#61

John, thanks, this is what I’m interested in. From the thread on AKG, I found this paper helped me better understand the mTOR signaling pathway and the relationship between mTOR, AKG (part of the TCA cycle) and Rapamycin (specifically in the brain).
https://www.sciencedirect.com/science/article/pii/S2667160322000266
Particularly about DNA methylation - “Another mechanism that could explain AKG’s control of lifespan is genome-wide demethylation. It is acknowledged that aging is accompanied by significant changes in genome-wide methylation levels. AKG controls the activity of DNA demethylases TET1-3, thus triggering the demethylation of GpC loci in DNA.”
And section 2.2. mTOR-related pathologies of the nervous system is very good about Rapamycin.

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#62

Although you can talk about particular pathways I think it is useful to talk about function. It appears to me that methylation is an indication of whether or not genes are being transcribed in that genes which are frequently transcribed tend not to have their DNA methylated whereas genes that are relatively inactive tend to be methylated.

Metabolic balances (levels of AKG, levels of acetyl-CoA, levels of NAD+) will affect the control systems, but it helps to have an idea of what informational function a particular modification has.

Coming back to my hobbyhorse, which is that certain genes (particularly the longer ones) tend to become quiescent with age. This would tend to link to the methylation of those genes.

However, the point about this is that the methylation although it has its own effects is secondary to the genes becoming quiescent.

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#63

A new paper from the Gladyshev lab at Harvard:

“Epigenetic clock work ticks forward” from @Danbelsky and CP Ryan on this study from @kejunying @gladyshev_la

Full Paper here:
https://rdcu.be/dwrVd

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#64

I’m trying to fully understand this process and working through this (Wikipedia: Gene Expression) as my study material. Essential, I would say.
https://en.wikipedia.org/wiki/Gene_expression

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#65

From the article:
“Existing epigenetic clocks lack aging-causal CpGs One open question for epigenetic clocks is whether their clock sites are causal to aging and age-related functional decline. To answer this question, we collected seven epigenetic age models in humans, namely, the Zhang clock, PhenoAge, GrimAge, PedBE, HorvathAge, HannumAge and DunedinPACE. We then performed an enrichment analysis of putative causal CpGs for all eight lifespan/healthspan-related traits for each clock. After correcting for multiple testing, none of the existing clocks showed significant enrichment for putative causal CpGs of any of the lifespan/healthspan-related traits (Fig. 3g). PhenoAge showed a nominal significant enrichment with CpGs causal to healthspan and healthy aging, but it was not robust to the choice of thresholds. This finding suggests that, although some clocks contain CpGs causal to aging (Table 2), they, by design, favor CpG sites with a higher correlation with age and thus are not enriched with putative causal CpGs.”
That’s matches my simple understanding - they’re not causal but only a reflection of your health status. And going back to the speedometer analogy - your speed is easy and quick to change, but the mileage on the odometer is not (without cheating),it only increases gradually with age. The question with epigenetic clocks is - yes, you can reduce the speed at which the miles are building up but can you make the odometer run backwards and reduce (for all practical purposes) the number of miles (years) travelled. This is what Bryan Johnson is trying to do.

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#66

Peter Fedichev’s presentation at the December Longevity Summit:

“Aging clocks, entropy, and the limits of age-reversal”

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#67

WTF?!!! I just watched that whole video. (and got nothing from it) The accent (Transylvania?) wasn’t the problem, I could understand him, but the presentation I found almost incomprehensible. There’s 23 minutes of my life that I’ll never get back!

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#68

Sorry - I also had a hard time following him. I was hoping someone else could get more from it than I did, but perhaps not!

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#69

Always listen or watch at 2 x speed.

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#70

So he sounds like a chipmunk from Transylvania?!!!

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#71

Still interesting. Find the presentation interesting, despite the staccato speed of delivery.

First takeaway - buy the book Scale, by Geoffrey West. Currently reading from a borrowed e-book copy from the New York Public Library.

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#72

Better audio of the same presentation. Not staccato.

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#73

Almost all video and audio playback actually adjust for the base in the voice so it sounds “normal” and not “chipmunky” even if faster.

(In the audible app it’s often comfortable to go up to 2.5x and depending on the book even 3x.

I think I heard that people on average read at about 3x the speed of speech - so the brain does not generally have problems when you start departing from 1.0x)

#74

Here is this piece that discusses the research paper (also in one of the Nature journals):

https://www.nature.com/articles/s43587-024-00570-ks

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#75

Aging from a physicist’s perspective; an enjoyable and informative read (for me).

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#76

This is why I prefer to read scientific papers rather than watch and listen to videos. I’ve gotten used to skimming through quickly and still picking up a large amount of useful information. I’m learning from them at a rapid clip. Videos on the other hand tend to be slow and a little wasteful of my time. I must say that being on this forum has been like learning a new language but that helps keep my brain young. So I count that as one of my anti-aging interventions. Maybe we should propose that to the ITP…time spent on RapamycinNewsForum and how it rejuvenates the aging brain.

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#77

How do we even measure the rate of aging in humans?

