The Selfish Gene can’t explain aging – but neither can Multi-level Selection

This is the second of two parts on the history of evolutionary theory.  Last week, I discussed the gene-eyed view of evolution that came to dominate evolutionary theory of the 20th century.  In the 1960s, this view hardened into a dogma, and provoked a reaction, in recognition of the many cooperative networks in nature that are difficult to explain in terms of “kin selection,” the only recourse of the Selfish Gene.  In Part Two, below, I continue with the science of multilevel selection (MLS), and talk about why aging is a tough nut to crack.  Clearly the selfish gene paradigm is inadequate to explain aging.  MLS provides a formal test for deciding whether a given trait can evolve via group selection, and according to these criteria, aging should not be able to evolve.  

Where do we go from here?  

What is missing from both systems is ecology.  When species’ interpendence is taken into account, it becomes possible to understand aging and many other cases where individuals sacrifice their own fitness to the community.


George Price and D.S. Wilson

We left off last week with kin selection, which is the only source of cooperative behavior that is recognized from the selfish gene perspective.  The science of kin selection was formalized by W. D. Hamilton in 1964 (part 1, part 2).

Just a few years after Hamilton, another young Brit named George Price expanded Hamilton’s rule, using math developed by Fisher himself.  Price’s contribution was to realize that a gene might be shared not just by brothers and cousins, but many distant relatives, and perhaps some unrelated individuals as well.  He read about the controversy surrounding group selection, and he proposed to resolve the question with statistical analysis.  The result was the Price Equation.

Following Fisher’s idea, Price imagined a large population of a single species that remained constant from one generation to the next.  What Price added was to divide the population into cooperative groups.  An altruistic gene, in Price’s model, would give the individual in which it appeared a disadvantage compared to others in that group that might not carry the altruistic gene.  But the group as a whole would thrive in competition with other groups, based on the number of copies of the altruistic gene that appeared in group members.  In other words, a cooperative gene is conceived as increasing the fitness of a group in proportion to how many members of the group have that gene.  This benefits the group in competition with other groups.  However, within each group, some individuals have the gene and others do not.  The individuals who carry the cooperative gene are at a disadvantage relative to selfish individuals within the same group that do not carry the gene.

The Price Equation applies analysis strictly by Fisher’s rules to to this situation, and it measures the balance between individual selection and group selection that can determine the fate of an altruistic gene.

Fig 1a.  This is the model of Fisher.  Genes increase or decrease in frequency over time within a population of fixed total size.

Fig 1b.  This is the model of Price.  Two things are going on simultaneously.  Genes increase or decrease in frequency within each population based on their individual effects.  Meanwhile, some groups are growing at the expense of other groups, based on the aggregate group effects of these same genes.

The form of the Price Equation may be something that only a statistician can love.  But the conclusions are actually quite sensible and intuitive.  In the competition between selfishness and altruism, how do we know which will come out on top?

Group selection prevails, and altruism wins when

  • the cost to the individual is small, and the benefit to the group is large
  • groups are well-segregated with altruists all clumped together in separate groups from non-altruists

Conversely, individual selection prevails, and altruism loses when

  • the cost to the individual is large compared to the benefit to the group
  • groups are integrated, with similar mixtures of altruists and non-altruists in each

The story of Price’s life is wonderfully colorful and tragically fated.  Price did not live to see his ideas spawn a cooperative cult within evolutionary biology, expanding to become a formidable and respected minority in the 21st Century.  It was David Sloan Wilson, a graduate student at Harvard and later a professor at Binghamton, who picked up the Price Equation and built his career promoting and popularizing the idea of group selection, citing hundreds of examples of cooperation in nature that are not well-explained by Hamilton’s Rule, and establishing the plausibility of Price’s model in explaining these.  Wilson promotes the science of multi-level selection (MLS) based on a dynamic balance between evolution operating simultaneously at the level of the individual, and larger communities, groups and ecosystems.

This would seem to be an inclusive position.  Why should it inspire such controversy?  Wilson has been extra-cautious in establishing the scientific basis for multi-level selection.  Nevertheless, he has become a lightning rod for charges that group selection is a fallacious idea, and that the science of MLS somehow lacks legitimacy.  Richard Dawkins, in particular, has become a vituperative antagonist of Wilson’s work.



