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Speaker 1: They say that only two things in life are certain,

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Speaker 1: death and taxes. For the lucky among us will pass

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Speaker 1: away quietly at an old age. But why is aging

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Speaker 1: and thus death inevitable? And how do we even define aging? Yes,

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Speaker 1: you can define it by the ticking of the clock,

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Speaker 1: but is there a biological way to define it that

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Speaker 1: gives you a better shot at really understanding how the

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Speaker 1: passage of time has worn away at your body. If

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Speaker 1: two people in their thirties can mix their old cells

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Speaker 1: together to make a brand new baby, then why can't

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Speaker 1: those same two people just start making young cells for themselves?

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Speaker 1: And shouldn't evolution favor living a really long time so

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Speaker 1: we can make more babies and be around to help

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Speaker 1: them grow. Aging can be a counterintuitive phenomenon, and Daniel

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Speaker 1: and I get many questions from our audience the extraordinaries

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Speaker 1: about the aging process. However, despite the furrows in my

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Speaker 1: forehead that get deeper each year and that my son

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Speaker 1: sometimes stares at, this biologist is not an expert in

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Speaker 1: the science of aging. But lucky for us, we were

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Speaker 1: able to get doctor Venki Ramakrishnan, author of Why We Die,

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Speaker 1: The New Science of Aging and The Quests for immortality

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Speaker 1: to come onto the show to answer your questions. Welcome

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Speaker 1: to Daniel and Kelly's extraordinarily Old Universe.

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Speaker 2: Hi.

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Speaker 3: I'm Daniel. I'm a particle physicist. I round my age

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Speaker 3: up two hundred.

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Speaker 1: Hello. I'm Kelly Waidersmith. I study parasites and space And Daniel,

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Speaker 1: last time we talked, you rounded up to fifty. Have

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Speaker 1: you just decided that, now that you're fifty, yere rounding

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Speaker 1: up to one hundred because that is not a helpful

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Speaker 1: wait around.

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Speaker 3: I think that's totally consistent. When I was forty eight,

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Speaker 3: I called myself fifty, and now that I'm fifty, I

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Speaker 3: got a round up to one hundred. It totally makes sense,

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Speaker 3: not to me. Plus, I think I look pretty good

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Speaker 3: for one hundred.

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Speaker 1: Yeah, you look great for one hundred, But I say

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Speaker 1: you look good for fifty two.

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Speaker 4: But like, I don't.

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Speaker 1: Yeah, I know that that kind of rounding doesn't make

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Speaker 1: sense to me. But that's all right, all right.

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Speaker 3: So here's my question for you today, Kelly. If you

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Speaker 3: could take a pill that would extend your life to

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Speaker 3: a thousand years or a million years, would you do

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Speaker 3: you want to live a super crazy long life?

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Speaker 1: Umm? Al right, So one, okay, So selfishly, I would

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Speaker 1: need to know about the quality of that life, and

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Speaker 1: if the quality of my life was going to be

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Speaker 1: as good it is now, for all of that time,

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Speaker 1: I would think about it. But to be honest, the

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Speaker 1: only reason I'm thinking that I might want to say

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Speaker 1: yes is that I want to live at least as

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Speaker 1: long as my son lives, because he's going to need

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Speaker 1: care his whole life, and I don't want him to

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Speaker 1: ever be alone. And so if I could live as

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Speaker 1: long as he lives, one hundred percent, So what about.

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Speaker 3: You, No, I see, life is like a hike. You know,

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Speaker 3: hikes are wonderful. There's oneful moments you're glad you went

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Speaker 3: on them. You're also glad when they're over. Nobody wants

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Speaker 3: to be in a hike that lasts until the end

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Speaker 3: of the universe. And maybe sometimes the best part of

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Speaker 3: a hike is when you get to sit down at

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Speaker 3: the end, you're like, oh wow, what a nice walk done.

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Speaker 1: Especially at the end of a good hike, Yeah, you're like, ah,

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Speaker 1: all right, I'm ready to be done.

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Speaker 3: Exact.

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Speaker 1: And my grandpa passed away recently, and I think he

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Speaker 1: had that kind of life. He hiked it was a

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Speaker 1: good trip, and at the end he told everyone, he's like,

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Speaker 1: I'm ready, and then he passed away into sleep, and

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Speaker 1: I was like, man, I really hope that's in the

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Speaker 1: genes somewhere, because that's pretty solid.

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Speaker 3: And I hope that listening to this podcast has improved

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Speaker 3: that everybody's quality of life out there were making your

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Speaker 3: hike through life more pleasant.

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Speaker 1: Maybe it will improve their quality of sleep, which at

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Speaker 1: the end of the episode will discover is an important

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Speaker 1: part of being healthy. So we're doing our part.

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Speaker 3: You're saying that listening to the podcast could technically scientifically

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Speaker 3: extend your lifespan.

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Speaker 1: Maybe maybe listen to our prior episodes about how you

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Speaker 1: evaluate scientific statements and see what you think, dear listeners,

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Speaker 1: and whether.

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Speaker 3: You should believe people who have skin in the game exactly.

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Speaker 1: Yes, all right, Well, so we get loads and loads

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Speaker 1: of questions about aging from the extraordinaries, and so I

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Speaker 1: pulled them all together. We found an amazing expert to

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Speaker 1: answer the question. Amazing, amazing, he does such a great job.

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Speaker 3: How do you know this Nobel Prize?

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Speaker 1: Kelly Oh, thanks for pitching myte We both were on

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Speaker 1: the short list for the Royal Society Book Prize. Yep,

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Speaker 1: Why We Die is thank you Rama Christnan's book, and

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Speaker 1: A City on Mars was my book, and we both

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Speaker 1: made the short list for the Royal Society Prize.

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Speaker 3: And you're just gonna omit those crucial piece of information

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Speaker 3: that you won the prize. So Kelly is the author

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Speaker 3: of a book which edged out a Nobel Prize winning

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Speaker 3: nonfiction science book.

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Speaker 1: I was not gonna mention that. Thank you for ult

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Speaker 1: over the years.

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Speaker 3: I appreciate it, all right, Well, this is a wonderful

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Speaker 3: conversation with a deep expert who also has the unusual

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Speaker 3: quality of being able to explain things clearly yes.

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Speaker 1: And being so nice, so nice.

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Speaker 5: Yeah.

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Speaker 1: Anyway, So I had so much fun. I feel so

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Speaker 1: lucky we got to do.

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Speaker 3: This interview before we bring on our expert, who want

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Speaker 3: to know abel prize in this area. We asked you

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Speaker 3: guys what you thought was the reason for aging. Here's

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Speaker 3: what people had to say.

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Speaker 2: I underested it to be oxidative pressures where new copies

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Speaker 2: of things just aren't quite as good as they used

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Speaker 2: to be and there are errors throughout. Short answer telemeres

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Speaker 2: real answer, so that there's someone to say I wouldn't

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Speaker 2: do that if I were you to the younger generations,

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Speaker 2: it's not like the clouds are going to yell at themselves.

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Speaker 6: Certain proteins that mark ourselves or do something along the

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Speaker 6: lines of maintaining how are unique gets repeated or transcripted deggregate.

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Speaker 3: Over time, the.

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Speaker 6: Body forgets how to make a new body the way

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Speaker 6: that it once did.

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Speaker 5: We age as a consequence of too many gas station burritos,

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Speaker 5: ninety nine cent big gulps and betrayal by Teilomeir.

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Speaker 7: At a molecular point of view, it's really hard to

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Speaker 7: maintain consistency in the gazillion times molecules and the cells

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Speaker 7: needs to produce these error skips that canap until the

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Speaker 7: whole body the case, my.

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Speaker 2: Short answer is we age due to the passage of time.

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Speaker 8: I think we age because we need to die ultimately.

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Speaker 8: I think it's conducive, if not crucial, to the evolution

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Speaker 8: of life itself, for organisms to have a finite lifespan.

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Speaker 2: Every beginning as an end.

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Speaker 9: So I believe I've read somewhere that the reason why

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Speaker 9: we age is because there is a shortening of some

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Speaker 9: kind of a protein or molecule within our cells.

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Speaker 2: And so for DNA current to fray and just gets

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Speaker 2: left up to entropy.

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Speaker 8: Aging and death are just part of the evolutionary.

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Speaker 7: Process and processes that have brought us to where we

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Speaker 7: are today.

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Speaker 2: It's just a fact.

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Speaker 3: Thanks everybody for your speculation on this concept. Now let's

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Speaker 3: talk to the expert and find out what we know

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Speaker 3: and what we don't know.

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Speaker 1: Doctor. Thank you. Rama Krishnan was initially interested in physics,

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Speaker 1: but I'm going to go ahead and give a point

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Speaker 1: to biology because he transitioned to focusing more on this

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Speaker 1: field and the biology stuff worked out well for him

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Speaker 1: because in two thousand and nine he received a Nobel

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Speaker 1: Prize for his work on ribosomes. He was President of

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Speaker 1: the Royal Society from twenty fifteen to twenty twenty and

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Speaker 1: recently wrote the book Why We Die, The New Science

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Speaker 1: of Aging and the Quest for Immortality. And today we'll

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Speaker 1: be talking about the science of aging. Welcome to the show.

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Speaker 2: Thank you, and thank you for having me.

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Speaker 1: Yeah, we're super excited to have you. We get so

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Speaker 1: many questions from our audience about aging, and every time

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Speaker 1: I'm like, look, I know when you look at me,

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Speaker 1: I look like the right person to ask about.

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Speaker 3: Well, they should look at me then, and there's so

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Speaker 3: much discussion out there about aging and how to prevent

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Speaker 3: it and if it's possible, and so much snake oil

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Speaker 3: being sold out there. It's so important to cut to

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Speaker 3: the chase.

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Speaker 2: It's certainly having a moment, and I'm a little bit cynical.

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Speaker 2: I think it has to do with my generation, the

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Speaker 2: boomer generation, that's used to having everything it wanted in life,

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Speaker 2: suddenly coming to terms with getting old, and so, you know,

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Speaker 2: there's a lot of anxiety in the air. Although having

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Speaker 2: said that, you know, this fear of death and fear

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Speaker 2: of aging is simply as old as humans, you know,

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Speaker 2: because ever since we learned about mortality, we've fretted and

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Speaker 2: worried about it. And I like to say we may

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Speaker 2: be the only species that's aware of mortality. Other animal

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Speaker 2: maybe are aware of death, but they're not aware that

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Speaker 2: they all have a finite lifespan and everybody is going

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Speaker 2: to die. I'm not sure that other species have that

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Speaker 2: understanding that we do. And when we somehow obtain that understanding,

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Speaker 2: perhaps as a result of cognitive development, language and so on,

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Speaker 2: ever since then, it has become it became a theme

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Speaker 2: and if you look at most religions. They're all about,

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Speaker 2: you know, how to deal with death and what happens

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Speaker 2: after we die.

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Speaker 1: I don't know if it's a blessing or a curse

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Speaker 1: that our species is aware of that.

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Speaker 2: Yeah, I mean many species aren't even aware of death,

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Speaker 2: you know, it just simply happens.

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Speaker 3: Well, can I start us off with a very broad

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Speaker 3: sort of philosophical question, which is, how do you define

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Speaker 3: aging biologically? Because as a physicist, I might think, well,

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Speaker 3: you have a clock and it starts and it stops,

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Speaker 3: and that's your age. But we're interested in more than that, right,

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Speaker 3: It's some sort of like decrease in the quality of life.

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Speaker 3: You're gradually moving towards death. It's this fact that you

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Speaker 3: don't just like live for sixty two years and then poof,

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Speaker 3: you're done. Your body degrades. How do we define aging

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Speaker 3: in a crisp way scientifically?

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Speaker 2: Yeah, so it's not. It's definitely related to the chronological

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Speaker 2: clock to time, but the rate is very different, not

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Speaker 2: only for species, it's vastly different for species, but it's

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Speaker 2: also different for individuals within a species. If you go

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Speaker 2: to your high school reunion, you will immediately be aware

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Speaker 2: of that the fact that people don't age at the

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Speaker 2: same rate, and I think aging molecular biologists would define

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Speaker 2: it as the gradual accumulation of changes and damage to

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Speaker 2: us over time that can happen different rates in different individuals.

