Article

ISSN 0006-2979, Biochemistry (Moscow), 2024, Vol. 89, No. 2, pp. 356-366 © Pleiades Publishing, Ltd., 2024.

356

REVIEW

Biological Clocks: Why We Need Them,

Why We Cannot Trust Them,

How They Might Be Improved

Josh Mitteldorf

Philadelphia, USA

aging.advice@gmail.com

Received December 22, 2023

Revised February 5, 2024

Accepted February 6, 2024

Abstract— Late in life, the body is at war with itself. There is a program of self-destruction (phenoptosis) imple-

mented via epigenetic and other changes. I refer to these as type(1) epigenetic changes. But the body retains a

deep instinct for survival, and other epigenetic changes unfold in response to a perception of accumulated damage

(type(2)). In the past decade, epigenetic clocks have promised to accelerate the search for anti-aging interventions

by permitting prompt, reliable, and convenient measurement of their effects on lifespan without having to wait for

trial results on mortality and morbidity. However, extant clocks do not distinguish between type(1) and type(2).

Reversing type(1) changes extends lifespan, but reversing type(2) shortens lifespan. This is why all extant epi-

genetic clocks may be misleading. Separation of type(1) and type(2) epigenetic changes will lead to more reliable

clock algorithms, but this cannot be done with statistics alone. New experiments are proposed. Epigenetic changes

are the means by which the body implements phenoptosis, but they do not embody a clock mechanism, so they

cannot be the body’s primary timekeeper. The timekeeping mechanism is not yet understood, though there are

hints that it may be (partially) located in the hypothalamus. For the future, we expect that the most fundamental

measurement of biological age will observe this clock directly, and the most profound anti-aging interventions will

manipulate it.

DOI: 10.1134/S0006297924020135

Keywords: phenoptosis, aging clocks, epigenetic clocks, methylation clocks, programmed aging

INTRODUCTION

“Biological clock” has three different meanings.

1.  The body’s circadian rhythm of wake/sleep cycles;

2.  Time-keeping over the life cycle, controlling de-

velopment, puberty, and (plausibly) aging;

3.  (Since 2013) Computer algorithms for calculating

biological age from epigenetic and other medical data.

In this manuscript, I shall refer to #1as “circadian

clocks”, #2 as “governing clocks”, and #3 as “algorith-

mic clocks”.

Mammalian circadian rhythms have been studied

and located in the hypothalamus. The timing of devel-

opment and gonadarche are governed by a mechanism

which has not yet been elucidated. If, as most readers

of this journal believe, the source of senescence can be

traced to an extension of the developmental clock into

a mode of self-destruction (phenoptosis) late in life,

then mechanistic understanding of this clock would

be a breakthrough in anti-aging medicine. Prospective

manipulation of this internal clock would be the true

Philosopher’s Stone.

Algorithmic aging clocks have been developed as

a shortcut to evaluate anti-aging interventions with-

out having to sample alternative mortality curves over

decades. If the algorithms truly reflect the governing

aging clock, then they can perform this function with

ideal precision. But since the detailed mechanism

of the governing clocks is yet unknown, algorithmic

clocks are based on the next best thing– gene expres-

sion changes which are, presumably, the principal way

in which the governing clocks affect the body globally.

A problem with this approach is that there are

other gene expression changes that occur with age.

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BIOCHEMISTRY (Moscow) Vol. 89 No. 2 2024

The best evidence is that the body is at war with it-

self late in life, and some of the observed changes

ingene expression are direct expressions of the body’s

phenoptosis program, while other changes are actual-

ly defenses against that program. The body detects the

(self-inflicted) damage, and deeply embedded programs

are activated to repair that damage.

With age, the body is simultaneously activating

a timed program of self-destruction and triggering a

program of self-defense. It is crucial for any algorith-

mic clock to distinguish between these two, because a

putative anti-aging intervention should be credited for

reversing the former, but debited for reversing the lat-

ter. The two may be impossible to distinguish based on

statistical analysis alone.

