The Mother of All Clinical Trials, Part II

Part II: Why We Should Trust the Methylation Clocks to Measure Aging

Last week, I proposed that methylation age could be used to measure the benefits of putative anti-aging interventions.  This procedure has the potential to slash the cost and the duration of testing. The reason is that we don’t have to wait for a small percentage of experimental subjects to become sick or die.  The vast majority of subjects in a human anti-aging trial give us no information whatever.  In contrast, with the aging clock, every experimental subject is a data point, and the effect on his aging might be measured in a year or two.

The proposal depends critically on the assumption that whatever slows aging will slow the methylation clock, and the converse: whatever slows the methylation clock slows aging.  Some people will find this hard to believe, because their fundamental conception of aging is an accumulation of damage, so that any association with methylation will be incidental or worse!  (What if the changes in methylation that accompany aging tell the story of the body’s increasingly powerful efforts to repair the damage from aging?)

But for those of us who come to the table open to the idea that aging is an epigenetic program, a close (causal) association between methylation and aging seems utterly expected.  For decades, developmental biologists have assumed that development in childhood is driven by age-dependent gene expression. The only thing that prevents us from seeing that the same is true about aging is a kind of prejudice from evolutionary theory that I have described in my book and in this blog.


4. Brief History of the Horvath Clock

Of many biochemical markers that cells use for epigenetic control, methylation of CpG sites is best studied.  If you know what that is all the better; if you don’t, all you need to know is methylation is a modification of the DNA by adding a CH3 group to Cytosine residues in a promotor region adjacent to a gene.  Regions with heavy methylation tend to suppress expression of the (usually) adjacent gene.  Methylation isn’t the only means by which gene expression is controlled — there are many others.   But it is far the best-studied and, given present technology, it is the only epigenetic marker that can be routinely measured, for a few hundred dollars in a small sample of blood, urine, or nanogram-scale biopsy of other tissue.  

The clock was developed by Steve Horvath at UCLA, and first published in 2013, built on an idea of Teschendorff from a few years earlier..  He identified patient records for methylation measurements of tissue samples from 8,000 individuals, with associated ages.  Methylation is recorded as a number between 0 and 1 for each Cytosine, indicating the proportion of that site that is methylated.  He scanned the entire genome for sites that changed most with age, and varied least from one tissue type to another.  In this way, he identified 353 sites, and optimized a set of 353 multipliers, such that multiplying levels of methylation at each site by each multiplier and adding the products produced a number that could be mapped onto chronological age.  About 45% of the multipliers are negative (sites losing methylation with age) and 55% positive (gaining methylation).

The original Horvath clock correlates 0.95 with chronological age.  The standard error in predicting any one individual’s age is + 4 years.  Averages of N individuals increase the accuracy of the clock by √N, so that the average of 100 individuals is accurate to 0.4 years.  (This is a general statistical principle that is useful to remember.)  For our purposes, the relevant question is: measuring the same individual at two different times, how accurate is the difference in Horvath age compared to the elapsed time?  There is no data on this yet, but we might safely assume that it is well under 4 years, since standard error of 4 years represents mostly individual departures from the average.

Five years after Horvath’s original publication, there are several other clocks based on methylation.  Just this spring, Horvath has developed a new clock, not yet published, which, to my knowledge, is the best standard we have.  This is the Levine/Horvath clock.  It is based on 513 methylation sites and it is calibrated not to chronological age, but to a tighter measure of age-based health, derived from blood lipid profies, inflammatory markers, insulin resistance, etc, which Horvath calls “phenotypic age”.  Consequently, it is less well correlated with chronological age than the original, but it is better able to predict mortality than either the classic Horvath clock or chronological age itself. By this measure, the scatter has been greatly reduced.

 

There is statistical evidence that the Levine clock reliably reports phenotypic age, and there is theoretical reason to believe that what the clock measures is close to the root cause of aging.

