. Over the coming months, the survival test will produce an answer to the important question whether a younger methylation age implies a younger biological age, in a form that is independent of theory.

Love and Longevity

One reason I love this topic is that I have centered my academic career around the thesis that aging is a socially-evolved phenomenon. Another is that it’s even more fun than intermittent fasting.


There has been a great deal of prejudice against this topic since the most popular evolutionary theory of aging [1957] hypothesized (with scant evidence) a tradeoff between reproduction and longevity. Religious taboos probably have played a role as well. In insects, there is some evidence that sexual activity is associated with shorter lifespan. But we’re not insects, and the data for humans and other mammals supports a robust role for sexual activity as a promoter of longevity. (I last wrote about this topic in 2018.)

This is the classic longitudinal study by Davey Smith [1997] which, to my knowledge, has not been replicated since.  In males age 45 to 70+,“mortality risk was 50% lower in the group with high orgasmic frequency than in the group with low orgasmic frequency,” Twenty-five years on, there is no excuse for a corresponding study not having been performed for women, but there is indirect evidence that women, too, live longer if they have more intimacy and sex in their lives.

Enjoyment of intercourse was in the three top predictors of longevity in women [1982]. Women have been found to be more sensitive to the quality of loving attention and the depth of their connections in love, while men tend to respond to the cruder quantitative variable of sexual activity [ref]. Women (>57yo) who reported sexual relations that were highly satisfying had higher risk of cardiovascular disease, but women who reported most intense pleasure from sex had lower risk. “These findings challenge the assumption that sex brings uniform health benefits to everyone.”

Frequency of sexual activity is associated with later menopause in women, and later menopause is associated with longer life.

I’ve been an advocate of the theory that aging is programmed by signal molecules in the blood. Only a few of these have been positively identified, and among these, the hormone oxytocin is probably best established as a longevity factor [thanks to the Conboy lab]. Oxytocin is expressed in experiences of intimacy, and also in childbirth. It’s not much of a stretch to guess that oxytocin is responsible for some of the health benefits of sex and intimacy.

Hugging and cuddling decrease levels of cortisol. Cortisol levels increase with age, and higher cortisol levels are associated with poor health in the elderly.

It’s no surprise that competent erectile function is associated with less depression and higher quality of life in elderly men, but someone had to do the study. As you can imagine, it is difficult to establish a direction of causality. “For the time being, it cannot yet be proved that “good sex promotes good health” since good health also favors good sex.” [Gianotten 2021]

Sexual activity contributes to better sleep in both men and women, and good sleep is an important longevity factor. Frequent sex stimulates the innate immune system, our first line of defense.


Long before modern methods and modern taboos

…there was an ancient literature associating sex with longevity.

In 1973, a Chinese tomb was excavated that had not been opened since 168 BC. Among other gems there in was the MaWangDui scroll. Harper translated and wrote about this scroll a few years later.

“The Mawangdui medical manuscripts bear the distinctive tendency of combining Daoist beliefs with medical knowledge. Some texts stress sexual intercourse as resembling the union of yin and yang. Others discuss sex’s benefits for physical well-being.” Although this, like all scholarly works of the era, was written from a male perspective, “the manuscripts emphasize the importance of women’s pleasure”. [ref]

The ms includes a poem which has been interpreted as a coded instruction manual for mystical sexual union.

There is a long Daoist tradition of sex ritual practiced to enhance health and longevity. There is also a prudish Confucian tradition which is scandalized, and seeks to suppress Daoist sexual practices. Consider this passage, already a thousand years into Daoism:

A man must not engage in sexual intercourse merely to satisfy his lust. He must strive to control his sexual desire so as to be able to nurture his vital essence. He must not force his body to sexual extravagance in order to enjoy carnal pleasure, giving free rein to his passion. On the contrary, a man must think of how the act will benefit his health and thus keep himself free from disease. This is a subtle secret of the art of the bed-chamber.

— Sun Si-miao, 7th Century Chinese scholar and medical researcher

Is it a pure rendering of the Daoist tradition, or is it tainted by defensiveness in the face of withering Confucian pressure?

“In the I Ching, the hexagram that symbolizes sexual union is the 63rd, called ‘Completion.’ It consists of the trigram for ‘water/woman/clouds’ placed over the trigram for ‘fire/man/light.’ This not only places Yin above Yang, it also suggests the image of water slowly coming to a boil over a fire. This is the quintessential Daoist image for human sexual intercourse, concisely symbolizing the essential differences between man and women in the sexual act. In order to last long enough to bring that cauldron of water to a rolling boil, the man must ration his fuel and carefully control his fire. If he burns his fuel too fast, his fire expires prematurely, leaving the water only lukewarm. But if he conserves his fire long enough to bring the water to a boil, then even the smallest flame suffices to keep it hot for a long time. — Daniel Reid

The yarrow tells of great good fortune now
(If not apparent yet, then very soon).
You’ve brashly prayed to God for Sun and Moon;
They’re granted! for your wish conforms with Dao.
You did not take the blame when things went wrong;
You must not gloat now that your luck has turned
No matter what you do, you won’t be burned,
But gains will be much greater if you’re “strong”—
Which just means “humble”—both connote the same.
To recognize that all depends on you,
Yet curb your will, avoid the urge to do,
Dissolve the Self, let intuition through,
Release control and laugh, forget your name—
No pride, no virtue, no judgment, no shame.