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Speaker 2: And it's not just damage. Some of it has changes

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Speaker 2: that occur with time. It may occur at different rates

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Speaker 2: in different individuals, and these changes may have a purpose

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Speaker 2: early in life, for example, modifications of our DNA, but

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Speaker 2: they cause us or at least they're strongly correlated with

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Speaker 2: aging later in life. So that's how I define it.

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Speaker 2: And this accumulation of changes in damage leads to a

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Speaker 2: gradual loss of function, and when that loss of function

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Speaker 2: reaches some point where some critical system fails, then you

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Speaker 2: have death. And so a death is a result of aging,

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Speaker 2: but its exact moment can't be predicted because in a

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Speaker 2: complex system, you can't predict exactly when a critical component

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Speaker 2: will fail.

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Speaker 3: Aging and changes, but that must mean very different things

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Speaker 3: to different parts of your body. You're talking about your

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Speaker 3: nerves or your skin or your eyes. Are there ways

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Speaker 3: we have to measure it?

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Speaker 2: It happens at every level. It happens at every level.

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Speaker 2: But I would say fundamentally it happens at the molecular level,

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Speaker 2: and that then manifests itself and each increasing level of complexity.

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Speaker 2: So you can go from molecules to collection of molecules

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Speaker 2: in our cell, to components of the cell, to cells themselves,

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Speaker 2: and then entire tissues, and you know the way cells

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Speaker 2: communicate with each other, like our immune system. So you

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Speaker 2: can see that, you know, it happens at the molecular level,

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Speaker 2: but it starts manifesting itself at increasingly higher levels, you know,

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Speaker 2: until the point that you know, we see aging as

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Speaker 2: various forms of frailty, you know. So in fact, a

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Speaker 2: very good measure of aging is actually something called the

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Speaker 2: frailty index. They'll measure things like can you get out

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Speaker 2: of bed? How fast can you walk you know, fifty yards?

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Speaker 2: What's your grip strength? How good is your eyesight? How

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Speaker 2: good is your cognition? You know, how good is your memory?

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Speaker 2: So all of those things are indications of frailty at

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Speaker 2: a macroscopic level, at a level that you and I experience.

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Speaker 2: But ultimately the underlying causes are molecular.

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Speaker 1: Okay, and is aging universal. So we're getting to one

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Speaker 1: of our first listener questions right now. One of our

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Speaker 1: listeners noted that they had heard stories about immortal organisms

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Speaker 1: and they wanted to know are they actually immortal.

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Speaker 2: Yeah, I had to say, there's a lot of hype.

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Speaker 2: What happens is people will study an organism that ages

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Speaker 2: very slowly, and suddenly they'll say, oh, this has no

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Speaker 2: sign of biological mortality. Let me back up and explain

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Speaker 2: what I mean by that. So, in normal species, the

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Speaker 2: likelihood of that we are going to die at any

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Speaker 2: given time keeps increasing exponentially. So for example, that chances

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Speaker 2: that you'll die when you're ten are very small, but

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Speaker 2: the chances you'll die in the next year when say

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Speaker 2: you're ninety five or one hundred or almost fifty percent, okay,

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Speaker 2: so the chances keep going up. Now, in some organisms,

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Speaker 2: it appears that that likelihood of dying you know, of

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Speaker 2: aging events, not of being eaten by a predator or

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Speaker 2: starving or anything else. Those are called external causes. But

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Speaker 2: you know aging, just dying of aging, that probability doesn't

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Speaker 2: seem to go up with time. And so there are

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Speaker 2: some species, like a freshwater species called the hydra. There's

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Speaker 2: another species called the immortal jellyfish, and these tend not

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Speaker 2: to show any signs of biological aging. That is, the

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Speaker 2: likelihood it's going to die just doesn't seem to change

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Speaker 2: with time. But in fact what is happening is it's

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Speaker 2: probably aging very very slowly. So if you looked, if

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Speaker 2: you simply followed a hydra in the wild, it'll die

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Speaker 2: of some other cause, not of old age. But if

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Speaker 2: you kept it safe and followed it long enough, you

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Speaker 2: will find that it too, gradually ages because no regeneration

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Speaker 2: is perfect. You know, the reason hydra and jellyfish appear

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Speaker 2: not to age is they constantly regenerate their tissue using

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Speaker 2: specialized cells called stem cells. In a way, they're like plants.

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Speaker 2: You know, plants have stem cells all over themselves, and

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Speaker 2: that's why you can take a cutting from a plant

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Speaker 2: and you know, grow an entirely new tree with it. Right,

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Speaker 2: we can't do that, but you know, some animals regenerate,

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Speaker 2: like starfish. You know, it cut off an arm and

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Speaker 2: it'll regenerate an arm. And you know, some of these

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Speaker 2: species can regenerate, you know, any tissue, and but it's

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Speaker 2: not perfect. And so I would say to your listener

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Speaker 2: that yes, everything will die, but they die at different rates.

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Speaker 2: I mean, they age at different rates.

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Speaker 3: And so everything ages. It's universal across organisms. Do we

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Speaker 3: understand why we age? Like, is it an inevitability of

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Speaker 3: like thermodynamics or molecular copying or something, or is it

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Speaker 3: an evolutionary advantage?

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Speaker 2: Well, there there are two ways of looking at it.

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Speaker 2: One is, you know, the physicist way would be that

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Speaker 2: you know, second law wins and there's always increase in

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Speaker 2: entropy and disorder and eventually things sort of degrade. And

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Speaker 2: you know, life is not a you know, equilibrium system.

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Speaker 2: The problem with that is that life is not a

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Speaker 2: closed system, and if you apply enough energy and enough

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Speaker 2: resources you can reverse damage. And in fact that's what

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Speaker 2: we do. So why is it then that we age

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Speaker 2: and die? Well, I'll tell you the evolutionary argument. The

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Speaker 2: evolutionary argument is resources are limited and throughout our history

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Speaker 2: and in fact, until recently, resources were limited for humans

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Speaker 2: as well. You know, we had to struggle to have

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Speaker 2: enough food to live and so on. When resources are limiting,

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Speaker 2: the organism has a choice to make. Does it put

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Speaker 2: more of the resources into maintenance and repair, which requires energy,

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Speaker 2: requires food, et cetera. Or should those resources be put

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Speaker 2: in too rapid growth and development? Now, if you take

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Speaker 2: a mouse. For example, a mouse lives about two years,

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Speaker 2: whereas a blue whale lives a few hundred years. So

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Speaker 2: why is it that there's this vast difference. Well, the

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Speaker 2: evolutionary argument is that evolution doesn't actually care how long

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Speaker 2: you live. Evolution simply cares about how successful are you

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Speaker 2: going to be at passing on your genes because it's

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Speaker 2: selecting for those genes, it's not really selecting for you

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Speaker 2: as an individual. And so in the case of a mouse,

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Speaker 2: there's no point in spending a lot of resources getting

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Speaker 2: a mouse to live to be forty years. And the

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Speaker 2: reason is that long before that it'll be eaten, or

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Speaker 2: it'll die of starvation or in a drought, or all

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Speaker 2: of a zillion external causes. And so in the case

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Speaker 2: of a mouse, it's more advantageous from an evolutionary point

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Speaker 2: of view for a mouse to grow very rapidly, produce

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Speaker 2: lots of offspring, and then you know, it doesn't matter

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Speaker 2: whether it dies, Whereas with larger animals, their metabolism is

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Speaker 2: also slower, so they take longer to mature, their offspring

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Speaker 2: take longer to produce and grow up and mature, and

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Speaker 2: so in there it does make sense for evolution to

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Speaker 2: have selected for longer lifespan in order to ensure fitness. Okay,

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Speaker 2: because otherwise it may men not actually have the chance

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Speaker 2: to reproduce, or not to reproduce enough. And it gets

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Speaker 2: worse than that. It's not even that evolution doesn't care

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Speaker 2: what happens to you after you've produced your offspring. Evolution

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Speaker 2: also will select for traits that are advantageous early in

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Speaker 2: life that will get you to maturity and reproduction, even

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Speaker 2: if those exact same traits will cause you to age

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Speaker 2: later in life. And there are many examples of that

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Speaker 2: in my book. For example, certain mechanisms that cause us

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Speaker 2: to age may have evolved as anti cancer mechanisms. Now,

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Speaker 2: of course you want to prevent cancer early in life,

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Speaker 2: but later in life they may cause aging, and ironically,

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Speaker 2: cancer itself increases as we age the likelihood of getting cancer.

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Speaker 2: But that's a different story.

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Speaker 3: Can you give us an example of an anti cancer

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Speaker 3: strategy that causes aging?

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Speaker 2: Leader Yeah, sure so. One very classic example is that

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Speaker 2: most of the cells in our body can only divide

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Speaker 2: a certain number of times, and then they reach a

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Speaker 2: state called sinesence. Senessen cells are these dysfunctional cells that

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Speaker 2: can't divide and they actually secrete inflammatory compounds. And as

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Speaker 2: we age, we accumulate more sinescent cells and that becomes

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Speaker 2: a problem and inflammation becomes a problem. Now, why do

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Speaker 2: cell stop dividing? Well, it turns out that our chromosomes

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Speaker 2: are linear DNA molecules and their ends are specialized struck.

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Speaker 2: It is called telomeres. Now, the copying mechanism for DNA,

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Speaker 2: every time it cell divides, that DNA has to be copied.

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Speaker 2: The copying mechanism is such that our chromosomes get slightly

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Speaker 2: shorter every time the cell divides. Okay, and these ends

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Speaker 2: have a special structure. Now, when they become too short,

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Speaker 2: that structure unravels. When it unravels, the end of our

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Speaker 2: chromosomes looks to the cell like a broken piece of DNA.

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Speaker 2: That the cell has evolved mechanisms that if there's a

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Speaker 2: DNA break, it will either try to repair it, or

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Speaker 2: if it can't repair it, it will send the cell

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Speaker 2: into Sinessence. Why, because a cell with a defective genome

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Speaker 2: is at cancer risk because it's about you know, it

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Speaker 2: could do all kinds of you know, abnormal things, and

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Speaker 2: it's much better to send that cell off to sinessence

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Speaker 2: and have it be removed by the immune system, then

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Speaker 2: have it continue with a DNA defect or a chromosome defect. Right,

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Speaker 2: So the cell has evolved as DNA response damage response

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Speaker 2: in order to get rid of cells that are problematic

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Speaker 2: in this way. But of course that same thing is

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Speaker 2: causing senessens and increase in senescent cells as we get

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Speaker 2: older and causing us to age. So that's a very

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Speaker 2: you know, clear example of how something that may have

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Speaker 2: evolved as an anti cancer mechanism early in life really

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Speaker 2: is a cause of aging later in life.

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Speaker 3: All Right, I want to hear a lot more about that,

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Speaker 3: but first we have to take a break. Okay, we're

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Speaker 3: back and we're talking about aging.

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Speaker 1: So you mentioned that as cells go on and replicate

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Speaker 1: the telomeres, you get shorter. But we and this is

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Speaker 1: another listener question, but we're able to, you know, combine

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Speaker 1: our gam meets with somebody else and make a fetus

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Speaker 1: that has all new cells. And you also mentioned that starfish,

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Speaker 1: can you regenerate an entire arm using stem cells? So

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Speaker 1: why is it inevitable that our cells will break down

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Speaker 1: when we seem able to set the clock back if

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Speaker 1: we want to so we.

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Speaker 2: Have evolved so that most of our cells have lost

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Speaker 2: that ability to regenerate, probably because you don't want all

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Speaker 2: of the trillions of cells in our body to be

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Speaker 2: able to keep dividing at will, because that is also

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Speaker 2: a cancer risk, okay, because they could acquire mutation and

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Speaker 2: then they could become cancerous. So we have specialized cells

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Speaker 2: called stem cells, which can keep regenerating. They don't go

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Speaker 2: into senescence, and these specialized cells their role is to

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Speaker 2: regenerate tissue. Now where do these stem cells come from, Well,

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Speaker 2: they came from the fertilized egg. The fertilized egg is

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Speaker 2: the ultimate stem cell because it's what is called a

402
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Speaker 2: toty potent stem cell. That means it can make everything

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Speaker 2: in the body, including the placenta. Okay, then that separates

404
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Speaker 2: off into placental cells and the cells that actually form

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Speaker 2: the fetus and the body, you know, and the organism.

