Aging in the news this week

In the press this week

  1. High-profile, misleading “proof” that aging is inevitable
  2. Disappointing results from Alkahest trials
  3. NewYorker article on exercise in a pill
  4. Splicing factors rescue senescent cells

  1. Mathematical proof that aging is inevitable

The headlines in the secondary scientific press said

Humans living forever is ‘impossible’ according to science

It’s mathematically impossible to beat aging, scientists say

Aging is Inevitable: Math shows Humans can never be Immortal

Mathematical models of aging are my specialty, but I’m not foolish enough to believe in the models.  I’m skilled and experienced at modeling so that I can adjust the assumptions to make a model do anything I want it to do.  I’ve seen time and again how tiny parameter changes can lead to opposite conclusions.  

Mathematical models can prove something is possible.  “Nature might arrange things in this way…”  But math models can never prove something is impossible.  Nature always has the option of arranging things in a way that’s different from the assumptions in your model.

In fact, the paper purports to be a general proof that aging is inevitable in all multicelled life.  But there are a few animals and many plants that don’t age.  Long periods of negative actuarial senescence (during which the probability of death goes down and down for years at a time) are common in trees, molluscs, and sea animals that keep growing without a characteristic, limiting size.  Turritopsis and Silphidae are capable of regressing to larval stage when starved and beginning life anew with a full life expectancy in front of them.  Annette Baudisch has made a career studying and documenting “negative senescence”.  So the idea that aging is some kind of mathematical certainty has about as much credibility as the authoritative declaration in Scientific American that flight by a heavier-than-air craft was impossible (1904 – more than a year after the Wright Brothers’ first flight).

The paper that appeared last week in PNAS is based on the premise that there is a kind of Darwinian competition among cells in the body.  Cells reproduce and mutate within the life of an organism.  In their model, somatic evolution–genetic change over time among cells in the same body–must navigate a course between Scylla and Charibdis.  The result is that mutations must accumulate, leading either to dysfunctional cells, too weak to do their job, or to cancer cells that have lost their allegience to the body and go on

They call this “aging,” but in fact somatic mutations do not contribute significantly to aging [ref].  Rather, in humans, the causes of aging include runaway inflammation, loss of insulin sensitivity, and thymic involution.  (In my view, most of these changes are driven in turn by programmatic epigenetic changes in gene expression.)  They redefine the term “senescent cells” to mean “cells that lose vigor due to cellular damage”, and then look for somatic mutations that cause the loss of vigor; but in general usage the term usually applies to cells that have critically short telomeres, or have otherwise entered a senescent state through epigenetic changes.  

The bottom line is that Masel and Nelson demonstrate a process that theoretically must kill us in the end, but their proof is silent about how long “in the end” might be, and they offer no evidence that the process they describe has to do with aging as humans (or other animals or plants) experience it.  Whatever “in the end” might mean, it must certainly be longer than 80,000 years, because that is the age of the Pando Grove which, last time I checked, qualifies as a multicelled life form.

Scylla and Charibdis

 

Blowing my stack (forgive me)

No one wants to think that death was handed to us with malice aforethought by evolution/nature/the gods.  In African myth, death was an accident caused by the laziness of a canine messenger of the gods.  In Judeo-Christian tradition, man would have been immortal if only Adam had not tasted the forbidden fruit.  William D Hamilton, one of the most insightful and best-grounded thinkers in evolutionary biology, proved that aging was an inevitable result of natural selection in 1966; forty years on, Baudisch and Vaupel used very similar reasoning to prove the exact opposite–that natural selection could never lead to aging [2004].  There are smart, famous people even today who argue that aging derives from the Second Law of Thermodynamics (Hayflick, of all people, is the man who discovered that cell lines run out of telomere).

We want to think that Nature is beneficient, that evolution has done her best by us and made us as strong and durable as possible.  If we get old and die, it must be because of something beyond evolution’s control.  But it’s just not true.  Natural selection first imposed aging on one-celled protozoans, and some of the same mechanisms that cause aging and programmed death in protozoans are active ingredients in human aging today (including telomere shortening and apoptosis).  Aging and programmed death have a long evolutionary history, and an ancient genetic basis.  We must conclude they exist for a purpose.

William Wordsworth asked, “Who shall regulate with truth the scale of intellectual ranks?”

Winston Churchill told us, “A lie gets halfway around the world before the truth has a chance to get its pants on.”

Arthur C. Clark said, “When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.”  

Young Paul Nelson may be excused for getting carried away by his mathematics, but his mentor (and my former colleague) Joanna Masel ought to know that what they have done is irresponsible.  These memes have consequences.   Arguably, the small and under-funded community of anti-aging research is the most promising frontier of medical science today, offering a vision that may eclipse multi-billion dollar research programs in cardiovascular disease, cancer and Alzheimer’s disease.  Articles like theirs have power because the people who make funding decisions are not experts, they don’t like to be ridiculed, and they’re easily swayed by general sentiment in the research community = people who are already getting the funding.  

If we do not correct this impression, it is likely to discredit the most innovative and dynamic field of medical research today.

 

  1. Disappointing results from Stanford’s first trials of infusions of young blood

Alkahest is a for-profit spin-off from the Stanford lab of Tony Wyss-Coray, doing research with blood plasma from young animals infused into older animals.  I first wrote about the project two years ago.  The company leapt ahead of animal studies to try infusions of young plasma as a treatment for human Alzheimer’s patients.  Last week, Science Magazine reported on a pre-printed meeting abstract: no change in cognitive trajectory of patients who received the infusions.

The people I know best in the field of young plasma are Irina and Mike Conboy.  When I visited them last Spring, Irina told me she expected Wyss-Coray’s protocol couldn’t work.  The dosage is not sufficient, the duration of treatment is too short, and (according to the Conboys’ research) it is more important to remove pro-aging factors from old blood than it is to add the factors found in young blood.

Wyss-Coray took a chance, and I wouldn’t want to criticize his ambition.  But the research world being what it is, this high-profile failure is likely to set back funding for a promising research field.  Let’s do what we can to make sure that research by Wyss-Coray, the Conboys and Amy Wagers continues apace.

 

  1. New Yorker touts the Exercise Pill

An article in last week’s New Yorker began with a long encomium to the drug GW501516, developed by GlaxoSmithkline some 20 years ago, sold in the grey market as Cardarine or Endurobol.  Looking behind the headline led me to learn about  a family of transcription factors called PPAR.  They seem to be promising targets for life extension drugs that are just beginning to be explored.

“In mice, GW501516, either when combined with exercise or at higher doses by itself, induces some hallmarks of [exercise] adaptation such as mitochondrial biogenesis, fatty acid oxidation, an oxidative fiber-type switch and improved insulin sensitivity via AMP-activated protein kinase (AMPK)” [source]  

Sounds pretty good, doesn’t it?  But

“To its detriment however, tumorigenic effects of GW501516 have been reported and development was discontinued by Glaxo in Phase II clinical trials.”   

How serious is the risk of cancer?  Are there ways to separate the benefits from the hazards, either by combing with other drugs or by chemical modifications to the structure of GW501516?  Is there anyone with a lab who is seeking answers to these questions?  

Cardarine=GW501516

Personally, at age 68 the three main ways that I feel my age are (1) decreased flexibility in yoga postures, (2) decreased speed in running and swimming, and (3) I can’t remember what the third one is.  I have charted my steady progression.  Swimming and running times are 30-35% longer than when I was 40, and increasing year by year on an accelerating schedule.  Exercise is my personal biomarker for age.  For reasons of vanity and vitality as well, I eagerly seek pathways to improved performance.  I also think that the activities of GW501516 and other PPAR agonists suggest potential for life extension, though there seem to be no lifespan studies either in rodents or humans.
Much of my source for what follows comes from a new paper summarizing exercise-mimetic drug state of the art, and references therein.

PPAR stands for Peroxisome Proliferator-Activated Receptor.  Peroxisomes are organelles in every cell that specialize in breaking down fat into short chains that the mitochondria can burn.  Thirty years ago, PPARs were discovered in the context of making more peroxisomes, but we now know that their most important function is to increase insulin sensitivity and signal a switch from burning sugar to burning fat.

Stimulating PPAR-α lowers LDL cholesterol and blood triglycerides.

PPAR-γ is a transcription factor that controls creation of new mitochondria.  (Mitochondria are the source of cell energy, and as we age, we have fewer of them and they become less efficient, linked to all diseases of age. [from my blog last summer: Part 1, Part 2]  Stimulating PPAR-γ improves insulin sensitivity and atherosclerosis.  PGC-1α is a protein that turns on PPAR-γ, indirectly creating more mitochondria.  Activating PPAR-γ has been discussed as an anti-cancer strategy.

Stimulating PPAR-δ (the modus of GW501516) switches the body from a preference for burning sugar to burning fat.  Great for weight loss and also for endurance.  You can double the running endurance of mice with GW501516.  Presumably, it was rather effective in enhancing performance in human long-distance runners before it was banned in 2009.  In calorie-restricted mice and long-lived mutants,    PPAR-δ is overactive.  (I’ve seen PPAR-β referred to only as similar to PPAR-δ. Maybe they’re the same.)

Joe Cohen at Self-Hacked sings the praises of GW501516.  Comments on this blog claim that (1) the increased cancer risk in rats was at very high doses*, and (2) the mechanism in rats doesn’t apply to humans.  Other commenters also minimize the cancer risk, but don’t offer references, and they may well be trolls for the companies that profit from GW501516.

“Although peroxisome proliferators have carcinogenic consequences in the liver of rodents, epidemiological studies suggest that similar effects are unlikely to occur in humans.” [source, ref, ref, ref, ref, ref].  “A number of experimental observations suggest that there is a species difference between rodents and humans in the response to PPAR agonists.” [same source] The article goes on to say that PPAR agonists may be more likely to create cancers in rat livers than human livers because rat livers have 10 times the PPAR expression compared to humans. It may be that tumorogenesis comes from the function for which PPARs were named: multiplying the number of peroxisomes.  But we now know that PPARs promote new peroxisomes in rodents but not in humans.

Here’s what I’ve been able to find out about PPARs, GW501516 in particular, and cancer:

PPAR is upregulated in colon cancer cells.  This shows that cancer causes PPAR, but not that PPAR causes cancer. There are many articles like this one, comprising evidence that activation of PPAR-δ promotes growth of existing tumors of the colon. The evidence is indirect, and gives no suggestion of the magnitude of the risk in humans who have colorectal cancer, let alone whether it in implies a risk for people who don’t have colorectal cancer.

PPAR-δ increases expression of COX2, the opposite of what aspirin and NSAIDs do.  NSAIDs decrease risk of cancer, and this suggests both that PPAR-δ increases risk of cancer and that the effect may be offset with NSAIDs.

