What is aging and how to measure it is an everpresent question in the field of aging research. Given the complexity of biology many give up on the task, proclaiming that "we" (either the field or humanity) don't understand aging. I don't. To me, what aging is is clear enough, and we can understand it as a fractal and emergent phenomenon within a system: there's aging of DNA, aging of cells, aging of organs, aging of organisms.

To understand aging and measure it we have to be reasonably acquainted with the systems we are studying, to come up with mental models and validate them against published evidence. The understanding required does not have to be complete and I of course do not claim to understand all of biology, but after having spent a long time thinking about the topic, what follows below is what seems most coherent with that evidence. In classic Nintil fashion, I aim to reach as close as possible conclusions that summarize and harmonize all previously published work by anyone who has ever had thoughts on the matter. This doesn't mean I have read everything, at some point one has to declare it sufficient and assume that what has not been read will still cohere with the model here presented.

I have in the past written some blogposts about aging that are useful to read before this one:

• Epigenetic clocks: A review
• Telomeres: everything you always wanted to know
• Aging is already solved in vitro. What comes next?
• Beyond lifespan as a metric in aging research: why reprogramming is promising.
• Making Cells Young
• What is aging?

The present post is an update of my views on the topic and any discrepancy between this and my previous posts should be resolved in favor of the current one.

Below I present different lines of evidence under the different sections. I tried to linearize it somewhat but ultimately the nature of knowledge is that of a web, not of a linear flow so I haven't tried to coerce too much it into what it is not. I recommend reading this to the end and taking it as a whole instead of being fixated by any one specific section. Some sections may seen like they simplify a lot, but if I had to stop to fully explain everything, it would lose focus.

Before going into it it is useful for me to say roughly what this is about, and what this is not about:

• Here I cover extensively methylation clocks results and whether they are a good proxy for aging, but I don't do this because I think they are in any way special; rather I do it because they are thematically relevant. They are used by consumers and in research.

• I discuss what I mean by 'aging' as it applies to various systems, including inanimate objects like a car or a glass of boiling water

• I reach slightly beyond methylation into proteomics to speculate about the results that might come into the future and what those might enable.

• I consider the case of iPSC reprogramming and what that might say about aging and the possibility of its reversal

• I do not consider in any depth the idea that aging is programmed (ie that we evolved purposefully to age in a group selection way) as I consider that to have been sufficiently argued against by others elsewhere.

  • While at the same time agreeing that while aging is a universal entropic phenomenon, its translation into phenotypes (how cells and their programs react to the damage) is encoded in the genome of each species and in this sense, it is programmed.
  • That is, the 'aging phenotype' of an individual is a function of chronological time, the environment, and its genome.

• I do not go in depth into the specifics of human aging (e.g. cancer in humans or cardiovascular disease). I focus mostly on cellular aging.

• I do not claim to have invented much here de novo, and my thoughts on the subject owe a lot to people like Gladyshev and Hayflick (Aging theory in general), Ocampo (Heterochromatin loss), Gorbunova, Seluanov, and Sinclair (DNA damage and its connection to aging), Kenyon (Genetics of aging), Levin (morphostasis) and Alon (Systems biology), and many others.

• At the end I have more concrete summary that hopefully is more actionable for specific applications

Aging by the numbers

For recreational reasons, I did an epigenetic aging test.

I sent a small sample of blood to TrueDiagnostic, a company that runs epigenetic clocks for consumers, and got back some results. I got the following results:

It's interesting that just from a blood sample one can get my actual chronological age within just 1 year, impressive isn't it!

What is going on in the charts above? One says my "telomere age is 17" and another that my "OMICage is 31.82", that my "rate of aging is 0.72" or that my "Gait speed epigenetic biomarker is higher than 95% of people", what does that mean? Is my biological age supposed to be 17? 31.82? Neither? All?

