Inside The Revolutionary Epigenetic Cellular Health Test (1,700 Biomarkers from a Drop of Blood)

Ryan Smith [00:00:00]:
So we’re finding so much information from just such a small drop of blood.

Nick Urban [00:00:05]:
You’re listening to High Performance Longevity, the show exploring a better path to optimal health for those daring to live as an outlier in a world of averages. I’m your host Nick Urban, bioharmonizer, performance coach and lifelong student of both modern science and ancestral wisdom. Each week we decode the tools, tactics and timeless principles to help you optimize your mind, body and performance span. Things you won’t find on Google or in your AI tool of choice. From cutting edge biohacks to grounded lifestyle practices, you’ll walk away with actionable insights to look, feel and perform at your best across all of life’s domains. Hey, I hope you’re doing amazing wherever you are tuning into this in the world. I’m going to stop doing these intros. I’m going to try it at least to see if that’s a better experience for you.

Nick Urban [00:01:05]:
Dear listener, I’m sure you’re busy and I want to let you get straight into the conversation I have with our guests. And like always, you can find the information for the episodes in the show notes which I will continue to publish. Those will be on outlier.com the number of the episode which will be in the show notes or if you’re watching on YouTube in the description, along with the usual things like the links to check out their work, any resources mentioned, their bio if you want to learn more about them, and a whole lot more. If in an episode like today’s there’s other context required, I will still be recording a short intro so that I can update you as necessary. For example, our guest today is talking all about a new form of testing I teased back in December, I think it was of last year. It’s a first of its kind test and in this episode we discuss my results. Specifically we walk through a breakdown and screen recording of my lab report. So this is one of those instances where the video version of the podcast will have a little more value, where you might want to check out the video version if possible.

Nick Urban [00:02:08]:
I work to still speak out and mention the different biomarkers so you can follow along via audio, but it’s one of those topics where the visual is hard to replace with audio. Nevertheless, this is an exciting frontier of diagnostics and testing that I wanted to bring to you and I haven’t heard anyone else talking about it really, so I hope you enjoy my conversation with repeat guest Ryan Smith. If you want to try the test, I actually released a written review of both of their products. This one is called True Health, and the Code Urban will save you on your order. The one that his company is most famous for is called True Age, and it’s a biological age test that seems to really be in a league of its own in terms of overall accuracy. We get into that why in the episode. And I’m actually currently compiling a little test I ran of five different products that claim to test your biological age. And I’m sharing my full results from each of them so you can compare and contrast to decide which one is right for you, if any.

Nick Urban [00:03:05]:
When that is done, it will be in the show notes for this as well. All right, without further ado, ladies and gentlemen, Ryan Smith. Ryan Smith, welcome back to the podcast for the third time.

Ryan Smith [00:03:17]:
Yeah, it’s great to be back. I know we’ve got a lot of updates.

Nick Urban [00:03:20]:
Yes, this is one industry that moves very quickly, this being the longevity industry and overall health diagnostics. If people want to get the Lay the land to understand the some of the basics of this stuff and also some of the interventions that we covered back in episode number 47 and 82, you guys can check those out. And then today we’re going to be picking up with a general overview. What is biological aging then epigenetic methylation, very briefly. Then we’re going to go on to some of the cool updates that have come out since our first podcast back in 2022, before the rejuvenation Olympics was a thing, before Brian Johnson had published the leaderboard, before the Kardashians had used True Diagnostic in your tests. So let’s start with the lay of the land. Ryan, what is biological aging at like a 10,000 foot overview?

Ryan Smith [00:04:12]:
Yeah, biological aging is the number one risk factor for every chronic disease and death. Right. So that’s why it’s important. But to sort of explain what it is, you know, we, it is simply trying to give a score to the molecular biology of how you are growing older and all the deficiencies that unfortunately come with that. We all know that chronological age is what most people think about when they talk about their age. But we also know people in their 70s who look 50 and vice versa. And we need a better way to measure this because it is such a big risk factor. And so biological age measurements have come out to do that.

Ryan Smith [00:04:46]:
But in recent years, probably the most prevalent and the most well validated have been these. These epigenetic methylation biomarkers, which are used to then tell you how your body is aging.

Nick Urban [00:04:56]:
Nice. Okay, so you guys have your original test that you’ve had since. I don’t. I don’t even know how long you’ve been using it. But then you’ve iterated on that. You’ve introduced new algorithms and you break down a lot of different scores and sub scores to help people understand what’s going on internally. What have you guys seen as some of, like, the big takeaways? Anything that stands out from your database of now? Thousands and thousands of people.

Ryan Smith [00:05:23]:
Yeah, there’s always interesting findings. You know, I think we’ve talked about a few of them even on some of the previous podcasts. I always like to preface this by saying what the overall things that we find are generally the things that are probably the least exciting, the things that are not surprises. Right. The things we already know we should be doing, which is exercising frequently, eating healthy foods. And by healthy, we generally mean natural non processed foods and generally a lot of leafy green vegetable type of foods as well. We also know that stress and environmental pollutants and lack of even social connection are all things that negatively impact this biological aging. And so I always like to start with the things that we already know.

Ryan Smith [00:06:09]:
I think that it’s also important that we see that biological age measurements back up what we already know, because we want to make sure that these things are working and telling us what is sort of classically all the things we’ve learned over these many, many years. And so those are the more tried and true methods. But there’s really interesting things we’re finding out all the time. One of my favorite things I think we talked about before is in data sets where we’re looking at bipolar, we found that lithium was one of these things that was correlated with lower biological age. And now there are several other studies which back this up, independent of mental illness patients, where lithium can be really exc. We just did a trial with EL where we released the outputs of 51 different aging interventions across all of the different clocks. And one of the best things on that benchmark probably actually was the best intervention for people who were already healthy across our cohort. And that was hyperbaric, which is probably again, not a huge surprise, but we saw a massive effect size for particularly high pressure hyperbaric.

Ryan Smith [00:07:10]:
So we’re seeing some things like that. We’ve looked at things like plasma apheresis that’s becoming really, really popular now. And actually there was a study published just two days though, from Dr. Borski, who actually said they didn’t see a lot of age rejuvenation, even though in some of our studies that we did with Dr. Kip, Rob and the Buck Institute, we did see some improvement of biological age and some reduction of some of those negative impact things like PFOS chemicals and a few others. But I think that we’re finding more and more every day now. We’re up to over 120 different interventional studies that we’ve done with DNA methylation that we’re all including to sort of benchmark. Now that we’ve got a universal measuring stick, we want to be able to benchmark all of these interventions on A1, on one basis.

Nick Urban [00:07:52]:
I love that. I hope you guys stack rank them somewhere and so we can look at them and see how things are faring in comparison to the others as that data becomes available to you.

Ryan Smith [00:08:01]:
Definitely we are, and we’ve published on some of that already, if you want to go look. We’ve done this with Yale and we’ve actually, with Yale created this platform we’re calling Translage, which will include all of the interventional data sets on DNA methylation be publicly available so you can benchmark those with any algorithm at any point.

