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). [healthline.com] 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.

Weight and Aging: A Paradox, Part 1

Caloric restriction is the gold standard life extension strategy, validated over thousands of experiments in many animal species. How can we reconcile this with consistent findings that people who are slightly overweight live longer than normal or underweight folks?

The one fact that everyone in the field of aging agrees on is that animals fed less live longer. This is the result that got me interested in the field 25 years ago, and it is still the most robust finding in the field, verified in dozens of species from yeast cells to Rhesus monkeys.

Are humans different from all other animals?

Last month, a study came out of Ohio State U based on the famous Framingham database, including medical and demographic information on 5,000 people and their offspring, tracked over 74 years. The take-home message was that the people who lived longest were average weight when young and gained weight during their middle years. There were not enough people who had actually lost weight to constitute a subgroup, but the group identified as “low-normal weight” all through their lives showed up with 40% higher all-cause mortality than those that gained weight.

I wrote about this subject in my book, and in one of my first posts on ScienceBlog, back in 2012. The post was titled Ideal Weight may be an Illusion, and I concluded that

For any given individual, it’s probably true that
the less you eat the longer you live.” 

The argument went thus: Weight is mostly fixed by genetics, and the genetic component of weight does not affect longevity. It is relative calorie intake that affects longevity, relative to genetics, body type, and metabolism. For example, a study of genetically obese mice found that they had shortened lifespans if they were fed ad libitem, however, if the obese mice were calorically restricted, they actually lived longer than genetically normal mice, and even longer than CR normal mice, despite the fact that they still appeared plump. 

This line of reasoning led me to hypothesize that the reason overweight people tend to live longer is that they are motivated to restrict calories, whereas people (like me) who don’t get fat no matter how much we eat feel no social pressure to restrain our gluttony.

I thought at the time that we ought to see this effect much more in women than in men, because overweight women are ostracized in our culture, whereas men are not. What I found, contrary to my prediction, was that the BMI with lowest mortality (in Japan) is 23-25 for men, compared to 21-23 for women [Matsuo, 2012].

So, is it time to consider the possibility that caloric restriction doesn’t extend human life expectancy?

New Ohio State Study

The new study is based on the 74-year-old Framingham cohort, people whose health and daily habits have been followed over time. Also followed was a Framingham Offspring cohort, the children of the original Framingham cohort. Almost all the original cohort have now died (so we have extensive mortality data), but many of the offspring cohort is still alive. The authors treat the two cohorts separately, and get somewhat different results for the two cohorts. Dr Zheng was kind enough to send me the full preprint with supplemental tables, and since it’s not yet available online, I’ve made it available for you to read here on GDrive.

The study looks not just at BMI but also at the change in BMI over mid- to late-life years. They classify the trajectories in seven groups, and analyze them using a Cox model. They find that the group that has lowest mortality had an average trajectory beginning at BMI=22 at age 30, increasing gradually to BMI=27 at age 80. The group was broadly defined, so that initial BMI could be anywhere from 18.5 at the low end to 25 at the high end.

Cox Proportional Hazard Model
This statistical method is standard for studies like this evaluating effect on mortality. It is designed to take into account the steep rise in mortality with age, and weight different deaths according to when they occur. The standard assumption is that the mortality curve with age is changed by a multiplicative factor associated with each variable. The mortality curve retains the same shape across ages, but it slides up or down (on a log scale) according to which factors apply to a given subgroup. For example, having a graduate degree may multiply your risk of dying by 0.9 across the board, and eating red meat may multiply your risk by 1.2, so the model actually derives these numbers by assuming that meat-eaters with a graduate degree will have a relative probability of death 1.08 times the control group, and this applies at every age. (where 1.08 = 0.9 * 1.2)Is this quantitatively realistic? Everyone knows it is not, but it yields a single number which is a good benchmark for different longevity factors, and it allows different studies to report their results in a common format for comparison.

Division of subjects into seven groups was somewhat arbitrary, and was done to facilitate statistical analysis. The red railroad tracks represents midline of the trajectory associated with “longest lifespan”, defined above as the minimum Cox factor. The lowest weight group was associated with a Cox factor of 1.4, meaning 40% more likely to die (at a given age) than the red railroad track trajectory.