HISTORICAL CONTEXT OF ADAPTIVE AGING

In the twentieth century, evolutionary theory

based on the “selfish gene” model of neo-Darwinism

guided understanding of the cause and the mecha-

nisms of aging in the animal kingdom. Aging has been

regarded as a passive accumulation of damage, result-

ing from a selection shadow at late ages (accumulat-

ed mutations [1]) or as an unavoidable side-effect of

selection pressure for maximal fertility (antagonistic

pleiotropy [2]). Late in the century, a few visionary re-

searchers had the courage to challenge these theoretical

paradigms based on observation and experiment; there

are aspects of the phenomenology of aging that defy

predictions of theory based on accumulated mutations

or antagonistic pleiotropy. The oldest and most robust

intervention for extending lifespan is caloric restriction

(CR), and the CR phenomenon does not fit well with ei-

ther of the two classical theoretical models [3,4]. Most

obviously, life extension flies in the face of the popular

disposable soma theory [5], which posits that aging is

caused by a need to budget food-derived energy [6].

The earliest proponents of aging as an evolved ad-

aptation were Libertini [7], Bowles [8], and Skulachev

[9]. There is now an abundance of plausible, published

models capable of explaining when and how natural se-

lection might prefer a fixed lifespan to an indeterminate

lifespan [10-16]. The conservative scientific community

avoided discussion of this challenge to neo-Darwinian

evolution, and the first suggestion of adaptive aging

to appear in a high-profile Western journal was [17].

Inmy opinion, the most plausible and general models

for evolution of aging are based on Gilpin’s

popula-

tion dynamics [15, 18] Others are based on dispersion

[19], on shortening the generation time to increase the

pace of evolutionary adaptation [20], and on keeping

up with an evolving pathogen [7, 13, 16, 21].

THE SIGNIFICANCE OF METHYLATION CLOCKS

In the first decade of this century, many inter-

ventions were discovered that significantly extend-

ed median lifespan of laboratory animals. Maximum

lifespan has been extended to a lesser extent in ver-

tebrates. These included metformin [22], rapamycin

[23], various short peptides [24], anti-inflammatories

such as aspirin and curcumin [25], hormones such as

vitaminD [26] and melatonin [27, 28], and mitochon-

drial modifiers [29, 30]. The question was ripe wheth-

er these measures would also extend lifespan in hu-

mans. But human lifespan studies are prohibitively

impractical.

–  The human lifespan is so long that significant

results require decades and tens of thousands of

study participants.

–  This makes human studies expensive, as well as

slow.

–  People cannot be expected to adhere to any

health regimen for decades at a time.

–  It is unethical to ask control subjects to refrain

from doing something that might extend their

lives.

In 2013, Horvath [31] and Hannum [32] indepen-

dently proposed a resolution to this dilemma. Their

hypothesis was that there is a meaningful, measurable

quality of a person or animal, dubbed “biological age”,

and that the effect of an intervention on biological age

could be used as a proxy for its effect on life expectan-

cy. The hypothesis sounds reasonable, and if true, then

“biological clocks” are indeed a great boon to research

in aging medicine. There are subtleties to the logic,

however, which neither Hannum nor Horvath was ea-

ger to discuss in publications, possibly because of the

conservatism of the evolutionary community, which I

referred to above. I only know this because I was for-

tunate to be privy to Horvath’s private thoughts on the

matter. Their clocks only make sense for the intended

use in the context of programmed phenoptosis, as I

shall argue below.

The Horvath and Hannum clocks were based on

methylation of cytosine, one of the four nucleotide bas-

es in DNA. Methylation is one mechanism of epigenetic

control, i.e., it is part of the way that the body express-

es genes selectively, where and when they are needed.

Itis widely accepted that methylation (along with other

mechanisms of epigenetic control) is part of the body’s

evolved, adaptive response. With age, the selectivity of

methylation is blurred; this is called epigenetic drift.

But there are also directional changes in methylation.

Thousands of genes are turned off gradually, mono-

tonically over a lifetime via hypermethylation, and

more thousands are turned on via hypomethylation.

* Gilpin, M. (1975) Group Selection in Predator-Prey Communities, Princeton University Press, doi: 10.2307/j.ctvx5wbvr.