There are no studies in humans.  There are many websites selling Cardarine, from which I guess that at least several thousands of people have taken taken it since 2005.  I have seen no sales numbers or estimates of the number of self-experiments, let alone cancer statistics. I have been unable to locate any anecdotes about cancer.

This 2004 review preceded GW501516, and reaches no conclusion.  It does, however, state baldly that PPAR-γ (not δ) is generally anti-cancer and that PPAR-α (not δ) causes cancer in rats but not in humans.

I have been unable to find published reports of the origina Smithkline-Glaxo experiment with rats that led to concern about cancer and abandonment of GW501516.

SR9009 is an unrelated mitochondria-growing drug sometimes mentioned in the same articles as GW501516.  There are no studies suggesting that it is carcinogenic, but that may be because it is much newer and there are so few studies altogether.

I don’t know whether Cardarine is too dangerous for human use, or whether similar drugs can be developed that target PPR-delta more safely.  But I’m outraged that the decision to abandon research on Cardarine has been made by investors in a board room who have no concern for public health and consider only the corporate bottom line.  This is an example of the worst kind of collision between capitalism and medicine–a collision which claims millions of casualties each year in the US alone.

I can’t blame the suits in the board room for doing their job, marching to the tune of those who paid the piper.  But this is emblematic of a gross failure of our regulation system, the FDA, and the reliance on for-profit drug companies to decide on our nation’s research priorities.  We now have (presumably) thousands of people taking a drug which may have large benefits and may have large dangers.  Most of them are motivated by wanting to be more buff or more sexy, and they are paying little heed to long-term consequences.  And because FDA has washed its hands of responsibility, there is no one even keeping records or collecting data to learn from the massive experiment about long-term health effects of GW501516.

Cardarine (GW501516) is available from LC Labs ($2240/g), from Monster Labs ($45/g), and from IRC Bio ($108/g, cheaper in quantity)

 

  1. Splicing Factors rescue senescent cells

I must admit that RNA splicing factors weren’t on my radar until this week, but I find this new experiment pretty convincing.  Eva LaTorre and colleagues from University of Exeter (UK) claim that splicing factors, more than sirtuins, are the pathway by which resveratrol (and analogs) extend life.

Sections of DNA (genes) are transcribed into messenger RNA, which finds its way to ribosomes, where the mRNA is translated into protein molecules.  But there is an in between step (in eukaryotes, but not in bacteria).  The DNA contains not whole (contiguous) genes but pieces of genes that need to be spliced together to assemble instructions for a whole protein.  Large sections of the DNA, called introns, are not intended for coding, and they need to be spliced out.  And, in fact, the pieces can generally be spliced together in different ways to make different useful proteins.  The work of splicing is performed by molecular complexes called splicing factors.  This is a process to which I had not given much thought until reading this article, but apparently it is a crucial step in epigenetics.  Epigenetics, the process of turning genes on and off, seems to get more complex with each passing year.

Resveratrol was identified about 15 years ago as a compound that extends lifespan in many species (but perhaps not in mammals).  Resveratrol has many effects, but the primary mode of action has been thought to be through SIR2 (or SIRT1) or related compounds called sirtuins that are selective gene silencers.  But the LaTorre group set out to show that the anti-aging benefit was through splicing factors rather than sirtuins.  They synthesized variations on the resveratrol molecule and tested them until they found one that promotes slicing factors but has no effect on sirtuins.  

Using this resveratrol analog, they were able to turn senescent cells back into fully functioning cells, with restored telomeres and other epigenetic changes.  They demonstrated that this was accomplished through splicing factors, and without sirtuins.

All this was done in (human) cell cultures, and it the horizons are now open to see what effect such rejuvenation has at the whole body level.

_____________

* Of course, there is no established dosage for GW501516, but pills come in 10mg and 20mg typically, corresponding to ~0.1 to 0.3 mg/Kg.  The highest doses I’ve seen discussed in  humans are ~2mg/Kg daily, nominally the same as the rat dosage.

Digging Deeper in Response to Reader Comments

Thank you, readers, for a lively dialog that has developed at the bottom of this page over the last few weeks, touching on some subjects that I have written about and many that I haven’t written about.  I will take this space to respond to some of what you’ve written about.  Some of my favorite topics include exercise, epigenetics, NSAIDs, and the gut microbiome.  Reports of whole-body rejuvenation with the four “Yakanaka factors” is especially promising. I’m grateful to Dr Paul Rivas for many of the ideas that I’ve expanded on here.


Aspirin, Ibuprofen, Naproxen

Background: COX2 inhibitors were found to reduce pain and inflammation of arthritis, but most COX2 inhibitors also inhibit COX1.  It is the COX1 inhibition that led to stomach damage and ulcer risk.  So in the 1990s, the pharma industry set out to find drugs that would inhibit COX2 without inhibiting COX1.  Only later, it came to light that these drugs elevated risk of heart disease, though they lowered the risk of cancer.  (Merck knew of the dangers of Vioxx before anyone else, but kept the stats under their hat as long as they could.) The worst offender, Vioxx=rofecoxib was taken off the market.  Only after CV statistics made the problem clear, researchers were led to ask, Why?  The problem is endemic.  Turns out that COX2 plays a role in maintenance of arterial health, and generally the NSAIDs increase heart risk to the extent that they inhibit COX2.  It turned out that Vioxx was dangerous because it did too well exactly what it was designed to do.

This story hangs together until we consider aspirin.  Aspirin inhibits both COX1 and COX2, and yet the preponderance of studies appear to show aspirin is associated with reduced CV risk [ref, ref].  This suggests there is a piece of the metabolic puzzle that is still missing.  Aspirin has many mechanisms of action, some of them unique to aspirin.

 

My advice, longstanding, has been to take ¼ to 1 whole aspirin or ibuprofen a day (not both; not to be mixed in the same week) after about age 50 for lowered inflammation and protection from heart disease and cancer.  Evidence for protective effect of aspirin has weakened a bit in recent years, but is still holding up [2016].  For patients who have already had a heart attack, aspirin remains standard protocol, and evidence for this population is strongest.

Readers pointed to this study [2017], which reports elevated risk of heart attack for people taking ibuprofen or naproxen.  The dosages they are looking at are several times higher than the daily dosage used for prevention alone.

All the NSAIDs have powerful effects in reducing cancer risk.  Glossing over the different numbers for different kinds of cancer with different NSAIDs in different studies, it’s a good rule of thumb that taking low-dose NSAIDs daily cuts cancer risk in half. [ref]

Effects on cardiovascular risk are more complicated.  I have been unable to find direct comparisons of aspirin vs ibuprofen and others, but there is “circumstantial” evidence in the literature that aspirin slightly decreases CV risk, while all the others slightly increase risk.  Different studies rank the NSAIDs differently.  There is suspicion of the “coxib” drugs which many people find work well for arthritis, but the latest studies show this seems to be unfounded.  This study [2016] finds Celecoxib (Celebrex) is safer than either ibuprofen or naproxen (Alleve), and results in both lower CV risk and lower all-cause mortality.

There may be other reasons to prefer one or another NSAID.  There are benefits for joint pain and stiffness; there are risks for gastric pain and ulcers.  It’s an individual choice, and I encourage you to experiment on yourself.  You can alternate different NSAIDs, but it’s best to do so week-by-week or month-by-month, rather than daily.  Don’t take aspirin and other NSAIDs in the same week.

 

Does too much exercise cause areterial calcification?

Readers pointed to this study [2017] from Mayo Clinic, in which young adults were followed for 25 years, and those who exercised most hours per week had elevated calcification of their arteries.  Calcification, in turn, is correlated with higher risk of heart disease.

There are several reasons I’m not turning on a dime to change my advice about exercise (which has always been, “the more, the better”).

  1. It’s a new finding.  The study is still in preprint form, and cites no precedent.
  2. It’s based on just 268 subjects.
  3. The people in the high-exercise/high-calcification group did the equivalent of 7 or more hours of jogging each week.  But the study didn’t separate recreational from occupational exercise.  Social class is a really big factor, and it may be that all we’re seeing is that working class people have more CV symptoms than the upper middle class.
  4. The fact that exercise is correlated with calcification and calcification is correlated with increased heart risk does not necessarily imply that exercise is correlated with heart risk.  This is such a common mistake.  (A correlated with B) and (B correlated with C) does not let you conclude that A is correlated with C.  In fact, the paper explicitly cites precedent that people who exercise most have lowest CV risk [ref, ref].
  5. So many benefits of exercise for so many aspects of health have been documented over the years that exercise is one of the solidest pillars of any health and longevity program.

The Copenhagen City Heart Study gave me more pause.  They found that joggers who ran at a moderate pace 2-3 hours per week had longest lifespans.  The benefit was about 6 years of life (a big number compared to every other life extension strategy that’s been studied, with the exception of caloric restriction).  But runners who worked longer and harder than this lost the benefit and, in fact, died early.  There is support for this thesis in other articles as well [ref, ref].  But there are also studies claiming that there is only a law of diminishing returns, and no amount or intensity of exercise that is actually bad for longevity [ref, ref].  

I have not figured out the reason that different studies come to different conclusions, but here is what they agree on:  

  • Exercise has a strong benefit for life expectancy, health, mood and productivity.
  • For low intensity exercise (yoga, walking, hiking, low-speed cycling, low-speed swimming) there is no evidence that too much can hurt you.
  • If there is a threshold above which exercise can increase cardiovascular risk and shorten life expectancy, it is only for intense exercise and long duration, typical of a marathon runner.

My guess (based on disagreement among experts) is that there are individuals for whom a great deal of high intensity exercise is beneficial, and there are others who damage their cardiovascular systems by pushing too far.  Doctors may be able to tell you if you have a heart condition that makes exercise hazardous.  My hope (based on personal experience with yoga) is that we might develop a sensitivity to our bodies, so that we can distinguish the pain of damage from the pain and resistance that always accompanies a strenuous workout.

 

IP6 is a new supplement for me

I’m grateful to Dr Paul Rivas whose comment in this blog led me to read a little about it.  Inositol hexaphosphate (IP6) is a bio-available form of Inositol, which is in the B-vitamin family.  It has a major benefit for certain kinds of anxiety and depression, and minor benefits for blood sugar, insulin sensitivity, and cancer prevention.

 

Extraordinary story of radiation hormesis

A reader referred us to this story in a comment last week.

It would be unethical to intentionally expose people, unknowing, to ionizing radiation.  But in Taiwan 35 years ago, construction steel was accidentally contaminated with Cobalt 60.  The Health Safety Society recommends that 50 millisieverts (mSv) is the maximum safe radiation dosage.  But 1700 people in apartments buildings in Taibei were exposed to this much radiation year after year for a period of 9-20 years until the contamination was discovered and they were evacuated.  These people were studied for adverse possible health effects, but the result was that they had dramatically lower rates of cancer and birth defects.