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Speaker 2: Early in development, those cells are called pluripotent because they

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Speaker 2: can make any kind of tissue. They could make kidneys,

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Speaker 2: they can make lungs, they could make brain cells, they

409
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Speaker 2: can make anything. But as the fetus, as the embryo

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Speaker 2: I should say, develops, the stem cells become more and

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Speaker 2: more specialized. And then you have amatopoetic stem cells, which

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Speaker 2: can make anything in the blood system, and that includes

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Speaker 2: all of our immune system and our red blood cells,

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Speaker 2: et cetera. Another kind can make anything in the nervous system,

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Speaker 2: you know, neurons, glia, all of those cells. Others can

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Speaker 2: make skin and hair and so on. So you get

417
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Speaker 2: the picture. The stem cells are becoming more specialized, but

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Speaker 2: those stem cells have a balancing act. They have to

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Speaker 2: reproduce so that they maintain the stem cell population, but

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Speaker 2: they also have to differentiate and produces more of the tissue.

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Speaker 2: So there's always this switch going on. Do they reproduce

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Speaker 2: more of themselves so you have more stem cells, or

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Speaker 2: do they make the tissue keep regenerating the tissue they are,

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Speaker 2: and there's always this balance. But as we get older,

425
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Speaker 2: our stem cells get depleted because they also get defective.

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Speaker 2: They also age, they also become sinescent, and so you

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Speaker 2: get this depletion of stem cells. You also get the

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Speaker 2: remaining stem cells are not optimal. They become what are

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Speaker 2: called clones. Instead of having a diverse population of stem

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Speaker 2: cells as when we're young, you get these clonal stem

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Speaker 2: cells which are suboptimal. They're selected for being able to

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Speaker 2: reproduce rather than being effective at generating tissue. So these

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Speaker 2: stem cells also decline. So that's why we can't keep

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Speaker 2: going forever, you know, by regenerating tissue. Now, the other

435
00:26:37,560 --> 00:26:40,879
Speaker 2: question your listener had was, you know what about our

436
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Speaker 2: germ cells. You know we can you know, we keep

437
00:26:43,359 --> 00:26:46,600
Speaker 2: producing babies that are age zero. They're not. You know,

438
00:26:47,280 --> 00:26:49,600
Speaker 2: in my book, I point out that a forty year

439
00:26:49,600 --> 00:26:53,840
Speaker 2: old woman doesn't give birth to a baby that's twenty

440
00:26:53,960 --> 00:26:57,000
Speaker 2: years older than a twenty year old woman. They're both

441
00:26:57,119 --> 00:27:01,480
Speaker 2: zero right, born at a time zero. So that's a

442
00:27:01,520 --> 00:27:05,840
Speaker 2: combination of two things. One is our germline cells are

443
00:27:05,920 --> 00:27:10,359
Speaker 2: highly protected against damage. They have better repair mechanisms for

444
00:27:10,480 --> 00:27:15,160
Speaker 2: repairing DNA damage. They're shielded against DNA damage, et cetera.

445
00:27:15,640 --> 00:27:20,280
Speaker 2: So that's one aspect. The others there's a brutal selection process.

446
00:27:20,359 --> 00:27:23,960
Speaker 2: You know, a female is born, a female human is

447
00:27:24,040 --> 00:27:27,679
Speaker 2: born with about a million or so eggs. But you know,

448
00:27:27,880 --> 00:27:30,240
Speaker 2: if you look at the number of menstrual cycles and

449
00:27:30,680 --> 00:27:33,760
Speaker 2: a woman over a lifetime, it's only maybe a few hundred.

450
00:27:34,440 --> 00:27:37,199
Speaker 2: So why do you need a million eggs? You know,

451
00:27:37,240 --> 00:27:39,879
Speaker 2: when you're really only going to use at the most

452
00:27:39,920 --> 00:27:43,640
Speaker 2: a few hundred, right, So that's because there's a lot

453
00:27:43,640 --> 00:27:48,160
Speaker 2: of selection in the process of going from the germline's

454
00:27:48,280 --> 00:27:53,560
Speaker 2: precursor cells to the egg that's actually eventually selected for ovulation.

455
00:27:53,680 --> 00:27:57,600
Speaker 2: There's a lot of selection. Sperm, of course, you know,

456
00:27:57,720 --> 00:28:01,760
Speaker 2: is highly selected. I mean, you know, each fertilization event

457
00:28:01,800 --> 00:28:04,160
Speaker 2: there you know, I don't know how many I would

458
00:28:04,160 --> 00:28:06,320
Speaker 2: had to guess and take a guess, but maybe it's

459
00:28:06,359 --> 00:28:10,119
Speaker 2: a million sperm cells or something, and out of that

460
00:28:10,240 --> 00:28:12,359
Speaker 2: is only one is selected, you know, So they have

461
00:28:12,440 --> 00:28:15,919
Speaker 2: to raise and they have to you know, win the competition.

462
00:28:16,640 --> 00:28:21,560
Speaker 2: So they are also selected for fitness for health. And

463
00:28:21,640 --> 00:28:27,760
Speaker 2: then after the fertilized egg is formed, you know, it

464
00:28:27,840 --> 00:28:31,760
Speaker 2: is also checked. So if the developing embryo is at

465
00:28:31,760 --> 00:28:35,400
Speaker 2: all defective, there'll be spontaneous abortion. Often a woman won't

466
00:28:35,440 --> 00:28:38,840
Speaker 2: even know it, you know, the very early spontaneous abortion.

467
00:28:39,720 --> 00:28:44,280
Speaker 2: Later abortions are what we call miscarriages, and that's another selection.

468
00:28:44,720 --> 00:28:49,000
Speaker 2: And even within the growing embryo, cells are selected against

469
00:28:49,080 --> 00:28:52,280
Speaker 2: if they're defective. The embryo keeps growing, but it kills

470
00:28:52,320 --> 00:28:56,959
Speaker 2: off cells that are defector, which I found remarkable. So

471
00:28:57,000 --> 00:29:04,360
Speaker 2: it's this combination of selection and protection that ensures that,

472
00:29:04,960 --> 00:29:09,719
Speaker 2: you know, the child that is born is has its

473
00:29:09,840 --> 00:29:15,600
Speaker 2: aging clock soon reset, okay, at each generation.

474
00:29:15,840 --> 00:29:18,600
Speaker 3: But is it technically possible for us to reset our

475
00:29:18,640 --> 00:29:21,880
Speaker 3: own clock? Is it just like a bad idea evolutionarily,

476
00:29:22,360 --> 00:29:24,120
Speaker 3: or is there something that prevents us from just like

477
00:29:24,520 --> 00:29:26,360
Speaker 3: constantly being at teko zero.

478
00:29:26,680 --> 00:29:30,440
Speaker 2: I don't see how you would reset your entire clock.

479
00:29:31,640 --> 00:29:36,160
Speaker 2: You know, in the whole organism there are people. So

480
00:29:36,240 --> 00:29:39,520
Speaker 2: if I were to back up just a little bit,

481
00:29:40,640 --> 00:29:43,760
Speaker 2: there is an example of taking a fully grown adult

482
00:29:43,880 --> 00:29:48,400
Speaker 2: cell and making a whole new animal from it, Okay,

483
00:29:48,600 --> 00:29:51,640
Speaker 2: And the first time that was done was by John

484
00:29:51,680 --> 00:29:53,960
Speaker 2: Gordon who received the Nobel Prize for it when he

485
00:29:55,120 --> 00:29:58,000
Speaker 2: cloned a frog from a skin cell. So he took

486
00:29:58,040 --> 00:30:01,280
Speaker 2: a skin cell from an adult frog and implanted the

487
00:30:01,400 --> 00:30:04,720
Speaker 2: nucleus of that cell into the egg of another frog

488
00:30:04,760 --> 00:30:09,200
Speaker 2: and then just grew it up and it resembled the

489
00:30:09,240 --> 00:30:12,200
Speaker 2: frog from which the skin cell had been taken, you know,

490
00:30:12,240 --> 00:30:15,280
Speaker 2: so it was essentially a clone. And then people asked

491
00:30:15,360 --> 00:30:18,520
Speaker 2: could they do it to mammals? And that made big

492
00:30:18,600 --> 00:30:23,000
Speaker 2: headlines when Dolly the Sheep was cloned. Now Dolly the

493
00:30:23,080 --> 00:30:25,520
Speaker 2: Sheep turned out to be very sickly sheep and died

494
00:30:25,560 --> 00:30:28,320
Speaker 2: at about half the age of a normal sheep. So

495
00:30:28,400 --> 00:30:32,520
Speaker 2: everybody said, ah, this is because Dolly the Sheep was

496
00:30:32,640 --> 00:30:35,680
Speaker 2: cloned from a fully grown adult cell which was already

497
00:30:35,800 --> 00:30:39,160
Speaker 2: kind of old and damaged and didn't go you know,

498
00:30:39,320 --> 00:30:43,479
Speaker 2: wasn't a normally produced sheep. It was done by this

499
00:30:43,520 --> 00:30:47,280
Speaker 2: weird cloning procedure. But it turns out that there are

500
00:30:47,440 --> 00:30:50,800
Speaker 2: many other cloned animals, and in fact, with Dolly, the

501
00:30:50,920 --> 00:30:54,280
Speaker 2: other cohorts like Daisy and Debbie, they're all females that

502
00:30:54,400 --> 00:30:58,400
Speaker 2: had d names and these sheep though by and large,

503
00:30:58,440 --> 00:31:04,280
Speaker 2: had normal lifespans. And so that means that you could actually,

504
00:31:05,280 --> 00:31:10,480
Speaker 2: you know, reset the clock to substantial degree by erasing

505
00:31:10,520 --> 00:31:14,520
Speaker 2: all the marks on the DNA. Okay, it's not perfect,

506
00:31:14,640 --> 00:31:17,440
Speaker 2: because the cloning itself involved lots of selection. You know,

507
00:31:17,480 --> 00:31:22,040
Speaker 2: it is very very inefficient. It only works small fraction

508
00:31:22,120 --> 00:31:24,400
Speaker 2: of the time, and most of them end up in

509
00:31:24,440 --> 00:31:28,520
Speaker 2: miscarriages or or they don't take and so on. So

510
00:31:29,120 --> 00:31:33,280
Speaker 2: at least in theory it's possible. Now, could we do

511
00:31:33,440 --> 00:31:37,680
Speaker 2: to cells in a more systematic way what Dolly the

512
00:31:37,720 --> 00:31:40,160
Speaker 2: sheep or John Gerdon did with his frog, Because they

513
00:31:40,240 --> 00:31:43,040
Speaker 2: just treated it in various ways, but they didn't have

514
00:31:43,080 --> 00:31:45,840
Speaker 2: a clear idea of what was what was it? What

515
00:31:45,880 --> 00:31:49,120
Speaker 2: were they doing to make that adult cell go back

516
00:31:49,200 --> 00:31:52,479
Speaker 2: to resembling a fertilized egg and start growing a new animal.

517
00:31:52,560 --> 00:31:56,760
Speaker 2: You know, it's like going backwards in time, right, And

518
00:31:57,240 --> 00:32:03,320
Speaker 2: so a Japanese scientist named Shinya Yamanaka asked, could you

519
00:32:03,400 --> 00:32:06,440
Speaker 2: take these stem cells that are in the final stage,

520
00:32:07,040 --> 00:32:09,640
Speaker 2: or even the final cells like a skin cell or

521
00:32:10,360 --> 00:32:15,360
Speaker 2: you know, lung cell or whatever, and have them go

522
00:32:15,480 --> 00:32:17,880
Speaker 2: all the way back to pluripotent stem cells so that

523
00:32:17,920 --> 00:32:20,600
Speaker 2: they could then, you know, become any kind of cell.