Aging shows many characteristics that indicate an evolved adaptation – most notably that the genetic basis for aging is hundreds of millions of years old, and seems to have been conserved from microbes to man.  But aging offers no selective advantage to the individual.  In fact, getting old, losing your fertility and dying can only decrease your individual fitness.  So evolution of aging is a prime candidate for explanation in terms of MLS.  Maybe aging evolved because of its benefit to the community.

So it is natural to apply Price’s criteria.  Do we expect group selection or individual selection to predominate in the case of aging?

  • The cost to the individual is high.  (This was the surprising conclusion of a survey of mammals and birds by Daniel Promislow in 1991, and confirmed later by Ricklefs and Bonduriansky with very different methodology.)  Though few animals in the wild drop dead of old age, there are many that begin to lose their competitive edge with age, and they succumb to predators or parasites from which young animals escape.  In other words, individual selection against aging is expected to be very strong.  Strike one.

  • The benefit to the group is hard to measure in the short run.  It comes from maintaining population diversity, and enhancing opportunities for reproduction, so that in the long run the population can evolve faster.  But before we even get to the “long run”, aging has a significant barrier to overcome in the short run, and in the short run, the genetic basis for aging ought to be disappearing more and more from each successive generation.  Strike two.

  • The group that benefits from aging is not well-confined to others that carry the genes for aging.  Far from it.  The only benefit from aging and programmed death is to open a space in the niche, and there is no guarantee that the individual who grows to fill that space will even be of the same species, let alone that it should carry the same gene for aging.  In the SG perspective and the MLS perspective alike, an altruistic trait can evolve only to the extent that the benefits of that trait accrue to others who carry that trait, and this is not the case with aging.  Strike three.

And so, as an explanation for the evolution of aging MLS strikes out.


Beyond the Price Equation

The critical omission in the analyses of all three scientists, Fisher, Hamilton and Price, is the volatility of population size, based on interactions among different species.  Population dynamics is the key to understanding why nature provides so many stunning examples of cooperation, and how the “cheater problem” has been avoided.  This was the insight of Michael Gilpin, a mathematical physicist who turned his attention to the problem of group selection in the mid-1970s.  At a time when computer models were already in common use among physicists but not yet among biologists, Gilpin brilliantly exploited the crude computational resources of the day to build a more sophisticated and general model than Fisher or Hamilton or Price.  His 1975 monograph, called Group Selection in Predator- Prey Communities, challenged fundamental assumptions of evolutionary theory, and created a model that is far more consonant with biological reality.  Gilpin’s work remains poorly understood and its significance unappreciated to this day.

The essence of Gilpin’s message was that the best strategy for individual success is to eat more and transform the biomass of your food into your own offspring, which take over the population.  This strategy leads at the individual level to success, and at the group level it leads to rapid population growth.  The catch is that “food” is also biological.  Whatever your species is eating will suffer a decline as you try to eat more and more, and simultaneously your numbers are growing and growing.  As your species prospers, your food species goes into decline.

Here’s the punch line:  Exponential mathematics makes this process rapid and dramatic.  One generation there’s plenty of food, the next generation there is no food at all.  The process of ecosystem collapse is very rapid.  It is a powerful Darwinian force, a fitting counter-balance to the raw strength of selection for selfishness.

(The idea that population regulation is an adaptive biological function, evolved by group selection was not new with Gilpin.  In his introduction, he credits V. C. Wynne-Edwards (1962) and Sir Alexander Carr-Saunders (1925) before him.  But Gilpin put these ideas on a solid mathematical footing, with an explicit mechanism of action.)

If you evolve to maximize your individual fitness, your children will starve.  This insight changes the landscape of evolutionary biology, and remains unappreciated to this day.

And what of the argument that ecosystems are stable and slow to change?  By all appearances they provide a constant biological background in which species may evolve slowly, one at a time.

The answer is that ecosystems are only stable because they are evolved to be stable.  If every individual were behaving selfishly, as Fisher postulated, then stable ecosystems would be an impossibility.  The stable ecosystems that we see only exist because, long ago, individual selection has been balanced by group selection at the level of the entire ecosystem, severely constraining individual selfishness.


Implications for Aging and Beyond

Virtually all of evolutionary theory is rooted in a paradigm where you can separate what happens in a single generation, and then think of multi-generation evolution as if it were an iteration of this single-generation process, over and over again.  Another part of the paradigm is that you can think about one trait evolving in one species apart from its effect on other traits and other species.  Not that anyone believes these things are literally true, but it is widely assumed that they are a rough-and-ready first approximation, a good start for a theory that can afford to refine itself later on, at a different level of detail.  This turns out not to be true.  Ecological interactions must be included from the get-go, or we get a very distorted picture of how evolution works.