On the other side

CR extends lifespan in almost every animal model in which it has been tried. I won’t dwell on this, because it’s so well known, but I’ll note that CR works better in short-lived animals, as a percentage of lifespan and the enthusiastic projections of Roy Walford now seem overstated. I have said that I think CR in humans is good for 3 to 5 years. Do I still think so? There is good evidence for CR in humans.

  • Food shortages during World War II in some European countries were associated with a sharp decrease in coronary heart disease mortality, which increased again after the war ended.[Fontana, 2007
  • Fontana performed in-depth metabolic profiles of people identified from the Caloric Restriction Society who were disciplining themselves to eat less. Relative to people at a comparable age, he found “a very low level of inflammation as evidenced by low circulating levels of C-reactive protein and TNFα, serum triiodothyronine levels at the low end of the normal range, and a more elastic ‘younger’ left ventricle, as evaluated by echo-doppler measures of LV stiffness.” [2007]
  • There is at least preliminary evidence that weight loss tends to set back the aging clock, as measured by several methylation algorithms [2020]
  • Higher BMI is associated with older methylation age [2019]
  • C-reactive protein in the blood, the most common measure of inflammation, increases with increasing BMI [2003]
  • Loss of insulin sensitivity is a hallmark of aging, driving many age-related diseases. There is a strong correlation between BMI and diabetes [2007]
  • BMI is linked to most common cancers, the #2 source of mortality. Here’s a good review by Wolin [2010].
  • BMI is also a factor in cardiovascular disease, the #1 killer. This study from Malaysia [2017] found a trend of increasing CVD at every BMI level, but — like other studies — also found that all-cause mortality was lowest for BMI 25-30, which has traditionally been called “overweight”.

So, why doesn’t weight gain show up as a risk factor for faster aging?

I will continue this discussion in Part 2, and try to resolve this paradox in part, but (spoiler alert) I remain puzzled, after a month of reading on the subject.

Source: REB Research https://www.rebresearch.com/blog/fat-people-show-less-dementia/

Universal Clock implies Universal Clockwork

A new methylation clock works in 128 different mammal species, using the same methylation signals. This is the latest evidence that at least some of the mechanisms of aging have been conserved by evolution—strong evidence that aging has a useful function in ecology, so that natural selection actually prefers a finite, defined lifespan.

Einstein taught us that time is relative. Indeed, there are rodents that live less than a year, and Bowhead whales that live more than 200 years. Some of this is just about size and has a basis in physics; but it is well-known that size is only part of the story. Bats and mice are the same size, but bats live ten times longer. Humans are much smaller than horses, but live three times as long.

The first time I met Cynthia Kenyon was circa 1998. She offered me a one-line proof that aging is programmed: the enormous range in lifespans found in nature defies any theory about damage accumulation, because no conceivable process of chemical damage could vary so widely in its fundamental rate. (Think mayflies and sequoia trees.) My own one-line proof is that yeast and mammals share in common some genetic mechanisms that regulate aging, though the last common ancestor of yeast and mammals is more than half a billion years old. These mechanisms include sirtuins and the insulin metabolism.

These intuitions about aging rate and evolutionary conservation have recently come to the world of big data. In this new BioRxiv manuscript, Steve Horvath collaborates with an all-star cast of biologists the world over to compile evidence that there is a universal mechanism underlying development and aging in all mammals, and it is a pan-tissue epigenetic program, not a process of chemical damage.

Brief background on methylation: It is increasingly clear that aging has a basis in gene expression. The whole body has the same DNA, and it doesn’t change over time. However, different genes are turned on and off in different times and places. Turning genes on and off is called “epigenetics”, and evolution has devoted enormous resource to this process. One of many epigenetic mechanisms is the presence or absence of a methyl group on Cytosine, which is one of the 4 building blocks of DNA (A, C, T, G). There are over 20 million regulatory sites in human DNA where methyls can appear or not. Of these, several thousand have been found to consistently correlate with age. The correlation is so strong that the most accurate measures of biological age are now based on methylation. There is (IMO) a developing consensus in the community that methylation changes are an upstream cause of aging, and there remains strong resistance to this idea on theoretical grounds. More background here

The team assembled tissue samples from 59 organs across 128 species of mammals, and looked for commonalities in the progression of methylation that were independent of species and independent of tissue type. They found thousands of methylation sites that fit the bill, attesting to an evolutionarily-conserved mechanism “connected to” aging. It is a short leap to imagine that “connected to” implies a root cause.