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The consistency of these patterns compels the

presumption that they are adaptive. But adaptive to

what end? Why would senescence be associated with

changes in gene expression?

1)  If you believe in phenoptosis, then the directed

changes in gene expression are means of self-destruc-

tion. Genes are turned on that increase inflammation,

destroying arteries and neurons [33]. Apoptosis is

up-regulated to the point where healthy muscle and

brain cells are dying [34, 35]. Protective anti-oxidants,

DNA repair, and autophagy are down-regulated [36].

If any intervention sets back the methylation clock,

then there is less self-destruction, more repair and

maintenance. We expect that the body will live longer.

2)  If you believe the neo-Darwinian theory that

the body cannot be purposely destroying itself, then

aging is an accumulation of incidental damage at the

cellular and molecular levels. If there are associated

epigenetic changes, these cannot be causing the de-

struction, so they must be a response to the damage.

Changes in gene expression as captured in the meth-

ylation clocks must be the body’s effort to protect it-

self with increased immune function, increased auto-

phagy, increased antioxidants, increased DNA repair.

If any intervention sets back the methylation clock,

then there is less repair and maintenance. We expect

that setting the aging clock back to a younger age will

actually decrease life expectancy. This insight is count-

er-intuitive, but, if correct, it changes the logic of meth-

ylation clocks.

Since 2013, there has been a kind of double-think

in the world of anti-aging research. Most researchers,

at least in public, continue to embrace perspective(2),

even as they adopt methylation clocks to evaluate the

interventions they develop.

I have been a leader in promoting perspective(1),

and most readers of this journal might be sympathet-

ic to the concept of phenoptosis. But in recent years,

I have become convinced that epigenetic changes of

both types(1) and (2) are taking place simultaneously

as the body ages. The body is at war with itself. The

self-destructive adaptations listed above are real: dial-

ing down repair and maintenance, promoting systemic

inflammation, apoptosis of healthy cells, derangement

of the immune system. But the body retains its pro-

tective responses, and there are also changes in gene

expression that ramp up the repair processes. All the

present clocks include a mixture of (1) and(2); thus,

we do not yet have a reliable metric for the efficacy of

anti-aging technologies.

A methylation clock algorithm may correlate tight-

ly with chronological age or even reflect expectancy of

remaining life more accurately than chronological age,

and still the same algorithm may give a deceptive as-

sessment of a medical intervention. This can happen

if the algorithm includes epigenetic markers (type(2))

for protective genes that are turned on in response to

perceived damage. An intervention that modifies the

epigenetics in such a way as to shut down the protec-

tive activity without actually repairing the damage will

score as a lower age, even as, in reality, it decreases

life expectancy.

To evaluate putative anti-aging interventions in

reasonable time, we need aging clocks. But we cannot

train these clocks on chronological age, or on measures

of impaired health, or even on time to death. Allthese

are likely to capture changes of both types(1) and(2).

It is crucial to tease apart these two types of changes,

but it is also difficult. It is a problem for students of

metabolism, not just students of statistics.

Two questions will motivate the remaining por-

tion of the present manuscript.

–  What is the evidence that changes of types (1)

and(2) are both components of all extant aging

clocks?

–  What are experimental methods by which we

might separate the two, so that we can develop

clocks based on (1)– (2) rather than (1)+ (2)?

Two other questions will be addressed briefly below:

–  What other issues with the current clocks might

be ameliorated, independent of the central

problem of distinguishing changes of types (1)

and(2)?

–  How does the body keep time? Changes of type(2)

do not have to be on a chronological schedule,

but changes of type (1) certainly do, and devel-

opment is certainly programmed in part via

programmed changes in gene expression. It is

reasonable to assume that the body keeps time

via some biochemical or bioelectric mechanism,

and we expect that interventions that set back

the body’s “odometer” would be a royal road to

rejuvenation, provided that a latent ability for

robust repair persists into late life stages.

WHAT IS THE EVIDENCE THAT CHANGES

OF TYPES(1) AND(2) ARE BOTH COMPONENTS

OF ALL EXTANT AGING CLOCKS?