— poem by JJM, from the I Ching Sonnet Project

Denis Noble, a medical professor at Oxford University, has a recent article describing an ancient Oriental literature of sex and health, and placing them in a modern, scientific context. (Noble has been an articulate advocate for expanding the Darwinian tradition beyond the Selfish Gene.)  He cites studies of telomerase activity related to physical intimacy and touts the powerful rejuvenation effects of spermidine on mitochondriaautophagy, and other aspects of aging.

Leslie Kenny is an Oxford medical researcher who is familiar with the ancient Daois traditions around sex and longevity. She

“wondered aloud whether the reason for arousal but non-ejaculation was so that the man would resorb his own spermidine and thereby benefit from a boost in cellular autophagy and the resulting beneficial biological effects. I too had wondered about the possible benefits of resorbing sperm to male health.”

Noble speculates about the traditional exchange of saliva in some of these ancient Chinese texts. Saliva contains exosomes, virus-like packets of DNA and proteins that transmit information both within the body and between individuals. The Chinese scrolls emphasize

slow and gentle movements, beginning with caressing of what seem to be the mysterious energy meridians within the body…Breath, gaze and heartrate between lovers become synchronised during foreplay until actual coitus occurs, but it would be harmful for the man to consummate the love act at this point. The text of Su Nu suggests that consummation should occur only 3 times out of 10, and only with a woman when wishing to conceive a child. All other uses of a man’s precious bodily fluids – in this case, semen – would be viewed as exhausting the man’s body, ageing it prematurely. Whereas a woman and her yin energy were greatly strengthened by reaching climax, this was to be avoided at all costs by the man, whose yang energy would be robbed.

Noble closes:

“As the 20th-century French sinologist Marcel Granet put it, sex for the ancient Chinese was ‘far more sacred than for us’. It can be so once again for us too.”

Omicron Origins—Omicron Optimism

The genome of Omicron has taken the community of public health scientists by surprise. Not only are there a large number of mutations, but some of these mutations have not been observed in the many previous genome analyses, thousands of which are being conducted in labs around the world. Among scientists, there are five competing explanations for this situation.

  1. The virus circulated and mutated in a region of the world where there are few scientific labs that might have reported its genome in intermediate states.
  2. A single immune-compromised patient might have harbored the virus for an extended period of “long COVID”, during which the virus mutated while replicating within him.
  3. The virus might have jumped to a mouse host and spread from mouse to mouse, in an environment where different mutations would be favored. The heavily mutated virus must then have jumped back to humans.
  4. The virus leaked from, or was released from, a laboratory in Durban, where experimenters were genetically manipulating the virus.
  5. Vaccinated populations have put intense selection pressure on the virus to evade the vaccine by mutating its spike protein, which is the only part of the virus to which vaccinated individuals have immunity.

As with everything COVID, we’ve seen significant censorship, both in the mainstream press and the medical journals. Three of these theories have been discussed out in the open. But #4 has been relegated to the fringes because scientists are still gunshy about discussing engineered bioweapons; and #5 has similarly been sidelined because it is politically incorrect to say anything bad about vaccines. The irony here is that evolution in vaccinated populations may have led to the emergence of a version of COVID that everyone can live with (see below).

First theory: Omicron was hiding out in darkest Africa

Christian Drosten, a virologist at Charité University Hospital in Berlin, has proposed that the virus evolved its prodigious ability to spread rapidly while hiding out in regions of Botswana and SW Africa. This region of the world has few virology laboratories that would have reported intermediate versions of the virus. In both Botswana and South Africa, just under half the population has been vaccinated (according to Reuters). This might explain the many mutations in the spike protein and Omicron’s ability to infect the vaccinated. (From a news article by Kai Kupferschmidt, published in Science Magazine last month.)

Second theory: Omicron gestated in the slow cooker of a single patient with long COVID

From the same Science article:

Omicron clearly did not develop out of one of the earlier variants of concern, such as Alpha or Delta. Instead, it appears to have evolved in parallel—and in the dark. Omicron is so different from the millions of SARS-CoV-2 genomes that have been shared publicly that pinpointing its closest relative is difficult, says Emma Hodcroft, a virologist at the University of Bern. It likely diverged early from other strains, she says. “I would say it goes back to mid-2020.” That raises the question of where Omicron’s predecessors lurked for more than a year…

 

Andrew Rambaut of the University of Edinburgh can’t see how the virus could have stayed hidden in a group of people for so long. “I’m not sure there’s really anywhere in the world that is isolated enough for this sort of virus to transmit for that length of time without it emerging in various places,” he says. Instead, Rambaut and others propose the virus most likely developed in a chronically infected COVID-19 patient, likely someone whose immune response was impaired by another illness or a drug. When Alpha was first discovered in late 2020, that variant also appeared to have acquired numerous mutations all at once, leading researchers to postulate a chronic infection. The idea is bolstered by sequencing of SARS-CoV-2 samples from some chronically infected patients.

