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 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:
“…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.
“…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 a 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.


[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.

Mitochondria in Aging, II: Remedies

The once-popular mitochondrial free radical theory of aging proved to be too glib. Aging isn’t fundamentally about dispersed damage; rather, dispersed damage is a result when the body’s defenses stand down in old age.  Nevertheless, the mitochondria do play a role in aging, largely through signaling and apoptosis.  Antioxidants targeted to mitochondria may be an exception to the rule that antioxidants don’t prolong lifespan.  And other supplements and strategies that either promote production of new mitochondria or enhance their efficiency of operation show promise for modest lifespan extension.

Growing new mitochondria

A ketogenic diet leads to generation of new mitochondria, as do caloric restriction and exercise.  Exercise when the body is starved for sugar (low glycogen) is the most potent stimulator of new mitochondrial growth.  Exercise while fasting, or continue to exercise after you “hit your wall”.

Hormones that promote mitochondrial proliferation include thyroxin, estrogens, and glucocorticoids.  Promoting new mitochondria has a tendency simultaneously to suppress apoptosis, programmed cell death [ref].  At later ages, apoptosis of cells that are still functional tends to be a larger problem than the failure of cancerous cells to eliminate themselves by apoptosis.  In other words, suppressing apoptosis is (on balance) a good thing for anti-aging, but the downside is it can also increase risk of cancer.



Coenzyme Q-10 (aka ubiquinone) is an essential part of mitochondrial chemistry, shuttling electrons along their way to the ATP molecules that mitochondria generate as their primary energy export to the cell.  It’s often called an antioxidant, but that’s not the primary role of CoQ10.

As a supplement, it is well-established with a good reputation.  There is lots of evidence for benefits to health markers, especially athletic endurance, several aspects of heart health, and erectile dysfunction.  If you have fibromyalgia or if you are taking statins, CoQ10 is strongly indicated.  For chronic fatigue syndrome, it’s definitely worth trying.  

But there’s no reason to expect it will increase your life expectancy.  Supplementing with ubiquinone increases the lifespan of worms but not mice or rats [ref, ref].

Worms that cannot make unbiquinone live 10 times as long.  Just saying…

A few years ago, ubiquinol was introduced as a more bioavailable form of ubiquinone.  It’s more expensive, but there is not clear evidence that it is more bioavailable.



Pyrroloquinoline quinone is helpful but not necessary part of mitochondrial chemistry.  Bacteria make a lot of it; plants less; mammals only tiny quantities.  Mice completely deprived of PQQ show growth deficiency, but the amount that they need is tiny compared to the quantities in PQQ supplements.

PQQ is a growth factor for bacteria, and the principal health claim for PQQ is that it can stimulate growth of new mitochondria.  The evidence is based on biochemistry and cell cultures.  In live mice, it has been shown that PQQ deficiency results in a mitochondria deficiency, but not that large quantities of PQQ lead to more mitochondria.

Bill Faloon (LEF) and Joseph Cohen (selfhacked) are big fans of PQQ, and you can read a list of benefits here.  Cohen claims PQQ helps with sleep quality and nerve growth, leading to better cognitive function.  

Small quantities of PQQ can be absorbed from many plant foods, but not animal foods.  Much larger quantities come in supplement form. 100 g of tofu has just 2µg (micrograms).  Supplements are usually 5-20 mg, hundreds of times as much as you’re likely to get from a vegetarian diet.  Here is a table of PQQ concentrations in foods:


SkQ and MitoQ

These are two closely related molecules, originally synthesized in Russia in the 1970s, but it wasn’t until the 1990s that their therapeutic value was documented by two New Zealand scientists.  One end of the molecule is CoQ10 (or a version found in plants, claimed to be even more powerful as an antioxidant).  The other end of the molecule is an electric tugboat that pulls the molecule into mitochondria.  

I’ve written a  detailed report three years ago.  At the time, I noted that the Russians claimed to extend lifespan of mice modestly with SkQ, and SkQ was found (also in Russian labs) to be a powerful rejuvenant for aging eyes.  The Russians sell SkQ as eye drops.  The Kiwis sell MitoQ as skin cream and also as pills.