Hormesis is a word for Improved health and longevity in response to challenges such as low doses of toxins, radiation, heat, cold exercise and fasting.

 

Cancer as atavism

Dr Green has outlined a theory that cancer [his comment] is a state of unconstrained cell growth characteristic of free-living cells half a billion years ago, before there was multicellular life.

First part of theory is cancer is normal growth from prior to 500,000 million years ago, prior to Cambian period. That was before plants and before oxygen rich atmosphere; life was fermentation, unlimited telomerase, no aging, cells were immortal.

This was new to me.  Cyanobacteria have been around for 2.5 billion years, with the capacity to turn CO2 into O2.  But apparently it was not until 800-600 million years ago that the oxygen in the atmosphere approached present levels.

Of more practical interest is Dr Green’s idea that it is epigenetics and not genetics that makes a cancer cell.  If this is true, then an entire anti-cancer industry based on the idea of mutations being the root cause of cancer is misguided.

 

Yamanaka Factors Used for Rejuvenation

I missed this article when it came out almost a year ago.  The “Yamanaka factors” (abbreviated OSKM) are four chemicals which, when applied together, can turn an ordinary differentiated cell (a skin cell, for example) back into the stem cell from which it came.  Pluripotent stem cells replenish all the cell needs in the body.  The offspring of a stem cell can be any kind of cell, hence “pluripotent”.  Up until ten years ago, it was thought that this was a one-way street, and that the process of differentiation was irreversible.  Then the Kyoto laboratory of Shinya Yamanaka reported success in “de-differentiating” cells by adding just four chemicals, initials O, S, K and M.  In other words, these four chemicals turn a regular skin or muscle or organ cell back into the stem cell from whence it came.

Summary of the Yamanaka-factor reprogramming experiment.

De-differentiation rejuvenates the cell, including lengthening of telomeres.  But can the rejuvenation be done without the de-differentiation?  That’s the subject of a Cell paper by Ocampo et al.  They report success in rejuvenating cells in a living mouse, without changing them back into stem cells.  They do this via intermittent doses of the same four Yamanaka factors.  The shorter duration (2-4 days) has the effect of epigenetically reprogramming cells to their younger state, without destroying their differentiated identity.

For several years, I have have been attracted to the idea that aging is essentially an evolved epigenetic program.  The holy grail would be to take cells that are programmed to be old and epigenetically reprogram them to be young.  The hitch in this plan is that to do this directly requires changing methylation at millions of separate sites, in addition to re-programming dozens of other kinds of epigenetic markers (besides methylation), some of which are just being discovered.  These sites are specific to cell type, introducing further complexity.  We have neither the knowledge of where all these sites are, and only rudimenteray ability to alter them with CRISPR and allied techniques.

These results raise the exciting possibility that epigenetic changes supersede/precede other aging hallmarks in the physiological aging process, as well, and may thus constitute a key target for future rejuvenation strategies. – Anne Brunet & Salah Mahmoudi

The finding last year by Ocampo et al offers the possibility that we don’t have to do any of this, that just four chemicals will instruct the body to do it all for us.  Watch closely—this may be the pathway to whole-body rejuvenation that so many researchers have been groping toward.

What about damage to the cells?  The good news is that epigenetically rejuvenated cells seem to be able to repair their damage better than we might do it with artificial interventions.  Somatic DNA mutations were repaired.  Mitochondria were returned to a younger appearance and performance.

Provisos and qualifications:

  • Lifespan increase has been demonstrated in genetically short-lived mice.  For normal lab mice, they report physiological markers of rejuvenation, but didn’t wait to see if the mice would live longer.
  • How do you get OSKM into the mice (or the humans)?  In this experiment, extra copies of the four factors were inserted into the mouse genome before birth in such a way that they were normally turned off, except in the presence of the antibiotic doxycycline.  This provided a convenient way to turn OSKM on and off at will, with injections of doxycycline.
  • In the genetically short-lived mice, the rejuvenation is temporary, only lasting 8 days before progeria asserts itself again.  We don’t yet know whether rejuvenation in normal mice will be short- or long-acting.

Brunet and Mahmoudi end by suggesting that induction of the four factors could be combined with removal of senescent cells, speculating that major life extension could result from synergy between the two.  (They also note that getting the four factors into cells of a living human being is a challenge we don’t yet know how to approach.)

Comparison of Various Rejuvenation Modalities

 

News from the world of telomerase activation

Thanks again to Dr Rivas for this article demonstrating that ashwagandha is a potent telomerase activator.  This article adds to the evidence that cells with the shortest telomeres are the problem, and average telomere length is less important.

 

Gut Microbiome

Once again it is Dr Rivas pointing us to this article.  Stool samples from 1,000 “extremely healthy” people of all ages were analyzed for RNA sequences associated with intestinal bacteria.  Their principal finding was that the composition of the bacteria depended more on health than on age.  There were major differences through childhood, and for people in their 20s, the bacterial colony was in a class by itself.  But after age 30, up through age 100, bacterial ecology of all the healthy individuals tended to look alike.

A recent consensus says that we lose gut diversity with age, possibly as an adaptation, but more likely with negative consequences for health.

 

Tocotrienols

These are variants of vitamin E.  They differ from vitamin E (tocopherol) in the same way that unsaturated fats differ from saturated fats.  They are more reactive, more easily manipulated by the body.  The normal varieyty of vitamin E (alpha tocopherol) does not have lifespan benefits, and may be a net negative.  Gamma tocopherol may be better, or it may be that we need a mixture of tocopherols in combination. The only human studies have been done with alpha tocopherol, and when you buy “vitamin E pills” that’s what you’re getting.

Early research suggests that tocotrienols protect against cancer and reduce inflammation. The body treats them differently from vitamin E, and they have separate activity. Tocotrienols occur naturally in foods including palm oil, wheat germ, and rice bran.  You can buy supplements of mixed tocotrienols, or gamma tocotrienol, or mixed tocotrienols with tocopherols.  

 

Inheriting Telomere Length

Unsurprisingly, telomere length at birth is inherited from parents, and is assumed to be correlated to lifespan.  Surpringly, a baby’s telomere length is inherited more from the father than from the mother.  More surprisingly, older fathers sire children with longer telomeres (though their own telomeres are, presumably, shorter).

 

Low-Dose Naltrexone

Naltrexone is a 35-year-old drug used to block opioid receptors and help people breaking addictions.  Soon afterward, Dr Bernard Birhari discovered naltrexone in low doses as a treatment for auto-immune disorders (allergies, lupus) and as an anti-inflammatory.  There has been some success with LDN as a cancer treatment.  Take LDN at bedtime, as it blocks pleasure receptors.  The theory is that blocking receptors during sleep increases the release of endorphins during the day.  There is anecdotal evidence for LDN as treatment for depression, PTSD, anxiety and sexual dysfunction.  LDN hasn’t been approved or tested for any of these uses, but informal experimentation off-label is gathering a critical mass. Advocacy site for LDN.


Thanks to all of you reading this column, and thanks especially for the intelligent and informative conversation that has grown up underneath this blog.  I hope you’ll please keep the ideas coming!

Air Pollution and Life Expectancy

Increased air pollution cuts victims’ lifespan by a decade, costing billions” blared the headline from Eurekalart last summer.  I spent five months of the last 15 in Beijing, with arguably the worst air quality in the world.  I call Philadelphia my home, with the 10th worst air pollution in the US.  In the past, before good statistics were available, I have been an advocate, board member and even expert witness in support of clean air legislation.  Now I dreaded discovering what air pollution might be doing to my long-term health.  I procrastinated, and left this project on a back burner for a year.  But when I finally chained myself to my desk to research this column, the results were not nearly so bad as my fears.


The above Eurekalert article referred to this research from Denmark, and the summary, it turned out was misleading.  The question it appears to be asking is, “if you live in a city with 10μg per m3 of particulate pollution, how much sooner must you expect to die?”  But in fact, it addresses a different question:  “Assume that air pollution has zero effect on the great majority of people, and that the entire burden of increased mortality comes from a small number of unlucky people.  If you are one of those unlucky people, how much is your life cut short because of air pollution?”  (Even for this unrealistic assumption, I am not convinced that the author did the calculation correctly.)

For context, the study was based on the concentration of the smallest particulate pollution, particles less than 2.5μm in size, which are thought to do the most damage.  A concentration of 10μg/m3 for such particles is a level typical of a large American city on an average day.  Philadelphia has many days each year exceeding this level.  Beijing air on a summer day has 150μg/m3, and winter days are typically 400-600μg.  If my reading of the Danish study is correct, it implies that the average citizen of Beijing loses 500 years of life to air pollution.

 

Questions

Beginning my reading, here are the questions I was curious about:

  • How much life is being lost to air pollution in American cities and Chinese cities?
  • What pollutants are responsible?
  • Is the risk linear with pollution, or is there a threshold?
  • Are sources of pollution predominantly local or regional?
  • Where are the best and worst places to live?
  • What diseases are associated with air pollution?
  • What can be done to mitigate health consequences of exposure to air pollution?
  • Is it better to exercise in polluted air or not exercise at all?

I came away realizing that some of these questions are difficult to address with field studies and epidemiology, and others have not been addressed, even though they are not so difficult.  But generally, I was re-assured that air pollution is not as big a health threat as headlines had led me to fear.

 

How big is the effect overall?

This study looked at day-to-day variations in death rates in Wuhan, a large, polluted city in China’s heartland.  They find that 10% of all deaths are due to respiratory disease, and some large fraction of respiratory deaths are triggered by the day’s SO2 level.  (Sulfur dioxide is a significant pollutant in China, but not America, because so much coal is burned in and near cities.)  This speaks of the  immediate effect only, and corresponds to less than one year of life lost.  But this kind of study can tell us nothing about long-term effect.  Another study in Eastern China (Jiangsu province) compares across cities, so is potentially sensitive to long-term as well as immediate effects.  They find a smaller effect of ozone (O3), corresponding to a few months of lost life.

One of my first discoveries in researching an early ScienceBlog column five years ago was that large differences in mortality correspond to small differences in life expectancy.  The deep cause of this counter-intuitive effect is the steep rise in mortality curves, building a wall of death into actuarial tables.  This is what Benjamin Gompertz realized two centuries ago, but I was a little late to the party.  

These mortality statistics are large enough to detect unambiguously, and a few percent increased mortality (up to 10% in China’s most polluted cities) sounds quite serious.  But when these numbers are translated into life expectancy changes, the results are far less alarming.  10% in the worst Chinese cities corresponds to less than 1 year of life expectancy.  1% – 2% typical of American cities corresponds to about a month of life expectancy.  Much more difficult to quantify is the extent to which the health effects of air pollution are focused on a subset of people who are particularly sensitive, and who will suffer a seriously early death.  This is the question addressed by the headline-grabber I quoted at the top of this column [ref].

