The Science Behind Ageless - Andrew Steele

i find a lot of the sort of wilder stuff that's going on really exciting and i'm just really fascinated to see how it pans out i think the the treatment i'd put most in this category is cellular reprogramming because i often say it feels like it's fallen through a wormhole from the future so hello and welcome to the cheeky science show where in this video i had the great pleasure of speaking with andrew still author of the book ageless i'm full-time science writer and presenter based in london andrew previously obtained a phd in physics from the university of oxford before staring towards computational biology here we discuss some of the fascinating discoveries in his book ageless and his thoughts on how computational biology can be applied to aging research i hope you enjoy our conversation oh hi andrew thank you for joining me today and welcome to the shaky science show right so great to have you on today thanks for having me it's a pleasure i'm looking forward to it thanks um so my first question before we go into like um the details of your book and your interest in the aging research um his first question is just um to get a better understanding of your background which i believe was a phd in physics so i was just wondering like what initially drew you to the area of physics and then what kind of led you from physics into the more computational biology routes yeah it's been a bit of a journey and i guess like an awful lot of scientists actually my sort of science origin story is back when i was my mom says about seven years old i'd look up at the sky i'd look at all the constellations up there all the stars and you sort of ponder my place in the universe and i think about that age i decided i wanted to be a theoretical astrophysicist and slowly but surely my career has sort of diverged away from this sort of incredible abstract you know fascinating big questions sort of end of science into the more practical you know trying to help people end of things so i ended up as you say i did a degree and a phd in physics um i worked on magnetism and super conductivity during my phd but toward the end of my phd i was trying to look for a way that i could use my career to make a difference in the world one of the things i considered for example was um becoming a climate physicist which would have been a bit more of a conventional transition trying to use those skills you know to understand and hopefully tackle the problem of climate change but at that time i started reading about aging biology and actually what i normally tell people which is a bit of an oversimplification but not much of one is that i changed career because of a graph and that graph is the graph of human mortality risk as associated with age so obviously i think you know everyone watching this channel will be familiar with the fact that humans risk of death doubles every eight years and you know as a physicist you look at that and you think wow um this is just this staggering thing that applies across human populations it applies across time i can take basically any human from any place on the earth at any time in human history and their risk of death will you know be different depending when they lived but it'll still have this same sort of ticking clock this doubling every eight years and so you look at that as a physicist you're trained to see you know see patterns in incredibly complicated phenomena and you think wow you know is there something going on here inside our biology that's causing this fundamental you know incredibly constant ticking clock and if we could go in there and intervene could we do something about it and so then i started reading about you know things like calorie restriction and rapamycin experiments just getting quite big around that time cause that was on the interventions testing program results who just came out about that there seemed to be loads of exciting stuff going on but i did sort of have this niggling doubt at the back of my mind i stopped studying biology formally at gcse so that's 16 years old for anyone who's not in the uk and you know obviously if i'm scratching my head you know maybe i'm a physicist in the classic physicist thing of looking at a field and thinking we can just go in there and nail that we can just you know draw a graph and understand the whole thing actually you know maybe it's just a bit more complicated than that and that's why biologists aren't concentrating on it as much as i perhaps thought they should um so i decided i was going to try and become some kind of biologist and there are various different ways as a physicist you can try and apply those skills to biology a common one which i've got a few friends who've actually done this route you can take the very practical lab bench experimental skills and become something like a microscopist so you can use all the optics that you learn as a physics undergrad and during your phd and use it to develop new microscope techniques or something like that and i sort of thought really you know what i want to understand are the big biology questions and although obviously it's hugely hugely important because these new microscopy techniques and so on allow us to do entirely new kinds of science they allow us to literally see things we couldn't see before and discover new stuff as a result i wanted to do something that gave me a bit more of an overview and so that's why i thought the computational biology and genomics and that kind of thing was a bit you know more than each for me you know you can still use the analytical skills obviously i'd learned a bit of coding as part of my phd as well um and take all of those skills and apply them to biological data instead of physics data and then i just felt that would give me a bit more of a sort of bird's-eye view of the field and hopefully you know try and understand whether aging was indeed something we could do something about and if so you know how and try and contribute to that that's a really good answer and i was actually going to say um i'm actually a bit jealous that you've done a phd in undergrad in physics because obviously i haven't done physics since a-level and obviously there's so much overlap between the different disciplines of science even in biology there's no um processes such as mechanical transduction and electrical signaling and actually having that kind of physics understanding um could be really useful to actually understand some of these more complex phenomenons in biology so um i was just wondering is there any any way you can elaborate on how you think your skill sets in physics help you to apply that to biological research i think at the moment we're at a point where um actually a lot of the physics stuff is a bit bleeding edging biology and i think quantum biology is a really good example i think it's gonna become abundantly clear over the next you know five ten years that quantum processes are going on in biology because evolution has had billions of years to optimize our enzymes and all different stuff going on inside ourselves at an incredibly detailed level it'll be frankly astonishing if it wasn't using quantum mechanics on some level to optimize all those different processes but the trouble is we are right at the bleeding edge of whether that's actually useful i mean are we going to find out that some quantum biological phenomenon is a 10th or 11th or 12th hallmark of aging i guess we'll find out but at the moment i think that like i say a lot of the physics stuff is about technique development and actually you know i ended up basically not using my physics knowledge at all i think it's it's quite cool just to have a really general grounding in science and you know another thing i could have gone into because of my magnetism and superconductivity background it's like mri imaging which obviously depends on these enormous magnets to you know build detailed pictures of what's going on inside the human body i think it is really important just to have a bit more of a general scientific background to understand you know how different fields work and perhaps just look at look at things with a different perspective because i think you know although i didn't have that biology training perhaps in some ways i didn't have that baggage either so it allows you to look at things in a different way so i don't really think there's like a simple formula i certainly wouldn't recommend if you you know if your passion is aging biology i wouldn't recommend that everyone go away and do a physics phd first it's a bit of a securities root but i think those things you know having a fresh perspective on stuff can definitely be an advantage as well well said and so to go to your your book ageless i'm going to take one quote from it which i think um kind of hints at your more physics um understanding which is you describe um life is under no thermodynamic obligation to age um so i was just wondering could you break down that statement for us yeah i think there are two ways of looking at that so people when they talk about aging i think even when you think about aging we often think of it as just a natural process of falling apart and you can really see why this would be the science of thermodynamics was born in the sort of 19th century primarily people were looking at steam engines they were trying to understand the actual physical processes going on inside them and you know machines like steam engines they rust they decay machines in factories they wear out as different components get you know basically friction rubbed against each other if you don't lubricate them properly etc and it just really seems like you know why would humans be any different from the machines that we can build well the answer is of course they're biological and so they've got these incredibly complicated self-repair mechanisms going on inside them but it might be that perhaps there is this problem of thermodynamics so the second law of thermodynamics is that entropy will tend to increase what's entropy well it's that's that's a whole sort of uh video interview in itself but entropy basically is a measure of disorder it's a measure of things becoming more disordered you're going from an ordered state into one where things are a bit of a mess and so the second law states that entropy will always increase with time and what that means therefore is that you expect an ordered physical system you know beautifully ordered human body for example just holding myself up here is the most beautifully ordered example of humanity by doing the hand gesture but my body should you know under the second law of the thermodynamics you might expect it to gradually deteriorate because at some point those repair processes aren't going to be able to keep up with with basically the you know endless march of thermodynamics the problem is that people who say that neglect the second half of the second law which is the entropy tends to increase in a closed system and what does a physicist mean by a closed system they mean a system that can't import an energy and can't export any of our entropy or disorder into its environment and clearly living things aren't such a system you know we can eat food which is obviously the way that we get our energy or if