Earlier this year, the Russian labs announced that SkQ had substantially extended lifespan of a mouse strain that was short-lived because of a mitochondrial defect.  None of the Russian claims have been reproduced in Western labs.  Three years ago, I was inclined to give the Russians the benefit of the doubt, but now I’m starting to wonder, since the New Zealand company has a laboratory arm, and they haven’t announced anything nearly so impressive.


Humanin and her sisters

Mitochondria have ringlets of their own DNA, encoding just 37 genes.  (That doesn’t mean that the mitochondria only need 37 proteins; the great majority of proteins needed by mitochondria are coded in chromosomes of the cell nucleus, and transported to the mitochondria as needed.)  Just 16 years ago, the first mitochondrial-coded protein to be discovered was named Humanin, because it was found to improve cognitive function to dementia patients, restoring some of their “humanity”.  In addition to being neuroprotective, humanin promotes insulin sensitivity.  Humannin’s action is not confined to the mitochondrion in which it was produced, but in fact it  circulates in the blood as a signal molecule.  Blood levels of humanin decline with age.


In experiments with mice, humanin injections have been shown to protect against disease.  Lifespan assays with humanin are not yet available.

To date, HN and its analogs have been demonstrated to play a role in multiple diseases including type 2 diabetes (25, 43), cardiovascular disease (CVD) (2, 3, 47), memory loss (48), amyotrophic lateral sclerosis (ALS) (49), stroke (50), and inflammation (22, 51). The mechanisms that are common to many of these age-related diseases are oxidative stress (52) and mitochondrial dysfunction (53). Mitochondria are major source of ROS, excess of which can cause oxidative damage of cellular lipids, proteins, and DNA. The accumulation of oxidative damage will result in decline of mitochondrial function, which in turn leads to enhanced ROS production (53). This vicious cycle can play a role in cellular damage, apoptosis, and cellular senescence – contributing to aging and age-related diseases. Indeed, oxidative stress is tightly linked to multiple human diseases such as Parkinson’s disease (PD) (54), AD (55), atherosclerosis (56), heart failure (57), myocardial infarction (58), chronic inflammation (59), kidney disease (60), stroke (61), cancers (62, 63), and many types of metabolic disorders (64, 65). We and others have shown that HN plays critical roles in reducing oxidative stress (6668). [2014 review]

Pinchas Cohen, MD (Dean, School of Gerontology, University of Southern California Davis, Los Angeles, California) is an expert in humanin, a protein (peptide) produced in mitochondria. Mitochondria are energy-generating organelles in cells, which have their own DNA separate from the DNA in the nucleus. The amount of DNA found in the mitochondria is much less than that found in the nucleus. As such, mitochondrial DNA contains codes for only a few proteins, humanin being one of them. Humanin was discovered by a search for factors helping to keep neurons alive in undiseased portions of the brains of Alzheimer’s disease patients. Humanin protects neurons against cell death in Alzheimer’s disease, as well as protecting against toxic chemicals and prions (toxic proteins)[ref].  Dr. Cohen’s team has shown that humanin also protects cells lining blood vessel walls, preventing atherosclerosis. In particular, they have shown that low levels of humanin in the bloodstream are associated with endothelial dysfunction of the coronary arteries (arteries of the heart).[ref] Humanin has also been shown to promote insulin sensitivity, the responsiveness of tissues to insulin. Because humanin levels decline with age, it is believed that reduced humanin contributes to the development of aging-associated diseases, including Alzheimer’s disease and type II diabetes. [Ben Best]

Personal notes: This lab near where I am visiting in Beijing is taking leadership in characterizing a group of short peptides similar in origin to humanin, and this company across the street from us is selling mitochondrial peptides.

If humanin were a patentable drug, there would be much excitement and multiple clinical trials for AD, probably leading to expansion into general anti-aging effects.