5. Statistical evidence that the Levine Clock=PhenoAge reliably measures biological age

What I find most convincing is the meta-analysis based on historic data.  Levine and Horvath use old, frozen blood samples to calculate a Horvath and Levine Ages as it was at some past date.  These are people who have died since the blood was drawn, and Horvath Age accurately “predicts” the remaining life expectancy of the subjects.  [Chen, Aging 2016].  There is less data available for the new Levine clock, but strong indications it performs much better than the Horvath clock for this purpose.

In addition, many of the life styles that promote long life have been confirmed to slow the Levine clock, while, conversely, obesity and high blood pressure and insulin resistance have been found to accelerate aging as measured by the Levine clock.

  • Epigenetic age correlates with progression of Alzheimer’s and Parkinson’s Disease [Levine 2016]
  • Same for Arthritis [Horvath 2015]
  • Menopause moves the methylation clock forward.  Early menopause is associated with accelerated methylation aging, and late menopause with younger methylation age.
  • Epigenetic age is accelerated by obesity, blood sugar, insulin, and inflammation
  • Epigenetic age is retarded by vit A, exercise, education (!), and by diets high in vegetables, fruits and nuts.
  • Stem cell transplants lower epigenetic age more dramatically than anything (from a study of leukemia patients [Stolzel 2017]).  Epigenetic age is set back ~8 years for a short period, but then accelerates to a set-forward a few years after treatment.

6. Theoretical foundation of the Horvath Clock

The original Horvath clock was developed by a statistical process that took into account only chronological age.  But Horvath age turns out to be a better predictor than chronological age for risk of all the diseases of old age.  This is powerful evidence that methylation is measuring something fundamental about the aging process.  If an individual’s methylation age is higher or lower than his chronologial age, the difference is a powerful predictor of his disease risk and how long he will live.  This can only be true if methylation is associated with a fundamental cause of age-related decline.

An emerging theory the last 7 years is that aging procedes under epigenetic control.  De Magalhaes, Rando, Blagosklonny, Johnson and Mitteldorfall have independently proposed an epigenetic basis for aging.  The root cause of aging—the reason our bodies are different at age 70 compared to age 20—is that different sets of genes are expressed at different times of life.  This priniciple is already well-accepted for growth and development [ref, ref].  During formation of the body in utero, gene expression rapidly changes, and in early childhood, the growth and mathuration of the body are widely agreed to occur under epigenetic control. But now we know that much of the change in methylation is continuous, from development through aging [ref].  I call this programmed aging.  Blagosklonny hedges and says “quasi-programmed”.  The difference is about evolutionary purpose and whether function is related to natural selection.  My view is that we are programmed for a fixed lifespan for the stability of the community. Blagosklonny’s is that the epigenetic changes that start in development continue afterward through a kind of inertia because there isn’t enough natural selection to turn these changes around.  

But for the sake of the reliability of the methylation clock in evaluation of anti-aging interventions, these two perspectives converge: they both support the expectation that methylation age will be an excellent criterion for trying and judging new ideas and combinations of old ideas.

Parabiosis experiments support the idea that factors circulating in the blood have a deep effect on the age of the body.  This is indirect support for the epigenetic foundations of aging, because these blood factors come from gene expression in cells–especially but not exclusively endocrine cells.

7. Counter-arguments

A) ‘Epigenetic drift’ Many authors still write about changes in methylation during aging as “epigenetic drift”.  For those who cannot accept the idea that aging is programmed, it is much more palatable to imagine a loss of order in gene expression, a randomization of gene expression.  Indeed, this is true. It is part of the story that gene expression does become more random with age.  But it is also true that there are specific gene expression changes associated with aging–the methylation clock is based on such programmed changes.  

B) Perhaps gene expression changes are a response to damage, the body’s attempt to mitigate aging.  This is the suspicion that haunts the aging clock.  If this is the case, interventions that thwart the mitigation would come out looking like age reversal, but in fact they’d have the opposite effect, increasing risk of disease and mortality.  Support for this idea comes from the prejudice that says “the body would never purpposefully destroy itself.” But there is no evidence for this idea, and in fact many of the programmed changes have been shown to be detrimental.  For example, signals for inflammation are increased, DNA repair is slowed down, and the anti-oxidant metabolism is suppressed.

