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.