Epigenetic clocks are simple (linear, most often) models trained on the methylation profile of a training set (very often cells collected from blood) to predict some outcome of interest. This outcome could be:

• Chronological age (Perhaps the most common one used, like the OG Horvath or Hannum clocks)

• Mortality risk (As in GrimAge or GrimAge2)

• Some composite endpoint of multifunctional health (Like PhenoAge)

  • Here we take a number of parameters like grip strength or VO2max and then we make them into an index (this can all be done independent of chronological age). Then, we can train models that predict grip strength or VO2max from methylation and in turn reconstruct the composite index from a blood sample.
  • This is a better approximation to the question "Relative to a reference population, how functional am I overall?" than using chronological age as predicted variable.
  • This is not a new idea: Whether someone is aging fast or slow relative to some established biomarker can be easily measured and aggregtead and ways to do this have been around for a while: Klemera-Doubal (2006) as a general way to do this sort of thing and Pace of Aging (2015) are two examples; the latter is what was used to train the DunedinPACE clock. For Pace of Aging it's things like cholesterol, CRP (inflammation), Hb1ac (diabetes), the waist to hip ratio, forced expiration, BMI, etc. But one could do others: There's something called intrinsic capacity from the WHO that sounds to me like a proxy for "aging": it is a framework comprising a set of faculties (locomotion, cognition, vitality (eg muscle strength), sensory, and psychological (wellbeing)). Intrinsic capacity predicts mortality and declines with age as one'd expect. One could also build an epigenetic clock that predicts that too but no one has yet.

• Rate of aging (Like DunedinPoAM or DunedinPACE). This one is particularly interesting and perhaps unique among the clocks beacuse of the way it was built, out of a multi-decade cohort and periodic blood sampling of the same individuals. Here they try to predict not how old you are now but how fast you are getting older; to construct it they also built these composite endpoints as in PhenoAge.

  • In theory this means that if I test 10 years from now, my DunedinPACE score might be exactly the same but my biological age (by some clock) will be only 7.2 years older instead of 10
  • Their datasets allows this clock to work around an issue the original clocks had: that training on longitudinal datasets could introduce a composition bias, as only the healthiest individuals would be represented at the later timepoints, as the sickest ones would die.

• Molecular markers like telomere length, inflammatory cytokines (IL-6), or cholesterol etc can all be proxied, in theory, via methylation-based models. Per some clockmakers, this is better than using the raw values of the marker because they can be noisier in the short term (IL-6 in particular, I'm looking at you). This is similar to how HbA1c is a more estable measure of diabetes risk than just measuring blood glucose, which tends to vary a lot.

  • These markers, to some extent, can be influenced by cell type composition. Someone with a greater proportion of naive CD8 T-cells (that have divided less) will present with longer telomeres if one measures telomeres from blood, so this may be a correlate of "lower lifetime exposure to infectious disease" as opposed to "slower aging", but it could also be a correlate of a more efficient immune system, and that might bona fide be a contributor to slower aging.
  • GrimAge2 uses as intermediate markers things like B2M (inflammation-related) or HbA1c (diabetes) as explicit intermediates which skews this clock towards diseases<sup class="footnote-reference" onmouseover=border_note("sidenote-1",true) onmouseout=border_note("sidenote-1",false)>1 as opposed to cellular aging.<div class="sidenote"

  id="sidenote-1" data-reference-id="1" > [1]. What is cellular aging and what is disease? Good question, more on that later

Here, have a map

Before continuing, the diagram below will be helpful for you to understand how I'm thinking about my core model of aging. This is written mostly with cellular aging in mind but it broadly generalizes: at its core sits DNA (nuclear and mitochondrial) from which everything else flows, and the code that helps cells interpret DNA (the chromatin state) which is comprised of modifications of DNA (like methylation) and their supporting structure (histone marks).

These together condition what tools a cell has available (if a gene is broken the cell can't use it) to do its job and how effectively, efficiently, and quickly they can de deployed to react to its environment. Empirically, it seems to be the case that it is chromatin disorganization that by far dominates in the aging process and hence iPSC reprogramming is able to reverse most of it which is quite convenient. Telomere shortening happens naturally when cells divide but it is also a function of our genetics (we could have been born with active telomerase). Through life, the cell constantly reacts to damage, and the response to this damage creates an epigenetic memory that eventually locks the cells in a state of constant inflammation (Patrick et al. 2024), which shifts resources away from the job of the cell (fibroblasts make less collagen, hepatocytes metabolize slower, etc).

So indeed if cells were able to repair their DNA damage faster than it gets produced, and were able to keep their telomeres from eroding, they would not meaningfully age. They would still get somatic mutations and that may eventually, absent cell division and natural selection of fit enough cells, end up in death. Induced pluripotent stem cells, which don't seem to age epigenetically in culture, do inded drastically upregulate all DNA damage repair pathways (Liedtke et al. 2015). This is existence proof that within our genomes are they are, without any editing, there is enough repair capacity to stave off damage for a long time. One potential wrinkle to this is the case of cells that don't divide; these accumulate age-related misfolded proteins and aggregates that some argue are not digestable even by young cells. Not much research has been done on actually testing it as most research is done on dividing cells. The one paper I am aware of that tested reprogramming in non-dividing cells did see a lowering of one such pigment, lipofuscin (Ivanova et al. 2024). The jury is still out on whether epigenetic rejuvenation can comprehensively reverse this too absent cell division but I am optimistic.

