November 18, 2024 • 52 mins
Two certainties are death and taxes; a third is that people will work hard to avoid them both. But why is it so difficult to extend our lifespan? We know how to do it in worms and mice; why is it tricky in humans? Why do so few companies study longevity? What does the near future hold? What would it be like if everyone lived a much longer life? Join Eagleman this week with longevity expert Martin Borch Jensen to discuss the hopes and challenges of longevity science.
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Episode Transcript
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Speaker 1 (00:05):
We know so much about biology nowadays, so why is
it currently so difficult to figure out how to make
a person live longer? I mean, we know how to
do this in worms and often in mice, but why
is the study of extending lifespans so hard in humans?
(00:25):
Why are there so few companies studying this? And what
is the future of longevity research and what would it
be like to live a much longer life. Welcome to
Inner Cosmos with me David Eagleman. I'm a neuroscientist and
an author at Stanford, and in these episodes we sail
(00:46):
deeply into our three pound universe to uncover some of
the most surprising aspects of our lives. Today's episode reaches
beyond neurobiology to our biology more generally and specifically about
(01:09):
whether we could live a lot longer if we just
understood which of the billions of tiny molecular signals in
a cell mattered for the aging process, and which ones
we could grab a hold of and tweak, and how
the whole network might shift in a way that keeps
everything young and optimized. It's often said that the only
(01:33):
two certainties in life are death and taxes, but it
turns out the third certainty is that people will put
in a lot of work to avoid those two things.
So today's episode is about the endeavor of living longer.
This is not about immortality, in other words, never dying. Instead,
(01:54):
this is about longevity, increasing your lifespan. So to introduce
today's topic, I'm going to read a short story that
I wrote some years ago and originally read on BBC Radio.
I have been asked to speak here at the funeral
of a one hundred and twenty two year old. As
(02:14):
many of you know, I have been asked to speak
not only because of my expertise in the history of aging,
but also because she is a distant relative. She is
my great great great great granddaughter. Her death is tragic
to us, not only because she has died so young,
(02:35):
but because she was haunted her whole life by an
incurable blood disorder and was well aware that she would
not have a long life. I have been asked by
her parents to perhaps ease the tragedy of her young
death by thinking back to the time when a life
span of one hundred and twenty two years was not
(02:56):
considered short but unusually long. At that time, not so
long ago, it was not typical for people to earn
multiple PhDs or to be citizens of multiple countries in
their brief twinklings of a lifetime. They tended to mature faster,
(03:17):
moving out of the house in their teens or twenties,
having families of their own by their thirties, retiring in
their sixties, and dying shortly thereafter, with only enough time
for one career and one family. All things ran on
an accelerated schedule. In each generation, they had to relearn politics.
(03:40):
They had not experienced the patterns before. Everything seemed new,
every message of progress seemed inspired. Of course, this had
one advantage. People did not burn with vendettas that belonged
to distant times. Instead, they died quick, and their children
(04:01):
held different attitudes. Politics could take sharper turns. We all
know what it's like to spot a forgotten lover a
century later, but imagine what it was like to never
achieve any meaningful temporal distance from your past choices. Everything
you've done is there at your heels to haunt you. Obviously,
(04:25):
our mistakes make life educational, but as we know, getting
second chances allows us to endure it. People with rapid
life spans felt quite close to their recent ancestors because
they only had four of them alive at best. We
typically have five hundred and twelve great great great great
(04:48):
great grandparents alive, enough to fill a good sized lecture hall. Therefore,
relationships sink to genetic interest rather than emotional salience based
on scarcity. For those with only a handful of decades,
the idea of staying with one partner throughout life was
something to be devoutly hoped for, and people would declare
(05:12):
that as part of their wedding vows. In modern times,
we know of several couples who have celebrated three hundred
year anniversaries, but it's obviously much more common to enjoy
dozens of marriages. As we mourn this loss today, we
should remember that those with a scarcity of years had
no shortage of one thing novelty. For the rest of us.
(05:37):
Time speeds up as we grow older. At my age,
decades pass like summers. We have seen life's patterns many times.
We have traveled, we have married, we have wrangled, made peace,
failed friends, impressed strangers, seen new groups rise and dwindle,
(06:00):
sought new narratives about our abilities and our purpose. But
in the end, as things become less new, we write
down fewer memories, unique experiences become increasingly scarce, And so
as we mourn this life cut short here today we
(06:21):
can be thankful that for her, at least everything remained novel.
She did not drown in the ocean of time. Every
day she continued to find new seashells at the edges
of its low lying shores. Okay, so that was my
(06:41):
short story about the social changes that would follow if
we were to succeed at longevity science. How would that
change our lives? Our decisions are traditions, how we spend
our time. But today I want to ask, is longevity possible?
Where are we in terms of the science. When you
(07:03):
look at life expectancy at birth, let's say two hundred
years ago, you find that it was about twenty nine
years old. At the beginning of the nineteenth century, even
the healthiest countries in the world had a life expectancy
no longer than forty years. Now in the twenty first century,
the world average is seventy three years. That's a huge
(07:26):
difference in just two hundred years. Is it due to
miraculous medical advances? Well, not exactly. Largely it's due to
simple advances like being able to control diarrhea and vomiting
and having antibiotics, and also on the simple public health
initiatives like sanitation and clean water and managing sewage. These
(07:50):
are the steps that have had massive impact in the
expected lifespan. So we've seen astonishing progress. But keep in
mind what we're talking about there is the average life span.
We prevented people from dying young, and that cranked the
average up and up, But the longest life spans, which
(08:12):
are influenced by biological aging processes, they haven't really changed.
People aren't generally growing older. In other words, a thousand
years ago you could have lived until eighty five, and
now you can live until eighty five. Despite all our advances,
there seems to be an upper limit biologically in the
(08:33):
low one hundreds. So at the moment we haven't yet
found ways to significantly slow or reverse aging itself. There
seems to be a biological sealing to human life, and
this is presumably set by genetics and properties of ourselves
that limit how long the human body can sustain itself.
(08:54):
This is despite all the improvements in health and medical care.
But on the lip side, we're living in a time
where there are a lot of studies with worms, with mice,
occasion with dogs or monkeys that seem to indicate there
may be molecular solutions or at least helpers to the problem.
If you follow the field or simply scan the news headlines,
(09:17):
you'll see articles about drugs like rapamycin, or chemicals like restrol,
or approaches like caloric restriction, and what you'll see is
that in animal studies these seem to increase lifespan. So
the question is should you be taking these drugs and
changing your diet. Do the studies translate over to humans?
(09:39):
And what dose should you go for or how many
calories should you restrict?
Speaker 2 (09:45):
Well, these are.
Speaker 1 (09:45):
Questions that short news articles tend to skate over the
complexity of and so to really understand the big picture,
we need to talk to experts in the field of longevity,
and that brings me to Martin borsch Jensen, a research
who takes the position that if we can really understand
the biological mechanisms of aging, that gives us our highest
(10:08):
leverage for alleviating human disease. Martin got his PhD at
the University of Copenhagen in aging research, then did a
postdoc at the Buck Institute before becoming a biotech entrepreneur.
He became the co founder and chief scientific officer at
Gordian Biotechnology, which is a company that puts the study
(10:29):
of longevity at the center. So here's my interview with Martin.
So what is aging biologically?
Speaker 2 (10:42):
That is an unanswered question by the field of aging biology, right,
So I think that's one of the core things here
that nobody can tell you, Like, here is exactly how
this works. You could sort of compare it to like
what is society or civilization, what is the economy. It's
sort of the complex thing where we know a lot
of components and we know some key factors. But let's
(11:08):
try to do our best here and say aging is
some set of changes that happen in your body biologically
that worsen your capacity for your body to restore itself
to homeostasis is the technical term, but basically, like your
body is in a certain state, and then the essence
of life is that when something gets it out of
(11:30):
that state, it sort of restores itself. Right, So if
you drink too much, you have a hangover, your liver
is going to work and so forth, and then you're
going to be fine again. If you get influenza, you
have an immune system that's designed to kill all of
those viruses and then you're going to be fine again.
And so that's sort of the return to homeostasis. And
I would say aging is a set of changes that
happen that lower your ability to like return to homeostasis,
(11:54):
and then over time that combines with everything that you're
going through in life to lead to you know, your
organs failing in different ways and you get weaker, and
you get slower and like all of those kinds of things.
So a set of processes that lead to all of
the bad things.
Speaker 1 (12:11):
And I want to heard you say that if we
were all twenty eight years old, we wouldn't have all
these departments in academic research institutes and hospitals and so on,
because we would just need to address a few things.
I think you mentioned testicular cancer a few other things,
but we wouldn't need giant cancer departments MD Anderson and
(12:32):
so on, because these are all things that come along
with aging.
Speaker 2 (12:36):
Yeah, Like if you just look at the incident's rate
of a lot of different diseases, you know, heart failure,
how many people do you know had a heart attack
like above age fifty, and how many people you know
that had a heart attack was like below age fifty
or below age thirty, right, and probably your anecdotal evidence
they are sort of aligned with just the stats we have,
(12:59):
which is, you know, like the rate goes up one
thousandfold or ten thousandfold or something like that, Right, And so, yeah,
when you are young, there's just a few things that
affect you, and when you're old, there's a lot of
things that affect you. And US healthcare spend is for
trillion ish and like the majority of that fraction is
for you know, like people who have age related diseases,
(13:23):
often multiple age related diseases.
Speaker 1 (13:26):
Now, when people go about trying to research this, you know,
it's limited in some way by the technologies that we have.
And so what people have done over let's call it
the last twenty years, is they look for particular biomarkers.
So tell us about that and how it's evolved in
the last couple of decades and what you're doing leading
to what you're doing at Gordian.
Speaker 2 (13:47):
Yeah. Absolutely, So the field of like, hey, we can
molecularly sort of study and manipulate aging is really only
about three decades old. In the early nineties, there were
some studies where our researchers changed a single gene and
some worms and it made the worms live twice as long.
And so before that it's like, well, what is aging
(14:07):
and can we really change it? And so forth. But
this shows that there is a way to regulate the
aging process that's already innate in sort of the way
our biology functions, and so that's cool. And so in
a worm, it lives three weeks after you extended the lifespan, right,
so you can you can do studies where you're just like,
does it live longer? Is it healthier as you get
(14:28):
closer and closer to humans? That's sort of impractical, right, Like,
aging by definition takes a lifetime, and so you don't
want to do forty year studies all the time. And
so people have done a couple of things in response
to that. One is like, find some area of biology
that like, this seems important. This accumulation of these sinesse
and cells or these particular changes in your body seems important.