How did the authors map age for a mouse onto age of a whale? Just as I might say, “I’m only 10 years old, in dog years,” a year for a whale might be a hundred “mouse years”. The authors took three different approaches. (1) Just ignore it, mapping chronological time directly. (2) Adjust time for the different species based on the maximum lifetime for that species. (3) Adjust time for the different species based on the time to maturity for that species.

Predictably, (1) produced paradoxes; (2) and (3) were similar, but (3) produced the best results. What they didn’t do — but might in follow-on work — was to optimize the age-scaling factor individually for each species to target the best fit with all the other species. Even better would be to choose two independent scaling factors to optimize the fit of each species. Ever since the original 2013 clock, Horvath has divided the lifespan into two regimes, development and aging: In development, time is logarithmic, moving very fast at the beginning and slowing down at the end of development. In the aging regime, time is linear. So it would be natural (optimum, in my opinion) to choose two separate scaling factors that best map each species’s life history course onto all the others. Mathematically, this is (roughly) as simple as matching the slopes of two lines. Horvath has told me he is interested in pursuing this strategy but for some species the existing data doe not cover the lifespan sufficiently to support it.

“Cytosines that become increasingly methylated with age (i.e., positively correlated) were found to be more highly conserved (Fig. 1a)  …Interestingly, although there were 3,617 enrichments of hypermethylated age-related CpGs [i.e., increased methylation with age] across all tissues, only 12 were found for hypomethylated [the opposite] ones.”

Interpretation: with age, we (and other mammals) tend to lose methylation, i.e., to turn on genes that shouldn’t be turned on. There are more sites that demethylate with age than that methylate with age. But the sites that gain methylation tend to be more highly conserved between species. I presume a lot of demethylation is stochastic. It’s easy for a methyl group to “fall off”, but attaching one in the right place requires a specialized enzyme (methyl transferase). What we are seeing here is stronger genetic determinism for the process that requires active intervention.

Question: Would it be useful to develop a methylation clock based solely on sites that gain methylation? What we would thereby avoid is the situation where the age algorithm combines a great many large positive numbers with a great many large negative numbers to make a small difference. This characteristic makes the algorithm overly sensitive to bad data from one or a few particular sites. We can see from the figure above that (red) sites from the top half of the plot have stronger evidence behind them than the (blue) sites from the bottom. What we would lose would be diversity in the basis of the measurement. If retaining that diversity is desirable, it would be possible to design a clock algorithm with both red and blue sites in such a way that all coefficients are relatively small, and no one site contributes inordinately to the age calculation, even if data for that site is completely missing.

Speculation for statistics geeks: I think the methodology that has become standard for developing methylation clocks is not optimal. The standard method is to identify N sites (typically a few hundred) where methylation is well-correlated with age, then derive N coefficients such that you can multiply each coefficient by the corresponding methylation, add up the products, and you get an age estimate*. The way I would do it is with a more complicated calculation, from a methodology called “maximum likelihood”. The idea is to choose the age that minimizes the difference between the expected methylation and measured methylation for the collection of the N sites. To be more specific, minimize the sum of the squares of the z scores for each site, where z is the number of standard deviations by which the measured methylation is different from the expected methylation.It may sound like a complicated calculation to find the age at which this number is a minimum, but it is not. Yes, it’s a guessing game; but the algorithm called “Newton’s method” allows you to make smart guesses so you home in on the best (min Σz2) age within four or five guesses. The calculation is more complicated to program, but it would still execute in a tiny fraction of a second. My proposed method requires maybe 10 or 20 times as many fixed parameters within the algorithm; but the data submitted from each sample is the same.
Caveat – This is all theoretical on my part. I don’t know how much performance would be improved in practice.
*Two footnotes: (1) A constant is also added. (2) In case the subject is young, below the age of sexual maturity, what you get is a logarithm of age, not age itself.