Some of the best-established interventions for ex-

tending lifespan do not affect the major algorithmic

clocks, or do so modestly compared to what might be

expected from their observed effects on lifespan. Rapa-

mycin extends lifespan of male mice without affecting

their methylation age in the Horvath rodent clock [37].

Participants in the CALERIE study who have adopted

25% CR diets showed no significant benefit according

to either the GrimAge or PhenoAge clocks [38].

Conversely, Katcher’s intravenous infusion of exo-

somes (E5) has a dramatic effect on the Horvath ro-

dent/human clock, reducing epigenetic age by half

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[39, 40], but thus far seems to extend lifespan less

consistently than the clock setback would imply [41].

The Conboys recently published a withering criticism

of the utility of current methylation clocks, and of the

machine learning algorithms from which they are cre-

ated [42]. They report that clocks in common use do

not respond as expected to known life-shortening con-

ditions, such as Down Syndrome, inflammaging associ-

ated with arthritis, and Parkinson’s Disease.

The GrimAge clock of Lu and Horvath [43] was

trained on actual mortality data, using historic blood

samples for which the future history of the donors was

known. This was a major advance from previous clocks,

based on chronological [31] age and on healthspan bio-

markers [44]. But one element of the GrimAge develop-

ment alerted me to the issue concerning type(2) chang-

es, as described above.

Part of the training of GrimAge involved a methyl-

ation image of the subject’s smoking history. Smoking

is known to accelerate aging and shorten life expectan-

cy. Certain patterns of methylation are associated with

smoking, and are also valuable predictors of time until

death. These were included in the GrimAge algorithm.

My assumption was that smoking decreases longev-

ity by damaging tissue of the lungs, not by turning on

the phenoptosis program. Therefore, if there are meth-

ylation changes associated with smoking, they are prob-

ably of type(2). In other words, the methylation signa-

ture of an “older” smoker is likely to include activation

of more protective pathways than a “younger” smoker.

This is an important clue. The methylation profile

of a smoker is useful in constructing a GrimAge clock,

but it should be counted in reverse. Methylation chang-

es associated with smoking are statistically associated

with shorter lifespan, but mechanistically with protec-

tion. These changes should have been included in al-

gorithmic clocks with negative coefficients, signaling a

younger biological age. This was not how the GrimAge

clock was constructed in fact. Methylation changes as-

sociated with smoking were included in the GrimAge

clock with positive coefficients.

In general, the methylation image of smokers is

an example of type  (2). All type  (2) changes should

be counted with negative coefficients in methylation

clocks, even though they are statistically associated

with older ages and shorter remaining life expectancy.

WHAT ARE EXPERIMENTAL METHODS

BY WHICH WE MIGHT DISTINGUISH,

SO THAT WE CAN DEVELOP CLOCKS

BASED ON(1)– (2) RATHER THAN (1)+ (2)?

The story of GrimAge carries a message that

suggests ways that methylation changes of types  (1)

and (2) might be teased apart in algorithmic clocks.

Present clocks do not distinguish between  (1) and  (2)

so presumably the two types of methylation changes

are combined in a way we might connote as (1) +  (2).

The goal would be to create a clock built on type  (1)

changes alone, or, more speculatively, penalize the

clock for type  (2) changes, so with the result that the

algorithm measures (1)  –  (2) rather than (1) +  (2).

The long-term goal would be to understand the

metabolic consequences of each CpG change, separate-

ly and in combination, so that a clock could be con-

structed with full confidence that it scores beneficial

and detrimental methylation changes appropriately.

Lacking this understanding in the interim, we

might make progress toward distinguishing(1) and  (2),

by learning from the smoking example. One way to

acquire a database of type  (2) changes is that animal

models might be injected with pro-inflammatory cy-

tokines, and their epigenetic consequences mapped.

