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.



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.)


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?


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.


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.

Letter to my readers

I struggled since childhood with night terrors, about death as an eternal void. My csreer as aging scientist helped me to crack that open, then gain insight through meditation practice.

Fear is not helpful. It is the worst motivation for life extension. Joyful living is the best. Fear is just a chenical programmed by evolution to get us through crises. No use in the long haul.

3 weeks ago, I was riding my bicycle, not paying good attention. A truck going the opposite way pulled into my lane to pass. I struck it head-on, and flew over the truck.

Moments later, I was lying on the ground, bleeding internally and externally many places. I was perhaps 1 hour away from bleeding to death. There was no fear. I had a premonition I would not die, but would endure a long and painful recovery. I think I am done with fear of death.

I have had 5 hi tech surgeries those first 2 weeks. Expert surgeons who also gained my trust as humans.

My vital systems were unharmed: heart, lungs, liver, digestion, spine, CNS, brain. All intact. My legs were shredded, also bad pelvic fracture. This is precisely where Western medicine shines.

And everything I have been doing the last 20 years to keep those systems young has been crucial for my survival and recovery.

Another thing you might not have thought about: No narcotics, no pain meds, just a little Tylenol from time to time. All those nerve signals inform the body’s healing.
And simehow my body has been smart enough to give me perceivable pain only when I need the feedback.

They are talking about full recovery. I am surrounded by loving friends and family.

Is aging inevitable?

A new study has appeared to support an old idea: Aging is inevitable and immutable, so anti-aging research is doomed in advance to failure.

In 1957, George Williams wrote

This conclusion banishes the “fountain of youth” to the limbo of scientific impossibilities where other human aspirations, like the perpetual motion machine and Laplace’s “superman” have already been placed by other theoretical considerations. Such conclusions are always disappointing, but they have the desirable consequence of channeling research in directions that are likely to be fruitful.

In 2002, three prominent aging scientists wrote (in Scientific American):

No Truth to the Fountain of Youth:
…no purported anti-aging intervention has been proved to modify aging…We find it ironic that a phony anti-aging industry is proliferating today…Some [researchers] Some assert that aging’s complexity will forever militate against the development of anti-aging therapies.

One of the three was Len Hayflick, who is most famous for having discovered and documented one of the clearest and most preventable mechanisms of programmed aging.

In 2017, Joanna Masel wrote

“Aging is mathematically inevitable. Like, seriously inevitable. There’s logically, theoretically, mathematically no way out.”

This new study is based on statistical analysis of human and primate populations. Among the 42 authors (!) who signed it, I am chagrined to find the name of J. W. Vaupel. Et tu, James? Over several decades, Vaupel has been the optimist of demography, telling us that somewhere in the world, human lifespan is always continuing to increase, as it has done since 1840, at the rate of about 1 year of new lifespan for every 4 years that passes. For the first 130 years of this advance, the improvement in lifespan was predominantly about preventing infant mortality and combatting infectious disease. But since about 1970, lifespan improvements have continued to benefit the elderly. My informal index is the number of 80-year-olds I see on the tennis courts. Vaupel and his former student, Annette Baudisch, also were prime movers in a comprehensive 2013 study of Aging Across the Tree of Life, which catalogued species that don’t age at all for decades at a time, and others that become demographically younger.

This new computer modellike all computer modelsis a translation into mathematical language of a set of assumptions about a natural phenomenon. The crank turns, and out pops a prediction. The sleight-of-hand, the conjuror’s trick, is that we are tempted to look at the mathematical machinery to see where these predictions come from. But equally important is to look at the assumptions on which the mathematics is built.