“DNA PhenoAge acceleration was found to be associated with increased activation of pro-inflammatory and interferon pathways and decreased activation of the transcriptional and translational machineries, the DNA damage response and nuclear mitochondrial signatures” [quote from Horvath 2018; footnote is to Levine 2018

C) Not all anti-aging interventions affect the Levine or Horvath clocks.  This is a substantial problem if it turns out that there are real anti-aging strategies that work, and yet the Levine clock won’t tell us that they work.  But we don’t really know this yet, because we don’t really know what works. “For example, within a 9-month follow-up period, the substantial weight loss resulting from bariatric surgery was not associated with a reduction in epigenetic age of human liver tissue samples”  [quote from Horvath 2018; footnote is to Horvath 2014]  To the extent we think that bariatric surgery is a legitimate anti-aging strategy, this is a problem

 

8. Improvements and adaptations of the Horvath clock

The “clocks” we’re talking about are really mathematical operations.  Given the output of a blood (or urine) test that reports what percentage of the DNA is methylated at each of hundreds of  thousands of different CpG sites, the “clock” is a computer program that distills this information down to a single number, the predicted age.

The Levine clock is a substantial improvement on the original Horvath clock, attained by calibrating it against health indicators and not just chronological age.  For prediction, it leaves its predecessors in the dust.

There are three more ways in which the methylation age test can be improved, and I have begun working with the Horvath lab to do the number crunching in support of these changes.

A) The original clock and all its successors have thus far been based on combining information from 353 different methylation sites in the simplest possible way.  They simply have 353 different multipliers. It is these 353 (positive and negative) numbers that have been optimized by the statisticians, so that each multiplier can be multiplied by each methylation, and the 353 products are added up to make a single number that indicates age.  My suggestion is to combine the 353 sites in a more flexible way. Some change rapidly during youth and then remain constant. Some change continually over a lifetime. Some don’t change much at all until aging sets in. There is no reason that all the 353 sites have to be treated the same way.  Using non-linear math that’s just a little more complicated, the 353 sites can be tracked in a way that corresponds to their peculiar lifetime trajectories. This will improve the clock’s accuracy for any application.

B) The clock might be specialized to the application of testing anti-aging effects on individual humans, i.e., comparing biological age for the same individual at two different times.  Some of the scatter in the plot of DNAmAge is due to variation from one individual to another, and some is due to other random factors that don’t depend on the individual. In the past, there was little data available for the same individual at two different times, but this is changing, and now it is feasible to separate the two kinds of scatter.  The clock can then be specialized to report age differences even more accurately.

C) Again, for the particular application proposed, there is no need for a clock that works generally on any age, from pre-birth to centennarian.  If all of the people in the study are between the ages of 50 and 70, then the clock might be specialized to be more accurate in this age range, at the expense of losing accuracy for younger and older subjects–who aren’t part of the study.  It may be worthwhile to take this idea even further and have four sub-specialized clocks, calibrated for ages 50-55, 55-60, 60-65 and 65-70.

In my brief experimentation with the data, I was able to raise the correlation from 95% to 96% using technique #1.  I’m guessing that with further work it can be raised to 98%. The reason that it pays to do this is that the cost of a human study depends on (A) how many people are studied and (B) how long a time they are followed.  As the scatter in the data is reduced by better statistical techniques, we can find out what we need to know with fewer subjects and a shorter study time. Raising the correlation from 96% to 98% will reduce the number of subjects needed for the experiment by a factor of 4.  Alternatively, for the same effort and expense, we wil be able to derive more information.

If we can indeed construct a clock with 98% accuracy, a new benefit will be available:  It will be accurate enough to distinguish changes for a single individual with no statistical averaging necessary.  This will be a gateway to individualized medicine.  There will always be treatments that work for some people but not others, and the future of medicine is connected to knowing what works for you as an individual.  Each of us will be able to use the methylation clock to know how we are doing. You can try a new supplement for a year and if it doesn’t work for you as an individual, you’ll know it and switch to trying something else next year.

