#402 ‒ NMR blood analysis: how heart disease risk, insulin resistance, inflammation, and mortality risk can be assessed from a single blood sample | Jim Otvos, Ph.D.

[Music] Hey everyone, welcome to the Drive Podcast. I'm your host Peter Atia. This podcast, my website, and my weekly newsletter, all focus on the goal of translating the science of longevity into something accessible for everyone. Our goal is to provide the best content in health and wellness, and we've established a great team of analysts to make this happen. It is extremely important to me to provide all of this content without relying on paid ads. To do this, our work is made entirely possible by our members, and in return, we offer exclusive member-only content and benefits above and beyond what is available for free. If you want to take your knowledge of this space to the next level, it's our goal to ensure members get back much more than the price of the subscription. If you want to learn more about the benefits of our premium membership, head over to peteratia-md.com/subscribe. [Music] I guess this week is Jim Ottvos. Jim is a biophysical chemist who pioneered the use of nuclear magnetic resonance or NMR spectroscopy to measure LIPE-approteen particles from plasma. After more than two decades on the faculty at North Carolina State University, he founded LIPE-A-Science in 1994, where he developed and commercialized the first FDA-cleared method for direct LDL particle or LDLP quantification. LIPE-A-Science was acquired by LabCore in 2014, where Jim served as chief scientific officer of the NMR diagnostic group. He's authored more than 200 peer-reviewed publications and holds numerous patents related to NMR-based biomarker activity. I wanted to have Jim on because his work sits at the foundation of a test many listeners have seen in their own blood work, but probably don't realize traces back to him. Many of you have probably had an LDL particle number, and you may even notice that it mentions that it's done by LIPE-A-Science. But what makes this conversation especially interesting is that the same NMR technology that began with LIPE-approot teams has evolved into a much broader way of looking at metabolic health, inflammation, insulin resistance, and even mortality risk. And that's where we spend a lot of our time today. So in this episode we go back and talk a little bit about the history, we talk about the unlikely story of how Jim turned a flawed cancer test into a new way of measuring LIPE-approot teams. What standard cholesterol tests can miss and why LDL particle number can reveal risk that LDL cholesterol alone does not. Why this notion of large fluffy LDLs being the nine is misleading, how LDLP and APOB help guide treatment decisions beyond LDL cholesterol alone. How NMR can reveal signs of insulin resistance before blood sugar rises, like A as a window into chronic low grade inflammation as an NMR biomarker, the metabolic vulnerability index or MVX and what it may reveal about frailty, resilience, and short term mortality risk. The surprising finding that MVX in healthy young adults may predict risk decades later and why NMR diagnostics remain underused despite the amount of information they can extract from a single blood test. So without further delay please enjoy my conversation with Jim Autos. So great to be with you again. We were just talking a minute ago that I had forgotten briefly that we were together once in 2013. But in many ways this is a pretty wonderful opportunity for me to sit down with someone whose work I've been following for literally 15 years. It was May, I still remember it was May of 2011 when Tom Dayespring introduced me to your work and I began voraciously consuming everything you had written in my personal obsession to better understand the fields of lipidology. Again, I think there can't be that many people listening to us that haven't at some point probably add an LDLP or HDLP test done and yet most of them will never realize until now presumably that that test goes all the way back to you and you are the creator of that. So maybe give us a little bit of a story your journey. You did a PhD in physical chemistry or chemistry and biochemistry. And yeah, so I was in academia for 20 years doing the kinds of things that people did in academia and do in academia using NMR spectroscopy as a structural tool. So NMR is a very common structural tool you can find NMR machines in every chemistry department in the country. So I had appointments in chemistry at the university with constant Milwaukee and then moved in 1990 to North Carolina State University. So I was basically doing my thing, minding my own business using NMR for the usual purposes. And for the listener, we are going to explain how NMR works because for people who didn't take chemistry and might not remember it, we'll come back to it. But I don't want to interrupt now to do that. Yeah, we'll come back. Yeah, I'm not sure that that's terribly relevant, but we can we talk about it. But anyway, the point is that NMR was and is very useful for a particular purpose, which is helping organic chemists determine the structure of molecules that they synthesize, for example. And I was using it to study biomolecules, so it was more challenging than small organic molecules and trying to get it understanding what was going on at the active side of zinc, zinc, zinc, zinc metallic enzymes. But anyway, so I was funded to do that and had an NMR machine in Milwaukee to do that research. And then in 1986, there was a paper published in the New England Journal with a lot of hoopla and in particular in the NMR field, people paid attention to this. I didn't normally read the New England Journal of medicine ever, but it claimed that a very simple NMR test could tell whether somebody had cancer or not. Plus or minus, irrespective of whether it was this cancer or that cancer. Nothing really in the paper that laid a mechanistic foundation for why this relationship should be it simply measured a couple prominent signals in the NMR spectrum of blood plasma and measured how wide the signal was halfway up the signal. If it was narrow, you had cancer, if it was not narrow, you didn't have cancer. And you know, normally this wouldn't be given much attention, but it was published in the New England Journal. And so everybody who had NMR machines was interested in seeing if they could replicate this. I was in a chemistry department not associated with the medical school. So I had no idea how to get my hands on plasma if I wanted to play around with this, but I went across the street to a hospital and talked to people in the lab to giving me six left over plasma samples from healthy people and pop that into my NMR machine. And sure enough, half of the signals were narrow and half were broad. Did half of these people have cancer? No, these were these three people were women who had just given birth. So pregnancy was one false positive that was given in this in New England Journal paper. So that it wasn't for that sort of linkage to something that seemed consistent with what was published. I probably never would have taken another spectrum of plasma, but just out of scientific curiosity, we started measuring plasma and notice that the signal that was this supposed cancer diagnostic didn't look like a nice symmetrical NMR signal. It had lumps and bumps and shoulders and and so what was up with that and pretty quickly when we started to ask where does the signal show up and what's the what are the molecules that are giving rise to these signals. It was clear that these were signals from the lipids and lipoprotein particles. And so we then very serendipitously got funding from Siemens medical systems, basically $100,000 after me giving a one hour presentation for what what I might learn with $100,000. So that was a pretty cool opportunity for a professor who had to go through a lot more hoops to get funding. So we had the wherewithal to get samples from people within without cancer and then separate the major lipoproteins VLDL LDO and HDL. And we looked at those signals and noticed that the VLDL signals were always to the left of the LDL signals, the LDL signals were always to the left of the HDL signals. And it was the superposition of these signals and their relative concentrations differing that gave rise to the different shapes of this composite mixture signal that you would see an aplasma sample. So it was obvious that that this signal was coming from lipoproteins. So what's up with the narrow signal meaning cancer? Well, that turns out to be due to the fact that those signals from those people were from people with higher triglycerides and lower HDL cholesterol and the combination of those two things made the signal narrower. So this was nothing to do with cancer per se at which to do with the lipoproteins, the lipid levels of people with cancer. One of yours before that, people had published that people with cancer on the average have higher triglycerides. triglycerides and lower HDL cholesterol. So anyway, we published in 1990 or '91 a paper in clinical chemistry that showed that