“Importantly, age-related methylation changes in young animals concur strongly with those observed in middle-aged or old animals, excluding the likelihood that the changes are those involved purely in the process of organismal development.”

These plots are adduced as evidence that aging and development are one continuous process under epigenetic control. They come from EWAS=epigenome-wide association studies. Start by asking which sites on the methylome are most closely correlated with age, across many different animals and different tissues in those animals. Start with just the young animals (different ages, but all before or close to sexual maturity. Arrange all the different sites according to how they change methylation with age (increasing or decreasing), just in this age range. Then repeat the process, re-ordering the sites according to how they change with age during middle age.

The left plot above includes a dot for each methylation site, ordered along the X axis according to how they change during youth, and along the Y axis according to how they change during middle age. The point of the exercise is that it is largely the same sites that increase (or decrease) methylation in youth and in middle age.

The middle plot shows the corresponding correlation between middle age (X axis) and old age (Y axis). The right-hand plot shows the correlation between young (X axis) and old age (Y axis). (I believe the labeling of the figure on the right is a misprint.)

This evidence points to a conceptual framework that views development and aging as one continuous process. Development is a lot more complicated than aging. Consequently, most of the sites in the clock are developmental.  Maybe a clock could be optimized for aging only, and it would be more useful for those of us who are using the clocks to assess anti-aging interventions.

“The cytosines that were negatively associated with age in brain and cortex, but not skin, blood, and liver, are enriched in the circadian rhythm pathway”

Here we see again the intriguing connection between the brain’s daily timekeeping apparatus and the epigenetic changes that drive development and aging.

“The implication of multiple genes related to mitochondrial function supports the long-argued importance of this organelle in the aging process. It is also important to note that many of the identified genes are implicated in a host of age-related pathologies and conditions, bolstering the likelihood of their active participation in, as opposed to passive association with, the aging process.”

Another theme in the set of age-correlated genes that the team discovered is mitochondrial function. Mitochondria have an ancient association with cell death, and a long, conserved history with respect to aging. The simple damage themes associated with the free radical theory have yielded to a more complex picture, in which free radicals can be signals for apoptosis or inflammation or enhanced protective adaptations.

The big picture

“Therefore, methylation regulation of the genes involved in development (during and after the developmental period) may constitute a key mechanism linking growth and aging. The universal epigenetic clocks demonstrate that aging and development are coupled and share important mechanistic processes that operate over the entire lifespan of an organism.”

This is cautiously worded, presumably to represent a consensus among several dozen authors, or perhaps to appease the evolutionary biologists looking over our shoulders. The statement is akin to what Blagosklonny has for years called “quasi-programmed aging”, to wit, there are processes that are essential to development that fail to turn off on time, and cause damage as the organism gets older. In the version put forward in this present ms, it is not the gene expression itself but the direction of change of gene expression that carries momentum and cannot be turned off.

Evolutionary theory

Modern evolutionary theory began with Peter Medawar, a Nobel laureate and giant of mid-century biological understanding. (He was 6 foot 5.) Medawar’s 1952 monograph contains the insight that launched all modern theories for evolution of aging. His fundamental idea was that it’s a dog-eat-dog world in which very few few animals live long enough for aging to be a factor in their death. The three main branches of evolutionary theory in response to Medawar are called Mutation AccumulationDisposable Soma, and Antagonistic Pleiotropy. According to Medawar’s thought (and all three theories that followed) old age exists in a “selection shadow” so random processes are at work in old age. It follows that we would expect the aging of a bat and a bowhead whale to be subject to very different random processes. If it is a burden of recently acquired mutations that natural selection has not yet had time to weed out, these should be different for different species. Or if it is about tradeoffs (pleiotropy) between needs of the young animal and the old animal, we would not expect the bat and the whale to be subject to the same tradeoffs.

The Medawar paradigm and its three popular sub-theories all predict that there should be little overlap between the genetic factors involved in aging of species that are adapted so differently. Therefore, the present work documenting a common epigenetic basis of aging is a challenge to the established evolutionary theories of aging.