In this case, the assumption is that natural selection has been trying to maximize lifespan, because the longer an individual lives, the more opportunity it has to reproduce. And reproductive output is the measure of success in neo-Darwinian logic.
But if we look at the biology of aging, it’s clear that evolution has not been trying to maximize lifespan. As we get old, genes are turned on that destroy us with inflammation and autoimmunity, and this epigenetic change shows every sign of being under the body’s control. As we get old, genes are turned off that rebuild and protect the body against chemical damage, most famously from free radicals. Again, it appears that this is deliberate. It is a product of natural selection, not a constraint on natural selection.
How can this be? How can a variety with lower reproductive success prevail in evolutionary competition against other varieties with higher reproductive success? This question has been the primary focus of my own research for 25 years, and my answer is the necessity to preserve stability of ecosystems.
My answer may be right or wrongit is still a minority opinion. But what is clear is that the lifespan of almost all living things is under epigenetic control. That is, aging is a programmed phenomenon. Aging is not the accumulation of damage. Aging is not the body wearing out. Rather, aging derives from processes of self-destruction that are under the body’s control.
In this perspective, aging looks a good deal less inevitable than this article claims. And indeed, there is cutting-edge science that appears to be turning back the clock of aging, turning old rats into young rats.
Specifically, what does the new study find? Looking at populations of humans and other primates, they find that longer average lifespans are associated with less variability in lifespan. In other words, the short-lived primates have deaths that are spread out, with some living much longer lives; but in the longer-lived primates, age-at-death is clustered up near the high end. This gives the appearance of some kind of wall at the high end of lifespan.
And where, specifically, is the flaw in the new paper?
“Understanding the nature and extent of biological constraints on the rate of ageing and other aspects of age-specific mortality patterns is critica…”
The implicit assumption about “biological constraints” is that the constraint is physical, or that in some way it is beyond the reach of evolution. The assumption is that natural selection has pushed against these constraints, and hit a brick wall. The alternative view (a view that is shared by some of the most prominent researchers who have studied physiology and biochemistry of aging) is that these “constraints” are actually baked in by natural selection itself. Far from being constraints on evolution, these constraints are actually the product of evolution. This is to say that the constraints are not fundamental physical limits, but features built into the epigenetic cycle of growth, development, and aging. The “constraints” become malleable as we tinker with the signaling mechanism by which the body imposes aging on itself.

A crucial caveat

I believe that as we understand more about epigenetics and the signaling mechanisms that control biological age, it will become increasingly feasible to manipulate lifespan. Indeed, we’re already doing this to a huge extent in lab worms and, to a good extent, in rodents.
But evolution isn’t so dumb. Limits on lifespan have been put in place to help protect against population overshoot. And (my opinion) humans are already in a state
of severe population overshoot, in the context of sustainable limits of Earth’s biosphere. I believe that whether or not biological science succeeds in further extending lifespan, it is an urgent matter for survival of our species (and many other species) that we shrink the human footprint on the biosphere and on the soil, water, and atmosphere that support Earth’s ecology. I think that living well with less is a relatively simple technical problem. We need only implement all currently known efficiency improvements in the use of resources, and continue to discover new ones. But it is a huge political problem that we have barely begun to confront, and I don’t have any good ideas how to make these changes a political reality. I’m going to stick to the science, and count on others who are more adept at politics than myself. As we extend human lifespan, there is an urgent need to move toward sustainable agriculture and to adopt energy-efficient technologies.

Paean to NAC

Long before NAC saved the life of someone dear to me, it was a staple of my supplement stack. I notice that now N-Acetyl Cysteine has become my favorite supplement, the one I reach for 3 or 4 times a day when I pass the kitchen cabinet. It’s been such a gradual process, that I don’t remember the reasons that installed NAC in my subconscious as a reliable life extension aid. I’m taking this opportunity to review the literature.

In the 1980s and 1990s, the oxidative theory of aging reached its pinnacle, and anti-oxidant supplements were all the rage. Trials of anti-aging supplements failed time and again, and often they led to shorter lifespans of test animals. Aging of animals turns out to be more complicated than rusting of iron, and part of the complication is hormesis, and ROS (Reactive Oxygen Species), particularly H2O2, are part of the signaling cascade that turns on hormetic protections.

One anti-oxidant that survived the massacre was glutathione. I continue to believe that glutathione promotes health, despite its close association with H2O2. Supplementing with N-Acetyl Cysteine (NAC) is the commonly-recommended strategy for raising glutathione levels, and it seems to work. The best promise of NAC (through glutathione) is in preserving our mitochondria, which weaken and reduce in number as we age.


Glutathione is a tripeptide, a mini-protein consisting of the 3 amino acids glutamate, cysteine, and glycine.

Our metabolisms (like all eukaryotes) use REDOX reactions to store and deploy energy, because they are far more energy-dense than the covalent chemistry of organic molecules. The energy metabolism has waste products which must be neutralized so they don’t latch on to delicate organic molecules and damage them. There are various toxic waste products (ROS), and various pathways for reducing them. The last stage is always H2O2, which must be neutralized to water. This is the primary job of glutathione. (Also catalase.) Unlike catalase, glutathione can perform diverse other detoxifying roles as well.

Glutathione acts like a rechargeable battery. Its reduced form (GSH) is available to detoxify H2O2, after which it exists as an oxidized form (GSSG), which must be “recharged”. GSSG is just two molecules of glutathione that are linked together by a disulfide bond, and a more complex protein called glutathione reductase comes along to separate the two molecules, recharging the battery. Another supplement, Alpha Lipoic Acid (ALA) is also helpful in recycling GSSG back to its useful form, GSH. Cells sense the ratio of GSH to GSSG to determine if they are in trouble. If the ratio becomes too low, the cell turns on NFkB [ref], which, in turn, initiates an inflammation cascade. A healthy cell has GSH:GSSG in the ratio 100 to 1, but a severely stressed sell can have more GSSG than GSH. Low ratios GSH:GSSG ratios can send a cell down a senescence pathway, terminating in apoptosis.