Aging contributes to taming population dynamics, to slowing the exponential growth, and to softening the inevitable population crashes that follow.  This is not a slow, long-term benefit (like population diversity), but an immediate and powerful force, providing protection against a danger that would otherwise wipe out a species in just a few generations’ time.   It is a powerful and tangible group-level benefit that simply is not captured in the world-view of the Price Equation.

Beyond aging, there are many other coooperative traits for which you might wonder, how could they have evolved “uphill” in the face of strong individual selection against them?  The answer is that the whole system of evolution has evolved to avoid rewarding individuals that exploit resources unsustainably.  This is part of the story of evolution of evolvability.

The way that evolution works is not what we naively imagined in the early 20th Century, but the dynamic of evolution itself has evolved to be far more sophisticated and subtle.

The Selfish Gene vs Multi-level Selection: Aging Doesn’t Fit

This is the first of two parts on the history of evolutionary theory.  First came neo-Darwinism, popularized as the selfish gene.  Genes are evolved to promote their own replication, and also copies of themselves that exist in relatives.  In the 1970s, the theory was extended by George Price to deal rigorously with groups that may or may not be related.  This is now known as multi-level selection (MLS).  There is an ongoing dialog in the evolutionary community about whether MLS is significant in nature, which is still the minority view.  The majority continues to hold that everything should be explainable in terms of the selfish gene.  But aging cannot be explained by the selfish gene; and even with the considerably broader perspective of MLS, the evolution of aging remains problematic.  What is missing from both systems is ecology.  When species’ interdependence is taken into account, it becomes possible to understand aging and many other cases where individuals sacrifice their own fitness to the community.

Darwin’s theory was descriptive and qualitative.  Sixty years after Darwin, A British mathematician named Ronald A. Fisher proposed the first fully quantitative model of how evolution might work.

In the 18th and 19th Centuries, the science of physics had advanced through reductionism: dividing systems into their constituent parts, and demonstrating that each part obeyed simple, universal mathematical laws. Fisher wished to do for biology what Newton and Maxwell had done for physics.  Physics built everything from simple point particles.  Fisher asked himself, what are the atoms of evolution – the irreducible elements from which he might construct a theory.

In populations of bacteria, Fisher might have taken the individual as an atom of inheritance.  Individual bacteria make exact copies of themselves, and the number of copies might be taken as a a measure of the individual’s success in evolutionary competition.  But in sexual populations, no two individuals are alike.  What is the “atom” of evolution for a sexual species?


In search of the “Atom” of evolution

Genetics was yet a new science in 1920, as Gregor Mendel’s breeding experiments in the mid-19th Century had just been rediscovered, and their importance for evolutionary science was first appreciated.  In 1909, Danish botanist Wilhelm Johannsen had coined the word gene to describe the theoretical unit of inheritance.  (Of course, it would be another 20 years before Erwin Schroedinger connected the gene conceptually to a length of DNA on a chromosome.)  Fisher took the gene to be the atom of evolutionary biology.  His theory would be about genes.

In Fisher’s model, there is a single species with a fixed population.  (In other words, exactly two offspring survive from each mating pair, so that the death rate exactly matches the birth rate, not just in the long run, but exactly in every generation.) Genes circulate freely among different individuals, as they are mixed and swapped and traded during sexual reproduction.  Different genes do not interact with one another, but each one contributes to making the individual better able to survive and reproduce, or not.  Those that make a positive contribution to survival and reproduction increase in gene frequency from one generation to the next.  This just means that there will be more individuals carrying that gene in the next generation than there are in the present generation, because the gene has been successful in enhancing either survival or reproduction.  This success, in Fisher’s model, is a property of the gene, and he identified the gene’s success in spreading through the population with what Darwin called fitness.

(Fitness is a property of the gene, not the indivdual animal or the population of animals or the species.  What might Darwin have thought of this?  Darwin’s was a very different scientific culture, built on a firm foundation of observation in the natural world, sparing of theoretical claims.  His “theory” would not be called a theory today, because he never defined terms rigorously, or specified the mechanistic details of natural selection.)