(14:50):
We're just going to study them and then sort of
like trust that they are important for the aging process.
The other thing people have done is try to find.
They're usually called like aging clocks, and so it's something
that you could measure that when you look at it
over time, it changes with age, and so you can
use whatever the measurement is to tell you sort of
(15:12):
like how far along is this person? Right, If we
do an analogy to a company, maybe it's like you
try to schedule a meeting with someone and whether they
can take the meeting, and like it's a young startup,
you can take the meeting in the next forty five minutes,
and if it's like a giant one hundred thousand person company,
it's going to take you a month and a half
to So that's like a marker of the aging of
(15:34):
you know, in this case the company. So we have those,
we have multiple of those for sort of human aging.
We don't yet have one where that when it responds,
that's actually our aging changing versus it's measuring something that
happens alongside aging. But it doesn't it doesn't necessarily mean
you're better off if the number goes down. So that's
(15:56):
sort of the main limitation here.
Speaker 1 (15:58):
As in I do something so that at this large company,
I can get a meeting quickly, but it doesn't change
the age of the company. It's still an old company,
even though I did some tweak to make the meeting
happen right, Like you.
Speaker 2 (16:10):
Could set up so that like every calendar in that
company must auto accept a meeting immediately. However, the company's
ability to actually act on what happens in the meeting
is not changed. The amount of bureaucracy is the same.
And so you could optimize that one all you want,
but it doesn't really do what you wanted to do.
Speaker 1 (16:30):
So the point here is that even if you do
something that changes these biomarkers, your cell might be just
as old and you know, having all the ills of senescence,
even though you think, hey, I fixed it.
Speaker 2 (16:44):
Yeah. Like, let's say, for example, a lot of these
are blood tests, right, so they would measure stuff in
immune cells, primarily because those are the ones circling around
your blood. And so I imagine if any your immune
cells will go and then when you have an infection,
they'll divide and proliferate and create more that's target disinfection
and so forth. Imagine that what a given aging clock
measures is actually how many times has this cell divided
(17:08):
That will tend to go up as you age, but whatever,
like TikTok tracks that, it's not the whole aging process.
There's all this other stuff going on in all these
other organs. And so I think that's the key thing
that sort of currently we need to address with these
kinds of aging clocks before we can really reliably use
them to accelerate the research, is how specific are they
(17:32):
for predicting the changes that we want to use them for.
Speaker 1 (17:37):
And I know that like everything in biology, you know,
what we're looking at are these vast networks that bankrupt
our language. They're so complex, and so what we've been
doing over the past decades is looking for little places
in the network where we say, aha, this is maybe
the sign that we need to read right, But what's
(17:59):
the what's your approach to this?
Speaker 2 (18:01):
Which I think is very clever. Yeah, absolutely so I
spent you know, after this sort of existentialist space, I
went into academic research. I did a PhD at an
aging institute, and I did postocical research and sort of
heading on the academic track to be a professor, all
like trying to figure out how does this work? You know,
because like one approach to fixing a system, let's say
(18:23):
a car or whatever is like, okay, well I understand
what each piece does, and I can see where the
broken thing is, and then I go in and I
like fix that broken thing. So that's one approach which
makes sense if you can like get to an understanding
in your lifetime, and so if you sort of capitulate
on that, which is honestly what I did. It's like
there's so much biology, like we don't even know what
(18:44):
we don't know yet, right, how do you find a
solution if you don't have an understanding of the whole.
And that's why the company I started with my co
found of Francisco, it's called Gordian after the sort of
legend of the Gordian nod where you try to untangle
it but it's so complicated you can't untangle it, and
then Alexander the then not yet Great, decides to cut
(19:06):
through it instead and find an elegant solution that questions
the assumptions. And so the biology version of that for
Gordian is, you know, I had a knowledge of how
complex everything was, and I knew that like, yeah, we
don't actually know what aging is. I can't give you.
Here's an accelerated version of aging that captures all the
important parts, because we don't know all the important parts.
(19:29):
And so if we want to find treatments that work
for different age related diseases. And this is really important because,
like we were talking about before, you know, most if
you just look up like top ten causes of death
in the US, seven of them, aging is the number
one risk factor and the eighth one like diabetes. It's
number two after diet, right, And if you just look
(19:50):
at like our spend, what are the things that we
can't cure, what are the things we're worried about, It's
like not tuberculosis anymore, we did good, but it's all
these age related diseases. And so so we've put a
lot of money into trying to treat these diseases and
find effective medicines, but we haven't succeeded in at scale yet.
Maybe that's because these diseases, as we talked about before,
(20:12):
they manifest in old people, and so some of these
physiological changes that are happening with aging are critical for
the disease, like being able to manifest and not being
just like repaired. So if we do all of our
drudge discovery in organisms that are young Let's say, you know,
like some model organism in the lab that is young
(20:34):
and we've sort of engineered it to get the disease.
Let's take Alzheimer's. People often like have these animals that
have like expressed the mutant proteins involved in the disease.
Speaker 1 (20:43):
These are mice, for example you're talking about.
Speaker 2 (20:46):
Yeah, exactly, And so we take these mice and we say, oh,
let's find every mutation that like happens in Alzheimer's patient
and throw all of them at the mice, and then
it's going to get Alzheimer's really quickly. But it's not
really Alzheimer's. It's like one specific dysfunction, and then we
can fix that. Like we have good drugs to treat
that in mice. We don't have good drugs to treat
Alzheimer's in people because it's probably more complicated.
Speaker 1 (21:08):
Because the rest of the adult human has five million
other biological issues going on at the same time. So
those mutations in the giant network that's happening, you get
different results then you do in a mouse who is
otherwise young and perfect but has these mutations.
Speaker 2 (21:26):
Yeah, that's right. You know, like you get a flat tire,
you know, driving to work and it's like it's fine,
I can deal with this, right, but like you get
a flat tire, like driving to the hospital with your
kid after you lost your job, it's like you can't.
You don't have the capacity to deal with this thing anymore.
That's probably a lot of what happens in these age
related diseases that like things aren't working as well, so
you don't have the capacity to compensate for whatever is
(21:49):
going wrong. Here's where we are. We've studied these diseases.
We've forced ourselves to like use these simplified models of
the disease because it's just not we didn't have a
practical way of trying a lot of things otherwise, and
then we failed. And so Gordian says, well, what if
we had a way to go into the most realistic
environment for the patient. What if we could instead of
(22:11):
like making this disease in an animal model in this
accelerated way, what if we could find an animal that
has the same disease as the human and developed it
over a long period of time the same way that
humans do. And then we could test a lot of
things there. And so for example, we could work with
you know, for ostereothritis, which is one of the diseases
(22:34):
that we're working on. A lot of people use have
these young mice and you do surgical injury and whatever.
We can find a horse that got ostereothritis from like
running around and living life, and then we can study
we can go in and see can we treat the
osterothritis in this context, and it's much more like a
(22:54):
human it's like an avatar for the human patient. Now,
anyone could do that, like anyone can go find a
whorse somewhere on a farm and then do this study.
But the way that most studies are done, you have
these large groups of animals with treatment A and large
group of animal with treatment B, so that you can
compare across the biological variability of the individual animals and stuff.
(23:17):
That's just impractical. It's plausible, but it's like prohibitively expensive
and like logistically challenging. And so Gordon started with the
idea that like, we can actually do this if we
invent a way to test hundreds of treatments in a
single animal, and so that's the core of the company.
We can go into the most predictive system for is
(23:38):
this going to cure disease X and a patient, and
then we can test a lot of things there, so
we don't have to be very smart. I try to
design it so that I can be not very smart
and still succeed. And so the way we do that
there's a lot of like cool biotechnology, and we have
these like re engineered viruses that can deliver therapies to
(23:58):
individual cells of the organ, and then we can like
pull those cells out and measure the activity of every
gene in that cell and then like predict, Okay, what
does this these gene changes mean for physiology. So there's
a lot of sort of hard stuff in actually making
that work, but the core of it is find the
(24:18):
thing that will actually predict the outcome you want, not
something that you sort of hope would predict the outcome
you want, but it's kind of very indirect, and then
do a lot of research there. And so we're using
that to find new treatments across four different diseases of aging,
heart failure, and fibrosis and osteothritis.
Speaker 1 (24:53):
So the key is you're looking at lots of things
at once instead of saying, hey, here's our single measure
that we're from these horses.
Speaker 2 (25:01):
Yeah, there's two things there. We're looking at lots of
different potential treatments, right, and so it's not like three
decades of research suggests that this is the right target
to treat this disease, which is often how it happens, right,
and like what we've had to do and what I
was just too impatient to stay in academia and do. Right, Instead,
we say like, well, let's test this like one hundred
(25:24):
different hypotheses all in the same animal. And then the
test is, yeah, measuring lots of things at once to
see what is the overall like state of this cell.
It was in a tissue that had let's say, al tereothritis.
There was a chondrocyite, which are the cells in your
like cartilage of your joints, and it's in this tissue
(25:44):
that has all the bad stuff going on. It's got
maybe some metabolic shifts that happen with age. There's an
immune system and all this stuff. And if we turn
this take this one genetic target, and turn it up
or down, does it now resemble a chondracite that is
in like a healthy whorese or has it changed in
its behavior to produce more cartilage which is the problem.
(26:05):
So we can look at the full sort of state
of the cell and we can say, you know, is
this more? Is this the state that we want to
go to in a way that doesn't require us to
make a single hypothesis around this is how the disease works.
So neither on the like, how do we try to
poke it? Or on the like is this better? Do
we rely on just like a single thing and like
(26:27):
this is the way of the disease because that's risky,
Like we've done that, you know neuroscience very well.
Speaker 1 (26:33):
Yes, yes, you know that's the way that biology is
having to move because we've spent so long looking at
individual pieces of very complicated networks, and we've seen the
ways in which that doesn't get us the answer we want.
Let me ask you a more general question, because you're
an expert on aging research. More generally, what do you
see in human longevity in terms of you know, people
(26:58):
are doing intermittent fasting, chloric restriction, and there are drugs
like risveritral and others that everyone's very interested in. What's
your view of the field as it stands right now?