Causality in biology

In biology, everything may potentially cause everything else. Feedback loops abound and disturbing one node enough will eventually disturb the rest. Impair autophagy and you probably get more DNA damage, induce DNA damage and you probably impair autophagy.

As a result, interventions targeting one aspect of the biology of aging can potentially ameliorate impairments in the rest, which can lead one to the wrong conclusion that they are all the same and there are no privileged hallmarks. "Impaired proteostasis" is more caused by "epigenetic alterations" than it is the other way around, for example.

To counter this idea and to defend a more hierarchical model with things resembling root causes, I want to point at the notion of turnover rates: most things in our body turn over. Proteins are produced and degraded, and the same goes for mRNA and most (but not all) cells. Even fibrosis turns over! That's right: fibrosis exists as a balance of generation of collagen and its degradation by macrophages, with a steady state equilibrium of "there's fibrosis" as opposed to the one where "there's no scar" (Alon, 2023).

The epigenome turns over as well: methylation marks are constantly being removed and added back. Methylation gets a lot of airtime because it is easier to measure than what argueably is the most important part of the epigenome: histone marks. These also turn over: they are deposited and removed. This may sound puzzling at first: if the cell is able to just put back marks "where it should" (ie to where young cells have them) why isn't it doing it? And the answer is that they do, but not perfectly, hence a constant ratchet towards aging.

Then there's the genome. The genome gets mutations and chromosomes get messed up to some extent with aging. But those mutations just a tiny fraction of a gigantic set of information. DNA gets copied with relatively high fidelity but it doesn't turn over much.

We should think of the genome not as a code but rather as a vast workshop with miriads of redundant tools the cell uses to do what it does. The epigenome is more aptly thought of as that code: the epigenome is the instruction set that's running on the workshop, determining whether the shop will be making neuron stuff or liver stuff, or aged stuff or young stuff.

There is then a hierarchy of turnover rates:

• DNA: ~almost no turnover informationally, though DNA gets copied (cell division happens every ~20hr)
• mRNA: 10 hr (Yang et al., 2003) to 16.4h (Wang et al. 2022)
• Proteins: From 30 min (Ornithine decarboxylase) to crystallins in the lenses (almost no turnover) as well as histones
  • Histones (of particular interest as they support epigenetic modification) bound to chromatin can last months (Mathieson et al. 2018; Shmueli et al. 2021)
• Mitochondria: Days to months ( this and Poovathingal et al. 2021) though these also come with their own DNA (mtDNA) so in a way they are like DNA
• Cells: Neutrophils (6-8 hours) to neurons (for life)

My claim: The slower the turnover rate of something is, the more it matters for aging. Turnover rate is like gravity in a way: Gravity extends infinitely in all directions, everything pulls on everything, but some objects pull more than others. Slow turnover rate of a biological entity is like its mass.

Think of a simple case: If you alter the proteome of a cell a little bit, briefly (you add 1 misfolded protein, other than a prion), what happens next? The cell will go back to equilibrium, degrading the old proteins and making new fresh ones. But if you were to wave that magic wand and set the entire epigenome to an aged state, then for a few hours or days you'd have an aged epigenome and some youthful proteins, but eventually given turnover rates, the aged epigenome would stay and the proteome would then be aged.

The heritability of longevity is higher than what we thought in the past (up to 50% per Shenhar et al. 2025 vs 10-30% I reported back then), and as I discuss in my Longevity FAQ it has been possible to make other species like worms live longer by 10x through manipulating their genome. Given that we already know a lot about the genetics of aging, one could make humans really long lived and slow aging by editing a handful of genes like FOXO3 or CIRBP. Genetic engineering is the easiest way to make something live longer: longevity is encoded in the genome, but for those already born and until we solve bodywide gene editing, rejuvenation of the epigenome is the second best thing we can hope.