The animals’ immune systems might be challenged, or

they might be subjected to laceration or small doses

of radiation, again to chart the epigenetic response to

compile a list of candidates for type (2) changes. These

experiments could not ethically be performed on hu-

mans, however there are humans whose aging is accel-

erated by non-epigenetic factors, including alcohol and

drug abuse. Such people might be tested as part of the

quest for type  (2) changes. People healing from phys-

ical and emotional trauma might also be presumed to

have epigenomes modified in the direction of type(2).

Other examples of hormesis [45] may be useful.

We might have most confidence in the epigenomes of

people and animals subjected to caloric restriction  [46].

Across the animal kingdom, CR is the most robust anti-

aging strategy known at present, and we can be con-

fident in subtracting CR-associated epigenetic changes

from any algorithmic measure of biological age.

In addition to CR, there are dozens of interventions

known experimentally to extend lifespan in rodents,

including juvenile exosomes [47], rapamycin  [48], cer-

tain peptides [24], vitamin D [26], NAC [49], SkQ  [50],

certain anti-inflammatories and angiotensin inhibitors.

Recently, some of these have been tested for their effect

on algorithmic clocks; in the future, the converse logic

might be used to calibrate clocks. If an intervention is

known to increase lifespan, then we may presume that

epigenetic changes observed in response to that inter-

vention are beneficial.

Some genes are known to be geropromoters, as,

e.g., FAT10  [50], mTOR, and SIRT1  [51]. Other genes,

e.g., FOXO  [52], AMPK  [53], and Klotho  [54] are thought

to be geroprotective. If methylation states can be found

that affect the expression of these genes, they might be

readily identified as types(1) or(2).

Before 2013, biological age was estimated with mea-

sures of performance and appearance: grip strength,

gait speed, athletic endurance, memory, exhalation

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volume (FEV), skin wrinkles, arterial inflammation,

cartilage integrity. In the age of epigenetics, these

physical characteristics retain their value as predictors

of mortality, and a hybrid clock might be devised, com-

bining physical and epigenetic factors.

A recent international collaboration [55] acknowl-

edged that some epigenetic changes with age are “adap-

tive” while others cause “damage”, and proposed an in-

novative way to separate the two using genome-wide

association (GWAS). Their method is to look across the

genome for SNPs that are doubly associated, first with

methylation at a particular CpG site, and second with

a change in life expectancy (shorter or longer). On the

presumption that the SNP is responsible for the meth-

ylation change and the methylation change is respon-

sible for an effect on lifespan, they categorize partic-

ular CpG methylation sites as pro-aging or anti-aging.

Some of these are sites change methylation status con-

sistently through a lifetime, and these changes can be

categorized presumptively as type (1) or type(2).

SENSITIVITY OF METHYLATION CLOCKS

TO LAB ERROR

This is an avoidable problem with most of the ex-

tant methylation clocks. Methylation is measured on

chips with microscopic dots containing antigen sam-

ples, each corresponding to a single CpG. It is easy for

a few of these dots to be misread because of quirks in

manufacturing or lab processing. Ideally, the clock al-

gorithms should be based on many sites in such a way

that democracy can minimize the importance of errors

at any one site.

The Horvath clocks are each based on a linear com-

bination of methylation levels (betas) at several hun-

dred methylation sites. Coefficients are derived with

an algorithm that optimizes accuracy and robustness.

Since hundreds of components are summed, it would

be reasonable to assume that a wild inaccuracy in any

one of them would affect the readout only to the extent

of a fraction of 1%. But this is not true. Roughly 60% of

the coefficients are negative and 40% positive. There

is cancellation between a large positive and a large

negative sum, with the difference being more sensi-

tive than necessary to potential anomalies that affect

a single CpG. It is my experience that a single CpG out

of 800,000 on the Illumina Epic chip can make a 10%

difference in the computed age.

A simple solution to this dilemma would be to sep-

arate the sites that are hypermethylated with age from

the hypomethylated sites, and run the same optimiza-

tion procedure on the positive terms and the negative

terms separately

. This procedure creates two separate

methylation clocks, one with only positive and one

with only negative coefficients. The two clocks inde-

pendently measure biological age, and each one has

the desirable property that no one site can affect the

output by more than 1%. An appropriately weighted

average of the positive and negative outputs can be

used as a best estimate of methylation age, avoiding

the sensitivity to individual sites on the Illumina chip.