This is another short peptide of mitochondrial origin, only recently discovered and characterized.  I was alerted to its existence by a study from a USC lab that was written up here in ScienceBlog just this month (reprinted from a USC press release).  Results are new but impressive.  Mice injected with MOTS-c had more muscle mass, less fat, more strength and endurance.  MOTS-c protected their insulin sensitivity when mice were fed a high fat diet [ref].  Lifespan studies haven’t been done yet.

Like humanin, MOTS-c is manufactured inside mitochondria from a template in mitochondrial DNA, but it is exported from the cell and appears in the bloodstream as a signal molecule.  Blood levels of MOTS-c decline with age.  It is a mini protein molecule with 16 amino acids, too big to survive digestion so it can’t be taken orally.  

MOTS-c holds much potential as a target to treat metabolic syndromes by regulating muscle and fat physiology, and perhaps even extend our healthy lifespan.”[ref]

Let’s keep your eyes on this one over the next year or two.


Gutathione / NAC

I’ve never heard anyone say a bad word about glutathione.  It’s the antioxidant with no downside.  Genetic modifications that upregulate glutathione have increased lifespan in worms, flies and mice.  

For a long while, it has been assumed that you can’t eat glutathione, because it doesn’t survive digestion.  Some researchers at Penn State disagree, finding impressive increases in tissue and blood levels when people were supplemented with up to 1 g per day raw glutathione.  Liposomal glutathione is an oral delivery form that gets around the digestion problem, especially when taken with methyl donors like SAMe.

The herb Sylimarin=milk thistle may increase glutathione.  For now, the precursor molecule N-Acetyl Cysteine (NAC) is the best-established supplement we have to promote glutathione.  In the one available study, supplementing with NAC greatly increased lifespan in male but not female mice.  NAC also increases lifespan in worms and flies.

N-Acetyl Cysteine



For the future, we might hope to do better.  Less than 20% of the cell’s glutathione actually makes its way to the mitochondria, where it is most needed.  There are esters of glutathione that, in theory, ought to be attracted into the mitochondria.  They have been tested in cell culture only, but are more than ripe for animal testing [ref].


Nicotinamide Riboside (NR) and other NAD+ enhancers

The chemicals NAD+ and NADH are alternative, cycled forms of an intermediate in the process by which mitochondria make energy.  Levels of NAD+/NADH decline with age.  NR is a precursor to NAD+, and it has been demonstrated (preliminary results in humans) that NR supplementation increases blood levels of NAD+.

It may be awhile before we know for sure whether this leads to better health or longer lifespan.  Niagen and Basis are heavily promoted with credible scientifists behind their products, and many early adopters offer subjective reports of short-term benefits.  There is one mouse study claiming to pull a 3% extension of lifespan out of the noise, and perhaps I am less open to the finding because the article, published prominently in Science, seems so breathless in describing benefits.



The primary role of melatonin is to regulate the body’s sleep/wake cycle.  Melatonin declines with age and the timing of our daily melatonin surge gets fuzzier and less reliable with age.sleep quality deteriorates.  Sleep quality suffers.

Melatonin is well-established in mice as a modest longevity aid, although results have been inconsistent.  12 out of 20 studies showed a lifespan increase, and the remaining 8 showed no increase or decrease.  Whether nightly supplementation affects mortality rates in humans has never been determined.

Melatonin is concentrated in mitochondria as much as 100-fold, and it may even be created there [ref], independent of the circulating melatonin that is secreted from the pineal gland at night.  One of its actions is as a mitochondrial antioxidant and scavenger of ROS.

Twenty years ago, Walter Pierpaoli promoted melatonin as a sleep aid, cancer fighting hormone that would enhance your mood and your sex life while keeping you young.  Russian labs have also been optimistic.  My take is that melatonin is a legitimate anti-aging hormone, and is especially useful for those of us whose sleep is disrupted with age.  It is widely available, cheap and safe.  Unless you’re fighting jet lag, 1 to 2 mg at night is all you need.