31 thoughts on “The Mother of All Clinical Trials, Part II

  1. Great article Josh- just one little thing to adjust ..you say the epigentic control of aging is emerging a a theory in the last 7 years starting wtih Rando and including>>
    “An emerging theory the last 7 years is that aging procedes under epigenetic control. De Magalhaes, Rando, Blagosklonny, Johnson and Mitteldorf—all have independently proposed an epigenetic basis for aging. ”

    I hate to keep harping on this but it actually all started with Al Mazin

    way back in 1993>>>
    Life span prediction from the rate of age-related dna demethylation in normal and cancer cell lines
    Author links open overlay panelAlexander L.Mazin
    Show more
    https://doi.org/10.1016/0531-5565(95)00004-ZGet rights and content
    Abstract
    A method has been proposed for the Hayflick Limit prediction by the analysis of the 5-methylcytosine content in DNA at earlier and later cell passages. The following facts were used as the basis of the method: (i) the rate of m5C loss from DNA remains approximately constant during cell divisions and it does not depend on the cell donor age; (ii) this rate is inversely proportional to the Hayflick Limit as well as to the life span of cell donor species; (iii) the period corresponded to loss of all m5C residues from the genome coincides with or somewhat exceeds the Hayflick Limit of normal cells.

    I then expanded on Mazin’s work (theoretically) in my 1998 paper the Evolution of Aging-A New Look at an old problem of biology- WHICH was all about epigentic control of aging (except that the word “epigenetics ” was relatively unknown back then) so I mostly described how loss of DNA methylation controlled aging….There was a lot of guesswork and it was kind like the first attempt to put dinsoaur bones together to see what the orignal thing looked like …but as far as the theory goes …there was no denying that it was all about epigeneitc control of aging and was on the mark. It is avaiable for your readers to read at researchgate>> https://www.researchgate.net/publication/13494609_The_evolution_of_aging_A_new_approach_to_an_old_problem_of_biology

    I handed his paper to Joao De Magalhaes at an aging conference in Spa Belgium in 2000, and I’m pretty sure it was one of the first papers you read about aging as well….Oh well that was so long ago it is easy to forget….

    • Thanks, Jeff. I should remember to cite Mazin as a predecessor. But in 1993, it was only possible to measure total methylation. No one dreamed that there might soon be an automated technique for measuring the methylation condition of every single site and put them into a database, all for a few hundred dollars.
      – Josh

      • hi Josh

        i wasn’t referring to the idea that methylation could be measured.recnetly …but rather your statement the theory that aging was under epigentic control “emerged in the last 7 years…from thin air?? I presume?>>>>>

        “An emerging theory the last 7 years is that aging procedes under epigenetic control”

        • Here is an excerpt form the abstract to my 1998 paper…sure it’as not perfect..(actually most of it is correct) but it definetly points everyine who read it in the right direction>>>>

          “Development and aging are timed by the gradual loss of cytosine methylation in the genome. Methylated cytosines (5mC) inhibit gene transcription, and deoxyribonucleic acid (DNA) cleavage by restriction enzymes. Cleavage inhibition prevents apoptosis, which requires DNA fragmentation. Free radicals catalyze the demethylation of 5mC while antioxidants catalyze the remethylation of cytosine by altering the activity of DNA methyltransferases. Hormones act as either surrogate free radicals by stimulating the cyclic adenosine monophosphate (cAMP) pathway or as surrogate antioxidants through cyclic guanosine monophosphate (cGMP) pathway stimulation. Access to DNA containing 5mC inhibited developmental and aging genes and restriction sites is allowed by DNA helicase strand separation. Tightly wound DNA does not allow this access. ”

          You see the last sentence also anticpates chromatin condensation as another form of epigeneitc control

  2. Hi Josh,

    I am extremely glad to see that you are working with Steve to improve the accuracy of the methylation clock. There is one issue with using single CpG sites in the regression that has been bugging me. To best describe the issue, let me first quote Steve’s recent paper:

    “Each clock CpG takes a value between 0 and 1, whereby 1 indicates methylated and 0 indicates unmethylated. importantly, the values of the clock CpGs (and the vast majority of all CpGs) are almost never 0 or 1 but a value in between. For example, a value of 0.66 indicates that 66% of the thousands of copies of that particular CpG, which were derived from thousands of cells, are methylated. this non-binary value occurs because DNA methylation measured by the Illumina Infinium platform is not from a single cell but a population of cells (in the thousands). Hence, the non-binary methylation values reveal that cells from a particular population or tissue are epigenetically heterogeneous, even if they seem morphologically homogeneous. This intercellular heterogeneity gives rise to the methylation values used by age estimators to measure epigenetic age. Of note, this change in heterogeneity is very small: the average change of a clock CpG between the ages 55 years is only 3.2%. these age-related changes probably reflect both intracellular changes and changes in cell composition. the changes in cell composition could reflect systematic changes in a subset of cells, which we term ‘clock cells’. DNAm age might track the loss of somatic stem cells in some tissues. However, this interpretation of DNAm age is not directly applicable to sorted neurons, which lend themselves to very precise DNAm age estimates.”
    https://doi.org/10.1038/s41576-018-0004-3

    What is troubling me is that many regulatory CpG regions are not single nucleotides, but are several nucleotides long (say, in exons), and sometimes hundreds of nucleotides long (CpG islands). So methylation of each particular CpG nucleotide *within* a CpG region could well vary from cell to cell, but the overall average methylation level _of_that_CpG_region_ should be pretty consistent in every cell of a given organ (which, statistically, would also translate into us seeing 66% of cells of that organ having a particular, say, central, CpG nucleotide methylated).

    In light of this, I wonder whether recomputing those 353 or 513 CpG methylation values by using an expanded aggregation window (expanded from a single nucleotide methylation value to a region of several (5-10-20?) adjacent CpG nucleotides) could improve the regression correlation coefficient.

    Best regards,
    Yuri

    • Yuri –
      I spoke to Larry Jia of Zymo Research last week, so far the only company that offers methylation testing commercially. He said exactly what you say, and Zymo’s algorithm averages over CpG islands rather than using individual CpG sites. They claim improved accuracy in some respects. Chart here.
      -Josh

        • Josh looks like you might want to add vitamin d3 to the testing list>>>
          Buck Institute study finds vitamin D may help extend life span

          GUY KOVNERTHE PRESS DEMOCRAT | November 7, 2016

          Vitamin D, a supplement already taken by millions of people — and delivered by sunshine — may help humans live longer, according to a new study by scientists at a Novato research center devoted to aging.

          Feeding vitamin D to tiny roundworms engineered so they mimic Alzheimer’s disease, a team at the Buck Institute for Research on Aging found their life span was extended by 33 percent, said Gordon Lithgow, a Buck faculty member and senior author of a paper published last month in Cell Reports, a life sciences journal.

          The worms, which “responded dramatically” to vitamin D in their diet, are ideal for longevity studies because they live for about 20 days, he said.

          While there’s no guarantee the same results would occur in humans, Lithgow said the study “strongly hints this is something worth looking into.” The aging process in the worms and mammals, including humans, is “thought to be similar,” suggesting vitamin D would have the same effect across the species, he said.

          Lithgow’s next step in the laboratory is to see if vitamin D boosts the life spans of mice, and he hopes its effect on human longevity will ultimately be studied. More research “is crying out to be done,” he said.

          • Yes, Vitamin D appears to have life extension benefits for human. Certainly anti-cancer. The experiment with worms is indirect evidence for this. Best direct evidence is the inverse correlation between blood levels of vit D and mortality.