Nick Urban [00:08:18]:
Yeah, and so the reason that I like this type of technology to begin with is that if you use a reputable test that’s doing things right, and we’ll discuss what that means in a second, then you can use this as a bit, as a benchmark to understand what’s going on internally in a way that you can’t necessarily with your typical blood labs or your wearables. It gives you a lot more granularity into your inner biological processes. But that requires us to have accurate data. And so I know there’s a lot of different companies now doing something similar. From the presentations I’ve seen you give and some of the data I’ve seen behind trudiagnostic, I know that you guys are doing things right and there’s a lot of other noise out there, if you will. What are some of the factors that you think are the most important when evaluating these types of services? Because I don’t think I told you this, but at this point months ago, I took like five different biological age tests in blood labs the same day to see what kind of differences. Some of them came back like eight years older than my chronological age and then most of them came back younger than my chronological age. And that makes more sense to me and people who watch my routines and everything.

Ryan Smith [00:09:22]:
And yeah, no, I could pontificate about this for forever, but. But to make it simple and easy for anyone listening, I think that There are, you know, for biological ages, there are two things that ultimately are the most important. And the first is, is it predictive? Right? And by that I mean, if we take a test and you see accelerated aging does risk for all of these negative disease outcomes, including death. If we can prove that it’s the most predictive, that’s already a very, very good clock because we know it’s informative, it’s telling you about your trajectory that you can change. And so in order to do that though, you have to do biobank validation studies. And so that means you have to take samples that have taken by 30, maybe 40 years ago and then prove that you can predict those outcomes. And so in the case of our clocks, we have three clocks that we prioritize. Our omic age clock we published to be the most predictive of death.

Ryan Smith [00:10:14]:
It’s 92% accurate within a five year period. Our symphony, or I should say our Dunedin pace, tends to be the most highly correlated to morbidity and mortality. So, you know, a lot of times people think about this whole idea of health span versus lifespan, right? Some people think, I don’t want to live till I’m, you know, really, really old and have a really poor quality of life, have a low health, a sort of a short health span, but a long lifespan. Generally I tell people not to worry because those things are generally correlated in the right direction. But the gene pace is the most predictive of quality of life related metrics, everything from facial aging to mental processing speeds to IQ to moving about the world. So omic age, best for longevity. Dunedin pace, best for overall health span. And then symphony age, which reports on the age of different organ systems, sits right there in the middle where it is the most predictive of outcomes of that organ system.

Ryan Smith [00:11:03]:
So our musculoskeletal clock is the most predictive of musculoskeletal aging. Our brain clock is the most predictive of cognitive aging. And so it is sort of the best in individual organ systems as well. So that’s why we sort of have all three. They’re all different clocks trained slightly differently, but all giving us a little bit different information. So again, the clocks need to be validated, they need to be published on, that’s the number one, because that way, you know, they’re predictive and therefore helpful for you as an individual. But the other part that I think is also important is can you act upon them, right? Can you actually influence change? And this has been a huge, huge issue with the clocks for years. Even whenever we first started, we could tell you an age, but we had no idea how to tell you why.

Ryan Smith [00:11:48]:
If you had a fast rate of aging, we’d recommend caloric restriction because we know that helps reverse it. But if you had a slow rate of aging, we’d still recommend caloric restriction because that’s again, what the data says. If we’re recommending the same thing for everyone, regardless of their score, why test in the first place? It might give you a good idea of where you’re at. But ultimately the recommendations are going to be the same. This new thing we do is what we call generation explainable clocks that not only give you a score, but then give you some resolution into maybe how you might impact that score. And that’s what omic age and symphony age do very well.

Nick Urban [00:12:22]:
And that’s the other thing that can be daunting about these clocks is like, in these, the companies who offer them is like, okay, I have this result, but now how do I actually translate this into lifestyle change so that I improve this score? Because it’s one thing to know the score. If I’m just concerned that it’s high, I can’t do anything about it, then it’s also kind of useless.

Ryan Smith [00:12:39]:
Yeah, exactly. And that’s where we’ve gotten a lot better. The Symphony age score, again, includes the age of the different organ systems and also the biomarkers used to train each so that you know that if you change that biomarker, you can then also change that overall aging rate. And so that’s certainly helpful. And then for the omic age, we actually have our epigenetic biomarker proxies which predict actual clinical values and can show you where you’re high or low or where you’re subpar.

Nick Urban [00:13:07]:
Okay, let’s just for one second look at my results just so people can get a visual of how these reports look. And you can look at my organ ages to see where, if you were me, you would focus. And so we’ll speak it out for people listening to the audio only version of the podcast. But this is my Symphony age right here. Can you see my screen?

Ryan Smith [00:13:28]:
Yeah, I’ve got it here. Yeah. And you can start to see, you know, three scores there, but we do a total of 11 here.

Nick Urban [00:13:35]:
Yeah. So I think this is all them.

Ryan Smith [00:13:37]:
And then we also benchmark it, I think, in a line graph. I’m not sure if you have that available as well, Nick. Oh, guess not. This is a different report, but we do report these in a ton of different ways. And then we also Again, have that rate of aging, which looks phenomenal at 0.7 years. That’s a really good score.

Nick Urban [00:13:54]:
This is not the advanced one. I have the advanced one, too. This one might be more useful. Here we go.

Ryan Smith [00:14:01]:
So, again, it looks like really good results. There’s only one specific system where you’re a little higher than your chronological age, which is a really good score.

Nick Urban [00:14:09]:
Yeah. Okay, so then if I go down and actually check this and I find hormone, that’s the one I’m high on. It says biomarkers. There’s two right here. Based on this, is there anything in particular that I could do to actually bring this back into range?

Ryan Smith [00:14:24]:
Yes, well, unfortunately, I think that you’re highlighting one thing which is, I think one of the biggest deficiencies of this particular algorithm, which is that the hormones are not trained on too many hormones, especially some of the biggest hormones that change with age, like sex hormones. Right. Including testosterone or estrogen, if you’re obviously a woman. And one of the problems with that is that these biobanks don’t actually measure hormones in every patient, especially like we do now. And so we don’t have historical data, which is why the hormone score is probably the least actionable out of everything on this report. But with that being said, I was just sort of mentioning earlier that. But we do know that if your IGF one goes up, your hormone score will decrease, because again, as the score has been modeled, we know that IGF1 significantly decreases as we get older. And so doing some things to increase IGF1, even things like exercise and weightlifting, can increase IGF1.

Ryan Smith [00:15:19]:
A lot of people like to use the growth hormone secretagogues like the CJC or the epamorelin. Those have also been shown to reduce that hormone score.

Nick Urban [00:15:29]:
Yeah, and that’s also a good note. It’s probably not as concerning if it’s only measuring two biomarkers versus some of these other ones that are measuring like 20 biomarkers. And if these are high, then I might want to focus on them. But from looking at this, could I figure out this information from a standard blood lab test and then act on anything that’s out of balance there? How do I go about interpreting this score of 29.4 and reducing it?

Ryan Smith [00:15:53]:
Yeah. Well, so generally what we’re doing here is looking at how all these biomarkers trend across age. Right. And we’re saying here are the most optimal scores. For instance, red cell distribution width is a good example. As we get older, our red cell distribution width goes up, and that’s because of how our ability to make red blood cells gets a little bit dysregulated. Really what we’ve tried to do is to take all of the work on knowing these individual scores and their trajectories with age completely out of it, and create a score which can actually tell you where you’re at based on all of these biomarkers and their signals through methy. Generally, you know, I.

Ryan Smith [00:16:34]:
I would only pay attention to the ones that are really out of range and then try and figure out, I think, if you have any sort of other information that might tell you which ones you really want to tackle.