Great question. The most effective definition of aging I know is that it’s the exponential increase of mortality. In humans, the risk of death from all causes doubles every eight years, but mortality is a population-level trait, and we need to connect these population figures with measurements in individual organisms.

This challenge is central to the aging biomarkers field. They want to measure something in you today that correlates with the characteristics of population mortality trends. This is why we began examining longitudinal datasets that contain multiple measurements from the same person. We wanted to understand how physiological parameters change throughout a person’s life.

We couldn’t do this with nematodes, since measurements would likely kill them. So, we turned to data from mice and humans. We started with a large public Mouse Phenome Dataset and added retrospective data from Andrei Gudkov’s years of mouse aging studies. We also procured a vast human dataset from a diagnostic company in Moscow with data points from individuals who took multiple blood tests over the company’s 20-year history.

When we started comparing mice and humans, we found something intriguing: although both species show an exponential increase in mortality, the dynamics of individual markers in humans and mice are completely different. Humans are not just bigger mice.

This discovery made us revisit our theories and rethink everything. We had to face the fact that humans are very different longitudinally. In mice, we see mortality increase exponentially, but so do biomarkers of aging. This pattern of exponential codependencies is everywhere.

Markers of inflammation, such as c-reactive protein, IL-6, and others are rising exponentially in mice. So is the burden of senescent cells. The exponential rate matches the mortality acceleration. This means that mouse aging is simple: we observe an exponentially accelerating breakdown of the organism’s state.

In humans, however, we know that after the age of 40, our mortality doubles every eight years. So, we see five doublings of mortality, a total 30-fold increase between 40 and 80.

It’s clear that not all aspects of aging in humans follow an exponential pattern. Our facial features do shift with age, but not exponentially. For instance, the space between our eyes might increase, but it doesn’t multiply by fivefold by the time we’re 80. Just picture that!

If you were to chart various human characteristics over time, you would likely find two distinct patterns. Many aspects change in a straight line, getting more varied with time – showing that their change is random. Then there are those markers that change faster than a straight line – hyperbolically. If you were to extend these lines, some would reach an infinite point at around 120 years – the current maximum lifespan. Interestingly, these are the same markers that show an exponential increase in mice.

This subtle but qualitative difference (hyperbolic vs. exponential) already shows you, even without any interpretation, that aging in humans is very different from aging in mice. For instance, mice experience an exponential rise in death rates until their average lifespan, then it plateaus. But in humans, once death rates begin to rise exponentially from around 40, they keep doing so beyond the average lifespan. In simpler terms, mice and humans age quite differently, and we need a theory to explain this.

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#78

I think the two key drivers in all species of animals are mitochondrial inefficiency and senescent cells. Both of these affect gene expression and as gene expression gets worse then there is more senescence and also less autophagy (hence more inefficient mitochondria). I think this drives Gompertz. It may be in humans that senescence is the bigger driver. In drosophila I think it is probably mitochondrial inefficiency.

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#79

This thread is full of interesting information. Thanks to all who have contributed. I have it marked to work through as time permits. From my scan, one of my first thoughts was that these IVs need to be tested, separately and in combination, against dependent variables such as lifespan and healthspan. Has that been done in a methodologically sound way? The high and low correlations I see contribute little to assessing any form of validity, predictive, convergent, or discriminant for the DVs important to all of us. If I have jumped the gun, I apologize and I will catch up to myself after more reading.

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#80

Rob, yes, I think that’s exactly what we’re getting at. This forum is full of experimenters as well as doctors and researchers that are trying to extend both lifespan and healthspan. A big challenge we face (without having the money of Bryan Johnson) is how do we measure success (without waiting to die). The simple way that many of us use is comprehensive blood testing - and plugging a limited set of results into the Levine Phenotype blood calculator (if we want a little ego boost). The other emerging test is the epigenetic BioAge test, that has many versions now available online (see GrimAge, myDNAage, NOVOS Age, TruDiagnostic, etc). The problem with them is they are expensive - about $300 - and not yet totally reliable (although evolving rapidly). What’s been proposed here is to come up with a more comprehensive set of tests, that first improves on the Levine blood test markers (by adding more inputs) and then supplement that with additions like blood pressure, various heart rate measures, some functional tests (grip strength, etc), maybe a DEXA scan or Vo2 max (although expensive, Attia says Vo2 max is the very best longevity measure), maybe tests for eyesight, hearing, olfactory sensitivity, mental acuity. These are all things that decline with age and impact healthspan. If we can find appropriate and accurate tests that aren’t too expensive or complicated or time consuming then we should do them on a regular schedule (every 3-6 months?) and post our results along with the interventions that we are using and a timeline that will show changes in both and how they correspond. That will give us a lot of very valuable data to show what is working and what isn’t. Some other factors that may be important are sleep quality, joint health, recovery time (from injuries), microbiome health, immune system health and maybe appearance measures - skin (wrinkles), hair (loss and graying), BMI (AnthropoAge test?) and eye (retinal age gap). Hopefully we can come to agreement on what’s most important and start collecting that information soon.

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