  3. Thought-provoking article Josh. There seems to be consensus that these epigenetic changes are both programmed and causative, by which I mean, causing the aging phenotype’s expression. I think this elevates DNA’s regulation of aging too far, it is metabolism to which aging and lifespan are coupled,.and metabolism that drives or is driven by the circadian clock. Saying that epigenetic changes in cells lead to cellular aging implies,that aging occurs at the cellular level and is a natural progression in a cell (rather than a statistical fact), to slowly change its DNA to a dysfunctional form by methylating it according to a program or not. Yet methylation is dependent on SAM which is dependent on metabolism. Methylation is also dependent on the local availability of CpG sites present in TADs (Topologically Associated Domains) So I am saying rather than determining events in aging, changes in CpG methylation just mark the passage of metabolic time, like a real clock records the passing of time (but doesn’t determine events), but in this case a physiological/metabolic time. These methylation changes may be an epiphenomena resulting from the various states of chromatin and chromosomes in exposing CpG sites to the environment and the chromosomal environment, either rich in SAM or not rather than reflecting a program, or perhaps reflecting a program dictated by the fundamental engine of life, the daily cytosolic change from oxidizing environment to reducing environment that is the main function of the circadian clock. With time the oxidizing environment grows and the reducing environment reduces resulting in overall oxidation. The maximum lifespan of a species seems to be determined by how much total oxygen they can process in a lifetime, a measure that depends on the efficiency of their mitochondria – in terms of ROS produced (which is why parrots can live so long, their mitochondria are more efficient). As I told my students last week in order to ‘blow their minds’ (it worked for those with minds) even if every base-pair in DNA could specify the location of a neuron, there wouldn’t be enough information to code of the location of the 100 billion neurons in the brain, much less their 10,000 synapses to other neurons. My point? Too much emphasis is placed on DNA which may well be more a recorder of change than an initiator of it.
    So I say that metabolism comes first, also the role of the changing 3-D structure of chromatin which is concerned with cell state. That decides, statistically, which sites will be subject to being exposed at times of high SAM levels and methylase availability, or being available for demethylation at other times. Funny thing is that we don’t even take the circadian cycle into account when we experiment on animals or even cells – will the same measurement obtain at midnight as at noon (mice are wakeful at night)? It’s more a matter of convenience I think. I wish there was a “Hovarth clock” for rats.

    • Harold –
      I agree that aging is programmed at the organismal level, not the cell level. A cell in a body is not an autonomous unit, but is taking its orders from the circulating signals in its environment.
      Methylation of a cell’s DNA is a response to those signals.

      I think there is a Horvath clock for mice–I don’t know about rats.
      -Josh

      • Are you sure about that? What about the hayflick limit for single human cells-fibroblasts? they aren’t getting extrinsic signals anymore yet they age…(stop reproducing)..Or can we say lack of a signal (telomerase in this case) is actually a signal sent by the body to the cell? This is getting to be a bit convoluted ..I think it is easier to say that aging occurs at both the organismal AND the cellular level..No?

  4. I have a question that I’m hoping someone can answer, as it seems that the term “epigenetics” is being used in two different ways. I’m a fan of the “Hallmarks of Aging” and one of the primary aging mechanisms there is “epigenetic” Reading the paper, it does show that they’re talking about the same thing we are discussing here, methylation, etc.

    The confusion came into play from the study where Spanish scientists used intermittent OSKM factors to partially rejuvenate mice last year. All the headlines, and the paper itself, described this as “epigenetic reversal of aging”. I’m sure they didn’t use Horvath’s clock in the study, but it seems like this is almost always used to refer to methyl groups, etc. Maybe they are just using it in a very broad sense.

    Anyway, what am I missing here? Even assuming that they do not mean epigenetics in the same way, it is still exciting to think that this intervention did in fact reverse epigenetic aging as described here. I wish these studies would run a battery of tests to see what actually happened in cases like this. The tests are cheap and the potential insight is massive.

    • HI there..

      I think we can learn a lot about aging reversal by looking into what happens when a mammal is cloned…there is massive demethylation followed by remethylation and restoration of the telomere lengths..ANd what is more of an aging reversal procesxs than turnoing an adult (cell) into an embryo!!

      From this link>>> https://www.fda.gov/AnimalVeterinary/SafetyHealth/AnimalCloning/ucm124803.htm

      In describing this phenomenon, Surani (2001) states that this “mechanism also erases any aberrant epigenetic modifications, so preventing the inheritance of epimutations, which consequently occurs very rarely.” The mechanism by which erasure of the epigenetic markings, including demethylation, in primordial germ cells is not yet understood. Other “resetting” mechanisms also occur in primordial germ cells, including the restoration of telomere length, and repair of lesions to the coding regions of the DNA (Surani 2001).