Also worth mentioning

Magnesium is required for manufacture of glutathione.  Selenium works along with glutathione.  Omega-3 fatty acids can promote synthesis of glutathione.  Acetyl L-carnitine transports fat fuels through the mitochondrial membrane.  Alpha-lipoic acid is part of the mitochondrial energy metabolism.

The Bottom Line

Commercial interests can make some messages louder than others, and the health news we hear is affected by what is profitable as much as by what is healthy.  Exercise is primary, but has no sales value.  Of the supplements reviewed here, NAC is the best-established for mitochondrial health and a possible effect on lifespan.  It is cheap and available.  Liposomal glutathione is certainly more expensive and possibly more effective.  Melatonin is even cheaper, and has been found to increase lifespan in multiple rodent studies, with broad benefits apart from modification of mitochondrial function.  Humanin and MOTS-c, not yet close to commercial availability, seem to be promising substances to explore for health, though not for profits.

Mitochondria in Aging, I Mechanisms and Background

A popular theory a generation back sought to trace aging to oxidative damage originating in the mitochondria.  Every cell in the body has hundreds or thousands of mitochondria, the sites of the high-energy chemistry that produces ROS as toxic waste. The hope was that by quenching the ROS, aging might be turned off. The “Mitochondrial Free Radical Theory” is built on a flawed theoretical foundation, and anti-oxidants don’t extend lifespan. Nevertheless, the mitochondria play a role in aging.  Historically, mitochondria were mediators of the first organized mechanisms of programmed death over a billion years ago, and they retain a role in processing signals that regulate lifespan.  Curiously, though a quadrillion mitochondria are dispersed through the body, they act in some ways like a single organ, sending coordinated signals that regulate metabolism and affect aging.

Mitochondria are in the cells of all plants and animalshundreds or thousands of mini power plants in each cell.  They burn sugar to make electrochemical energy in a form the cell can use.  They are loyal and essential servants.  But it wasn’t always so.  More than a billion years ago, mitochondria came into the cell as invading bacteria.  Though they’ve long ago been domesticaed, they retain a bit of their autonomy as a relic of the past.  Mitochondria have their own DNA.  Like bacteria, mitochondrial DNA is in the form of loop, a plasmid rather than a chromosome.  Each mitochondrion keeps several copies of the plasmid.  

Mitochondria retain from their distant pathological past the capacity to kill the cell.  This is an orderly process known as apoptosis=programmed cell death.  Mitochondria are not the jurors that sentence the cell to death, but only the executioners acting on external signals.

Aging of the body as a whole is centrally coordinated, though the nature and location of the clock(s) remain a major unsolved problem.  Communication about the age state of the body is carried through signal molecules in the blood, and tissues respond accordingly.  Mitochondria not only pick up on these signals, they also contribute circulating signals of their own.  Apoptosis is dialed up in old age.  Along with inflammation, it is a primary, local mode of the self-destructive process that is aging.  We lose too many cells to apoptosis, cells that are still healthy and useful, and mitochondria are the proximate cause of this loss.

Portrait by scanning electron microscope, artistically colorized


Signaling, up, down and sideways

The big picture is that mitochondria take their orders from the cell nucleus, where the vast majority of the DNA is housed.  The transcription factors that determine what mitochondrial genes are expressed are housed in the nucleus.  In addition, there is feedback, retrograde signaling, by which mitochondria communicate to the nucleus the state of their own health and of the cell’s energy mtabolism in general.  The nucleus responds with changes in transcription based on communication from the mitochondria.

A great part of the diverse benefits of caloric restriction, and perhaps of exercise, too, are thought to originate in signaling from the mitochondria.  

In addition to sending and receiving signals from the cell nucleus, mitochondria talk to each other.  They coordinate extensively within a cell, and they also generate hormones that are transmitted through the bloodstream, talking to distant cells and foreign mitochondria.


Mitochondria and Cancer

Cancer cells have impaired mitochondrial metabolism.  They don’t burn sugar through the usual, high-efficiency mode that combines with the maximal amount of oxygen; rather they use fermentation—anaerobic breakdown of sugar.  Cancer cells do this even when oxygen is plentiful, despite the fact that it generates much less energy per sugar molecule.  Cancer cells are starved for energy, and they gobble up sugar at a high rate.  (PET scans are able to visualize tumors on the basis of their sugar consumption.)  Eating a very-low-carb diet is a cancer therapy.  

