Latest / Gladden Longevity Podcast / How Fast Are You Aging? Understanding the Dunedin Pace and the Power of Epigenetics - Episode 262

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How Fast Are You Aging? Understanding the Dunedin Pace and the Power of Epigenetics - Episode 262 · Gladden Longevity Podcast

• 0:02
  Welcome everybody to this edition of the Gladden Longevity Podcast.
• 0:05
  I'm your host, Dr.
• 0:05
  Jeffrey Gladden.
• 0:06
  And today we are talking with Hannah Wendt.
• 0:13
  Hannah is a co -founder of TrueAge, which is probably the leading omics age company, certainly in the U S maybe in the world.
• 0:24
  I don't know if she could speak to that, but they have over 75 ,000 people in their database now and
• 0:32
  their Omics stage.
• 0:33
  We've been using it really ever since they first introduced it, which was probably about three and a half or four years ago now almost.
• 0:42
  But this is gonna be a really interesting conversation.
• 0:45
  So hang on to your seat, you're gonna learn something.
• 0:47
  So Hannah, welcome to the show.
• 0:49
  Thanks Dr.
• 0:49
  Gladden, I'm excited to be here.
• 0:51
  Yeah, absolutely.
• 0:52
  So, so you and Ryan basically founded this company.
• 0:58
  Is that kind of what happened?
• 0:59
  Yes, that is correct.
• 1:01
  Yeah.
• 1:02
  We, you know, I think you knew Ryan originally from, from the old, old peptide pharmacy days, but you know, we needed a way to add a lot more quantitative data behind those
• 1:11
  peptide markers to take it and, you know, serve it to the FDA, to all of these regulators and say, Hey, people aren't just feeling better subjectively from these amazing novel
• 1:22
  molecules, but it's actually proving a difference in their number one risk factor.
• 1:26
  which is aging, right?
• 1:28
  For all cause mortality, morbidity.
• 1:30
  So we kind of went off the pharmacy path a little bit and did some discovery on, well, what is that number one risk factor?
• 1:38
  Like I mentioned, aging as it relates to your overall health.
• 1:40
  And how do we measure this?
• 1:41
  Is it through grip strength and other physical function measurements?
• 1:45
  Is it through phenotypical aging, so facial aging, or is it through something like telomere length?
• 1:52
  And we realized
• 1:54
  All of those were missing a certain component or piece that we wanted, whether it was accuracy, reliability, responding to interventions.
• 2:01
  And then these epigenetic DNA methylation markers came out and just really became more clinically applicable a couple of years ago.
• 2:08
  And we took it and ran with it.
• 2:10
  We have the full in -house lab in Lexington, Kentucky now.
• 2:12
  Yeah.
• 2:13
  Yeah.
• 2:13
  I mean, you guys really ran with it in a really big way, which has been really kind of fun to see since we've been working with you, which is, think, kind of right from the
• 2:23
  beginning or very close to it.
• 2:25
  You've probably had about four or five different iterations of your reporting.
• 2:29
  Right.
• 2:29
  So and there's another one that's going to that's coming out right now that we're going to let people know about.
• 2:34
  So let's just let's just talk about.
• 2:38
  Epigenetics for a moment.
• 2:39
  We can talk a little bit about how this got started.
• 2:42
  If you want to talk maybe a little bit about Steve Horvath and kind of the work he did and then how this translated into humans just to kind of bring people up to speed.
• 2:50
  So.
• 2:50
  definitely.
• 2:51
  we actually, Ryan and I went and visited Steve Horvath when we had this idea.
• 2:54
  We went out, saw him at UCLA, met him in his office at the genetic center there and wanted to get him on board.
• 3:01
  He's way more interested, I would say, in more of the science behind these markers as it relates to more animal models rather than kind of the clinical utility.
• 3:10
  So he didn't have as much interest back then and still doesn't.
• 3:14
  Now he's working more on kind of reprogramming these markers and setting cells to a stage zero.
• 3:20
  He did some amazing work in 2013 where at a conference he presented work showing how these epigenetic markers are predictive of chronological age.
• 3:30
  And that's really important, of chronological age.
• 3:33
  I wasn't at that meeting.
• 3:34
  I know a lot of people who were and they said their jaw just dropped when they saw this R correlation value being above, I think, 0 .97.
• 3:41
  It was maybe even 0 .99 to be exact.
• 3:44
  So you just don't see those correlations in science and any type of studies really.
• 3:48
  So it's saying, okay, wait a second.
• 3:50
  We're looking at these epigenetic markers, which to define that as your DNA regulation, how your genes are being expressed, whether it be turned on or turned off or somewhere in
• 3:59
  between, and can basically predict your chronological age by measuring that.
• 4:05
  So, yeah.
• 4:07
  understands, as we go through life, and this is true for all mammals and Horvath has shown this across many mammalian species, is that as we go through life, know, certain DNA
• 4:19
  sequences are turned on and certain ones are turned off.
• 4:21
  The most obvious one that I explained to people is, you go through puberty, right?
• 4:25
  All of a sudden, certain genes get turned on.
• 4:27
  Other ones get shut down.
• 4:30
  And the same is true as you go through the aging process, menopause, andropause, and then on and on through the aging process, certain genes get up regulated and others down
• 4:38
  regulated, some in response to internal biological mechanisms and some in response to the environment, right?
• 4:45
  So it's really interesting that you can look at this pattern, if you will, of what's going, being turned on and what's being turned off.
• 4:55
  And it's not just that things get turned off as you age or things get turned on as you age.
• 4:59
  It's actually the pattern of what goes on and what goes off that tends to correlate with the aging process.
• 5:06
  And that in the initial clocks, they were, they were seeing, well, if, this is true, how close can we get to predicting somebody's chronological age or a mammal's chronological
• 5:16
  age?
• 5:16
  And that was what the original clocks were kind of focused on.