you're a plant you can photosynthesize you can use the light from the sun to you know to generate energy yourself and then we can export that entropy through a variety of different means that i won't go into too much detail about into our environment so we can get rid of that disorder we can get rid of waste effectively and so there's no sort of physics reason why it would be that we should age and so that sort of demolishes that argument just by looking at it from a purely theoretical standpoint i think the thing that's more compelling to me though actually is then to go to the biology um because you know you do get to a point where you don't care about the physics if you've already got a biological example where this doesn't happen i already mentioned that humans risk of death doubles every eight years there are loads of creatures out there whose risk of death doesn't double at all it stays flat when they become adults and the reason there's a tortoise on the cover of my book is that giant tortoises are one such creature they've got a risk of death that doesn't change with time they don't get any more frail depending on how old they are uh they even stay reproductively active until they're late in life so in a very real sense they don't age so not only is not aging not against the laws of physics because it doesn't defy entropy it's also not against the laws of biology because we can point to tortoises and salamanders and fish and naked mole rats all these animals out there in the world that don't seem to age either and so i think there's this sort of i can understand where this fatalism comes from because it does seem that aging is inevitable and we aren't really exposed to these creatures you know that there are obviously a handful of people who have a pet tortoise but they probably haven't done a detailed demographic study of a thousand pet tortoises in order to come to these kinds of conclusions so we now actually assume that everything ages but actually it's not that inevitable at all that's i think one of the most exciting things about this field exactly that's really well said so talking more generally about your book what kind of motivated you to write the book in the first place i was inspired to write the book actually by my work as a biologist because i found that a lot of the biologists i interact with during my work um i'd often know more than they did about aging biology that isn't because i'm some kind of genius you know again not wanting to sound like a physicist who's barging into the field and feels like they suddenly know everything the reason was that you know i'd meet people with incredible degrees from incredible universities and they'd never have had a lecture on aging biology there's not a single page in a lot of biology textbooks about aging and that's just sort of staggering to me because it's one of the most universal processes we've just heard it's not quite universal but a huge number of species age and it's a you know evolution is perhaps the most universal phenomenon in biology but aging is definitely up there as one of the others and i think there's this sort of strange phenomenon that aging has been quite a small field historically perhaps because of what we were just talking about actually because people have thought is this inevitable process of falling apart that we can't really do anything about maybe it's just too complicated for biologists to take a serious interest in and because the field is small historically other fields have been larger it sort of creates this vicious cycle and you find that you know if you're never lectured about aging biology you're never going to get excited about it so you're not going to apply for a phd in it and even if you want to apply for a phd in it there just aren't that many labs with phds to apply for and that means that you know when you get to the end of your actual phd in cancer research or in virology or whatever it is what are you going to do you've already got a few papers you've already got a bit of experience in that field you're going to go on you know become another cancer researcher or another virologist yourself and that means that when you're lecturing your students and inspiring the next generation you're not going to be telling them all about aging biology are you going to be telling them about your field of expertise and so on and so on this sort of perpetuates this cycle which means that aging biology and i assume therefore other neglected fields of science remain neglected purely because they never quite gain this critical mass and i really think that's a i sort of diagnosed that pathology and i thought that i want to write a book for a variety of reasons um basically to convince lots and lots of people about how exciting this field is and obviously among those people i count the general publics i think that people just don't understand this science they think it's some kind of sci-fi kooky weirdness when you talk to them about it um in that same sort of category i put politicians and policymakers that people are funding the science they don't realize you know every politician knows a friend or a relative who's died of cancer so cancer research gets comparatively within science a you know larger slice of the pie perhaps whereas people don't think you can die of aging they don't see cancer as a disease caused by the aging process so again you've got to try and communicate to that group you know what aging biology is all about but then beyond that i needed to communicate to the biologists as well i need to show them that this is a legit field this isn't some again some sort of cranky snake oil you know people trying to live forever nonsense it's actually genuinely hard science that you know that works we've got you know dozens and dozens of labs around the world showing really promising results in this now and actually i i then went on to marry a doctor and i found that doctors also don't know much about aging biology you know to the extent that they're taught about aging uh when they have their lectures on aging it's about well it's very complicated to care for these geriatric patients they've often got multiple different diseases so you have to think about that there are social problems because you know perhaps they're living alone at home and so that complicates things they're taking you know five or ten different drugs so if you want to prescribe them a new drug you've got to check how those drugs are going to interact but nothing on the fact that you know during my wife's career she's quite likely to have to prescribe people these first generation of anti-aging drugs it's not wildly outside the bounds of possibility and yet when she first met me i was crazy she thought you know what what is this sort of mad kooky sci-fi anti-aging stuff and so in order to get the message out to that whole range of different audiences i just thought i've got to try and do something about this and that's what made me decide to write a book yeah well said and i have to say having read your book is very well explained and i think it's very accessible to people from any like background knowledge so yeah congratulations to you for that and so in your book you discuss a lot of uh theories of aging and to begin with you speak a lot about the different evolutionary theories of why we my age um so i was just wondering like having done all this background reading to write your book what theory do you think makes most sense to you at the moment to explain why it's why that humans age compared to these other organisms you've discussed that seem to not show um phenotypes of aging i think the most important thing to remember about the evolution of aging is the sort of strange paradox of its center which is that the reason that animals die of aging is because ultimately they die of other things so to sort of unpack what that means if you find yourself in a very very high risk environment let's imagine you're an animal like a mouse and mice live you know maybe six months or a year in the wild so they've got very short lifespans compared to an animal like a human what that means you know basically they're at high risk they've got cats chasing after them all the time they can get infectious diseases they're tiny little animals so they can literally just die of exposure it's called you know they can get so cold on a winter's night they just freeze to death and so there are loads of different ways that a mouse can meet a sticky end and so if your evolution you know looking at this scenario and trying to design a mouse's sort of reproductive strategy you're going to say make a lot of babies and make them as fast as possible so that your genes have been passed on to the next generation you have to make lots of babies because lots of them are going to die in horrible ways as well unfortunately and so ultimately their reproductive strategy is sort of have a lot of kids and have them fast and if that comes at the expense of lifespan you know say that means that they put a bit more energy into reproducing or growing more quickly and a bit less energy into creating incredibly robust anti-cancer defenses for example that are going to allow a mouse at the age of two and a half to not get a particular tumor well they're just not going to bother with that are they because they'd rather spend that energy on reproducing because all the mice in the wild are already going to be dead by the time they get to the age when they're going to be experiencing age related problems so that's sort of one side of the coin and that's why the evolution of aging this sort of drive to to not bother with any kind of um long-term preservation strategy evolves in animals that have a naturally short lifespan for other reasons because they have you know external causes of mortality if we then look at the other end of the scale a nice example this is bats so bats they're also you know they're mammals they're quite closely related to mice and this all evolutionary tree but the key difference between a bat and a mouse even though they're about the same size as well a bat can obviously fly and it's not just because flying is brilliant that these things live a lot longer but if you look in look out out in nature then a bat can live to maybe 40 years i think is their record i think it might be 41 years old so why is it that bats can live so much longer it's because they're in a much safer environment precisely because they can fly they've got far fewer predators and so you know that that means that they can afford to put more energy into getting rid of cancer into you know trying to keep keep their bodies running for a much much longer period of time and that entirely changes their evolutionary strategy and this manifests in a few different ways it can manifest by something called mutation accumulation theory which is something that happens um over evolutionary time where mutations that are really bad in late life can accumulate in an animal like a mouse but a bat would de-accumulate those mutations it will try and you know get rid of anything that might kill it you can then have um unfortunately all that all the ideas in aging biology aging evolution have really really terrible names um perhaps one of the most explanatory theories is this