The most recent comparison of South and North China (where coal was burned freely for winter heat) is featured in  Eurekalert with the sensational headline, Air Pollution Cuts 3 Years in Northern China, but the research article behind it reports 8 months.

 

My own informal study: Life Expectancy in American Cities

Can we see an effect of pollution on life expectancy in America’s largest cities?  I looked up the data, and found a surprisingly large variation in life expectancy.  Here is a scatterplot of life expectancy plotted against EPA’s measurement of average morning pollution levels for the smallest particles (PM2.5).

There is a correlation that goes in the expected direction, but not statistically significant, and no clear visible trend.  For comparison, look at the plot of life expectancy vs per capita income:

Here there is a statistically significant correlation (p=0.01) and a trend that is visible to the eye.  Across 25 cities, 29% of the variance in life expectancy can be explained by wealth alone.
[Source for pollution data]
[Source for life expectancy data]
[Source for income data]

Mechanism of long-term damage

When mice breathe air with particulate pollution, their arterial walls thicken and stiffen, arterial plaques increase, and inflammation rises over a period of months [ref].  Similar effects in humans would be expected to increase risk of heart disease and ischemic stroke.  Much of this damage is thought to be reversible after ths source of the pollution is removed [ref].

Joel Schwartz of Harvard School of Public Health has persisted through a long career in creating some of the most solid and credible connections between pollution and its health consequences.  This classic study, more than two decades old, uses conservative statistical methods to separate effects of weather from pollution.  (Weather is known to be highly correlated with daily mortality, more so than pollution, and pollution, of course, is correlated with daily weather and also with season.)  The result is a robust conclusion that TSP of 100 μg/m3 increases risk of death by a factor 1.06.  The weakness of this finding is that, since the time of this study, TSP=“total suspended particulates” has gone out of fashion as a measure of pollution.  TSP measures large particles more heavily than small, but we now know that the smallest particles are most damaging.

Air quality in America has improved in the last 20 years, and most days, most places are compliant with EPA limits.  Nevertheless, a difference in mortality rates can be detected between the good days and the bad.  A recent study from Schwartz’s group investigated the question of low-level pollutants.  They are able to detect effects from three pollutants: PM2.5, O3 and NO2, and report a total ~1% increase in daily mortality.

 

Dose-Response

This is a large unanswered question, very difficult to pose in an epidemiological study design.  It is plausible that high exposure for a short time is more damaging than low exposure for a longer time, but the opposite is possible.  It is plausible that the combination of chemical irritants (e.g., O3, SO2, NO2 with micron-size particles is worse than either of the two separately, but we don’t know.  A “latency” is often assumed, such that today’s exposure to bad air can produce hidden damage that shows up a decade later to cause disease or death.  But it is just as plausible that those who are fortunate to escape disease in the immediate aftermath of pollution exposure suffer no long-term consequences.  We do know that hospital admissions and both cardiovascular and pulmonary mortality rise in times of major pollution events.  But smaller day-to-day fluctuations in air pollution also produce smaller fluctuations in a city’s mortality and morbidity rates, and these can be correlated in long-term studies.  

 

Is there a threshold, below which low levels of pollutants cause no problem?

Probably not.  This study by Schwartz found that 1% or 2% of all deaths in Boston are arguably attributed to particulate and ozone pollution, and Boston air is cleaner than most large American cities, and was within EPA guidelines virtually all during the time of the study (2000-2009).  A study across different cities in Eastern China also could find no evidence of a “safe threshold”.  

 

Do filter masks do any good?

Masks are common in China

These cheap, simple respirator masks are a common sight in Beijing.  They are so thin that it is easy to imagine that they can’t be doing much of anything, but apparently this simple measure is quite effective.  This study from University of São Paulo was based on metabolic response to pollution, and found the response was reduced to undetectable levels by wearing a mask.

Also common in China are indoor air purifiers that continually circulate air through a HEPA filter.  The Berkeley Wellness Letter offers some suggestions and emphasizes limitations.  A room air purifier provides less effective protection than a mask.

Room air purifier

 

Can B Vitamins Shield you from Harm?

This study looked at short-term effects of particulate pollution only.  These include elevated heart rate, suppressed immune function, certain epigenetic changes (DNA methylation),  and a decrease in heart rate variability.  (The latter is a somewhat mysterious but apparently robust measure of health that has begun to gain recognition as an indicator in recent years [ref].)  By all these measures, a modest course of B vitamin supplementation for several weeks preceding exposure completely prevented the physiological response.  On the one hand, it’s a very impressive result; on the other hand, what we care most about is long-term damage to the lungs and CV system, and the short-term protection may or may not correspond to long-term protection.

To Exercise or Not to Exercise?

This study finds that the benefits of walking and cycling outweigh the damage done by breathing more polluted air.  The claim is that this is overwhelmingly true in moderately polluted Western cities, and remains true in all but the most polluted cities of the developing world.  The methodology of the study looks good to me, although the data on which it is based are uncertain.  The study doesn’t address high-intensity exercise, which necessarily involves rapid hyperventilation.  It is hard to know if lung damage might be caused at an extra-high rate when the body’s cleansing mechanisms are overwhelmed, as they are in cigarette smoking.  People in China tend to exercise less on high-pollution days, but when they live in high-pollution cities, they make the most of it and exercise indoors, or outdoors when the pollution is as good as it’s going to get [ref].

Early morning Tai Chi, an old Chinese tradition all year ’round

 

The Bottom Line

Mitigating air pollution is an important environmental project, with health benefits that far outweigh the costs.  It is indeed a travesty that our EPA is bowing to pressure from GM and Exxon, decade after decade.   Mitigation is well worth pursuing in the US, let alone in developing Asian cities.  Nevertheless, even in the worst areas of China and India, the air pollution is a major health problem only for a sensitive segment of the population, and overall robs city-dwellers of less than a year off life expectancy.


Two Personal Notes

  1. I fasted for five days last month, coordinated to end on the Jewish fast day of Yom Kippur.  The last two days I took large doses of quercetin, thinking to purge senescent cells.  Fasting is supposed to protect normal cells, while sensitizing senescent cells to toxins.  Quercetin is a supplement commonly found in health food stores, a flavonoid extracted found in onions and green tea.  It has been identified as a senolytic.  Results:  Difficult to say with any certainty, but I did feel an ease and speed in swimming after I began re-feeding, and perhaps an easing of chronic stiffness in my low back.
  2. I have a yoga practice that goes back to 1972 and, I believe, has helped me to retain range of motion.  The place I feel loss of suppleness most is my lower spine, and MRIs showed a loss of discs beginning 20 years ago.  I take daily aspirin, 325 mg at bedtime, and I think I associate this with an easing of flexibility in the low back.  Recently, I’ve noticed that if I substitute naproxen (200 mg) for the aspirin, my low back feels less stiff in the morning.  Naproxen is a stronger over-the-counter NSAID than aspirin, more likely to produce side effects in sensitive stomachs; some studies claim to detect long-term heart risks.  The best reason to prefer aspirin over naproxen is the long history attesting to the safety of aspirin (for most people).  

    I intend to try more controlled experiments over the next few weeks to see if my first impressions of naproxen’s benefit hold up.

 

Is Cancer a Mitochondrial Disease?

“Cancer is a genetic disease.  Its primary cause is mutagens in the environment, abetted by time and bad luck.  A cell is controlled by the chromosomes in its nucleus, and when just the wrong combination of mutations happens to occur, a cell can begin to grow and multiply uncontrollably.  The next crucial step occurs when the cell acquires the ability to travel through the bloodstream and implant somewhere else.  The whole pathway from errant cell to malignant cell proceeds via chance mutations. From inception to metastasis, cancer is driven by genetics.”

This theory of cancer is more than 100 years old, but it didn’t become the dominant view until the 1950s, when, after Watson and Crick, genes assumed an exalted position in the study of biology.  The “somatic mutation theory” continues to dictate the course of cancer research and treatment today.

It is uncontested that cancer cells have abnormal chromosomes.  Dozens of different mutations have been found in malignant cells.  They have been catalogued as different oncogenes, and because they are so different in their functions, cancer has been re-conceived from a single disease to a category containing many different diseases with similar symptoms.

Are mutated genes the root cause of cancer?  Toxins that commonly break DNA (teratogens) are also found to cause cancer (carcinogens).  Radiation, ditto.  “Ionizing” radiation packs enough wallop in each photon to break a chemical bond, and is associated with cancer, while non-ionizing radiation (visible, infrared, and radio waves) is not mutagenic and generally not carcinogenic*.  This has been taken as powerful circumstantial evidence for the prevailing theory.

A direct answer to the question of whether cancer originates in the nuclear DNA is available from an experiment that is simple in principle: Swap nuclei between two cells, one normal and one malignant.  Take the mutated DNA out of a cancer cell and put it in a normal cell, to see if it becomes malignant.  Take the un-mutated DNA out of a normal cell and put it in a cancer cell to see if the cell is rescued and restored to health.

This experiment has been technically feasible for more than 30 years, and indeed Barbara Israel and Warren Schaeffer actually performed both experiments at UVM and wrote them up in 1987 [ref, ref].  The results were exactly the opposite of what was expected: The cell with normal cytoplasm and cancerous nucleus was normal; the cell with normal nucleus and cancerous cytoplasm was cancerous.  This result has been confirmed in other labs [reviewed by Seyfried, 2015].  Still, the genetic paradigm has a stubborn grip on cancer research and treatment to this day.

An alternative theory of cancer as a metabolic disease was put forth by the Nobel polymath Otto Warburg in the 1930s.  The principal proponent of this theory today is Thomas Seyfried of Boston College.  Seyfried cites evidence that damage to the nuclear DNA, conventionally thought to be a root cause of cancer, is actually an effect of the damaged mitochondria and irregular metabolism.  “The metabolic waste products of fermentation can destabilize the morphogenetic field of the tumor microenvironment thus contributing to inflammation, angiogenesis and progression.”

 

Respiration and Fermentation

Every cell in our bodies (and almost every cell in all eukaryotes everywhere) makes uses of energy in the form of ATP, adenosine triphosphate.  ATP is manufactured in the mitochondria, usually by a controlled burning of sugar to form CO2 and H2O. Highly energy-intensive cells such as muscles and nerves have thousands of mitochondria in each cell.  The word “respiration” in this context is used to mean burning sugar in an efficient energy conversion process, yielding 38 ATPs for every sugar molecule.  But when oxygen is scarce, perhaps because you’re breathing as fast as you can or sprinting in deep anaerobic mode, another process can be used to rapidly convert available sugar stock to lactic acid, requiring no oxygen at all, but yielding only 2 ATPs per sugar molecule.  The latter process is called “fermentation”.  (This observation explains the extraordinary effectiveness of interval training (sprints) for weight loss.)