Nick Urban [00:16:43]:
Got it. Okay. Yeah, that would make sense. It’s like a. It can give you an area to focus on once you know what to focus on, and you can zoom in some more and find out which particular one’s out of balance. If you want to go that route, and if you don’t have anything that’s out of range, then you’re fine. Exactly.

Ryan Smith [00:16:57]:
And you can always do a little bit better. But I think, you know. Yeah, we want to see, you know, decreases or not increases from every single time we measure.

Nick Urban [00:17:06]:
Yeah. And so this is immune health. And so now this report breaks down each of the different sections that’s involved in those composite scores, and it helps you understand where you’re. You’re in range or out of range.

Ryan Smith [00:17:19]:
Yeah, correct. So one of the things we can do is actually tell you your percentage of each immune cell with less than a 3% error versus flow cytometry. And the way that we can do that is every cell has a unique epigenetic identity signature. So we can see at what level those signatures are represented to tell you the percentage of each cel. And so this is really helpful information if you have some type of deficiency. So one of the ways we like to look at that is that CD4 to CD8 T cell ratio, if you’re low on that, that’s a really good indicator of something going wrong with your immune system. So that’s one of the biomarkers, obviously, that people might use in really extreme conditions like hiv, where that would be super low. But at the same time, you know, even chronic diseases will actually drop that CD4 to CD8 T cell ratio.

Ryan Smith [00:18:03]:
So what we like to do is, again, make sure that those are all within range. You certainly are. Here we every once in a while, some immune cells that are a little bit higher. But. But again, if you’re. Generally. I would really focus on those CD4 to CD8T cell ratios.

Nick Urban [00:18:17]:
Okay. And there’s telomeres, inflammation, omic m, fit age. So I have a fit age is based on my grip strength and gait speed and VO2 max and something else.

Ryan Smith [00:18:30]:
Yeah, this one might be good to go into as well, because I know that, you know, a lot of people ask us, first and foremost, how are you getting my gait, gait speed or grip strength from DNA methylation? And the answer is we’re not necessarily, but we’re predicting it. And the way that we do that is by taking grip strength in a couple thousand individuals along with DNA methylation and then seeing if we can predict that. And generally, I want to mention here, these are not the best predictors. They’re really just especially compared to the other clocks, which are phenomenal. These are really meant to just gamify fitness behavior because we know that if you do more exercise, you’ll see these things change.

Nick Urban [00:19:04]:
Change. Yeah. It seems to me like the most impactful things are going to be around like behavior change, such as smoking risk, alcohol, immune system health, these types of things. And then we can supplement the other information just to add some extra motivation to things. But it’s perhaps not going to be as accurate as actually measuring your grip strength with a, a device that is designed specifically for that.

Ryan Smith [00:19:25]:
Definitely, definitely. And, and you know, with that being said, you know, people like VO2 max and grip strength, because they’re longevity biomarkers in and of themselves, they’re highly correlated to those same outcomes. Head to head. These biological clocks way outperform even all those physical measurements.

Nick Urban [00:19:41]:
Okay, and so is this part of the report still part of the standard true age report? Right here? It is.

Ryan Smith [00:19:47]:
Yeah, it is, because these are the epigenetic biomarker proxies we have included into that omic age calculation.

Nick Urban [00:19:54]:
Will you break down what an epigenetic biomarker proxy is?

Ryan Smith [00:19:58]:
Yeah, certainly. So I mentioned how we are predicting grip strength and gate speed. Right. We have a lot of couple thousand individuals where we measure it and we see if we can predict it with DNA methylation. And really that’s the same thing that we’re doing with the epigenetic biomarker proxies. But instead of predicting physical outputs, we’re predicting other molecular measurements. So things like your metabolites. And so for metabolites, we’re talking about your hormone levels or your neurotransmitters or your omega 3 fatty acids, where your antioxidant levels.

Ryan Smith [00:20:28]:
And then so we do that with metabolites, we can also do with proteins, and then we can also do it with just the clinical values that you get. And you could see here things like albumin, HbA1c, hematocrit, or hemoglobin. And so we’re basically predicting with DNA methylation, these other biomarkers. And this has been, quite frankly, a really, really big surprise breakthrough for us. When we originally did this, we were just hoping to have some type of information that could explain the biological age clocks. However, upon validation of the tools we created here, we actually think that they’re better than most of the clinical blood testing that you’re doing already, and that’s because they’re just generally more predictive. We never compared all 1700 of the different things measured within the Harvard cohort. 62% of the time, the epigenetic biomarker proxy was better at predicting or diagnosing disease than the marker itself.

Ryan Smith [00:21:21]:
So, for instance, if we’re comparing systolic blood pressure, systolic blood pressure measured through your clinic is not as good at predicting cardiovascular disease as our epigenetic prediction of systolic blood pressure.

Nick Urban [00:21:31]:
Yeah, Ryan, I want to underline and underscore what you just said there, that this is 62% more predictive than the. The measurements we’re getting in labs like LabCorp and Quest Diagnostics, these types of places. A traditional blood test. This is actually more predictive than those.

Ryan Smith [00:21:47]:
Yeah, absolutely. Head to head, we can compare those. Right. Because we’re getting that same data, and we’re looking at outcomes, and these are just more predictive. And so ultimately, that goes back to why are we measuring these things in the first place? Right. To inform us about our outcomes or what we’re at risk for. And these just do a better job. And not to mention that they do it without having to do phlebotomy.

Ryan Smith [00:22:07]:
So you don’t need to. To take really large amounts of venous blood. And we can do a scale that’s just unprecedented. We can do 1700 of these things from the same data set. Whereas if you wanted to do that with blood, you’d have to take 15 vials of that. And then some of the times, you have to spin it down to collect plasma and then refrigerate it, ship it back. And so there are a lot of collection and ease of access issues, but there’s also the price to scale benefits, and then just ultimately, they’re better biomarkers in the first place. I think the most exciting part is that we’re sort of being able to create better screening tools, essentially.

Ryan Smith [00:22:47]:
And one of the reasons for this is I Like to always use the example of fasting glucose versus HbA1c. Your fasting glucose can vary all the time based on what you’re eating or how you’re exercising. And so it can go up and down throughout the day. So if you were to get a fasting glucose that was above, let’s just say 120, you might be worried, but you also could have just had a donut or something that spiked it like crazy. Crazy. And so it doesn’t mainly tell you about your risk of diabetes outside of some context. Whereas HbA1c is really now used as a standard to diagnose diabetes because it is sort of a three month running average of your glucose concentrations. And so that is way more informative than a single point in time.

Ryan Smith [00:23:33]:
Right. If your 3 month average is really high, we can say you’re probably at much higher risk of diabetes. Whereas if your 3 month running average is super low, we can say you’re probably not at a risk of diabetes. And really, these epigenetic biomarker proxies, we think, are essentially longer running averages of all of these biomarker concentrations, which is why they tend to be more predictive, just like HbA1c being more predictive than fasting glucose.

Nick Urban [00:23:56]:
Yeah. Okay, that makes sense. So if I was to look at this right here, I have my fasting glucose score, and this is 18.1% methylation. Is there a way of translating that to what it would be in a traditional blood chemistry, blood labs?