90 years ago, a Nobelist and Big Thinker in biomedicine named Otto Warburg gave us the hypothsis that mitochondria with impaired glucose metabolism are the root cause of cancer.  We usually think of cancer as starting with mutations that lead to uncontrolled growth and proliferation, but in the Metabolic Theory of Cancer, mutations and proliferation are secondary to this change in mitochondrial chemistry.  Today, proponents of the Warburg Hypothesis are a small but enthusiastic minority, armed with facts and arguments that I have not yet found time to assess.  But I am struck by the fact that when the nucleus of a cancer cell is transplanted into a healthy cell, the healthy cell remains healthy; and when the nucleus of a healthy cell is transplanted into a cancer cell, the cell remains cancerous [ref, ref].  This seems to be prima facie evidence that the essence of cancer is not to be found in chromosomes of the nucleus.


Fewer, less efficient, and more toxic waste with age

We have fewer mitochondria as we age, and this is plausibly connected to lower muscle strength and endurance as well as energy in the organ that uses energy most intensively=the brain [ref].  The relationship is subtle enough that it is not completely nailed down, despite decades of work from true believers.  Since mitochondria mediate apoptosis, it is also plausible that loss of muscle cells and nerve cells with age (at least partially through apoptosis) is also mediated by mitochondria.

Cells that need a lot of energy have a lot of mitochondria. Heart muscle cells are packed with them.

Compounding the problem, the mitochondria that we do have become less efficient with age.  They are giving us less energy, and they are generating more reactive oxygen species (ROS).  Simultaneously, the cell is generating less of the native anti-oxidants that protect from ROS.  Glutathione, ubiquinone, and superoxide dismutase all decline with age.  This is one of the ways the body destroys itself.  Oxidative damage accumulates in old but not young people.  Oxidative damage may also contribute to telomere shortening.

Somehow, ROS generated by impaired mitochondria produce damage that accumulates, but ROS generated by exercise signal the body to ramp up the repair processes, and produce a net gain in health.  It is not clear how the two processes are distinguished.  The reason that anti-oxidants don’t work to extend lifespan is probably that they interfere with the signaling functions of ROS.

The best-documented way in which mitochondria deteriorate is that their DNA develops mutations.  I find this something of a conundrum—not that mitochondria should accumulate mutations over the course of a lifetime but that they don’t accumulate mutations from one generation to the next (in the germline).  Mitochondria proliferate clonally, without sex.  Sex shuffles genes in many combinations, so that the good genes can be separated from the mutated ones, and the latter eliminated before they get fixed into the genome.  Without sex, how do mitochondria avoid accumulating mutations over the aeons?  And since they largely do manage to avoid accumulating mutations over millions of years, why can’t they avoid accumulating mutations over the course of a few decades within a human body?


Are mutations in mitochondrial DNA a cause of aging?

Mitochondrial mutations accumulate with age.  Genetically modified mice with a defective gene for replication of mitochondrial (but not nuclear) DNA age faster and die earlier.  This has generally been taken as proof that mitochondrial mutations are a factor in aging, but it need not be so.  In fact, mitochondria function well with a high tolerance for genetic errors, and it is not clear whether levels of mitochondrial mutations in aging humans cause significant problems, or even whether mutations are related to the general decline in mitochondrial function with age.  An alternative explanation for the mito-mutator mice is that they have developmental problems already in utero, and these may lead to premature aging even without accumulation of mito mutations.