524
00:32:21,320 --> 00:32:26,480
Speaker 2: And remarkably, he found that if you take four genes

525
00:32:27,720 --> 00:32:32,440
Speaker 2: and introduce them into one of these adult cells and

526
00:32:32,480 --> 00:32:36,160
Speaker 2: turn them on, you could change the genetic program of

527
00:32:36,200 --> 00:32:39,560
Speaker 2: the cell and have it go backwards all the way

528
00:32:39,560 --> 00:32:43,560
Speaker 2: back to pluripotence. Now, this has created a big industry

529
00:32:44,000 --> 00:32:45,960
Speaker 2: in the stem cells because stem cells are going to

530
00:32:45,960 --> 00:32:48,600
Speaker 2: be useful for all kinds of things. For example, if

531
00:32:48,640 --> 00:32:51,960
Speaker 2: you want to replace damaged tissue, you know, let's say

532
00:32:51,960 --> 00:32:55,800
Speaker 2: you want to replace pancreas in diabetics so that they

533
00:32:55,800 --> 00:32:59,000
Speaker 2: can produce insulin. There are all kinds of things being

534
00:32:59,040 --> 00:33:02,440
Speaker 2: talked about, and they're you know, cartilage and a guy

535
00:33:02,560 --> 00:33:06,800
Speaker 2: like me with very bad joints. So or for a

536
00:33:06,840 --> 00:33:09,720
Speaker 2: guy like me with you know, very little hair, you

537
00:33:09,760 --> 00:33:14,520
Speaker 2: could imagine stem cells stimulating new hair growth, Okay, and

538
00:33:14,560 --> 00:33:16,040
Speaker 2: that would be a billion dollar industry.

539
00:33:16,120 --> 00:33:18,000
Speaker 3: Yeah, if you could develop some like gun you pointed

540
00:33:18,000 --> 00:33:20,400
Speaker 3: a part of your body and you're like, make this younger.

541
00:33:20,720 --> 00:33:25,280
Speaker 2: Exactly, So people asked, now, the problem with going all

542
00:33:25,320 --> 00:33:29,920
Speaker 2: the way back is that you have the risk of cancer,

543
00:33:30,160 --> 00:33:34,440
Speaker 2: you know, because it's you're taking these cells. They're not

544
00:33:34,680 --> 00:33:40,040
Speaker 2: quite exactly the same as a normal embryonic development is

545
00:33:40,440 --> 00:33:44,600
Speaker 2: it's the somewhat artificial process that you're using to go

546
00:33:44,720 --> 00:33:48,800
Speaker 2: backwards in development. And when they try to grow those

547
00:33:48,880 --> 00:33:52,040
Speaker 2: plur iportent stem cells, they often would get these tumor

548
00:33:52,600 --> 00:33:56,360
Speaker 2: like growths called teratomas, and so there is definitely a

549
00:33:56,440 --> 00:34:00,360
Speaker 2: cancer risk. But what a number of scientists asked was

550
00:34:00,920 --> 00:34:06,080
Speaker 2: supposing you turn on these Yamanaka factors transiently, you know,

551
00:34:06,320 --> 00:34:09,239
Speaker 2: just turn them on and then figure out a way

552
00:34:09,280 --> 00:34:15,600
Speaker 2: to turn them off after a while, then what would happen. Well, astonishingly,

553
00:34:15,640 --> 00:34:19,960
Speaker 2: they tried this in mice and they found that the mice,

554
00:34:20,600 --> 00:34:25,080
Speaker 2: you know, resembled younger animals. They suddenly had better fur

555
00:34:25,239 --> 00:34:30,640
Speaker 2: and muscles, and you know, by various markers they seemed younger.

556
00:34:31,280 --> 00:34:35,880
Speaker 2: So this idea of cellular reprogramming is a big area

557
00:34:36,200 --> 00:34:41,240
Speaker 2: in the longevity field, but it's still in early stages.

558
00:34:41,320 --> 00:34:44,160
Speaker 2: Even though there's a lot of excitement the idea that

559
00:34:44,200 --> 00:34:46,719
Speaker 2: tomorrow you're going to go and get a treatment that

560
00:34:46,760 --> 00:34:52,200
Speaker 2: will suddenly make all yourselves younger, it's really not going

561
00:34:52,239 --> 00:34:55,440
Speaker 2: to happen anytime soon, and it's because there are lots

562
00:34:55,440 --> 00:34:58,480
Speaker 2: of problems. One is, you know, you have to get

563
00:34:58,480 --> 00:35:01,000
Speaker 2: the right dose, you have to make or it's safe.

564
00:35:01,120 --> 00:35:03,120
Speaker 2: You have to make sure it goes to the tissues

565
00:35:03,160 --> 00:35:07,680
Speaker 2: and just the right amounts. These are all big challenging problems.

566
00:35:07,719 --> 00:35:09,879
Speaker 2: And you know, of course a long term cancer risk

567
00:35:10,040 --> 00:35:15,160
Speaker 2: is another problem. So I think it's very exciting and promising,

568
00:35:16,000 --> 00:35:19,879
Speaker 2: but it's not something that's around the corner as it's

569
00:35:19,920 --> 00:35:23,920
Speaker 2: often hyped. I mean that's my opinion. Of course, you know,

570
00:35:24,000 --> 00:35:27,080
Speaker 2: people will disagree with me those but remember a lot

571
00:35:27,080 --> 00:35:29,759
Speaker 2: of these people have quite a lot of skin in

572
00:35:29,800 --> 00:35:34,000
Speaker 2: the game. They have financial interests, they've founded companies and

573
00:35:34,080 --> 00:35:39,000
Speaker 2: so on. So you have to slightly take what they

574
00:35:39,040 --> 00:35:40,240
Speaker 2: say with a pinch of salt.

575
00:35:40,560 --> 00:35:43,320
Speaker 1: So you've mentioned that one of the reasons that we

576
00:35:43,640 --> 00:35:45,680
Speaker 1: age and die is because it has something to do

577
00:35:45,719 --> 00:35:47,560
Speaker 1: with resources.

578
00:35:46,840 --> 00:35:50,640
Speaker 2: And with evolutionary choice. Basically. Yeah.

579
00:35:50,800 --> 00:35:53,920
Speaker 1: So now many humans like me live in an environment

580
00:35:53,960 --> 00:35:57,280
Speaker 1: where there are too many resources maybe, and we should

581
00:35:57,320 --> 00:35:59,640
Speaker 1: take in fewer resources, and we live in an environment

582
00:35:59,640 --> 00:36:02,120
Speaker 1: where we're better and better at being able to treat cancer,

583
00:36:02,120 --> 00:36:05,279
Speaker 1: because it seems like we keep coming up across you know,

584
00:36:05,360 --> 00:36:07,520
Speaker 1: cancer is the thing that's holding us back. So if

585
00:36:07,560 --> 00:36:10,480
Speaker 1: we were in a high resource environment and we could

586
00:36:10,480 --> 00:36:12,920
Speaker 1: figure out how to cure cancer, do you think we

587
00:36:13,000 --> 00:36:15,879
Speaker 1: might be able to get our life spans up one

588
00:36:15,920 --> 00:36:17,040
Speaker 1: hundred years or something.

589
00:36:17,400 --> 00:36:23,840
Speaker 2: Well, somebody did a calculation. Demographer named Jay Olshansky from Chicago,

590
00:36:23,920 --> 00:36:27,759
Speaker 2: who's a leading expert in this area, did a calculation

591
00:36:27,840 --> 00:36:31,200
Speaker 2: a number of years ago, maybe twenty five thirty years ago,

592
00:36:32,000 --> 00:36:36,480
Speaker 2: which suggested that if there are four major causes of

593
00:36:37,680 --> 00:36:41,000
Speaker 2: major diseases of old age that caused death, one you

594
00:36:41,080 --> 00:36:45,080
Speaker 2: mentioned cancer, the other one is diabetes, a third one

595
00:36:45,120 --> 00:36:49,360
Speaker 2: is heart disease, and the fourth one is dementia. Neuer

596
00:36:49,360 --> 00:36:53,160
Speaker 2: degenerative diseases and of course the newer degenerative diseases are

597
00:36:53,280 --> 00:36:58,680
Speaker 2: among the hardest to treat. But let's say you could

598
00:36:58,719 --> 00:37:02,399
Speaker 2: eliminate all four of them. The suggestion is you're only

599
00:37:02,440 --> 00:37:06,560
Speaker 2: gain about fifteen years of lifespan if you eliminated all

600
00:37:06,600 --> 00:37:10,439
Speaker 2: of these four causes. And the reason is that they

601
00:37:10,600 --> 00:37:15,480
Speaker 2: will not affect the normal process of aging, you know,

602
00:37:15,600 --> 00:37:19,240
Speaker 2: which leads to frailty of you know, system wide frailty.

603
00:37:20,120 --> 00:37:23,640
Speaker 2: And there's always this argument, is aging a disease And

604
00:37:24,480 --> 00:37:28,320
Speaker 2: people say, well, you know, all of these major things

605
00:37:28,360 --> 00:37:32,000
Speaker 2: like diabetes, cancer, etc. The risk goes up with age.

606
00:37:32,000 --> 00:37:34,960
Speaker 2: In fact, the biggest risk factor is age. The older

607
00:37:34,960 --> 00:37:36,640
Speaker 2: you are, the more likely you are to get one

608
00:37:36,640 --> 00:37:39,799
Speaker 2: of these things, or more or several of them. But

609
00:37:40,520 --> 00:37:44,800
Speaker 2: the other argument is that, well, these diseases don't happen

610
00:37:44,840 --> 00:37:48,200
Speaker 2: to everybody. Not everybody dies of cancer, not everybody has

611
00:37:48,239 --> 00:37:51,600
Speaker 2: heart disease, and also young people get cancer, so it's

612
00:37:51,680 --> 00:37:55,799
Speaker 2: not directly related. And aging, on the other hand, is

613
00:37:55,800 --> 00:38:00,000
Speaker 2: something that happens to every single person and it's inevitable.

614
00:38:00,520 --> 00:38:03,440
Speaker 2: So how can you call something that's both ubiquitous and

615
00:38:03,520 --> 00:38:08,680
Speaker 2: inevitable a disease. It's simply a process of life. And

616
00:38:08,760 --> 00:38:11,600
Speaker 2: I tend to agree with that. But the reason they

617
00:38:12,280 --> 00:38:14,640
Speaker 2: want to call it a disease is because then it's

618
00:38:14,680 --> 00:38:19,000
Speaker 2: easier to get approval for clinical trials. Well, I think

619
00:38:19,040 --> 00:38:22,440
Speaker 2: they ought to try some other thing. For example, they

620
00:38:22,480 --> 00:38:28,000
Speaker 2: can choose a target, a disease target that's strongly correlated

621
00:38:28,040 --> 00:38:33,520
Speaker 2: with aging, for example ostere arthritis or loss of various

622
00:38:33,640 --> 00:38:36,799
Speaker 2: functions and so on, and then they could use that

623
00:38:36,960 --> 00:38:42,200
Speaker 2: as the measure of success of their drug. So there

624
00:38:42,239 --> 00:38:44,680
Speaker 2: are ways to get around it. But I don't think

625
00:38:45,239 --> 00:38:49,759
Speaker 2: that just eliminating these diseases will increase lifespend that much.

626
00:38:49,800 --> 00:38:53,440
Speaker 2: And in fact, even people who in the aging field

627
00:38:53,480 --> 00:38:56,920
Speaker 2: who have bet so. Olshansky was on one side of

628
00:38:56,960 --> 00:39:02,600
Speaker 2: a bet with another gerontal just named Stephen Ostad. Stephen

629
00:39:02,640 --> 00:39:05,680
Speaker 2: Ostad made a bet with him that the person who

630
00:39:05,719 --> 00:39:08,840
Speaker 2: lives to be one hundred and fifty has already been born, okay,

631
00:39:09,320 --> 00:39:11,759
Speaker 2: And that bet was made some time ago, and they

632
00:39:11,800 --> 00:39:14,120
Speaker 2: bet it so that in one hundred and fifty years,

633
00:39:14,920 --> 00:39:17,880
Speaker 2: you know, the amount would be worth that a billion

634
00:39:17,920 --> 00:39:21,680
Speaker 2: dollars or something. Of course, you know, maybe it'll cost

635
00:39:21,680 --> 00:39:25,800
Speaker 2: a billion dollars to buy a sandwich, but by that time.

636
00:39:26,040 --> 00:39:29,640
Speaker 2: But anyway, but they made this bet. Now. Stephen Ostad

637
00:39:30,280 --> 00:39:32,600
Speaker 2: also doesn't believe that it's just going to be because

638
00:39:32,640 --> 00:39:36,919
Speaker 2: of eliminating disease. Rather, what he thinks is that we're

639
00:39:36,960 --> 00:39:42,160
Speaker 2: making progress in slowing down or arresting aging itself, and

640
00:39:42,239 --> 00:39:45,600
Speaker 2: that's the reason why we may end up living longer.