Fisher successfully argues that his simple model is an accurate reflection of reality

From the beginning, there were questions about whether genes could be considered as free agents, with individual interests of their own.  A gene only makes sense within a given environment.  That environment includes other genes, other individuals, and other species.  Systems in biology are just too interdependent to admit the kind of reductionism that worked for physics, argued Sewall Wright, who was Fisher’s intellectual sparring partner in the 1930s and 40s.  Fisher was a brilliant mind and a commanding presence.  He won the debates.

The contentious assumptions on which Fisher’s model was based were

  • No interaction between different genes (= no epistasis)
  • No interaction among indiivdual animals or plants of the same species
  • No change in the environment, or in other species while the gene is propagating
  • Genes spread through the population and are mixed at random, so combinations of genes that work particularly well together are no more likely to occur than combinations that work at cross purposes.

Fisher (who was trained in mathematics and not biology) was not so naïve as to think that these assumptions were literally true.  But in defending his model through the following decades, he argued that the world is a big place, and that in the long run you can focus on what matters most, and everything else will average out.  It is the genes that matter most, because it is the genes that are evolving.


The Dilemma of Altruism

Fisher never heard of the “selfish gene”.  The term was coined by Richard Dawkins a few years after Fisher’s death in 1962.  But selfish genes were exactly the subject of his model, and Fisher’s theory can fairly be called a “selfish gene” theory.  Biologists looking at the real world see a lot of cooperation.  Animals make sacrifices for the sake of their children all the time.  Almost as common is the tendency of animals to nest together, to hunt together, to cooperate in groups that may or may not share the same genes.  Often an individual will sacrifice its own fitness for the benefit of the community, and the community, in turn, makes possible a much more secure environment in which the individual may flourish.  (Here’s a striking example from amoebas – which may not be the first image to come to mind when you think of the English word altruism, but which is a classic of evolutionary altruism.)  But what prevents individuals from “cheating”?  Why don’t they evolve to mooch off the community, taking all the benefits while shirking the sacrifice and communal responsibility.  A gene that promoted this behavior would be successful, because its host would receive all the benefits of the community, and in addition would save energy by not contributing to the communal welfare, and so it would have extra energy to channel into its own success in propagating into the next generation.  Why don’t cheaters evolve to undermine cooperative communities?  Why do we see any cooperation at all in nature? Fisher’s model didn’t readily account for this, and evolutionary biologists looked for a way to expand his theory so as to explain cooperation.


Kin Selection and the Legacy of W.D. Hamilton

In the early 1960s, William Hamilton was a promising young grad student at the London School of Economics when he conceived an answer to this dilemma, which was quickly recognized as a seminal contribution to the field, and to this day remains the only mechanism that is universally accepted by the many scientists who work within the Fisher model. Hamilton’s idea: a copy of a gene acts within a particular individual, but brothers and children and cousins of that individual have a certain probability of carrying an identical copy of that same gene, because of their relatedness.  It is the gene that is selfish after all, and not the individual.  Genes will evolve to promote copies of themselves that exist in other individuals, even at the expense of the individual carrying that gene.  Hamilton’s rule  is a simple mathematical expression of the balance between effects that a gene’s action has on the self, and on relatives that might carry copies of the same gene.  A quip attributed to Hamilton references the math: “I would lay down my life for two brothers or eight cousins.”  What this means is that if I have a particular gene, then my brother has a 50% probability of having the identical gene, because each parent contributes half his or her genes to each child, randomly selected.  Similarly, the probability that my first cousin shares a copy of this gene (which impels me to risk my life for a relative) is ⅛.

Group Selection

During the 1960s, the community of evolutionary scientists was assimilating the mathematical framework that Fisher had developed several decades earlier, and grappling with the question how cooperation can arise in a process of natural selection.  Conceptually, there were two big ideas that became competing models.  One was group selection, the idea that natural selection pits not just individual-vs-individual but group-vs-group.  A gene for altruism may be defined as one that depresses the fitness of the individual that carries it, but that promotes fitness of the trait group within which the individual cooperates.  If evolution operates on the level of groups, that solves the dilemma of altruism.  Groups that have more copies of the altruism gene simply out-compete groups that have fewer copies.  This is a Darwinian process analogous to the one that Fisher modeled, but operating at a higher level of selection.  The second alternative was that Hamilton’s Rule explains all.  The conservative assumption is that only closely-related groups can maintain their cohesiveness and avoid evolving “cheaters”.  Group selection is an unnecessary complication, a fifth wheel of evolutionary theory.  George Williams and  John Maynard Smith, two bright young leaders in the fast-developing field of mathematical biology, argued that group selection was not only unnecessary but improbable as well.  Look at ecosystems in the real world, they said.  Individual deaths are common, but group exctinctions are rare.  Gradual changes in gene frequency are common.  Group selection requires one group to drive another out of business, and this doesn’t seem to happen very often.