thing called antagonistic pliotropy so let's unpack that antagonistic obviously means that these things pull in different directions and plyotropy is the word that biologists give when a gene has more than one characteristic so you know so it can do different things at different times or in different situations and so that that then means a gene that might be useful for gearing up rapidly to reproduce in youth actually causes some kind of problem later in life and that's you know again it really makes sense for the mice they might have some gene that makes them grow more quickly but makes cancer more likely as they get older and so again you can see this antagonistic thing the player should be the gene behaves differently depending on which part of the life the mouse is in and then finally uh the other theory that i discussed in the book is this thing called disposable soma which is just the idea that again they've all got awful names disposable obviously means what it means soma means the sort of body and that's just the sort of um the biologist's way of saying evolution doesn't care about you it cares about your genes being passed on to the next generation so again if you're apportioning energy between looking after your body and looking after the next generation well most the time particularly in a dangerous environment you're going to put it towards um towards the next generation rather than yourself which sounds all very altruistic but ultimately of course leads to us aging so how is it given all of these things i've just described three different mechanisms by which animals can evolve in order to age and it's not really on purpose it's sort of an accidental byproduct of the fact they get killed by other things but nonetheless how do you animals that don't i think it really varies depending on their reproductive strategy i think the clearest example of this is in fish so i just mentioned that you know mice love to have loads and loads of kids when they're young but fish have a very very different life cycle and that's to say that a huge amount of the reproduction by fish is done by massive old fish and there's a great acronym for this it's buffs which is big old fat fertile female fish let me get that right and these fish they're massive underwater matriarchs basically they produce far far more eggs than uh younger female fish the eggs are of higher quality so they're more likely to grow up into healthy fish themselves and that means that a huge fraction of the population's reproduction is done by sort of this comparative handful of really old female fish so evolution suddenly has a huge incentive to make sure that old female fish don't just die of cancer or die of something else wants to carry on you know keeping them alive and all that they can carry on churning out babies so again it just completely changes the calculus and is obviously scientists are slightly squirming in their seat now because i'm giving evolution a sort of a motive i'm saying evolution wants this and wants that but you can see in that situation why evolution would want to keep that older fish alive and depending on your life course evolution doesn't care how long you live evolution will do whatever it does whatever it can to maximize the propagation of your genetic material onto the next generation and so if that in the case of the fish means that you might become negligibly senescent you might even become negatively senescence you might have a reducing chance of death with time if that's what evolution thinks is important on the other side of the coin if you're a mouse and you're in a really dangerous environment you're going to crank out kids and you're going to age very rapidly so i think basically evolution doesn't care how long you live it'll just try and maximize the number of kids you have and unfortunately that means that we are one of those animals that does age yeah well said i was actually gonna ask you about the buffs because that was something i hadn't learned about before and it's kind of fascinating to see examples where you seem to get promotion to just um enable to increase fertility but also to um reduce signs of aging so that these um older fish can also remain reproductive as well so that's obviously a really interesting example and i think understanding different organisms in general is very useful because we can always use insights from different species to develop even human therapy sex um so kind of shifting gears more to the i guess the molecular underpinnings of why we age and how we can do something about it you mentioned a nice example in your book about uh lipofruskin and um and how maybe it felt like a question um you mentioned examples i say life effusion but again it's one of those words you know you you read it and you almost never actually say it that's right yeah so we can go with one or the other i'm happy um but you mentioned how there's actually some enzymes so this is something that accumulates in lysosomes but it's something that our bodies can't naturally degrade we have no enzymatic activity to do it but there's been some recent research that you describe and we'll let you explain it in more detail but it's the idea that we can effectively use information and um biology biology from different organisms to help uh treat aging and humans yeah and i think there are just some really fascinating examples of trying to draw in enzymes because there obviously are creatures that can break down like a few sin somewhere because otherwise you know with the entire surface of the earth would basically be just covered in huge huge piles of stuff but something must be degrading in the environment so we can look to things like soil dwelling bacteria that might be able to degrade it i think the one that i found most fascinating this i'm not sure this is technically lipofusine but it's about um oxidized cholesterol that builds up in the lysosomes of um cells that are part of atherosclerosis when you're you know basically they're furring of your arteries as you get older the cholesterol gets gobbled up by these um by these immune cells but unfortunately they can't really do anything with it they just tuck it away in their lysosomes it often gets oxidized it often gets all kinds of other reactive stuff stuck all over it and we were scratching our heads about how on earth we could possibly you know get rid of this stuff it's very very hard to degrade which is obviously why they can't do it and what the researchers found was there's there's been this long-standing mystery about how tuberculosis bacteria survive hiding out inside immune cells potentially for years or even decades because tuberculosis is an infection that can go dormant and then come back out at times of stress and what they found was that the tuberculosis bacteria were surviving by using some kind of cholesterol metabolism so they were effectively digesting the cholesterol they found inside these immune cells and so what the scientists did was they extracted obviously the enzyme that was responsible inserted it into some cells in the lab and they found that it did indeed successfully break down this oxidized cholesterol the only problem was that the byproduct it created during that reaction was itself toxic to the cell so it wasn't a sort of long-term solution but you can see how with a bit of tweaking that's something that we could use to attack the root cause of atherosclerosis which is you know the leading cause of heart disease basically is the cause of heart disease um and that's really exciting because the way that we currently deal with heart disease is just to try and reduce the level of cholesterol in the blood which does slow down the accumulation of these plaques but can never ultimately stop them but if this can go in there and clear out the stuff that's causing these all these immune cells to die and causing these plaques to form in the first place then you know that's one of the dreams of aging biology you know aging researchers to go in there and actually solve these problems before they accumulate to the point where they cause disease rather than just slowing these things down so i think this is one of the more sort of exciting but speculative areas of research that i talk about in the book and i really hope we can sort of take some insights from other species and crack it yeah so you raised a really interesting point there that there are kind of i guess two main strategies for trying to treat aging one is trying to the more like um preventative strategy to target the root cause and prevent the i guess the bad of things from happening and then the alternative strategy is to kind of clear away the damage that's already accumulated um and so i was wondering if you could maybe talk more about the latter strategy as do do you think that that's probably the more likely um therapeutic outcome we'll see first before we see these more preventative strategies i think it's an interesting question and i think that often it's um it's perhaps overplayed in the sense that there are there are maybe some things that fall really clearly into one category or another but there are lots of things that seem to probably do both like if you think about rapamycin this is something that might be characterized as something that just slows down the aging process it's a drug that goes in it it clogs up an enzyme that effectively um is part of the response to calorie restriction and we know that if animals calorie restrict if they eat a lot less they can live a lot longer and they can do so in good health so by jamming up this particular enzyme it tricks your cells into thinking that you're eating far far less than you are but in doing so therefore activates some of those longevity pathways that seem to be activated through calorie restriction and you might think oh you know therefore what you're doing calorie restriction doesn't allow animals to live forever it just slows down their aging it doesn't make them negligibly senescent at all so um you know maybe that is just a drug that slows down aging but actually rapamycin seems to have some effect on cellulosin essence as well it seems to get rid of senescent cells and you might class getting rid of senescent cells as one of these sort of damage repair type therapies because you're taking these senescent cells that accumulate in your body over time and clearing them out so i think you know while there's sometimes seem to be this battle between those two factions i think often it's not quite as clear-cut as you know proponents might suggest and actually of course um you know it's also the case that even researching things that you might think only slow down the aging process and perhaps you don't think that's as beneficial that can throw up insights into which hallmarks are being affected it can you know what's accumulating more rapidly and more slowly in this case raging has been slowed down so it's really not clear to me exactly how we should be approaching the funding because we just don't know what's the most exciting thing at the moment yeah well said i'm so speaking more about i guess like why we age in general like what do you what would be your main contributors to the aging process or do you think it is more a holistic effect of multiple different