Ryan Smith [00:24:10]:
There is, there is. However, we don’t do it on our reports, mainly because we don’t want to confuse people. Our fasting glucose is essentially a running average of fasting glucose because of that longer duration. And so if you measure these things head to head, let’s say you take our test the exact same time you take a blood test, you might not see the most compatibility because generally our correlations are, are about 0.4 to 0.6, 0.7. So they’re highly correlated, but they’re still different. But again, if we compare them in how they’re predicting disease, ours are better, and that’s why we think ours is the better biomarker in the first place. But they’re technically different biomarkers than what you are seeing here. We’re not actually measuring fasting glucose.

Ryan Smith [00:24:57]:
We’re using DNA methylation to predict your average fasting glucose.

Nick Urban [00:25:00]:
Yeah, and the reason this is important is because the whole point of going in and getting these blood labs done traditionally is to know how this correlates or the Likelihood of this causing issues down the line, perhaps disease, perhaps like early deterioration, like a lot of things like that. And so having a greater predictive power by 62% is tremendous.

Ryan Smith [00:25:23]:
Yeah, exactly. And small increments make a big difference in diagnosis of disease, especially early. And that’s where this is going in the future is, is just like we have scores to tell you your age, we’ll have scores to tell you your risk of every different disease, all within one test. They can then look at the biomarkers associated with that and make personalized recommendations. So I think it’s, you know, our diagnostics are great, but we need more personalized recommendations to really improve where we’re at. And I think that’s where we’re going is we’re getting so much information from just DNA methylation and the algorithms we can run on that data that we can then provide a lot of information that is specific to you as an individual ritual.

Nick Urban [00:26:05]:
Ah. So with traditional blood labs, there’s also issues with some of them and I guess negative influences by other things. For example, if you supplement creatine monohydrate, which you were ahead of the bandwagon by the way. You mentioned using it for mental performance back in 2022, before it was all the rage. If you take that, then your creatinine levels can go up and that might show a false positive on a traditional blood lab. And it looks here I take creatine and it’s not out of reference range, but sometimes I take blood labs like normal blood labs, and it is out of reference. Rang. But if I stop for a couple of days before, a couple of days, maybe a week before I take the test, then it comes back down into normal ranges.

Nick Urban [00:26:43]:
Are these more resistant to lifestyle induced fluctuations?

Ryan Smith [00:26:49]:
I think on a day to day basis, yes. And that’s also why they have so much benefit, Right. Is that they’re getting longer average signals. However, if you’re doing something consistently over time, we’ll absolutely see it here. But the good news is that again, because we’re measuring sort of gene patterns, we’re less fluctuating to, to, I would say things that don’t make as much sense. So. Right. Obviously if you increase your creatine, your breakdown products will increase your creatinine and that can look like a kidney issue.

Ryan Smith [00:27:17]:
We don’t see the same associations there as well. So I think it’s a little protected on some of those things. And that also gives me another thing I’d like to just sort of talk about, which is precision of these tests and classical blood tests. A lot of people like to critique these biological age clocks for their lack of precision. But I always like to give examples of the fact that these are just way better than even your classical labs. The total allowable error for something like HDL cholesterol, if you go to your LabCorp or Quest, the national standard is a 15% variance on that. So 15% in either direction is allowable, essentially. So that’s a 30% range.

Ryan Smith [00:28:04]:
Right. Know these epigenetic clocks generally won’t vary by more than 3%. They are extremely specific in way, way lower in their relative variation than almost any clinical lab that you would do. And so again, if we’re, if we’re taking the standards that most people have, these are incredibly precise measurements as well.

Nick Urban [00:28:26]:
So also there’s, that’s also helpful to know. But then if I was to take the same sample, or maybe not the same exact sample, but I was to take one sample right now, now, and then say in 10 minutes, say an hour, take another sample. A lot of the early biological age tests, especially through different methods, like telomere tests, depending on the tissue type, depending on what time of the day, depending on like, a lot of different factors, you would get dramatically different results. Like, how consistent are these results in comparison? I guess not in comparison, just overall.

Ryan Smith [00:28:54]:
Yeah, well, so it’s a good question. If we’re taking the exact same sample, they’re highly, highly precise. They’re almost giving you again, less than a 3% variance. We know they’re that because we calculate this is what we call this intraclass correlation. However, there are certainly circadian rhythm changes. There are changes. We actually just did a very cool analysis that I have never shared before, but we just did analysis on fasting versus non fasted. So how do people take this test not eating, then affect people versus people who are eating? How does that change results? And we actually found something very, very interesting, which is, is they had significant differences in their biological ages.

Ryan Smith [00:29:34]:
However, whenever we controlled for the immune cell subsets. So we sort of said, hey, if the immune cell subsets, we factor that out, we include that in the model, we saw no change in biological age, which is really interesting. So what that means is essentially, people who are eating have immediate changes in their immune cell subsets. And if you don’t take this into account, you could get the wrong biological age. But that’s again, why we include those immune cell subsets on there is because we factor it into these al, so that things like fasting won’t actually impact that. And so for us, we think things like immunity convolution are really important. But right now, we’re even creating correction methods for menstrual cycles. We’re creating correction methods for circadian rhythm changes.

Ryan Smith [00:30:19]:
We’re doing it for a host of wide variety of things to make sure that these are as biologically replicable across the same day as possible.

Nick Urban [00:30:27]:
Wow, that’s really cool. And I know last time we talked, you mentioned the importance of the immune system and overall health and biological aging. And then there’s folks like Joel Green. He has his immune code where it’s immune centric medicine. And obviously a lot of the popular therapeutics like rapamycin are immunosuppressive and they have like, they modulate the immune system in different ways. What do you think? Like, what’s your latest understanding of the role of the immune system? Do you still think it’s important or how is your opinion on it changed since we talked two years ago?

Ryan Smith [00:30:59]:
You know, the, the more data I look at, the more and more important the immune system becomes. You know, I, I like to even. I’ll just give some classical examples, like even senescence. Right. We know that senescence can be targeted by natural killer cells, those innate natural killer cells. So if you have good natural killer cells, you might have lower burdens of senescence, even just inflammation, the difference between M1 versus M2 macrophages and their differing levels controlling inflammatory burden. And so the immune system plays a role in everything that we do. And I also should mention that whenever we’re looking at blood, we’re really, really just measuring immune cells.

Ryan Smith [00:31:33]:
That’s the main thing that we’re looking at. But we still, within blood, see almost an 80% correlation in methylation values to every other tissue. The exception there is the brain. But just to give you an idea, 80% of the signal we see within the immune cells are the same across almost every tissue in the body, which I think is a pretty good signal of just how important it is across your entire system.

Nick Urban [00:31:54]:
Wow. And so are there other tests you like to understand immediately immune system function adequately, or do you think this gives you enough insight that you can make significant enough changes to actually impact, I guess, the expression behavioral change type stuff?

Ryan Smith [00:32:10]:
Yeah. So even in our paper, we basically showed how immune cell subsets can change by all these different lifestyle behaviors, such as coffee. So I can even share that. And so certainly lifestyle changes. But most of the time when people do immune diagnostics, they really are just quantifying the percentage or count of an immune cell that’s almost, almost all of these immune diagnostics have been. They’re not necessarily grading activity necessarily right. Or saying how good are your T cells? And so I think that there are certainly a lot of really cool immune diagnostics coming out that will give you a little bit more resolution. But right now I think that really quantifying cells of what the history of clinical medicine has been and the DNA methylation can do that very well.