Glutathione’s importance is underscored by its large concentrations in every cell in the body. Your average human cell is using glucose for fuel, but the cell has as much glutathione as glucose in the cytoplasm.


Glutathione levels normally decline with age. 

In addition to anti-oxidant activity, glutathione is now known to have many other roles, including DNA repair, protein synthesis, and chemical signaling. These functions may be even more important than detoxifying H2O2. Most important for slowing age-related degeneration, glutathione has anti-inflammatory effects [ref], especially in the lungs [ref], which may be why NAC has been helpful in protecting against COVID [ref] It is well-established that severe COVID depletes glutathione, especially in late stages involving a cytokine storm [ref].

Table 1 Functions of Glutathione

  1. Direct chemical neutralization of singlet oxygen, hydroxyl radicals, and superoxide radicals
  2. Cofactor for several antioxidant enzymes
  3. Regeneration of vitamins C and E
  4. Neutralization of free radicals produced by Phase I liver metabolism of chemical toxins
  5. One of approximately 7 liver Phase II reactions, which conjugate the activated intermediates produced by Phase I to make them water soluble for excretion by the kidneys
  6. Transportation of mercury out of cells and the brain
  7. Regulation of cellular proliferation and apoptosis
  8. Vital to mitochondrial function and maintenance of mitochondrial DNA (mtDNA)

Table from J Pizzorno [2014]

Dietary Glutathione

Fruits and vegetables are a substantial source of dietary glutathione [ref], but bioavailability is low, so most of the body’s glutathione is home-made.

Can you just take glutathione pills? Yes, but they are expensive and poorly absorbed. Does supplementation with NAC really increase availability of glutathione where it is useful? Evidence is good [ref, ref, ref]. Just two years ago, I advised readers of this blog to eat glutathione, but I’m backing off from that suggestion now, because I think NAC supplementation is not just cheaper but more effective.

The rate-limiting step of glutathione synthesis does not appear to be the activity of either enzyme under normal conditions, but rather the provision of one of the amino acids (L-cysteine) making up the tripeptide. [ref]

Agricultural and industrial chemicals, ubiquitous in our environment, are not the primary cause of aging, but they cause severe symptoms for some, and may be degrading the metabolisms for all of us in subtle ways. Glyphosate has become impossible to avoid. Glyphosate, mercury, and other chemicals increase the body’s need for glutathione, as glutathione is essential to the body’s detox machinery. IBS, Crohn’s disease, and other inflammation syndromes increase the need for glutathione, and can potentially benefit from NAC supplementation.

What benefits of NAC have been documented in humans?

Best evidence is for preservation of the eyes with age. This is from an article on eye health and aging by BIll Sardi:

Numerous studies link glutathione with the prevention of cataracts, glaucoma, retinal disease and diabetic blindness. Here is a sampling of the evidence concerning glutathione and eye health.

Glutathione has been shown to detoxify the aqueous fluid of the inner eye [ref] and may help maintain adequate fluid outflow among glaucoma patients. [ref, ref]

Glutathione exists in unusually high concentrations in the lens and is essential to maintain its transparency. [ref] However, glutathione levels decline in the lens with advancing age; the decline is especially rapid prior to cataract formation. [ref]

NAC has been observed to have neuroprotective properties, but whether it lowers risk of dementia or PD is still not established [ref]. Emerging evidence suggests that NAC supplementation protects the brain in the event of ischemic stroke [ref]. Intravenous glutathione has been tried as a therapy for Parkinson’s disease with unimpressive results. Psychiatric applications are still under development. NAC has shown promise for treating addiction, AD, PD, autism, OCD, schizophrenia, depression, and bipolar disorder [ref].

Infusion of NAC increased endurance in trained cyclists [ref].

Intravenous NAC is used in ERs for detoxification of acetaminophen. It is also used for heavy metals [ref, ref], chloroform, monoxide and other poisons. [ref]

Table 2 Diseases Associated with GSH Depletion

  • Neurodegenerative disorders (Alzheimer’s, Parkinson’s, and Huntington’s diseases, amyotrophic lateral sclerosis, Friedreich’s ataxia)
  • Pulmonary disease (COPD, asthma, and acute respiratory distress syndrome)
  • Immune diseases (HIV, autoimmune disease)
  • Cardiovascular diseases (hypertension, myocardial infarction, cholesterol oxidation)
  • Chronic age-related diseases (cataracts, macular degeneration, hearing impairment, and glaucoma)
  • Liver disease
  • Cystic fibrosis
  • Aging process itself

Table from J Pizzorno [2014]

Old mice have half as much glutathione in their muscles, compared to young mice [ref]

Life extension in lab animals, including rodents

There are many studies in worms and flies demonstrating life extension via NAC. There is just one study in mice [ref], but it was so successful that I don’t know why it hasn’t been replicated. 24% increase in mean lifespan and 45% increase in maximal lifespan in the only arm of this Jackson Lab broad screening study that showed promise.