Speaker 2 (27:08):
The key thing that we're missing is that measurement, right
and so if we had something that we had really validated,
like and bivalidated, I mean something along the lines of like, Okay,
I'm I'm postulating that this thing will predict whether you,
like you're risk of getting any disease, let's say, or
your risk of like dying before a certain age. So
(27:31):
I'm postulating that this is this measurement, your DNA methilation
clock or whatever like predicts that. So then we should
test it, right, you should put a bunch of things
in at least in mice right that we know will
extend miles lifespan, and then a bunch of other things
that like we know won't and then see just how
accurate are your predictions. If we have something like that
(27:51):
where we're like, oh yeah, this like has great accuracy.
When this thing moves per our previous conversation, that means
you're healthier, then we would have a much better sense
of like, Okay, is this spiritual thing working? Is this
repemison thing working, you know, like it's the fasting doing something.
There are a lot of sort of attempts to like
(28:12):
people who have this vision that like, look, we should
really get away from what is currently called healthcare, but
it's more like sick care, right, like wait until you
get disease and then get drugs for that disease. Towards
are more like can we just like measure your overall
health and your you know, homeostatic capacity or biological age,
(28:34):
whatever we want to call it, and try to prevent
you from having these diseases in the first place. That certainly,
if we can do that, well, that's a much better approach.
It's cheaper, like answer prevention, right, And the tricky part
of doing that is knowing the future, right, So we
need to build these tools that allow us to know
the future because currently you know. The other way with
(28:54):
that we do that is sort of controlled clinical trials
where you just like run the experiment and you have
a bunch of people and then you randomize them to
different groups, treat one and not the other one, and
then we see do we get the outcome that we want?
But doing that for aging it both you know, like
if you're doing that for like do you live or die?
It takes a long time. It's fairly expensive, and in
(29:16):
the US it's not obvious like is that something insurance
covers kind of should be, but like we haven't really
figured it out, So there's some financial risk involved there.
Speaker 1 (29:26):
What's your intuition if you're even looking retrospectively. I don't
know if there are groups of people who have had
rest for all their whole lives and others that haven't,
or whatever.
Speaker 2 (29:35):
But when you.
Speaker 1 (29:36):
Look at the data and look at the whole picture,
what do you feel is the thing that maybe you
would do that might be useful?
Speaker 2 (29:45):
Yeah, I think there's if you look at that. And
then the other thing you look at is like all
the animal studies, right, so like what actually makes a
mouse live longer? And the answer is calarge restriction of
some sort, so limiting the number of calories or are reprimicin,
which is a drug that's used as an immune suppress
(30:05):
and it's FDA approved at a high dose. So at
high dose it reduce prevent your immune system, it reduces activity,
and so we use it for like organ transplants. That's
not going to make you live longer suppressing your immune system, right,
but so at a much lower dose in mice. There's
some recent data in monkeys, and there's a trial on
going in dogs called Triad, where people are giving repromised
(30:28):
into dogs and see if they live longer. Repromizon has
the strongest data Other than this like eating regimen thing,
especially for kilored restriction, there's a caveat of like, well,
how much restriction and how much restriction once you multiply
that by like how much do you exercise? Right, Because
it's like in the lab mice that generally are in
(30:48):
cages and they have ad lib food and so forth,
you know you'll get a certain response, but then when
people are very different, they don't have the same genetics.
What is the right amount of restriction? There probably is
some amount, I mean there's for everyone. There's definitionly some
right amount. For probably a lot of people. It's probably
lower than where we're currently at, but it's it can
be you know, like there's a U curve. You just
(31:10):
eat less and less, it's definitely going to be bad
for you. So that's where we're back to, like needing
a measurement. Rappromizin is the other one, but similarly like
two higher dose is bad and so finding out like
what is the right dosing here where there's actually a
benefit that feels tricky. The nonprofit I started has funded
some clinical trials of rappromizing, like early stage trial Phase
(31:32):
one trials for different This one for like a reproductive health,
there's one for oral.
Speaker 1 (31:37):
Health, and this is in humans.
Speaker 2 (31:39):
That's in humans. Yeah, so there's four different trials that
we funded, and I think there's three others, and.
Speaker 1 (31:45):
So you won't know the longevity piece for several decades.
Speaker 2 (31:50):
None of those will give us the longevity piece, right,
So I think at that point, you're looking at this
dose seems safe and has a beneficial effect on this potentially,
and so if we then extrapolate like a broader health
benefit from the animal studies, your like expected value of
this might go to positive, but it's also positible, like
(32:13):
it's still it's still unclear. Yeah, So so those are
the two with the strongest data. Then there's some other things.
You know, mid Foreman, which is a diabetes drug. There's
a lot of push for that, you know, like we
should do a trial here in humans. There's a not
yet started but sort of like they're trying to fundraise
for it, thing called tame Game. And the mid form
(32:35):
of data it's more sort of observational what you said, like, Okay,
well we've given this to diabetics, so we have like
tens of millions of person years of data. What that
certainly tells us is like, this is not super dangerous.
What it may tell us is like, oh, there might
be lower overall cancer risk. But it's hard with these
like retrospective studies because you always have to be sure
(32:56):
that like the people that we looked at was there's
some unintended way that we selected for certain people that
already had a lower risk of having cancer diagnosis and
compared them to another one. So there's big caveats. That's
why we do these trials. But there's some sort of
human stuff the animal studies are less clear on, like
mid Foreman doing a whole lot, but might still be
(33:19):
beneficial and probably a safe.
Speaker 1 (33:21):
What do you do personally? Do you take med forman
or wrap am icin.
Speaker 2 (33:26):
I don't take rap of icein, Yeah, because I'd rather
see rather than somebody else find the safe dose, right,
I mean the basic stuff you already know. Right, It's
just like you should exercise, you should sleep, you should
like eat less bacon and more vegetables, definitely eat less sugar. Right,
So like most people is boring. I think like optimizing
for the longevity hack right now, you'll get a bit
(33:49):
but like the delta between I'm a hardcore like longevity
biohacker and I'm just like generally healthy. Maybe it's three
years or something. Like. I think the much more important
for the field is, like, let's put our effort into
doing these technological advancements that allow us to measure things,
that allow us to test more things at once, and
(34:11):
like really try to solve because like we know there
is potential. We know that like you or I could
have a kid that's like zero years old, so like
one cell in your body has the capacity to create
a functioning, completely non age system, right, And we also
know that there's animals that live much longer than humans
and there's great variability and like how long different animals live,
(34:32):
and we know that we can like you know, engineer
genetic engineer, there's worm or there's mouse to live longer.
So we know it's malleable. We know it's doable, and
it's possible to have like a big effect. I don't
know how hard it is, Like I don't know if
we like fifteen years we'll have some tremendous results with
partial reprogramming or something, or if it's like it's actually
much harder it'll take longer, right, sort of like AI,
(34:54):
it's like it's just around the corner and that suddenly
it takes off.
Speaker 1 (34:58):
So if we were to fast forward to twenty five
years from now, where do you think the longevity field
is going to be.
Speaker 2 (35:04):
Where I hope it would be is sort of like
rigorous testing of aging clocks happening in the next couple
of years probably will reveal many things to be fixed,
and then like deliberate efforts to create these sort of
like biomarkers, and then I think that's very doable on
like a five year horizon with concerted effort. Not guarantee
(35:27):
that that would happen, but there are different actors that
could like make that happen, such as the Altos lab
startups or our PAH in the US, or evolution in
Saudi Arabia. So that's one or even you know, frankly
like private philanthropists, like this is sort of like a
seed stage startup level effort or maybe a bit more.
So that's one. We definitely need that. Then we need
(35:49):
to figure out, Okay, what is the clinical trial we're
going to run? Are we going to try with the
current round of things like wrapamycin or synelytics. People are
excited about them. At foreman got to pick the right
thing or do a portfolio and then just start to
run those trials, and then I expect, I mean, this
is what happened in drug discovery in general. You try
to run the trial and then you find out some
of my endpoints, like we're not sensitive and off whatever,
(36:12):
so we could run we could do another round of trials.
So I think those things should happen. That's sort of
the ecosystem. And then at the same time people are
trying to do drug discovery for start getting different mechanisms
of aging. So if we did things in the right sequence,
and this is sort of like okay and prayer of
the world, you could like pm it all to like
(36:33):
work out, and then there's reality which relies on all
these intentives and whatever. But like all the companies now
and there's more and more of them that are trying
to find like age specific therapies, those would then have
an outlet to be not just we're going after And
so what happens now is like if I find a
drug for aging. Let's say I'm at Stanford, I'm doing
a bunch of research. I have this cool thing, I
(36:55):
think at least, and then I start a company and
then we raise fifty million dollars going to clinical trials. Bioage.
Just iPod yesterday that actually, yeah, that was from Stanford,
So that sort of a fits the story, right, and
they have clinical trials going. But even though the company
started with like, we're looking at aging, they had the
(37:16):
sort of incentive constraint to like, well, we don't know
how you run an aging trial yet. So everyone is like,
if we want to succeed here, if we want a
good readout for the investors, the patients, and so forth,
we're going to narrow down to a specific disease of aging.
And so you run that trial, which is good, Like
I hope that that treats patients. One could imagine, and again,
(37:37):
if we're being just blue skies ambitious, one can imagine
that the US decides, Look, we're doing this sick care thing.
We have like fourteen different institutes and medical specialties and
all this kind of stuff. But there's clearly biology that
spans those right, Like when your immune system dysfunctions, there's
a whole bunch of diseases. There's inflammation stuff that like
(37:58):
effects a whole bunch of diseases. We should stop doing
these trials where we test for one drug one disease, right,
match one of like thirty thousand things to one of
eight thousand diseases, and then try to do all by
all there you will take infinity time, right. Instead, we
should develop ways, and again this is sort of like
an our page kind of thing. We should develop ways
(38:20):
where when we run one trial, we can take some
blood samples and then we can look for these markers
that imply that the drug might work on a bunch
of other diseases. And so we sort of turn each
trial we run in this country into something that has
like an integrated overall beneficial effect on health or doesn't, right.
(38:41):
And so if you took that approach, you could have
fewer trials that lead us to a portfolio of medicines
that have a bigger overall impact. There's various reasons why
we're not doing that yet. One, we don't have those
measurements right, so there's technological development needed to do it right.
Another one is we probably don't have the perspective right. Like, again,
we used to you have one disease, and like that's
(39:02):
the thing that we the unit of sort of like
medical care. And then another one is the way that
the FDA is set up. It's sort of like nothing
bad can happen, but slowing things down has a secret
cost of like lots of people dying or whatever, but
like nothing bad can happen. And so you imagine if
you measure like is this good for all these different diseases, well,
what if one of them is bad? What if this
(39:24):
drug like will prevent Alzheimer's and heart failure, but it
increases your risk of cancer by ten percent, that might
still be like a good deal for a given human patient.