As I see it, the expression of genes is exquisitely timed for many purposes, so we must view gene expression as subject to tight bodily control. “Accidents” or “mistakes” or “evolutionary neglect” are implausible. For some genes, methylation changes from minute to minute in a way that is adaptive and responsive. Blagosklonny’s idea that there are genes turned on for development and then the body forgets to turn them off doesn’t feel right. Equally, the idea that certain genes are being turned on (or off) progressively through development and then, after development has ended, the process has a momentum of its own so the body can’t stop further turning on (or off) of these same genes is equally implausible. I assume the body is adapted to do exactly what it wants with gene expression, and if the body expresses a combination of genes that causes aging, it’s because that’s what natural selection has designed the body to do. Of course, this looks to be a paradox, as aging is completely maladaptive according to the notion of Darwinian fitness that became accepted in the first half of the 20th century; but evolutionary biologists have broadened the notion of fitness since then, and I’ve written volumes concerning this paradox.

The bottom line

For personal application to individuals who want to know how well they are doing and their future life expectancy, I recommend Horvath’s Grim Age clock as the best available. (Elysium has done a lot of work on their Index product, and it may be as good or better, but it’s impossible to evaluate unless they release their proprietary methodology.) For application to studies of anti-aging interventions (including my own project, DataBETA), the choice of clocks is not clear, because it depends not just on statistics but on theory. We want a clock that is not only accurate, but that is based on epigenetic causes of aging, not epigenetic responses to aging. The multi-species clock is a welcome contribution, precisely because epigenetic processes that are conserved across species are more likely to be linked to the root cause of aging. For the future, I’ve made suggestions above for ways the multi-species clock might be made even better.

A Science of Wholeness Awaits Us

Just as the melody is not made up of notes nor the verse of words nor the statue of lines, but they must be tugged and dragged till their unity has been scattered into these many pieces, so with the World to whom I say Thou Martin Buber

We creatures of the 21st Century, grandchildren of the Enlightenment, like to think that our particular brand of rationality has finally established a basis for understanding the world in which we live. Of course, we don’t have all the details worked out, but the foundation is solid. 

We might be chastened by the precedent of Lao Tzu and Socrates and Hypatia of Alexandria and Thomas Aquinas and Lord Kelvin, who thought the same thing. I wonder if the foundation of our world-view is really made of more durable stuff than theirs. In fact, founding our paradigm in the scientific method offers us something that earlier sages did not have: we can actually compare in detail the world we observe and the consequences of our physicalist postulates. The results are not reassuring. In recent decades, the science establishment has willfully ignored observations of phenomena that call into question our foundational knowledge.

Reductionism is the process of understanding the whole as emergent from the parts. The opposite of reductionism is holism: understanding the parts in terms of their contribution to a given whole. It’s fair to say that all of science in the last 200 years has been reductionist. Physical law is the only fundamental description of nature. Chemistry could, in principle, be derived from physics (if only we could solve the Schrödinger equation for hundreds of electrons); living physiology could be understood in terms of chemistry; and ecology could be modeled in terms of individual behaviors. 

Curiously, there are holistic formulations of physics that are mathematically equivalent to the reductionist equations, but in practice, physicists use the differential equations, which are the reductionist version. 

Biological function is explained by a process of evolution through natural selection that made them what they are. Holism in evolution is called “teleology”, and is disparaged as unscientific. But when features of physics appear purposeful, there is no agreement among scientists how to explain them. Most physicists would avoid invoking a creator or embedded intelligence, even at the cost of telling stories about vast numbers of unobservable universes outside our own. This is the most common explanation for the fact that the rules of physics and the very constants of nature—things like the charge on an electron and the strength of the gravitational force—these things seemed eerily to have been fine-tuned to offer us an interesting universe; most other choices for the basic rules of physics might have produced dull uniformity, without stars or galaxies, without chemistry, without life.

But I am racing ahead of the story. The question I want to ask is whether we are missing something in reasoning exclusively from the bottom up, explaining all large-scale patterns as emergent results of small-scale laws. I want to suggest that this deeply-ingrained pattern of thought may be holding science back. Are there large-scale patterns waiting to be discovered? Are there destined outcomes that help us understand the events leading to a predetermined denouement? Even formulating such questions is controversial; and yet, we see hints pointing in just this direction, both from micro-science of quantum mechanics and from studies of the Universe on its largest scale.