Warburg was among the first to notice [1931] that most cancer cells use fermentation rather than respiration as an energy source.  Metabolic studies pointed to damaged mitochondria in tumor cells that had become inefficient in producing sufficient energy through respiration.  He theorized that impaired mitochondrial function is the root cause of cancer.  In fact, Warburg did some of the early work establishing the role of mitochondria as cellular energy factories.

So most cancer cells are sugar addicts.  They consume enormous amounts of sugar, both because they are actively growing and dividing, and also because they use sugar so much less efficiently than normal cells.  A PET scan can be used to visualize concentrations of sugar in the body, and PET technology is often used to locate tumors.

Sugar is easily made from carbohydrate foods, and when you eat a diet containing carbs, sugar is the fuel of choice.  Ketones are an alternative fuel used by the body when burning fat, either stored fat or ingested animal fat or vegetable oils.  (Medium chain saturated fatty acids like coconut oil seem to be most effective in inducing metabolic ketosis.)  Unlike sugar, ketone bodies cannot be fermented.  They generate ATP energy only through oxidative respiration in the mitochondria.

The logical question:

Are zero-carb diets an effective treatment for cancer?

Some well-known cancer drugs (Gleevec, Herceptin) already target the fermentation metabolism.  Acarbose has been proposed but not yet tried.  But might it be safer and more effective to starve cancer cells by cutting carbohydrates in the diet to zero?  There is a robust literature suggesting, “yes” [e.g., ref, ref, ref, ref, ref, ref, ref] but so far the results have been less than earth-shaking.

A search of ClinicalTrials.gov yields 25 trials of ketogenic diet variants for cancer treatment.  Most are in early stages, 5 have been completed, 2 have results.  In this study, the ketogenic diet, with or without chemotherapy, did not cure glioma.  This small study found modest benefits in a variety of advanced cancers.  These results are consistent with many mouse studies, in which some benefit was recorded from the ketogenic diet, but not a dramatic difference.  The most encouraging results I have found was a study in which 9 of 11 mice treated with a combination of radiation and a ketogenic diet were cured of brain cancer.  Clearly, this is no miracle cure, but it’s too early to give up–we’re just figuring out how to make the diet work, and it has not yet been tried except at late stages, after all else has failed.

Fasting shows more promise than ketogenic diets.  (Perhaps fasting lowers blood sugar even more than ketogenic diets.)  A series of studies by Valter Longo make the case that fasting simultaneously sensitizes cancer cells to chemo or radiation and de-sensitizes normal cells.

Seyfried has proposed a “press-pulse” system based on this vulnerability, targeting the glucose metabolism and the glutamine metabolism with hyperbaric oxygen.  Besides glucose, glutamine is also a major fuel for tumor cells.  Drugs will be required to target glutamine, as glutamine is the most abundant amino acid in the body and can be easily synthesized from glutamate.  Hyperbaric oxygen requires a patient to be enclosed in a pressurized oxygen chamber or room filled with pure oxygen at 2.5 x atmospheric pressure.  There is one highly encouraging case report for the success of this triple combination—hyperbaric oxygen, glucose inhibitors, and low-dose chemo—in which a late-stage, resistant breast cancer is driven to total remission.

Last week, a research paper from Duke U suggested a target for attacking the fermentation metabolism of cancer cells, and a marker for identifying which cancers are likely to be sensitive to it.  The research group of Jason Locasale found a protein called GAPDH which switches to the fermentation metabolism, and a compounded called koninjic acid, extracted from fungi, that inhibits GAPDH.  They have tested koninjic acid extensively in cell lines, and have begun testing in live mice.  Whether such drugs are more effective than simply restricting glucose is a topic for investigation.

Explanatory diagram from the Duke study of GAPDH

 

Mito-targeted Cancer Prevention

 Supplements that promote mitochondrial health include CoQ10, PQQ, mitoQ/SkQ, alpha lipoic acid (ALA), carnitine, and melatonin.  Can they lower risk of cancer?  So far, we have just a few hints; this is a promising area for research.

CoQ10 was studied in the 1990s as a cancer treatment, with some encouraging results [ref].  PQQ has been shown to kill cancer in vitro [ref].  One mouse experiment looked at ALA as part of a cancer treatment [ref].  Use of carnitine remains theoretical [ref].  Most has been written about melatonin [ref, ref, ref], but even here, there is no epidemiological evidence.

 

The Bottom Line

All the evidence for radiation and other mutagens causing cancer might be re-interpreted in terms of mutations to mitochondrial DNA.  (Mitochondria live in the cytoplasm, outside the cell nucleus, but they have a bit of their own DNA and ribosomes for transcribing it.)  Damaged mitochondria can also cause cancer even when their DNA is intact, and Seyfried (after Warburg) makes a strong case that mitochondrial damage is the root cause of cancer.  Inflammation is probably the single worst source of mitochondrial damage. Do we need one more reason to minimize inflammation?  Viruses often target mitochondria for their own ends, and this may explain cases in which viral infections are associated with etiology of cancer.

The insight that mitochondrial damage is the root cause of cancer (preceding nuclear mutations) also has broad implications for cancer prevention.  As for treatment, there have been a few disappointments and also some promising pilot studies, especially in combining glucose deprivation with radiation or chemo to finish the job (“press-pulse”).  This is a research field that deserves much more attention.

__________

*There are exceptions to both these generalizations.  There is controversy whether ionizing radiation at low dosages causes cancer [ref]; and cell phones (non-ionizing) have been linked convincingly to cancer risk, presumably by a different mechanism than breaking chromosomes [my column last year].

I sent a draft of this column to Thomas Seyfried, who was kind enough to edit it in detail and add references of which I was unware.

I was led to this subject by my co-author’s publisher, Chelsea Green, publishers of
Tripping over the Truth, by Travis Christofferson. 

Follow-up on Telomeres: Genetic Studies vs Straight Epidemiology

My August post on this subject stretched my readers’ patience with technical detail. On the other hand, that column has generated a larger volume of discussion than any other in the history of this blog. For readers who are put off by numbers, I promise to get back to a juicier topic next week. But today, here’s another one for the geeks…

While I was at the National Biological Institute in Beijing this past July, I taught a seminar to biology grad students which I called “Intuitive Statistics”. I asked them to put aside the powerful software that would calculate all manner of statistical indicators automatically, and instead to play with numbers in Excel, where they could see what they were doing, monitor each step, and think about why the results came out the way they did.

Already in 1954, Darrell Huff titled his all-time best-selling text, How to Lie with Statistics. Today’s menu-driven statistical software tempts the professional statistician to deceive himself first, others incidentally. The guts of the calculation are performed automatically. It’s so easy to obtain right answers to wrong questions. The professional division of labor between biologists and their statistical consultants adds abundant opportunity for miscommunication, and the spreading of responsibility among a large team of authors leads to the unintended consequence that no one feels the burden of personal responsibility that the reported results make sense.

The first principle is that you must not fool yourself — and you are the easiest person to fool.
— Richard Feynman

I wanted the students to begin to develop a feel for numbers that would inoculate them against the embarrassment of deep forays into their lab data that were fundamentally misguided. I knew that they would be working with professional statisticians, and I wanted them to be able to do back-of-the-envelope calculations that would tell them when the advice they were getting was outside the range of the plausible. Where the detailed answer differs from the back-of-the-envelope calculation, it’s important to understand the difference before assuming that the more sophisticated calculation is correct.

Here’s one of the exercises we did:

A is positively correlated with B. B is positively correlated with C. What do you expect about the relationship between A and C?

Of course, our first expectation is that A is likely to be positively correlated with C. Can you prove this? Can you think of a counter-example that disproves it? The answer, as it turns out, is that our expectation is only valid if the relationships (A,B) and (B,C) are quite strong. In that case, we are justified in assuming that (A,C) are likely to be a positive correlation. But it is not hard to come up with examples where the opposite is true. The correlations (A,B) and (B,C) can be highly significant, though less than 0.5, and (A,C) can be negatively correlated.

I asked the students to construct an example with made-up data. There are also plenty of real life examples. My favorite has been salt: Salt consumption is positively correlated with high blood pressure. High blood pressure is positively correlated with cardiovascular risk. But salt consumption is negatively correlated with CV risk.  Yes, eating more salt may raise your blood pressure and also decrease your chance of having a heart attack. [Link to the course web page]

What this has to do with telomeres

Longer telomeres are positively correlated with certain genetic variants (SNPs). The SNPs are positively correlated with higher risk of cancer. A series of genetic studies [refrefref] claims, on this basis, that long telomeres pose a risk of cancer.

Advocates of this kind of study (called GWAS), say that it avoids mixup between cause and effect, because your genome is always a cause, never an effect. But the hitch in this reasoning is that we have to accept an extra level of indirection, so we’re really back where we started. What I mean is this: The authors are trying to establish that long telomeres can cause cancer. Their data shows that certain SNPs (genes) are correlated with long telomeres, and the same SNPs are correlated with cancer. So they conclude that

SNP long telomeres cancer

But they cannot exclude the possibility that

SNP long telomeres AND

SNP cancer (directly, bypassing the telomere)

This is especially problematic because the correlations are quite low – under 10% for each of the SNPs separately. The SNPs cause very small differences in telomere length, and the statisticians find very small differences in cancer risk. Then the software works automatically to “standardize” the result, and report what the cancer risk would have been if there had been a large difference in telomere length. For esoteric reasons of mathematics, the risk estimate is extrapolated exponentially. The result ends up looking quite scary. 5 times the risk of brain cancer and 3 times the risk of lung cancer for people whose telomere length is in the top 16% (1 sigma). The 2% with the longest telomeres (2 sigma) would be projected to have 10 times elevated rates of lung cancer and 28 times elevated risk of brain cancer.

Such high levels of risk for long telomeres have not been observed in previous studies that look directly (not through genetic intermediates) for correlations between disease and telomere length. What these studies have tended to show is small increases in some cancers, decreases in others connected to telomere length. If people in the top 2% of telomere length really had 28 times the risk of getting brain cancer, then more than half the people with brain cancer would have extra-long telomeres and everyone with brain cancer would have longer-than-average telomeres. It is difficult to imagine that such a huge effect could have been missed.

Phillip Haycock, first author of the large GWAS study I described last month, has been gracious enough to write to me generously and to comment directly on this blog page. Below, I respond to some of his comments (his comments in purple).