• 5:19
  So just to give the audience a sense of context there.
• 5:22
  Yeah.
• 5:22
  you're exactly right.
• 5:23
  And those first generation clocks, mean, that's what they're defined as is kind of the first iteration.
• 5:28
  These first generation clocks able to predict chronological age.
• 5:32
  They were so exciting for so many reasons, but mainly that they were able to predict our ability to become diseased or even death later on in life better than chronological age
• 5:46
  itself.
• 5:46
  So.
• 5:47
  That's super impressive.
• 5:48
  It's a new biomarker, just like we're testing and using any other biomarker, whether from a blood -based panel or any of our hormones, as you mentioned as an example.
• 5:55
  So it's just another biomarker, way to measure something that's predictive of outcomes.
• 6:00
  These, yeah.
• 6:01
  the individual a sense of where they are in the aging process and then also beyond that risk of death, right, was kind of one of the initial things that came out, right?
• 6:12
  So maybe you can talk a little bit about that in terms of how this can be predictive of when someone's gonna die.
• 6:20
  Yeah, definitely.
• 6:21
  So what we're doing again is kind of looking at these on and off switches and saying, hey, we've also measured these on and off switches and other cohorts or populations or bio
• 6:31
  banks of people who are diseased or people who have become really close to passing away or people who have passed away.
• 6:37
  So we're able to measure.
• 6:40
  We like to call them fingerprints.
• 6:41
  When you think of a genetics, almost think of like a fingerprint of the body.
• 6:44
  We're able to measure these fingerprints on you, which is just a pattern based on your environment.
• 6:50
  your routine, your habits, how you interact with the world, essentially.
• 6:56
  So we're able to perform some, I would say, some really unique bioinformatics processes to say, hey, this is how your genetic expression, your DNA expression levels look like.
• 7:06
  And they tend to look like someone who is at risk for X disease or X type of death date, essentially, which is a little creepy to say, right?
• 7:17
  But yeah, it's become a really useful tool in clinics like you run, Dr.
• 7:20
  Gladden, where you can start to get a baseline or a sense of where you're at.
• 7:24
  And then if you're accelerated aging or even a slower type of aging, you can start to implement different plans to hopefully decrease that aging later on as well.
• 7:34
  One other thing I want to say just to hopefully relate to the audience a bit more as well, those first generation clocks or iterations, again, they're trying to produce
• 7:41
  chronological age.
• 7:42
  They're actually really useful in a lot of applications.
• 7:46
  like identifying the age of DNA at crime scenes, they are useful for refugees to see if they're old enough to seek asylum.
• 7:55
  And then even in some maybe odd confidential -based cases where people don't know their birth date or even orphans as well, right?
• 8:04
  We're really lucky to know our date of birth, but not everyone does.
• 8:07
  So they're really useful in those senses.
• 8:09
  That's fascinating.
• 8:10
  Yeah.
• 8:10
  I wasn't aware of some of the forensic uses of that.
• 8:13
  And that's pretty cool.
• 8:15
  So in those early clocks, then there came around this idea of the grim age, which was basically a sense of when somebody was going to die.
• 8:25
  And it was comprised of, think about, I don't know, 12 or 13 different markers that were, that were not DNA methylation markers.
• 8:32
  They were things like smoking and certain levels of certain cytokines and things like this.
• 8:38
  And
• 8:39
  And then what was interesting to me was to see that you were able to say, you know what, using DNA methylation patterns, we think we can actually estimate what the grim age is
• 8:54
  going to be.
• 8:54
  We don't have to actually draw your blood and measure these different cytokines and markers in the blood.
• 9:01
  We can actually look at DNA methylation and from that be predictive of when you might, you know,
• 9:09
  go, so to speak.
• 9:10
  do you want to speak to that for a little bit?
• 9:13
  How you figured that out?
• 9:14
  Yeah, definitely.
• 9:15
  this is, Grimmage is a great clock.
• 9:18
  This is by Dr.
• 9:20
  Steve Horvath.
• 9:20
  This came about five, six years later.
• 9:24
  So we're talking 2018, 2019, very, very new data and new science.
• 9:30
  But essentially it's an advanced version of those first generation clocks.
• 9:34
  Like you mentioned, Dr.
• 9:35
  Gladden, we're not only looking at DNA methylation or epigenetic data, we're actually looking at
• 9:42
  More information, additional biomarkers.
• 9:45
  In particular, there were lifestyle factors included in the calculation, like smoking, for example, and then also a lot of age -related proteins as well.
• 9:55
  So, GDF -15, PAI -1, a couple others like leptin, ADM, as well, just to name a few for those who are more interested in those individual proteins.
• 10:07
  They took about 12 of these, and unlike those
• 10:10
  first generation clocks that I mentioned that were trained only on chronological age, the Grimm age integrates this DNA methylation data with circulating proteins, with lifestyle
• 10:20
  factors, making it more sensitive to health related risk.
• 10:23
  So it offers what I think was probably the first ever epigenetic clock, which was really adding more clinical utility to it, right?
• 10:34
  It's been used in a lot of studies.
• 10:36
  It's been validated to really assess impact of lifestyle interventions, diseases on biological age.
• 10:41
  So I love the GrimAge.
• 10:43
  It just came out with a V2 even more recently, which they added a little bit more proteins, I think like four or five more to make it even better than that V1 or that first
• 10:53
  iteration.
• 10:54
  Yeah, that's very cool.
• 10:55
  Yeah, because when you think about it, most people in the audience do know their birth date.
• 11:00
  And so if you pay to have a test done, that's basically going to say, well, this is your chronological age.
• 11:06
  I mean, how useful is that to you?
• 11:07
  Right.
• 11:07
  It's like, well, it's off by a year or two.
• 11:09
  I mean, it's it's not that helpful.
• 11:12
  Right.
• 11:12
  So what we really want to do is move away from predicting chronological age into more biological age.