the NMR signals from isolated VLDL, the LDL and HDL from people with and without cancer didn't differ at all. So there was nothing distinct about whether the sample came from a cancer patient or not. But what was very reproducible, and we didn't understand why, was this phenomenology of the VLDL signals not showing up in exactly the same place as the LDL signals or the HDL signals. So we got the brilliant idea, which I didn't think was very brilliant. I thought it was obvious to use this putative cancer signal as a source of information about the concentrations of lipoproteins in the blood. So with a fairly simple low-tech NMR spectrum that anybody could do with any NMR machine, you could generate this signal who shape and amplitude could be used to deduce the concentrations of the VLDL, the LDL and HDL that were giving rise to that composite signal. And so we have been funded by Siemens. We published a paper in I think 1991 saying that yes, it was feasible that one could get VLDL, LDL and HDL simultaneously from this simple NMR spectrum. So that seemed to have some advantage over the usual way of measuring triglycerides and LDL and HDL cholesterol via normal chemical methods. So that's as far as we thought we could go. And if it wasn't for the fact that Siemens people were advising me that I never would have filed for a patent on how to do this, what did I know about patenting and what did I care about commercialization. But I did file a patent and the patent was issued. So if this seemed to be something useful and clinically useful, then it would have to be clinically translated and that would have to be via some sort of commercial entity, because there were no NMR machines in clinical laboratories. In fact, there are no NMR machines to this day in clinical laboratories. So we needed to figure out how to transition NMR spectroscopy into clinical laboratory medicine. And we needed a commercial vehicle to do that. So that was an idea that evolved over the early 1990s to about 1995, '96. Had a couple NIH grants to support the analytical development. And what we didn't expect to be able to do but found that we could was not only to differentiate VLDL, LDL and HDL, but the different size subspecies that make up what we call total VLDL, total LDL, total HDL, smaller, medium size, larger particles. Because these signal positions are so close to each other, it just didn't seem feasible that you'd be able to work backwards from the composite signal and accurately get the concentrations of the subspecies. But by that time I had been reading the literature a bit and Ron Krauss at UC Berkeley, a donor laboratory, was showing that via a quite laborious separation method, gradient gel electrophoresis and others that you could differentiate LDL on the basis of size and found that people with a prevalence of small dense LDL had greater cardiovascular risk at a given LDL cholesterol level than somebody with large LDL. So this was something that was very interesting and had been replicated in literature and people were talking about and yet it took a couple days from start to finish to do this electrophoresis and get the result. So it wasn't really clinically translatable and inefficient. And just to see if Enemark could do this, I hooked up with Ron. He sent me about 45 samples along with the gradient gel electrophoresis tracing so I could see who had paternay at a large LDL, patern B, the small LDL. And sure enough, when we applied our analysis for decomposing the composite signal into its parts, we could definitely tell the difference between large and small LDL. So aha, okay. Now we spent a couple years seeing if we could refine the methodology for quantifying small LDL, large LDL, small HDL, large HDL. And that was quite successful. So it really was with this idea that there was something really clinically useful about being able to differentiate the size of LDL particles that drove us to take the step that I was very unqualified to take, which is commercialization of Enemark testing technology. It really did seem that yes, you not only could generate the same information as a lipid panel by Enemar, but really what would drive the utility of Enemar testing was if it could measure something better and different. And so if we could measure small dense LDL, paternay and B, threefold greater risk associated with that at a given level of LDL cholesterol, well, that would be a pretty useful thing clinically. So Jim, tell us how, I'm going to have to go into great detail, but tell us how a plasma system, so you go to the doctor, they draw your blood. The last thing the patient sees is that tube of dark blood that's leaving them. Tell me what has to happen from there until they get a basic lipid panel back, which says total cholesterol is this many milligrams per desk leader LDL cholesterol, HDL cholesterol triglycerides, all in milligrams per desk leader. How do they get that out of that tube? What is the basic? How do they do that? Chemically? How do they do that? Yes, chemically and in a clinical laboratory. Yeah, so these are standard chemistry-based assays that are like all such assays. You add a reagent that reacts with what you're trying to measure like triglycerides. Actually, triglycerides is interesting because you know, triglycerides are a fatty acid that is terrified to glycerol. So what actually happens in that assay is the blood is exposed to a lipase that hydrolyzes that separates the fatty acid from the glycerol. It leaves the glycerol and then the glycerol is what something else is added to make a color change in proportion to the amount of glycerol. So this is how you're accounting the glycerol. You're accounting the glycerol. You're imputing how much triglycerides you have because you know, it was a three to one ratio. Yes. The clinical issue, it's not a common, but there are situations where somebody has a lot of glycerol, not a terrified. So the assays for standard assays for triglyceride would say that this person has very high triglycerides. They don't have triglycerides. They actually don't have high triglycerides. Anyway, so the same thing with cholesterol. So you're adding chemicals, adding reagents that cause a color change or a change in the UV spectrum that is monitored and you have a standard curve that relates known amounts of LDL cholesterol to the signal to the color that's created and you can work backwards from that measurement. So these can be completely automated. You're using totally optical density or something like that. That's right. And that's right. UV detection or visible light detection. So these are really standard, very, very efficient auto analyzers do this. The challenge with cholesterol though, I mean, so HDL cholesterol. So the problem is you're measuring the cholesterol inside the LDL, LDL on HDL. So when does that get broken? When do the lipoproteins break open in what part of the assays so that you are just looking at the total amount of cholesterol contained? Right. So the original interest in cholesterol in its relationship to cardiovascular disease risk was just total cholesterol. So basically the reagents find all the cholesterol inside all these particles don't differentiate where it's coming from. If you want LDL cholesterol, you have to separate the LDL particles. These are spherical containers that contain the cholesterol and separate it from the HDL containers and the V LDL containers and then do a regular cholesterol assay on what you've separated. So there's a separation step, same thing for HDL. And for many years until fairly recently, HDL cholesterol was always measured by first getting rid of the V LDL and LDL by precipitation and then it left HDL that you then did a cholesterol assay. LDL cholesterol is more tricky because you would have to separate V LDL from LDL. And that takes an ultra-central fugestep, which is laborious and even clinical laboratories today don't even have ultra-central fugies. So people devised a way of calculating LDL cholesterol by measuring total cholesterol minus HDL cholesterol, which is V LDL plus LDL cholesterol. And then estimating V LDL cholesterol by dividing triglyceride by 5. Triglycerides are mostly in V LDL. So it was an easy but not terribly accurate way of quantifying or estimating V LDL cholesterol from which you could subtract the HDL cholesterol from total cholesterol and get LDL cholesterol. It's safe to say Jim that when a patient gets a lipid panel today, unless it says LDL direct, which we'll talk about. And it just says LDL cholesterol equals 127 milligrams per desolate, is it a safe assumption that it may have been done using that exact same methodology you described? Yes, absolutely. And the only thing that's changed fairly recently is recognition that dividing triglyceride by 5 doesn't give a very accurate V LDL cholesterol estimate and that impacts the accuracy of the LDL cholesterol estimate. And so now there are equations that interrogate other things non-HDO cholesterol, triglycerides. There's an NIH equation that I help people develop that is used by lab corps and other major laboratories. And so you