Ryan Smith [00:32:54]:
I think we won’t be as good at some of the other future diagnostics that get really specific about activity. But right now I think DNA methylation and those immune cell subsets are more than sufficient to give you what you would get on most flow cytometry. And even flow cytometry is a $6 billion industry where you need refrigerated samples and large volumes of collection. We can do all the same again with that same data set.

Nick Urban [00:33:17]:
Yeah, I’m sure you hear this all the time, but people mentioning epigenetic methylation as only one of the hallmarks of aging and it can’t possibly predict your entire biological age because it’s only one piece of. Of the piece puzzle. How do you respond to that?

Ryan Smith [00:33:31]:
I think that’s probably a good critique, quite frankly. I think that as I mentioned, we’ve measured the full, what we call multiomics. Right. So the multi omics is sort of like classification of all the different measures you can take. Right. Everything from genomics to epigenomics to RNA and transcriptomics. And so we’re experienced with a lot of different biological measurements from proteins to metabolites to rna. And so I think that eventually a model which integrates of these things is going to be the most important.

Ryan Smith [00:34:03]:
But unfortunately, to measure all of those things is really expensive. Right. And so I don’t know that it would be the most cost effective, but it certainly, if you’re combining all those data sets and you have them, I think you could probably create a better model. The one thing that we’ve seen is with those epigenetic biomarker proxies is that DNA methylation can accurately predict the levels of proteins, metabolites, clinical values, even predicting some genetic polymorphisms. But we haven’t seen that for any other biomarker yet. We haven’t seen proteins that predict metabolites or metabolites which predict epigenetics. And so I think that right now DNA methylation offers something unique in that category. But I certainly think the more data, the better.

Nick Urban [00:34:44]:
Yeah, I mean, hard to argue against that. But if one Metric is very predictive, then it’s a good one to go off of because even the traditional labs, as you already mentioned several times, they’re not 100% accurate. They’re actually in some cases pretty inaccurate. So it’s not like you’re comparing one emerging new technology versus one that’s established and perfect. It’s a lot more nuanced than that.

Ryan Smith [00:35:09]:
Yeah, definitely. But even if you compare these head to head, there have been actually a couple studies which compare metabolite measurements or proteomic clocks or metabolome clocks with epigenetic clocks. And generally in every head to head study, the epigenetic clocks come out on top. And so right now, from a single diagnostic, I think Epigenetics is still the most well validated with the best prediction capabilities. Ultimately, if you added other data sets into there, I think it would improve it. But if we’re looking at one measurement platform, I think currently epigenetics is still the leader.

Nick Urban [00:35:38]:
So then you released a new product. I think it was this year or late last year, but it’s brand new on the market. There’s nothing else like it that I’ve come across. And that is True Health. When you first described it to me, I was thinking this sounds like what a company called, called Theranos was doing, but they actually have science behind it here at True Diagnostic and they are doing a good job and they lay out the research and there’s a lot of, I guess, information available to the public that’s showing this stuff actually works. Can you explain what True Health is specifically?

Ryan Smith [00:36:11]:
Yeah, certainly. So, so I, you know, for what it’s worth, we’re as surprised as everyone else. I didn’t think that we expected these to be as good as we, we thought they were, were, but basically True Health is a biomarker report, much like you might get from a really in depth biomarker panel that you get a LabCorp request. The only difference again is that we’re using DNA methylation and the epigenetic biomarker proxies to predict your levels of these different factors. And so we published this as a preprint in December of last year and then started doing this really around February of this year as a commercial test. And in that time we’ve had lots of different validation studies across multiple different cohorts, which really tells us the same story, which is that we’re adequately getting the picture of the clinical picture of these biomarkers in the right way. And again and again, again we see that these are more predictive than the actual clinical surrogates that we’re using. And so we’re super excited about this because again, of the scale, the use of collection and all of the different information they can tell us.

Ryan Smith [00:37:10]:
So in our true health report, we’re reporting everything from your omega 3 fatty acids to NAD biomarkers to exposure to PFAs, chemicals or heavy metals, metals, even your APOB particle sizes and your VLDLs and HDLs. And so it’s a really, really diverse report with a lot of data. But we have these again, validated in our cohorts to be predictive of outcomes. And so we’re hoping that they can be sort of a one stop shop for all the biomarkers you need. Right now, there’s probably only one big gap on those reports, which is that right now they don’t do hormones. But I think that outside of that, they still have a lot of really good, good data.

Nick Urban [00:37:50]:
And with these, part of the reason that it excites me a lot is because there’s already organic acids tests and those can give you information depending on the practitioner you ask. Sometimes they’re valuable, sometimes they’re not, sometimes they’re like very expensive. But then there’s also a lot of limitations there about what they can and cannot measure. And your traditional blood labs, if you look for serum minerals, you’re not going to see much change unless there’s a real issue because your body’s going to pull from bones and other storage depots throughout your body to make sure that the blood serum stays very safe, steady. What about on something like this, are you going to be able to understand like deficiencies and where your body like needs extra support based on the EBPs and the levels you’re seeing in the reports?

Ryan Smith [00:38:32]:
Yeah, absolutely. You know, some of my, my favorite examples include some of the interventional trials that we’ve done. You know, we, I think as we were talking even previously, we did a growth hormone trial where we were basically taking people who were growth hormone deficient. They had very low IGF1s, they failed any stimulation testing. They had super low IGF1s. And so we were then giving them growth hormone. We’re seeing that IGF one from the epigenetic biomarker proxies go up. We were seeing it both in the initial test, the low IGF1s in the subsequent testing, much higher IGF1s.

Ryan Smith [00:39:05]:
And that makes perfect sense if we’re administering growth hormone. We also saw then reductions of our visceral adipose tissue predictions, which again we saw clinically as well. So some of those interventional trials are great. I like to use. We did a kidney dialysis and kidney transplant where we saw high creatinine, high blood urea nitrogen and then we began after transplant we saw all of those factors go down even in gastric bypass. Leptin, which is what people call the Sadie hormone, the thing that makes you less hungry. We saw that go up significantly after gastric bypass and so we’re seeing a lot of these change. So.

Ryan Smith [00:39:43]:
Absolutely. I certainly think it’s able to pick out the 50 deficiencies or you know, in the other way things that are out of range in a negative way.

Nick Urban [00:39:50]:
Yeah, I think we, it’d be helpful to share my reports. We can go over this really quickly and discuss what you’re seeing and like how it actually looks because people have questions I’m sure about like how these reports compare and contrast to true age and also to like their typical blood labs.

Ryan Smith [00:40:05]:
Yeah, definitely feel free to bring it up and I can share some, some other information as well in case it might failful. But, but yeah, you know this is a long report I think. And so I think what we’re looking at right now are the different categories that we would have. And so we do things like vitamins, amino acids, antioxidants, you know, lipids, blood pressure markers, immune markers, inflammation, toxins, mitochondrial function. Again, a really, really long list. Right. And I hate to say it, but these are only even a fraction of what we can do. We include 92 I believe on this report.

Ryan Smith [00:40:40]:
But we as I mentioned have developed now over 1700 biomarkers that do. We can even right now do things like tau and amyloid proteins for Alzheimer’s that we don’t include on this report. So there’s a lot here that we don’t even do yet. These are just the ones that are by far the most validated and we’ve seen changes with longitudinally in many of these clinical trials.