• 11:18
  Right.
• 11:18
  Which is kind of where are we in the aging process?
• 11:21
  Are we younger or are we older?
• 11:23
  And, you know, as the audience has heard me say many times, there's no single age that we have.
• 11:29
  We're a mosaic of many ages, right?
• 11:31
  A brain age and a heart age and a blood vessel age and actually a telomeric age and a DNA methylation age and a mitochondrial age and, you know, on and on and on.
• 11:40
  So there's many, many different ages that we have.
• 11:44
  So when the DNA methylation clock started to look at no longer trying to predict what your chronological age was, we're trying to give you an idea of
• 11:52
  biological ages.
• 11:54
  Initially, it was kind of a biological age, right?
• 11:57
  There was an intrinsic and an extrinsic age.
• 11:59
  Maybe you can speak to that.
• 12:00
  We'll start there and then we'll show how the technology has expanded into even being able to tell you more.
• 12:07
  Yeah, definitely.
• 12:08
  So the intrinsic and extrinsic epigenetic age, that was the first iteration of what Tru Diagnostic, my company offered, which seems like ages ago.
• 12:15
  You were right, Dr.
• 12:17
  Gladden, we really ran our first test in about July of 2020.
• 12:20
  So we're a little over four years old now.
• 12:22
  And at the time, again, those first generation clocks were the best that we had to offer.
• 12:28
  So those intrinsic and extrinsic epigenetic ages are considered first generation clocks.
• 12:33
  They're still better at predicting all -cause mortality, morbidity than
• 12:36
  just your chronological age itself, because as a little thought experiment, think of your chronological age.
• 12:42
  Think of your friends, your peers who are the same age as you.
• 12:45
  Some probably look a lot older.
• 12:47
  Some probably look a lot younger, right?
• 12:49
  People vary on a biological cellular level, and that's what we're really capturing in those algorithms and those clocks.
• 12:58
  To date, we now don't offer intrinsic and extrinsic, so we actually removed that away from the reporting because we've created
• 13:05
  better iterations of these clocks or second generation clocks, which, you know, I don't think everyone's happy with change, right?
• 13:12
  Moving around kind of the information provided on the report.
• 13:16
  We're still able to historically provide you with anything we've ever reported out on, but you know, we want to hold ourselves as a company to the highest level of scientific
• 13:24
  integrity that there is.
• 13:26
  And we believe you really get there by.
• 13:28
  removing a little bit of the noise and maybe focusing on the ones that are more predictive of outcomes.
• 13:33
  They're more precise, they're more reliable.
• 13:36
  And that's how we know those second generation clocks are better is because they're even more predictive of outcomes than those first generation ones.
• 13:43
  think to your point, think part of the consternation of the clinician and the clients or the patients was that it was difficult to track things over time because the original
• 13:55
  extrinsic age and intrinsic age, which had to do with whether or you were excluding methylation points related to the immune system, if you were going to try to follow
• 14:06
  yourself over five years, all of a sudden it's like,
• 14:10
  the scoring system changed.
• 14:11
  And so it was very difficult to have apples to apples comparisons in your own world.
• 14:15
  And so you could think that you were doing well and then your next test came back and showed you, no, you're not doing as well as you thought.
• 14:21
  And that was a little bit, you know, of a conversation piece, right?
• 14:25
  Like, how do we make peace with this and how do we actually understand it and go forward from here?
• 14:30
  But I do agree that the integrity of the approach is what stands to test the time.
• 14:36
  You don't want to keep reporting something when you have something better to report.
• 14:39
  or more accurate to the report.
• 14:41
  So congratulations on doing that.
• 14:43
  So, you know, as your report has expanded now, it's really expanded into many different sections of a report, right?
• 14:52
  And maybe we could start with the omics reporting that you do.
• 14:55
  So now there's an omics section and you can get a DNA methylation omics age.
• 15:03
  But the interesting thing about that is that it also now becomes predictive of
• 15:08
  you know, risk of death, whether you have an increased risk of death or a decreased risk of death, heart disease, lung disease, cancer, you know, a number of different things go
• 15:17
  into that.
• 15:18
  Do you want to talk a little bit about that omics piece?
• 15:21
  Definitely, think Grimmage, even version two Grimmage on steroids, right?
• 15:26
  We took some of their methodology and added a twist to it, but basically the omic age, as Dr.
• 15:34
  Gladden said, is this idea of the multi -om.
• 15:37
  We're aging in all these different ways.
• 15:39
  We have our genes, which is our hardware that our epigenetics sits on top of.
• 15:44
  It's the way our...
• 15:45
  genes are expressed and then we move to transcription.
• 15:49
  So again, how are those genes being read and actually transcribed into protein folding and then giving off metabolites, which ends up in our phenotypic outwards expression.
• 15:57
  that process there is kind of this idea of the multi -ome.
• 16:01
  So we actually measured 61 clinical lab variables, 8 ,500 metabolites, and 27 ,000 untargeted proteins to say, all right, let's really add as much.
• 16:13
  biological underlying data and information as possible to get the most predictive clock that's ever been created.
• 16:18
  And that's what we really did with omic -mage.
• 16:21
  Kind of along with that, we're able to produce those risk scores, as you mentioned, and then something else we're able to do that hasn't really been done before is we're able to
• 16:28
  give you reasons as to why you're aging faster or maybe slower.
• 16:34
  And these are called EBPs or epigenetic biomarker proxies.
• 16:38
  Mm -hmm.
• 16:39
  they're those individual clinical lab values, metabolites or proteins, which are the most statistically significant or correlated with that aging.
• 16:47
  So, it's gamified now, right?
• 16:49
  We're able to give you actionable insights and say, hey, let's work on reducing maybe your interpretation of HbA1c or fasting glucose.
• 16:57
  Maybe it looks like a little bit of an insulin sensitivity issue.
• 17:02
  Exactly.