FDA regulation

Glutathione and NAC have both been readily available supplements, available without prescription for many years. NAC is preferred as a less expensive pathway to augmenting GSH levels within cells. Recently, NAC was reclassified as a prescription drug by FDA. There is no concern with safety, and the only reason offered by FDA is that NAC has been promoted as a hangover remedy after excess alcohol consumption. Since glutathione can detoxify alcohol breakdown products in the liver, NAC probably has some usefulness in this role. I believe the real motivation for making NAC harder to get is that it is useful in treating COVID, and there appears to be an agenda for suppressing inexpensive and effective treatments (chloroquine, ivermectin, vitamin D, zinc, quercetin) in favor of vaccination.

(Off-topic: If you’re interested in a comprehensive guide to the general principles and the subtleties treating COVID, I highly recommend this interview by Dr Darrell Demeo of Mumbai.)

The Bottom Line

The evidence for NAC as a life extension supplement is mostly indirect, but there are many good reasons to boost our glutathione levels, especially as we age, and especially in an age of ubiquitous chemical toxins.

Unthinkable Thoughts

This essay is inspired by Dr Mercola’s announcement last week that (reading between the lines) his life and his family’s have been threatened if he doesn’t remove from his web site a peer-reviewed study demonstrating the benefits of vitamin D and zinc in prevention of the worst COVID outcomes. In the present Orwellian era, where propaganda and deception are ubiquitous, one of the signposts of truth that I have learned to respect is that the most important truths are the most heavily censored.

This is not what I enjoy writing about, but as I find dark thoughts creeping into my consciousness, perhaps it is better to put them on paper with supporting logic and invite my readers to help me clarify the reasoning and, perhaps, to point a way out of the darkness.

Already in January, 2020, two ideas about COVID were emerging. One is that there were people and institutions who seemed to have anticipated the event, and were planning for it for a long time. Gates, Fauci, the World Economic Forum, and Johns Hopkins School of Medicine were among the prescient. (I credit the (now deleted) videos of Spiro Skouras.) Second was the genetic evidence suggesting that COVID had a laboratory origin. Funders of the scientific establishment have lost their bid to ridicule this idea, and it has now leaked into the mainstream, where it is fused with the classical yellow peril propaganda: “China did it!”. I have cited evidence that America is likely equally culpable.

The confluence of these two themes suggests the dark logic that I take for my topic today: Those who knew in advance, not only that there would be a pandemic but that it would be a Coronavirus, were actually responsible for engineering this pandemic.

Immediately, I think: How could people capable of such sociopathic enormities be occupying the most powerful circles of the world’s elite? And what would be their motivation? I don’t have answers to these questions, and I will leave speculation to others. But there’s one attractive answer that I find less compelling: that it’s a money-maker for the large and criminal pharmaceutical industry. The new mRNA vaccines are already the most profitable drugs in history, but I think that shutdown of world economies, assassinations of world leaders, deep corruption of science, and full-spectrum control of the mainstream narrative imply a larger power base than can plausibly be commanded by the pharma industry.

Instead, I’ll try to follow the scientific and medical implications of the hypothesis that COVID is a bioweapon.

The Spike Protein

The spike protein is the part of the virus structure that interfaces with the host cell. SARS 1 and SARS 2 viruses both have spike proteins that bind to a human cell receptor called ACE-2, common in lung cells but also present in other parts of the body. Binding to the cell’s ACE-2 receptor is like the wolf knocking at the door of Little Red Riding Hood’s grandmother. “Hello, grandmama. I’m your granddaughter. Please let me in.” The virus is a wolf wearing a red cape and hood, pretends to be an ACE-2 enzyme molecule seeking entrance to the cell.

In order to enter the cell, the virus must break off from the spike protein and leave it at the doorstep, so to speak. This is an important and difficult step, as it turns out. Unique to the SARS-CoV-2 virus is a trick for making the separation. Just at the edge of the protein is a furin cleavage site. Furin is an enzyme that snips protein molecules, and it is common in our bodies, with legitimate metabolic uses. A furin cleavage site is a string of 4 particular amino acids that calls to furin, “hey — come over here. I’m a protein that needs snipping.”