And you could also further segment down to people who
are not at elevated cancer risk, like this might be
extra good for them. But it's a tough sort of
political decision to say this is you know, we're utilitarian
(39:47):
in our healthcare or whatever that has not been made.
And consequently, for a farmer company of whoever's running the
clinical trial, there's a disincentive to have anything look bad
because that might tank the w whole thing. Even if
like there's you get you know, like twice as much
good stuff but some bad stuff, that's probably bad news
for approval right now.
Speaker 1 (40:23):
So let me ask you something about this issue that
you mentioned about looking at one.
Speaker 2 (40:27):
Disease at a time.
Speaker 1 (40:28):
If you were extrapolating, you know, fifty years from now,
do you think that the names of the medical professionals
will change, so we don't have a neurologist and a
cardiologist and a liver specialist and so on, but what
we have or things that are more general or broad.
Speaker 2 (40:43):
Optimistically, yes, pessimistically I feel like we will add something
new and just sort of like staple in on top
of the old, so you'll still have a neurologist and
so forth.
Speaker 1 (40:54):
Right, if you were in charge, if you were czar
of the hospital system and could say what it should be,
what kind of things would you set up?
Speaker 2 (41:02):
I mean I think it I probably would, you know, Like, yes,
it's not like throw away all the old, right, Like
you still have a heart, and like if we want
to know what's going on, someone who knows a lot
about the heart and can talk in detail about the
exact rhythm of your heart and what that means for
like your champers and stuff like that is a good
thing that we want. Right then, the question is like
(41:22):
what have we what do we want to lump and
what do we want to split?
Speaker 1 (41:26):
Right?
Speaker 2 (41:26):
Like what are the things that we've called different things
but actually they're a similar thing, right, And then what
do we want to split up? I think one thing
for sure we want to split up is like Alzheimer's
and aging. So if you look at the National Institutent
on Aging right now, something like half the budget is
earmarked for Alzheimer's research, which is like brain specific and
just one specific disease, right, And so it's something that
(41:49):
a lot of people are very afraid of, and so
that's why it happened, right, But like it's not it
doesn't make sense that like that's just like lumped in
under aging. So I think the things to me that
strike me as like there is a multi organ thing
going on here for sure, like inflammation immune function. And
so we obviously have immunologists and we have people that
(42:13):
look at like the function of the immune system. But
the overlap between the state of like infections and then
the amount of inflammation you have and like aging and
the amount of different organs like solid tissues and the
amount of inflammation you have, and like the interplay between
those that feels like a clear you know something we're
(42:35):
probably overlooking. I mean, COVID is sort of a big
boost here where people are looking at like, okay, exposure
to this virus, what does it do in the long term,
And a lot of us, especially when we're young, right,
we're used to like, Okay, got cold, you know, immune
system killed cold. We're fine. It's just like it's a
separate class of things. But more and more evidence, like
(42:56):
it does affect your chance of having Alzheimer's, Like you're
amount of bacterial like mouth bacteria correlate well with risk
of Alzheimer's. It seems like, you know, more research, so
I think that's one. And then like if we just
think about like what goes wrong in your tissues, there's
sort of some common things that happen. One is like
(43:17):
an out of control inflammation loop that leads to fibrosis.
So this is sort of scarring of your tissue. And
this will happen in your kidney, in your heart, your liver,
and your lungs. And sometimes we call it like pulmonary
fibrosis or COPD chronic obstructive pulmonary disorder, so like smoker's
lung that's when it's real bad. But the process is
(43:38):
happening in like most of your tissues at all times.
So that's like a thing. What is this particular loop
of something is like misfunctioning in the cells of that
tissue and then it triggers immune infiltration and then that
triggers fibrosis. Like that could be something that people are
specialized in, which is true in academia. There are people
who are like, look at that specifically, right, But medicine
(44:00):
is like different because then you start thinking, okay, we
have this drug. Might this drug actually improve multiple organs, right,
because the same thing is sort of happening, or maybe
like this drug it was approved just for this organ,
but it won't work in this or the organ because
like the biology is a bit different. So those are
some and then there's the whole like why do we
(44:22):
lose cells? So there's a whole bunch of organs in
your body that lose cells. The most shocking one might
be like the thymus, and so the thymus here is
like where your immune cells get told, you know, like
what should you attack and what should you not attack?
And that whole organ is like basically replaced with fat
by age forty, So it's just like you have an
(44:44):
organ that you start out with and it's gone obviously
for women like you're reproductive, organs like you get this
and for men, I don't know if you get it,
but that's one. Then there are other organs, right like
your brain. You lose cells in your brain, you lose
cells in your muscle, all what gets replaced with fat.
So there are some organs where like the way they
fail is that you lose them cells over time and
(45:06):
they don't get restored because that organ the cells don't
really divide, like the cells in your brain very little,
muscle very little. And so that's another area where there's
like something that happens consistently across tissues that isn't covered
by a single medical specialty.
Speaker 1 (45:22):
So let's say we extrapolate a thousand years from now
and all of the mysteries of biology where we're now
sort of at the foot of the mountain. We've summitted
the peak and we're there. We understand the whole network computationally,
it's all worked out. My question is do you think
there are natural limits to how long the human body
can live? Or is longevity something that has no limit
(45:45):
to it?
Speaker 2 (45:46):
If you make no changes whatsoever to the human body.
Then yes, it does seem like there are natural limits, right,
Like few people live over one hundred and fewer still
live more than one hundred and twenty. Right, But that
assumes that we don't change in anything, that we don't
have any technology, right, And so if we have endless
(46:06):
technology and we are willing to change to some degree,
like what is a human body, right, like we're willing
to which we do already, right, So like if you
lose a leg, you get robot leg, right, that's you
have a cyborg now, right, And similar if you have
a pacemaker, it's less visible. And so I think so
far we're okay with some cautious pace of like we
(46:30):
are actually augmenting the human body in order to avoid disease.
Even that a vaccine would fall into this category, right,
And so if you put that in, there's no no,
I don't think that there's any limit to some definition
of the human body. And we can see that because
like the human race exists still and so so many
(46:51):
generations of humans have produced a new body out of
a single cell, out of the same DNA code, right,
Like life happened once and it's still going for many,
many millions of years, right, And so it's not and
we see that, you know, you have a tree that
lived five thousand years. That's a very different kind of body.
But like it's possible to set things up to last
(47:13):
that long.
Speaker 1 (47:14):
And do you see any ethical or philosophical issues about
what that would be to have a lifespan of two hundred,
three hundred and five hundred years.
Speaker 2 (47:25):
Yeah, for sure. I mean I think there's a lot
of stuff, and I think that the not very serious
version of engaging with that is like it's weird and
unnatural and we need to like just not do it.
Because by that argument, if you look at I can
send you like this graph of like lifespan over time.
And so there have been these statements that like, well
(47:45):
it can't go above seventy you can't go so you
have these like horizontal lines and then you just have
like a straight diagonal line just going up since you know,
hundreds of years of like you know, human existence or
human civilization. Right. So anyway, like if you want to
take the stance that like living longer is bad because X,
and there's lots of different X, there's like what if
(48:06):
bad people live longer? What if a tyrant lives longer? Right?
Or like what if we have wealth inequality and then
it gets perpetuated for longer. Some of those are like, well,
you know, like we have inheritance so that it does
anyway or whatever. But like, but even if like that's
a legitimate issue, I find it fundamentally not serious that
your proposal is to kill everyone, right, Like it's it
(48:29):
let pretend that we were we had three hundred year
lifespans and then we found that, like, oh, we have
a lot of wealth inequality because the ones who get
ahead early they accumulate more and more over time. It's like,
let's kill everyone at a young age. It's not a
serious solution to this, Like, let's find a societal solution philosophically.
Speaker 1 (48:49):
What does it mean if we're all living three hundred
and five hundred years? What does what does that mean
for society?
Speaker 2 (48:54):
You know, there are concepts that we are used to
working a certain way that might not work that way.
The most obvious one is retirement, right, So, like retirement
is fundamentally like we recognize that humans are like not
very capable, then become capable, and then are less capable
again in old age, right, And so then we've structured
society so that the ones who are most capable are
(49:15):
supporting the ones who are not capable, including children, including
infirm aged people. Right. And so that's just like the
way things are. And so obviously if the older people, like,
if you just stretch that period of like competency, good
news is that you don't bankrupt medicare, which is what's
going to default happen now, right, But of course it
means like you're not going to retire at sixty or
(49:36):
sixty five or whatever it is. Right, Let's say we
really extended lifespan. You live for like three hundred years.
You might have multiple careers in different areas. You might
have like a work for this long and then go
on like retirement sabbatical thing where you just like go
back to learning a new thing for ten years, and
you're not very useful again because like you used to
(49:57):
be a scholar and now you're going to be an
artist or whatever it is, right, and then you come
back and you do a new thing. It'll be interesting
to see what happens. Right.
Speaker 1 (50:10):
That was my interview with Martin borsch Jensen, who's longevity
researcher and co founder and chief scientific officer of Gordian Biotechnology.
And one of the things this conversation surfaces is why
the science is so difficult. Biology is full of feedback
loops that cause nonlinear responses, and that makes it really
(50:32):
difficult for us to move from simple experimental observations like
the blood level of some molecule type that we measure
in worms or mice, to reliably producing meaningful physiologic changes
in humans, or even knowing what the right changes to
aim for. So how far are we from a world
(50:53):
where anti aging therapies are as routine as vaccinations. It's
not happen anytime soon. But on the other hand, we
now have billions of young brains on this planet getting
great educations, and with the exponential pace of technology and
the increasing pace of medical innovation, maybe we can get there.
(51:16):
Maybe we can not only prevent people from dying young,
as we have done over the past couple centuries, but
also extend a healthy lifespan such that we actually will
someday give heartbroken funereal speeches lamenting a person dying at
the tender young age of one.
Speaker 2 (51:36):
Hundred and twenty two years old.
Speaker 1 (51:38):
Perhaps we will come to understand the giant biochemical puzzle
of ourselves and actually be able to shift around some
of the pieces. Perhaps a few of us listening to
this podcast may just be lucky enough to live to
see that day, and there to see lots of days
(52:02):
past that as well. Go to eagleman dot com slash
podcast for more information and to find further reading. Send
me an email at podcasts at eagleman dot com with
questions or discussion, and check out and subscribe to Inner
Cosmos on YouTube for videos of each episode and to
(52:24):
leave comments until next time. I'm David Eagleman, and this
is Inner Cosmos.