processes occurring i think we're probably going to find that it is a really holistic thing a massive interconnected tangled you know chaotic web of nonsense going inside our bodies basically i think it's good to break it down to hallmarks or pillars or you know types of damage or whatever it is that you're trying to do in order to help come up with some kind of conceptual framework and i think that what you know the results in the lab show that we're going to actually potentially have some quite large successes just going after single hallmarks i'm really quite excited by the fact that you can give mice analytic drugs you can clear out their senescent cells a single hallmark of aging and it seems to impact basically the whole of their biology in terms of aging it seems to turn back that you know the whole of their aging clock it makes them live longer it makes them have less disease it makes them less frail it makes them less cognitively i can't say that i'm not less cognitively impaired right so i'm starting to suffer from that a little bit myself and it also you know just makes them look great i was a computational biologist i've never dealt with mice in the lab but mice that have had senescent cell treatment they just look fantastic they've got much better effects and you know it's really going after the cosmetic stuff as well which you know unfortunately is going to get a certain certain sections of the population excited perhaps more so than the medical side of things but nonetheless you know going after the single hallmark has this huge collection of benefits and it might be that going after you know two or three of the hallmarks or two or three different processes has a synergistic effect so you know has an effect that's greater than the sum of its parts it might be that some things have an effect that's less than the sum of their parts to effectively duplicate duplicating efforts somehow but ultimately and i talk about this right at the end of the final sort of science chapter of the book um i think the way that we're going to really nail aging is going to be by understanding a sort of systems biology model of it because i think that i mean and you can already see this actually in the discussion around students in cells although senescent cells were first named because they looked like old cells they were cells that divided so many times that they didn't you know weren't able to divide anymore and that's why they were thus christened we now understand that senescent cells are a vital part of development they're used in the developing embryo to fashion certain parts of your body they're using wound healing and that's again sort of a place where you want fresh tissue to grow back and senescent cells secrete these factors that cause cells to grow um they seem to be implicated in some way in the in the the growth of cancers as well so again that's sort of a growth related thing um so it's clear that sort of senescent clapped out old isn't necessarily the right way to think about them and you know you see all these arguments about how do we define a senescent cell you know you can say it's a cell that doesn't divide anymore but is it a cell that's p16 positive is it a cell that um is positive for the celestions associated with galactose is it i think you know there are all these various ideas galactose this is exactly your field isn't it so i'll take any corrections you're offering me um so if you know is it positive for these various markers um i suspect none of these will actually be appropriate for what is a senescent cell and we'll probably have to come up with some complicated composite marker it might be that we end up taking out you know the optimal for aging uh delay might be to take out an entirely different subset of cells which somewhat overlaps with what we currently call senescence cells but it's actually somewhat something else it might be different in different parts of the body and i just think that is ultimately the way we're probably going to end up solving this problem because it can't be as simple as you know just taking out one particular class of cell and one particular molecule and everything's sorted or you feel like biology you know evolution might have managed to do that with a little bit more reason in creatures out there in the wild so i think the ultimate solution is going to be a sort of big holistic systems biology um you know load of chaos in some ways but what's more optimistic than that in the short term is that we do have these treatments you can do interventions in in the lab and they can have dramatic effects on the lifespan of mice the health of mice most importantly um even though you only seem to be doing one single thing so my hope is that you know we can sort of use the knowledge that we gain from giving people these sort of laser guided just senescent cells or just telomeres or just whatever it is treatments and as we understand how that interacts with their aging more broadly we can start to intervene in a slightly more intelligent way and hopefully you know address multiple hallmarks and eventually all the hallmarks at once yeah well simon i completely agree about the whole system's biology approach um and so you've mentioned certainlytics but what kind of other uh strategies from obviously writing your book do you think are most promising or seem most exciting at the moment for training agent i think there's a real range and i'm excited about them all for sort of quite different reasons this analytics i really feel like are the most exciting in the sort of near term for what i'd call sort of a real hallmarks based anti-aging treatment because you know we've got results in mice that show all these exciting things i just talked about we've got human trials going on for these drugs now because some of them are pre-existing approved drugs that have been approved for different conditions so it's something that we can extend into humans um these trials at the moment are obviously for specific diseases where we know senescent cells are a problem but hopefully if the drugs are effective so obviously if they help with those diseases but most importantly if they're safe we could then think about rolling them out to people who haven't got some you know such severe problems and eventually if they're safe enough we could start thinking about rolling them out preventatively you know maybe you're just 60 or 65 so if you've accumulated deficient cells that's worth giving you one of these things preventatively and if all goes well with that you know we could easily have semalytics in hospitals in the next few years for specific conditions if those clinical trials work and then it's not hard to imagine that you know maybe five maybe ten years after that we could start be thinking about giving them out preventatively and that really is i think the ultimate sort of paradigm shift in terms of the way that we practice medicine so that's something i'm very excited about for that sort of variety of reasons i've just mentioned um the sort of near-term stuff that's less exciting in terms of the big picture but probably more exciting in terms of breaking the ground and the things like metformin and rapamycin i've already mentioned these you know existing drugs that we think might be able to slow down the aging process and there's this trial i'm sure a lot of your viewers have heard of the tame trial in the u.s where they can actually a proper randomized trial trying metformin to see if it really does slow down the aging process or if the sort of hints we've got so far have maybe been experimental artifacts and that you know the the people who are doing tame even they aren't necessarily that you know this isn't going to double human lifespan if net forming doubled human lifestyle we'd know about it already but the fact is it's going to lay the regulatory groundwork that allows us you know in the past we've only found it easy to approve drugs that are against specific medical they call them indications in the jargon of regulation um so specific diseases basically cancer diabetes whatever it is and if they can lay the groundwork with that study and show firstly show regulators that this is a thing that we can do we can give people you know people who would currently classify as healthy a drug to prevent them becoming unhealthy and also just show the public and policymakers that we can develop drugs that slow down aging i think that's a really exciting thing even if it isn't necessarily going to result in the biggest breakthrough um in the longer term i i find a lot of the sort of wilder stuff that's going on really exciting and i'm just really fascinated to see how it pans out i think the treatment i put most in this category is cellular reprogramming because i often say it feels like it's fallen through a wormhole from the future because we've got this sort of collection of four genes they were originally discovered by yamanaka who was looking for a way to turn you know adult body cells into a stem cell and he was interested in those stem cells because they can then turn into any other kind of cell he wasn't looking for something that could turn back the sort of biological aging clock but what we've since discovered is that these four genes almost as a side effect of that process do indeed turn back the aging in those cells and they do so in a surprisingly you know wide widespread way they they de-age whole you know they de-age their what's called the epi what's called the epigenetic clock this measure of you know how old things how old the cells are looking that way they can make the mitochondria behave in a more youthful way and there are just loads and loads of changes that are going on and now we've even shown that you can um give these things to mice and as long as you only turn them on intermittently and therefore don't cause the cells to go all the way back to becoming a stem cell you seem to be able to make those mice healthier so it's an exciting result but the real question for me and the reason i say it feels like it's falling through a wormhole in the future is you know can we can we scratch our heads and like find a way to turn this into a therapy that we can actually give to humans in anything like the near term because obviously gene therapies are starting to come online now we've got the first of those being approved in the clinic for again for specific conditions for people who are willing to take a punt on something experimental um but i'm certainly not going to be lining up to have the yamanaka factors injected into me anytime soon so the question is you know can we find a safer way to do this can we identify some genes that perhaps you know the amino factors they're these skeleton keys inside the cell they perform such a huge range of changes can we find some that are a little bit more targeted and can just address the sort of aging aspect of this without changing the nature of ourselves and potentially putting us at risk of cancer and stuff like that or you know ideally speaking could we find a drug that can awaken whatever process this is and you know it would be ideal if this could be taken as a pill rather than something more exotic so the real question with those kinds of therapies is you know can we come up