Third theory: Omicron jumped to a mouse, then back to humans

This study from the Chinese Academy of Sciences (Beijing) cites genetic evidence from the Omicron genome to support the thesis that the virus jumped to mice, then back to humans. The frequency of different kinds of mutations (different amino acid substitutions) is different within the mouse physiology compared to the human physiology. These authors find that the types of mutations found in Omicron are more characteristic of mouse than human physiology.

A creative idea! but perhaps that it is its main weakness: 1) There are a huge number of mutations of every kind when the virus replicates, either in a mouse or a human. The ones that stick are the ones that are adaptive, i.e., the ones that help the virus to replicate or spread more effectively to another host. This is not addressed in the Chinese study. 2) A great many adaptations would be needed for a virus to effectively infect a mouse population. These would have to be established to accomplish the jump into the mouse population, then undone for the virus to jump back to humans. Still, there is some precedent in the known ability of SARS-CoV-2 to infect a herd of white-tailed deer. 3) Both these objections could be obviated if the virus were deliberately passaged through humanized mice in a laboratory.

Fourth theory: Omicron escaped from a gain-of-function laboratory

In April, a laboratory in Durban, South Africa published this paper, describing their genetic modification of the SARS-CoV-2 virus  In November, the Omicron variant was first discovered in the area of Johannesburg / Pretoria, about 600 km away from Durban. Were the two events related? The 501Y mutation which is the subject of the Durban study is present in the Omicron variant, but many of the other mutations listed in the Durban ms are missing from the Omicron genome.

Many scientists are convinced, based on its genetic signature, that the original Alpha strain of COVID was engineered in a bioweapons laboratory. Normally, the spike protein of a virus is just evolved to latch firmly onto a host cell. But in the case of the COVID virus, the spike protein does a lot of nasty things as well, including blood clots and damage to nerves and arteries. The spike protein seems on its face to be designed for toxicity.

Ironically, the early Nature Medicine article that tried to put the lab-origin theory to rest claimed only that the spike protein was not fully optimized to bind to human cells, QED. (When Dr Fauci’s emails were FOIAed, we learned that Fauci himself had commissioned this article, and that its authors included suspects for channeling NIAID funding of bioweapons research to China.) So now it appears that the spike protein was designed as a compromise between optimal infectivity and optimal toxicity.

If Omicron is engineered, then perhaps it has been designed as an antidote rather than a weapon. Omicron seems to spread so fast that it has rapidly displaced Delta in the African population where it originated, and yet it is causing remarkably mild illness and few if any deaths.

All of these four stories have adherents and there is logic behind them. Any may turn out to be correct. But there is a simpler hypothesis, which involves no extra assumptions, but relies only on the principles of natural selection. The main weakness of this hypothesis is that the number of mutations in Omicron and the rate of evolution seem to be anomalously high; but perhaps it is being ignored because of publishing taboos.

Fifth theory: Omicron evolved to evade the vaccine

Viruses always evolve toward higher transmission rates and lower fatality rates. The higher transmission rate is what allows it to out-compete other variants and spread through the population. The lower fatality rate is less obvious—viruses can spread better if the host is feeling well and circulating in the population; and if the host dies, the virus dies with him.

The Omicron variant seems to be an unusually large step in both directions. This is the reason that most epidemiologists are looking for a specialized explanation for its origin. A more mundane explanation is the pressure to adapt that has been created in vaccinated populations. Communities with high vaccination rates have created an ideal environment for the Corona virus to mutate. All parts of the virus are mutating all the time, but not all help the virus to be successful. If the spike protein mutates, this can throw the vaccinated immune system off the scent, because vaccination produces a highly focused immune response to the (Wuhan original) spike protein.Dr Geert vanden Bossche prominently predicted that this would happen early in the distribution of the COVID vaccines.

The Omicron variant demonstrates that vanden Bossche got this exactly right. It includes 37 new mutations in the area of the spike protein, and Omicron has largely evaded the vaccines. Vaccinated people are as likely or more likely to get Omicron compared to unvaccinated.

Vanden Bossche anticipated tragic consequences for all of humanity, but this does not seem to be what is happening. Rather, this cloud has a silver lining. As mentioned above, the spike protein is the toxic payload of the COVID virus, responsible for most of the damage that the virus does to blood vessels and neurons. (It appears that the spike protein was engineered for this purpose in a gain-of-function experiment.) As the spike protein has mutated, it has become less toxic. As a result, the Omicron variant is far milder than original Wuhan COVID. The Omicron mortality rate, according to UK figures, is only 1/10 as high. (The UK has had 10,866 Omicron cases and 14 deaths for a mortality rate of 0.0013. For comparison, the 2-year total of COVID deaths and cases in the UK was 148,000/11,800,000 = 0.013, almost exactly ten times higher.)