can do better than estimating it by a free-to-world formula. But most laboratories, I think, to this day still use the free-to-world divide by five to get the LDO cholesterol. So let's talk a little bit now about NMR. So again, maybe someone remembers back in an organic chemistry class that one of the problems you would receive on an exam or something was you would be showing a picture. And the picture was very much like how you were just describing your experience in the 80s and 90s where you had a long and X-axis line and then it would have these spikes and they would sort of correspond. So tell us, what did the X-axis correspond to and what did the amplitude or Y-axis correspond to? And of course, I want you to get to the point of these are protons, but get there in your own way, of course. Yeah, so you're exactly right. That's what the output of a normal NMR spectrum looks like. Irrespective of whether you're detecting protons, hydrogen, nuclei, or carbon-13 or nitrogen-15, et cetera. So on the X-axis is frequency. It's just frequency. These are signals that have different frequency. And their amplitude is proportional to how much of the molecules carrying this, the hydrogens, let's just talk about proton hydrogen NMR. They show up in different places. They have different frequencies depending on their chemical environment. And that's why this is a useful structural tool for organic chemistry. So a CH2 group next to another CH2 group is opposed to a CH2 group and a double bond or whatever it might be. They show up in very different places and there's other differences that are structure dependent. So you can work back from an NMR spectrum and deduce the structure of the compounds they're giving rise to that. So this was never used though as a quantitative analysis tool. It was used. So it's a relative signal intensities would tell you how many protons are here in a molecule, how many protons are there on the molecule. Does that fit the structure that you were attempting to synthesize, for example? And so just I wish we were to make sure people understand what you're getting out there. So you're trying to synthesize something. You know the structure of the thing you're trying to synthesize. You think you know it. And therefore you should know what its spectra looks like. And now you're basically trying to match the NMR of what you've synthesized to say, look, this should have a carbon, a double bond, a carbon, a single bond over here is going to be an oxygen that's going to produce. And it's, I mean, it's a really fun game. It's, I mean, not to be too nerdy about it, but it's a super fun puzzle to solve effectively. Yeah. And it's pretty complicated and I haven't done that sort of thing in 50 years. So I moved to a different application, which is using NMR to detect a single signal. So I didn't care about what was in the rest of the spectrum. I cared about the signals that came, that were these putative cancer signals from the terminal methyl groups of the fatty acid chains that are carried on different types of lipids that are carried in these lipoprotein particles. By the way, did the NMGA, New England Journal Medicine authors know that they were looking at lipoproteins? I think they said lipids. I can't remember, you know, and again, and I think yes, that they did. And they suggested that if you had cancer, there was some structural alteration in the lipoproteins that gave rise to a different signal. And that's what we've disproved by showing that that wasn't true. So anyway, back to this issue of whether we could use that so-called cancer signal as a source of quantitative information about the lipids and lipoproteins of the lipoproteins themselves. So what I told you is true, it was empirical observation that there was this very consistent relationship between where the frequency of the signal from the very same methyl groups on the very same molecule so that the molecules don't differ at all in terms of what's carried in VLDL, LDL and HDL. It's the same lipids, so you'd expect they would all show up in exactly the same place. But for some magical physical chemical reason that is explained by complex equations that I don't even understand very well, it's been shown that a larger particle will always give rise to a signal that has a slightly lower frequency and a smaller signal will have a slightly higher frequency. So the same lipids in different sized packages show up in slightly different places, but very reproducibly different places. And so the idea is that if you understand exactly where the signal shows up from a particular diameter lipoprotein particle and also measure that because the shape of the signal will differ. That's another complexity. It adds to the accuracy of what we're able to come up with with a complete understanding of what the different parts are that make up the mixture, make up the whole. The whole idea is that you measure the whole and then you decompose it into the parts. So the sum of the parts equals the whole. What's efficient about the methodology is that you're measuring something with a really low-tech simple NMR spectrum that you can obtain in 30 seconds. A computer then with a deconvolution model that has in it what the signals look like from all the different size VLDL and HDL subclasses and then take that measured 30 second measured composite signal and spit out how big the signals must be from all the different constituent parts to when they superimpose they will recreate that shape of the composite signal. So that's the idea that the concentration information comes from how big the deduced NMR signal intensities are in this mixture, blood. Now the problem is that one point to just add to that, Jim, that a person who's looking at their own NMR result in their blood test will notice that the units are reported as nanomoles per liter as opposed to milligrams per deciliter. So it's not a mass concentration, it's a molar concentration. Maybe explain to people that's just where I was going to go next. As I told you, the chemical constituents in these particles and they're quite heterogeneous, there's different size legs of fatty acid chains, some are saturated, some are mono and saturated, some are polyunsaturated. All of these things give rise to the lipid complexity of these particles. But the way the NMR detection at least of this signal knows nothing about any of that. It's basically a lipoprotein particle signal. And so what should be the case if that's true is that how big that NMR signal is from the particle should relate to the number of particles irrespective of what the lipid concentrations are. And there are variable amounts of cholesterol and triglyceride in most lipoprotein. So what we realized at the beginning was if this was a lipoprotein particle signal, we were interrogating, we could get lipoprotein particle concentration information. But we could not and should not report lipoprotein cholesterol levels or lipoprotein triglyceride levels because we actually weren't able to differentiate the signals from those different chemical species. So that is what we started to produce when we did this commercialization thing, which I may come back to a little bit. But as you said, what's reported LDLP is the particle concentration in nanomoles per liter. So there's 6.02 times 10 to the 23rd particles in a mole of LDL. And so this is 10 to the minus 9th anyway. So it's a big number. It's still 10 to the 16th particles. So there's a lot of these particles in your blood. But you're reporting the concentration of the package, not the lipid molecules in the package. So then the question is, one question is, is there any advantage to that? I mean, because by then, so I'll go back a little bit to the commercialization because as I said, the commercialization was driven by the idea that small dense LDL is much more atherogenic, much more to worry about than large LDL. And our own studies, when we started measuring small and large LDL by NMR and did it in large population studies, we found exactly the same thing that Ron Kraus and others did. And which population did you look at? First Mesa framing him? Framing him for sure. Back in the day, Mesa came a little bit later. I can't remember what we initially looked at. But the point is that when looked at through the lens of at a given quantity of LDL, if the LDL is small versus large, does it make a difference in your cardiovascular risk? When I said the concentration of the quantity of LDL, the way everybody thinks of the concentration of LDL is LDL cholesterol. And that's what Ron Kraus and that's what we did. We said, okay, at a given level of LDL cholesterol, you take people to the hospital. when you stratify them according to low medium and high LDL cholesterol or you do a multilininger regression and put LDL cholesterol in the model and now you're asking just the size of the LDL add anything to LDL cholesterol in cardiovascular risk production and sure enough it does and it's quite an impressive increment of risk so we reproduced what Ron crashed it however we realized because we were also in the business of