We know so much about biology nowadays, so why is
it currently so difficult to figure out how to make
a person live longer? I mean, we know how to
do this in worms and often in mice, but why
is the study of extending lifespans so hard in humans?
(00:25):
Why are there so few companies studying this? And what
is the future of longevity research and what would it
be like to live a much longer life. Welcome to
Inner Cosmos with me David Eagleman. I'm a neuroscientist and
an author at Stanford, and in these episodes we sail
(00:46):
deeply into our three pound universe to uncover some of
the most surprising aspects of our lives. Today's episode reaches
beyond neurobiology to our biology more generally and specifically about
(01:09):
whether we could live a lot longer if we just
understood which of the billions of tiny molecular signals in
a cell mattered for the aging process, and which ones
we could grab a hold of and tweak, and how
the whole network might shift in a way that keeps
everything young and optimized. It's often said that the only
(01:33):
two certainties in life are death and taxes, but it
turns out the third certainty is that people will put
in a lot of work to avoid those two things.
So today's episode is about the endeavor of living longer.
This is not about immortality, in other words, never dying. Instead,
(01:54):
this is about longevity, increasing your lifespan. So to introduce
today's topic, I'm going to read a short story that
I wrote some years ago and originally read on BBC Radio.
I have been asked to speak here at the funeral
of a one hundred and twenty two year old. As
(02:14):
many of you know, I have been asked to speak
not only because of my expertise in the history of aging,
but also because she is a distant relative. She is
my great great great great granddaughter. Her death is tragic
to us, not only because she has died so young,
(02:35):
but because she was haunted her whole life by an
incurable blood disorder and was well aware that she would
not have a long life. I have been asked by
her parents to perhaps ease the tragedy of her young
death by thinking back to the time when a life
span of one hundred and twenty two years was not
(02:56):
considered short but unusually long. At that time, not so
long ago, it was not typical for people to earn
multiple PhDs or to be citizens of multiple countries in
their brief twinklings of a lifetime. They tended to mature faster,
(03:17):
moving out of the house in their teens or twenties,
having families of their own by their thirties, retiring in
their sixties, and dying shortly thereafter, with only enough time
for one career and one family. All things ran on
an accelerated schedule. In each generation, they had to relearn politics.
(03:40):
They had not experienced the patterns before. Everything seemed new,
every message of progress seemed inspired. Of course, this had
one advantage. People did not burn with vendettas that belonged
to distant times. Instead, they died quick, and their children
(04:01):
held different attitudes. Politics could take sharper turns. We all
know what it's like to spot a forgotten lover a
century later, but imagine what it was like to never
achieve any meaningful temporal distance from your past choices. Everything
you've done is there at your heels to haunt you. Obviously,
(04:25):
our mistakes make life educational, but as we know, getting
second chances allows us to endure it. People with rapid
life spans felt quite close to their recent ancestors because
they only had four of them alive at best. We
typically have five hundred and twelve great great great great
(04:48):
great grandparents alive, enough to fill a good sized lecture hall. Therefore,
relationships sink to genetic interest rather than emotional salience based
on scarcity. For those with only a handful of decades,
the idea of staying with one partner throughout life was
something to be devoutly hoped for, and people would declare
(05:12):
that as part of their wedding vows. In modern times,
we know of several couples who have celebrated three hundred
year anniversaries, but it's obviously much more common to enjoy
dozens of marriages. As we mourn this loss today, we
should remember that those with a scarcity of years had
no shortage of one thing novelty. For the rest of us.
(05:37):
Time speeds up as we grow older. At my age,
decades pass like summers. We have seen life's patterns many times.
We have traveled, we have married, we have wrangled, made peace,
failed friends, impressed strangers, seen new groups rise and dwindle,
(06:00):
sought new narratives about our abilities and our purpose. But
in the end, as things become less new, we write
down fewer memories, unique experiences become increasingly scarce, And so
as we mourn this life cut short here today we
(06:21):
can be thankful that for her, at least everything remained novel.
She did not drown in the ocean of time. Every
day she continued to find new seashells at the edges
of its low lying shores. Okay, so that was my
(06:41):
short story about the social changes that would follow if
we were to succeed at longevity science. How would that
change our lives? Our decisions are traditions, how we spend
our time. But today I want to ask, is longevity possible?
Where are we in terms of the science. When you
(07:03):
look at life expectancy at birth, let's say two hundred
years ago, you find that it was about twenty nine
years old. At the beginning of the nineteenth century, even
the healthiest countries in the world had a life expectancy
no longer than forty years. Now in the twenty first century,
the world average is seventy three years. That's a huge
(07:26):
difference in just two hundred years. Is it due to
miraculous medical advances? Well, not exactly. Largely it's due to
simple advances like being able to control diarrhea and vomiting
and having antibiotics, and also on the simple public health
initiatives like sanitation and clean water and managing sewage. These
(07:50):
are the steps that have had massive impact in the
expected lifespan. So we've seen astonishing progress. But keep in
mind what we're talking about there is the average life span.
We prevented people from dying young, and that cranked the
average up and up, But the longest life spans, which
(08:12):
are influenced by biological aging processes, they haven't really changed.
People aren't generally growing older. In other words, a thousand
years ago you could have lived until eighty five, and
now you can live until eighty five. Despite all our advances,
there seems to be an upper limit biologically in the
(08:33):
low one hundreds. So at the moment we haven't yet
found ways to significantly slow or reverse aging itself. There
seems to be a biological sealing to human life, and
this is presumably set by genetics and properties of ourselves
that limit how long the human body can sustain itself.
(08:54):
This is despite all the improvements in health and medical care.
But on the lip side, we're living in a time
where there are a lot of studies with worms, with mice,
occasion with dogs or monkeys that seem to indicate there
may be molecular solutions or at least helpers to the problem.
If you follow the field or simply scan the news headlines,
(09:17):
you'll see articles about drugs like rapamycin, or chemicals like restrol,
or approaches like caloric restriction, and what you'll see is
that in animal studies these seem to increase lifespan. So
the question is should you be taking these drugs and
changing your diet. Do the studies translate over to humans?
(09:39):
And what dose should you go for or how many
calories should you restrict?
Speaker 2 (09:45):
Well, these are.
Speaker 1 (09:45):
Questions that short news articles tend to skate over the
complexity of and so to really understand the big picture,
we need to talk to experts in the field of longevity,
and that brings me to Martin borsch Jensen, a research
who takes the position that if we can really understand
the biological mechanisms of aging, that gives us our highest
(10:08):
leverage for alleviating human disease. Martin got his PhD at
the University of Copenhagen in aging research, then did a
postdoc at the Buck Institute before becoming a biotech entrepreneur.
He became the co founder and chief scientific officer at
Gordian Biotechnology, which is a company that puts the study
(10:29):
of longevity at the center. So here's my interview with Martin.
So what is aging biologically?
Speaker 2 (10:42):
That is an unanswered question by the field of aging biology, right,
So I think that's one of the core things here
that nobody can tell you, Like, here is exactly how
this works. You could sort of compare it to like
what is society or civilization, what is the economy. It's
sort of the complex thing where we know a lot
of components and we know some key factors. But let's
(11:08):
try to do our best here and say aging is
some set of changes that happen in your body biologically
that worsen your capacity for your body to restore itself
to homeostasis is the technical term, but basically, like your
body is in a certain state, and then the essence
of life is that when something gets it out of
(11:30):
that state, it sort of restores itself. Right, So if
you drink too much, you have a hangover, your liver
is going to work and so forth, and then you're
going to be fine again. If you get influenza, you
have an immune system that's designed to kill all of
those viruses and then you're going to be fine again.
And so that's sort of the return to homeostasis. And
I would say aging is a set of changes that
happen that lower your ability to like return to homeostasis,
(11:54):
and then over time that combines with everything that you're
going through in life to lead to you know, your
organs failing in different ways and you get weaker, and
you get slower and like all of those kinds of things.
So a set of processes that lead to all of
the bad things.
Speaker 1 (12:11):
And I want to heard you say that if we
were all twenty eight years old, we wouldn't have all
these departments in academic research institutes and hospitals and so on,
because we would just need to address a few things.
I think you mentioned testicular cancer a few other things,
but we wouldn't need giant cancer departments MD Anderson and
(12:32):
so on, because these are all things that come along
with aging.
Speaker 2 (12:36):
Yeah, Like if you just look at the incident's rate
of a lot of different diseases, you know, heart failure,
how many people do you know had a heart attack
like above age fifty, and how many people you know
that had a heart attack was like below age fifty
or below age thirty, right, and probably your anecdotal evidence
they are sort of aligned with just the stats we have,
(12:59):
which is, you know, like the rate goes up one
thousandfold or ten thousandfold or something like that, Right, And so, yeah,
when you are young, there's just a few things that
affect you, and when you're old, there's a lot of
things that affect you. And US healthcare spend is for
trillion ish and like the majority of that fraction is
for you know, like people who have age related diseases,
(13:23):
often multiple age related diseases.
Speaker 1 (13:26):
Now, when people go about trying to research this, you know,
it's limited in some way by the technologies that we have.
And so what people have done over let's call it
the last twenty years, is they look for particular biomarkers.
So tell us about that and how it's evolved in
the last couple of decades and what you're doing leading
to what you're doing at Gordian.
Speaker 2 (13:47):
Yeah. Absolutely, So the field of like, hey, we can
molecularly sort of study and manipulate aging is really only
about three decades old. In the early nineties, there were
some studies where our researchers changed a single gene and
some worms and it made the worms live twice as long.
And so before that it's like, well, what is aging
(14:07):
and can we really change it? And so forth. But
this shows that there is a way to regulate the
aging process that's already innate in sort of the way
our biology functions, and so that's cool. And so in
a worm, it lives three weeks after you extended the lifespan, right,
so you can you can do studies where you're just like,
does it live longer? Is it healthier as you get
(14:28):
closer and closer to humans? That's sort of impractical, right, Like,
aging by definition takes a lifetime, and so you don't
want to do forty year studies all the time. And
so people have done a couple of things in response
to that. One is like, find some area of biology
that like, this seems important. This accumulation of these sinesse
and cells or these particular changes in your body seems important.
(14:50):
We're just going to study them and then sort of
like trust that they are important for the aging process.
The other thing people have done is try to find.