This is the argument that carried the day, and among evolutionary scientists the idea of group selection came to bear a stigma.  Over time, skepticism of group selection grew to be a prejudice that has outlived the memory of where it comes from and on what grounds it is based.  It continues today as a scientific bias.

Are there many examples in nature of behaviors that Hamilton’s Rule can’t explain?
TBC next week

Molecules in the Blood that Signal Self-Destruction

My theme the last few weeks has been the signals that trigger the body’s self-destruction with age.  The easiest to target are molecules that circulate in the blood.  Last week, I covered a few that decrease in blood concentration as we age, and that cripple our defenses.  This week, I’ll talk about signals that increase as we age, and that activate self-destruction.  There are two principal mechanisms of active self-destruction:  inflammation and apoptosis=cell suicide.  Both mechanisms are important to the healthy functioning of the body.  Inflammation is our first defense against invading pathogens.  Apoptosis elimnates diseased and cancerous cells before they can become a problem.  But in old age, both are co-opted for self-destruction.  Healthy tissues in the body are destroyed by apoptosis, and inflammation leads to arterial damage and heart disease, DNA damage and cancer.  

The blood signals, too, are have helpful roles earlier in life, but turn traitorous with age.

Two weeks ago, I listed some protective hormones that are down-regulated with age.  In this column, I review some hormones and other signals in the blood that rise with age, and contribute to weakness, loss of function and the diseases of old age.



The pro-inflammatory hormone NFkB was first recognized in the 1940s as a risk factor for cancer.  Circulating NFkB increases with age, and it is hard to imagine any benefit to the body from this increase.  Last May, the laboratory of Dongsheng Cai at Einstein Med School reported that NFkB is secreted by the hypothalamus — a tiny endocrine gland embedded within the brain, which is known as the clock that controls the daily rhythm of wake/sleep cycles.  (More speculatively, the hypothalamus also controls the timing of development and, by extension, aging as well.)  Cai was able to shorten life span in mice with extra NFkB, and to lengthen life span (10% increase in maximum life span) by inhibiting NkB.

One of the least-understood aspects of ageing is its coordinated and stereotyped progression in all organ systems. Although researchers have long suspected that the brain orchestrates systemic ageing, compelling evidence of this in mammals has been lacking. Furthermore, we have had no clear understanding of how ageing is affected by inflammation, which is a hallmark of age-related diseases such as diabetes, cardiovascular disease, arthritis and Alzheimer’s disease. In this issue, Zhang et al.1 (page 211) help to make this connection by documenting the integration of inflammatory responses with systemic control of ageing by the hypothalamus — a part of the brain that controls growth, reproduction and metabolism*.  [Ref]



Smad proteins are circulating transcription factors.  In other words, they are a way that a central command in the body can send a signal through the blood and affect which proteins are transcribed from DNA and manufactured in the peripheral cell.

Smad3 affects creation of new cells which is necessary not just for healing but for keeping muscles healthy and strong.  Stem cells in the muscles are called satellite cells, and these are activated every time we exercise to renew and strengthen the muscles.

In aging muscle cells, P-Smad3 increases and holds back the cells’ regenerative power.  In vitro studies show that satellite cells can be rejuvenated by reducing P-Smad3.  [Ref]



Wnt is a signal from outside of the cell that affects gene transcription inside the cell nucleus, and thus has a powerful, general effect initiated from the outside.  It’s not a single protein, but many that effect signaling by the same pathway, with different results.  (cancer and insulin resistance fr overexpression…)  Wnt is overexpressed in cancer cells

One of the most dynamic and innovative aging labs in the world is run by Irina and Michael Conboy at UC Berkeley.  I have previously cited them in connection with parabiosis experiments.  The Conboys reported back in 2007 that Wnt signaling changes with age (in mice) in ways that inhibit healing and muscle regeneration.   Unfortunately, it’s not as simple as “too much Wnt”, because Wnt is not a single hormone but a whole family of signals. [follow-up ref;  another ref]