The most compelling evidence for a laboratory origin of COVID is that coronaviruses don’t have furin cleavage sites, and until last year, this trick has never evolved naturally.

How we think about natural disease

The classical understanding of a viral or bacterial disease is this: A parasite is an organism that uses the host’s resources for its own reproduction. It is evolved to reproduce efficiently. If it has co-evolved with the host, it may be evolved to spare the host’s health, or even to promote it, because this is the optimal long-term strategy for any predator or parasite. But newly-emerged parasites can do well for awhile even if they disable or kill their hosts, and this is the kind of disease that is most damaging to us. The damage is done because the (young) virus’s strategy is to reproduce rapidly and disperse itself into the environment where it can find new hosts. The virus has no interest in harming the host, and was not evolved to this end, but this is a side-effect of commandeering the body’s resources for its own reproduction.

How engineered diseases can be different

A bioweapon virus is designed to cause a certain kind of harm.

  • What kind of harm? It depends on the projected use for the weapon.
  • Doesn’t the virus have to reproduce? Probably, for most weapon applications; but a bioweapon is not necessarily designed for rapid reproduction. A bioweapon can be designed as a “sleeper” to remain dormant for months or years, or to cause incremental disability over a long period.

If COVID had evolved naturally, we would expect that its spike protein would be adapted to mate well with the human ACE-2 receptor. There’s no reason to suspect it being otherwise biologically active. But if COVID is engineered, it may be that the spike protein itself has been designed to make us sick.

One reason this is significant is that the vaccines have all been designed around the spike protein, assuming that the spike protein were metabolically neutral. If the virus had been naturally evolved, this is a reasonable assumption. But if it came from a laboratory (whether it leaked or was deliberately released) the spike protein might actually be the agent of damage. There are several reasons to suspect that this is the case.

The Spike Protein as an Active Pathogen

Back in February, 2020, this article noted that the spike protein was not perfectly optimized to bind to human ACE-2 and put this forward as a proof that “SARS-CoV-2 is not a purposefully manipulated virus.” But if someone were designing the virus to cause harm, the spike protein would be a convenient locus for the damage vector, so the spike might have been designed with twin purposes in mind, binding and toxicity. The spike protein appears in many copies around the “crown” of the coronavirus. Since each copy has a furin cleavage site at its base, many spike proteins will break off into the bloodstream. We now have several reports and hypotheses concerning the spike protein as an active agent of damage. The spike protein is suspected of causing blood clots, of inducing long-lasting neurological damage, and of causing infertility. Many anecdotes describe injuries to un-vaccinated people who have been in close proximity to vaccinated, prompting speculation about “shedding” the spike protein.

“Individuals with COVID-19 experience a vast number of neurological symptoms, such as headaches, ataxia, impaired consciousness, hallucinations, stroke and cerebral hemorrhage. But autopsy studies have yet to find clear evidence of destructive viral invasion into patients’ brains, pushing researchers to consider alternative explanations of how SARS-CoV-2 causes neurological symptoms….


If not viral infection, what else could be causing injury to distant organs associated with COVID-19? The most likely culprit that has been identified is the COVID-19 spike protein released from the outer shell of the virus into circulation. Research cited below* has documented that the viral spike protein is able to initiate a cascade of events that triggers damage to distant organs in COVID-19 patients.

Worryingly, several studies have found that the spike proteins alone have the capacity to cause widespread injury throughout the body, without any evidence of virus.


What makes this finding so disturbing is that the COVID-19 mRNA vaccines manufactured by Moderna and Pfizer and currently being administered throughout the U.S. program our cells to manufacture this same coronavirus spike protein as a way to trigger our bodies to produce antibodies to the virus.” [Global Research article, Feb 2021]

Note: the Astra-Zeneca and J&J vaccines are also based on the spike protein, and cause the spike protein to be created in the vaccinated person.

* “Research cited below” refers to this study in Nature which reports that the spike protein, injected into mice, crosses into the brain, where it causes neurological damage.

Bigger news came just this week from a study in which researchers from California’s Salk Institute collaborated with Chinese virologists. They have found that the bare spike protein without the virus (injected in mice) can cause damaged arteries of the kind that lead to heart disease and strokes in humans. The original paper was published in Circulation Research, and the Salk Institute issued a news report describing the research.

One of the most credible dangers of the spike protein involves fertility. None of the vaccines were tested in pregnant women, and yet many government and other authorities are recommending it as safe for pregnant women. VAERS has reported 174 miscarriages to date after COVID vaccination. VAERS is notoriously underreported. I find the anecdotes less concerning than the fact that no one is taking this seriously, and research is being actively discouraged in the best-respected science journals.