Unknowns and the Future

We know historically that the natural immunity of a recovered patient provides the best immunity we know.  People (mostly Chinese) who recovered from SARS eighteen years ago seem to have full immunity to COVID, though the two viruses are substantially different. This should mean that Omicron sweeps through the population, and many, many people will recover after a mild and abbreviated illness, with permanent immunity to all forms of COVID. This would be the dawn of herd immunity and the end of COVID. The question is whether recovering from Omicron will provide full immunity to future variants. We see that recovery from past variants does not provide sufficient immunity to protect against Omicron. Is this because Omicron is an exception to the general rule about robust immunity in recovered patients? Or is it an artifact of faulty testing, people who have been told that they recovered from COVID when they really had the flu? Or is it an artifact of vaccination after recovery, which seems to be counter-productive, narrowing some of natures’s robust, acquired immunity?

Meanwhile, CDC press releases and mainstream reports are using Omicron as a booster for the fear-porn industry, citing exploding “case” statistics while ignoring the simultaneous drop in “death” statistics. Pfizer is developing a new mRNA vaccine for Omicron, which they plan to release in March. Will they double down on their tragic mistake in basing the vaccine on the toxic spike protein? Or will the new vaccine be derived from a less dangerous part of the virus?  We have reason to hope that Omicron will spell the end of COVID, but only time will tell.

Immune senescence, Christian theology, and the Spike protein

One of the things that happens to our immune systems with age is that a preponderance of naïve B-cells (in youth) gives way to a diverse body of memory B-cells (in older adults), each trained to respond to a specific pathogen from the past. (Valter Longo claims that fasting eliminates some of the memory B-cells, which are replaced by naïve B-cells upon re-feeding.)

We know that old and young people have very different responses to COVID and to the COVID vaccines. There is a link between the B-cell story and the differential responses of old and young if we look at a recently re-discovered phenomenon called original antigenic sin. (The term was coined in a 1960 article on influenza.)

(For anyone looking for the Christian theology in this blog, that was it. I apologize for the jokey headline.)

The innate immune system is our first and best line of defense. It is strongest in youth. Neutrophils engulf and digest bacteria and viruses. In addition to neutrophils and natural killer cells, there are short proteins in mucus membranes that protect us.

The mucus layer also contains substances that kill pathogens or inhibit their growth. Among the most abundant of these are antimicrobial peptides, called defensins, which are found in all animals and plants. They are generally short (12–50 amino acids), positively charged, and have hydrophobic or amphipathic domains in their folded structure. They constitute a diverse family with a broad spectrum of antimicrobial activity, including the ability to kill or inactivate Gram-negative and Gram-positive bacteria, fungi (including yeasts), parasites (including protozoa and nematodes), and even enveloped viruses like HIV. Defensins are also the most abundant protein type in neutrophils (see below), which use them to kill phagocytosed pathogens. It is still uncertain how defensins kill pathogens.
Molecular Biology of the Cell, 4th Edition

How do these simple, generic defenses distinguish invaders from self? There are certain molecules that are characteristic of bacteria and absent in eukaryotes.

The pathogen-associated immunostimulants are of various types. Procaryotic translation initiation differs from eucaryotic translation initiation in that formylated methionine, rather than regular methionine, is generally used as the first amino acid. Therefore, any peptide containing formylmethionine at the N-terminus must be of bacterial origin. Formylmethionine-containing peptides act as very potent chemoattractants for neutrophils, which migrate quickly to the source of such peptides and engulf the bacteria that are producing them….Short sequences in bacterial DNA can also act as immunostimulants.
Mol Biol of Cell, 4th Ed

Innate immunity is based on inflammation. I’ve seen several sources that describe how the brilliant, all-purpose system of innate immunity turns to chronic, un-targeted inflammation with age, but no explanation as to how the inflammatory response loses its way and attacks the body generally.

The great resistance that young people have to the COVID virus seems to be due to a strong innate immune system; conversely, the second line of defense, the adaptive immune system, which older people rely on, seems to have more trouble with COVID.

Original antigenic sin (OAS): When the immune system first encounters a pathogen, a tiny subset of randomly-generated antibodies that happens to match a subregion (about 120 AA bases) of some protein in the invader is copied in an exponential process that leads to enormous amplification. Thereafter, the body has a memory of some protein fragments of the pathogen, but not others. When the same pathogen is detected months or years later, the immune system will favor its remembered response, rather than exploring its naïve cells for a new one.

The problem called “original sin” arises when the new invader is a related pathogen, not identical to the one first encountered. The immune system recognizes some subsequences, and figures, based on its memory, “we’ve got this one covered”. But sometimes the response that worked well with the original pathogen is sub-optimal for the new one. The body may fail to fight off a new virus simply because it has encountered a similar one in the past. This is the phenomenon that Thomas Francis dubbed “original sin”.

The relevance to present-day pandemic epidemiology is this: Coronaviruses are ubiquitous, and have been around longer than humans; we have all been exposed to many of them. When our bodies first encounter SARS-CoV-2, they are likely to yawn and say, “this looks a lot like something I’ve seen before”. And indeed, this seems to work well for a lot of bodies. No less a light than John Ioannidis has estimated that up to 80% of people cast off the COVID virus with symptoms so mild that they never know they had it. But there are other people for whom the remembered response to some generic coronavirus is not sufficient, and their immune systems get stuck in an obsolete paradigm. Original sin.