641
00:39:46,360 --> 00:39:50,400
Speaker 2: And for example, you know, there's a drug called wrappamicin

642
00:39:51,480 --> 00:39:56,400
Speaker 2: which is somewhat is related to caloric restriction, which also

643
00:39:56,920 --> 00:40:01,360
Speaker 2: allows animals to live longer. That, for example, can increase

644
00:40:01,600 --> 00:40:05,880
Speaker 2: lifespan in mice by you know, twenty or thirty percent. Well,

645
00:40:06,000 --> 00:40:09,000
Speaker 2: if we live you know, ninety years, you know, thirty

646
00:40:09,040 --> 00:40:12,000
Speaker 2: percent of that would already get us to one hundred

647
00:40:12,000 --> 00:40:15,360
Speaker 2: and twenty or so. You see, So maybe he's counting

648
00:40:15,440 --> 00:40:18,960
Speaker 2: on on things like that. I tend to be on

649
00:40:19,040 --> 00:40:27,759
Speaker 2: the Olshansky side. I think that I'm really fundamentally increasing lifespan,

650
00:40:27,920 --> 00:40:31,319
Speaker 2: and especially healthy lifespan. Is not going to be as

651
00:40:31,360 --> 00:40:35,839
Speaker 2: easy as they say, because it's highly multi factorial. There's

652
00:40:35,920 --> 00:40:37,120
Speaker 2: so many things going on.

653
00:40:37,640 --> 00:40:39,359
Speaker 3: Well, how do we know you're not just a shell

654
00:40:39,440 --> 00:40:40,120
Speaker 3: for big death?

655
00:40:40,320 --> 00:40:40,480
Speaker 2: You know?

656
00:40:40,480 --> 00:40:42,120
Speaker 3: Are you being paid by the death industry?

657
00:40:45,040 --> 00:40:46,719
Speaker 1: All right, well, take a break, and when we get

658
00:40:46,719 --> 00:40:47,480
Speaker 1: back we'll talk.

659
00:40:47,320 --> 00:41:08,600
Speaker 4: More about aging, and we're back.

660
00:41:08,760 --> 00:41:11,000
Speaker 1: So we have another question from a listener, and here

661
00:41:11,040 --> 00:41:14,960
Speaker 1: it is, I'm curious why immune systems seem to decline

662
00:41:14,960 --> 00:41:18,719
Speaker 1: with age. Shouldn't they get supercharged because by then, when

663
00:41:18,760 --> 00:41:20,880
Speaker 1: you're old, you've basically seen everything.

664
00:41:21,480 --> 00:41:24,600
Speaker 2: It is true that immune systems are exposed to more

665
00:41:24,719 --> 00:41:30,160
Speaker 2: things as we age, but immune systems are essentially a

666
00:41:30,200 --> 00:41:34,640
Speaker 2: collection of cells, and the cells themselves age, and so

667
00:41:34,680 --> 00:41:39,879
Speaker 2: they don't respond as well as they do when we're

668
00:41:39,960 --> 00:41:44,440
Speaker 2: younger or when we're in our prime. And this has

669
00:41:44,520 --> 00:41:50,120
Speaker 2: to do with molecular damage affecting higher levels like the

670
00:41:50,239 --> 00:41:55,720
Speaker 2: cell and communication between cells, and so for all kinds

671
00:41:55,719 --> 00:41:58,920
Speaker 2: of reasons, our immune system as a result of this

672
00:41:59,040 --> 00:42:02,880
Speaker 2: accumulated damage doesn't function optimally.

673
00:42:03,239 --> 00:42:05,640
Speaker 3: So it's got a lot more wisdom, but like less

674
00:42:05,760 --> 00:42:06,879
Speaker 3: energy and effectivity.

675
00:42:07,080 --> 00:42:10,080
Speaker 2: Yeah, and actually it doesn't function as well. For example,

676
00:42:11,320 --> 00:42:15,480
Speaker 2: it responds in an aberrant way. It's not as well regulated.

677
00:42:16,480 --> 00:42:18,920
Speaker 2: You know, the immune system always has to be very

678
00:42:18,960 --> 00:42:22,120
Speaker 2: finely regulated because you don't want to react against yourself

679
00:42:22,680 --> 00:42:26,440
Speaker 2: or against harmless things. You only want to react against

680
00:42:26,880 --> 00:42:33,600
Speaker 2: truly dangerous entities. So that fine balance is disrupted, and

681
00:42:33,719 --> 00:42:37,279
Speaker 2: so you get essentially a dysfunctional immune system, and you

682
00:42:37,360 --> 00:42:39,799
Speaker 2: also get a lot of inflammation as a result. So,

683
00:42:40,320 --> 00:42:43,759
Speaker 2: for example, I mentioned those sinescence cells. The reason those

684
00:42:43,800 --> 00:42:48,239
Speaker 2: sinessn cells secrete inflammatory compounds is as a signal to

685
00:42:48,280 --> 00:42:51,359
Speaker 2: the immune system that hey, there's something wrong here, come

686
00:42:51,400 --> 00:42:55,200
Speaker 2: and clear it up. And so the immune system will

687
00:42:55,239 --> 00:42:57,560
Speaker 2: come there. It may be the side of a wound,

688
00:42:57,680 --> 00:43:01,279
Speaker 2: or an infection or or some other stress, and it

689
00:43:01,360 --> 00:43:04,960
Speaker 2: will deal with it. But as we get older, not

690
00:43:05,080 --> 00:43:07,759
Speaker 2: only do the number of sines and cells increase, but

691
00:43:07,800 --> 00:43:11,600
Speaker 2: the immune system doesn't respond as well to the signals,

692
00:43:12,080 --> 00:43:15,719
Speaker 2: and so you get this sort of auto catalytic or

693
00:43:15,800 --> 00:43:20,080
Speaker 2: you know, you get this essentially, this explosion in the

694
00:43:20,120 --> 00:43:23,000
Speaker 2: growth of sines and cells and inflammation.

695
00:43:23,400 --> 00:43:27,120
Speaker 3: And is this something that's understood across species. One of

696
00:43:27,160 --> 00:43:30,680
Speaker 3: the listeners asked why cats and dogs have the same

697
00:43:30,840 --> 00:43:33,600
Speaker 3: age related diseases that we do, but they appear at

698
00:43:33,640 --> 00:43:36,759
Speaker 3: a younger age, maybe smaller number of years.

699
00:43:37,040 --> 00:43:41,759
Speaker 2: That's simply the fact that this allocation of repair to

700
00:43:42,000 --> 00:43:47,719
Speaker 2: maintenance and repair to growth and reproduction, that balance is

701
00:43:47,760 --> 00:43:51,560
Speaker 2: different for different species. You know, you could ask why

702
00:43:51,600 --> 00:43:54,080
Speaker 2: do whales live so long? Well, one reason is they

703
00:43:54,080 --> 00:43:57,840
Speaker 2: have a slower metabolism than say animal like a mouse.

704
00:43:58,360 --> 00:44:02,120
Speaker 2: But the other reason also is but they have a

705
00:44:02,239 --> 00:44:05,279
Speaker 2: large number of repair enzymes. You know, if you look

706
00:44:05,280 --> 00:44:08,400
Speaker 2: at just DNA repair enzymes, they have many different repair

707
00:44:08,480 --> 00:44:12,080
Speaker 2: enzymes when they sequence the genome of some of these species,

708
00:44:12,600 --> 00:44:16,480
Speaker 2: and elephants, for example, have many more copies of a

709
00:44:16,560 --> 00:44:20,279
Speaker 2: DNA repair enzyme than mice.

710
00:44:20,080 --> 00:44:23,040
Speaker 3: Do because they live longer, so they need more repairs.

711
00:44:23,719 --> 00:44:26,600
Speaker 2: Yeah, and they have to they have to maintain that. Also,

712
00:44:26,920 --> 00:44:29,759
Speaker 2: there is a paradox They have many more cells, and

713
00:44:29,800 --> 00:44:32,880
Speaker 2: so the chance that one of their cells could become

714
00:44:33,000 --> 00:44:36,120
Speaker 2: cancerous and kill the whole animal is much higher in

715
00:44:36,239 --> 00:44:40,040
Speaker 2: a larger animal than in a small animal. But paradoxically

716
00:44:40,080 --> 00:44:44,120
Speaker 2: it's mice get cancer more often than elephants, and that's

717
00:44:44,200 --> 00:44:50,120
Speaker 2: because the elephants do have this additional capacity to repair.

718
00:44:50,680 --> 00:44:56,840
Speaker 2: So it's all evolution really just optimizing for fitness. Remember,

719
00:44:56,920 --> 00:45:00,759
Speaker 2: evolution does not optimize for long life. It doesn't care

720
00:45:00,800 --> 00:45:04,440
Speaker 2: about long life. It cares about survival of genes because

721
00:45:04,480 --> 00:45:05,520
Speaker 2: that's what it selects for.

722
00:45:05,880 --> 00:45:07,560
Speaker 1: This might be a little too far off topic, but

723
00:45:07,600 --> 00:45:11,200
Speaker 1: I've seen articles that say, like green sharks never get cancer.

724
00:45:11,560 --> 00:45:14,880
Speaker 1: Are there actually species that never get cancer or it

725
00:45:14,960 --> 00:45:16,640
Speaker 1: just takes them away and we don't see it often.

726
00:45:17,040 --> 00:45:20,879
Speaker 2: It is almost entirely that we don't observe them long enough.

727
00:45:21,440 --> 00:45:25,319
Speaker 2: So for example, I'll give you an example of Glapicus tortoises, right,

728
00:45:25,600 --> 00:45:28,120
Speaker 2: you know, they live to be two hundred years old.

729
00:45:28,200 --> 00:45:30,920
Speaker 2: And I like to joke that there's probably a Galapicus

730
00:45:30,920 --> 00:45:34,000
Speaker 2: tortoise wandering around now that might have actually met Darwin.

731
00:45:34,520 --> 00:45:37,640
Speaker 2: Oh you know, that's a cool thought, right, But anyway.

732
00:45:37,840 --> 00:45:41,200
Speaker 3: Let's have them on the podcast so exactly, so.

733
00:45:41,320 --> 00:45:44,080
Speaker 2: You know, if they could talk, they might be able

734
00:45:44,080 --> 00:45:47,080
Speaker 2: to tell you quite a bit. But anyway, No, it

735
00:45:47,160 --> 00:45:50,440
Speaker 2: was thought for a while that these things, these tortoises

736
00:45:50,480 --> 00:45:53,319
Speaker 2: don't age. Well, actually they do age. If you look

737
00:45:53,320 --> 00:45:58,560
Speaker 2: at old tortoises, they have terrible eyesight, and you know,

738
00:45:58,600 --> 00:46:02,759
Speaker 2: there's slow moving, there's skins, you know, old you know

739
00:46:02,800 --> 00:46:03,640
Speaker 2: they have all these.

740
00:46:03,480 --> 00:46:05,360
Speaker 3: They don't know how to use the VC exactly.

741
00:46:05,400 --> 00:46:08,799
Speaker 2: They have all of the same problems, and it's just

742
00:46:08,920 --> 00:46:10,760
Speaker 2: that it happens more slowly, Okay.

743
00:46:11,360 --> 00:46:13,279
Speaker 1: And I gotta say, Daniel, I think the VCR joke

744
00:46:13,360 --> 00:46:14,720
Speaker 1: aged you more than anything.

745
00:46:15,960 --> 00:46:17,799
Speaker 3: Well, the fact that you laughed at it aged you.

746
00:46:18,120 --> 00:46:22,040
Speaker 1: Oh, you got me. That's true. That's true. So let's

747
00:46:22,120 --> 00:46:25,200
Speaker 1: jump back, if that's okay, to another example of folks

748
00:46:25,239 --> 00:46:29,080
Speaker 1: trying to extend lifespan. So I've heard of examples of

749
00:46:29,160 --> 00:46:32,000
Speaker 1: like taking blood from young mice and giving it to

750
00:46:32,120 --> 00:46:34,040
Speaker 1: old mice, and then I think there's a guy, Brian

751
00:46:34,160 --> 00:46:37,960
Speaker 1: Johnson who's trying to limit aging by using his son's blood.

752
00:46:38,480 --> 00:46:41,840
Speaker 1: Is there any evidence that that's anything other than nuts.