• 17:03
  No, it's really quite fascinating.
• 17:04
  And the thing about it is it gives you targets, right?
• 17:08
  It gives you not only a sense of where you are in the aging process, but helps you understand where to take action.
• 17:14
  And I think that's what everybody's looking for.
• 17:17
  It's like, OK, it's one thing to tell me that I've got, you know, a particular biological age or I'm at a particular risk for something.
• 17:26
  Well, what am I going to do about it?
• 17:27
  Right.
• 17:27
  And I think, you know, people
• 17:30
  that come to us, sometimes when we have the initial conversations are like, okay, so you're to test all these things, but you know, what are we going to do about it?
• 17:39
  Right?
• 17:39
  Are you just going to tell me that I've got a problem and we can't do anything about it?
• 17:43
  And we say, quite honestly, everything that we ever test is actionable.
• 17:47
  And to your point, what you're now doing is actually linking interventions and actions, potential interventions and actions into the results that you're, that you're reporting.
• 17:57
  So that's pretty exciting.
• 17:59
  Yeah.
• 17:59
  Yeah, absolutely.
• 18:00
  And that's what we want to do, right?
• 18:03
  It's give actionable insights.
• 18:04
  We want to make sure these things are changing in a good direction with things that we'd expect to change it with, like caloric restrictions, analytics, which the first generation
• 18:14
  clocks have issues kind of tracking.
• 18:17
  So we're super confident and happy with the algorithms as of now.
• 18:21
  So, you know, when you think about the interventions that have made the biggest impact, and we'll go through some of the other things you're reporting on, but you just mentioned
• 18:31
  calorie restriction and it does seem like, you know, fast -mimicking diets or fasting itself.
• 18:38
  And I don't know if there's data yet on the use of the supplement Mimeo that you're probably familiar with, which is kind of a supplement that mimics fasting, if you will, in
• 18:47
  certain ways without you having to fast.
• 18:50
  I'm just curious, what have you seen makes the biggest impact on somebody's omic age, so to speak?
• 18:59
  Yeah, this paper was released August 8th of this year.
• 19:05
  It's a preprint right now, but it's called Multiomics Analysis Reveals Biomarkers That Contribute to Biological Age Rejuvenation in Response to TPE or Therapeutic Plasma
• 19:15
  Exchange.
• 19:16
  This was a paper published out of the Buck Institute with Dr.
• 19:20
  Kiprov, and it's really new, and it's now putting our money where our mouth is.
• 19:25
  And we saw really great
• 19:28
  results in terms of biological age clock reversal with therapeutic plasma exchange, which I think a lot of physicians we work with have been doing and are excited about.
• 19:38
  Well, that's very cool.
• 19:39
  I see that paper because it hasn't come out yet, I guess.
• 19:43
  yeah, so when I know Dobrik Kiprov and, you know, he works with the conboys there at Berkeley and he's a big fan of plasma freezes where you basically for the audience, you
• 19:56
  get hooked up to a machine.
• 19:57
  They pull out your blood.
• 19:58
  goes into a centrifuge.
• 19:59
  They separate the cellular portion from the liquid portion called the plasma.
• 20:05
  They throw away about two liters of your plasma, depending on your size.
• 20:09
  And then they give that volume back to you with normal saline and some albumin, which is the primary protein in plasma so that you maintain both blood volume and the proper what's
• 20:20
  called on chronic pressure.
• 20:21
  So, you know, there's enough protein to keep the liquids from moving around into your different compartments.
• 20:27
  So so anyway, with that.
• 20:31
  The convoy showed that there was could be a rebooting of stem cells across all three different germ layers, right, which would be all the different tissues that we have and
• 20:39
  the derby is a term actor term and that it could be kind of a rejuvenating kind of technology that was in animals.
• 20:47
  And they felt like it was even equivalent to and maybe even better than young plasma at that point in time.
• 20:54
  So I'm curious with Dobry's paper, I suppose that this was
• 21:00
  did not involve young plasma or anything.
• 21:03
  It probably just involved plasma for Isis with saline and albumin.
• 21:08
  And can you leak any information as to how big the impact was with just that approach?
• 21:14
  Yeah, they tested all of the different clocks and I'll definitely send you this paper as a follow -up as well.
• 21:23
  I want to see, you we saw the biggest result though in our kind of individual systems age scores, which we'll talk about later, specifically the metabolic score, brain score,
• 21:38
  inflammation, kidney and immune as well had the biggest age acceleration difference.
• 21:43
  Nice, nice.
• 21:45
  Yeah, that's pretty exciting.
• 21:47
  That's pretty exciting.
• 21:48
  So I know that when I first did plasmapheresis, I was doing it on a monthly basis for about six treatments and then doing it quarterly thereafter.
• 21:59
  And it has been very useful.
• 22:01
  Do you have any data on young plasma in this regard?
• 22:06
  Yeah, not in this particular study.
• 22:08
  we, we again have had a lot of people doing the young plasma, as well.
• 22:13
  I think we, like young plasma too.
• 22:15
  I'd love to see him, think head to head and, compared across each other, but we don't have that.
• 22:21
  Unfortunately, it's definitely a need though.
• 22:24
  Yeah, interesting.
• 22:25
  Yeah, because there are other things that of course could be done that could be rejuvenating as well besides plasma exchange.
• 22:32
  You know, there's different cell factors from placenta and cord blood.
• 22:36
  There's stem cells of sorts of different varieties that can be rejuvenating as well.
• 22:42
  And then calorie restriction back to that calorie restriction, I think, can also have a significant impact on.
• 22:50
  on some of the omic ages.
• 22:51
  Do you have any data that you can share on that per se?
• 22:55
  Yeah, so just traditional caloric restriction, not necessarily intermittent fasting or time restricted feeding.
• 23:01
  So just reducing your overall caloric intake.
• 23:05
  We love that for kind of the inhibition of mTOR, right?