“Original sin” can apply to vaccines as well. The COVID spike protein binds to the ACE2 receptor, and has this in common with spike proteins from many past coronaviruses. This makes it likely that parts of the SARS-CoV-2 spike protein have similar regions to other common coronaviruses from the past, (including the original 2003 SARS). The spike protein, of course, is the element of the virus that was chosen by all Western vaccine manufacturers to induce with their vaccine products. So we see a possible reason why young people and old people have such different reactions to the vaccine: young people are responding to the vaccine from the innate immune systems, while older people are responding by selectively amplifying antibodies from their immune memory.

Age and Vaccine Side Effects

The current crop of mRNA vaccines have caused in 11 months about twice as many adverse reactions, including deaths, as the total of all previous vaccines in the 30-year history of VAERS. These post-vaccination events deserve to be counted and addressed. CDC is in denial.

Reported heart attacks (9,746 cases) and deaths (19,532) after vaccination are skewed toward older people. The average age for heart attacks is 62. [these numbers from OpenVAERS]

Myocarditis and pericarditis (15,403) are skewed toward the young, average age 32, and toward boys more than girls.

When adults do have myocarditis following the jab, it is equally likely to be after the first or second dose. But when young people (<20) get myocarditis, it is most likely to be after the second dose. My interpretation: Adults have been around the block, and they have seen spike proteins before. Their response to the vaccine is from memory B cells. Young people are more likely to be responding from naïve B cells. Something terrible (that I don’t claim to explain) happens when they see the same antigen 3 weeks later.

Neurological damage, including Bell’s Palsy, paralysis, and Guillain-Barre, peak in middle ages (average age 50)

Middle-aged people are also more likely than the young or old to have anaphylactic responses to the vaccines (8,301 total cases). This is surprising, not only in light of the elevated inflammatory response in older people, but also because the old are more likely to have a problem from original sin.

 

OVS?

A related phenomenon might be called original vaccination sin. It is peculiar to the newer, cheaper crop of vaccines that are based on a single protein extracted from the virus, rather than on a weakened whole virus, which had been the basis of classic vaccines.

When we develop a vaccine for a pandemic virus based on one small subset of the viral genome, quite predictably, the virus squirm its way out of this artificial barrier by mutating exactly that part of its genome that the vaccine targets. The new variant, with mutations in just the target part of its genome, expands  in just a few months from a rare sub-species to become the dominant infection. Meanwhile, the pharmaceutical manufacturers are geared up to mass-produce a vaccine that no longer targets the current version of the virus. A seasoned Dutch vaccine specialist predicted back in April that just this would happen. As the omicron variant emerges with 37 mutations in the spike protein, scientists who certainly know better feign surprise that so many mutations could arise so quickly, and in just the part of the virus that vaccinated individuals respond to. A high school student’s understanding of natural selection makes it obvious why the COVID virus is mutating in this way.

The good news is that these mutations are likely to make the virus less deadly. The spike protein of SARS-CoV-2 is not an ordinary, evolved spike protein which is evolved to bind well to a receptor and gain entrance to a host cell. This spike protein was engineered in a bioweapons lab to be toxic in multiple ways (in addition, of course, to binding to ACE-2), to break off and enter the bloodstream, spreading its damage far and wide. So when the spike protein mutates to avoid the vaccine, it is likely to become less toxic (while retaining the ability to bind to ACE-2, because that’s what helps the virus to transmit itself.)

OAS and ADE

ADE = antibody-dependent enhancement (or pathogenic priming) is much better known these days than OAS. ADE or PP refers to any situation in which having been exposed to a virus or bacterium once, the patient becomes sicker on the second exposure. It is much discussed now because of the fear that vaccines could induce ADE, so that some vaccinated people might have worse cases of COVID than if they had not been vaccinated. And indeed there is some evidence for this.

There is no agreement in the community about why ADE happens in some patients some of the time, and there is not even good agreement about how to define ADE. It is possible that the antibody binding to the virus can actually enhance its ability to infect, rather than marking it for destruction.

Some of the definitions of ADE are broad enough to encompass OAS. For example, here is a definition from AAAS. Derek Lowe describes ADE:

Dengue fever is a classic example, because it infects humans through four distinct serotypes. If you are infected with one of these and raise a successful immune response, you may well be at increased risk of serious infection with one of the other serotypes. The neutralizing antibodies for one of the types are often not neutralizing for the others, but instead allow that cell-antibody-receptor mechanism to kick in (easier infection of human monocytes), known as “extrinsic ADE”. There’s also an “intrinsic ADE” seen with dengue, which leads to greater viral replication inside infected monocyte cells before they burst and release their contents. The mechanisms for that are still being worked out, but seem to involve suppression of cytokine pathways.

Here is how Eric Brown describes OAS:

Memory B cells producing antibodies of high affinity and specificity established following a primary exposure to one subset of antigens can prevent or significantly dampen responses by naive B cells to new antigens if they are part of a profile that includes antigens present during the primary exposure (56). This is not a problem if the memory response produces neutralizing antibodies to antigens associated with the secondary exposure; however, problems do arise if memory B cells produce nonneutralizing antibodies to the antigens shared between primary and secondary exposures as reported recently in humans exposed to related human coronaviruses (hCoVs) and later infected with SARS-CoV-2 (78). In such a scenario, not only can the memory response be ineffective, it can significantly attenuate the response of newly activated B cells that could have responded effectively to new antigens absent from the original priming event.