Mitochondrial mutator mice

Stem cells keep dividing and producing new functional (differentiated) cells through the life of the animal.  They seem smart enough to minimize the damage from mitochondrial mutations.  Stem cells have been observed to hold on to the best mitochondria, and pass the damaged ones off to the cells that have a limited lifetime. This helps keep the errors from proliferating, and is in the best interest of the organism as a whole.  It’s interesting that mother budding yeast cells do the opposite—they hold on to their damaged mitochondria and pass the cleanest and purest on to their daughter cells [ref].  Mammalian mothers also seem able to choose the best mitochondria to pass to their daughters, purifying the germline [ref].  In other words, though their behavior is the opposite of stem cells, both behaviors are adaptive for the long-term interest of the organism (and its progeny).

In summary, the age-related increases in oxidative damage and ROS production are relatively small and may not explain the rather severe physiological alterations occurring during aging. Consistent with this hypothesis, the absence of a clear correlation between oxidative stress and longevity [across species] also suggests that oxidative damage does not play an important role in age-related diseases (e.g., cardiovascular diseases, neurodegenerative diseases, diabetes mellitus) and aging. Experimental results from mtDNA mutator mice suggest that mtDNA mutations in somatic stem cells may drive progeroid phenotypes without increasing oxidative stress, thus indicating that mtDNA mutations that lead to a bioenergetic deficiency may drive the aging process [but this is not assured, since these mice seem to suffer substantial damage already in utero]. There is as yet no firm evidence that the overall low levels of mtDNA mutations found in mammals drive the normal aging process. One way to address this experimentally would be to generate anti-mutator animal models to determine whether decreased mtDNA mutation rates prolong their life span. [Bratic & Larsson review]


Mitochondrial evolutionary conundrum

Mitochondria reproduce clonally, like bacteria.  In fact, all the mitochondria in your body were inherited from one of your mother’s egg cells, and she got her mitochondria from your maternal grandmother, and so forth back in time—matrilineal all the way.  How is it that defects don’t accumulate in the mitochondrial genome?

As far as I know, the way in which the integrity of the mitochondrial genome is maintained remains an unsolved problem.  We do know that mutations in mitochondrial DNA increase with age in some tissues but not others [ref].  The reason you have to speak up when you talk to your grandmother is probably related to mitochondrial defects in neurons [ref].

Over the course of millions of years, mitochondria do not lose their genetic integrity, though the mitochondrial genome evolves more rapidly than the nuclear genome, and different species tend to have distinctive mitochondrial genomes.  The mystery is why detrimental mutations should accumulate over decades, but not over aeons.

To me, this is powerful evidence that there is a mechanism for managing the evolution of the mitochondrial genome.  It probably involves selection by the cell so that mitochondria that are functioning efficiently are encouraged to reproduce.  The cell acts like a human lab that is breeding tomatoes or Labrador retrievers for specific characteristics that the breeder or the cell finds most useful.  Probably there is also gene exchange among the different copies of the plasmid within a mitochondrion, and between mitochondria as they sometimes merge during the lifetime of a cell (my speculation).


What’s going on?

A theme in this blog (and in my thinking) has been that aging is not a dispersed process of locally-occurring damage, but is centrally orchestrated.  Well, mitochondria are about as far from “central” as you can get.  We have about a quadrillion of them, dispersed through every cell in the body (except red blood cells).

Mitochondria talk to each other within a single cell.  They merge and they reproduce, coordinating with one another and with the cell nucleus.  Now it appears they also send signals through the bloodstream (more next week).  Could they be acting like a single organ, dispersed through the body? Maybe.  Sensing the body’s state of energy usage and fuel sufficiency, they send signals that contribute to calculations about lifespan.

My guess is that aging is coordinated by a few biological clocks (centralized like the suprachiasmatic nucleus and the thymus or dispersed like telomeres and methylation patterns), and that mitochondria are not counted among the clocks.  But mitochondria are important intermediates.  The old story is that they generate energy and generate tissue-damaging ROS.  The new story is that they are also centers of signal transduction, probably based on their first-hand knowledge of the energy status of the body.

End of Part I.
Next week, I will discuss some supplements
and health strategies based on mitochondria.