measuring LDL particles that by definition if an LDL particle is smaller versus larger it's always full of lipid it's you don't get a partially filled container of LDL it's always full so a smaller LDL particle means by definition that it's carrying less cholesterol less lipid per particle than a large LDL particle so the phenomenology is at a given level of LDL cholesterol people with small dense LDL have higher cardiovascular risk the trouble is that people with small dense LDL at a given level of LDL cholesterol have more LDL particles their LDL P is higher than would have been imagined from the LDL cholesterol measurement so an alternate explanation for the extra risk that small dense LDL seemed to confer is that there are simply more particles rather than the size of the particles being the determining characteristic of the authenticity of these particles. Let me just use a silly childlike example to make this point so imagine you had four lipoproteins that each contain three units of cholesterol they're fully saturated at three units of cholesterol so you have four of them so you have 12 units of cholesterol now imagine you have three spherical lipoproteins that each have four units of cholesterol they're fully saturated so they're obviously bigger but they also collectively have 12 units of cholesterol so here you have two people one has 12 units of cholesterol but it's being carried with four lipoproteins the other has the same 12 units of cholesterol carried by three you're telling me all the data say the first person is at higher risk question is are they at higher risk because their spheres are smaller or are they at higher risk because they have more spheres which happen to be smaller correct so you said you explained that very well and so this is basically how science works so you have a different explanation for the phenomenology of small dense LDL having this seemingly extra-authority so it's then testable so it's it's quite straightforward at that point to ask the question at a given level of LDL particles if you stratify people according to LDL P and then say some people have small LDL some people have large is there any difference in their risk and the answer is not not a bit and we do again the many study examples is you have two patients that each have 20 particles yeah one of them is 10 big 10 small the other one is 15 big 5 small if the particle size matters the first one should be at higher risk if the particle size is irrelevant once you've corrected for total number they should be at the same risk you're saying they're at the same risk that's right and and why does this matter I mean if you're only interested in assessing the risk of a person you're equally well off with the cholesterol information and the size information as the particle information and the size of the particle information the size doesn't add to that the reason that's important is that if you believe that small LDL is bad and you can make it less bad by making the particles bigger they're aputically somehow then you will be telling patients that at a given let's say they get treated with statins and they lower their LDL cholesterol or their LDL particle concentration to an acceptably low level but the particles are still small somebody who believes small then cell D.O. as particularly bad will then try to do something to make them bigger and imagine that there's clinical benefit and a lot of drugs actually have that effect CTP inhibitors are one of them niacin you know HDL drugs of different types so triglyceride lowering automatically will will will have this effect so there really are a lot of clinical trials that imagined when they set out to do the clinical trial that they there would be a lot of efficacy like niacin for example because not only did that modestly raise HDL cholesterol and lower triglyceride which were two good things seemingly but it made the LDL particles bigger and it made the HDL particles bigger because there's also a similar argument about the size being important in HDL and and yet there was no efficacy when they did the outcome studies and and this has been reproduced in with you know the fibrates and and and and other drugs so so Jim what would you say to the person listening who because I hear this all the time who says hey guys I do have a high LDL cholesterol and and even my LDL particle number is very high on my NMR test but I'm very pattern A you see all of my LDL are very large and they even use words like fluffy and do boyan I have large fluffy boyan yes LDL so my LDL particle number is over 2000 animal per liter which probably places me at the 80th percentile or so but I don't need to worry about it because they're all big I don't have that pattern B I don't have that what would you say to that person I would say that's a fallacious idea and the data that I mentioned supports it completely and then you also have to think about the fact that one of the best known or best accepted genetic reasons for cardiovascular risk is FH familiar hypercholesteroemia people with FH have very high LDL cholesterol levels they also have very high LDL particle levels but those particles are large they're not small and there's you know these are people that die when they're 30 or 35 years old when they're homozygous FH so this idea that somehow fluffy large LDL particles are not to worry about our benign is completely fallacious let's talk about one more thing on this before we pivot away from this which you alluded to very briefly but we went off on a different path which is the potential discordance that exists between LDL cholesterol and LDL particle number in fact when I first came to learn about your work Jim this was actually one of the first papers I read of yours literally 15 years ago almost to the to the month and it was in the Mesa population and it was showing four Kaplan Meyer curves so I'll let you explain what a Kaplan Meyer curve is but it was basically the four scenarios which was discordance between LDL P and LDL C when one is higher than the other each time and then concordance between them so maybe there were three curves then that's exactly there were three curves okay so concordance between LDL C and LDL P and discordance in favor of LDL P discordance in period okay so that was that was a very very eye-opening paper to me and I'd love you to just kind of explain that finding and what the significance are because to this day it's still a very important thing for clinicians and patients to understand now it is and and prior to that Mesa paper in 2011 was a 2007 paper so it was the first one that we sort of introduced this idea of assessing the situation by whether the LDL P and the LDL C agreed or not and in that case was a framing ham study and in that case one question that always comes up is what defines discordance and and basically it doesn't matter and everybody defines it differently and it it doesn't matter because we're not saying discordance is a risk factor right we're simply trying to disentangle a situation where most people don't have a discrepancy between LDL C and LDL P so if you're trying to understand whether LDL P is a better measure of LDL associated cargobasca risk and you do a whole study population and 80% of those people have concordant or in agreement LDL C and LDL P and just explain what that means because the nut people will say well the numbers are different how can they be in agreement okay so what one way to do it which we did in the in the Mesa paper is transform the milligram per decilator cholesterol number into a percentile so that defines people according to their rank low high intermediate and if you do the same thing to the LDL P now you're comparing apples to apples percentile of one percentile of the other and you plot that and then you you see the the data points all over the place and the ones that track on the diagonal the ones that are close to the diagonal are the ones for which a 50% ile LDL P is close to a 50%ile LDL C so that's what we call concordance and in that case we picked this weird number of a 12%ile difference was our cut point plus or minus so everybody within 12 percentile units of each other we called concordant y12 one not one out around no for and it was because we were trying to make the concordant population about equal and number to the discordant in one direction and the discordant in other direction population So you have 50% in one 25 and 50% in the two discordant groups. No, it's actually we're trying to make it sort of 30, you know, I see 30, 30, 30, 30, 30. Okay. And so that's what what we did and then we simply took these three groups of people and you talk about Catholic Meyer. All this is basically cumulative incidents of cardiovascular events. So on the X axis, you can see the number increasing. If it increases a lot, this is a higher risk subpopulation than somebody whose risk is much shallower and doesn't increase very much as a function of concentration. So that very clearly showed that for people who's LDL, P and LDL, you agree, you can make no argument about why LDL P is a better thing to measure than