with a way of turning them from this idea that works in cells in a dish works in mice in the lab perhaps but can we get it to a point where we can you know consider safely deploying that in human patients so there's this real range of excitement from the very sort of pragmatic near-term stuff to the incredible sort of stuff that's on the horizon the question is can we get that stuff on the horizon a little bit closer to actual practice exactly and i agree i also think cellular programming is also really exciting um in particular stem cell therapies and so you mentioned in the book in a bit more detail about the different potential therapies and so why do you think we'd see stem cells being used first sorry so that again um why do you think we'd see stem cell therapies being used first like in the clinic if they ever get like safety approval i think we're probably going to see them being used again in cases and a lot of these will be aging related this is one of the parts of the book where i was more excited because the developments weren't going on so much as a sort of accidental side effect of looking into other things a lot of the diseases of aging are caused by loss of cells of one kind or another i think the two things that are most advanced in the clinic at the moment are stem cell therapies against parkinson's disease and this honestly really shocked me quite how long ago we were doing this the first stem cell therapies against parkinson's so parkinson's is this disease where you basically lose control of your motor function gradually because of death of a particular kind of neuron in a particular part of the brain and because it's this very specific type of cell in a very specific localized region scientists thought you know can we replace those cells and thereby you know give people a bit of their lives back and actually scientists in sweden first tried transplanting stem cells into parkinson's patients in the late 1980s so it's not like this is some wild cutting-edge technology that you know we just developed last year this has got a really really long pedigree and um what they found was you know they were doing these experiments using um cells that were extracted from fetuses and they had to do this incredibly complicated procedure pulling out this tiny little ball of cells i think smaller than the size of a pinhead from a fetus that was maybe a couple of centimeters long at that stage in development then they'd culture those in the lab and inject them into parkinson's patients and it was a very very small number of people right you know as a handful of patients but they showed these remarkable results and there's been this ongoing controversy because the that number of patients was so small it was then then some labs in the us attempted to replicate it and they didn't quite manage and the labs in sweden hit back that they hadn't been doing the technique correctly and you know had therefore that was what the problem was there's clearly still a bit of excitement about this because there's a massive study going on in europe at the moment across different um hospitals all over the continent trying to replicate these results again in a much more rigorous way across you know for a whole variety of different locations and actually i think the results of that i do out pretty soon and not only that at the same time we've got this huge advance because of that discovery by yamanaka that we can make these uh pluripotent stem cells we don't have to you know go on this sort of scavenger hunt through tiny little fetuses that's incredibly complicated and obviously you know some people have ethical problems with we can generate these cells from other people's adult cells or even the patient's own adult cells and turn them into whatever kind of cell we want including the cells that are lost in this particular part of the brain in parkinson's so i think that you know potentially obviously stem cell therapy feels like it's it's been on the horizon for quite a number of years now but it really does feel like the parkinson's that's starting to come together and the other place that i think there is there are some really advanced trials is looking at age-related macular degeneration so that's the most common form of blindness in older people it's when you're macular which is part of the retina the back of your eye starts to degenerate as you get older um again there are some really cool stem cell experiments going on in japan um there was this incredibly detailed um very practical safety study that was done in pigs in the u.s and pigs have just chosen because they're quite anatomically and biologically close to us in this particular situation where they tried extracting stem cells from genuine old donors so old human donors to make it as realistic as possible and they went through the whole process as though they were doing a human uh amd treatment the point being to check that this thing is absolutely safe there's no risk of something like cancer coming up when you try and modify the genes inside inside these cells in order to turn them into you know back into cells to the back of the eye and that seemed to pass with flying colors so again there's just it's a really big job i think getting something um particularly something biological you've got to nail down so many different things before you feel confident sticking that into a human being but i think those are the two that are probably furthest ahead at the moment cool and so you mentioned parkinson's disease which is i guess in general uh categorized as a neurodegenerative disease and another really big neurodegenerative disease associated with aging as alzheimer's and in your book you mentioned actually a couple of examples where there's some really exciting research um being categorized to treat alzheimer's um as i was just wondering could you elaborate on some of those strategies that's a really fascinating thing and i i wonder if we're going to at least partly cure alzheimer's or not cure but defer alzheimer's i should say by accident because i think that there's been obviously this enormous focus within the alzheimer's field on one particular kind of accumulated protein this stuff called amyloid beta and for the last 20 maybe 30 years scientists um this is how alzheimer's was actually first identified with these these plaques and tangles inside people's brains in post-mortems and you know so it was obviously thought that these these things are obvious if you cut a brain open perhaps this stuff there's an obvious biological you know change that you can see if you're a pathologist maybe that's what's driving the disease and so um and i mean the other thing that really really motivated that hypothesis in the 70s and 80s was the discovery of people who've got what's called early onset alzheimer's so that's a form of the disease where you have a particular genetic mutation in your amyloid and sorry in your a beta production which means you produce a lot more of this amyloid and you can get diagnosed with alzheimer's i think even as early as your 30s but more typically in your 50s and 60s which is a long time before the age-related form of the disease would normally kick in so that's been a really really dominant theory and we spent an awful lot of research time trying to clear out this amyloid beta to try and basically clear out what seems to be waste um inside the brain in order to try and sort out dementia and although we got very very good at clearing the amyloid unfortunately that doesn't seem to have translated to improvements in anything about patient outcomes you know people aren't getting cognitively better or anything like that so that's you know on the one hand as biological success but a medical failure so um now there's just a whole plethora of different strategies i think the failure of this hypothesis and the conclusive failure because so so many drugs have failed to improve outcomes has meant that a whole different range of hypotheses are being explored and actually i think a serious one is just is looking at something like inflammation this sort of increase in inflammation as we go through our lives and it's cellulase in essence again appears to be at least somewhat implicated in dementia i just think that quite a lot of these other approaches although dementia is certainly worth studying in and of itself and trying to uncover what exactly the causes are i think that by improving all kinds of things in our aging bodies and we're talking about lysosomes earlier as well you know improving the clearance of the live effusion from inside lysosomes which also seems to be a really important factor in all neurodegenerative conditions that we've looked at um i don't really know what's going to happen with alzheimer's i think it's really up in the air at the moment but i do feel like it's it's such a disease of aging right so we already said earlier that the risk of death doubles every eight years or so in a human being uh whereas dementia is actually even more extreme than that your risk of dementia is basically zero unless you've got one of those early onset forms that i mentioned earlier before the age of 60. and then it doubles every four and a half years after that so substantially faster than the rate of aging it really is an incredibly strongly age-associated disease so hopefully by going after some of the other hallmarks my sort of my fingers are crossed that we're going to at least defer dementia by doing that cool that's really interesting um it's another area of like um the field i find quite interesting at the moment is like more the personal approach and how obviously we're all very different and we're going to respond differently i might need different therapeutics and say one thing that i know kind of links back to some of the work you did as a computational biologist is about um using data and machine learning tools to kind of predict the correct treatment options and so i believe you did some work looking at um electronic health records and being able to predict um the risk associated with cardiovascular disease and so i was just wondering if you could explain more about what exactly it was you were doing and how machine learning approaches could be used to to help with uh personalized therapeutics yeah the idea of that project was um we wanted to you know take advantage of this enormous rich data set because in the uk we're very fortunate to have the nhs and that means that literally everybody basically is on the same medical records database and so we had data from people's gps we had data from hospital visits we had data on operations that had had medications they'd been given all this different stuff and what that had been used for in the past was to try and predict cardiovascular outcomes like you say so um you know so say you're a doctor you're starting your doctor's surgery and someone comes in and you're trying to decide whether to prescribe them some preventative like statins now statins are a drug that obviously have side effects and so you don't want to just be handing them out like candy to everyone over a particular age you want to be handing them out to people who are actually at risk of having a heart attack and so what you like is a prognostic risk score they call it so you