753
00:46:41,400 --> 00:46:46,000
Speaker 2: That's an excellent question. And it is true that when

754
00:46:46,040 --> 00:46:50,640
Speaker 2: they connected an old rat to a young rat, the

755
00:46:50,800 --> 00:46:54,200
Speaker 2: old rat benefited by the exchange of blood and the

756
00:46:54,239 --> 00:46:57,439
Speaker 2: young rat actually suffered. And then they were wondering whether

757
00:46:57,480 --> 00:47:00,719
Speaker 2: it was really due to the blood itself, or maybe

758
00:47:00,840 --> 00:47:04,880
Speaker 2: the young rat had better liver and kidneys to detoxify

759
00:47:04,960 --> 00:47:07,799
Speaker 2: the blood, and so it wasn't just the blood, but

760
00:47:07,880 --> 00:47:10,279
Speaker 2: it was just that it had better organs to clean

761
00:47:10,360 --> 00:47:15,320
Speaker 2: up blood. So they separated them and simply give them transfusions,

762
00:47:15,880 --> 00:47:19,640
Speaker 2: and they found that, in fact, the effect was still there,

763
00:47:20,440 --> 00:47:23,920
Speaker 2: but it was more that the old rat had things

764
00:47:24,000 --> 00:47:26,759
Speaker 2: in it that were harmful to the young rat. That

765
00:47:26,960 --> 00:47:30,359
Speaker 2: was more the case than that the young blood was

766
00:47:30,440 --> 00:47:34,120
Speaker 2: beneficial to the old rat. But it did. But there

767
00:47:34,200 --> 00:47:38,040
Speaker 2: was some effect both ways. Now this is true, and

768
00:47:38,360 --> 00:47:40,759
Speaker 2: when the people discovered it, they caught all sorts of

769
00:47:40,800 --> 00:47:44,600
Speaker 2: creepy phone calls from rich people asking, you know, whether

770
00:47:44,640 --> 00:47:48,840
Speaker 2: they could get young blood and so on, and in fact, companies.

771
00:47:48,440 --> 00:47:50,480
Speaker 3: Where do babies? It's kind of exactly.

772
00:47:52,120 --> 00:47:56,560
Speaker 2: And in fact companies sprouted up, and as you can imagine,

773
00:47:56,560 --> 00:47:58,320
Speaker 2: mostly in California. I think.

774
00:48:01,360 --> 00:48:03,440
Speaker 3: That's what I would get, you mean, the center of

775
00:48:03,520 --> 00:48:06,440
Speaker 3: innovation and forward thinking and creativity.

776
00:48:06,480 --> 00:48:12,879
Speaker 5: That's why you said, ye, anyway, that's somehow obsessed with youth.

777
00:48:13,239 --> 00:48:19,600
Speaker 5: But but anyway, some of these companies would would get

778
00:48:19,680 --> 00:48:22,160
Speaker 5: blood from young donors and sell them at a huge

779
00:48:22,200 --> 00:48:26,080
Speaker 5: markup to rich people wanted them. And in one case,

780
00:48:26,120 --> 00:48:28,880
Speaker 5: the FDA actually tried to shut one a company down

781
00:48:28,920 --> 00:48:31,799
Speaker 5: and then it opened up under a different name. And

782
00:48:31,840 --> 00:48:35,680
Speaker 5: in one case the CEO said, well, look, our people

783
00:48:35,960 --> 00:48:38,759
Speaker 5: simply don't have the time to wait for clinical trials,

784
00:48:38,840 --> 00:48:45,360
Speaker 5: you know, Oh my goodness. It was really bizarre coming from,

785
00:48:45,680 --> 00:48:50,040
Speaker 5: you know, a CEO of you know, a health based company.

786
00:48:50,239 --> 00:48:52,839
Speaker 3: But you're saying that there are real benefits to having

787
00:48:52,880 --> 00:48:54,840
Speaker 3: transfusions of blood from young people.

788
00:48:54,960 --> 00:48:58,680
Speaker 2: Well, there's certainly seemed to be in animals, and so

789
00:48:58,880 --> 00:49:02,160
Speaker 2: there's a big bar of research to find out what

790
00:49:02,320 --> 00:49:05,880
Speaker 2: is changing in blood as we get older, and what

791
00:49:05,920 --> 00:49:08,680
Speaker 2: do these factors do. You know, if they're harmful in

792
00:49:08,800 --> 00:49:12,279
Speaker 2: old age, what do they do? Maybe we can inhibit them,

793
00:49:12,440 --> 00:49:15,600
Speaker 2: or if they're beneficial in early life, maybe we can

794
00:49:16,120 --> 00:49:21,360
Speaker 2: take advantage of that and introduce them into older people.

795
00:49:21,719 --> 00:49:25,600
Speaker 2: So I think that's a very legitimate and broad area

796
00:49:25,640 --> 00:49:28,920
Speaker 2: of research and lots of very you know, top scientists

797
00:49:28,960 --> 00:49:33,080
Speaker 2: from very well known universities are actually working on that.

798
00:49:34,239 --> 00:49:38,520
Speaker 2: But you know, this idea that you should just take transfusions,

799
00:49:39,160 --> 00:49:43,000
Speaker 2: you know, it's not really going to help that much

800
00:49:43,040 --> 00:49:48,360
Speaker 2: at this point. And Brian Johnson whom you mentioned, actually

801
00:49:48,400 --> 00:49:50,880
Speaker 2: did this experiment of keeping it all in the family.

802
00:49:51,080 --> 00:49:53,640
Speaker 2: He took blood from his son and gave his blood

803
00:49:53,640 --> 00:49:57,640
Speaker 2: to his dad. But he's also, I mean to give

804
00:49:57,719 --> 00:50:01,000
Speaker 2: him some credit, he's obsessed with a you know, or

805
00:50:01,080 --> 00:50:04,799
Speaker 2: not aging to be more precise. He spends like a

806
00:50:04,800 --> 00:50:08,360
Speaker 2: couple of million dollars a year on all kinds of

807
00:50:08,400 --> 00:50:13,480
Speaker 2: longevity treatments and measurements, and you know probably has you know,

808
00:50:13,800 --> 00:50:15,920
Speaker 2: fitness programs and all sorts of things.

809
00:50:16,160 --> 00:50:19,160
Speaker 3: Okay, Well, the thing that fascinates me about Brian Johnson

810
00:50:19,360 --> 00:50:21,640
Speaker 3: is that he does take a lot of data, right,

811
00:50:21,680 --> 00:50:22,000
Speaker 3: he is.

812
00:50:21,960 --> 00:50:26,120
Speaker 2: Exactly focused data on metrics, right, He's focused on metrics.

813
00:50:26,320 --> 00:50:28,800
Speaker 3: But he doesn't look young, Like even though he says

814
00:50:28,920 --> 00:50:30,960
Speaker 3: he has all these metrics which are equivalent to an

815
00:50:30,960 --> 00:50:33,839
Speaker 3: eighteen year old, he still looks like a vampire. So

816
00:50:33,880 --> 00:50:36,239
Speaker 3: he sort of captures this like, well.

817
00:50:36,160 --> 00:50:39,799
Speaker 2: I would say, no, I'll give him credit. He's his

818
00:50:39,920 --> 00:50:43,040
Speaker 2: late forties. He looks pretty good for late forties. But

819
00:50:43,120 --> 00:50:45,200
Speaker 2: I'll tell you my son is in his late forties.

820
00:50:45,320 --> 00:50:48,200
Speaker 2: Yeah he does none of this stuff. Yeah, okay, but

821
00:50:48,280 --> 00:50:51,520
Speaker 2: he runs regularly and eats well, and he looks just

822
00:50:51,560 --> 00:50:53,840
Speaker 2: as good as Brian Johnson. And I'm not just being biased.

823
00:50:53,880 --> 00:50:56,360
Speaker 2: You could look him up on online. He's a cellist.

824
00:50:56,920 --> 00:51:00,719
Speaker 3: So well, you're definitely biased, but I don't don't not

825
00:51:00,800 --> 00:51:02,840
Speaker 3: believe you, But you know, I think it raises a

826
00:51:02,880 --> 00:51:05,239
Speaker 3: deeper question, which is, like, is it possible to be

827
00:51:05,840 --> 00:51:08,799
Speaker 3: young biologically by all of these metrics, as you say,

828
00:51:08,840 --> 00:51:11,920
Speaker 3: you know, you're measuring the damage to whatever molecular mechanisms,

829
00:51:12,080 --> 00:51:15,719
Speaker 3: but still somehow not be young in the sort of

830
00:51:15,719 --> 00:51:16,440
Speaker 3: social sense.

831
00:51:17,080 --> 00:51:20,480
Speaker 2: That's that's a very good question. You know. So you

832
00:51:20,520 --> 00:51:23,000
Speaker 2: know I mentioned the high school reunion and how we

833
00:51:23,080 --> 00:51:26,280
Speaker 2: all look different. Yeah, so that's led to this quest

834
00:51:26,440 --> 00:51:31,600
Speaker 2: for biological markers of age. Okay, yeah, because you want

835
00:51:31,640 --> 00:51:34,360
Speaker 2: to know. You know, your birthday may have been, you know,

836
00:51:34,440 --> 00:51:37,279
Speaker 2: forty years ago, but how old are you really in

837
00:51:37,320 --> 00:51:40,319
Speaker 2: biological terms? Right, So the people have come up with

838
00:51:40,360 --> 00:51:43,799
Speaker 2: different clocks, you know. So one clock is this so

839
00:51:43,880 --> 00:51:47,600
Speaker 2: called DNA methylation clock. So these are little tags that

840
00:51:47,800 --> 00:51:51,760
Speaker 2: get attached to our DNA from the time we're conceived. Okay,

841
00:51:51,800 --> 00:51:56,160
Speaker 2: it happens even in utero. We're aging even in utero, okay,

842
00:51:56,840 --> 00:52:05,160
Speaker 2: And that's apparently better correlated with with mortality than chronological age.

843
00:52:05,480 --> 00:52:07,960
Speaker 2: You know, So chances that you're going to die are

844
00:52:08,000 --> 00:52:10,520
Speaker 2: more correlated with your data infilation than they are with

845
00:52:10,640 --> 00:52:14,640
Speaker 2: your date of birth. So so that's you know, used

846
00:52:14,640 --> 00:52:15,240
Speaker 2: as a clock.

847
00:52:15,640 --> 00:52:18,680
Speaker 3: But does that suggest that if you could somehow adjust

848
00:52:18,719 --> 00:52:21,120
Speaker 3: that you would extend your life? I mean, is it

849
00:52:21,200 --> 00:52:22,520
Speaker 3: causal or is it correlated?

850
00:52:22,800 --> 00:52:26,080
Speaker 2: That's the real question. You know, we don't know the

851
00:52:26,160 --> 00:52:30,160
Speaker 2: extent of causality. The other case is that as we age,

852
00:52:30,680 --> 00:52:36,120
Speaker 2: extra sugar groups get added to our proteins. It's called lication.

853
00:52:36,840 --> 00:52:41,920
Speaker 2: And so you can measure this, you know, addition of

854
00:52:41,960 --> 00:52:45,640
Speaker 2: sugar groups to our proteins, and when that happens to

855
00:52:45,680 --> 00:52:49,040
Speaker 2: our proteins of our immune system, it also doesn't work

856
00:52:49,080 --> 00:52:52,440
Speaker 2: as well. So people think that it has some connection

857
00:52:52,600 --> 00:52:56,120
Speaker 2: with this, you know, decay of the immune system. But anyway,

858
00:52:56,640 --> 00:52:59,839
Speaker 2: that's another clock. Now people will sell you kits. They'll

859
00:52:59,840 --> 00:53:04,200
Speaker 2: tell your denim infylation kit or a glacation kit or

860
00:53:04,200 --> 00:53:08,359
Speaker 2: a full blood you know library. You know, they'll just

861
00:53:08,520 --> 00:53:11,399
Speaker 2: analyze a bunch of stuff in your blood to give

862
00:53:11,440 --> 00:53:13,839
Speaker 2: you a sort of biological age, and each one will

863
00:53:13,880 --> 00:53:17,680
Speaker 2: say this is our thing, is the most accurate? Okay. Now,

864
00:53:18,160 --> 00:53:22,160
Speaker 2: I think these are all very useful research tools because

865
00:53:23,360 --> 00:53:28,080
Speaker 2: if you have a longevity intervention, like an anti aging intervention,

866
00:53:29,040 --> 00:53:31,880
Speaker 2: you can see are these markers changing more slowly or

867
00:53:32,480 --> 00:53:34,959
Speaker 2: are they changing at the same rate? Okay, and they'll

868
00:53:34,960 --> 00:53:37,160
Speaker 2: give you a good idea of you know, are you

869
00:53:37,200 --> 00:53:41,719
Speaker 2: aging faster or not. But people need to come together

870
00:53:42,400 --> 00:53:46,359
Speaker 2: and agree on a panel. You know, I don't think

871
00:53:46,440 --> 00:53:50,320
Speaker 2: a single clock is going to tell you the whole story. Yeah, okay,

872
00:53:51,120 --> 00:53:54,000
Speaker 2: I think they need to agree on a panel and

873
00:53:54,080 --> 00:53:58,440
Speaker 2: then say okay, here's a panel and this is what

874
00:53:58,600 --> 00:54:02,120
Speaker 2: it represents, and it might be a complex thing. There's

875
00:54:02,120 --> 00:54:05,200
Speaker 2: no point in talking about your biological age because your

876
00:54:05,320 --> 00:54:08,040
Speaker 2: liver may not be the same age as your kidney

877
00:54:08,160 --> 00:54:10,719
Speaker 2: or your lung. You know, you can imagine if you're

878
00:54:10,719 --> 00:54:14,600
Speaker 2: an alcoholic, your liver might be older than other parts

879
00:54:14,600 --> 00:54:17,640
Speaker 2: of your bodies. So I think people need to have

880
00:54:17,680 --> 00:54:21,680
Speaker 2: a more complex view of aging, of biological age.