There is a credible mechanism, in that the spike protein is partially homologous to syncytin. Syncytin, in fact, was originally a retroviral protein, inserted into the mammalian genome many aeons ago, and evolved over the ages to play an essential role in reproduction, binding the placenta to the fetus. An immune response that attacks syncytin might be expected to impose a danger of spontaneous abortion. In any ordinary times, this would be a subject that medical researchers would jump on, with animal tests and field surveys to assess the danger. But these are no ordinary times, and the risk is being dismissed on theoretical grounds without investigation. This is especially suspicious in the context of history: a Gates Foundation vaccination program in 1995 was allegedly promoted to young women, causing infertility. (Yes, I know there are many fact-checkers eager to “debunk” this story, but I don’t find them convincing, and some of these fact-checkers are compromised by Gates funding.)

Even doing what the spike protein is supposed to do — tying up ACE2 — can be a problem for our lungs and arteries, which are routinely protected by ACE2.

The most dangerous possibility, suspected but not verified, is that the spike protein causes a prion cascade. Prions are paradoxical pathogens, in that they are misfolded proteins that cause misfolded proteins. Their evolutionary etiology is utterly mysterious, so much so that it took Stanley Prusiner a decade after describing the biology of prions before the scientific community would take prion biochemistry seriously. But prions make potent bioweapons, which laboratories can design outside of natural evolutionary dynamics. The possibility of prion-like structures in the spike protein was noted very early in the pandemic based on a computational study. This recent review combines theoretical, laboratory, and observational evidence to make a case for caution. Once again, I find it disturbing that this possibility is being dismissed on theoretical grounds rather than investigated in the lab and the field.

Where did the idea come from that all vaccines are automatically safe? Why do so many journalists dismiss the suggestion that vaccines should be placebo-tested individually, like all other drugs? Why has it become routine to ridicule and denigrate scientists who ask questions about vaccine safety as politically-motivated luddites, or “anti-vaxxers”? How did we get to a situation where the “precautionary principle” means pressuring young people who are at almost no risk for serious COVID to accept a vaccine which has not been fully tested or approved? I don’t have answers, but I do know who benefits from this culture.

Putting together all the evidence

  • Knowledge beforehand
  • Suppression of treatments and cures
  • Toxicity of the spike protein which, if it had been made by nature, should have been benign
  • Inclusion of the toxic spike protein in the vaccines that are supposed to protect us
  • Heavy promotion of  these scantily-tested vaccines and
  • Censorship of scientists and doctors who question the vaccines’ safety

… putting together all this evidence, it is difficult to escape the inference that powerful people and organizations have engineered this pandemic with deadly intent.

Weight and Aging: a Paradox, Part 2

The paradox: In animal models there is a consistent relationship between eating less and living longer. But studies in humans find that people who are a little overweight live longest.

Last week, I introduced this paradox and offered evidence, both that lab animals live longer when they are underfed, and that humans live longer when they are overfed. In the article below, I introduce nuances and confounding factors, but in my opinion, the paradox remains unresolved.


BMI is an imperfect measure of how fat or thin someone is for his height. That’s because it is calculated with the square of height, but body volume (for a given shape) is proportional to the cube of height. The result is that tall people will have a higher BMI than shorter people with equivalent proportions of body fat. For example, BMI=20 for a person 5 feet tall means a weight of 102 pounds, an average weight for that height; whereas BMI=20 for a person 6 feet tall means a weight of 147, which is borderline emaciated.

Short people tend to live significantly longer than tall people, and the effect is substantial.  Males under 5’7” live 7½  years longer than males over 6’ [ref]. This fits with the fact that short people tend to have less growth hormone in their youth. There is a genetic variant in parts of Ecuador that prevents growth hormone from transforming to IGF1 (Laron dwarfism); these people are generally about 4 feet tall and tend to live longer. From domesticated animals, we also know that small dogs live longer than large dogs, small horses longer than large horses. Between species, larger animals live longer, but within a single species, smaller animals live longer.

The height association deepens the weight paradox, because short people will tend to have a lower BMI, which we would expect to skew the association of BMI with longevity downward.

Growth Hormone and IGF1

Growth hormone (which is translated into IGF1 in the body) is genetically associated with shorter lifespan, but we have more of it when we’re young and it promotes a body type with more muscle, less fat. According to this Japanese study, IGF1 increases with weight for people who are thin, but decreases with weight for people who are fat. So maximum longevity is close to maximum IGF1.

Here are some partial explanations for the paradox.

Most variation in weight is explained by genetics, not food intake. The explanation I have proposed in the past is that the CR effect is about food intake, not genetics. And people who are congenitally stout are more likely to be restricting their calories. CR humans are not necessarily especially thin.