LDL C. Then it's a matter of well, from first principles, what would we expect the cholesterol to be a better measure of risk or the particle number? And really, if you're honest about it, there is no mechanistic explanation that you should really be too comfortable with for one versus the other. And we explain that in that paper that what has happened with when Ron Krauss showed that small dense LDL was more athergenic and that this was based on epidemiologic population studies. The question was, well, why is that? And then basically the speculation was ultimately supported by various lines of evidence that, yes, small LDL particles are likely to get into the arterial wall easier than a bigger particle. And then the difference in the shape of the stuff on the surface of the particles can bind more avidly to molecules that are in the arterial wall and retain it more and then it's more readily oxidized. So all these things, I can't tell you how many papers I've read were in the discussion section is this obligatory paragraph about why small dense LDL was so much more athergenic. The flip side argument is that, okay, large LDL particles also get into the artery wall. And when they get oxidized, they're more retained. No, no, they're not more retained. I mean, you could make that argument, right? Well, let's just say that they get to the end of the trail and they get taken up by macrophages and deposit their cholesterol contents into the arterial wall. Well, bigger particles have more cholesterol. More cholesterol. So you think the more cholesterol rich particles should be more athergenic. But if that's counterbalanced by not getting to the end of the party as often as the small particles, maybe it's a wash. How do you answer the question? You do the study. And so that's what the discordance analyses allowed us to do. Whenever there's a discrepancy in one direction or another, the cardiovascular risk tracks with a particle number, not the cholesterol. Yes. So the curve really had a beautiful separation of those three figures. You had the concordance in the middle and then above that line, which means these are people that are dying quicker. That's when LDLP was above LDLC. And below the line, these people died much slower than you would expect. It was the flip. Cholesterol was high, but their particles were low. Exactly. Yeah. So yeah, that. I mean, and I can't tell you how many debates I had to have with people. I mean, usually very well regarded cardiologists. Tell me about it. Who just refused to acknowledge there was ever a need to look at anything beyond LDL cholesterol. Right. Well now, let me sort of go somewhere about the clinical utility of this because what people would imagine from having said all this is that LDLP should be a much better thing to use to assess cardiovascular risk. And the reality is that the way cardiovascular risk is assessed today is the same, pretty much the same way it's always been assessed. But by equations that take into account total cholesterol, HDL cholesterol, diabetes, present abs and smoking, present abs and hypertension. So these are risk equations. And there's updated risk equations, just fairly recently a new one. They all have total and HDL cholesterol in it. When you ask the question, if I use LDLP or I add LDLP to a model that has those things in it, is my risk assessment better? And the answer is no. It's not better. And this sort of kills you if you're trying to make a commercial argument that you should be testing LDLP instead of LDLC for risk assessment. The reason I think that that is true is because the HDL cholesterol is in the model. And we still don't really understand whether it's HDL is bringing extra risk assessment to the table above and beyond what the LDL and the total cholesterol brings. Whereas at the triglyceride-rich particles, now people have swung away from the idea that HDL cholesterol is important because the HDL cholesterol raising trials were negative or weren't positive. And then because there's an inverse correlation between triglyceride and HDL cholesterol, when HDL cholesterol is low, triglycerides are high. Oh, maybe it's the triglycerides. Well, it doesn't make sense that the triglycerides per se, the molecules triglycerides are authentic, but the triglyceride-rich particles that carry them could well be because they also get into the arterial wall and deliver a lot of cholesterol. Now, Jim, I don't know if this was one of your papers, but I think it was, and it was around that time, probably 2012, 2013. And if I recall the paper, the figure specifically, it was, it was a histogram, right? So on the X-axis, you had 0, 1, 2, 3, 4, 5, and you were looking at number of criteria met for metabolic syndrome. So for the listener, just to remind everybody, metabolic syndrome has these five criteria, and the more of them you have, the more likely you are to be insulin resistant. So there's one about having high blood pressure, obesity, so the trunkal diameter, trunkal girth, blood pressure, fasting glucose, and HDL cholesterol. I think those are the five, right? Doesn't include, yeah. Okay. And what this figure showed was if you had zero of them, the probability that you were discordant between your LDLC and LDLP was very low. If you had one of them, the probability went up a little bit. Two of them, considerably, three, a lot, and it was a monotonic increasing relationship, Jim, such that by the time you had five out of five met sin criteria, you were virtually guaranteed to be discord, right? And so I bring that up to say earlier you mentioned there's nothing wrong with being discordant per se. Although it's, you could also argue that the more, the more discordant you are, the higher the probability that you're discordant means the higher the probability that you probably have some underlying metabolic insulin resistance or risk. Yeah. Yeah. And so the question then becomes, is the reason that very elaborate multi-parametric risk models can erase the LDLP is that they are simply capturing all of the cardiometabolic risk indirectly and directly. Yeah. You basically just explained what I was getting at, which is because HDO cholesterol is in the standard risk assessment models. When you have low HDO cholesterol, you're likely to have more LDLP than the LDO cholesterol indicates or suggest. And so your risk is higher because you have higher LDLP, not because HDO cholesterol is low. But you're not doing better in risk assessment because the HDO cholesterol, for the wrong reason, if you will, I'm not a causal reason, but an association reason, is telling you the same thing. So this was really important to liposcience back in the day because, and this is something that I still argue with, but I, Alan Snyderman and I will get to the question of ABOB. Yes, yes. But, you know, he insists, he keeps wanting to convince people that ABOB is important for risk assessment. And what we pivoted to in light of the evidence that we found ourselves, convinced people that LDLP was a better thing to measure for risk assessment to a better thing to measure for risk management. Because what you do when you have high risk is you manage it by lowering your LDL cholesterol. This is the best tool we have in the toolbox that are not actually very many others. We have statins and then we have things that are doing the same job as statins, but better. So we can lower the heck out of LDL. And the question for a patient with high cardiovascular risk is, how much LDL lowering do I need? So what you really would like is a biomarker that tells you my LDL associated risk is adequately controlled. I've gotten my LDL low enough. If it's not, I should add, if I'm on high dose statin and I'm not there, then I want to add a PCSK9 inhibitor or something else to lower it even more. So you want the best objective measure of LDL related risk, not total risk, but LDL related risk. In that case, it's very clear that LDL P or APOB is a better biomarker because a lot of people who achieve very low LDL cholesterol have not achieved equally very low LDL particles or APOB. And they therefore with visibility to that would be candidates for more aggressive LDL lower. And so we absolutely notice that as clear as day in our practice gym because we are very aggressive at managing these things. Is there a biologic reason for why the discordance really happens at low levels in that way or is it a chemical assay? Is it a property of the assays that's causing that? No, it's like everything that you ask. It's complicated. You can get into the weeds. But what's true is that the lower your LDL level is. The more likely that the cholesterol in the LDL particle is replaced to some extent by triglyceride. Because there's always this interchange, this swapping of cholesterol ester and triglyceride in the core of the particle. And it's driven by the relative amounts of the triglyceride rich particles and the LDL particles, which are cholesterol rich. The triglyceride rich particles, the bigger that gap is, put triglyceride into LDL in exchange for cholesterol ester. And so as you lower LDL with statins or