Science is all about observing nature and noticing patterns which might be articulated as theories or laws. When these patterns connect nearby events that can be observed at one time by one person, they are easy to spot. When the patterns involve distant events and stretch over time and space, they may go undetected for a long while. This can lead to an obvious bias. Scientists are more inclined to formulate laws of nature that connect contiguous events than laws that connect events that are separated spatially and temporally, just because these global patterns are harder to see.

The physical laws that were formulated and tested in the 19th and 20th century were all mediated by local action. The idea that all physical action is local was formalized by Einstein, and has been baked into our theories ever since. But there is a loophole, defined by quantum randomness. Roughly speaking, Heisenberg’s Uncertainty Principle says that we can only ever know half the information we need to predict the future from the past at the microscopic level. Is the other half replaced by pure randomness, devoid of any patterns that science might discern? Or might it only appear random, because the patterns are spread over time and space, and difficult to correlate? In fact, the existence of such patterns is an implication of standard quantum theory. (This is one formulation of the theorem about quantum entanglement, proved by J.S. Bell in 1964.) Speculative scientists and philosophers relate this phenomenon to telepathic communication, to the “hard problem” of consciousness, and to the quantum basis of life.

I hope to explore this topic in a new ScienceBlog forum beginning in 2021. Here are four examples of the kinds of phenomena pointing to a new holistic science.

1. Michael Levin and the electric blueprint for your body

We think of the body as a biochemical machine, proteins and hormones turned on in the right places at the right times to give the body its shape. Levin is clear and articulate in making the case that the body develops and takes shape under a global plan, a blueprint, and not just a set of instructions. This is true for humans and other mammals, but it is easier to prove it for animals that regenerate. Humans can grow back part of a liver. An octopus can grow a new leg; a salamander can grow a new leg or tail tail; a zebrafish can grow back a seriously damaged heart; starfish and flatworms can grow back a whole body from a small piece.

Consider the difference between a blueprint and an instruction set. An instruction set says

1. Screw the left side of widget A onto the right side of gadget B.
2. Take the assembly of widget+gadget and mount it in front of doodad C, making sure the three tabs of C fit into the corresponding holes in B

A blueprint is a picture of the fully assembled object, showing the relationship of the parts.

Ikea always gives you both. With the instructions only, it is possible to complete the assembly, but only if you don’t make any mistakes. And if the finished object breaks, the instruction set will not be sufficient to repair it. The fact that living things can heal is a strong indication that they (we) contain blueprints as well as instruction sets. The instruction set is in the genome, together with the epigenetic information that turns genes on and off as appropriate; but where is the blueprint?

Prof Michael Levin of Tufts University has been working on this problem for almost 30 years. The answer he finds is in electrical patterns that span across bodies. One of the tools he pioneered is voltage reporter dyes that glow in different colors depending on the electric potential. Here is a map of the voltage in a frog embryo, together with a photomicrograph.

from Levin’s 2012 paper

Levin’s lab has been able to demonstrate that the voltage map determines the shape that the tadpole grows into as it develops. Working with planaria flatworms, rather than frogs, their coup de grace was to modify these voltage patterns “by hand”, creating morphologies that are not found in nature, such as the worm with two heads and no tail.

This is stunning work, documenting a language in biology that is every bit as important as the genetic code. Of course, I am not the first to discover Dr Levin’s work; but it is underappreciated because the vast majority of smart biologists are focusing on biochemistry and it is a stretch for them to step out of the reductionist paradigm.

(I wrote more about Levin’s work two years ago. Here is a video which presents a summary in his own words.)

2. Cold Fusion

Two atomic nuclei of heavy hydrogen can merge to create a single nucleus of helium, and tremendous energy is released. This process is not part of our everyday experience because the hydrogen nuclei are both positively charged and the energy required to push them close enough together that they will fuse is also enormous. So fusion can happen in the middle of the sun, where temperatures are in the millions of degrees, and fusion can happen inside a thermonuclear bomb. But it’s hard as hell to get hydrogen to fuse into helium, and, in fact, physicists have been working on this problem for more than 60 years without a viable solution.

Except that in 1989, the world’s most eminent electrochemist (not exactly a household name) announced that he had made fusion happen on his laboratory bench, using the metal palladium in an apparatus about as complicated as a car battery.