Environmental vs intrinsic aging

This fact, that given a normal environment for a species, its lifespan is determined mostly by genetics (compare say a mouse vs a bowhead whale) is phrased by some (like Cynthia Kenyon) as aging being programmed. This is a different use of the term from those that argue that "there's an aging program that purposefully drives the organism towards aging".

The phrase "normal environment for a species" is doing some work there. For many years cardiovascular disease has been a key component of human aging. But suppose that statins, PCSK9 inhibitors and other such interventions manage to make CVD a rare disease. Humans now live longer. Suppose that we also manage to have perfect cancer treatements and we also cure cancer. Now humans live 10 years longer. In that case we would be leaving a central part of what aging is untouched: the thing that human and worm and mouse aging have in common.

That part is cellular aging. Whereas in multicellular organisms new ways of aging arise, for any one single cell, regardless of whether it is yeast or a mammalian cell or a worm cell, they all age in a similar way. Roughly speaking, the main thing that goes on in cellular aging is:

1. DNA damage happens (In the nucleus and mitochondria)
2. That leads to DNA mutations (which can later on become cancer) and epigenetic/chromatin alterations
3. Cells have to decide whether to spend energy maintaining their chromatin or fixing their DNA damage. Indeed some of the proteins that help upkeep chromatin are also involved in repair (like sirtuins).
4. This loss of chromatin stability means that regions that should be open are closed and those that should be close are open. The cell becomes less functional and resilient over time.
5. Eventually an insult to the cell is stronger than its declining resiliency and dies.

Besides DNA damage, telomere shortening is the other variable I'd look at as a driver of aging, though we know, as will be discussed later, that one can fix telomere shortening and one still ages similarly.

Later in the post I will argue that all that has ever been done to extend the lifespan of organisms, from caloric restriction to reprogramming ultimately boils down to mostly fixing DNA damage or reducing its production.

Clocks and their weights: DunedinPACE

I found DunedinPACE quite intriguing beacuse it predicts a rate of aging, not an absolute number, and it is popular enough that there are people out there taking the test and publishing their own results eg here.

This below is from the DunedinPACE paper showing the correlation of the marker with age for their training set (The Dunedin cohort). There is some correlation but not that as strong as clocks that predict chronological age. This makes sense because DunedinPACE was built to predict how healthy someone is and that gets worse exponentially with chronological age, and so the rate of decline has to be greater later in life.

Now if you take this and apply it to two totally different datasets (The Normative Aging Study and the Framingham Offspring Cohort) one can ask the question: does a higher score in DunedinPACE predict higher mortality and morbidity? And the answer is yes it does:

Framingham analysis included n = 2471 members of the Offspring cohort (54% women) with average age of 66 years (SD = 9) at DNA methylation measurement during 2005–2008. Over follow-up through 2018, 23% died, 13% were newly diagnosed with cardiovascular disease (CVD), and 6% had a first stroke or transient ischemic attack (TIA). Disability follow-up was conducted through 2015 based on participant reports of limitations to activities of daily living (ADLs) on the Nagi, Katz, and RosowBrelsau scales. Participants with faster DunedinPACE at baseline were at increased risk for CVD, stroke/TIA, and mortality (CVD HR = 1.39 [1.26–1.54]; stroke/TIA HR = 1.37 [1.19–1.58]; mortality HR = 1.65 95% CI [1.51–1.79], Figure 4B). They were also more likely to develop disability (Nagi ADL IRR = 1.40 [1.19–1.65]; Katz ADL IRR = 1.33 [1.16–1.53]; Rosow-Breslau ADL IRR = 1.39 [1.24–1.56]).

The average starting age in Normative aging was 77 years old and in Framingham, 66 years old. The effect sizes seem quite different and may have been affected by selection bias: In Normative Aging if you have already survived to be 77 years old you are probably healthier to start with so the clock struggles to tease out among a population of already healthy people whereas in Framingham the effect is clearer (and the sample size bigger).

In the supplement, they look at predictive power for mortality or quality of life (disability) after controlling for age. Controlling for smoking is also useful because smoking is an easy way to predict mortality that has little to do with age and we wouldn't want clocks just overindexing on this. This is quite important: there is no value if a clock is just predicting chronological age because we already have access to that. Unsurprisingly, DunedinPACE (and GrimAge, a clock built to predict mortality) do much better than clocks that try to predict just age:

One might object to this that while these clocks work on a normal population, they might fail to detect out of distribution interventions. That is: If we were to subject a human (or a mouse) to caloric restriction or rapamycin or an intervention that turns on their FOXO3 (the one gene most associated with human longevity), would this show up in the clocks? Or, for that matter, if we just give people statins (which should lower LDL cholesterol), will that be reflected in the signal?