whatever, the LDL particles are lower. But the LDL cholesterol is even lower because not only has the particle number gone down, but the cholesterol in the particles independently is going down. That's actually a great explanation. I did not know that and that makes sense because we see that as clear as day. One other thing I want to talk about on this front before we pivot is at least to my knowledge, the first composite score that you then developed out of that, which was the LPI R score. Was that indeed sort of your first foray into who will composite scores? Yes. And it was used to this day. So many people are listening to us. We'll have an LPI R score every time they get their blood test. No, it's interesting because as I said, we first got into this game because we could measure small LDL. And so the ability to differentiate different size, lipoprotein particles seemed like it was clinically useful. But at the end of the day, as I've gone through, it turns out that it's really the particle number that matters. And if you can convince people to not pay so much attention to LDL cholesterol and pay more attention to APOB or LDLP, then you're better off. But that means that measuring the size of LDL doesn't matter. Well, that's a bummer because we have a great efficient way of doing that. So then the question was, well, are the lipoprotein subclass distributions useful for something else besides cardiovascular risk assessment and management? And the answer is absolutely yes. There is a well-known association between higher triglycerides and lower HDL cholesterol and insulin resistance and diabetes. And insulin resistance leading to diabetes risk or leading to diabetes. So it's already known that there's a lipid signature for insulin resistance. And the idea was if we could measure the different sizes of VLDL, LDL and HDL, could that do a better job than just the triglyceride over HDL cholesterol ratio? So the poor man's insulin resistance measure from a lipid panel is triglyceride over HDL cholesterol ratio. And Jerry Reven, who was really discovered and promoted the idea of insulin resistance being extremely important, he advocated that it be used because people were getting lipid panels. And this information was not being used for anything. So with this LPR score, which brews together six VLDL and LDL and HDL size and subclass concentration parameters, it brews them into a score from 0 to 100 higher scores being more insulin resistant. Then the question was, does this LPR score do a better job in assessing whether somebody is likely to become diabetic? So the pathophysiology has to be talked about a little bit here because there's some interesting parallels to where we are today with respect to primary prevention of cardiovascular disease versus primary prevention of diabetes. So in the cardiovascular situation, as everybody pretty well understands, the initiating causal factor there is elevated cholesterol or elevated LDL or elevated WB. But it's acting over time. So it requires an integration of exposure over a long period of time. And that gradually leads to cholesterol deposition in the artery wall. That's atherosclerosis. Atherosclerosis over time starts little more, more. But that doesn't trigger any clinical concern. This is a subclinical manifestation of cholesterol doing its dirty deed over a long period of time. And then at some point you might have a myocardial infarction or a stroke. And that's when the atherosclerosis has transformed into the clinical event. The good news from a measurement biomarker standpoint is that the initiator, the causal factor is cholesterol, which is easily measured. So now what about diabetes? Diabetes is very similar. It's a time-integrated process where if you are insulin resistant over time, your beta cells have to spit out more insulin to keep your glucose under control. So you're making the beta cells work harder if you want to think about it that way, if you're insulin resistant versus insulin sensitive. And so insulin resistance times time leads gradually to hyperglycemia, elevated glucose. So if it's an idea below your AOK, but over time if you're going to convert to diabetes, you go through a transition of the glucose going higher and higher until it crosses this magic 126 milligram per desolate line that defines diabetes. Just for folks to know we're talking average. Absolutely. So the causal factor is insulin resistance. The thing that's sort of equivalent to atherosclerosis on the CBD side is hyperglycemia sub less than 126, so not diabetes, but pre-diabetes. So when glucose gets over 100 before it goes from 126, you're pre-diabetic. Guess what's easy to measure glucose? So in that case, the effect of the cause is measurable. The cause itself is not. And so what that means is that from a prevention standpoint, what you really want to do is to keep insulin resistance from transforming over time into hyperglycemia and ultimately diabetes. If you don't know that you're insulin resistant, you just you're waiting for the easily measured glucose to become elevated. And now once that happens, you've lost about 50% of your beta cell function. So the opportunity for real effective prevention, primary prevention, primordial prevention is to act on people whose glucose is okay and hasn't gone to this transition yet because the beta cells have started dysfunctioning. So I want to pause you there because the way you've laid that out is very elegant and I like the I've never thought of it the way you just explained it. So I'm repeating it just as much for me as for others. People who listen to this podcast know we constantly use the way you describe this CBD prevention thing, which is you have a causal marker. You don't need to wait until disease is measurable to treat it and the example I always give is smoking and lung cancer. We have a causal marker for lack of a better word smoking. We always have to specify causality doesn't mean one to one mapping. There are some smokers that never get lung cancer. There are some never smokers who still get lung cancer. None of those facts erase the causality of tobacco and lung cancer. Do we need to wait for a smoker to develop a small cancer to tell them to stop smoking? Absolutely not. That would be malpractice. You always eradicate causal drivers of disease the moment they appear. And that's why when you have elevated LDLC or APOB or LDLP, you treat it immediately. Not once they have disease, same with hypertension, same with smoking as it pertains to cardiovascular disease. Now let's pivot to what you said, which is look, we know that insulin resistance is the cigarette to diabetes as cancer. Do we want to wait until we actually see the glucose rise, which by the way is the biomarker that defines diabetes, when in reality by the time that's happening, there's potentially already cellular damage at the level of the beta cell in the pancreas and it's basically running out of steam. And what else can we measure? Now I want to come back to the idea of there are things that we can do, but they're very laborious. So an oral glucose tolerance test is a fantastic way to find out that canary in the coal mine years before it shows up. But it's so fallen out of favor as a clinical test because it takes two hours. It's, I mean, it's just so cumbersome to do that outside of our practice and a few others. I just don't imagine many people want to do it. So Jim comes along and says, what if somewhere in this NMR spectrum is a whole series of things that turn out to be a fantastic marker for insulin resistance that we can use as causal proof that you're on the wrong path before your glucose goes up. Yes, and it seemed to us a very compelling case that you would want to act on the causal factor and not the effect or like the downstream effect of the action of insulin resistance. But it's interesting, I mean, back to convincing people, clinical translation. I mean, this is, I use that word. I don't think I was familiar with that word when I started this company called LIFO Science to try to get NMR testing introduced in the clinical laboratories. But I thought the argument of LDL P versus LDLC was quite compelling and that people really should be using it as the biomarker to determine management of LDL. Tremendous resistance by the establishment could never understand why it was so, why there was so resistant. The messaging people learn about cholesterol and medical school, we'd have to tell a different story and people wouldn't get it and all sorts of, you know, reasons that seemed pretty weak to me. So on the diabetes side, it's the same thing. People imagine that hyperglycemia, free diabetes, is a risk factor for diabetes. It's not a risk factor. It is the disease. It's just in a less manifest form. So why not address the cause? A lot of resistance to that. I sort of blew my mind and we did NMR analysis in a number of studies to show the efficacy of, or at least the relationship between insulin resistance score and likelihood of future diabetes. And by the way, transitioning to pre-diabetes by no means means that you're going to get