know basically a score of how likely are you to have a heart attack in the next five years and so the way those are traditionally calculated is a bunch of nerds basically get together a bunch of you know doctors and epidemiologists and scientists and they might look at this medical records database and say okay we think these are the 20 or 30 most important factors some stats nerds then go away and turn those factors into a model and we say okay what this can do is this model can show us with a certain degree of accuracy what your likelihood of having a heart attack is in the next five years and then your gp when you come in and um and visit them rather than sort of looking at your history and sort of having to have a bit of a think and try to base this entirely on their judgment they can type your various scores or like ideally they could take all the blood test results it would automatically move into this model and it would then say okay your risk of a heart attack in the next year uh in the next five years is five percent and i say okay my threshold for prescribing statins is four percent so here are your statins you know be on your way and what we thought was there's just this huge opportunity to put machine learning into this situation because uh these scientists uh the the the this this model i just described in slightly hypothetical terms have actually been made by some clinicians and epidemiologists and they were using i think it was it was just over 30 of these of these numbers but there are literally hundreds maybe thousands of different codes inside these medical records and so you're just throwing away so so much information by using these expert models and the other thing we wanted was you know not only could you use more information more isn't always better because sometimes it can lead to phenomena like overfitting where you sort of get spuriously accurate results that then don't generalize to the wider population not only could you have more of these things you could also potentially uncover things that the experts might not have thought of and so that's what we did we used a variety of different techniques uh one of them called elastic net regression which if anyone's familiar with epigenetic clocks it's actually the same statistics that's used to derive those and i also use something called random forest which is just another statistical technique to try and sift through these hundreds and hundreds of variables and come up with the best possible model and um what i managed was well few first of all we did slightly beat the clinicians and epidemiologists that was a little bit more accurate with all that data it wasn't sufficiently more accurate for that to be like a huge huge success because we beat them by you know a fraction of a fraction of a tiny tiny amount but what i thought was more exciting was that we did all this without having to understand anything you know about cardiology i'm not a cardiologist as you as you know i'm trained as a physicist so i knew nothing about hearts before we started that project and yet completely naive obviously the machine didn't know anything about hearts either it could pull out the variables that were most important and some of those variables did overlap with what the experts had thought you know things like heart rate and blood pressure obviously they're important as to you know whether or not you're going to have a heart attack but we also pulled out some stuff that you really wouldn't necessarily have expected and i think my favorite example of that was um something that's prognostic of whether or not you're going to have a heart attack is if you've had a telephone consultation with your gp now i guess that variable might not work so well in in the covert era because obviously all of us if we want to interact with rgb have to do it by telephone or certainly encourage very strongly to do so but back in those days having a telephone uh encounter or even a home visit is sort of the more extreme version of that if your gp has to come to your house what that means is you're probably so ill that you can't make it to the surgery and therefore um what that suggests is that you know perhaps you're at higher cardiovascular risk and so this is sort of almost an administrative code that's put in there it's nothing to do with the patient's actual health or blood markers or anything like that and yet we found it was quite predictive so what this allows you to do then is to you know uncover these things that can be useful that can help a prognostic score but that wouldn't necessarily have been thought of by experts and i think what's most exciting about that is um as i say my score didn't do vastly better than that that the cardiologists already had but the fact is that heart disease is a very well characterized disease um you know we we know a lot about the risk factors uh if you get a doctor in the room you know they'll immediately reel off the 10 things they think are most important it's fairly well understood but the fact is you know building those expert models firstly it requires that understanding we just talked about dementia you know we don't necessarily have the understanding of the etiology of some of these uh some of these other diseases and secondly there are just loads of diseases so if you want to do this for some slightly more obscure condition you know you might have to get a bunch of experts together and it takes the same amount of time to build this hand-built expert model and yet it might only apply to a far smaller fraction of the population so it might just not be worth doing economically or or whatever so what's cool about the machine learning is whatever the condition is and whatever the state of the expert understanding of it you can try and see if the model can pull something out and if it can then you potentially got a prognostic tool for something you otherwise wouldn't have been able to have so that was what we were hoping to do and yeah i think that's why that's so exciting the question of whether it's going to lead to personalized treatment i think is a much thornier one because the thing with prediction is you've got quite good data and particularly something like a heart attack or death like it's very easy to find when someone has one of those things it's quite difficult you'd think to dodge you know you're that appearing in your gp record actually intriguingly one of the other difficulties with using this it's called sort of found data when you use a record that already exists rather than a record that was specifically created um i've forgotten the numbers now but it's remarkable so we had access to four different databases there's the gp records the hospital records a special heart disease database and there was a fourth one that i've forgotten right now but there was it was quite shocking you know a heart attack you think is very major you can't fail to go to hospital if you have a heart attack and your gp cannot fail to receive a letter from the hospital telling them that you've had a heart attack if that happens and yet there were still some huge percentage of patients who weren't recorded in all four of these data sources and it's just as i say this is where the hazards of using found data it's often got lots of missing values and so it doesn't necessarily have everything you want nonetheless heart attacks death they're quite um unambiguous outcomes shall we say whereas if you're looking for something a bit more subtle it can be a bit more challenging and the particular challenge with treatment is that outside of the context of clinical trials doctors normally prescribe the best treatment for that particular patient in their you know in their estimation it's normally the treatment is actually the first line treatment the first on the list and then move to the second one if that doesn't work for some reason which often might not be that but not not particularly clear so the question is is there going to be a diverse enough range of data in terms of what treatments people were given in order for the machine to learn and my sort of dream for the future is firstly that just machine learning techniques generally will advance i think we're already seeing that particularly neural networks are really hot and exciting at the moment but i guess you know these techniques are evolving all the time and getting better and better at digesting and making sense of huger and huge quantities of data but i think will be really exciting and this is again particularly applicable to a country like the uk where we do have a national health service and literally everybody is enrolled into effectively the same system i'd like to see a lot more adaptive clinical trials happening in a healthcare setting so quite often you know patients have complicated diseases it's not necessarily 100 clear what the absolute best drug for them would be and in that case it would be nice if the computer could randomize them to one of a few relatively good options and then you know over sort of months and years they watch those patients afterwards you can then draw that data together and as it becomes clear that certain treatments are better for certain types of patients maybe you could increase the probability that a patient with those characteristics gets that treatment so people are always getting or the the largest percentage of people are always getting the best possible treatment but there's always a little bit of experimentation going on in the system as well just to make sure that what we think is the best possible treatment is the best possible treatment the issue of course then is that's really exciting and i think you know firstly it's exciting to nerds like me and secondly i think it probably would save the most lives overall if it were carefully implemented but the problem is that clinicians and members of the public i think um have this real uncertainty about randomization in a treatment context i think if you're randomizing a trial then everyone's like oh i understand why we're doing this we've got to find out if one drugs better than the other but by the time you get to the clinic you want everyone to be getting the same treatment you don't want to feel like you're missing out on the best treatment and often that's um in a sense slightly misguided because we don't always know what the best treatment is for a particular situation but at the same time you know you don't want to feel like you're the person who's been randomized to placebo or you know you're the person who's been randomized to the second best treatment because that's because that's what the computer told you to do that makes it feel even worse if there's not necessarily a human making every aspect of the decision so i think there's a lot of exciting potential for that um but it's just a question of you know how can we implement those things in a healthcare setting yeah now that was really fascinating i that was amazing detail um so in terms of also the potential of using these machine learning techniques what kind of measurements do you think are going to be most um necessary to have for example like blood measurements or even things like box office that seems to also be important um and i guess on from that what are kind of like the major