They're usually called like aging clocks, and so it's something
that you could measure that when you look at it
over time, it changes with age, and so you can
use whatever the measurement is to tell you sort of
(15:12):
like how far along is this person? Right, If we
do an analogy to a company, maybe it's like you
try to schedule a meeting with someone and whether they
can take the meeting, and like it's a young startup,
you can take the meeting in the next forty five minutes,
and if it's like a giant one hundred thousand person company,
it's going to take you a month and a half
to So that's like a marker of the aging of
(15:34):
you know, in this case the company. So we have those,
we have multiple of those for sort of human aging.
We don't yet have one where that when it responds,
that's actually our aging changing versus it's measuring something that
happens alongside aging. But it doesn't it doesn't necessarily mean
you're better off if the number goes down. So that's
(15:56):
sort of the main limitation here.
Speaker 1 (15:58):
As in I do something so that at this large company,
I can get a meeting quickly, but it doesn't change
the age of the company. It's still an old company,
even though I did some tweak to make the meeting
happen right, Like you.
Speaker 2 (16:10):
Could set up so that like every calendar in that
company must auto accept a meeting immediately. However, the company's
ability to actually act on what happens in the meeting
is not changed. The amount of bureaucracy is the same.
And so you could optimize that one all you want,
but it doesn't really do what you wanted to do.
Speaker 1 (16:30):
So the point here is that even if you do
something that changes these biomarkers, your cell might be just
as old and you know, having all the ills of senescence,
even though you think, hey, I fixed it.
Speaker 2 (16:44):
Yeah. Like, let's say, for example, a lot of these
are blood tests, right, so they would measure stuff in
immune cells, primarily because those are the ones circling around
your blood. And so I imagine if any your immune
cells will go and then when you have an infection,
they'll divide and proliferate and create more that's target disinfection
and so forth. Imagine that what a given aging clock
measures is actually how many times has this cell divided
(17:08):
That will tend to go up as you age, but whatever,
like TikTok tracks that, it's not the whole aging process.
There's all this other stuff going on in all these
other organs. And so I think that's the key thing
that sort of currently we need to address with these
kinds of aging clocks before we can really reliably use
them to accelerate the research, is how specific are they
(17:32):
for predicting the changes that we want to use them for.
Speaker 1 (17:37):
And I know that like everything in biology, you know,
what we're looking at are these vast networks that bankrupt
our language. They're so complex, and so what we've been
doing over the past decades is looking for little places
in the network where we say, aha, this is maybe
the sign that we need to read right, But what's
(17:59):
the what's your approach to this?
Speaker 2 (18:01):
Which I think is very clever. Yeah, absolutely so I
spent you know, after this sort of existentialist space, I
went into academic research. I did a PhD at an
aging institute, and I did postocical research and sort of
heading on the academic track to be a professor, all
like trying to figure out how does this work? You know,
because like one approach to fixing a system, let's say
(18:23):
a car or whatever is like, okay, well I understand
what each piece does, and I can see where the
broken thing is, and then I go in and I
like fix that broken thing. So that's one approach which
makes sense if you can like get to an understanding
in your lifetime, and so if you sort of capitulate
on that, which is honestly what I did. It's like
there's so much biology, like we don't even know what
(18:44):
we don't know yet, right, how do you find a
solution if you don't have an understanding of the whole.
And that's why the company I started with my co
found of Francisco, it's called Gordian after the sort of
legend of the Gordian nod where you try to untangle
it but it's so complicated you can't untangle it, and
then Alexander the then not yet Great, decides to cut
(19:06):
through it instead and find an elegant solution that questions
the assumptions. And so the biology version of that for
Gordian is, you know, I had a knowledge of how
complex everything was, and I knew that like, yeah, we
don't actually know what aging is. I can't give you.
Here's an accelerated version of aging that captures all the
important parts, because we don't know all the important parts.
(19:29):
And so if we want to find treatments that work
for different age related diseases. And this is really important because,
like we were talking about before, you know, most if
you just look up like top ten causes of death
in the US, seven of them, aging is the number
one risk factor and the eighth one like diabetes. It's
number two after diet, right, And if you just look
(19:50):
at like our spend, what are the things that we
can't cure, what are the things we're worried about, It's
like not tuberculosis anymore, we did good, but it's all
these age related diseases. And so so we've put a
lot of money into trying to treat these diseases and
find effective medicines, but we haven't succeeded in at scale yet.
Maybe that's because these diseases, as we talked about before,
(20:12):
they manifest in old people, and so some of these
physiological changes that are happening with aging are critical for
the disease, like being able to manifest and not being
just like repaired. So if we do all of our
drudge discovery in organisms that are young Let's say, you know,
like some model organism in the lab that is young
(20:34):
and we've sort of engineered it to get the disease.
Let's take Alzheimer's. People often like have these animals that
have like expressed the mutant proteins involved in the disease.
Speaker 1 (20:43):
These are mice, for example you're talking about.
Speaker 2 (20:46):
Yeah, exactly, And so we take these mice and we say, oh,
let's find every mutation that like happens in Alzheimer's patient
and throw all of them at the mice, and then
it's going to get Alzheimer's really quickly. But it's not
really Alzheimer's. It's like one specific dysfunction, and then we
can fix that. Like we have good drugs to treat
that in mice. We don't have good drugs to treat
Alzheimer's in people because it's probably more complicated.
Speaker 1 (21:08):
Because the rest of the adult human has five million
other biological issues going on at the same time. So
those mutations in the giant network that's happening, you get
different results then you do in a mouse who is
otherwise young and perfect but has these mutations.
Speaker 2 (21:26):
Yeah, that's right. You know, like you get a flat tire,
you know, driving to work and it's like it's fine,
I can deal with this, right, but like you get
a flat tire, like driving to the hospital with your
kid after you lost your job, it's like you can't.
You don't have the capacity to deal with this thing anymore.
That's probably a lot of what happens in these age
related diseases that like things aren't working as well, so
you don't have the capacity to compensate for whatever is
(21:49):
going wrong. Here's where we are. We've studied these diseases.
We've forced ourselves to like use these simplified models of
the disease because it's just not we didn't have a
practical way of trying a lot of things otherwise, and
then we failed. And so Gordian says, well, what if
we had a way to go into the most realistic
environment for the patient. What if we could instead of
(22:11):
like making this disease in an animal model in this
accelerated way, what if we could find an animal that
has the same disease as the human and developed it
over a long period of time the same way that
humans do. And then we could test a lot of
things there. And so for example, we could work with
you know, for ostereothritis, which is one of the diseases
(22:34):
that we're working on. A lot of people use have
these young mice and you do surgical injury and whatever.
We can find a horse that got ostereothritis from like
running around and living life, and then we can study
we can go in and see can we treat the
osterothritis in this context, and it's much more like a
(22:54):
human it's like an avatar for the human patient. Now,
anyone could do that, like anyone can go find a
whorse somewhere on a farm and then do this study.
But the way that most studies are done, you have
these large groups of animals with treatment A and large
group of animal with treatment B, so that you can
compare across the biological variability of the individual animals and stuff.
(23:17):
That's just impractical. It's plausible, but it's like prohibitively expensive
and like logistically challenging. And so Gordon started with the
idea that like, we can actually do this if we
invent a way to test hundreds of treatments in a
single animal, and so that's the core of the company.
We can go into the most predictive system for is
(23:38):
this going to cure disease X and a patient, and
then we can test a lot of things there, so
we don't have to be very smart. I try to
design it so that I can be not very smart
and still succeed. And so the way we do that
there's a lot of like cool biotechnology, and we have
these like re engineered viruses that can deliver therapies to
(23:58):
individual cells of the organ, and then we can like
pull those cells out and measure the activity of every
gene in that cell and then like predict, Okay, what
does this these gene changes mean for physiology. So there's
a lot of sort of hard stuff in actually making
that work, but the core of it is find the
(24:18):
thing that will actually predict the outcome you want, not
something that you sort of hope would predict the outcome
you want, but it's kind of very indirect, and then
do a lot of research there. And so we're using
that to find new treatments across four different diseases of aging,
heart failure, and fibrosis and osteothritis.
Speaker 1 (24:53):
So the key is you're looking at lots of things
at once instead of saying, hey, here's our single measure
that we're from these horses.
Speaker 2 (25:01):
Yeah, there's two things there. We're looking at lots of
different potential treatments, right, and so it's not like three
decades of research suggests that this is the right target
to treat this disease, which is often how it happens, right,
and like what we've had to do and what I
was just too impatient to stay in academia and do. Right, Instead,
we say like, well, let's test this like one hundred
(25:24):
different hypotheses all in the same animal. And then the
test is, yeah, measuring lots of things at once to
see what is the overall like state of this cell.
It was in a tissue that had let's say, al tereothritis.
There was a chondrocyite, which are the cells in your
like cartilage of your joints, and it's in this tissue
(25:44):
that has all the bad stuff going on. It's got
maybe some metabolic shifts that happen with age. There's an
immune system and all this stuff. And if we turn
this take this one genetic target, and turn it up
or down, does it now resemble a chondracite that is
in like a healthy whorese or has it changed in
its behavior to produce more cartilage which is the problem.
(26:05):
So we can look at the full sort of state
of the cell and we can say, you know, is
this more? Is this the state that we want to
go to in a way that doesn't require us to
make a single hypothesis around this is how the disease works.
So neither on the like, how do we try to
poke it? Or on the like is this better? Do
we rely on just like a single thing and like
(26:27):
this is the way of the disease because that's risky,
Like we've done that, you know neuroscience very well.
Speaker 1 (26:33):
Yes, yes, you know that's the way that biology is
having to move because we've spent so long looking at
individual pieces of very complicated networks, and we've seen the
ways in which that doesn't get us the answer we want.
Let me ask you a more general question, because you're
an expert on aging research. More generally, what do you
see in human longevity in terms of you know, people
(26:58):
are doing intermittent fasting, chloric restriction, and there are drugs
like risveritral and others that everyone's very interested in. What's
your view of the field as it stands right now?
Speaker 2 (27:08):
The key thing that we're missing is that measurement, right
and so if we had something that we had really validated,
like and bivalidated, I mean something along the lines of like, Okay,
I'm I'm postulating that this thing will predict whether you,
like you're risk of getting any disease, let's say, or
your risk of like dying before a certain age. So
(27:31):
I'm postulating that this is this measurement, your DNA methilation
clock or whatever like predicts that. So then we should
test it, right, you should put a bunch of things
in at least in mice right that we know will
extend miles lifespan, and then a bunch of other things
that like we know won't and then see just how
accurate are your predictions. If we have something like that
(27:51):
where we're like, oh yeah, this like has great accuracy.