diabetes. I mean, there's, you really, my wife has been pre-diabetic 105 or so milligram per decilator for 25 years. It doesn't change. So the insulin resistance score can tell you whether you're more or less likely, once you're pre-diabetic to transition. And then at that point, even though you'd like to have intervened earlier, it's not too late. It still do something about it. And so Jim was the LPR score validated on longitudinal data to predict the development of type two diabetes or okay. So in that sense, we'll talk about MVX and how similar that was in that regard. Has there ever been a comparison that says how well does it do versus an oral glucose tolerance test? Yes. And so, well, not oral glucose tolerance test, because in the real world, it's too complicated. So you're really fasting insulin is sort of an easier way to assess insulin resistance. It has some analytic issues and practical issues. So nobody has really been interested in using insulin for that purpose, generally, in clinical practice. But the LPR score has been compared to fasting insulin and is better. The study that shows that the best is one that unfortunately hasn't been published yet. It's it got very close to having the manuscript be written. It's in the diabetes prevention program. So you couldn't ask for a better study because this was a study that put intervention lifestyle intervention that form on on the map in terms of being able to do something about transitioning to diabetes once you had hypergysemia, once you were pre diabetic. So this study was done about 20 years ago. We have baseline samples and then one year samples post treatment. All that everybody in the study had to enter more analysis done LPR and other things that we can measure by enomer that make FPR better if they're if you want it to be better. We're shown to be independently predictive better than insulin. But most importantly, because that was an intervention study, you know, lifestyle change and weight loss was shown to significantly reduce the incidence of diabetes in these people. Metform and less effective but significant comparative placebo. So what we were able to show really nicely is that the LPR score was reduced significantly by lifestyle less significantly by metform and branching amino acids, which we haven't talked about yet, but higher branching amino acids are also related to insulin resistance and can improve the LPR score. And we actually have developed another score called the diabetes risk index that integrates or adds branching amino acids to LPR. Okay, I was going to ask you that. So just to confirm the the DRI the diabetes risk index is the LPR score inclusive of the three branching amino acids. Right. And when do you recommend using one of those versus the other? Well, you know, it really never has gotten to be far enough along or to have the amount of acceptance that that discussion has even occurred. Is DRI commercially available with lab core? It is. It is at lab core. Okay. We should also point out for folks who are wondering, your company, LIPA science that you found it in mid 90s was acquired by lab core 10 years ago, a little more than 10 years ago. And so for those of us like me, the dinosaurs, like we used to still order a LIPA science test. Now it just all happens through lab core. So that presumably that has increased, I assume some uptake of the test. So I wanted at some point to get into the question of why hasn't broad clinical translation occurred because it hasn't right now. You can only go to lab court for this information. So I'll go there now briefly if you don't mind. Please. So LIPA science began as I told you as a spin off of the university and I left the university. And we we tried to convince people that size didn't matter and L the LP did matter and we did that for quite some time. And we were a laboratory testing company. And so samples were said to LIPA science and you get the results back. But the additional beautiful you had beautiful results. I had color report. I loved it. Why not use color printers? No, it was beautiful. But the business objective was never to be a lab that got bigger and bigger and did more testing. It was to make the ability to do NMR testing available to any laboratory in the world. So you know we started by what was available. I talked about NMR machines being in every chemistry department. These are research NMR machines. They're engineered for multi functionality. You can do any weird NMR experiment. There's lots of variations on the theme. We wanted NMR to do one thing very efficiently and as rapidly as possible. And so you know ideally 30 seconds or less. Pop one sample in automatically another sample comes in boom, boom, boom. So we realized that we couldn't use a research NMR spectrometer for that purpose. We used them for the initial years at LIPA science because that's all there was. But we wanted to transition from a lab testing company into an IVD company in vitro diagnostics company. All laboratories rely on IVD companies to supply them the machinery and the reagents to do all these assays. So lab corp does 3000 assays. They rely on other people, Roche, other people to provide them with the wherewithal to do the testing. Those are IVD companies. We wanted to be an IVD company at LIPA science. Make an NMR analyzer that looked just like a regular chemistry analyzer to a MedTech that had no experience, no knowledge of NMR. Walk up to it with 200 samples, present the tray, push the green, grow, go button and walk away. So that's what we actually did and a lot of investment and a lot of time and effort was put into that. And that is the Ventera NMR analyzer. It's the only existing NMR analyzer in the world. And we went to the FDA because we you know these analyzer have to be FDA cleared in order to go into different laboratories. That was an adventure because what did FDA know about NMR spectroscopy and it was a new platform, a new way of testing that had to be understood. So anyway, it's when did you get the clear we got the no, not clear. We got FDA clearance for LDLP and the Ventera analyzer in 2011. Oh wow. Okay. Yeah. 2011. And so these these Ventera analyzers, a number of them were manufactured. And then about the time that just before the year or so before liposcience was sold to lab corps, these analyzers were started to be distributed to major laboratories. LabCorp was one of the recipients of these analyzers. So they could do the testing in-house rather than having to send the samples to liposcience. Couldn't the laboratories hate sending send-outs samples. And when that's for a rare cancer or something when it's a fairly rare event, no big deal. If you're doing hundreds of these a day, you want to you don't want that hassle. So they were very happy to receive the Ventera analyzer so they could do the testing in-house. And every time they did, they would pay liposcience for each analysis. Okay. That was the business model. We also made Ventera analyzer available to the Mayo Clinic, Cleveland Clinic, Scripps Clinic, A-Ruppa, a big reference laboratory in Utah. So we're on the way to making it available broadly because we didn't want to be the only people that could do antimartesting. And also we wanted to convince people that this wasn't, you know, magic. It was real. And it was analytically in many respects much better than reagent-based chemistry testing. Unfortunately, by that time liposcience had gone public, I was no longer on the board. I was basically the investment that we needed to start liposcience. We basically took too long to get to the payout of the initial investors. So people, those the investors were not patient. They were very patient up until then, but you almost couldn't blame them because they wanted to get their money back venture capital in particular. So the company went public and then LabCorp came along and said, "We'd like to buy you because we can make more money if we don't have to pay whatever number dollars to liable science every time we do this test." So it was a purely financial decision. LabCorp really didn't care about measuring things other than the NMR lipo profile, the LDLP, et cetera. It was financially based. That was too bad for the vision of having NMR analyzers in every laboratory because LabCorp is a lab test company. It's not an IVD company. So too bad, the IVD business model, the IVD vision was ended in 2014. So yes, did you join? Did you go and become a scientist? Research group largely was retained by LabCorp. So we had a pipeline of things like LPR and DRI, things that were coming down the pike that we'll talk about later. So really the best was yet to come in terms of the clinical value and the things that could convince people that having Bantaire analyzers in their lab was a commercially useful thing but also a clinical useful thing. So the bad news is that LabCorp understandably not sharing the IVD vision and being a laboratory that wanted to have NMR be proprietary to themselves took back these analyzers that had been placed in these other reference laboratories. And then unlike lipo science, that knew that