      Although the preceding discussion has focused on methylation as the primary marker of imprinting, it is important to remember that there are other modifications that may contribute to the retention of “epigenetic memory” in germ cells whose identity and mechanism remain to be characterized (Davis et al. 2000; Fazzari and Greally 2004).

      • Erasure and reprogramming to Age=0 is a natural process, and therefore less difficult to replicate artificially. But we don’t want to turn the body’s cells into embryos! They would lose their function and there is experimental evidence they might form tumors.

        What we want to do instead is to turn the clock back part-way. This never happens in nature, where the clock runs always forward except when it is reset to zero.

        Just last month, there are promising results from Univ of Edinburgh applying the Yamanaka factors for a brief time, then withdrawing them. Cells seem to become younger without losing their differentiation, which is what we want. https://www.biorxiv.org/content/early/2018/03/31/292680

        • HAHA- i was not proposig to turn all our cells to embryos. I was suggesting that if we studied the cloning process more closely we might find a huge bucket of factors that get turned on and off to do the reprogrammig. Within that huge bucket we might fbe bale to find the few factors that can partially reverse aging. Im sure the Yamanaka factors are in there…there might be some more…Also given that complete remethylation of the genome during his process occurs while the telomeres are restored to their normal lengths might suggest that the 2 processes are linked . Lots to learn from cloning!! And it seems to be being ignored by aging researchers.

          • some clones have shorter lifespans…due to reprograming errors .
            while some have normal lifepsans….

            I have not heard of any having an increased life span…

            but if you consider an adult animal as being one cell and then having that one cell reprogrommed to 0 and living another entire life…in that sense you doubled its life span

  5. I think I’ve answered my own question.

    I was curious if epigenetic reprogramming with OSKM factors would lead to a rejuvenation based on Horvath’s clock. The breakthrough study on partial reprogramming (in mice) that came out a couple years ago did not address this. But Blasco’s team tested the telomere dynamics of the same partial reprogramming mechanism and found that telomeres were also rejuvenated.

    Now I have just found a study that shows complete rejuvenation based on Horvath’s clock using the same exact partial reprogramming technique (See study title below), at least in dermal fibroblasts.

    This raises a ton of questions, but if we were to trust all three of these studies, they show that there is no inverse correlation between rejuvenation of telomeres and epigenetics. In fact the studies would show that partial reprogramming rejuvenates on both the epigenetic and telomere levels. Another study that tested the cells of centenarians in vitro showed that reprogramming resets a bunch of aging biomarkers, including telomeres, oxidative stress, etc, etc.

    It’s beginning to look like partial reprogramming using these factors (probably don’t need c-MYC) is the holy grail we’ve been looking for. Although the technique obviously needs a lot of refinement.

    It’s unlikely to be a complete magic bullet considering things happening in the ECM and proteostasis, but still looking pretty fundamental.

    The study:
    “Partial reprogramming induces a steady decline in epigenetic age before loss of somatic identity”

  6. No one seems to be mentioning this, so maybe it’s just me, but the newest methylation clocks seem to be a little too accurate for my liking. If they can indeed predict with great precision how long I have left in this world, then I’m not sure how comfortable I am with that . I mean, I don’t really want to know, and if it said something like 5 years it would create a certain immobilizing panic perhaps.
    The ideal situation would be to try something like rapamycin and then find out in a year or two if it yielded a positive response, and if so, to what degree. I could then try out different things and get repeat testing without having too much information which could be somewhat terrifying.

    • Paul, I agree that I wouldn’t want anyone to tell me what day I will die on. But I think there is no danger of this. The Levine methylation clock was created using standard biomarkers of health, like inflammation markers, glucose tolerance, blood lipids, blood pressure. Is the information provided by the Levine clock more precise than the composite of all these tests? I think that remains to be seen.

      A life expectancy is not a date certain on which you will die, but only a statistical average. The readout from the Levine clock will be “You are 60 years old but you have the life expectancy of a 50-year-old.” Or, in the more troubling case, “You are 60 years old but you have the life expectancy of a 70-year-old.”

      • Josh, how accurate can a test be when it includes lipids as part of the process ?