Nick Urban [00:40:59]:
Yeah. So it looks like on my report I have sub optimal in two categories, amino acids and then lipid peroxidation and I have critical in ketones. And I didn’t tell you this initially but I did did take this test. Both of the tests about four or five days after a two day dry fast. So I’m not sure if that’s going to impact these markers at all. I’d assume that it will. Perhaps next time I won’t take it five days after a dry fast.

Ryan Smith [00:41:25]:
Well, it should. Yeah. We should have seen those ketones really high after that dry fast. So I’m surprised to see that it looks like it looks deficient.

Nick Urban [00:41:33]:
Well, it was actually five days after, so it could have been like a refeeding syndrome. I’m not sure what exactly, but it was, it wasn’t like the five hours afterward or anything.

Ryan Smith [00:41:42]:
Yeah, no, certainly. But yeah, again we see these highly correlated to health outcomes and highly correlated to current status. And there are a lot of really good examples of this. But what we’re really trying to do is to say are you deficient or are you too high? And we’re setting those reference ranges based on their associations to disease outcomes. So for instance, things like vitamin B6 and vitamin B5, it’s really hard to be too high because it’s a water soluble vitamin that you can excrete via your urine. Right. So we don’t have anything there that’s necessarily too high, but we do have things that are too low, for instance. And then so, so it really just depends on the marker in terms of where we’re sitting.

Ryan Smith [00:42:21]:
Those range in terms of risk, but we sort of say where you’re at. And then obviously by supplementing with these things, we would obviously see some change.

Nick Urban [00:42:28]:
Yeah. So right here, vitamin B6, this is the active form of B6 P5P, the pyridoxine hydrochloride, the inactive form people tend to have issues with because it can actually, I think, as far as I understand, dis displace P5P. Is it possible to measure the inactive form as well?

Ryan Smith [00:42:43]:
You know, it’s probably available. I don’t know that we measured it in our metabolite studies though. And, and so we were using a company called Metabolon to do most of our metabolomics. I don’t know that they had that on their list.

Nick Urban [00:42:58]:
Gotcha. Okay. And for certain of these, like vitamin A, and it looks like the metabolite is called vitamin A a retinol, vitamin A M, it says the lower end is 10% and I’m at 12%. So I’m a bit low there. Would I want to be supplementing more vitamin A or eating more vitamin A rich foods?

Ryan Smith [00:43:18]:
I would. Yeah. You know, you’re on the threshold, which means that, you know, it’s probably subclinical if anything. But, but with that being said. Yeah. You know, one of the things we see across the board is carotenoids. Right. Which are obviously found in a lot of foods that are vitamin A rich.

Ryan Smith [00:43:33]:
Like, you know, carrots for instance, are across the board great for reduct of a lot of different diseases. If you don’t mind, I would love to show you maybe just something from my screen really quickly as well. This is some of how we do our validation. For instance, what you can see here is all the different things that we would measure or report on a true health report. Then you can go ahead and see all the different diseases that we would see as outcomes here. These are heat maps. Generally, if we’re looking at hydroxyfluorine sulfate, an environmental metabolite that’s bad, we know that it increases significantly your risk of COPD or death. Same with this bone lead predictor.

Ryan Smith [00:44:12]:
We can see really high associations with asthma, Whereas carotene dial has negative associations for almost all of these different diseases. So again, talking about carotenoids, this is one of those things we’d really like to see. Even vitamin D shows reduction of disease across many different types of diseases. This is how we do our validation to say is it have the same associations that we would typically see with many other things. And so even these things like glycoprotein, acetyls, these things we know are bad for cardiovascular disease, are across the board, terrible for a lot of other things as well. And so we can sort of actually quantify how something is good or bad for you across many of the most common chronic diseases.

Nick Urban [00:44:50]:
Okay, so if I’m reading this graph right, we got a bunch of diseases, a bunch of metabolites, and we have a bunch of colored squares. And the things that are healthy and beneficial are blue. And the darker blue, I’m guessing the more beneficial they are.

Ryan Smith [00:45:08]:
Yeah. Cancer out. Yeah.

Nick Urban [00:45:10]:
The greater the red. There’s like multiple different colors. Blue is beneficial, red is detrimental. You want to choose interventions that have the most blue possible and the least red.

Ryan Smith [00:45:22]:
Exactly. And, and, and so, and, and you know, to play this in numbers. If it’s over 1, it increases your risk of that disease. If it’s under one, it’s protective of that disease. And so, so, you know, any. We’re not looking at every subclass of disease. This is just a really crude chronic disease measurement. But you can see, for instance, things like HDL reducing your risk of being associated with lower diabetes or lower cardiovascular disease.

Ryan Smith [00:45:49]:
These make sense. Even look at glucose. Glucose here has really negative impacts across almost every disease that we would see. You can start to see that these are backing up the same types of associations that we would see and most of the clinical medicine that we would do. But we can get all of this information from a really, really, I would say you Know, low cost, high yield sample. Yeah.

Nick Urban [00:46:12]:
And right there I’m seeing glycine with a lot of blue. Then over on the left hand side I see Alcar, acetyl, Acetyl, L, carnitine. And that’s often used as a supplement for mitochondrial health and a lot of other things. But all I see is one yellow or orange.

Ryan Smith [00:46:29]:
Yeah.

Nick Urban [00:46:29]:
For square. Does that mean it’s a negative association or are there a bunch of other potentially beneficial squares outcomes that are not included in this graphic?

Ryan Smith [00:46:41]:
So, so this is looking at any significant association across any of these conditions, but it’s only including the significant ones. So there might be some non significant factors here. And again there’s also obviously sometimes confounders as well. There might be, you know, in this cohort they might have some type of disease or something that might, might change these hazard ratios. And so that’s why you always need multiple different cohorts and multiple validation studies.

Nick Urban [00:47:06]:
That’s cool to see. Let’s go back into my report because I think there’s some other things that might be interesting to explore together like vitamin D. This correlates pretty well to my typical blood labs. I usually get like 62, 63 somewhere around there. I’m not sure the unit nanocr or milligrams a deciliter, maybe choline. I consume a lot of eggs and choline sources and nootropics and different things. So that makes sense. Betaine.

Nick Urban [00:47:36]:
Betaine. I expect it to be higher now that I started supplementing it. So that’ll be interesting to see in a follow up how it changes.

Ryan Smith [00:47:43]:
Track it.

Nick Urban [00:47:44]:
There’s certain things like ergothioneine. I’m surprised that you guys include that. It’s pretty cool. It’s a supplement I’ve been taking for a little bit. I think I started, I restarted after this test but before I got the results. So it’ll be interesting to see. It seems like this is a good report card, like how effective your supplements are in actually achieving their said purpose. Like if I’m, I’m deficient or I’m, I’m not taking this and all of a sudden I’m, I’m here, then I start taking it.

Nick Urban [00:48:08]:
I’d expect to see the levels to increase this way.

Ryan Smith [00:48:10]:
Definitely. And we would expect that as well. And, and for what it’s worth, ergothioneine is one of those that has multiple benefits across multiple different diseases. You know, it’s one of those things that obviously helps improve the mitochondrial mitochondria. And generally anytime you’re improving the mitochondria, you’re improving a host of outcomes. It actually ranks as actually one of the top three metabolites for overall longevity.