Preventing Dementia

Abating Risk of AD

Dementia seems to appear out of nowhere.  People feel like helpless victims of their genes.  But this is a misconception.  How much does the dreaded ApoE4 gene increase your chance of getting dementia?  This is not a straightforward calculation precisely because the correlation must be controlled for many diet and lifestyle factors that are proving to be just as important as genetics.  For women with ApoE4 from one parent, risk of AD is doubled, but this factor is easily reclaimed with diet, exercise and supplements.  For men, the effect of one copy of ApoE4 is small enough to be lost in the noise.  For men and women with two copies of ApoE4, risk of AD is very high, but even here factors under your control offer hopeful remediation.

I confess that I love public attention.  For a long time, I thought of this as a personal failing, and consciously avoided pursuing publicity.  When I came up with radical notions about evolutionary biology and the direction of medical research, I counted on people to think for themselves and evaluate the evidence; if they looked only at the credentials of the theorist, I was seriously outgunned.

Reputation shouldn’t matter, but it does  Only in the last decade, I have come to realize that people are receptive to ideas from those they already trust, and skeptical of ideas that come from an unknown source. I’m learning to burnish my reputation…

So when I got an inquiry from a CBS news intern asking “What can we do to improve our odds of avoiding dementia?”, I set aside time to review the literature before I talked to him.  Here’s what I found.


Exercise is most important

Five areas that I investigated are:

  1. Physical exercise
  2. Mental stimulation and social factors
  3. Metabolic syndrome / insulin sensitivity
  4. Diet
  5. Brain-targeted drugs and supplements

As I set out on my quest, I was prepared to find that #2 was the most important.  For longevity, social connections are more important than anything else; and I reasoned that for keeping the brain healthy in late life, this should be even more true.  But I was surprised.  #2, mental stimulation and social factors are effective in some studies but not others.  The clear winner in the literature is #1.  There is robust evidence that physical exercise lowers your risk of dementia.  Here’s a meta-analysis from 2011 and a review from 2017.  Modest amounts of exercise are way better than being a couch potato.  There is some extra benefit from being a gym rat, but there are diminishing returns with intensity.

Social connection and intellectual stimulation

The most positive study connecting social factors with a slowing of cognitive decline was in 2001.  A team from UCLA followed 1200 men and women over 7.5 years, and concluded that, among social factors, “emotional support” was the most important factor.

But in subsequent studies, the importance of emotional support faded.  In a review in 2010, no social variables made the list of protective factors.  And this 2016 study found social connectivity to be connected to base levels of cognitive function at midlife but not to 20-year change in cognitive performance.


The MIND diet

Of several diets that have been tested for neuroprotective benefit, the MIND diet, developed by Martha Morris of Rush University in Chicago seems to be the best documented.  In the only test so far, Morris claims it can cut AD risk by 50%.  The M in MIND is for Mediterranean, of which the MIND diet is a variant.  Start with lots of leafy greens, berries and nuts, fruits and vegetables, olive oil, whole grains and fish for protein and omega 3s.  The MIND variant emphasizes blueberries and walnuts, which seem especially good for the brain. [Details at WebMD]

Habitual coffee drinking is associated with a modest decrease in risk [2016], but tea, especially green tea is better [2015].   This study [2006] found a factor of 2 benefit for the heaviest tea drinkers in Japan.  Caffeine seems to be moderately neuroprotective [2008], but the best benefit comes from catechins=polyphenols, for which decaf tea works as well.)  Green tea is part of the MIND diet.


Metabolic Syndrome

Beginning 20 years ago, a strong association was found between metabolic syndrome and AD [1997].  Insulin treatments seemed to worsen the risk [1999] — indeed, excessive insulin seems to be at the heart of the connection.  Some people started saying we should think of AD as type 3 diabetes.  Metformin is the standard treatment for diabetes, and this study [2011] found that taking metformin brought the risk of AD in diabetics back down to a rate comparable with age-matched non-diabetics.  But this study [2013] found that taking metformin actually increases risk of AD.  A strong hypothesis is that metformin uses up vitamin B12.  B12 is essential for functioning of neurons, and is concentrated in the brain.  Many people are deficient in B12 even without metformin, and anyone taking metformin should be supplementing with B12.  Whether there is residual risk of AD from metformin even with B12 remains to be seen, but probably it’s not just people on metformin who should be supplementing with B12 [2014, 2015].  Low protein diets are protective for many aspects of aging, and the MIND diet is mostly vegetarian.  B12 is one essential nutrient that can’t be derived from vegetable sources.