Levine has proposed a more elegant solution to

this problem based on principal component analy-

sis (PCA) [56]. She has reproduced the training algo-

rithms of the major Horvath clocks, but instead of

individual betas as primitives, she uses principal com-

ponents. A principal component is a linear combination

of thousands of betas that points in a direction of beta

space defined by the shape of the statistical distribu-

tion in the training set [57]. Early analysis of this new

approach [58] suggests that it is more robust to error

but not yet as accurate as the older methods with be-

tas for primitives, and that larger training sets could

generate PCA clocks that are both more accurate and

less error-prone.

IS THE GOVERNING CLOCK

LINKED TO THE CIRCADIAN CLOCK?

The body’s circadian rhythm is controlled by the

suprachiasmatic nucleus, located within the hypothal-

amus in the brain’s endocrine region [59]. Individu-

al cells have their own timekeeping mechanism, and

these are networked so as to create a consensus. The

system is also subject to influence, particularly by light

and by activity, which can reset the “time zone” without

modifying the internal rate-generating circuits [60-62].

Circadian cycles can be affected with a small num-

ber of circulating hormones, whereas development and

aging probably require timing and integration of an

epigenetic network that is both complex and plastic in

response to signaling from the metabolic and external

environments. All the more reason to expect that a lo-

cus of a clock for aging and development might be situ-

ated in the neuroendocrine regions of the brain.

Cavadas [63, 64] has investigated the effects of neu-

ropeptide Y (NPY), a short peptide deriving from the

hypothalamus. She has collected evidence for a role of

NPY in regulating aging at a systemic level [65]. Levels

of NPY decline with age and in mice, NPY seems to be

necessary for the life extension effects of CR [66].

Cavadas links six modes of aging to NPY levels:

–  loss of proteostasis;

–  stem cell exhaustion;

* Hypermethylated and hypomethylated should not be confused with type(1) and type(2). These ways of categorizing CpG

sites are independent and cut across each other.

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BIOCHEMISTRY (Moscow) Vol. 89 No. 2 2024

–  altered intercellular communication;

–  deregulated nutrient sensing;

–  cellular senescence;

–  mitochondrial dysfunction.

Though NPY may be a promising target for anti-

aging therapies, it is probably not an upstream deter-

minant of age because it is a neurotransmitter and not

a transcription factor. FOXO and SIRT1 are transcrip-

tion factors strongly linked to aging, but are not cen-

trally sourced in the brain. Orexin and oxytocin derive

from the hypothalamus and both have been linked to

effects on aging [67]. Age-dependent increase in the

pro-inflammatory signal NF-κB seems to emanate di-

rectly from the hypothalamus [68].

Cai et al. have demonstrated that aging could be

slowed in mice by inhibiting the inflammatory cyto-

kine NF-κB and the related cytokine IKK-β just in the

hypothalamus. “In conclusion, the hypothalamus has

a programmatic role in ageing development via im-

mune–neuroendocrine integration…” They summarized

findings from their own lab, suggesting that metabolic

syndrome, glucose intolerance, weight gain and hyper-

tension could all be exacerbated by signals from the

inflamed hypothalamus. In agreement with Cavadas,

they identified GnRH as one downstream target, and

were able to delay aging simply by treatment with this

one hormone. IKK-β is produced by microglial cells

in the hypothalamus of old mice but not young mice.

Genetically modified IKK-β knock-out mice developed

normally but lived longer and retained youthful brain

performance later in life [68].

Cai’s group identified micro-RNAs, secreted by the

aging hypothalamus and circulating through the spinal

fluid, that contribute to aging. A class of neuroendo-

crine stem cells from the third ventricle wall of the hy-

pothalamus (nt-NSC’s) was identified as having a pow-

erful programmatic effect on aging by secreting other

micro-RNAs. Mice in which these stem cells were ab-

lated had foreshortened life spans; old mice that were

treated with implants of hypothalamic stem cells from

younger mice were rejuvenated and lived 12% longer,

despite the lateness of the intervention [69].