challenges to also trying to use this approach more globally i guess things such as security of the data or um actually trying to pull data from different sources i think in answer to the first thing we just don't know what are the most important things and i i we can probably make some reasonable guesses in terms of you know doctors can obviously tell you with more wisdom than i can what are you know considered to be important blood tests but at the same time you don't necessarily want to always go with what doctors think are the most important things because you know i massively respect doctors for the ridiculous amount of knowledge they hold in their heads about human bodies there have also been a lot of times in the past and what seems to be the medically obvious uh particular answer has turned out not to be correct at all so i think we have got to sort of try and throw a bit of randomness in there and sort of let the machine learning models do their thing um in terms of the data security yeah that is a huge huge challenge but i think that's definitely something we're all waking up to not just in medicine but in our everyday lives as well because you know we've got facebook and google and all these different companies that are just basically their business model is to aggregate as much data about us as possible and that's really starting to include health data as well because google is starting to do work um they've been doing machine learning and medical records data as well because it's clearly you know a huge huge business opportunity as well as one for improving human health um luckily i think because there is that awareness we are starting to get a bit better at this and actually covid here in the uk has driven some really big innovations in this area there was a sort of family of trials called open safely and i think that's some tortured acronym but i'm afraid i don't know what for but the idea there was that um this was this was actually as i say specifically to look at covert they were trying to find out which patients are at highest risk um based on their medical records based on you know hospital admissions and who comes into hospital with covert and so on and the way that that system was set up was that everybody's medical records were stored on an entirely secure central database and then if you wanted to analyze those records then what you had to do was submit a job which was then processed on this central database and then it would spit out the answers but you never actually saw you know you never had your hands on the actual data and so that's just a really great model because then what that means is that you know researchers can effectively do this thing do this kind of stuff almost entirely anonymized the real challenge is that actually although we deal with anonymized data it's sometimes called pseudonymized because um you know you don't actually it's very hard to truly anonymize stuff so when you when you say pseudonymized it might mean it doesn't have your name it doesn't have your actual date of birth you might round it to the nearest month it doesn't have uh your nhs number for example it's got some random identifier that we then give you know then give the patients the problem is that even if you hand over what seems like a really well pseudonymized data set it's actually remarkably easy to start de-anonymizing people to certainly work out who these individuals actually are because if you think about you know the number of unique characteristics we each have obviously you know you've got your gender even knowing your month of birth is a good start knowing which part of the country you live in you know you can you can start to isolate people very quickly particularly if they have a rare condition or something like that you might be able to identify them down to just a handful of people in the uk just because of one particular disease that they've been diagnosed with so there are definitely challenges there but i think we're hopefully getting better and better at navigating them so it's uh yeah watch this space i guess yeah that's really interesting and so you mentioned elastic net regression models that you use um for your studies and how that's also used in epigenetic clocks as i was just wondering if you could first maybe comment on your opinion on epigenetic clocks at the moment and then maybe just discuss a bit further like where else do you see the use of maths in trying to treat agent yeah i think um i actually think to answer the second part of your question first i think that biomarkers are one of the most exciting places that maths and machine learning can come to bear because we do have all kinds of data sets uh you know and i i think a really cool example of this actually is just just looking at people right so there's this fascinating study that came out in 2009 where uh people were asked to rate how old they thought other people looked and what they found was that people who are rated as looking older seem to actually be biologically older than their people you know other people the same age who looked younger they got more diseases they died more quickly and so on and you know since then we sort of tried to formalize that a little bit by using machine learning you know take a photo of someone's face and then the computer will you know analyze the various various things on the image and obviously it being machine learning we can never be quite 100 sure what it is it's analyzing uh but it'll then spit out a number that tells you how biologically old you are based on that and that's a really cool application because rather than taking you know loads of blood tests or doing a full body mri scan or however else we could get you know huge huge volumes of data to put into these models you can just take a photo and actually there was an example of trying to do this for mice as well and again you know if you're doing a mouse experiment you can take all you know you can measure the epigenetic age and do all this you know complicated blood tests or you can just take a before and after picture and if you could get that to work with sufficient accuracy that's a huge huge improvement so i think that's a really cool place that we can use maths in terms of the epigenetic clocks i think it's sort of a case of waiting and seeing unfortunately they're clearly very good at predicting the biological age of people who are living in our current normal environment but the problem with all machine learning or a lot of machine learning is it's often looking for correlations and not what's actually causative so it might be if i've got you know this particular spectrum of values of blood tests and that can then be used to derive an epigenetic clock and that then tells me that i'm this old you know you see people trying to optimize a particular blood test value but that's kind of cheating because is it that blood that blood test is actually the thing that's driving your aging or is it that whatever value it's measuring actually you know is indicative of a whole range of other things going on in your body or that value only makes sense in combination with the other values that have been put into the to the biological aging clock that you're looking at and what i think we really need to see to get properly excited about this stuff is if we do some therapies that we know slow down the aging process and then the epigenetic clock responds in a commensurate way then we can say that we're measuring something meaningful and not just measuring you know some aspect of the way that we currently live our lives effectively and there are some early signs that this work so we you see that in animals that are undergoing calorie restriction for example their epigenetic clock seemed to take more slowly and there was even this trial of um thymus rejuvenation which showed that people's epigenetic clocks were reduced by i think it was a few months or maybe a year after the treatment had been done so i think it's really exciting but it's just a question of watching this space and trying to find out um yeah trying to try to find out if these things actually do show us what we need them to show us because at the moment the ideal way to do a trial and the way the tame metformin trial i talked about earlier is going to be done is they're going to give a bunch of people i think it's between the ages of 60 and 79 metformin are a placebo and they're going to watch them for a few years that trial has to be big because ultimately even a 65 year old only has a one percent chance of dying that year and that means that you know in order to actually see one 65 year old die you need to have 165 year olds in your trial and that means if you want to see a difference between the treatment and placebo armor of the trial you have to have hundreds and hundreds of people involved you have to watch them for several years in order to get good enough statistics to actually draw conclusions whereas if we could do epigenetic clock measurements for example you could take a blood test from everyone before the trial started you could give them metformin for six months and then every person in your trial is a data point rather than just a handful who died because everyone has an epinephrine engaged before and they've got an epidural they've got an epigenetic age after if i can say that and that means that everyone's a data point so in a far shorter time with far fewer participants potentially you can come up with the same results and that's obviously really important for for us as agent researchers because we don't want to be waiting you know 10 20 30 years for the ultimate clinical trial to find out if giving us analytics to 50 year olds is something that's going to slow down their aging it's far far more useful if we can get results in you know months or small numbers of years and the smaller the number of participants you can use the less expensive these trials are so the more of them you can do in parallel the more stuff we can try out so i think it's really exciting it's just a question of you know are they going to work as we hope they're going to work yeah exactly and so evidently you're very good at communicating science and i believe now that you do science communication as kind of like your your full-time career so i was just wondering if you could maybe talk about some projects that you're working on or what is it you're you kind of get up to on a daily basis besides talking to people like me yeah it's a good question it's a very strange job this so i spent a lot of time writing this book right so that's the i was fortunate enough to get a book deal um toward the end of my second postdoc and basically decided to write full time and i'm so glad i did because it effectively took me two years full time to write the book and it's just because aging biology is such a huge field it obviously touches on every aspect of biology and so it's not possible to be an expert in all of those things before you start and i certainly wasn't that meant i had to talk to you know loads and loads of scientists i had to read loads and loads of papers and i wanted to make it really really robust as a book as well because um i've tried as you sort of mentioned earlier um to make it as accessible as possible to