When this thing moves per our previous conversation, that means
you're healthier, then we would have a much better sense
of like, Okay, is this spiritual thing working? Is this
repemison thing working, you know, like it's the fasting doing something.
There are a lot of sort of attempts to like
(28:12):
people who have this vision that like, look, we should
really get away from what is currently called healthcare, but
it's more like sick care, right, like wait until you
get disease and then get drugs for that disease. Towards
are more like can we just like measure your overall
health and your you know, homeostatic capacity or biological age,
(28:34):
whatever we want to call it, and try to prevent
you from having these diseases in the first place. That certainly,
if we can do that, well, that's a much better approach.
It's cheaper, like answer prevention, right, And the tricky part
of doing that is knowing the future, right, So we
need to build these tools that allow us to know
the future because currently you know. The other way with
(28:54):
that we do that is sort of controlled clinical trials
where you just like run the experiment and you have
a bunch of people and then you randomize them to
different groups, treat one and not the other one, and
then we see do we get the outcome that we want?
But doing that for aging it both you know, like
if you're doing that for like do you live or die?
It takes a long time. It's fairly expensive, and in
(29:16):
the US it's not obvious like is that something insurance
covers kind of should be, but like we haven't really
figured it out, So there's some financial risk involved there.
Speaker 1 (29:26):
What's your intuition if you're even looking retrospectively. I don't
know if there are groups of people who have had
rest for all their whole lives and others that haven't,
or whatever.
Speaker 2 (29:35):
But when you.
Speaker 1 (29:36):
Look at the data and look at the whole picture,
what do you feel is the thing that maybe you
would do that might be useful?
Speaker 2 (29:45):
Yeah, I think there's if you look at that. And
then the other thing you look at is like all
the animal studies, right, so like what actually makes a
mouse live longer? And the answer is calarge restriction of
some sort, so limiting the number of calories or are reprimicin,
which is a drug that's used as an immune suppress
(30:05):
and it's FDA approved at a high dose. So at
high dose it reduce prevent your immune system, it reduces activity,
and so we use it for like organ transplants. That's
not going to make you live longer suppressing your immune system, right,
but so at a much lower dose in mice. There's
some recent data in monkeys, and there's a trial on
going in dogs called Triad, where people are giving repromised
(30:28):
into dogs and see if they live longer. Repromizon has
the strongest data Other than this like eating regimen thing,
especially for kilored restriction, there's a caveat of like, well,
how much restriction and how much restriction once you multiply
that by like how much do you exercise? Right, Because
it's like in the lab mice that generally are in
(30:48):
cages and they have ad lib food and so forth,
you know you'll get a certain response, but then when
people are very different, they don't have the same genetics.
What is the right amount of restriction? There probably is
some amount, I mean there's for everyone. There's definitionly some
right amount. For probably a lot of people. It's probably
lower than where we're currently at, but it's it can
be you know, like there's a U curve. You just
(31:10):
eat less and less, it's definitely going to be bad
for you. So that's where we're back to, like needing
a measurement. Rappromizin is the other one, but similarly like
two higher dose is bad and so finding out like
what is the right dosing here where there's actually a
benefit that feels tricky. The nonprofit I started has funded
some clinical trials of rappromizing, like early stage trial Phase
(31:32):
one trials for different This one for like a reproductive health,
there's one for oral.
Speaker 1 (31:37):
Health, and this is in humans.
Speaker 2 (31:39):
That's in humans. Yeah, so there's four different trials that
we funded, and I think there's three others, and.
Speaker 1 (31:45):
So you won't know the longevity piece for several decades.
Speaker 2 (31:50):
None of those will give us the longevity piece, right,
So I think at that point, you're looking at this
dose seems safe and has a beneficial effect on this potentially,
and so if we then extrapolate like a broader health
benefit from the animal studies, your like expected value of
this might go to positive, but it's also positible, like
(32:13):
it's still it's still unclear. Yeah, So so those are
the two with the strongest data. Then there's some other things.
You know, mid Foreman, which is a diabetes drug. There's
a lot of push for that, you know, like we
should do a trial here in humans. There's a not
yet started but sort of like they're trying to fundraise
for it, thing called tame Game. And the mid form
(32:35):
of data it's more sort of observational what you said, like, Okay,
well we've given this to diabetics, so we have like
tens of millions of person years of data. What that
certainly tells us is like, this is not super dangerous.
What it may tell us is like, oh, there might
be lower overall cancer risk. But it's hard with these
like retrospective studies because you always have to be sure
(32:56):
that like the people that we looked at was there's
some unintended way that we selected for certain people that
already had a lower risk of having cancer diagnosis and
compared them to another one. So there's big caveats. That's
why we do these trials. But there's some sort of
human stuff the animal studies are less clear on, like
mid Foreman doing a whole lot, but might still be
(33:19):
beneficial and probably a safe.
Speaker 1 (33:21):
What do you do personally? Do you take med forman
or wrap am icin.
Speaker 2 (33:26):
I don't take rap of icein, Yeah, because I'd rather
see rather than somebody else find the safe dose, right,
I mean the basic stuff you already know. Right, It's
just like you should exercise, you should sleep, you should
like eat less bacon and more vegetables, definitely eat less sugar. Right,
So like most people is boring. I think like optimizing
for the longevity hack right now, you'll get a bit
(33:49):
but like the delta between I'm a hardcore like longevity
biohacker and I'm just like generally healthy. Maybe it's three
years or something. Like. I think the much more important
for the field is, like, let's put our effort into
doing these technological advancements that allow us to measure things,
that allow us to test more things at once, and
(34:11):
like really try to solve because like we know there
is potential. We know that like you or I could
have a kid that's like zero years old, so like
one cell in your body has the capacity to create
a functioning, completely non age system, right, And we also
know that there's animals that live much longer than humans
and there's great variability and like how long different animals live,
(34:32):
and we know that we can like you know, engineer
genetic engineer, there's worm or there's mouse to live longer.
So we know it's malleable. We know it's doable, and
it's possible to have like a big effect. I don't
know how hard it is, Like I don't know if
we like fifteen years we'll have some tremendous results with
partial reprogramming or something, or if it's like it's actually
much harder it'll take longer, right, sort of like AI,
(34:54):
it's like it's just around the corner and that suddenly
it takes off.
Speaker 1 (34:58):
So if we were to fast forward to twenty five
years from now, where do you think the longevity field
is going to be.
Speaker 2 (35:04):
Where I hope it would be is sort of like
rigorous testing of aging clocks happening in the next couple
of years probably will reveal many things to be fixed,
and then like deliberate efforts to create these sort of
like biomarkers, and then I think that's very doable on
like a five year horizon with concerted effort. Not guarantee
(35:27):
that that would happen, but there are different actors that
could like make that happen, such as the Altos lab
startups or our PAH in the US, or evolution in
Saudi Arabia. So that's one or even you know, frankly
like private philanthropists, like this is sort of like a
seed stage startup level effort or maybe a bit more.
So that's one. We definitely need that. Then we need
(35:49):
to figure out, Okay, what is the clinical trial we're
going to run? Are we going to try with the
current round of things like wrapamycin or synelytics. People are
excited about them. At foreman got to pick the right
thing or do a portfolio and then just start to
run those trials, and then I expect, I mean, this
is what happened in drug discovery in general. You try
to run the trial and then you find out some
of my endpoints, like we're not sensitive and off whatever,
(36:12):
so we could run we could do another round of trials.
So I think those things should happen. That's sort of
the ecosystem. And then at the same time people are
trying to do drug discovery for start getting different mechanisms
of aging. So if we did things in the right sequence,
and this is sort of like okay and prayer of
the world, you could like pm it all to like
(36:33):
work out, and then there's reality which relies on all
these intentives and whatever. But like all the companies now
and there's more and more of them that are trying
to find like age specific therapies, those would then have
an outlet to be not just we're going after And
so what happens now is like if I find a
drug for aging. Let's say I'm at Stanford, I'm doing
a bunch of research. I have this cool thing, I
(36:55):
think at least, and then I start a company and
then we raise fifty million dollars going to clinical trials. Bioage.
Just iPod yesterday that actually, yeah, that was from Stanford,
So that sort of a fits the story, right, and
they have clinical trials going. But even though the company
started with like, we're looking at aging, they had the
(37:16):
sort of incentive constraint to like, well, we don't know
how you run an aging trial yet. So everyone is like,
if we want to succeed here, if we want a
good readout for the investors, the patients, and so forth,
we're going to narrow down to a specific disease of aging.
And so you run that trial, which is good, Like
I hope that that treats patients. One could imagine, and again,
(37:37):
if we're being just blue skies ambitious, one can imagine
that the US decides, Look, we're doing this sick care thing.
We have like fourteen different institutes and medical specialties and
all this kind of stuff. But there's clearly biology that
spans those right, Like when your immune system dysfunctions, there's
a whole bunch of diseases. There's inflammation stuff that like
(37:58):
effects a whole bunch of diseases. We should stop doing
these trials where we test for one drug one disease, right,
match one of like thirty thousand things to one of
eight thousand diseases, and then try to do all by
all there you will take infinity time, right. Instead, we
should develop ways, and again this is sort of like
an our page kind of thing. We should develop ways
(38:20):
where when we run one trial, we can take some
blood samples and then we can look for these markers
that imply that the drug might work on a bunch
of other diseases. And so we sort of turn each
trial we run in this country into something that has
like an integrated overall beneficial effect on health or doesn't, right.
(38:41):
And so if you took that approach, you could have
fewer trials that lead us to a portfolio of medicines
that have a bigger overall impact. There's various reasons why
we're not doing that yet. One, we don't have those
measurements right, so there's technological development needed to do it right.
Another one is we probably don't have the perspective right. Like, again,
we used to you have one disease, and like that's
(39:02):
the thing that we the unit of sort of like
medical care. And then another one is the way that
the FDA is set up. It's sort of like nothing
bad can happen, but slowing things down has a secret
cost of like lots of people dying or whatever, but
like nothing bad can happen. And so you imagine if
you measure like is this good for all these different diseases, well,
what if one of them is bad? What if this
(39:24):
drug like will prevent Alzheimer's and heart failure, but it
increases your risk of cancer by ten percent, that might
still be like a good deal for a given human patient.