Six months later, at an MIT press conference, scientists from prestigious labs around the world lined up to announce they had tried to duplicate what Fleischmann had reported with no success. The results were un-reproducible. Cold Fusion was dead, and the very word was to become a joke about junk science. Along with the vast majority of scientists, I gave up on Cold Fusion and moved on. 22 years passed. Imagine my surprise when I read in 2011 that an Italian entrepreneur had demonstrated a Cold Fusion boiler, and was taking orders!

The politics of Cold Fusion is a story of its own. I wrote about it in 2012 (not for ScienceBlog). The Italian turned out to be a huckster, but the physics is real.

I began reading, and I became hooked when I watched this video. I visited Cold Fusion labs at MIT, Stanford Research Institute, Portland State University, University of Missouri, and a private company in Berkeley, CA. I went to two Cold Fusion conferences. I became convinced that some of the claims were dubious, and others were convincing. There is no doubt in my mind that Cold Fusion is real.

Physicists were right to be skeptical. The energy for activation is plentiful enough, even at room temperature, but the problem is to concentrate it all in one pair of atoms. Left to its own devices, energy will spontaneously spread itself out— that’s what the science of thermodynamics is all about. To concentrate an eye-blink worth of energy in just two atoms is unexpected and unusual. But things like this have been known to happen, and a few times before they’ve taken physicists by surprise. Quantum mechanics plays tricks on our expectations. A laser can concentrate energy, as billions of light particles all march together in lock step. Superconductivity is another example of what’s called a “bulk quantum effect”. Under extraordinary circumstances, quantum mechanics can leap from the tiny world of the atom and hit us in the face with deeply unexpected, human-scale effects that we can see and touch.

There are now many dozens of labs around the world that have replicated Cold Fusion, but there is still no theory that physicists can agree on. What we do agree is that it is a bulk quantum effect, like superconductivity and lasers. When the entire crystal (palladium deuteride) asks as one quantum entity, strange and unexpected things are possible.

For me, the larger lesson is about the way the science of quantum mechanics developed in the 20th Century. The equations and formalisms of QM are screaming of connectedness. Nothing can be analyzed on its own. Everything is entangled. The quantum formalism defies the reductionist paradigm on which 300 years of previous science had been built.

And yet, physicists were not prepared to think holistically. We literally don’t know how. If you write down the quantum mechanical equations for more than two particles, they are absurdly complex, and we throw up our hands, with no way to solve the equations or even to reason about the properties of the solutions. The many-body quantum problem is intractable, except that progress has been made in some highly symmetrical situations. A laser consists of a huge number of photons, but they all have a single wave function, which is as simple as a wave function can be. Many-electron atoms are conventionally studied as if the electrons were independent (but constrained by the Pauli Exclusion Principle). Solid state physics is built on bulk quantum mechanics of a great number of electrons, and ingenious approximations are used in combination with detailed measurements to reason about how the electrons coordinate their wave state.

Cold Fusion presents a huge but accessible challenge to quantum physicists. Beyond Cold Fusion lie a hierarchy of problems of greater and greater complexity involving quantum effects in macroscopic objects.

In the 21st Century, there is a nascent science of quantum biology. It is my belief that life is a quantum state.

3. Life coordinates on a grand scale

There are many examples of coordinated behaviors that are unexplained or partially explained. This touches my own specialty, evolution of aging. The thesis of my book is that aging is part of an evolved adaptation for ecosystem homeostasis, integrating the life history patterns of many, many species in an expanded version of co-evolution. My thesis is less audacious than the Gaia hypothesis.

  • Monarch butterflies hibernate on trees in California or Mexico for the winter. In the spring, they migrate and mate and reproduce, migrate and mate and reproduce, 6 or 7 times, dispersing thousands of miles to the north and east. Then, in the fall, the great great grand offspring of the spring Monarchs undertake the entire migration in reverse, and manage to find the same tree where their ancestor of 6 generations spent the previous winter. [Forest service article]
  • Zombie crabs have been observed in vast swarms, migrating hundreds of miles across the ocean floor. Red crabs of Christmas Island pursue an overland migration

  • Sea turtles from all over the world arrange for a common rendezvous once a year, congregating on beaches in the Caribbean and elsewhere. Their navigation involves geomagnetism, but a larger mystery is how they coordinate their movements.
  • Murmuration behavior in starlings has been modeled with local rules, where each bird knows only about the birds in its immediate vicinity; but I find the simulations unconvincing, and believe our intuition on witnessing this phenomenon: that large-scale communication is necessary to explain what we see.
  • Monica Gagliano has written about plants’ ability to sense their biological environment and coordinate behaviors on a large scale. This is her more popular book.