In observational studies almost everything is correlated with everything, making judgements about causality basically impossible. For example, observational studies tend to find that telomere length is associated with everything-under-the-sun (from meditation to stroke).

The way I think of it, the correlation motivates us to look for a plausible causal mechanism, and some are much easier to imagine than others. I don’t think anyone has proposed a theory that telomerase makes people more likely to take up a mediation practice. Common sense tells us the causal order is that meditation promotes release of telomerase, not vice versa. In the other direction, when we find that short telomeres now are predictive of disease several years down the road, we don’t argue that the future disease has reached back in time to cause telomere shortening.

So there are two possibilities: A) Short average telomere length usually means a high number of cells with critically short telomeres. These cells become senescent, and spew out inflammatory cytokines. The resultant inflammation is already known to be a cause of cancer, AD, and cardiovascular disease. B) The body has suffered infections and toxins in the past that have prompted extra cell divisions, shortening telomeres. The same infections and toxins have raised the risk of cancer, AD and CV disease by a mechanism that has nothing to do with telomeres.

Clearly, the presumption is in favor of (A), that short telomeres contribute to the diseases of old age. All the steps are filled in and previously established. This doesn’t disprove (B), but it establishes the burden of proof. For those who want to argue in favor of (B), the next logical step is to do a prospective study including as independent variables both telomere length and the infections, toxins, pollution, smoking, etc that could cause both telomere shortening and disease risk. This is exactly what was done in the Rode study two years ago, and they found that short telomeres were still correlated with cancer and (especially) CV disease even when correcting for history of infections and smoking. In fact, the correlations with infections and smoking were far weaker than the correlations with telomere length. At this point, (A) looks very strong.

In contrast, genetic variants do not generally correlate with classic environmental and lifestyle factors (predicted in theory by Mendel’s laws and observed in practice).

Let’s be specific here. The primary finding of GWAS studies like Haycock’s is that certain genetic variants (SNPs) are associated with slightly higher risk of cancer. The interpretation which Haycock and other authors offer is that the effect is indirect, mediated entirely by the effect of the SNP on telomere length

SNP long telomeres  cancer

I note that

1) In contrast to (A) above, there is no plausible mechanism offered. The mechanism is never spelled out, but here is what I think is the implicit hypothesis: A pre-cancer cell is replicating and mutating. Because its telomeres are slightly longer than others, it has more time to mutate before it runs out of telomere and dies of cell senescence. Therefore the pre-cancer with long telomeres has a higher probability of neoplastic conversion than a cell line with shorter telomeres.

I think the reason that this hypothesis remains implicit and is not spelled out (let alone tested with computational models) is that it doesn’t make sense quantitatively. The difference in telomere length from the most powerful of these SNPs is a fraction of 1%. This corresponds on average to much less than one cell division. It’s hard to imagine this having a detectable effect on cancer risk.

2) The correlation between each of these SNPs and telomere length is very low, accounting typically for less than ½% of the variance in telomere length. SNPs generally have more than one effect. So it is easy to imagine that some of the SNPs have a direct effect on cancer risk.

3) The direct effect doesn’t have to be very large. All of the observed increases in cancer risk associated with the SNPs are under 25%. Odds ratios less than 1.25 are generally discounted in epidemiology, and for good reason.

4) Another plausible explanation for the observed correlation is that SNPs are not randomly distributed through the population, but are significantly correlated with many other genetic, geographic and cultural variants. Let’s spell out the premise of “Mendelian randomization”: Literally, it relies on the assumption that nothing that could be associated with cancer risk is at all correlated with the “telomere SNP.” Of course, this is very far from being true. As Haycock says – everything is correlated.

This kind of thing is a hazard in all forms of epidemiology; but what makes it more treacherous in this case is that the effect you’re looking at is so small. OR<1.25. This can be caused by literally thousands of different associations unrelated to telomeres. For example, these SNPs may be associated with more people from cultures that have higher rates of smoking; more people of African descent; more people who come from Northern climates… Haycock doesn’t control for any of these possibilities, and, of course, neither do any of the other authors of GWAS studies. Controlling for other variables is supposed to be unnecessary because of “Mendelian randomization”. But in reality, Mendelian randomization is far from complete.

Observational studies of directly measured telomere length provide opposite conclusion.

Our findings are generally in strong agreement with prospectively designed observational studies (where telomere length is measured before cancer diagnosis). The apparent conflict you cite is almost entirely due to the retrospective studies, where telomere length is measured after cancer diagnosis, and which generally do find that shorter telomeres increase cancer risk. In my opinion this is due to reverse causation bias…

The only observational study that I referenced was Rode, because it’s the only one I have read carefully, and because it is the largest (65,000 people), it uses a homogeneous population, and (crucially) telomere length is measured before onset of disease.

Contrary to your claim, our findings are in strong agreement with the findings from these studies. The studies are large with samples sizes ranging from 47,000 to 96,000. The main studies are:

https://www.ncbi.nlm.nih.gov/pubmed/23468462
“…Short telomere length is…not [associated] with cancer risk”

Short telomeres are associated with older ages. In any study that includes a range of ages, there is a choice of ways to tease apart the effect of age. Age always wins, but some statistical methods will make it look like age is the whole story, while others will say that short telomeres are a risk independent of age.

This study found a strong correlation between short telomeres and incidence of cancer, also of short telomeres and risk of death from cancer. After correcting for age, the association with cancer incidence disappeared, but the association with death from cancer remained strong. I can’t see how this is “in strong agreement” with your claim that short telomeres protect against both cancer incidence and mortality.

https://www.ncbi.nlm.nih.gov/pubmed/25862531
“…genetically determined short telomeres were associated with low cancer mortality…”

This is the Rode study, about which I have written extensively. A small part of the Rode study used GWAS methodology, and its conclusions were, unsurprisingly, more similar to other GWAS studies than to conclusions in the main section of the same paper.

Results are due to direct effect of SNPs on cancer

This possibility would be a violation of assumption 3 above – that the SNPs affect cancer exclusively via their effect on telomere length. Horizontal pleiotropy is a well known genetic phenomenon that could induce such direct associations between SNPs and cancer that bypass telomere length. This is the most important potential limitation of Mendelian randomization studies. We observed some evidence for this in our results and we admit in the discussion that we cannot entirely exclude this possibility.

Remember that all it takes is a very small direct effect to mimic the very large indirect effect.

“The new studies require very large implicit extrapolation that is not necessary in the old studies. The 50 to 1 extrapolation is very speculative, and it magnifies the noise along with the signal.”  We standardise the results to reflect a 1-SD (standard deviation) change in telomere length and therefore you are correct that we are extrapolating beyond the observed effect sizes of the SNPs. However, the extrapolation you describe is more like a 7 to 1 than 50 to 1 extrapolation because the average effect size of the SNPs is 0.13 SD units per copy of the telomere length raising allele.

Thanks for this information! I didn’t have the number 0.13 when I was writing the column last month, and in fact I assumed 0.05. Working backwards from reported odds ratio of 5.27 for brain cancer, I said they must have started with 1.08; working backwards from reported odds ratio of 3.19 for lung cancer, I said they must have started with 1.06. These numbers should be corrected to 1.24 and 1.16, respectively.

These numbers are still very low. Epidemiology is well-known to be full of uncertainties, and an odds ratio of 1.24 is just near the lower edge of what might be considered actionable. For the genetic telomere studies, however, it is the highest risk ratio they observed (reported as 5.27 times extra risk for brain cancer).

Question for Dr Haycock: Is 0.13 sigma the average increase (or decrease) in telomere length for subjects in your study? How was the 0.13 computed? Is it consistent with Table 1 in your paper, in which the highest percent of variance in telomere length explained by any one SNP was less than 0.5% ?

You write about GWAS and Mendelian randomization as if they are the same techniques. They are actually quite distinct methodologies and analytical approaches. In GWAS we measure the association between genetic variants and human traits across 100s of thousands to millions of loci across the genome (focus is gene-trait association). Mendelian randomization is the use of genetic variants as instrumental variables to appraise causality in hypothesized exposure-disease associations (i.e. the focus is the exposure-disease association).

Thank you for the correction. I gather that, though GWAS studies rely upon Mendelian randomization, the term “Mendelian Randomization Study” is reserved for a different animal.

“Another possibility is that one or more of the SNPs happen to be more common in a segment of the population that is prone to cancer, for whatever reason.” The problem you are referring to is known as confounding by population stratification – the tendency of cases and controls to have slightly different genetic ancestries and which can introduce confounding into genetic association studies. This issue is taken very seriously by genome-wide association studies. We did our best to take this into account. For example, our analyses were either adjusted for principal component scores of genome-wide genetic variation or we found little evidence for population stratification in diagnostic plots (these are standard techniques in the field). However, I agree and acknowledge in the paper that we can’t entirely rule out this possibility. More details in the discussion section of the paper.

Agreed.

[Concerning tradeoff between benefit for heart disease and liability for cancer]: We don’t know what the net benefits are at the population level and can’t infer that from our study. This requires detailed statistical modelling of absolute as well as relative risks.

This is a computation that I did myself, very approximately, combining risk ratios from the Haycock study with data on death rates from each of the cancers, and from heart disease. The answer that I got was that in the Haycock study, the two effects approximately cancel each other out, but in the epidemiological studies there is a large net benefit from longer telomeres. I didn’t think it worthwhile to do the computation more precisely because the data it relied on was highly uncertain.

Evolutionary tradeoff
There’s an interesting literature about potential evolutionary tradeoffs in cancer and vascular disease risk and the impact of body size and telomere length. Cancer incidence doesn’t seem to increase with increasing mammal body size (about same rates in mice and humans, known as Peto’s paradox). See this interesting review on “Telomere Length and the Cancer–Atherosclerosis Trade-Off”:

This study, published just this last summer, claims that, in humans, telomerase levels have evolved to be low and telomeres short, creating an optimal compromise between deaths from cancer and deaths from CV disease.

As you have guessed, I’m skeptical. I have staked my career on the thesis that the evolutionary theory of aging on which this paper is based is wrong. However, the paper cites several references on telomere length and cancer which I will read with an open mind.

[Savage, 2013; Anic, 2013; Nan, 2011; Machiela, 2014; Seow, 2014; Sanchez-Esperidion, 2014; Pellatt, 2013; Qu, 2013; Lynch, 2013; Julin, 2015]

How I see it: Aging is an evolved adaptation. Telomeres are short on purpose, as a clock that regulates lifespan.

New Evidence that Long Telomeres cause Cancer, and Why I Think It’s Wrong

Since 2003, I’ve been saying that long telomeres are a path to long life.  The opposing view says that nature allows our telomeres to shorten to protect us against cancer.  Up until this spring, there has been little evidentiary support for the cancer theory.  Now, a major new study uses genetics to argue that longer telomeres increase risk of cancer as much as five-fold.  The study contains many statistical checks, but I’m going out on a limb to say I think the experts have made a conceptual error.