And you could also further segment down to people who
are not at elevated cancer risk, like this might be
extra good for them. But it's a tough sort of
political decision to say this is you know, we're utilitarian
(39:47):
in our healthcare or whatever that has not been made.
And consequently, for a farmer company of whoever's running the
clinical trial, there's a disincentive to have anything look bad
because that might tank the w whole thing. Even if
like there's you get you know, like twice as much
good stuff but some bad stuff, that's probably bad news
for approval right now.
Speaker 1 (40:23):
So let me ask you something about this issue that
you mentioned about looking at one.
Speaker 2 (40:27):
Disease at a time.
Speaker 1 (40:28):
If you were extrapolating, you know, fifty years from now,
do you think that the names of the medical professionals
will change, so we don't have a neurologist and a
cardiologist and a liver specialist and so on, but what
we have or things that are more general or broad.
Speaker 2 (40:43):
Optimistically, yes, pessimistically I feel like we will add something
new and just sort of like staple in on top
of the old, so you'll still have a neurologist and
so forth.
Speaker 1 (40:54):
Right, if you were in charge, if you were czar
of the hospital system and could say what it should be,
what kind of things would you set up?
Speaker 2 (41:02):
I mean I think it I probably would, you know, Like, yes,
it's not like throw away all the old, right, Like
you still have a heart, and like if we want
to know what's going on, someone who knows a lot
about the heart and can talk in detail about the
exact rhythm of your heart and what that means for
like your champers and stuff like that is a good
thing that we want. Right then, the question is like
(41:22):
what have we what do we want to lump and
what do we want to split?
Speaker 1 (41:26):
Right?
Speaker 2 (41:26):
Like what are the things that we've called different things
but actually they're a similar thing, right, And then what
do we want to split up? I think one thing
for sure we want to split up is like Alzheimer's
and aging. So if you look at the National Institutent
on Aging right now, something like half the budget is
earmarked for Alzheimer's research, which is like brain specific and
just one specific disease, right, And so it's something that
(41:49):
a lot of people are very afraid of, and so
that's why it happened, right, But like it's not it
doesn't make sense that like that's just like lumped in
under aging. So I think the things to me that
strike me as like there is a multi organ thing
going on here for sure, like inflammation immune function. And
so we obviously have immunologists and we have people that
(42:13):
look at like the function of the immune system. But
the overlap between the state of like infections and then
the amount of inflammation you have and like aging and
the amount of different organs like solid tissues and the
amount of inflammation you have, and like the interplay between
those that feels like a clear you know something we're
(42:35):
probably overlooking. I mean, COVID is sort of a big
boost here where people are looking at like, okay, exposure
to this virus, what does it do in the long term,
And a lot of us, especially when we're young, right,
we're used to like, Okay, got cold, you know, immune
system killed cold. We're fine. It's just like it's a
separate class of things. But more and more evidence, like
(42:56):
it does affect your chance of having Alzheimer's, Like you're
amount of bacterial like mouth bacteria correlate well with risk
of Alzheimer's. It seems like, you know, more research, so
I think that's one. And then like if we just
think about like what goes wrong in your tissues, there's
sort of some common things that happen. One is like
(43:17):
an out of control inflammation loop that leads to fibrosis.
So this is sort of scarring of your tissue. And
this will happen in your kidney, in your heart, your liver,
and your lungs. And sometimes we call it like pulmonary
fibrosis or COPD chronic obstructive pulmonary disorder, so like smoker's
lung that's when it's real bad. But the process is
(43:38):
happening in like most of your tissues at all times.
So that's like a thing. What is this particular loop
of something is like misfunctioning in the cells of that
tissue and then it triggers immune infiltration and then that
triggers fibrosis. Like that could be something that people are
specialized in, which is true in academia. There are people
who are like, look at that specifically, right, But medicine
(44:00):
is like different because then you start thinking, okay, we
have this drug. Might this drug actually improve multiple organs, right,
because the same thing is sort of happening, or maybe
like this drug it was approved just for this organ,
but it won't work in this or the organ because
like the biology is a bit different. So those are
some and then there's the whole like why do we
(44:22):
lose cells? So there's a whole bunch of organs in
your body that lose cells. The most shocking one might
be like the thymus, and so the thymus here is
like where your immune cells get told, you know, like
what should you attack and what should you not attack?
And that whole organ is like basically replaced with fat
by age forty, So it's just like you have an
(44:44):
organ that you start out with and it's gone obviously
for women like you're reproductive, organs like you get this
and for men, I don't know if you get it,
but that's one. Then there are other organs, right like
your brain. You lose cells in your brain, you lose
cells in your muscle, all what gets replaced with fat.
So there are some organs where like the way they
fail is that you lose them cells over time and
(45:06):
they don't get restored because that organ the cells don't
really divide, like the cells in your brain very little,
muscle very little. And so that's another area where there's
like something that happens consistently across tissues that isn't covered
by a single medical specialty.
Speaker 1 (45:22):
So let's say we extrapolate a thousand years from now
and all of the mysteries of biology where we're now
sort of at the foot of the mountain. We've summitted
the peak and we're there. We understand the whole network computationally,
it's all worked out. My question is do you think
there are natural limits to how long the human body
can live? Or is longevity something that has no limit
(45:45):
to it?
Speaker 2 (45:46):
If you make no changes whatsoever to the human body.
Then yes, it does seem like there are natural limits, right,
Like few people live over one hundred and fewer still
live more than one hundred and twenty. Right, But that
assumes that we don't change in anything, that we don't
have any technology, right, And so if we have endless
(46:06):
technology and we are willing to change to some degree,
like what is a human body, right, like we're willing
to which we do already, right, So like if you
lose a leg, you get robot leg, right, that's you
have a cyborg now, right, And similar if you have
a pacemaker, it's less visible. And so I think so
far we're okay with some cautious pace of like we
(46:30):
are actually augmenting the human body in order to avoid disease.
Even that a vaccine would fall into this category, right,
And so if you put that in, there's no no,
I don't think that there's any limit to some definition
of the human body. And we can see that because
like the human race exists still and so so many
(46:51):
generations of humans have produced a new body out of
a single cell, out of the same DNA code, right,
Like life happened once and it's still going for many,
many millions of years, right, And so it's not and
we see that, you know, you have a tree that
lived five thousand years. That's a very different kind of body.
But like it's possible to set things up to last
(47:13):
that long.
Speaker 1 (47:14):
And do you see any ethical or philosophical issues about
what that would be to have a lifespan of two hundred,
three hundred and five hundred years.
Speaker 2 (47:25):
Yeah, for sure. I mean I think there's a lot
of stuff, and I think that the not very serious
version of engaging with that is like it's weird and
unnatural and we need to like just not do it.
Because by that argument, if you look at I can
send you like this graph of like lifespan over time.
And so there have been these statements that like, well
(47:45):
it can't go above seventy you can't go so you
have these like horizontal lines and then you just have
like a straight diagonal line just going up since you know,
hundreds of years of like you know, human existence or
human civilization. Right. So anyway, like if you want to
take the stance that like living longer is bad because X,
and there's lots of different X, there's like what if
(48:06):
bad people live longer? What if a tyrant lives longer? Right?
Or like what if we have wealth inequality and then
it gets perpetuated for longer. Some of those are like, well,
you know, like we have inheritance so that it does
anyway or whatever. But like, but even if like that's
a legitimate issue, I find it fundamentally not serious that
your proposal is to kill everyone, right, Like it's it
(48:29):
let pretend that we were we had three hundred year
lifespans and then we found that, like, oh, we have
a lot of wealth inequality because the ones who get
ahead early they accumulate more and more over time. It's like,
let's kill everyone at a young age. It's not a
serious solution to this, Like, let's find a societal solution philosophically.
Speaker 1 (48:49):
What does it mean if we're all living three hundred
and five hundred years? What does what does that mean
for society?
Speaker 2 (48:54):
You know, there are concepts that we are used to
working a certain way that might not work that way.
The most obvious one is retirement, right, So, like retirement
is fundamentally like we recognize that humans are like not
very capable, then become capable, and then are less capable
again in old age, right, And so then we've structured
society so that the ones who are most capable are
(49:15):
supporting the ones who are not capable, including children, including
infirm aged people. Right. And so that's just like the
way things are. And so obviously if the older people, like,
if you just stretch that period of like competency, good
news is that you don't bankrupt medicare, which is what's
going to default happen now, right, But of course it
means like you're not going to retire at sixty or
(49:36):
sixty five or whatever it is. Right, Let's say we
really extended lifespan. You live for like three hundred years.
You might have multiple careers in different areas. You might
have like a work for this long and then go
on like retirement sabbatical thing where you just like go
back to learning a new thing for ten years, and
you're not very useful again because like you used to
(49:57):
be a scholar and now you're going to be an
artist or whatever it is, right, and then you come
back and you do a new thing. It'll be interesting
to see what happens. Right.
Speaker 1 (50:10):
That was my interview with Martin borsch Jensen, who's longevity
researcher and co founder and chief scientific officer of Gordian Biotechnology.
And one of the things this conversation surfaces is why
the science is so difficult. Biology is full of feedback
loops that cause nonlinear responses, and that makes it really
(50:32):
difficult for us to move from simple experimental observations like
the blood level of some molecule type that we measure
in worms or mice, to reliably producing meaningful physiologic changes
in humans, or even knowing what the right changes to
aim for. So how far are we from a world
(50:53):
where anti aging therapies are as routine as vaccinations. It's
not happen anytime soon. But on the other hand, we
now have billions of young brains on this planet getting
great educations, and with the exponential pace of technology and
the increasing pace of medical innovation, maybe we can get there.
(51:16):
Maybe we can not only prevent people from dying young,
as we have done over the past couple centuries, but
also extend a healthy lifespan such that we actually will
someday give heartbroken funereal speeches lamenting a person dying at
the tender young age of one.
Speaker 2 (51:36):
Hundred and twenty two years old.
Speaker 1 (51:38):
Perhaps we will come to understand the giant biochemical puzzle
of ourselves and actually be able to shift around some
of the pieces. Perhaps a few of us listening to
this podcast may just be lucky enough to live to
see that day, and there to see lots of days
(52:02):
past that as well. Go to eagleman dot com slash
podcast for more information and to find further reading. Send
me an email at podcasts at eagleman dot com with
questions or discussion, and check out and subscribe to Inner
Cosmos on YouTube for videos of each episode and to
(52:24):
leave comments until next time. I'm David Eagleman, and this
is Inner Cosmos.
Host
David Eagleman
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