 

The bottom line
Our immune systems are more complex than we understand. They are brilliantly effective most of the time, but respond to novel stimuli in ways we can’t predict. In general, it seems true that educating the immune system about a pathogen in advance adds protection when that pathogen is encountered later. But there are known and unknown mechanisms by which previous exposure can make a new infection worse.

Vaccine development is an experimental science. The immune system is modified in permanent ways, and there is no theory to tell us whether the benefits or the detriments of an intervention will play out over the years. There is no substitute for long-term trials.

I’ll save the best news for last

The Delta variant had significantly lower mortality than the Wuhan original SARS-CoV-2. Omicron is the up-and-coming strain of COVID, and it has a dramatically lower mortality. There is a simple explanation for this direction of evolution, and I think it’s something we can count on.

In general, viruses evolve to become more contagious and less harmful. It’s in the virus’s interest to co-exist with the host, doing no harm, so it can spread freely. In the case of COVID-19, this evolution has been more rapid and more dramatic than usual. Here’t why:

The spike protein is the part of the virus that is engineered as a bioweapon. The spike protein is responsible for damage to arteries, to nerves, and to the heart that make COVID a fearsome disease. But the spike protein is also the only part of the virus that is induced by the vaccines. Hundreds of millions of people have immunity to the spike protein and nothing else. The virus can continue to spread to the extent that it evades vaccine immunity, and the best way to evade vaccine immunity is via mutations to the spike protein. [recent survey from the SF bay area] These mutations tend to de-fang the spike protein, which was engineered by humans to have multiple toxic effects.

The vaccines are doing their job by guiding the evolution of the virus toward a more benign form. The end game will be that those of us who have not already lived through COVID will be exposed to omicron or something even more benign, and we’ll come through with a lifetime of immunity to all new COVID strains.

A New Approach to Methylation Clocks

A new approach to methylation clocks from Morgan Levine uses massive computer resources and sophisticated mathematics. I am enthusiastic about it, not just because it produces better results than previous methods, but because I suspect it is better aligned with the way that biological systems actually work.

The Clockmaker’s Dilemma

Your goal is a robust and accurate measure of biological age. You start with a sample of (for example) 5,000 people, and let’s suppose for now you know their “biological age” (we’ll come back to this). For each person, you also have 850,000 methylation levels — a number for each of 850,000 spots on the human chromosome where methylation is known to vary, called “CpG’s”.

Here’s the paradox. You could easily come up with a formula that “predicts” biological age precisely for all 5,000 people, because you have 850,000 parameters to play with. In general, you can always make a formula that works perfectly in N cases if you have N different knobs you can turn to make the formula fit. In this case, you have a lot more knobs than cases. In mathematical terms, you have more unknowns than you have constraints.

In this way, you could construct a clock that is perfectly accurate for all 5,000 in your group. But it’s been jerry-rigged to do that. The clock you develop will be unreliable for predicting the age of someone who is not in this group of 5,000. Peculiarities about this particular set of 5,000 have been incorporated into the model, distorting its priorities. Statisticians call this phenomenon “overfitting”.

The opposite approach would be to look through the 850,000 methylation sites (CpG’s) and find the one that best correlates with “biological age” for your sample population,  The result will be an aging clock with much less accuracy; but chances are strong that it will work just as well with a new set of people as it did with your original 5,000.

Between these two extremes, you, the Clockmaker, look for a formula relating multiple CpGs that fits your 5,000 sample subjects well, while avoiding overfitting. But how can you know if you’re overfitting? There is no standard answer, and various methods are used with names like LASSO and elastic net and “Leave one out” . Some general principles are

  • The total number of CpGs referenced should be much less the total number of calibration subjects. The best clocks reference a few hundred CpGs, and are calibrated with many thousands of subjects.
  • Don’t get too fancy. If a single CpG or combination of 3 or 4 is highly correlated with age, that is probably real, but more complex combinations are suspect.
  • Spread the algorithm out, so that no single CpG (or a few CpGs) can have a big effect on the outcome.
  • Large positive and large negative components that cancel each other out just right to produce the age prediction tend to be fragile, and can produce large errors if a single CpG is mismeasured.

The methods listed above are common to all the best methylation clocks (and also to their cousins, based on the proteome or the microbiome or the immune system). The differences among methylation clocks are based on what is defined ahead of time as the target “biological age”. Steve Horvath’s first clock was calibrated with chronological age — which is already a pretty good surrogate for biological age. The Levine/Horvath PhenoAge clock was calibrated using a combination of metabolic factors that correlate with health, including inflammation, DNA transcription, DNA repair, and mitochondrial activity. The Lu/Horvath GrimAge clock was calibrated with actual mortality statistics, derived from banked blood samples from decades in the past, so the future lifespan of the donors was now known. Other mortality-related data were also involved, and the GrimAge clock is presently most accurate for predicting all-cause mortality.

Creation of methylation clock algorithms illustrate this open secret: Statistics is as much an art as a science. Experience and sound judgment are more important than mathematical sophistication.