as many people as possible but at the same time and if you're a biology nerd and you're thinking oh this all sounds a bit sci-fi and kooky there are hundreds of references in the back of the book where you can chase up everything i've said you know make sure that i'm not talking total nonsense and it was really really important to me that was as scientifically robust as possible so it couldn't just be dismissed out of hand as some sort of fringy fringy theories being passed around so that was the first thing i've spent an awful lot of time was just just writing the book at the same time i've been trying to make some youtube videos to talk about various aspects of science not just aging biology so i think it's really important to try and engage people on a variety of different cool scientific things i mean i guess ultimately one of the reasons why i want to live a longer healthier life is to have more time to explore this wonderful universe we find ourselves in i'm still you know that astronomy bug that i had back at the age of seven hasn't entirely gone away um i've also been writing articles for newspapers and doing a lot of press are often around the book um and that's just been a really really cool opportunity there's some talk of maybe a tv series but these things are totally totally unknown i've done a bit of tv presenting on other things so it's just a very strange sort of collection of stuff i guess i'm starting to think about a second book now so i guess we'll just have to see awesome that sounds really exciting and i also i think i saw you on the russell howard show recently which is also super exciting um yeah it's really cool that's definitely a sort of childhood dream of mine i've always been fascinated by comedy so to be honest sort of comedy news you know topical satire show is absolutely fantastic and also what's really cool about that is um i'm genuinely passionate about getting the message out about aging to as many people as possible and so it's all about you know going to these really mainstream outlets be you know mainstream news shows or comedy shows or whatever it is and just telling them about this stuff so it doesn't seem fringe and weird you know it's obviously i love talking to scientists you can probably tell you know i'm happy to nerd out about anything for any level of detail but i think the thing that really excites me is talking to people who haven't necessarily been exposed to these ideas at all before and showing them this is mainstream exciting stuff exactly um as i'm talking more about um generally just aging and health in general um and they'll see a lot of these treatment options there isn't enough data to uh talk about them or implement them but in general what kind of advice would you give to someone who is trying to be more healthy there's a health advice chapter in the back of the book and i think it falls kind of into two categories one of them is the stuff you've probably heard a hundred times before but i hope that understanding a bit more about aging biology will really encourage you to stick to that advice and that's the sort of a big part of the sort of my health regimen has been driven by that in that i knew that you're supposed to you know not eat too much and try and eat a balanced diet and try and get some exercise and obviously not smoke is the big thing that should be in you know red flashing lights um that you really really shouldn't do these things the thing that really encourages me to you know watch my diet a bit and try and make sure i do get plenty of exercise is that these things literally slow down the aging process so if you think about you know something like exercise it's obvious when you exercise that you're you know you can feel your heart beating in your chest you can feel your lungs going you can feel your muscles being worked out it's clearly a cardiovascular workout it's clearly helping your muscles but as you understand more about the biology you realize that it's going to be you know decreasing inflammation throughout your body it doesn't just decrease the risk of heart disease which is this obvious one it decreases the risk of quite a few different kinds of cancer even decreases the risk of dementia so it really suggests that you know again just like analytics in a way you're intervening in a whole range of different aspects of the aging process and that makes that seem really really cool to me um the other thing that i find is that there are some bits of health advice that you just wouldn't expect but for aging biology i think my favorite example of that is uh trying to brush your teeth so it's been noticed throughout um throughout the last 10 20 30 years that people with worse oral hygiene tend to go on to have uh worse heart outcomes whereas cardiovascular problems and you might be sort of scratching your head and thinking well this is another example where correlation doesn't equal causation because maybe it's the people who have you know have more time to take care of their teeth they have less tooth decay they have less gum disease but also they have more time to exercise they have more time to you know cook really good meals they have more time to take care of their health generally and so these two things are related but they're not causally related but actually as you start to unpick the biology a bit more you realize this is again a source of that chronic inflammation that i've alluded to a few times because you know in youth you have this inflammatory process it's very very important it's the way that your body responds to external threats so if there are bacteria in your mouth you'll get inflammation your immune system will rush your mouth and try and clear out those bacteria the problem is of course that your immune system can never quite get rid of all those bacteria that's why dentistry is in a way so archaic you know because they literally drill bits of your tooth out because there's no other way to get rid of the bacteria um so what happens is there's this sort of constant fizzing going on inside your mouth of of inflammation and so the inflammation goes from acute which is like turns on very rapidly when there's a problem and turns off very rapidly when that problem is finished to chronic so it's sort of constantly going on it's sort of a paranoia in your immune system and we know that that can drive the whole aging process and in fact that seems to be the causal link between having clean teeth and having a well-functioning heart there's even some evidence that again it could slow down dementia so you know if there wasn't enough um sort of incentive to avoid dental bills and to avoid painful tooth decay to keep your teeth cleaned you know to floss and brush your teeth every night as thoroughly as possible then i think the fact that it can literally slow down aging is something that's really encouraged me to keep on top of my dentistry that's really good advice um i'd say more generally do you have any advice for those wanting to kind of get into the field of aging biology obviously you've had a kind of more unconventional approach but do you have any yet just general advice about having a scientific career i think it's just important to um sort of have your goal in mind it would be ideal to have your goal in mind from the start right don't do the physics phd but at the same time don't feel constrained by your qualifications so far or you know whatever you are already you feel like you've got expertise in because it is possible to make these career switches at any stage of your career you know don't feel constrained by your a-levels or your degree or your phd even because you can make these changes whenever you want and i think it's also important just not to be disheartened because one of the real challenges of aging biology i sort of mentioned this earlier there aren't that many labs doing it so it might be that you can't find your perfect master's project your perfect phd working you know in exactly the group that you wanted studying exactly the aspect of the aging process you wanted but the fact is a lot of the knowledge that you acquire you know maybe as i said i wouldn't recommend a physics phd but if you go and do some research in an excellent cancer research lab and you learn loads of lab techniques and you learn you know pipetting and molecular biology of which i cannot possibly going because i've never worked in a biology lab but all all those techniques are incredibly cross-applicable if you become an expert in a certain particular kind of microscopy then that microscopy is probably going to be applicable to the aging process as well so by the time you get to the level where you're applying for your own funding you can start to move your research you know sort of segue into that direction which means you could perhaps carry on doing whatever your core expertise is and you know make sure you can keep on churning out those papers which is so important for your academic career but at the same time start to redirect a bit of your energy towards aging biology so basically i think just keep your head down and carry on as best as possible if you can get a position doing directly working on exactly what you want from the beginning that's great but if you can't just press on you know keep gathering expertise and eventually you'll probably be able to find a way to apply that expertise to aging yeah exactly um so where can people go to find out more information about you and the work that you do so you can check out the book at ageless.link um i've got a website andrew steele.com k i'm also a lot on twitter which is at stato um my youtube channel is dr andrew steele like to keep all these things different just to keep everyone on their toes and i'm on a variety of other social media stuff as well if you just search for me you'll find me there okay great i'll put some of those links into the description as well so yeah andrew it's been great to have you on today um i think we've covered like so many different uh topics and it's been really informative so yeah just thank you very much for the work that you're doing thank you so much for having me it's been a lot of fun thank you so i hope you've learned something in this video thank you to my patreon supporters and thank you for listening you

Channel: The Sheekey Science Show

Views: 3,863

Keywords: andrew steele, andrew steele aging, andrew steele youtube, aging, longevity, ageless, interview, podcast, healthspan, anti aging, senolytics, cellular reprogramming, neurodegeneration, stem cells, science communication, biochemistry, physics, computational biology, science behind aging, why do we age, reverse aging

Id: MUHC3pig36k

Length: 66min 19sec (3979 seconds)

Published: Sat Dec 18 2021

Reddit Comments

[u/lunchboxultimate01]

Ageless was a very good read if you're looking for your own reading material or a gift for someone else.

[u/HesaconGhost]

I love Sheekey's work, I just wish the interviews were available in podcast form.

[u/Verzingetorix]

I was watching this today. A good interview overall. But I was surprised he said rather insistently he has to tell other biologists aging isn't crackpot science.

Who are these people? Are they not in touch with the world outside their disciplines? Or is it a UK thing?