        It’s an old theory that LDL-C causes CVD and a theory which is gradually being abandoned. For example I keep on coming across research which states that over the age of 65 higher LDL-C is associated with longer life span.

        While statins which are prescribed to reduce LDL-C, even in the pharmaceutical companies own research studies is associated with 2-3 extra days of life !

  7. Maybe John Koza’s method should be used to breed such clock functions.

    One could download John Koza’s Little Lisp code, and then write the custom fitness function to assign a fitness that is proportional to the accuracy of its argument program’s outputs when compared to the historical data.

    Then when you run the program, the Little Lisp code automatically makes a population of random computer programs.It uses your fitness function to calculate how accurately they each predict remaining lifespan when fed the historical methylation data. They will all perform poorly in the first generation. Then the program automatically makes a new population by taking high scorer and performing crossover, and repetition, and mutation on the programs. It evaluates them again using your fitness function. Repeats this process 4 million times or whatever.

    In the final population of programs maybe the highest scoring program would be doing a very complicated calculation on the 353 arguments it takes. Maybe it would achieve 98 percent accuracy.

    Then it could be tested on some other set of historical data just to make sure it wasn’t responding too much to the idiosyncrasies of the data set it on which it was bred.

    • Algorithms like this work well in some situations and not in others. I like to use them when I know nothing about how to solve the problem, and then a trial-and-error process of machine learning is appropriate. But the more you know about a situation, the more likely you can do better than the general-purpose program that learns by trial and error.

      Here’s an extreme example: Suppose you want to know what number multiplied by 7 gives 63. The trial-and-error method would be to try 100 random numbers between 1 and a billion and multiply each one by 7. Take the 10 numbers that come closest to 63 and explore 10 random numbers close to each of those in the second round. Again we have 100 guesses, from which we choose the 10 best and use them to seed the next round…Eventually this process will lead to a very good estimate of the best answer. But if we know something about multiplication, we could short-circuit the whole process and say “the opposite of multiplication is division. All we have to do is divide 63 by 7 to get 9.”

      Our situation with the methylation is not so obvious as this, but not so random that we need the trial-and-error procedure. I have been working on a smart algorithm that I think can do better than the linear method and also better than the LISP program could do. It is based on finding the trajectory of each methylation site as a function of age. Put ~300 of these together and you have a trajectory in 300-dimensional space. Each time you want to estimate someone’s age, first locate him as a point in 300-dimensional space, then find the point of closest approach to that trajectory, and call that his age.

      – Josh

  8. Reading Harold Katcher’s comment reminds me of the “What comes first; hen or egg?” type of situation.

    As a mere amateur surrounded by biochemical experts, my abilities to contribute anything useful are obviously limited.

    Nevertheless, are both, epigenetic and organism, not mutually dependent on each other?
    Is, after the initial start, in the following situation, in which one reaction drives the next, one the other, epigenetic the organism and vice versa,really important who was the initiator?

    Harold Katcher seems to complain, at least in my understanding, that the genes are numerically speaking so much inferior to the rest of … well, everything else.
    But it takes just one stone to start an avalance.

    A sucessful intervention could theoretically begin everywhere, could be epigenetical, or systemwide. A lot of sucessful interventions have shown that after such procedures (on the organismal level) genetical expressions have changed to a much more “younger” state. So, yes, the question if epigenetic can influenced, and even be changed, to a more favourable level, is a clear “yes”. I am not aware of the opposite case, epigenetic change resulting in optimized organismal state, although one can argue that the highly promising interventions regarding partial reprorgamming through the oskm factors constitute one.

    Seen in that light, the practical relevance of who’s the initiator seems not all that great for me. Scientifically highly interesting, and generally very revealing and informative, yes.

    I must confess that i am very doubtful about any attempt to answer the question what exactly drives aging in the first place at the present time. There seem to be more holes in the actual knowledge than said knowledge. With all that new information one could easily forget that.
    But just today i read an article about just how many genes are simply not researched much, if at all. Too complicated, too obscure, too less interesting for business … there could be a complete new world out there, unknown to us all.
    Facts which might change the way we look at everything.

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