Transplanting a SCN from young hamsters into old

hamsters cut their mortality rate by more than half,

and extended their life expectancies by 4 months [70].

At Xiamen University, Leng etal. [71] have discov-

ered that the decline of hypothalamic menin signaling

with age is correlated with cognitive decline and possi-

bly lifespan regulation in mice.

AGE-RELATED EPIGENETIC CHANGES

IN DISPERSED CELLS

Effectiveness of the methylation clocks attests

to a major role for methylation in the aging process;

but questions remain regarding the relationship of

methylation in dispersed cells to the central regula-

tion of aging.

– Is the dispersed methylation state of somatic

cells an independent clock that determines the body’s

age state, or is methylation an intermediate transmis-

sion of information about the body’s age, information

that derives from a separate source, perhaps the su-

prachiasmatic nucleus itself?

– How much of the methylation change observed

to take place with age is entropic “dysregulation”, and

how much is directed?

The governing clock(s) that we wish to identify

must have two conflicting properties. Of course, it must

keep time reliably to trigger the phenotypes of growth,

development, and then senescence on schedule.

Itmust also be homeostatic. If the clock is perturbed,

it must be able to find its way back to a remembered

biological age. Homeostasis is a fundamental property

of life. All biological systems tend to restore their state

when deranged by the environment.

The need for homeostasis is a general property of

biological systems. But how can a clock be homeostat-

ic? If the body’s clock is knocked far off the biological-

ly-determined age, where is the reference information

from which it can be reset?

The only way that the clock can both keep time

and restore itself after perturbation is with sever-

al independent time-keeping mechanisms which are

continually exchanging information. This condition

derives from theoretical considerations– a dangerous

way to draw conclusions about biology. So, I am taking

a chance to put forward this hypothesis: there ought

to be several independent time-keeping mechanisms in

the bodies of complex organisms like the human ani-

mal, and there is continual cross-talk by which they are

able to establish consensus, and reset the readout if one

clock should differ substantially from the others.

Evolutionary history has installed a fail-safe sys-

tem in most higher animals to ensure that death hap-

pens on a (flexibly adaptive) schedule. The fast-acting

force of individual selection seeks always to defeat the

imperative of obligate death, and thus the death pro-

gram is deeply embedded with alternative pathways so

that it cannot easily be mutated away. This argument

was made with different emphasis in George Williams’s

influential antagonistic pleiotropy paper of 1957 [2].

We have identified a central clock in the neuroen-

docrine center of the brain, the suprachiasmatic nucle-

us, and we have found a probable second clock in the

epigenetic state of dispersed cells around the body [72].

Horvath [73] has developed a pan-tissue methylation

clock, and has measured differences in aging rates in

different organs. Epigenetic changes in stem cells may

deserve the status of a separate clock [74]. Telomere

length in stem cells may constitute another clock [75];

MITTELDORF362

BIOCHEMISTRY (Moscow) Vol. 89 No. 2 2024

a stem cell loses some of its telomere each time that it

divides, and when telomeres become critically short,

itbecomes a senescent cell, secreting cytokines that not

only cause systemic inflammation but also can trigger

senescence in nearby cells. The immune system ap-

pears to have its own aging schedule [76]; and perhaps

oxidative status of the dispersed mitochondria consti-

tutes a sixth clock [77,  78]. A new possibility is raised

by the research from Michael Levin’s Tufts University

laboratory [79]. Levin has demonstrated a role for elec-

trical patterns in morphogenesis, healing, and regenera-

tion. His model for cancer is not genetic; rather he has

demonstrated that he can create tumors from normal

genomes by disrupting their electrical connection to

one another, and he can cure cancer in highly-mutated

tumors without killing the cells, merely by restoring the

cells’ electrical connectivity [80]. Levin has speculated

that electrical patterning is lost with age on the ground

that many vertebrates are able to regenerate limbs or

parts of limbs early in life, and gradually lose this ca-

pacity as they mature [81]. We should not be surprised

to discover other independent clocks.