Transforming Growth Factor-beta proteins are multifunctional cytokines (fancy name for a signaling molecule), secreted by numerous cell types. They are capable of signaling to virtually every cell type and broadly control cell proliferation, differentiation, apoptosis, inflammation and scarring in various tissues [Ref].  TGF-β may contribute to too much apoptosis and too little stem activity as we age.  The Conboy lab, writing about TGF-β and Wnt:

The results shown here argue against the notion of systemic TGF-β1 endocrine activity and strongly suggest that TGF-β, released by the known process of platelet activation during sera collection, inhibits satellite cell responses in vitro. These findings also suggest that young sera may contain a functional and natural decoy of TGF-β1, or a competitor of TGF-β1 signaling pathway (either endocrine or released by platelets). Lastly, our results demonstrate that Wnt antagonizes, rather than synergizes with TGF-β1-mediated satellite cell response inhibition. [Ref]


…in other words, we have more TGF-β as we get older, and its activity is inhibited when we are younger. TGF-β plays a role in inactivating stem cells, which we need for repair and rebuilding.

LH and FSH

Luteinizing Hormone and Follicle-Stimulating Hormone have their best-known role in the female menstrual cycle.  Men have much less than women at all ages.  But surprisingly, both these “gonadotropins” increase with age.  What are women doing with extra menstrual hormones after they stop menstruating?  In fact it is now accepted that high levels of FSH are programmed from the pituitary (brain) as women approach menopause, and that FSH is part of the hormonal signal that initiates menopause.

Back in 1998, Jeff Bowles published an evolutionary hypothesis that LH and FSH were signals that induce aging and purposefully increase mortality in both men and women.  Remarkably, a good deal of circumstantial evidence has accumulated since that time in support of Bowles’s idea.

Female mice that overexpress FSH receptor age prematurely. [Ref]

In women, high FSH is associated with bone loss and osteoporosis, and also with growth of fat cells and weight gain.

 In men, high FSH is associated with enlarged prostate and with prostate cancer.  (The prostate is one source, secreting FSH.)  High (age-adjusted) FSH is associated with an increase in all-cause mortality in men.  In middle-aged males, high FSH is associated with muscle pain and increased frailty.

There is a literature of antibodies against the gonadotropins (LH and FSH) going back at least to 1934.  Anti-gonadotropins are used to treat sex-associated cancers, and here is a clinical trial that is trying antigonadotropins as an Alzheimer treatment.  I am not aware of anti-aging therapies based on blocking the action of FSH and LH, but this would not be difficult to accomplish, and I think the experiment is well worth trying.


Cortisol is the opposite of inflam-aging.  Cortisol damps the body’s immune response, making it more tolerant.  And yet, cortisol increases as we age (more in women than men), and there are reasons to believe that the results are not good for us.

Cortisol provides the trigger that causes the Pacific salmon’s body to self-destruct after it has returned to fresh water and spawned.  Cortisol increases sugar in the blood, part of the insulin resistance of metabolic syndrome.  Cortisol levels rise with chronic stress.  Cortisol is associated with age-related memory decline, with depression and dementia.

Cushing’s Syndrome is the name given to a constellation of symptoms associated with over-exposure to cortisol, and many of these symptoms sound like “normal aging”: thinning of the skin, rubber tire around the waist, sleep disorders, baldness in men, high blood pressure and a tendency toward osteoporosis.

Drugs that block cortisol receptors are known, but are not in common use.


P53 promotes apoptosis, which is programmed cell death.  It is important for eliminating diseased cells and suppressing cancer.  But as we age, we have too much p53, and many non-cancerous cells begin to commit suicide, with the result that we lose healthy tissue in the muscles and, even more important, neurons in the brain.

P53 does not circulate in the blood, and in this sense does not belong in the present list.  It is produced endogenously in each cell.  The amount of p53 (and most other proteins) is regulated by a balance between transcription and degradation.  Transcription is the process of reading a DNA gene into messenger RNA, which travels to a ribosome, where its instructions are translated, 3 by 3, into a sequence of amino acids that makes a unique protein.  Degradation is managed by first tagging of proteins targeted for destruction using the label molecule ubiquitin.  A protein with several ubiquitin tags is recognized by the cell and dragged to the nearest proteosome for recycling.