Mechanistically, one can investigate the methylation sites (CpGs) that the clock is picking up and see what these clocks might be picking up. This is rarely done!

I pulled the CpGs from their GitHub and slapped the Illumina annotations on top, then did some ChatGPT-assisted plotting. First, ouf of 173 CpGs in the clock, only a handful are quite large, followed by a long tail (Pareto principle at work!)

But are these causal? You can already see that one of the top driving CpGs is known to be driven by statin use. People on statins are plausibly less healthy but the statins are not what is making them sick. So were someone to go off a statin, their DunedinPACE score would show rejuvenation but their mortality risk would increase.

This is why it is important to look at how these clocks are constructed and investigate whether the CpGs have reasons to be causal or not.

It is also useful, I think, to present stratified mortality curves according to other biomarkers just to illustrate that mortality prediction is not anything magical that only clocks can do.

LDL cholesterol

[source]

Glycated hemoglobin (Hb1Ac)

[source]

Walking speed

[source]

Physical strength

[source; in this plot maximum quadriceps torque is depicted]

Can you lift you way to immortality? The search for causal aging markers

These examples are illustrative of the way the causality of disease and aging work: aging causes disease, and disease causes mortality and disability. As a result, disease is causally closer to what we observe (people dying and suffering) so if one tries to make predictors of those one will pick up stronger drivers or correlates of that than of the underlying driving factor (aging).

And because in the natural population the decline of everything is correlated to an extent, one can use one (grip or leg strength) to predict another (mortality) despite the fact there is a very small direct causal link between having really strong forearms and dying; rather strenght acts there as a proxy for overall health.

This is not the case for causal biomarkers like LDL cholesterol which cause the relevant disease (such that modifying it modifies the disease) and we can measure directly.

Hence we can measure the downstream consequences of aging very easily and we can predict outcomes of interest reasonably well, but that can all be done without touching the measurement of cellular aging at all. We don't even need these clocks to do that!

Suppose you could either know your DunedinPACE score or know your Hb1ac (diabetes risk), LDL/ApoB (CVD risk), your lean mass %, and the usual blood counts and metabolic panels? What would give you a better picture of your health and mortality risk. To me, it is uncontroversially clear that classic validated biomarkers are not only superior predictors but they are also directly actionable compared to epigenetic clocks. If your LDL is high you can take statins. If your DunedinPACE score is high what are you supposed to do?

To my knowledge, no analysis has found that methylation clocks are superior to composites of traditional blood based biomarkers to predict mortality and disability. If you are aware of any such analysis, let me know!

But if we have these blood-based biomarkers that work so well, what's missing? What's missing is models that estimate aging, instead of those two other downstream things.

Sure, but who cares about aging you might ask? We care about its downstream consequences right? If an aging predictor cannot predict mortality better than LDL cholesterol, what would be their use?

Good question! One that doesn't get asked often enough. Moreover, is aging even a thing you can measure with a single number? Right now we don't measure "health", we measure diabetes risk and CVD risk separately.

One answer could be "for research purposes". If we can slow down the rate of aging permanently in a mouse, then we can know that very quickly whereas waiting for a cohort of mice to die and/or get frail can be expensive and time-consuming.

Another use could be for clinical trials, if one shows age slowdown in mice and wants to take that to the clinic, one could use such a biomarker to have an early readout of whether the treatment is working or not and if its not, discontinue and save money on the trial.

In the appendix to this essay I discuss proteomics clocks, which I argue are superior to methylation clocks, and superior (and deeper) to the simple baselines of LDL+HbA1c. They do something methylation clocks struggle with: sampling different tissues with specificity; methylation clocks tell us the methylation status of a given cell type (blood cells), but not say how the brain is aging. But there are plasma-based biomarkers of CNS disorders like Alzheimer's: the organs leak information about their state into the blood, and that can be read out with proteomics. So I argue that in the future, paired proteomics clocks (for intantaneous organ-specific function) with methylation clocks (aging of blood cells) will give us a reasonable picture of how a living being is aging much better than what we currently have right now.

But now, back to the methylation clocks! We said the issue is that if we train them on endpoints like mortality, we bias them towards disease, not aging. What can we do about that?