If my hypothesis is correct, then all these clocks are

exchanging signals, cross-checking to establish a con-

sensus age, and resetting accordingly. How do the vari-

ous clocks exchange information? Given developments

of the recent past [40,  82,  83], the obvious place to look

is in exosomes carried in the blood  [84]. This reason-

ing leads to the inference that exchanging young blood

plasma for old ought to be a robust anti-aging strategy.

Of course, this work commenced nearly two de-

cades ago. Experiments in heterochronic parabiosis

[85,  86] have provided a proof of principle that rejuve-

nation through blood exchange is feasible. The Conboys

have gone on to promote a perspective in which old

age is established affirmatively by molecular species

in the blood of old animals. Katcher and Wyss-Coray

and Wagers have promoted the opposite perspective,

that senescence is linked to a dearth of youthful sig-

nals in the blood of older animals. I think it likely that

the most effective strategies for rejuvenation will in-

volve both removal of pro-aging factors and addition

of anti-aging factors to the blood plasma.

Wyss-Coray has championed therapeutic plasma

exchange as a treatment for Alzheimer’s disease [87].

Human umbilical plasma has been used to rejuvenate

mice [88]. Wyss-Coray’s company, Alkahest, has been

acquired by the Spanish giant, Grifols, which has been

a major player in plasmapheresis therapies. Research

in the Conboy lab has centered on enhanced rate of

healing in old tissues exposed to young plasma. Their

results convince them that it is nothing in young plas-

ma that makes the difference but rather the absence of

inhibiting factors in old plasma. Simply removing plas-

ma from an older animal and replacing it with saline

solution plus albumin was shown to enhance rates of

healing [89]. In support of this strategy, blood donation

is reported to extend life expectancy in humans [90].

To test this idea in therapeutic practice, the Conboys

have allied with Dr.  Dobri  Kiprov to conduct a clini-

cal trial of blood dilution  [91]. Other American clinics

experimenting with therapeutic plasma exchange in-

clude the Apeiron Center (Austin, TX) and Maxwell

Clinic (Brentwood, TN). The experiments of Harold

Katcher  [39] have demonstrated clock rejuvenation

and life extension in rats using young exosomes.

OTHER CLOCK IDEAS

If methylation is one of the body’s methods of

controlling gene expression, then gene expression it-

self might be a more direct measure of changes with

age. This suggests a clock based on the proteome. Com-

pared to a methylation clock a proteomic clock is one

step closer to metabolism. Thus, it should be easier to

separate type (1) from type(2) [92].

The technology of proteome measurement is not

yet as well developed as methylation measurement.

But the proteome is more easily understood in terms

of its metabolic effects; thus, it should be possible

to separate type (1) from type  (2) changes based on

known physiology. The first proteomic clock [92] is not

yet as tightly correlated with age as the best methyla-

tion clocks, and it is far more expensive, but this is to

be expected in the early stage of development.

The Conboy team has proposed a methylation

clock that is not based on methylation values (betas)

that change with age, but on dysregulation of methyl-

ation at CpGs that, on average, remain constant over

a lifetime [42]. They find that the noise in these betas

increases monotonically with age, where “noise” is de-

fined as standard deviation in the absolute difference

from the mean. It is not clear from their published

description of their algorithm whether the standard

deviation is computed within a population, and if so,

how the algorithm could be applied to measure age of

an individual.

LOOKING TO THE FUTURE

In the last decade, epigenetic clocks have opened

the door to testing anti-aging interventions in humans

on a short 1-2-year time scale. This is a major method-

ological breakthrough, but there are signs that exist-

ing clocks are not giving us a full and accurate report.

I offer observations herein in service to the research

effort in improving measurement of the body’s age

state and, potentially, modifying the signaling by which

the body elicits coordinated, age-appropriate respons-

es systemically.

BIOLOGICAL CLOCKS 363

BIOCHEMISTRY (Moscow) Vol. 89 No. 2 2024

Funding. This work was carried out by the author

with no external funding.

Ethics declarations. This work does not contain

any studies involving human and animal subjects.

The author of this work declares that he has no con-

flicts of interest.

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