Nick Urban [00:48:31]:
Whoa. I wrote an article on it a long time ago, and I haven’t looked back into it since then. It’s cool to hear that it’s gaining more steam in that world. I think some of the researchers called it a longevity like. Or a vitamin like substance.

Ryan Smith [00:48:43]:
Yeah. In every analysis we do, it always pops up there. I was even mentioning some of our depression specific studies. And even in depression, it’s highly correlated.

Nick Urban [00:48:53]:
So here are some of the neurotransmitters, amino acids in, like tyrosine that’s used for dopamine production. And it’s a bit low. It’s not like, deficient. But what do you think would be causing this? And what should I do? Or is it not really an issue because it’s still not at the 5% threshold. It’s at 14% instead.

Ryan Smith [00:49:09]:
Yeah. I would say that you’re still pretty far away from that threshold, so probably not a huge issue. You know, I think that a little bit lower or at least on the previous reports we had some things like dopamine and serotonin predictors. I think that it would be cool to see if you saw those together. But I think right now you’re so. Well, without the range where we consider the problem.

Nick Urban [00:49:28]:
Okay. Because even though it looks like it’s really close by, I guess 14% is pretty significantly greater than 5%.

Ryan Smith [00:49:34]:
Yeah. And again, you got to remember that we’re setting these thresholds based on their hazard ratios to outcomes. Right. So it’s not significant until you hit that different area.

Nick Urban [00:49:44]:
So this isn’t an inside where, like, I want greater dopamine or production, so I’m going to want greater levels. And I could just take some L tyrosine or the.

Ryan Smith [00:49:52]:
Because this is specifically trained on.

Nick Urban [00:49:55]:
Yeah, yeah. Well, this is trained on, like, outcomes. So it’s not necessarily trained on, like, what’s optimal for mental performance.

Ryan Smith [00:50:02]:
Correct. Correct. Not yet. And do we hope it’ll get there? Absolutely. And we’ll be able to do that with the universal data set, because, for instance, any study that uses DNA methylation and looks at performance outcomes like cognitive function or even things like sprint or athletic performance, we’ll start to be able to correlate these individual levels without having to measure plasma versions of all these. So this unified data set is really where we’re starting to unlock lots of correlation analysis across many different cohorts and many different studies. So I think that’s one of the most exciting things about it, is that we can now go into, and I think I mentioned this to you earlier, but into all cylinders. Alzheimer’s disease cohort.

Ryan Smith [00:50:43]:
We can then start to look at all of the different epigenetic biomarker processes that are associated with Alzheimer’s disease, find insights, and then automatically go back and update your report. Even though they’re two different cohorts, we can use the same algorithms across them to find insights and translate that across different, different settings.

Nick Urban [00:50:56]:
Well, Ryan, that’s also one of the things that I find so cool about this is like you have the data already and you’re running it through algorithms. So say you. You discovered in a week or a month, like 25 new metabolites that you want to show. All you have to do is just click a button and regenerate the reports and then that information becomes available to us.

Ryan Smith [00:51:16]:
Exactly. And we actually imagine that at one point in time we’ll be able to customize this for you. Right. Where we can. Then you can say, hey, this is what I’m interested in, or these are the supplements I’m taking. And then we can report directly on those things without going too much into it. I’d love to show you just the scale of, of these because I think it visually is a little bit overwhelming. But this is our list of EBPs.

Ryan Smith [00:51:42]:
So just here, these are all of the different metabolites that we can do. So here you can see the sub pathways. But these are in terms of amino acids. We have hundreds of amino acids. And we then have cofactors and vitamins. And we have things of the TCA cycle like alpha ketoglutarate or citrate or malonate. Then we would have your lipid hormones, like your DHEA versions, or your EPI endosterones. Or we then have lipids across the board.

Ryan Smith [00:52:11]:
So many lipids, sphingomyelins, phosphatidylcholines. This goes on and on. Nucleotides, peptides, xenobiotics. This is probably one of my favorite categories because these xenobiotics are all about exposures. So have you taken X drug? So we’ve got things like acetaminophen here, or we’ve got ibuprofen or, or. Or proton pump inhibitors.

Nick Urban [00:52:31]:
And so say one of those are high. Say, like you have the metabolite of proton, one of the proton pump inhibitors, that’s high. What would that actually yield in terms of actionable insight? Like, would you be Able to. I mean, obviously you already took it, so that’s hopefully in the past, maybe in the present also. But then what do you do with that?

Ryan Smith [00:52:51]:
Well, so in terms of clinically, not a lot. Right? Because the. You already know you’re taking it. Right. But, but the thing that it helps us do is let’s just say that we didn’t know someone’s taking that in a big cohort. But that’s obviously then one of the things we know is that proton pump inhibitors are associated with lower lifespan. We know this, it’s been published a lot. But if we didn’t know that, we couldn’t factor it into our prediction of lifespan.

Ryan Smith [00:53:14]:
So what we can do now is we can use it as a control feature. Whereas if someone has a high version of the proton pump drug, we can then factor it into our analysis in order to get better predictions of the things we actually care about. And those are just the metabolites. Then we have proteins and thousands and thousands of proteins again that you would see here. So we’re finding so much information from just such a small drop of blood. But again, we know that these are predictive of outcomes. We know that these change with intervention and that’s what makes them so exciting. And they’re more predictive than even the classical blood based biomarkers.

Nick Urban [00:53:50]:
What I could also see here is if you have some kind of intervention where it’s a lot of, of the research, especially epidemiology, is notoriously inaccurate because people are asked to self report things that they don’t know or remember. It’s like, what’d you have for lunch two weeks ago? And I couldn’t tell you what I had exactly. But then it’s like you have this information available to them or to researchers and they can figure out, okay, they might not have realized or remembered to write down they’re on proton pump inhibitors or taking acetaminophen or something. But we can see the metabolite is very high right here. And you’re not going to have that metabolite high by chance. It doesn’t just randomly, namely happen in the body. So we can figure out they’re on these supplements and drugs or whatever. And therefore we’re not going to have skewed results in our analyses because they just forgot to tell us that they’re taking it.

Ryan Smith [00:54:33]:
Exactly. It’s a control feature, just like we were talking about for the immune cell subsets. Fasting or non fasting, that’s important. Right? And now we can actually see that. And I showed this to you as well too. But you know, these are, this is all the, on the left we have the epigenetic biomarker proxies. On the right we have the actual measurements we took within this cohort. But we look at depression.

Ryan Smith [00:54:51]:
So people who have been diagnosed with depression and we see all of a sudden one metabolite just jump out of the roof. And that, I don’t know if you can see it here, but it’s serotonin, which makes perfect sense because the moment you get diagnosed with depression, most of the time you put on ssri. And we can actually see that again just molecularly. We don’t even have to have someone responded on a survey. We can just see that serotonin is super high.

Nick Urban [00:55:15]:
Yeah, but then there’s also the, I guess, confounders where people are now put on SNRIs and other things as alternatives to SSRI size. So hopefully that won’t mess up your model. You’ll be able to see those deviations as well.

Ryan Smith [00:55:26]:
Yeah, you know, we’ll see. I, I, we don’t, we haven’t looked at it specifically, but I, I mean, you know, epinephrine, norepinephrine, really, you know, those things are things we can measure here too.