Doing (aging prediction) by non-doing (aging prediction)

There are a couple of options to work around the limitations of the data we have with methylation clocks

1. Correct the mortality/disease signal by broadening or standardizing the population:

   1. Try to include other species in the dataset so that it picks up what is common across them and not just in humans (diabetes, etc)

   2. Develop clocks for populations with standardized environments and genetics (like lab mice or cells in culture!) to remove the effects of specific diseases from biasing what the clock picks up

   3. Leverage EWAS to try to pick up CpGs that are associated with specific diseases

2. With a theory of aging at hand that points to drivers of aging:

   1. Hand-pick CpG sites that are posited to be drivers of aging as opposed to letting a model optimize the weights (One man's taste is another's bias).

For the first approach there we can look at the CpGs of this other clock (PanTissue, PanMammalian). But this is not great: It doesn't uniquely pick out the genuine slower rate of aging of dwarf mice (thought they did claim this in the preprint and then took it back!), which by all metrics age slower, nor does it pick out the substantial epigenetic rejuvenation of iPSCs. So perhaps adding all species is bad and adds noise.

What about clocks just trained on human data, and then applied in vitro? That is more promising: these clocks show that the cells age in culture in a linear fashion (just like humans in the wild), just 60-65x faster.

The Pan-tissue clock also seems to work in patients with certain progerias (accelerated aging)

And it also performs well for Ercc1- (a DNA repair gene defect) mice (Where genetic and environmental variablity is controlled), at least for some tissues even though this clock was trained on humans not mice.

Retrotransposon clocks

Another approach to make better clocks is to handpick CpGs. A good candidate for this are CpGs that suppress retrotransposons. Retrotransposons cause DNA damage when they insert themselves in DNA and they get derepressed with aging. One can build a clock that looks at CpGs that are in Retrotransposons exclusively:

Intriguingly the authors note

Notably, we observed ourRetroelement-Age V2 clock overlapped with 9 CpGs (cg06672696,cg07286682, cg08822136, cg16936289, cg16810279, cg22277154, cg13261390, cg22277154, and cg24251135) used in AdaptAge,CausAge, and DamAge causality-enriched epigenetic clocks recently developed using Mendelian randomization (Ying et al., 2024). This observation suggest some of the signal from our Retroelement-Age V2 may include sites that contribute and/or protect against aging.

This works for reprogramming:

It also predict aging in vitro:

Alas, this clock hasn't been applied to other datasets in eg slow aging mice or mice subjected to caloric restriction. A similar approach, however, has, and that shows the expected effects:

For context, these Snell dwarf are a well known case of slow aging:

It also seems to match with the idea that bigger dogs age faster (bigger dogs have their retrotransposons derepressed faster):

I really like the retrotransposon clocks! They do seem to be neatly tied to DNA damage. With these, I think we have some grounds to believe, given what we know about retrotransposons, that if one were to see a slowdown in this clock, one would have thus slowed down aging. But we could not be maximally confident: If someone developed an epigenetic editor to methylate all these retrotransposons, that could drop the Retrotransposon age to zero, and yet that wouldn't necessarily reset aging as it is not the only source of DNA damage. Using this clock would be valid especially if one's not specifically targeting retrotransposons, but even then I would suggest using a panel of approaches and not just this one.

Aging clocks with mouse-only data

Other clocks, developed specifically for mice are also able to pick this up, doing it better (greater separation) than what the Retrotransposon-only clock does:

And later this one as well, also from the Gladyshev lab.

Aging clocks through EWAS

More recently, we got this paper from the Gladyshev lab that I really liked trying to build "causal clocks". The idea here is to carefully tease out what CpGs might be associated with pro-aging vs those that are differentially altered with chronological aging as a response to aging. The fist one they call DamAge clock, the second, AdaptAge. DamAge passses the reprogramming test (age drops with reprogramming):

It picks out progeroid damage, exposure to cigarette smoke, and exposure to UV light (ie skin samples exposed to the sun vs non exposed)

Here the other clocks fail to pick this up, in fact some say UV exposure slow down aging! But no, what they are probably picking up is mechanisms that counteract aging in those cells. DamAge correctly assigns a score similar or greater to these exposed cells.

In vitro, the effects are more stark, exposing cells to cigarette smoke shows up cleanly only in this clock:

Intriguing. So what is this clock picking up? The weights are here. Doing the same as before, here's some ChatGPT interpretation.