4. The Anthropic Coincidences, or the Improbability of Received Physical Laws

For me, this is the mother of all scientific doors, leading to a radically different perspective from the reductionist world-view of post-enlightenment science. Most physicists believe that the laws of physics were imprinted on the universe at the Big Bang, and life took advantage of whatever they happened to be. But since 1973, there has been an awareness, now universally accepted, that the laws of nature are very special, in that they lead to a complex and interesting universe, capable of supporting life. The vast majority of imaginable physical laws give rise to universes that are terminally boring; they quickly go to thermodynamic equilibrium. Without quantum mechanics, of course, there could be no stable atoms, and everything would collapse into black holes in short order. Without a very delicate balance between the strength of electric repulsion and the strong nuclear force, there would be no diversity of elements. If the gravitational force were just a little weaker, there would be no galaxies or stars, nothing in the universe but spread-out gas and dust. If our world had four (or more) dimensions instead of three, there would be no stable orbits, no solar systems because planets would would quickly fly off into space or fall into the star; but a two-dimensional world would not be able to support life because (among other reasons) interconnected networks on a 2D grid are very limited in complexity.

Stanford Philosophy article
1995 book by Frank Tipler and John Barrow
Just Six Numbers by Martin Rees

Most scientists don’t take account of this extraordinary fact; they go on as if life were an inevitability, an accident waiting to happen. But those who have thought about the Anthropic Principle fall in two camps:

The majority opinion:  There are millions and trillions and gazillions of alternative universes. They all exist. They are all equally “real”. But, of course, there’s no one looking at most of them.  It’s no coincidence that our universe is one of the tiny proportion that can support life; the very fact that we are who we are, that we are able to ask this question, implies that we are in one of the extremely lucky universes.

The minority opinion:  Life is fundamental, more fundamental than matter.  Consciousness is perhaps a physical entity, as Schrödinger thought; or perhaps it exists in a world apart from space-time, as Descartes implied 300 years before Schrödinger; or perhaps there is a Platonic world of “forms” or “ideals” [various translations of Plato’s είδος] that is primary, and that our physical world is a shadow or a concretization of that world.  One way or another, it is consciousness that has given rise to physics, and not the other way around.

If you like the multi-universe idea, you will want to listen to the recent Nobel Lecture of Roger Penrose. He races to summarize his life’s work on General Relativity to end the lecture with evidence from maps of the Cosmic Microwave Background of fossils that came from black holes in a previous universe, before our own beloved Big Bang.

I prefer the minority view, not just because it provides greater scope for the imagination [Anne of Green Gables]; there are scientific reasons that go beyond hubristic disregard of Occam’s razor in postulating all these unobservable universes.

  • Quantum mechanics requires an observer.  Nothing is reified until it is observed, and the observer’s probes help determine what it is that is reified.  Physicists debate what the “observer” means, but if we assume that it is a physical entity, paradoxes arise regarding the observer’s quantum state; hence the “observer” must be something outside the laws that determine the evolution of quantum probability waves.  Cartesian dualism provides a natural home for the “observer”.
  • Parapsychology experiments provide a great many indications that awareness (and memory) have an existence apart from the physical brain.  These include near-death experiences, telepathy, precognition, and clairvoyance.
  • Moreover, mental intentions have been observed to affect reality.  This is psychokinesis, from spoon-bending to shading the probabilities dictated by quantum mechanics.

Finally, the idea that consciousness is primary connects to mystical texts that go back thousands of years. 

Dao existed before heaven and earth, before the ten thousand things.  It is the unbounded mother of all living things.

                     — from the Dao De Jing of Lao Tzu

Please look for my new page at ExperimentalFrontiers.ScienceBlog.com, coming soon.