• 23:08
  It's this mechanistic target of rapamycin, protein that plays a big role in regulating cell growth, metabolism, survival.
• 23:15
  So the thought is, hey, if we can target this and inhibit that, we're slowing down kind of the growth of our body and our cells.
• 23:23
  So we really like that.
• 23:24
  The calorie randomized control trial was published last year in January.
• 23:27
  It showed a big drop with Dunedin pace.
• 23:30
  And we see in our internal cohort as well that a lot of the omic -age, the symphony -ages, they go down statistically significant with caloric restriction.
• 23:40
  And anything else that would inhibit mTOR, right, and kind of follow that same pathway.
• 23:44
  Yeah.
• 23:45
  up.
• 23:46
  Rapamycin, sulfur, the broccoli extract is looking really good, I think, as a supplement as well, people really like to do.
• 23:54
  Also, it just has really great antioxidant -related effects on cellular stress, cellular oxidation, inflammation too.
• 24:03
  So people really like that.
• 24:04
  so the audience knows this comes from broccoli.
• 24:07
  It comes from other cruciferous vegetables too, but it's pretty rich in broccoli and broccoli seeds have more than broccoli sprouts, which has more than broccoli itself.
• 24:17
  There's an enzyme called myrinase that's required to activate the sulforaphane.
• 24:21
  And so there's a supplement out there now that combines those two sulforaphane with myrinase and it's called abmacol.
• 24:30
  And it's
• 24:31
  It's an interesting supplement.
• 24:32
  When you go through genetic testing and you look at all the different genes that are actually impacted in a positive direction, which means they're either upregulated or
• 24:42
  downregulated by sulforaphane.
• 24:45
  It's literally hundreds of genes that are positively impacted by this molecule.
• 24:51
  So we've been using sulforaphane in our practice for everybody for, you know, I don't know, over a decade now, probably when we first learned about it in Australia.
• 25:00
  And so that's pretty interesting to see that it's actually now having an impact on omiclocks as well.
• 25:05
  So pretty cool.
• 25:07
  Yeah.
• 25:07
  We like that one as well.
• 25:08
  So, yeah, a couple of tips and tricks on, on ways to, you know, slow, slow that overall, I'll make image.
• 25:14
  think point being is, Hey, we can get really granular now start to pinpoint specific systems of the bodies or start to, you know, kind of look at these themes.
• 25:23
  Dr.
• 25:23
  Michael Snyder out of Stanford actually explains these types of aging themes as agio types, where you're putting someone into a particular category based on all of these ways
• 25:32
  they're aging.
• 25:33
  to try and say, let's work on these mechanisms or pathways and target these particular genes.
• 25:38
  So more to come on that.
• 25:40
  Yeah.
• 25:40
  exactly.
• 25:41
  So, let's- You did mention the Dunedin pace, which is, basically,- I feel like I never pronounce it properly.
• 25:47
  It is named after a New Zealand town where data was collected, kind of like the Framingham study here in the US, looking at mortality and omics ages and things like that.
• 25:58
  And this has become a very sort of easy-to-grasp, easy-to-understand
• 26:06
  metric, if you will, in the aging process, where it's talking about really your rate of aging.
• 26:10
  And so, you know, you think, 'Well, I'm on the planet Earth for 365 days, so I aged a year.' But it turns out you could be aging more than a year or you could be aging less
• 26:21
  than a year.
• 26:22
  And Brian Johnson, who's become very popular, has used this as a metric of his Longevity Olympics.
• 26:30
  It's really the one metric that they look at for that.
• 26:33
  You know, the interesting thing is that-
• 26:35
  Maybe you can talk us through a little bit about the data behind the Dunedin Pace and the quality of that metric and then we can kind of juxtapose that to some of the other things
• 26:44
  that we see.
• 26:45
  Yeah, definitely.
• 26:47
  This is probably my favorite one to talk about.
• 26:49
  I think it's just so unique.
• 26:50
  It's, you know, we've talked a little bit, so far in this episode, about first generation, second generation clocks...
• 26:56
  This is actually called a 'third generation'.
• 26:58
  Again, just talking about the differences between how they're built.
• 27:01
  One is not necessarily better than the other.
• 27:03
  It depends on what you're looking for in your specific application.
• 27:07
  But, I think you said it right, Dr.
• 27:09
  Gladden the, the Dunedin pace was, you know, created out of a
• 27:14
  cohort in Dunedin, New Zealand that started in 1972 and 1973.
• 27:19
  They looked at about, 1,100 people over that 50-year longitudinal trial and they're still studying them today.
• 27:25
  I think that's what's really unique here is this cohort they're studying because they have a 96% retention rate, which is crazy.
• 27:33
  You just don't hear about that.
• 27:34
  You would never get that replicated in, you know, in the States and they are.
• 27:38
  There were four waves in the study
• 27:42
  that researchers would go out and collect a full biomarker panel on them, collect all of these like physical function measurements, even dental images, kind of eye health, any and
• 27:52
  all biomarkers you can imagine.
• 27:54
  And they basically created an algorithm of specific bioinformatic processes and formula to track those biomarkers over time and create this pace.
• 28:03
  So instead of an age, you get kind of a number between 0.6 and 1.4
• 28:08
  that tells you how quickly you're aging biologically for every one chronological year.
• 28:12
  So, this one is really different than the others in the fact that it's more of like a three to six month running average of how you've been aging.
• 28:20
  And it is, when you compare it to all other clocks ever created first and second generation, it responds the quickest to intervention.
• 28:29
  So, people like to do this one a little bit more often, maybe even every two to three months, usually.
• 28:34
  And this is one that I do believe calorie restriction has a big impact on.
• 28:38
  Like, if you do a five-day fast-mimicking diet, you could test yourself prior and you could test yourself a week after you finish the fast and it would show some changes.
• 28:47
  I think it can- It's that responsive.
• 28:50
  So, just so people understand.