Nick Urban [00:55:34]:
One of the supplements I’m curious about, there’s been a lot of debate over the years on this one particular, I guess, class of ingredients and that is the NAD plus precursors. Based on what you’re seeing in the the results of True Health, are you finding that either nicotinamide riboside NR or nicotinamide mononucleotide NMN is superior?

Ryan Smith [00:55:59]:
Yeah, I’ve got some good data here. So this, this I should say is, is not, we’re not linked this to outcomes and we have not linked this to longitudinal interventional trials. What we did is we basically took our cohort and we said let’s look at everyone who’s taking nmn, let’s look at everyone who’s taking NR and let’s compare their levels of everything in the NAD salvage pathway. And so we did that and what we saw was that the people who were taking NMN really had high levels of nicotinamide, but generally everything downstream was non significant or reduced. Whereas alternatively, those people were taking nicotinamide riboside had increases of everything downstream, including some of the bad things like 2Py or 4Py, which can be cardiac or uremic toxins at too high of levels. And so they’re metabolites of that NAD pathway. And so it’s all good news, but at the same time, there’s sort of this classical argument. At one side you have the creator of nicotinemide riboside, Charles Brenner, and on the other side you have David Sinclair, who’s a really big proponent of nmn.

Ryan Smith [00:57:11]:
Charles says there’s no cellular transporter for nmn. David Sinclair argues that there is. Based on our data here, it looks like that David is probably not right. And I would say just in a very limited data set, because what we’re seeing is that it looks like that in order for NAD for it to get into the cell, it’s being converted back into nicotinamide. And that’s why we’re seeing such huge increases in nicotinamide, but nothing down downstream. And so based on this analysis, it would look like we’d recommend NR to get cellular absorption better than nmn. But with that being said, we’re only talking about the cellular effects. There’s probably a lot of extracellular effects as well that are probably beneficial.

Nick Urban [00:57:54]:
Okay, so in general, this is only going to capture the cellular effects of different substances and ingredients and interventions, not necessarily the extracellular.

Ryan Smith [00:58:03]:
Yeah, I think it’s difficult for us to say right now because we haven’t actually quantified both. But we do know that particularly for nad, there are extracellular benefits and then intracellular benefits, each with different pathways and different mechanisms. And so I don’t want to rule out NMN being positive for anyone. I don’t think we have enough information. But I think we do know that based on these epigenetic biomarker proxies, it looks like that nicotinamide riboside is increasing most of the things in the NAD salvage pathway.

Nick Urban [00:58:29]:
And either way, it’s like there’ll be individual effects where it doesn’t work as well for me, or it works better for me than for your average person, which is literally why biochemical individuality is so important. It’s a huge consideration. And we’ll be able to see these types of results, like personalizations reflect in our own results.

Ryan Smith [00:58:48]:
Yeah, exactly. And, and, and I think that that’s where we’re getting, right, is that in order to have that personalization, you have to have a lot of data. Right. And, and that’s what DNA methylation can do a little bit better. And it’s still not perfect. Right. Like, as I mentioned, even with the nicotinamide or the NAD downstream products, we’re still not exactly sure about all of their associates associations because we haven’t looked into it directly. But eventually we will be able to.

Ryan Smith [00:59:08]:
And we can capture all that data with a low cost, easy to collect blood drop.

Nick Urban [00:59:13]:
Yeah, I had a statistics professor who said all models are wrong, some are useful. And it seems to be the case really with a lot of this stuff, you’re not going to get. You’re not going to have a perfect model. But it can still give you a lot of valuable insights. Ryan, if people want to choose between the two or use them in conjunction with each other. True age or true true health. How do they go about deciding which one to use?

Ryan Smith [00:59:35]:
Yeah, I think that if you’re interested in aging, true health is the one to go with. And I would argue it’s probably even more beneficial for the large majority of people because again, aging is the biggest risk factor. And if you’re going to measure one thing, it is probably that if you’re a biohacker like you are Nick or me, then I think the true health might be more interesting as well because then you can start to look at specific pathways.

Nick Urban [00:59:58]:
True age is most helpful for most people, what you’re saying.

Ryan Smith [01:00:01]:
Yeah, I think, I think that, you know, if you’re looking at a diagnostic to do, I would do true age because again, aging is that biggest risk factor. But if you’re, if you’re, you know, I would say an expert and really care about each of those individual markers you, you like to supplement with different things, you like to try things out, then I think the true health can be really fun and really informative.

Nick Urban [01:00:19]:
And it seems to me that, like, you could do the true age more frequently to see how things are trending. And then also every like, say once a year, maybe once, every great once in a while, then you do a true health to figure out, are these supplements just a waste of money, basically? Are they actually making a difference?

Ryan Smith [01:00:34]:
Yes. Yeah, exactly. Spermidine is one of my favorites. Right. Where they saw that, you know, spermidine might not raise serum levels of spermidine. And, and so I think it’s one of those examples where, you know, you get to learn, I think, based on an individual basis.

Nick Urban [01:00:47]:
Well, that was one of the recommendations in my report, is to add spermidine. I used to take it, then I stopped. And then lo and behold, my results show that that’s one of my top supplement recommendations. So it’ll be interesting to see after I add it in for a while. And I don’t know if it probably matters if I take a spermidine tri hydrochloride or whatever it is versus naturally derived, that also has Spermine and putrazine and other things in it. That’ll be a fascinating little experiment to run for myself.

Ryan Smith [01:01:12]:
I’d love to see the data. Yeah, you know, we haven’t done a lot of that with spermidine, especially the different types. We don’t even ask that on our survey, unfortunately. We just ask, are you taking spermidine? Just like we don’t ask you, are you taking creatine? We don’t ask about monohydrate or hydrochloride or anything of that nature. So hopefully with additional data, we’ll get much better at our recommendations too.

Nick Urban [01:01:28]:
Perfect. Well, Ryan, you have a hard stop in one minute. So if people want to connect with you to try true health or true age tests, how do they go about that?

Ryan Smith [01:01:39]:
Yeah, the best way to do it is just to go on our [email protected] and, and you can order there. If you have any questions or want to talk to anyone, you can always reach out to us. I’m on link LinkedIn if anyone would like to reach out with any additional questions.

Nick Urban [01:01:51]:
Cool. And I will put a link. I’m going to publish a post that’s going to share my results so you guys can check that out. If you want to try these tests, you can use the code Urban to save. I think it’s 12% on the tests. Ryan, thank you so much for joining me again. This new product you guys have released is super cool excited frontier of modern medicine. Any final words? People made it this far.

Ryan Smith [01:02:12]:
Oh, yeah. Well, thanks for sticking around mainly. But I think, you know, hopefully I’ll be on again for the fourth time and have even more to talk about as we get into methylation risk scores and, and hopefully we might even have an aging diagnostic in in with the FDA in the works as well.

Nick Urban [01:02:26]:
Nice. Congrats.

Ryan Smith [01:02:27]:
Yeah.

Nick Urban [01:02:28]:
All right, take care.

Ryan Smith [01:02:29]:
Thanks so much.

Nick Urban [01:02:31]:
Thanks for tuning in to high performance longevity. If you got value today, the best way to support the show is to leave a review or share it with someone who’s ready to upgrade their health span. You can find all the episodes, episodes, show notes and resources [email protected] until next time, stay energized, stay bioharmonized and be an outlier.