The CR effect is proportionately smaller in long-lived humans than in short-lived rodents or shorter-lived worms and flies. [ref] If life extension via CR evolved to help an animal survive a famine, then it seems reasonable that the benefit should be limited to a few years, because that is as long as most famines in nature are likely to last.

The CR effect may be due to intermittent fasting rather than total calorie intake. Traditional CR experiments conflate intermittent fasting with overall calorie reduction, because food is provided in a single daily feeding, and hungry rodents gobble it up, then go hungry for almost 24 hours. More recent experiments attempt to separate the effect of limited-time eating from the effect of calorie reduction, and the general conclusion is that both benefit longevity. It may be that humans who are skinny tend to graze all day, while people with a comfortable amount of fat more easily go for hours at a time without eating. 

Mice carry less fat, have less food craving, and have better gut microbiota if they are fed at night rather than during the day [ref]. Mice are active nocturnally; so translating to humans, it probably means that we should eat in the morning. Conventional wisdom is that eating earlier in the day is better for weight loss and health [ref], but I know of no human data on mortality or life span. This classic study in mice [1986] found caloric restriction itself was the only thing affecting lifespan, and there was no difference whether the mice were fed night or day, in three feedings or one.

Smokers tend to be thinner than non-smokers, but they don’t live longer for reasons that have to do with smoking, not weightSo this is a partial explanation why heavier BMI might be associated with longer lifespan. But note that the recent Zheng’s Ohio State study claimed there was no change in the best weight for longevity when correction was introduced for smoking.

Cachexia is a “wasting” disorder that causes extreme weight loss and muscle atrophy, and can include loss of body fat. This syndrome affects people who are in the late stages of serious diseases like cancer, HIV or AIDS, COPD, kidney disease, and congestive heart failure (CHF). [] If cachexia subjects are not removed from a sample, it can strongly bias against weight loss, because once cachexia sets in, life expectancy is very short. But the Zheng study was based on Framingham data, collected annually over the latter half of a lifetime; Cachexia is not expected to be a significant factor.

Timing artifact – The Framingham study covers a 74-year period in which BMI is increasing and also lifespan is increasing, probably for different reasons. The younger Framingham cohort is living ~4 years longer than the older cohort and is ½ BMI point heavier. This could create an illusion that higher BMI is causing greater longevity. However, the Ohio State study made some effort to pull this factor out. Greater lifespan is associated with gradually increasing BMI, and this is true separately in both cohorts.

Differential effects on CVD and Cancer This chart (from Zheng) shows how the mortality burden of cardiovascular disease has decreased over the last century, but not so cancer.

But CV disease risk increases consistently with BMI, while cancer risk, not so much (also from Zheng):

These numbers in parentheses are odds ratios from a Cox proportional hazard model. What they means is that a person in the Lower-Normal weight group had 20% less chance of getting heart disease compared to someone of the same age in the Normal-Upward group, but a 60% increased chance of getting cancer. These appear to be large, concerning numbers. But remember that the underlying probabilities are all increasing exponentially with age. Translated into years of lost life, 60% greater probability of cancer is only 1 year of life expectancy at age 50. (60% greater overall mortality would subtract 4½ years from life expectancy.) In my experience, hazard ratios in the range 0.7 to 1.5 don’t necessarily mean anything, because of the difficulties in interpreting data. The numbers in parenthesis after 1.60 in the above table (1.12 2.30) mean that statistical uncertainty alone is a range from 1.12 to 2.30.There are plenty of large effects with hazard ratios of 3 or more. For comparison, the hazard ratio for pack-a-day smokers getting lung cancer is 27.

Zheng’s study found a longevity disadvantage to being underweight, and it was exclusively due to a higher cancer risk. In fact, incidence of cardiovascular disease among the lowest BMI class was lowest (0.8); but their cancer risk more than made up for it (1.6). 

This means that as time goes on and most Americans are getting heavier, their risk of dying from CVD is blunted by improved technology. The mortality risk from CVD is down by 40% in this century [NEJM], while the cancer risk is unchanged [CDC]. So people are dying of cancer who would have died of CVD in previous generations. 

This means that low BMI has less benefit for longevity than it used to have, and the trend over time tends to exaggerate the appearance that higher weight is protective against all-cause mortality.

Is it true that cancer risk does not go up with BMI?

The Framingham result is puzzling and difficult to reconcile with a well-established relationship between higher BMI and higher cancer risk. This review by Wolin [2010] finds a modest increase in risks of all common types of cancer associated with each 5-point gain in BMI. (The RR numbers are comparable to hazard ratios above.)