• 28:54
  The interesting thing is that when we when we test our people across the whole
• 29:01
  comprehensiveness of the test report that you put out, we can see that somebody can actually be aging at a 0.84 rate of aging.
• 29:11
  And yet, based on their omics age, they could be almost a decade older.
• 29:17
  They could have a risk of death that's over 100% of predicted.
• 29:21
  And so, it's very, very misleading to, and this is my one critique, I suppose, of the
• 29:29
  Longevity Olympics, is that it's it can be misleading to hang your hat on a single...
• 29:33
  on a single metric.
• 29:34
  And I think the audience should understand that there is no one metric that actually captures what's going on, which is why you guys are reporting on so many different things.
• 29:42
  Right?
• 29:43
  So, just realize that you could- you could have that rate of aging that's, you know, point...
• 29:48
  point eight or whatever, looks really fantastic but it's not a guarantee.
• 29:52
  You really have to look a little deeper to see.
• 29:54
  And really, the idea of doing these tests is not
• 29:57
  so much for bragging rights as it is to identify opportunities.
• 30:01
  Where can I take action to optimize my life and my health?
• 30:04
  Right?
• 30:04
  That's the question I think to ask, not how do I compare, but how do I take action?
• 30:09
  Right?
• 30:10
  So...
• 30:10
  Yeah, definitely.
• 30:12
  No, I think you're completely right.
• 30:13
  Aging is extremely multifactorial.
• 30:16
  It's super complicated.
• 30:18
  We don't know what we don't know.
• 30:19
  And there's, you know, I think more...
• 30:21
  We're We definitely have way more questions than we do answers when we first started TruDiagnostic, which is, is always fun to build a business off of.
• 30:29
  But I think you're, you're exactly right.
• 30:32
  You know, someone could be doing all of the things correctly now, but maybe, historically, they've aged at an accelerated
• 30:39
  rate that makes their, you know, OMICm age a lot higher.
• 30:42
  So there there's- What's that?
• 30:45
  Let's see.
• 30:46
  showing her a diagram that I've- This is the aging process.
• 30:49
  These are all the different, 18 different hallmarks and how they're interrelated.
• 30:53
  And that's how complex it is, right?
• 30:56
  So, if you want to see a page with, know, 18 things around the periphery and lines going everywhere, connecting everything, and they're all interrelated...
• 31:03
  That's what I'm showing her.
• 31:04
  It is- Aging is very, very complicated and it's all interrelated.
• 31:09
  And so, trying to tease that knot apart is really the task that we have.
• 31:14
  And I think AI is going to be very helpful to us in doing that.
• 31:18
  But anyway, just to her point, it's complicated.
• 31:21
  Yeah.
• 31:21
  I always bring up that example too, usually.
• 31:23
  I'm like, 'Hey, we just published on nine hallmarks of aging in what, 2013 or something.
• 31:28
  And now we've updated it and we have 12, right?' And we have hundreds, if not thousands of other hypotheses as to why we may be aging.
• 31:36
  I think what's really interesting with the epigenetic data is we're able to maybe capture a lot of that signal or information.
• 31:44
  But it doesn't mean it's the only test out there.
• 31:46
  I think you still need to
• 31:49
  do this alongside of many other different tests, as you mentioned.
• 31:52
  It's not a replacement.
• 31:53
  It's an add-on.
• 31:53
  It's complementary to everything you're doing.
• 31:56
  Yeah, no, that's right.
• 31:57
  And that's how we use it.
• 31:59
  We use it that way.
• 32:00
  Exactly.
• 32:01
  Like you'll estimate a telomere length, we'll actually measure the telomere lengths directly.
• 32:05
  Right?
• 32:05
  And then you may estimate a VO2.
• 32:07
  Well, we'll measure the VO2 directly for the individual because we learn so much more from the tests, the direct measures, than just what correlates to the to what the omics is
• 32:19
  predicting.
• 32:20
  For example, with VO2, it's good to know your VO2, but what's your anaerobic threshold?
• 32:24
  Right?
• 32:25
  And what's your heart rate-work rate slope that correlates to myocardial ischemia with exercise and things like that.
• 32:32
  Right?
• 32:32
  You just- It's hard for the for the omics- I mean, God bless it, it's giving us great information, but it's hard for it to actually replicate everything that you can get from
• 32:41
  other tests that also become super useful.
• 32:44
  Y'know, for example, we we just had an 18-year-old
• 32:49
  woman in here who presented with MS, early MS going with intermittent blindness, right?
• 32:54
  And so, the question was, 'Well, what's causing this?' And so, we deconstructed the situation.
• 33:01
  We did not do any omics aging on her.
• 33:03
  But what we found is that with a glucose tolerance test that her hemoglobin A1c was 5.3, her fasting blood sugar was 88, right?
• 33:13
  When we gave her a glucose tolerance test, she went from 88 to 135 and then back down to 120 in two hours.
• 33:19
  But her insulin was 13 at rest and at one hour went to 230.
• 33:27
  And at two hours was 220.
• 33:29
  It should go up no more than five times, right?
• 33:32
  So, let's say it started at 10, it shouldn't go higher than 50.
• 33:35
  Or let's say it started at five, it shouldn't go beyond 25.
• 33:38
  So, what she has here is massive insulin resistance, completely undetectable by any other metric.
• 33:45
  And even if you saw an endocrinologist and they did a glucose tolerance test, they would have missed it.
• 33:50
  So, it's really fascinating how important it is to actually be so meticulous about deconstructing the situation to get to the answer.
• 33:58
  And that was only one of many answers we found for her as to why she had it.
• 34:02
  I won't go into all that right here, but the point is that when you're doing the omics,
• 34:07
  it's always important to understand what it's telling you and understand what it's not telling you.
• 34:13
  And that's really, really key with any test, quite honestly, so...
• 34:17
  Yeah, I think that's a good point.