First, most of the weights of the model are negative: this means the CpGs picked up are to be interpreted as "the more methylated the gene is (the less expressed), the lower the damage"

These are the top 20 CpGs

Then here's pooled by gene

This looks much better than DunedinPACE: the CpGs and genes picked up are not obviously tied to diabetes and metabolic syndrome! But it's still hard to interpret.

Aging clocks with in vitro data: Polycomb Repressive Complex sites

One last way I'll discuss of constructing more robust models to track cellular aging is training them in vitro where organismal-level disorders are not relevant. This, as it turns out also has its problems.

The EpiTOC/pcgtAge clock (Yang et al. 2016) was constructed by handpicking as opposed to running a regression which makes it more robust to allegations that it's picking just a correlation. What they do is pick PRC2-related promoters that get hypermethylated with age in blood samples and that start with no methylation across a range of fetal tissues, then the score for a given sample is simply the average methylation across those CpGs. This can be used to predict human chronological age, and it also correlates with the number of cell divisions estimated for stem cells in different tissues, which matches also with other clocks that pick up tissues that turn over faster as faster aging. Note that in this picture below each point is a different tissue from a person of a different age.

A later model, EpiTOC2 (Teschendorff 2020) further expands this methodology, using fewer CpGs.

The original Horvath clock has 353 CpGs that are jointly fitted to estimate the chronological age of a sample. This is not great because

​ a) Plausibly some people might have aged faster or slower but we are telling the model that two samples have an age of exactly their chronological age

​ b) Different tissues plausibly have aged faster and slower but we again tell the model to assume that they all have the same age

EpiTOC2 instead models each CpG individually, they say that the methylation at a site equals is a function of someone's age, how often stem cells divide per tissue, and how likely is that a cell division will lead to an increase in methylation. Doing this they fit one model per CpG (3 values to fit), then pick the modal value of the stem cell division rates estimated (should be the same across CpGs because they are all replicated jointly) and fit the model again, this time with just 2 values (de novo methylation probability and the ground state methylation).

Though this all is done in blood, their model is posited as universal per cell division so then this model fitted in blood can be applied to other tissues. So given say a colon sample, one can measure the actual methylation and knowing the age of the person one can calculate the number of stem cell divisions for a tissue. They find their estimate of this parameters correlates reasonably well with literature estimates of how often stem cells divide per tissue

Moqri et al. (2024) did something similar in concept (picking PRC2-associated sites and averaging their methylation) but without modeling, they just find those PRC2 sites, pick 1000 with the most PRC2 binding and average the methylation of that.

This, they explicitly note, is what explains ELOVL2 (discussed later): It is one of those PRC2 associated sites!

Exciting, isn't it! But: Is this causal? If we were able to revert this clock while leaving everything else unchanged, would we revert aging? I don't think so!

I looked into why is it that cells keep adding methylation at these sites and the reason seems to be that initially those genes are silenced during development when cells are comitted to a fate, by depositing H3K27me3 (a histone mark) on it. But during cell division, this gets diluted and to compensate, the cell methylates those sites to continue to repress them (Yang et al. 2023). This means that these PRC2 methylation sites are non causal! They are an adaptive response. The implication of this is that if we were to remove them without adding back H3K27me3 we would make the cells younger "by the clock" but make them less functional. Conversely, this means it is also possible to make the cells genuinely younger while seeing no change in an epigenetic clock that heavily indexes on these sites. It also means that the sometimes reported age acceleration in cancer might just litearlly be a proxy for their higher rate of cell divisions (Rozenblit et al. 2022).

Everything is connected: epigenetic clocks are not unique, but they are convenient

The state of a cell is the aggregate of everything that's happening in the cell: how many mRNAs of each type there are, concentrations of proteins, what chromatin is closed, etc.

But it's all connected: One influences the rest. As a result, if one measures deep enough a cell, one can reconstruct the rest of the state from that subset. From the genome and chromatin one can predict gene expression, and from gene expression one can predict protein abundance and chromatin accessibility<sup class="footnote-reference" onmouseover=border_note("sidenote-3",true) onmouseout=border_note("sidenote-3",false)>3.<div class="sidenote"

id="sidenote-3" data-reference-id="3" > [3]. This requires the entire transcriptome; the correlation between mRNA of gene X and abundance of protein X is very noisy as this is influenced by post-translational regulation among others. This effects can be addressed by sampling the whole transcriptome which will contain e.g. those genes participating in post-translational regulation