881
00:54:21,440 --> 00:54:24,600
Speaker 3: But don't we also need to unravel this question of causality.

882
00:54:24,600 --> 00:54:27,239
Speaker 3: I mean, if you identify a marker, even or even

883
00:54:27,280 --> 00:54:32,160
Speaker 3: a complex panel that indicates biological age, adjusting those results

884
00:54:32,200 --> 00:54:35,359
Speaker 3: doesn't necessarily make you younger. It's like I can turn

885
00:54:35,440 --> 00:54:38,000
Speaker 3: back the clock literally and it will read a different number.

886
00:54:38,200 --> 00:54:41,280
Speaker 3: Doesn't make me younger. And if this is just a comment,

887
00:54:41,320 --> 00:54:43,960
Speaker 3: one more thing which is one of my favorite mechanisms

888
00:54:43,960 --> 00:54:46,520
Speaker 3: that Brian Johnson keeps track of is that, and I

889
00:54:46,560 --> 00:54:48,759
Speaker 3: love that he's totally transparent about his data, is that

890
00:54:48,840 --> 00:54:52,680
Speaker 3: he measures his erection quality during the night and he

891
00:54:52,760 --> 00:54:55,839
Speaker 3: posts this data online, which I think is hilarious. But

892
00:54:55,920 --> 00:54:58,239
Speaker 3: you know, just as an easy example, if the guy

893
00:54:58,320 --> 00:55:00,520
Speaker 3: took a viagara every night when he went to you

894
00:55:00,600 --> 00:55:03,440
Speaker 3: probably would have like glorious erections all night long. It

895
00:55:03,480 --> 00:55:05,240
Speaker 3: wouldn't make him any younger, right.

896
00:55:05,400 --> 00:55:10,400
Speaker 2: That's true. But you know, let's take dinner methylation. You know,

897
00:55:10,719 --> 00:55:15,520
Speaker 2: so one of the things about those reprogrammed cells is

898
00:55:15,560 --> 00:55:18,840
Speaker 2: that they have changed the methylation pattern as well, you know.

899
00:55:19,000 --> 00:55:23,040
Speaker 2: So I mean that one of the distinct things about

900
00:55:24,160 --> 00:55:26,840
Speaker 2: going back to an early embryonic state is that the

901
00:55:26,880 --> 00:55:31,920
Speaker 2: methylation pattern is different. So there may be some element

902
00:55:31,960 --> 00:55:36,319
Speaker 2: of causality, because methylation does change the program of our

903
00:55:36,440 --> 00:55:40,360
Speaker 2: gene expression. So if you're going back to an earlier state,

904
00:55:40,840 --> 00:55:45,520
Speaker 2: you maybe you're going back to an earlier program. But

905
00:55:45,640 --> 00:55:49,560
Speaker 2: I agree that, you know, causality, you know, needs to

906
00:55:49,560 --> 00:55:53,920
Speaker 2: be established by careful experiments. You know, is is it

907
00:55:54,040 --> 00:56:00,600
Speaker 2: sufficient to reverse methylation and without cause on automatic cause

908
00:56:01,239 --> 00:56:03,880
Speaker 2: something to look younger. There are some scientists who claim

909
00:56:03,960 --> 00:56:07,560
Speaker 2: that they have reversed aging just by this process, but

910
00:56:07,600 --> 00:56:08,799
Speaker 2: it's highly controversial.

911
00:56:08,920 --> 00:56:10,759
Speaker 1: So I imagine our listener is going to want to know,

912
00:56:10,920 --> 00:56:13,719
Speaker 1: as an expert in aging who doesn't believe, you know,

913
00:56:13,760 --> 00:56:15,719
Speaker 1: that there's a magic pill out there that's going to

914
00:56:15,719 --> 00:56:18,040
Speaker 1: give us an extra fifty to one hundred years, what

915
00:56:18,280 --> 00:56:21,600
Speaker 1: do you do to slow the aging process?

916
00:56:21,800 --> 00:56:28,359
Speaker 2: Yeah, yeah, I should say, you know, there's no theoretical

917
00:56:28,440 --> 00:56:30,839
Speaker 2: reason why we couldn't all start living to be one

918
00:56:30,920 --> 00:56:35,799
Speaker 2: hundred and fifty eventually. Okay. The thing that I'm what

919
00:56:35,880 --> 00:56:39,560
Speaker 2: I'm saying is that we don't know how to do

920
00:56:39,600 --> 00:56:43,200
Speaker 2: that at this point, and more importantly, we don't know

921
00:56:43,239 --> 00:56:45,719
Speaker 2: how long it's going to take. And that's where I

922
00:56:45,840 --> 00:56:49,960
Speaker 2: differ with some of the more extreme optimists that the

923
00:56:50,000 --> 00:56:53,879
Speaker 2: field is full off. Okay, but what we can do

924
00:56:54,360 --> 00:56:58,040
Speaker 2: right now before then? I want to address one question.

925
00:56:58,080 --> 00:57:02,359
Speaker 2: If you ask most aging research, they would say, oh,

926
00:57:02,360 --> 00:57:05,960
Speaker 2: we're not interested in extending lifespan. We're really interested in

927
00:57:06,000 --> 00:57:11,800
Speaker 2: extending health span. Okay. And this whole thing is based

928
00:57:11,840 --> 00:57:15,000
Speaker 2: on an idea called compression of morbidity. So as we

929
00:57:15,040 --> 00:57:19,600
Speaker 2: get older, we start accumulating various morbidities. You know, you

930
00:57:19,600 --> 00:57:23,360
Speaker 2: could say diabetes is one, or heart disease or dementia, accounts,

931
00:57:23,840 --> 00:57:27,080
Speaker 2: et cetera. You know, frailty of various kinds or morbidities,

932
00:57:27,760 --> 00:57:32,200
Speaker 2: and the ideal life would be that you're extremely healthy

933
00:57:32,320 --> 00:57:35,360
Speaker 2: and then suddenly we have undergo a rapid decline. Okay.

934
00:57:35,680 --> 00:57:38,760
Speaker 2: This is called compression of that morbidity into a very

935
00:57:38,800 --> 00:57:42,560
Speaker 2: short space of time. You have a span of time.

936
00:57:43,160 --> 00:57:49,280
Speaker 2: So that's the goal. The question is is that even possible. Well,

937
00:57:49,640 --> 00:57:55,160
Speaker 2: in the last few decades we are all living healthier

938
00:57:55,240 --> 00:57:58,600
Speaker 2: as a result of improvements in health, but it's also

939
00:57:58,640 --> 00:58:01,920
Speaker 2: extended our lives, so that our period of morbidity has

940
00:58:02,000 --> 00:58:06,760
Speaker 2: not changed. Okay, so it's just postponed. And in fact,

941
00:58:06,920 --> 00:58:10,440
Speaker 2: you know, we're living more years and it's sort of

942
00:58:11,120 --> 00:58:15,200
Speaker 2: decline than you know veras before, we might have died

943
00:58:15,560 --> 00:58:20,280
Speaker 2: brutally quickly, okay, as soon as something went wrong, you know,

944
00:58:20,880 --> 00:58:23,520
Speaker 2: would collapse and die, and now we're sort of prolonging

945
00:58:23,560 --> 00:58:27,480
Speaker 2: it and have a long period of morbidity. So it's

946
00:58:27,520 --> 00:58:32,680
Speaker 2: not clear that as we improve things, we're going to

947
00:58:33,720 --> 00:58:37,720
Speaker 2: somehow keep healthy and reach some fixed limit and then collapse.

948
00:58:38,120 --> 00:58:40,680
Speaker 2: It may simply be that we'll live a bit longer

949
00:58:41,200 --> 00:58:45,560
Speaker 2: and still have that inevitable period of decline. That's an

950
00:58:45,640 --> 00:58:50,160
Speaker 2: unsolved question, no matter what people will actually say. The

951
00:58:50,200 --> 00:58:55,280
Speaker 2: one exception to this are super centenarians. These are people

952
00:58:55,320 --> 00:58:57,880
Speaker 2: who live to be over one hundred and ten and

953
00:58:57,960 --> 00:59:00,160
Speaker 2: even over one hundred and five. They tend to be

954
00:59:00,200 --> 00:59:03,840
Speaker 2: extremely healthy. Many of them have never seen a doctor

955
00:59:03,920 --> 00:59:07,880
Speaker 2: until they're one hundred or so, and then they suddenly

956
00:59:07,880 --> 00:59:11,880
Speaker 2: go into a decline and die. Now you could ask

957
00:59:12,280 --> 00:59:15,560
Speaker 2: why is that, Well, it could be that they're selected

958
00:59:16,680 --> 00:59:20,919
Speaker 2: and they're just there's a selection bias there. First of all,

959
00:59:21,000 --> 00:59:23,800
Speaker 2: they may be lucky in the combination of genes that

960
00:59:23,840 --> 00:59:28,520
Speaker 2: they have, but they each combination, there's no fixed combination.

961
00:59:28,640 --> 00:59:32,000
Speaker 2: They may be different than each individual, but somehow these

962
00:59:32,040 --> 00:59:36,880
Speaker 2: combinations give them that edge. Another is that they may

963
00:59:36,920 --> 00:59:42,000
Speaker 2: simply have been lucky in avoiding various diseases and cancer

964
00:59:42,120 --> 00:59:46,120
Speaker 2: and accidents and so on. And you're looking at the survivors, okay,

965
00:59:46,680 --> 00:59:51,360
Speaker 2: and so it's not something that's translatable to the rest

966
00:59:51,400 --> 00:59:55,760
Speaker 2: of the population necessarily. So that's still debating. And people

967
00:59:55,840 --> 00:59:58,440
Speaker 2: are studying centenarians, which I think is a great idea,

968
00:59:58,960 --> 01:00:01,280
Speaker 2: and trying to find out more or about their lifestyle

969
01:00:01,360 --> 01:00:05,280
Speaker 2: and their genome and also their methylation patterns and so on.

970
01:00:05,840 --> 01:00:11,480
Speaker 2: Now you asked, what could WeDo, Well, I advocate the

971
01:00:11,520 --> 01:00:18,200
Speaker 2: trio of diet, exercise, and sleep. It's been known in

972
01:00:19,080 --> 01:00:24,280
Speaker 2: many species that caloric restriction improves lifespan and improves health

973
01:00:24,400 --> 01:00:28,920
Speaker 2: in old age. And of course caloric restriction is extreme.

974
01:00:29,080 --> 01:00:32,200
Speaker 2: It means you're consuming just the bare minimum number of

975
01:00:32,240 --> 01:00:36,480
Speaker 2: calories required to have a steady state. In other words,

976
01:00:36,520 --> 01:00:39,480
Speaker 2: you're not losing weight and starving, but you're just steady.