Up until now, epidemiological studies in humans and lab studies in animals have shown consistently that shorter telomeres increase risks for all the diseases of old age.  People’s telomere length tends to decline with age, but among people of the same age, those with shorter telomeres tend to die sooner.

The new study finds a very different conclusion: that shorter telomere length leads to much lower risk of cancer, while longer telomere length leads to slightly lower risk of heart disease.  Put these two together, and you predict pretty much the same life expectancy for people with long telomeres and short telomeres.

The new studies are based on genetics and account for telomere length only indirectly.  Nevertheless, it is claimed, they are more reliable than the old studies (based on direct observation) because they are able to eliminate a statistical anomaly that (they claim) is super-important.  

I believe the new study is actually less reliable, and that we should believe the more direct studies like the ones I have reported here in the past.  My reasons are that

  • The previous studies are straightforward, direct correlations.  Methodology in the new study relies on very small differences in telomere length, tiny differences that are lost in the noise and very difficult to detect.
  • The new studies require very large implicit extrapolation that is not necessary in the old studies.  The 50 to 1 extrapolation is very speculative, and it magnifies the noise along with the signal.
  • It is likely what these new studies are seeing are actually direct effects of genetics on cancer risk.  Even very small (direct) effects of genotype on cancer would appear in their methodology as though they were huge (indirect) effects of telomere length.  This is what I believe is happening, and why I don’t trust their results.

I may be wrong about this.  I’m questioning seasoned experts in the field based on my general knowledge of statistics.


Two years ago, I reported on a Danish study linking short telomeres to higher mortality, especially heart disease (CV).  I took this as clear proof that telomere length was not just a marker of aging but a cause.  The implication is that you can live longer by adopting lifestyles and taking supplements that extend your telomeres.

The core of my argument, based on the the Danish study, was this:

  • Impact of telomere length on mortality, raw data:   3.38 (meaning that the 10% of people with the shortest telomeres were dying at a rate 3.38 as high as the 10% with the longest telomeres)
  • Same calculation, corrected for age:  1.54
  • Same calculation, corrected for age and all other hazard variables:  1.40

Conclusion: This demonstrates that age is the biggest factor in mortality, and telomere length is second, with a strong effect, independent of age.  All the health variables together have only minor effect compared to age and telomere length.

The Danish study did a multivariate analysis, also called ANOVA.  This is a statistical technique designed to separate out the factors that contribute to an outcome (in this case, mortality) and assign percentages of causality.  What their analysis revealed was that the strongest cause of increased mortality is age itself, and that telomere length comes second.  Everything else, from smoking to depression to a history of infections, is much less important than age and telomere length.  I interpret this to say that short telomeres are probably a direct cause of increased disease risk.

A popular theory is that the association of short telomeres with higher mortality is only incidental.  Stresses, infections, smoking, etc. cause both shorter telomeres and higher mortality.  But these are separate pathways.  It is not the shorter telomeres that are causing higher mortality, but short telomeres happen to be associated with higher mortality because both are caused by various stressors in a person’s past.  If this is true, you can’t improve your odds of living longer just by extending your telomeres.

But I believe that the Danish study disproves this theory.  If the stressor theory were correct, then the Danish analysis would have found that the relationship between stressors and mortality was stronger than the relationship between telomere length and mortality.  In fact, they found the opposite.

 

The New Genetic Study

The result reported by the new study is that longer telomeres creates a very much higher risk of several common cancers.  On the other hand, longer LTL (=leucocyte telomere length) protects against heart disease.  The protective effect for heart disease is much smaller, but many more people die of heart disease than of these particular cancers.  The result is a wash.  Longer LTL is neither a net benefit to health nor is it a net risk.  People with longer and shorter LTLs have similar overall mortality risk, about the same life expectancy.

 

Disease Odds Ratio
Glioma 5.27
Ovarian cancer 4.35
Lung cancer 3.19
Neuroblastoma 2.98
Bladder 2.19
melanoma 1.87
Testicular 1.76
Kidney 1.55
Endometrial 1.31
Basal cell skin 1.22
Breast Cancer 1.06
Heart disease 0.78

“Odds ratio” refers to a person’s probability of contracting the corresponding disease.  For example, the first line means that people whose telomere length is one standard deviation (1 sigma) longer than average have a risk of glioma 5 times greater than people who have average telomere length.

This result gains credibility because it is exactly what the theory would predict.  Nature has optimized LTL by compromising between two risks.  If the average LTL for our species were longer, then we’d get more cancer.  If it were shorter, we’d get more heart disease.  The reason there is so much variation among the population, people with much longer and much shorter telomere length, is that it doesn’t matter very much.

So here is agreement between experiment and theory, a tidy situation that scientists like to see.  What is more, there is a widely-held belief that the methodology of the new study is more reliable than studies in the past that are more direct and simpler.  Nevertheless, I’m about to offer my opinion that the previous studies were right, the theory is wrong, and, in fact the design of the new study is seriously flawed.

This was the latest and far the largest in a series of GWAS studies going back four years [ref, ref, ref].  GWAS stands for Genome-Wide Association Study.  The idea is to work around life experience variables that might create a correlation without a causal connection.  In the present case, the target is to detect any causal relationship between leucocyte telomere length (LTL) and various diseases, while filtering out associations between LTL and disease risk that might be incidental, as described above.  The researchers looked for small genetic differences (called SNPs) that are linked to telomere length.  These vary from one individual to the next, and they persist through a lifetime.  The next step is to compare numbers of people with a particular SNP variant among those who have the disease and those who don’t have the disease.  Are people who have the SNP associated with longer telomeres more or less likely to develop the disease?  From the answer to this question, they infer a causal relationship, not between SNP and the disease but between telomere length and the disease.

Observational studies look for a direct relationship between LTL and disease.  GWAS studies look for an indirect relationship between SNP and LTL, SNP and disease.  The indirect study is widely considered to be a more reliable indicator of causal connection than the direct study.  Why?

“Mendelian randomization studies are less susceptible to confounding in comparison to observational studies…Given the random distribution of genotypes in the general population with respect to lifestyle and other environmental factors, as well as the fixed nature of germline genotypes, these results should be less susceptible to confounding and reverse causation than those generated by observational studies.”

The reasoning is that people have their genomes for their entire lives, independent of how they live, what they do, what they are exposed to.  By working with the genome, the statisticians can be sure to eliminate the standard objection that (for example):

  • Stress directly decreases LTL
  • Stress directly increases risk of disease
  • Therefore, short LTL will appear to be linked with disease, even though short LTL doesn’t cause disease.

 

Problems with GWAS studies

But the GWAS methodology also introduces new problems of its own.  The main problem is that the statistical sensitivity of the study is seriously reduced.  This is because the relationship between SNP and LTL is very weak.  All sixteen SNPs together constitute a very small factor among many larger ones that create difference in LTL between one person and the next.

“The selected SNPs correspond to 10 independent genomic regions that collectively account for 2% to 3% of the variance in leukocyte telomere length”

And of course, very few people have all 16 SNPs going in the same direction.  The study is forced to work with people who have, for example 10 positive SNPs out of 16 compared to others who may have 5 positive SNPs out of 16.

Their LTL is really quite close together.  To compensate for this, the statisticians divide by a small number to extrapolate outwards.  For example, the difference between typical people in the study is about 1/20 sigma*.  And the difference between risk of glioma (brain cancer) for these people is only about 0.08** .  But the difference is reported as “what would have been the risk of brain cancer if the difference had been not 1/20th but one full sigma.  They extrapolate exponentially, so the conclusion comes out quite startling: They claim that people with 1 sigma of extra LTL have 5 times greater chance of getting brain cancer.

What they find: people with 0.05 sigma extra LTL have 1.08 times the risk of getting brain cancer.

What they report: people with 1 sigma extra LTL would have (by extrapolation) 5 times the risk of getting brain cancer.

They conclude that there is a large effect of telomere length on cancer, but they do this by the following reasoning:

  • There is a small effect of these genetic variations on telomere length.
  • There is a small effect of these genetic variations on cancer risk.
  • Dividing the small by the small, they conclude: if the mechanism for these genetic variations affecting cancer is mediated by their effect on telomere length, then the effect of telomere length on cancer must be quite large.

I’m sorry to belabor this, but it’s important, and it’s hidden in the methodology.  People who do these studies know that an odds ratio (OR) of 1.08 means nothing.  The state of the art in epidemiology is rarely able to attach meaning to odds ratio that is close to 1.  It is lost in the nosie.  But an OR of 5 is something easy to see.  It stands out from the noise and is easy to detect.

The description of the methodology in this study hides the fact that they are working with ORs less than 1.08 and extrapolating exponentially outward to make the ORs look very large and significant.

 

What I think is really going on

The study finds a large and consistent result that demands some explanation.  I’m claiming that the explanation they offer (in terms of telomere length) is wrong.  So why do I think they get the results that they did?

A few of the sixteen SNPs that are considered in the study correspond to slight variations on the form of the telomerase molecule.  I’m guessing that these mutated forms of telomerase cause an increased risk of cancer.  The increased risk doesn’t have to be large.  As in my example above, the increased risk for brain cancer would have to be just 8%, and the increased risk for lung cancer (more important because it is more common) only 6%.  Because of the extrapolation by an exponent of 20 that is implicit in their methodology, these small effects would be reported as though they were odds ratios of 5 (for brain cancer) and 3 (for lung cancer).

Another possibility is that one or more of the SNPs happen to be more common in a segment of the population that is prone to cancer, for whatever reason.  It may be that a particular SNP is more common in an ethnic group that has high smoking rates, or that is prone to melanoma because of lighter skin, or has a diet and lifestyle that leads to a slightly greater risk of cancer.  For example, it is known that people of African extraction have SNPs associated with longer telomere length, and they also have higher risks for many cancers, including lung and [ref].  (Africans have lower risk of glioma, so the correlation goes in the wrong direction for this particular example.)  At the risk of beating a dead horse, I emphasize again that even a small increased risk would be magnified by the extrapolation that is implicit in the methodology of the GWAS, and appear very large and scary when misinterpreted as an effect of telomere length.

GWAS is also referred to as “Mendelian randomization studies” because they depend very much on the assumption that different SNPs are randomly distributed in the population.  Of course, this assumption is not literally satisfied.  How significant is the deviation from random distribution?  I will be investigating this question, and I’ll let you know what I find.

 

The Bottom Line

There is a sharp conflict between the new GWAS results [Haycock, 2017] and the observational results [Rode, 2015] reported two years ago.  They can’t both be right.  If the GWAS results are as Haycock claims, there would have been glaring increases in cancer risk that Rode could not have missed.  If Rode is correct, then the methodology of Haycock must be flawed.

