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