Interlude: How biological systems are different from machines

How many of us have had the experience of sitting in a plane, leaving the gate, and after a long time out on the tarmac, a voice comes over the PA system saying, there’s a bad valve in the hydraulic system for the left aileron and we’re waiting for them to locate the part in the warehouse?

Yes? How many have been on that plane when the pilot comes on again a minute later — never mind, we have a spare capacitor for the on-board radar and it’s the same size and shape as the valve, so we’ll use that instead. “Flight attendants, prepare for takeoff!”

No? You haven’t had that happen? It’s because airplanes, like computers and washing machines and radio telescopes, are engineered from parts that are individually optimized for one function only, and then the parts are assembled and linked together in one very particular way that makes the machine work.

But evolution is not an engineer, and living things are not constructed out of parts that are separately optimized for exactly one function. Your bones support the body’s frame, but they also store calcium and manufacture blood cells. Your lymph nodes collect and channel cellular waste products, but they also generate an army of lymphocytes to fight infection, and they are responsible for fluid homeostasis. Your liver stores glycogen and also generates hundreds of different molecules important for digestion, regulation, and metabolism, even clotting factors for the blood.

Early geneticists were “flying blind”, with no knowledge of the molecular mechanisms of inheritance; still they figured out very early that the body doesn’t work the way a human designer would have designed it. The word “epistasis” was coined in 1909 by Gregory Bateson. It meant gene interactions. Several genes combine to create one phenotype. The very next year, Ludwig Plate coined the word “pleiotropy”. It is the converse: A single gene has multiple effects. At the time, these words were coined because they were thought to be exceptions to the rule of one-gene-one-trait.

Now we know that one-gene-one-trait is the exception. The body is not engineered the way a machine is engineered. Every molecule has multiple functions. Every function is regulated by multiple pathways. Before we curse the body for being organized this way, consider the benefit: We’re not waiting on the tarmac every time there’s a single part that doesn’t work. The body is wonderfully, amazingly, robustly homeostatic. Far more so than any human-engineered machine that is designed for maximum “fault tolerance”.

Levine’s Innovation for more robust aging clocks

For aging clock technology, the message from the above story is that using individual CpGs for a starting point may not be optimal. We suspect that CpGs, like other biological entities, work together closely in teams. Anything that we might identify as a function (e.g. growth, inflammation, aging itself) might be regulated not by a single CpG, but by a team. Just as the members of a sports team might vary from day to day, the particular CpGs on a team might vary slightly from one individual to the next. But the team has a function and an identity and a signature that is robust.

This spring, the following paper appeared on BioRxiv: A computational solution for bolstering the reliability of epigenetic clocks by scientists at Yale and Elyisium Health, headed by Morgan Levine. Levine’s innovation was to use the same statistical methodology I described above (LASSO, etc) as applied to CpGs, but instead she applied this methodology to teams of CpGs. How do you identify the team members? This leads to Principal Component Analysis (PCA), which is the mathematical part of the story. (more mathematical treatment here)

Simple example of PCA

Imagine charting for 1,000 children their age, height, and weight. Imagine a 3D graph where the x, y, and z axes are age, height and weight. Each child is a point in this space. The points fill a blob in 3-dimensional space, and perhaps the blob is cigar-shaped, because age, height, and weight all tend to increase together. The cigar doesn’t point toward any of the three axes (x, y, z), but it points obliquely out into 3-space.

The direction that the cigar points is called the first principal component. Chances are that the cross-section of the cigar is not round but oval shaped, because taller children tend to be heavier at the same age. The direction in which the cigar is widest is called the second principal component, and the direction in which it is flattened is the third principal component. This example has only 3 principal components, because it exists in 3-space.

PCA for methylation CpGs

Levine uses 78,464 CpGs that vary with age, so instead of 3-space, each person’s methylation profile represents a point in 78,464-dimensional space. Some combination of these tends to vary together most consistently, and that combination is the first principal component. There are 78,464 principal components, but perhaps only a few hundred that are interesting.

The mathematical procedure for finding principal components proceeds in two steps. Step 1 is to compute the correlation coefficient between each of the 78,464 CpGs and every other one, and laying all these numbers out in a giant square matrix. Step 2 is to diagonalize the matrix. The directions of the principal components are called eingenvectors of the matrix.

For you computer geeks, the number of arithmetic operations required to diagonalize a matrix goes up with the cube of the rank of the matrix. So the number of operations for a 78,464 square matrix is in the range 500 trillion. On a desktop computer capable of 1TFlops = 1 trillion floating point operations per second, this suggests the diagonalization might require just a few minutes.

Why I’m enthusiastic about PCA methylation clocks

As I wrote above, this approach seems to be well-aligned with biological complexity. It is a departure from the tendency of most scientists to be more comfortable with reductionist paradigms. Biology works with teams of molecules, The set of CpGs that form a principal component tend to vary together, turning on and off in a coordinated way. It is reasonable to think of a principal component as a “team”. We expect the team to function more consistently than any of its individual members.