In unstressed cells, p53 levels are kept low through a continuous degradation of p53. A protein called Mdm2 (also called HDM2 in humans), which is itself a product of p53, binds to p53, preventing its action and transports it from the nucleus to the cytosol. Also Mdm2 acts as ubiquitin ligase and covalently attaches ubiquitin to p53 and thus marks p53 for degradation by the proteasome. However, ubiquitylation of p53 is reversible. [hfrom Armando Rivera-Malo in Google Books]

If there is too much p53 late in life, this is executed at the level of the cell, but it happens in response to (yet unidentified) blood-borne signals that carry directives from the brain.



Homocysteine is a variant of an amino acid used as a basic protein building block.  It is a small molecule.  Increased amounts of homocysteine in the blood are a risk factor for CV disease.  Folic acid (a B vitamin) can reduce homocysteine in the blood.  But whether the association is causal is controversial [Ref – No;  Ref – Yes]

Homocysteine’s health risks are thought to come from deterioration of endothelial cells in the linings of blood vessels.  This damage can be reduced by dietary supplementation with curcumin (the anti-inflammatory agent in the Indian spice-root turmeric).


Sex hormones

Sex hormones decline with age.  There is a lot of data on hormone replacement therapy in women (estrogen and progesterone), and less but also considerable data on testosterone replacement in aging men.  Different studies produce different results, and interpretations are contentious.  Are sex hormones a mortality risk, or a protection?  This is a big topic, and I’m going to have to defer it to another day.

The Bottom Line

There are several known blood signals that promote age-related destruction and disease, and probably many more that are not yet known.  Already we know enough to be able to target these signals with anti-bodies and other molecules that block their action.  What are we waiting for?

Forget exercise

Physical activity is one of the best things you can do for yourself, even if it didn’t increase your odds for a long and healthy life. For those who would like to be more active, the trick is to find activities that don’t feel like a chore, but that you enjoy for their own sake. Then, gradually build more of these into your routine. There’s no hurry. There are five kinds of exercise that contribute to retaining a youthful brain and body. Only two of them hurt.


Mean and max lifespan

When a mouse cage is provided with an exercise wheel, most mice will run on it for hours every night (mice are nocturnal). Those mice tend to live longer. Do the mice have something to teach us?

There are more than a few life extension advocates who dismiss exercise as a longevity strategy because it increases mean but not maximum life span. Translation: when groups of mice that exercise are compared to similar groups of mice that are sedentary, the mice that exercise live about 10% longer on average. But the longest-lived mice in the sedentary (control) group live about as long as the longest-lived mice that exercise. This is often translated to mean that “exercise doesn’t affect the rate of aging, but helps to prevent premature death.” I would rather say that there are a few percent of mice (and people, perhaps) who will live to extraordinary old age even if they don’t exercise.  Unless you know that you are one of these, exercises increases your chances of living a long time. Even if you’re sure you’re in the lucky group, you may choose to exercise because it puts you in a good mood, protects you from getting headaches and colds, and adds a dimension of interest to your life.



I’ve taught one yoga class a week since 1976, a small but very consistent part of my routine that means a lot to me. For my class, I define yoga as any physical activity that is done with concentrated awareness on the body. All exercise should be yoga. The more attention we pay to our bodies the more we learn to decode the signals about what our bodies need. This is important not just for safety, but for a general guide to self-care. It’s related to “do what feels good,” but it’s not quite the same. There are some sensations that indicate a real danger, and it is important to stop what you’re doing right now. Other sensations may be equally unpleasant, but they come from stressing the body in ways that are a fast track to strength and health. This is hormesis.  Eustress. The intermittent challenge that makes us strong.

For those of us who seek a longer life, certainly it is conscious life that we value. Time we spend going through the motions of unaware, habitual activity can barely be said to be “alive”.  In this sense, yoga is training for being more alive.


1. Cardio-pulmonary – exercise for the lungs, heart, and circulation

This is exercise that gets you out of breath. You have two choices: low intensity for a long time (endurance training) or high intensity for a short time (interval training). Until a few years ago, endurance was considered the way to go, simply because it seemed to make sense. But now there is evidence that high intensity exercise is not only a more efficient path to fitness, but also works better.  [Ref 1; Ref 2Ref 3;  Ref 4]

(This is all based on physiological reasoning, because it is not possible to do controlled experiments with humans to see which strategy results in more life extension, and even if volunteers for such experiments could be arranged, the results would take decades to accrue.)

Some people enjoy running or hiking for hours on end, but have an aversion to the grueling output associated with interval training. Others welcome the intensity of interval training, and have no time in their week for endurance training.  To paraphrase the Delphic Oracle, accommodate thy individuality.