The reasoning in GWAS studies depends on a huge extrapolation.  I am saying it is more likely that the effect of genetic variations on cancer risk is direct, not (as per Haycock’s assumption) mediated by telomere length.  It could be that a very small direct effect of one of these SNPs is reported as though it were a large indirect effect, working via telomere length.

For now, I’m sticking with my previous counsel: Lengthening telomeres is a viable strategy for improving health and longevity.  If you take supplements that promote telomerase, you are not adding to your cancer risk.  Because of the large net benefit, lengthening of telomeres should be a major target for medical research.

But as I said at the outset, I am criticizing the new study from the outside, and it is quite possible that I have misunderstood the methodology.  I have sided with the direct observational studies and I have been skeptical of the GWAS studies, but it may be that the consensus in the field is correct, and that GWAS studies really are more reliable indicators of causality.

I intend to get to the bottom of this, and will report my findings in future columns.

__________

* Sigma is a standard deviation of telomere length in the population at large. If you know what that means, that’s great; if you don’t it doesn’t matter to the logic of what I’m saying.

** Disease risk is typically reported as an odds ratio.  In this case, 0.08 would mean that, in their raw data, people in the study with the longer LTLs had a risk of 1.08 times as great as people with shorter LTLs.  You get to 1.08 not by adding 1 but by raising e to the power 0.08.

Building the Case that Aging is Controlled from the Brain

Last week, a new study came out fingering the hypothalamus as locus of a clock that modulates aging.  This encourages those of us who entertain the most optimistic scenarios for anti-aging medicine.  Could it be that altering the biochemistry of one tiny control center might effect global rejuvenation?  

First some background….

I have staked my career on the interpretation that aging unfolds under the body’s full control.  Even those aspects of aging that look like random damage are actually damage that is permitted to accumulate as the body pulls back its defense mechanisms late in life and dials up some biochemical processes that look an awful lot like deliberate self-destruction

I believe that aging is governed by an internal biological clock, or several semi-independent and redundant clocks.  There are

  • A telomere clock, counting cell divisions on a flexible schedule, eventually producing cells with short-telomeres that poison us.
  • The thymus, crucial training ground for our white blood cells, shrinks through a lifetime.
  • An epigenetic clock alters gene expression over time in directions that give rise to self-destruction.
  • A neuroendocrine clock in the hypothalamus
  • Perhaps other clocks, yet to be identified.

 

A dream is to be able to reset the hands of the clock.  If we’re lucky, then changing the state of some metabolic subsystem will not just temper the rate at which we age, but actually restore the body to a younger state.  Most of the research in anti-aging medicine is still devoted to ways to engineer fixes for damage the body has allowed to accumulate; but I belong to a wild-eyed contingent that thinks the body can do its own fixing if we understand the signaling language well enough to speak the word “youth” in the body’s native biochemical tongue.

Some of these clocks are more accessible and easier to manipulate than others.  The epigenetic clock is most daunting, because it presents the spectre of a global network of signal molecules circulating in the blood, transcription factors that mutually support one another in a state of slowly-shifting homeostasis.  This system could be so complex that it might take decades to understand, and then hundreds of different signal molecules in the blood would need to be re-balanced in order to recreate homeostasis in a younger condition.  (For several years, the Mike and Irina Conboy have been looking for a small subset of molecules that might control the rest, but in a private conversation they recently told me they are less optimistic that a small number of factors controls all the rest.)

At the other end of the spectrum, the hypothalamic clock presents the most optimistic scenario.  It is tightly localized in a tiny region of the brain, and might be relatively easy to manipulate, with consequences that rejuvenate the entire body.  The hypothalamic clock hypothesis is an attractive target for research because, if correct, it will offer direct and straightforward control over the body’s metabolic age.

That aging unfolds according to an internal clock remains a controversial claim, but what everyone agrees is that the body has some way to know how old it is.  There has to be a clock for development that determines when growth surges and stops, when sex hormones turn on and, if it’s not too great a stretch, when fertility ends and menopause unfolds.

The clock that governs growth and development has yet to be elucidated—a major metabolic mystery by my lights.  The clock that we know about and (sort of) understand is the circadian day-night clock that governs sleep and waking, giving us energy at some times of the day but not others.

Is the life history clock linked to the circadian clock?  Maybe the body just counts days to tell how old it is?  This possibility was eliminated, at least for flies, using experiments with cycles of light and dark that were consistently longer or shorter than 24 hours.  Flies living with fast day-night cycles (less than 24 hours) lived shorter, as predicted; but flies living with long day-night cycles failed to have longer lifetimes,  In fact, deviation from 24 hours in either direction shorten the fly’s lifespan [2005].  

But this study suggests the short-term clock and the long-term clock may be linked in a way that is less straightforward.  Melatonin may be another reason to expect a connection.  Melatonin is the body’s cue for sleep, and Russian studies have documented a role for melatonin in aging.  A third motivation comes from the fact that aging disrupts sleep cycles, and (in a downward spiral) disrupted sleep cycles are also a risk factor for mortality and diseases of old age.

Cells seem to have their own, built-in daily rhythms.  I want to say “transcriptional rhythms”, adding the idea that gene transcription is the locus of control; however, red blood cells are the counterexample—they exhibit daily cycles, even though they have no DNA to transcribe [2011].  Individual cycles are designed to be 24 hours, but they would soon drift out of phase with day and night if they weren’t centrally coordinated.  The reference clock that keeps the others in line is in the SCN, the suprachiasmatic nucleus, a handful of nerve cells in a neuroendocrine part of the brain called the hypothalamus.

Think of a million pendulums that are all tuned to swing with a period of 24 hours.  All that it takes is a tiny nudge to all these pendulums each day to keep them in phase with one another, so they are all swinging together.  The SCN provides this nudge in a smart way, based on information from the eyes (light and dark) and endocrine signals that indicate activity and sleep.  The SCN is upstream of the pineal gland, and supplies the signal that tells the pineal gland when it’s time to make melatonthematic index of scarsonatas.  The natural resonances of individual cells become entrained in a body-wide response.

 

What does all this have to do with aging?

Experiments in the 1980s and 90s showed that the SCN is related to annual cycles, but the relationship seems to be not as strong or as simple or as direct.  For example, squirrels in which the SCN was removed had no daily sleep-wake cycles at all, but their annual cycles of fertility and oscillations of weight were affected inconsistently, more in some animals than others.  Transplanting a SCN from young hamsters into old hamsters cut their mortality rate by more than half, and extended their life expectancies by 4 months [1998].

I have written in this column [one, two] about research from the laboratory of Claudia Cavadas (U of Coimbra, near Lisbon) indicating that inflammation and inflammatory cytokines in the hypothalamus are at the headwaters of a cascade of signals that lead to whole-body aging.  They have emphasized the role of TGFß binding to ALK5 and of the neurotransmitter NPY.  We usually think of inflammation as a source of damage throughout the body, but in the hypothalamus, inflammation seems to have a role that is more insidious than this, with full-body repercussions.  Blocking inflammation in the hypothalamus is a promising anti-aging strategy.

New Paper on micro RNAs from the Hypothalamus

Along with Cavadas, Dongshen Cai (Einstein College of Medicine) has been a leader in exploring neuroendocrine control of aging that originates in the hypothalamus.  Several years ago, Cai’s group demonstrated that aging could be slowed in mice by inhibiting the inflammatory cytokine NF-kB and the related cytokine IKK-ß just in one tiny area of the brain, the hypothalamus.  “In conclusion, the hypothalamus has a programmatic role in ageing development via immune–neuroendocrine integration…”  They summarized findings from their own lab, suggesting that metabolic syndrome, glucose intolerance, weight gain and hypertension could all be exacerbated by signals from the inflamed hypothalamus.  In agreement with Cacadas, they identified GnRH (gonadotropin-releasing hormone) as one downstream target, and were able to delay aging simply by treatment with this one hormone.  IKK-ß is produced by microglial cells in the hypothalamus of old mice but not young mice.  Genetically modified IKK-ß knock-out mice developed normally but lived longer and retained youthful brain performance later in life.

In the new paper, Cai’s group identified micro-RNAs, secreted by the aging hypothalamus and circulating through the spinal fluid, that contribute to aging.  A small number of stem cells in the hypothalamus were found to keep the mouse young, in part by secreting these micro-RNAs.  Mice in which these stem cells were ablated had foreshortened life spans; old mice that were treated with implants of hypothalamic stem cells from younger mice were rejuvenated and lived longer.  A class of neuroendocrine stem cells from the third ventricle wall of the hypothalamus (nt-NSC’s) was identified as having a powerful programmatic effect on aging.  These cells are normally lost with age, and restoring these cells alone in old mice extended their life spans.

Exosomes are little packets of signal chemicals. Micro-RNAs from stem cells in the hypothalamus are collected into exosomes and shipped down through the spinal fluid.  These exosomes seem to constitute a feedback loop.  On the one hand, they are generated by the hypothalamic stem cells.  On the other hand, they play a role in keeping these same cells young, and producing more exosomes.

Life extension of about 12% was impressive given that there was just one intervention when the mice were more than 1½ years old, but of course it’s not what we would hope for if the master aging clock were reset.  For really large increases in lifespan, we will probably need to reset two or even three of the clocks at once.

 

The Bottom Line

The reason the body has multiple, redundant aging clocks is to assure that natural selection can’t defeat aging by throwing a single switch.  That means the clocks must be at least somewhat independent.  Nevertheless, I judge it is likely that there is some crosstalk among clocks, because that’s how biology usually works.  To effect rejuvenation, we will have to address all aging clocks, but we see some benefit from resetting even one, and expect more significant benefit from resetting two or more.

The most challenging target is the epigenetic clock,built on a homeostasis of transcription and signaling among hundreds of hormones that each affect levels of the others.  Reverse engineering this tangle will be a bear.

The idea of a centralized aging clock in the hypothalamus seems far more accessible, and is promising for the medium term.  Still, it does not suggest immediate application to remedies.  The hypothalamus is deep in the brain, and you and I might be reluctant to accept a treatment that required drilling through the skull.  A treatment based on circulating proteins and RNAs from the hypothalamus would be less invasive, but even that might have to be intravenous, and include some chemistry for penetrating the blood-brain barrier.  RNA exosomes seem to be our best opportunity

As Cavadas’s group has already pointed out, it is inflammation in the hypothalamus that is amplified by signaling to become most damaging to the entire body.  This raises the interesting question: could it be that the modest anti-aging power of NSAIDs is entirely due to their action within the brain?  In other words, maybe “inflammaging” is largely localized to the hypothalamus.