• 34:19
  I think, too, we're creating a new field, right?
• 34:22
  New novel biomarkers that have never been created before.
• 34:24
  So, this technology is newer than the first iPhone, which is crazy to say, and we've already discovered so much from it.
• 34:33
  So, be patient with us, we're working on training this a little bit better, validating it further too.
• 34:39
  But all of these are published algorithms and predictive of many different outcomes.
• 34:43
  So, tell the audience a little bit about this research project you do with Harvard.
• 34:48
  Yeah, yeah, so that research project we did with Harvard is the omic image.
• 34:55
  So the one that I was mentioning where we measured all of those multi -omic type of variables, right?
• 35:02
  The 27 ,000 protein groups again, 8 ,500 metabolites, 61 clinical lab variables, a million DNA methylation markers, and about 5 ,000 people.
• 35:12
  So the usefulness of this is not only did we create
• 35:16
  the best second generation biological age clock that out competes at GrimAge, PhenoAge, and other historical really great clocks, it gives us resolution.
• 35:27
  So we're able to actually now produce, Dr.
• 35:29
  Gladd, at about 4 ,000 of these EBPs, or epigenetic biomarker proxies.
• 35:37
  Clinical lab values, again, metabolites or proteins, meaning, well, again, what's the point?
• 35:41
  Why is this useful?
• 35:42
  Ideally, we just need one blood spot card.
• 35:46
  at one price point with all of your results delivered in one place to get CBC values, to get hormone values, sexual function outcomes, immune cell subset information, even
• 35:58
  micronutrient panel, right, vitamin levels and kind of all of the above in one place.
• 36:03
  Of course, we won't be perfect with all of them.
• 36:06
  We'll have to make sure we're predictive of those individual outcomes, that we have good correlation values, but it's really, really exciting.
• 36:15
  what we're most excited about here at Tru Diagnostic, even outside of just the aging itself.
• 36:21
  Yeah, no, it's impressive.
• 36:23
  And tell us a little bit about the symphony of aging that you have.
• 36:30
  Yeah, the symphony age, you actually said mosaic earlier during this conversation.
• 36:35
  It used to be called mosaic or sometimes you'll see it called systems age too.
• 36:40
  This is a second generation clock we actually just created with our partners at Yale and it's available now, but it's kind of gives you this idea of, hey, we all age at a
• 36:52
  different rate according to the organ level.
• 36:54
  We know we all don't get lung disease at the same time or.
• 36:58
  liver disease or neurological dysfunction.
• 37:01
  So we break this down into 11 different organ systems such as lung, metabolic, musculoskeletal, blood liver inflammation, kidney, heart, hormone, immune, and brain age.
• 37:13
  So again, it gives you resolution.
• 37:15
  We're not just giving you a biological age and you're guessing about what you need to change.
• 37:20
  We're saying, musculoskeletal age looks a little bit older compared to the rest of them.
• 37:26
  -hmm.
• 37:26
  we need to look at your muscle mass, your bone health, your vitamin D3.
• 37:31
  We can go into that musculoskeletal age and say, hey, here's what it was trained off of.
• 37:35
  Let's dig into those biomarkers a little bit more.
• 37:38
  Yeah, yeah, absolutely.
• 37:40
  You know, the EBT seem like a rich area for expansion, right?
• 37:46
  Because you're identifying different molecules or different measurements that people can get in other ways that could be driving the aging process or at least are associated with
• 38:02
  the aging process and could become sort of targets for that individual to focus on.
• 38:06
  Do you want to talk a little bit about that?
• 38:07
  Do you have plans to kind of expand on the EBT picture with recommendations about interventions or that sort of thing?
• 38:17
  Absolutely.
• 38:18
  So I think our next product we're thinking about creating that true diagnostic through these EVPs is going to be almost like a NutriEval report.
• 38:28
  I don't know if you do any type of that reporting now.
• 38:31
  Yeah.
• 38:32
  But again, basically being able to give you feedback on certain vitamin levels, right?
• 38:37
  Do you have enough B vitamins?
• 38:39
  Are you even methylated properly, which you can get that information from a genetic test right now, but we'll probably be able to give you that too.
• 38:47
  So we'll give you ranges.
• 38:48
  We'll say, you're extremely low.
• 38:50
  You're kind of in range.
• 38:53
  You're too high.
• 38:54
  Here's what to do to get optimal ranges.
• 38:56
  What's really cool about that, though, is we actually have a pretty cool company we're partnering with to actually then give you kind of little micro dosing of those nutrients
• 39:09
  or those supplements.
• 39:10
  So it's a different idea.
• 39:13
  Again, we're not going to be offering supplements.
• 39:15
  company is going to offer it based on your results if you're interested.
• 39:18
  We don't offer supplements.
• 39:20
  We don't believe in offering you the test and the solution.
• 39:22
  But you have the option to say, all right, what is my specific micronutrient profile based on my DNA expression patterns and what I'm seeing?
• 39:32
  So I'm really, really excited for that.
• 39:35
  I think that the sky's the limit there.
• 39:37
  And what's really cool is a lot of outside universities and academic kind of
• 39:44
  publications have come out about this idea of using epigenetics as a proxy for other outcomes.
• 39:50
  And people are replicating this work.
• 39:53
  I mean, it's here, right?
• 39:54
  We made a pretty good guess.
• 39:56
  We had some luck along the way.
• 39:57
  Yeah, exactly.
• 39:58
  Now, it's pretty interesting.
• 40:00
  Let's switch gears for a minute and talk a little bit about David Sinclair and the work that they've done with the Yamanaka factors and now some of the some of the chemicals that
• 40:08
  they're using to actually reprogram epigenetic agent in cells, really in tissue culture.
• 40:16
  You know, the Yamanaka factors were Nobel Prize winning for factors that were found that when they were activated, they could actually take a.
• 40:25
  a skin cell and turn it back into a pluripotent stem cell.