You have to be breathing hard to get the benefit. For aerobic exercise, this can be a modest elevation that can be sustained for half an hour or more. For interval training, you want to be pushing to your limit for 1 to 4 minutes at a time. A popular form of interval training that many find more tolerable is to push even harder for half a minute at a time, repeated 5 to 10 times with brief breaks of 1 to 4 minute in between.

You can do anything that elevates your heart and breathing rates rapidly. Swimming the butterfly, sprinting, jumping “red hot pepper”, squat thrusts, calisthenics, pushups, pullups can all work. The elliptical trainer is my favorite instrument of torture.


2. Strength.

Just a few years ago, weight training was associated with the vanity of body-builders, and health benefits were under-studied. Then doctors began to recognize the benefits of weight-bearing exercise for osteoporosis. For 90-somethings, strength training increases independence, stair-climbing ability, walking speed, and slashes mortality risk associated with frailty.  Now, far more general benefits are associated with training for muscle strength, and not just cardiovascular endurance.  A Tufts University review found that

In addition, strength training also has the ability to reduce the risk of osteoporosis and the signs and symptoms of numerous chronic diseases such as heart disease, arthritis, and type 2 diabetes, while also improving sleep and reducing depression.

Again, you have two choices: many repetitions of modest challenges, or a few repetitions of exercises that are at the edge of your muscular strength. Particularly effective and particularly unpleasant seems to be that last effort in which you work your hardest and just can’t lift the weight (or whatever) one more time.

The grueling super-slow style of exercise consists in pulling or pushing or lifting or lowering over a period of about 10 seconds, so that instead of a sudden oomph of exertion (with follow-through from momentum), the muscles are engaged continuously over their full range of motion. Dr Mercola, the internet health guru, has just this year become an enthusiast of “super-slow” weight training.  The Mayo Clinic offers a tentative endorsement. 


3. Balance, coordination and reflex.

This is exercise for the nervous system more than the muscles. We lose motor skills and not just strength as we age, and with daily practice we hold on to these skills much longer.

Learning new skills is an anti-aging tonic for the brain. 

Tai chi chuan and Qigong are disciplines originally derived from the martial arts that have practiced by Chinese elders for many centuries.

On a cushioned floor, practice falling, and breaking the fall with your hands.  Practice rolling into a tumble. Reflexes that you develop in this exercise will be there for you during those split seconds when you need them: tripping on a root in the woods, falling down the stairs, hitting a pothole at high speed on a bicycle.

Surfing, diving, skateboarding, unicycles, juggling, headstand, tightrope and other circus tricks can provide challenges for many years. My model for balance and coordination exercises is Stephen Jepson.  In his 70s, he continues to walk on the tightrope and juggle while riding his unicycle. Video here.


4. Flexibility

You can’t become flexible by overcoming tightness with force. But if you move to the limit of your range of motion and consciously relax the muscles that are holding you back, the range of motion will slowly increase over time. Be patient, both in seconds and in months.

There is an ancient tradition in India and Tibet associating yoga with long life. Science may yet catch up.

At 95, Tao Porchon-Lynch continues to teach yoga in New York. 


5. Keep moving

Even among people who exercise regularly, hours at a time spent sitting are an independent risk factor for cardiovascular disease.  Sitting at a desk also makes us dull and stifles creativity.

Get up at least once an hour and walk or stretch or do sun salutations or a form of tai chi. Better yet, work at a treadmill desk.

Walk up stairs instead of taking the elevator. Bicycle or walk to market and destinations in your neighborhood. Gradually expand your notion of “neighborhood”.


Forming new habits

Build activity into your life style. Change gradually, rather than making bold resolutions. Do what’s fun, and don’t let exercise be a chore. Jump on a trampoline. Jump in a lake.  Dance.  Skip rope.  Volunteer to referee for the middle school soccer league.  Kayak.  Learn to skateboard, bongo board and unicycle.

Forget exercise.

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A major new review came out in JAMA last month summarizing 40 years of experiments with anti-oxidants and longevity.  It was a meta-analysis of 78 different randomized, controlled trials between 1977 and 2012.  Anti-oxidants don’t increase life span. There is a small increase in mortality associated with anti-oxidant vitamins E, C and beta carotene.  I wrote a full post about this subject last year.

Anti-oxidant sales continue to boom, and “anti-oxidant” is on the label of thousands of health foods.