977
01:00:40,080 --> 01:00:43,760
Speaker 2: But that will leave you hungry and cold, and your

978
01:00:43,960 --> 01:00:47,680
Speaker 2: loss of libido and all sorts of side effects which

979
01:00:48,120 --> 01:00:52,720
Speaker 2: maybe not worth it. Yeah, you know, it reminds me

980
01:00:52,760 --> 01:00:54,680
Speaker 2: of that joke about the doctor who said, you know,

981
01:00:54,960 --> 01:00:57,840
Speaker 2: if you do these things, you'll live live longer, and

982
01:00:57,920 --> 01:01:00,480
Speaker 2: the patient said really, he said, well, I'm not sure,

983
01:01:00,520 --> 01:01:06,040
Speaker 2: but it will feel like it so anyway. So, but

984
01:01:06,040 --> 01:01:08,280
Speaker 2: but you could have a moderate diet, you know. And

985
01:01:08,680 --> 01:01:12,800
Speaker 2: it is true that a healthy and moderate diet will help.

986
01:01:13,280 --> 01:01:16,560
Speaker 2: And exercise has all kinds of things, including, by the way,

987
01:01:17,200 --> 01:01:22,640
Speaker 2: those regenerative abilities, regenerating muscle, and even regenerating mitochondria, which

988
01:01:22,640 --> 01:01:26,840
Speaker 2: are these organelles in our cells. So exercise has huge

989
01:01:26,960 --> 01:01:30,920
Speaker 2: benefits that are only now becoming clear. And then the

990
01:01:30,960 --> 01:01:35,680
Speaker 2: third which I think Americans need to take more note off.

991
01:01:36,280 --> 01:01:38,560
Speaker 2: And by the way, I am an American who lives

992
01:01:38,600 --> 01:01:42,080
Speaker 2: in Britain, although I'm now also a British citizen. So

993
01:01:42,240 --> 01:01:49,440
Speaker 2: Americans particularly ignore sleep. Okay, and sleep is really important

994
01:01:49,480 --> 01:01:52,320
Speaker 2: because that is when a lot of the repair and

995
01:01:52,400 --> 01:01:57,400
Speaker 2: maintenance mechanism of the cell, the clear, clearing out garbage,

996
01:01:58,520 --> 01:02:03,360
Speaker 2: you know, repairing damage, et cetera. Much of that occurs

997
01:02:03,360 --> 01:02:06,200
Speaker 2: when we sleep. And there's actually a very nice book

998
01:02:06,240 --> 01:02:09,720
Speaker 2: called Why We Sleep by Matthew Walker, which talks about

999
01:02:10,040 --> 01:02:13,360
Speaker 2: all of the things about sleep. So though that trio

1000
01:02:13,480 --> 01:02:17,800
Speaker 2: is extremely healthful. Now, things like stress, cause you know,

1001
01:02:18,000 --> 01:02:21,520
Speaker 2: accelerate aging. But you know, if you exercise and sleep,

1002
01:02:21,840 --> 01:02:24,640
Speaker 2: you will also be less stressed. And if you exercise

1003
01:02:24,720 --> 01:02:28,440
Speaker 2: you'll sleep better, sleep better, you're less likely to overeat

1004
01:02:28,520 --> 01:02:31,800
Speaker 2: and you know, snack and so on. So there's it's

1005
01:02:31,840 --> 01:02:34,320
Speaker 2: like a three legged stool that help you know, each

1006
01:02:34,400 --> 01:02:36,200
Speaker 2: one helps the other two.

1007
01:02:36,360 --> 01:02:38,600
Speaker 3: But then let me ask you about that specifically, because

1008
01:02:38,640 --> 01:02:41,200
Speaker 3: it feels like as we get older, it's harder to

1009
01:02:41,240 --> 01:02:43,880
Speaker 3: sleep longer it is well. And yet you're telling me

1010
01:02:43,920 --> 01:02:46,360
Speaker 3: that sleep is crucial for old age, and so it

1011
01:02:46,400 --> 01:02:49,160
Speaker 3: seems like a death spiral there exactly.

1012
01:02:49,200 --> 01:02:52,480
Speaker 2: And that's why if you exercise and eat well, you're

1013
01:02:52,520 --> 01:02:55,720
Speaker 2: more likely to sleep well. And and then it's a

1014
01:02:55,840 --> 01:02:58,280
Speaker 2: it's a kind of virtuous cycle. They help each other,

1015
01:02:58,360 --> 01:03:02,280
Speaker 2: each each leg help the other two. And then there

1016
01:03:02,280 --> 01:03:06,680
Speaker 2: are social things. For example, again, Daniel, you mentioned causation

1017
01:03:06,960 --> 01:03:11,880
Speaker 2: versus correlation. But there's strong evidence that people who are

1018
01:03:11,920 --> 01:03:15,040
Speaker 2: socially well networked in old age, for example, they have

1019
01:03:15,120 --> 01:03:20,040
Speaker 2: circles of friends, family, they're socially involved, tend to have

1020
01:03:20,120 --> 01:03:23,880
Speaker 2: lower mortality rates. And people with a sense of purpose

1021
01:03:23,920 --> 01:03:27,400
Speaker 2: in life, independently of the social network they have a

1022
01:03:27,480 --> 01:03:32,040
Speaker 2: sense of purpose in life also tend to live longer.

1023
01:03:32,640 --> 01:03:36,320
Speaker 2: And so this would argue for being socially involved and

1024
01:03:36,440 --> 01:03:40,640
Speaker 2: perhaps you know, contributing, you know, maybe volunteering and having

1025
01:03:40,680 --> 01:03:46,280
Speaker 2: some sort of purpose just beyond watching your Netflix Q,

1026
01:03:47,520 --> 01:03:49,919
Speaker 2: although that some people would argue that's a purpose too,

1027
01:03:50,760 --> 01:03:54,000
Speaker 2: but anyway, but having a real purpose in life might help.

1028
01:03:54,280 --> 01:03:57,560
Speaker 2: Now again you might say, well, people who are healthier

1029
01:03:57,600 --> 01:04:01,960
Speaker 2: and you know, not as fast may be more inclined

1030
01:04:02,000 --> 01:04:06,320
Speaker 2: to do these things. So there is this correlation causation issue,

1031
01:04:06,360 --> 01:04:08,760
Speaker 2: but I think it's it's well worth considering.

1032
01:04:09,400 --> 01:04:11,000
Speaker 1: Is it time for the alien question, Daniel?

1033
01:04:11,120 --> 01:04:13,880
Speaker 3: I think it is. So we often wonder on this

1034
01:04:13,920 --> 01:04:17,160
Speaker 3: podcast not just about the scientific mysteries here on Earth,

1035
01:04:17,200 --> 01:04:20,920
Speaker 3: but scientific mysteries more broadly in the galaxy. And so

1036
01:04:21,000 --> 01:04:23,840
Speaker 3: since we're in this moment where we you know, maybe

1037
01:04:23,880 --> 01:04:27,240
Speaker 3: on the cusp of discovering aliens on the other planets

1038
01:04:27,280 --> 01:04:30,480
Speaker 3: in the next decade or whatever. Do you think that.

1039
01:04:30,760 --> 01:04:32,680
Speaker 2: I'm very agnostic about that, by.

1040
01:04:32,520 --> 01:04:36,000
Speaker 3: The way, as Ama, I sure, though enthusiastic. But say

1041
01:04:36,000 --> 01:04:39,240
Speaker 3: that we're there, you're an astrobiologist, you're on a mission,

1042
01:04:39,280 --> 01:04:41,920
Speaker 3: you're landing on the planet. Do you expect that life

1043
01:04:41,920 --> 01:04:44,840
Speaker 3: cycles on alien planets will also have the same sort

1044
01:04:44,880 --> 01:04:47,040
Speaker 3: of aging patterns that we see here on Earth.

1045
01:04:47,560 --> 01:04:50,680
Speaker 2: I think so, because I think natural selection is a

1046
01:04:51,560 --> 01:04:54,439
Speaker 2: universal process. You know, if you think of life as

1047
01:04:55,080 --> 01:05:00,240
Speaker 2: essentially the ability to reproduce, self, replicate, and evolve are

1048
01:05:00,280 --> 01:05:04,280
Speaker 2: two essential characteristics of life. So if you have that,

1049
01:05:04,360 --> 01:05:08,840
Speaker 2: you will have natural selection, and so it will if

1050
01:05:09,080 --> 01:05:14,640
Speaker 2: inevitably have these trade offs of resource versus maintenance and repair,

1051
01:05:15,320 --> 01:05:18,360
Speaker 2: and and of course if it's carbon based, then you

1052
01:05:18,400 --> 01:05:22,360
Speaker 2: know it's more likely even to have that. And and ultimately,

1053
01:05:22,680 --> 01:05:27,240
Speaker 2: ultimately the laws of physics, which you know are result

1054
01:05:27,320 --> 01:05:32,680
Speaker 2: in chemistry, which results in damage that's not going to change,

1055
01:05:33,480 --> 01:05:35,040
Speaker 2: you know, somewhere else.

1056
01:05:35,360 --> 01:05:39,240
Speaker 3: So everywhere across the galaxy there are grumpy old aliens

1057
01:05:39,280 --> 01:05:41,120
Speaker 3: telling those young kids to get off their lawn.

1058
01:05:42,000 --> 01:05:45,840
Speaker 2: That that that I would I would bet on that

1059
01:05:45,880 --> 01:05:49,560
Speaker 2: if I had to. But I'm I'm somewhat skeptical about

1060
01:05:50,320 --> 01:05:52,760
Speaker 2: I think we don't know what the probability of life

1061
01:05:52,800 --> 01:05:56,439
Speaker 2: here is, and until we know that, we have no

1062
01:05:56,560 --> 01:06:00,919
Speaker 2: idea whether life elsewhere is very likely or whether we're

1063
01:06:00,960 --> 01:06:05,440
Speaker 2: alone or somewhere in between. We just don't know. I

1064
01:06:05,440 --> 01:06:10,920
Speaker 2: should say some of the enthusiasm for extending lifespan to

1065
01:06:11,160 --> 01:06:14,400
Speaker 2: very very long lifespan is by people who want to

1066
01:06:14,440 --> 01:06:17,600
Speaker 2: do extra galactic travel. You know, there are people who

1067
01:06:17,600 --> 01:06:20,840
Speaker 2: feel that we may be the only intelligent species and

1068
01:06:20,880 --> 01:06:25,360
Speaker 2: we should go off and colonize not just Mars, which

1069
01:06:25,400 --> 01:06:29,200
Speaker 2: you guys have pointed out as extremely hard anyway, but

1070
01:06:30,360 --> 01:06:33,720
Speaker 2: you know, even other galaxies, and so they figure, well,

1071
01:06:33,920 --> 01:06:35,240
Speaker 2: if we have to do that, then we have to

1072
01:06:35,280 --> 01:06:37,360
Speaker 2: be able to survive the voyage, you know, and so

1073
01:06:37,400 --> 01:06:40,480
Speaker 2: we should you know, we need to start working on longevity.

1074
01:06:40,800 --> 01:06:44,960
Speaker 2: So it seems like a crazy idea, but anyway, that's

1075
01:06:45,000 --> 01:06:45,480
Speaker 2: how it is.

1076
01:06:45,760 --> 01:06:47,280
Speaker 1: I think for a lot of people, it's like, you know,

1077
01:06:47,320 --> 01:06:49,280
Speaker 1: they'll say, oh, I want humanity to do it, but

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Speaker 1: what they mean is that I want to be the

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Speaker 1: one who does it in particular.

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Speaker 2: So yeah, yeah, yeah, absolutely, yes, all.

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Speaker 1: Right, Well, thank you so much for being on the show.

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Speaker 1: This was fascinating. I'm sure our listeners are going to

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01:06:59,560 --> 01:07:01,920
Speaker 1: be thrilled with all of the answers, and thank you

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01:07:01,960 --> 01:07:02,520
Speaker 1: for your time.

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Speaker 2: Thank you, it's been a real pleasure chatting with both

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Speaker 2: of you. And by the way, I really enjoyed your book.

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Speaker 1: Oh thanks, I loved your book. Daniel and Kelly's Extraordinary

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01:07:17,320 --> 01:07:20,520
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01:07:20,560 --> 01:07:21,040
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Speaker 3: We really would. We want to know what questions you

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01:07:24,280 --> 01:07:26,920
Speaker 3: have about this Extraordinary Universe.

1092
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1093
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01:07:37,280 --> 01:07:40,480
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01:07:41,720 --> 01:07:45,520
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