• 40:29
  And that was very exciting because in that pathway is the ability to make a cell young again.
• 40:36
  And so Sinclair played with that.
• 40:40
  And what they found was that if you gave all four of the factors, you would take the cell back so far in time that it would actually start to form tumors.
• 40:50
  So they they took out one of the factors.
• 40:52
  I forget which one.
• 40:53
  of the four, but they started using three factors and then they started pulsing three factors for shorter periods of time, like for nine days or 12 days, enough to turn the
• 41:04
  cell back in time, but not take it too far back.
• 41:07
  And I believe they could use the amino, the antibiotic erythromycin actually turn them on and turn them off, something like that.
• 41:14
  So when they would give the erythromycin, I think it would start the action going and then when they would stop it, would, it could pause it in time.
• 41:23
  And that's really fascinating because they were showing based on the epigenetics that they were measuring that the cell was actually getting young again.
• 41:31
  Right.
• 41:31
  So that's pretty fascinating.
• 41:32
  Then they went on to do another study where they used five sort of chemicals, one of which was valproic acid, some other ones that just are letters in the paper, know, letters and
• 41:42
  numbers.
• 41:42
  You're not sure exactly what the what the substance was.
• 41:44
  But the idea was using chemicals.
• 41:48
  they could actually replicate what was being done with the Yamanaka factors right at a genetic level.
• 41:53
  So that was pretty interesting.
• 41:55
  And they showed again that the epigenetic age was going back in time.
• 42:01
  So anyway, that's a very exciting area as well.
• 42:04
  And I don't know if you're collaborating with them on any of the stuff that they're doing or whatever.
• 42:10
  But I would think there might be some interplay there.
• 42:13
  So.
• 42:13
  Yeah, yeah, mean, mean, definitely it's so interesting.
• 42:16
  And there again, it warrants, I think more questions than it does answers.
• 42:21
  But there's a really cool conference that our team goes to usually once a year when they kind of have aging as the area of interest called GRC or the Gordon Research Conference.
• 42:32
  And it's like the best of the best, the who's who, all of the work has to be, you know, peer reviewed, published.
• 42:38
  And they kind of talk about this idea of resetting the cells or these Yamanaka factors.
• 42:44
  they really, I mean, it's happening.
• 42:47
  Like this is happening in a lab and on a cellular level that you can take these fully formed differentiated cells and kind of take them back to stage zero.
• 42:56
  But then I think once you have them in stage zero, how did the cells learn to get back to stage zero, right?
• 43:03
  They're doing this work.
• 43:05
  They know it can be done, but they're just like trying to understand it more.
• 43:09
  Like what does it actually mean?
• 43:10
  How can it mature again?
• 43:12
  How do you make it kind of clinically applicable, which is so cool because I'm able to see the research and everything happening, but then also see people like you, Dr.
• 43:21
  Gladden, are pioneers in this work, put it into practice with their patients.
• 43:25
  So I think it's so exciting and Altus Labs where Steve Horvath, Dr.
• 43:30
  Morgan Levine is now.
• 43:32
  pioneers in this epigenetic space are working on that day in and day out.
• 43:37
  Yeah, yeah.
• 43:38
  And I think that's something that's very exciting for everyone.
• 43:41
  It was interesting in reading those papers that when you do rejuvenate a cell, there are some portions of the cell that aren't fully rejuvenated.
• 43:51
  And so it kind of makes you wonder if it's like the clone is not as good as the original copy necessarily.
• 43:59
  Although I think that's been sorted out to where you can make a clone that is as good as the original copy.
• 44:03
  my point is that
• 44:05
  It makes you wonder whether that cell that's actually been reprogrammed to youth, whether it will now age down a different trajectory, either faster or with different stages or
• 44:16
  different accelerants or different, you know, elements that may either slow it or make it faster as it ages.
• 44:23
  It's there's really a lot of unknowns here, right?
• 44:25
  What's to your point?
• 44:26
  You take it back in time, but how's it going to now move forward?
• 44:30
  Right.
• 44:30
  And so there's a lot to be sort of sorted out there, too.
• 44:33
  But fascinating stuff.
• 44:35
  Yeah.
• 44:36
  Yeah, yeah.
• 44:37
  So super cool.
• 44:37
  Well, again, there is a lot to come, I think, a lot of research -based questions we need answered.
• 44:43
  And again, how can we make this as clinically applicable as possible?
• 44:46
  I still think it is paving the way in aging, although we're able to predict a lot of different things as well.
• 44:51
  Yeah, absolutely.
• 44:53
  Well, it's been fabulous chatting with you.
• 44:54
  I appreciate you taking the time to to come in and do this with us.
• 44:59
  I think the audience has learned a lot.
• 45:00
  It's been very enlightening.
• 45:02
  If you're not familiar with true age, you can go online.
• 45:06
  I don't know.
• 45:06
  Can people order the test themselves or is it done through practitioners or how does that work?
• 45:11
  Yeah, we were always recommending going through a practitioner.
• 45:13
  You're to get the full reporting and you're going to get guidance.
• 45:15
  You're going to get, you know, hopefully consultation that's included.
• 45:18
  they can get it on, the website.
• 45:20
  It's going to be more expensive.
• 45:21
  It's going to only have, you know, a subset of those small reports.
• 45:25
  so we were, we recommended going through a healthcare provider for sure.
• 45:28
  yeah, I think it's complicated enough.
• 45:30
  You do want somebody to help you kind of navigate it and and ultimately decide how you're going to take action.
• 45:36
  Right.
• 45:36
  That's the point of these tests.
• 45:38
  And then, you know, not just a test once, but a second time, right?
• 45:41
  Third, fourth, right after you take action as well.
• 45:43
  that's right.
• 45:44
  Well, great.
• 45:45
  Well, thank you so much.
• 45:47
  Thanks, Dr.
• 45:47
  Gladden