Drugs, Hormones and Supplements

The most common pharmaceutical approach is to enhance the neurotransmitter acetylcholine.  Acetylcholine is found wherever nerves are found, for cognition, sensation and motor control, and it seems especially imortant for memory.   Alpha GPC (glyceryl phosphoryl choline) is an acetylcholine precursor which can be taken orally.  In this study [2003], 132 AD patients taking Alpha-GPC tested better after 90 and 180 days than at the outset, compared to worsening in those taking placebo.  Whether A-GPC is nootropic or neuroprotective is less clear.  Here is a bibliography from

I’ve been slow to recognize the potential of angiotensin receptor blockers (ARBs), prescription drugs which seem to be a new class of age-slowing drugs which I have not discussed previously.  The best known is Losartan. This study [2010] found a 25% reduction in AD.  And in studies with rats, ARBs increase lifespan [2009].  They work by relaxing the arteries, lowering blood pressure, relieving in the process the risk of all cardiovascular diseases.  The trouble with ARBs is that people don’t like them.  Some people feel lethargic, weak and unmotivated.  Some people cough.  Telmisartan is supposed to be the drug in this class with the least side effects.

Both male and female sex hormones are protective against dementia [2017].  A modest way to get this benefit without taking sex hormones is by supplementing with DHEA.  DHEA levels decline with age, and serum DHEA is lower in AD patients, suggesting but not proving that DHEA might be protective.

Pregnenolone is another hormone precursor, and in fact the body makes DHEA from pregnenolone.  As with DHEA, we have less pregnenolone as we get older.  Studies with mice have found pregnenolone leads to increased memory retention in the here and now.  Evidence that it is neuroprotective is lacking.

Poor sleep quality, sleeping more than 8 or less than 7 hours per night, and sleep apnea are all risk factors for dementia [2016].  The effect is large, but the data is thin.  Sleep is commonly disrupted in AD, with melatonin levels that are low and not synched to the circadian cycle.  Melatonin supplementation is a natural thing to try, and there has been some limited success with melatonin as a treatment for AD [2010 review].  I found no data on whether it helps as a preventive, but sleep-inducing pharmaceutical alternatives are associated with increased risk.


Honorable mention

These supplements are reported to be neuroprotective or nootropic or both.  It’s interesting to me that there is an overlap.  We don’t have to choose “now or later”, but can have our cake and eat it, too.

Primarily nootropic Primarily neuroprotective
Ginkgo biloba
Gotu kola
Lion’s Mane Mushroom
Acetyl L-Carnitine
Nigella sativa (=kalonji=charnoucha)
Curcumin (turmeric)
Omega 3 fatty acids
Alpha lipoic acid

Hope for the doubly unlucky

One person in 50 has two copies of the ApoE4 gene.  Age-adjusted risk of dementia is more than 10 times higher for both male and female.  For these folks, there is a theory [1989] that keeping blood cholesterol low can completely ameliorate the risk.  The theory comes from a mechanistic analysis and extrapolates from the odd fact that Nigeria has the highest prevalence of ApoE4 in the world, but there is no elevation of dementia.  More recent [2016] is speculation that ApoE4 carriers need much more omega 3 than the rest of us.

My favorite Alzheimer’s study comes from Dale Bredesen at Buck Institute [2014], who offered intensely personalized treatment protocols to ten Alzheimer’s patients, and dramatically improved cognitive performance for 9 of the 10.  This is a stunning example demonstrating what medical science is capable of when institutional barriers are removed and clinicians have time to work with patients individually.

Dale Bredesen

Bredesen’s work offers incidentally the hopeful possibility that the benefits of some of the measures I have described are cumulative, and that with carefully crafted individual programs they can be combined to push Alzheimer’s risk back many years.