Lung cancer is the big exception, and Wolin explains the inverse relationship with BMI by the fact that people smoke to avoid gaining weight. This would suggest a resolution to the conflict with Zheng’s study, but for the fact that Zheng explicitly corrects for smoking status and finds it makes no difference at all — a result which is puzzling in itself.

Alzheimer’s Disease is the third leading cause of death, and the corresponding story is more complicated. Lower weight in middle age seems to be mildly protective, while it is certainly not protective in the older years when AD is most prevalent.

“Hazard ratios per 5-kg/m2 increase in BMI for dementia were 0.71 (95% confidence interval = 0.66–0.77), 0.94 (0.89–0.99), and 1.16 (1.05–1.27) when BMI was assessed 10 years, 10-20 years, and >20 years before dementia diagnosis.”  [ref]

This, too, is unexpected in light of previous consensus. Alzheimer’s Dementia has been recast as Type 3 Diabetes, because of its strong association with insulin metabolism. Overweight is supposed to be the greatest life-style risk factor for diabetes. When this study [2009] out of U of Washington found that high BMI is protective against dementia, the authors were unwilling to draw the standard causal inference, so they conjectured instead  that weight loss is a consequence of AD’s early stage. 

There may be a better explanation hidden in their data. AD is the most common cause of dementia, but vascular dementia, a separate etiology, accounts for roughly ⅓ of cases in the Kame data set:

There is a suggestion here that higher BMI protects against vascular dementia, but not against AD.

From you, my readers

Here are some of the suggestions offered in the comment section of last week’s blog:

  • Fat people are happier.  I don’t doubt that happiness has a lot to do with longevity but a lot of overweight is due to compulsive eating by people who are not happy with their lives. Obesity is associated with lower socio-economic status, and lower SES is independently associated with shorter lifespan and lower life satisfaction.
  • Higher BMI can mean more muscle mass, not necessarily more fat mass. Good point. I don’t know how big a factor this is.
  • This study [BMJ 2016] found greatest longevity for BMI in the range 20-22.  I take your point that the larger studies with longer follow-up tend to report lower optimal BMI. The BMJ study is a meta-analysis of a huge database covering 9 million subjects.
  • Dean Pomerleau writes at the CR Society web page about brown fat, cold resistance, and greater longevity.
  • Thin people have greater insulin sensitivity, which can lead to glucose going into cells instead of being stored as fat. This is interesting, and deserves more follow-up. But good insulin sensitivity also means lower blood sugar, so its not obvious to me which direction the effect ought to go.
  • I was grateful for a pointer to Valter Longos recent work, recommending that time-restricted eating becomes counterproductive after about 13 hours a day of fasting. Longer fasts several times a year are still highly recommended.
  • Paul Rivas is my go-to authority on weight, and he recommended this 2015 study, which emphasizes the paradox as I describe it.
  • This study out of Emory U [2019] recommends different diets for different BMI groups for minimizing inflammation.

What story does methylation tell?

Aside from mortality statistics, I regard methylation age as the most reliable leading indicator we have. I’ll end by reviewing data on BMI and methylation age.

The Regicor Study [2017] looked for methylation sites associated with obesity. They reported 97 associated with high BMI and an additional 49 associated with large waistline. I compared their lists with my list of methylation sites that change most consistently with age. There was no overlap. What I learn from this is that there is no association with genetically-determined weight and longevity. If you were born with genes that make you gain weight, there is a social cost to be paid in our culture, but there is no longevity penalty.

Horvath [2014] did not discern a signal for obesity with the original 2013 DNAmAge clock, except in the liver where the signal was weak, amounting to just 3 years for the difference between morbidly obese and normal weight. But a few years later with 3 different test groups [2017], a moderate signal was found, as expected, linking higher BMI to greater DNAmAge acceleration. (Age acceleration is just the difference between biological age as measured by the methylation clock and chronological age by the calendar.) 

This study [2019] from the European Lifespan Consortium found a modest increased mortality from obesity, corresponding to less than a year of lost life by most measures, based on two Horvath clocks and the Hannum clock. This Finnish study [2017] found a small association between higher BMI and faster aging in middle-aged adults, but not in old or young adults.

This study from Linda Partridge’s group [2017] found a strong benefit of caloric restriction on epigenetic aging—in mice, not in humans. 

The bottom line

I’ve had a good time with this project, seeking explanations for the paradox, and I’ve passed along some interesting associations, but in the end, the essential paradox remains. I don’t know why the robust association of caloric restriction with longevity doesn’t lead to a clear longevity advantage in humans for a lower BMI. My strongest insight is that the largest determinants of BMI are genetic, not behavioral, and the genetic contribution to weight has no effect on longevity. But what do I make of the fact that life expectancy in the US has risen by a decade over my lifetime [ref] even as BMI has increased 5 points.