Second, there are quirks and errors in lab technique and in quality control for individual bead chips (Illumina Corp) that process the DNA samples and measure methylation. These can have large effects on any single CpG site, but they are unlikely to affect an entire principal component in a consistent way. So we expect the PCA methodology to be more robust against variations in lab technique and variability from one bead chip to the next.

Third, in practice the Levine team reports that their computational method already produces the most precise age measurements yet. PCA computation slashes the uncertainty introduced by technical and lab issues by a factor of 6.

…And why I’m cautious

Steve Horvath is the father of methylation clocks and also the person who has published more research in this area than anyone else. Several years ago, I asked Steve about PCA analysis, and he said that he had tried using it a decade ago, and abandoned the PCA methodology on the way to his groundbreaking 2013 clock.

When we work with individual CpGs, we usually have some sense of what genes are associated with each CpG, and we have a sense of what those genes do. When we work with PCs, we are flying blind — they are just mathematical constructs, so there is no known associated physiological function. “Correlation is not the same as causation”, so it is even possible that these thousands of CpGs are correlated, but that they don’t really work together as a team at all. The major danger of using CpGs is excessive abstraction. We are manipulating mathematical objects formally and trusting that the results will be meaningful. This increases the risk of the “overfitting” problem I described above. Here is a rather technical cautionary editorial.

Of course, there is no guarantee that the first principal component or the second will be correlated with age. Looking for PCs that correlate well with age is just like looking for individual CpGs that correlate well with age. And certain PC’s will be found to work well together to predict age, just as in the classical method certain CpGs work well together.

PCA methylation clocks are a new technology without a track record, and for now the established and validated clocks should serve us well.

The future

The Levine paper already contains many computational tests and interesting results, but it is new and not yet peer reviewed. Still, I’m hopeful that this represents a new direction for methylation and other aging clocks. It has the feel of a right approach.

Levine is committed to open science even though she is affiliated with the for-profit Elysium Health, which has its own proprietary methylation clock, and even though universities are jealously guarding IP rights in this era. The good news is that the peer-reviewed version of her paper will be published shortly, and full details of the algorithms will be available on GitHub and script in the R programming language will be released for the use of other researchers. I hope there are others who pick up on this technology so it moves rapidly forward.

If PCA clocs correlate well with previously validated clocks but offer tighter uncertainties, we’ll know we’re on the right track.

Statistical Fraud in the FDA Vaccine Approval Process

The Pfizer vaccine is on a fast track for FDA approval, hearings possible today according to the NYTimes. I suspect the political pressures are enormous. Still, how can approval be possible when the safety record of this vaccine is far worse than any vaccine in the past, including many that were pulled from the market?

Efficacy

Part of the answer is that CDC is not reporting the statistic most relevant to measuring efficacy. That is: Vaccination status of COVID patients. How many of the new infections and new deaths are in vaccinated individuals, and how many in unvaccinated? Of course, this information is known in CDC databases; it is scandalous that the numbers are not being made public. I do not know if they are being provided to FDA. Israel is more honest, and numbers from there are not encouraging.

Likewise, data about transmissibility, comparing vaccinated and unvaccinated populations, are not being reported, and were not part of the clinical trials last fall.

Safety

There are several systems for reporting vaccine reactions, including deaths, but the only one available to the public is VAERS. It is incomplete, because it relies on voluntary reporting, there is no incentive to report to VAERS, and it is a cumbersome process.

We may compare reports of the COVID vaccines to past years, when there were also hundreds of millions of vaccinations, including annual flu shots and childhood vaccine schedules. The comparison is dramatic.

There were more than twice as many deaths related to the COVID vaccines this year as the sum total of all vaccine deaths in the 30-year history of VAERS.

Given this safety record, how is there any possibility of approval? Here is where the statistical fraud comes in. [I am grateful to have been alerted to this situation by Matthew Crawford]

The safety criterion they have chosen is an obscure computation called PRR for Proportional Reporting Ratio. As the name implies,  it is based on RATIOS of different event types and is utterly blind to the ABSOLUTE RATE of such events.

PRR measures the distribution of different kinds of adverse events, e.g. blood clots, heart attacks, and deaths. If those ratios are severely out of line with the great variety of vaccine reactions in the past, PRR would detect that. For example, if the new vaccines caused an extraordinary risk of myocarditis, but everything else was low, then PRR would flag that. But if myocarditis was just one risk among many that have been reported from past vaccines, then PRR would not pick that up.

The real scandal is that PRR is blind to the absolute risk numbers. PRR is defined in such a way as to look for unusual PATTERNS of adverse events, but it is completely insensitive to unusual RATES of adverse events. Of course, it is the rates and not the patterns that are of primary concern, and the PRR is designed NOT to reflect that.

For example, suppose we have 2 vaccines:  

Vaccine A has 1 reported death per million vaccinations, 3 reported heart attacks per million, and 20 reported headaches per million.

Vaccine B has 1 reported death per hundred vaccinations, 3 reported heart attacks per hundred, and 20 reported headaches per hundred.

Vaccine A is quite safe, and vaccine B is extremely dangerous. And yet the formula for PRR will produce the same result for vaccine A and B!

Clearly, PRR is not an appropriate criterion for evaluating safety of any particular vaccine. Someone has arranged to cook the books.