when it developed a new test, it needed to go to the trouble of creating awareness and interest and doing the studies to prove the clinical efficacy of these tests. That's a very important activity. LabCorp doesn't do that because they're not an IVD company. They are reactive, they're not proactive. They're very good at being reactive and in COVID they ramped up the COVID testing. So I'm not saying that LabCorp doesn't do a really good job at what they do but they really didn't know what to do with new knowledge that they generated in-house by acquisition of lipo science. And so there was no marketing, no awareness creation. And basically, lipo science went invisible and is still largely invisible. Most of the testing is still done by the people that were interested in the testing thanks to lipo sciences efforts. So these X number of analyzers that were produced 15 years ago are what LabCorp is using to produce this information and this more exciting. And what's the life of these analyzers? Good question because nobody's ever, so the magnets themselves, these superconducting magnets last a long time and don't degrade but they're using PCs, you know, 10-year-old PCs. And anyway, they're all the moving parts. So they're not going to last much longer. And this is what maybe I'm most concerned about and most interested in people hearing about because what needs to happen for this to continue, this clinical translation to continue at LabCorp but also ideally broadened to the rest of the world is for an IBD company to come in and basically acquire the lipo science technology and there's a lot of patents intellectual property associated with this. A lot of expertise that comes from in terms of service and keeping these machines functioning well. And you know, learn, I mean, we've done so many millions of tests and you learn by doing. So what people don't know is that at some point in the future, maybe sooner rather than later, these vantara analyzers, first generation, only generation are going to cease to function and it would be a real shame, especially with the things that we're going to talk about that are more exciting than LPR and in my mind, LDLP, etc. So there's an issue here. There's a problem and hopefully it'll be addressed by an IBD company taking over. I've tried to make the case to big multinational IBD companies but NMR is too exotic. I mean, I basically show them that this is a completely de-risk proposition because we've already gone through the regulatory hurdles. We've already made the analyzers. We already have the experience. So it's not like starting from scratch with something that you're unfamiliar with, technology you're unfamiliar with. But you know, these big companies have a lot of inertia and talking to the right people or having these big companies be entrepreneurial. So I think this is maybe if it happens, it'll be possibly a smaller IBD company or a new startup IBD company basically taking the torch that LIPA science said that they would be willing to sell those assets, the IP. It's lab corp that owns all of that. Sure, that's what I mean. His lab corp said that they're willing to sell that. Yes, and I think they realize that they're interested in licensing. But most of the patents have to do with the assays, so LPR and MBX that we'll talk about. So absolutely. But who's going to license them if there's no machinery to produce the information? So what is your estimate of the cost per machine now if you were going to make a gen too? Well, so these machines cost on the order of $400,000 or $400,000. But the beauty of it is, of course, that they last a long time, which you've shown, but they have no consumables associated with the assays. So it costs just as much to do 10 assays a day as a thousand assays a day. So in a high volume setting, these tests are very cheap. And what we've talked about, we talked about LDLP and the NMR LiPo profile was the report that gave people the LDLP information. And that comes from this simple NMR spectrum that can then use to be extract much more information than LDLP, LPR, DRI. It's just part of the story. There's all these other things that we'll talk about later. So in terms of efficiency, analytic efficiency, you couldn't ask for anything better because if you want to use NMR to produce a lipid panel and actually, after telling you that NMR only measured particles and not cholesterol, the basic information is encapsulated in all these NMR signals. So you can actually feed NMR data to a machine learning algorithm, AI, and train it to produce accurate lipid panel and APOB information. So we published this four or five years ago that you can actually use the NMR spectrum to produce an extended lipid panel, APOB plus lipids, no incremental cost to a lipid panel. The APOB doesn't add cost. It more than doubles the cost of a lipid panel if you want to do it by chemistry and use regular reimbursement in the US. So it's extremely analytically efficient and cost effective. The thing that people probably don't appreciate and I didn't appreciate as a naive professor, I thought, well, surely this will be commercially attractive if it can cut the cost of doing these tests. The cost of these tests is so much smaller than the price that is charged for these insurance pays for these tests that cutting the cost in half of doing the analysis makes no difference whatsoever. So this is, you know, maybe get into this later because what is true is that NMR, a single scam can tell you a heck of a lot more than just your cardiovascular risk, your inflammation level, your diabetes risk, your overall mortality risk. Because we're about to discuss this all comes in the same assay essentially. Well, I can think of no better way to introduce the MVX assay. So I'll tee it up for you and then take, take away the story. So by the way, I went to have my first MVX drawn. We've been doing it on our patients for about four months now and I just haven't got around to doing a blood test on myself. So I went out to LabCore two and a half weeks ago because I wanted to have the results back when we were sitting here and wouldn't, you know, at Jim, they screwed up the assay. So I don't have it. They got everything else. Every other test we ordered they got, but they butchered this one. So I don't have my own to talk about. But the MVX is a composite score that measures if I recall six things. So small HDLP, like A, which we haven't introduced yet and we'll talk about citrate and the three branch chain amino acids. So a loose, isolacine and valine. So why don't you tell us the story of how you develop this score and I'll just give the punchline so the listener knows why they should be paying attention. This score seems to have remarkable, when it's normalized to a zero to a hundred. The higher the score, the worse it is. This score seems to have remarkable predictive value of all sorts of things we want to avoid starting with death, cardiovascular death, liver disease. While the first study that I saw was done in a very, very high risk group of catheterized [BLANK_AUDIO] and it would be easy to dismiss that it was only valuable in that population. It's been demonstrated in healthy populations as well. So tell us about the score. Yeah, so it's a really interesting story. And one question that gets asked is, well, why did you think of measuring those things, or did you have some mechanistic reason for focusing on these things that ended up contributing to this MVX metabolic vulnerability index score? And the answer is, no, that's not how it happened. And I just, I do want to make this point because MVX, and especially MVX, raises more questions than answers right now. Okay, so you really would like to understand if it predicts mortality so well, why does it do that? Is it causal? Can we intervene? Oh, you know, these are the things that really matter clinically. So it wasn't starting out with some idea that branch gene amino acids are really important mechanistically even though they are. This was because NMR analysis has given us a very efficient tool to measure baseline samples from studies that follow people over time, 10 years, 20 years, 30 years longer, to see what develops. And so you really would like the ultimate idea on a biomarker is that it predicts the future. You want to know not if it's related to cardiovascular disease, but is it related to getting cardiovascular disease in the future? And so we have availed ourselves of over the years of baseline samples for many very large clinical studies. You mentioned Mesa, multi-ethnic study of arthritis growth. That's a NIH funded study that started in about 2000. It's had more than 20 years of follow-up now. We were asked to measure the baseline samples 15, 20 years ago. For free, now I work for a company that has to make some money, but I had enough freedom to be able to offer that test. Initially I asked for money, but then they finally said, well, we just can't find the money for that. But we'd really like to have the information. So it was measured for free. Same thing from other very large studies. Women's Health Study, 26,000 women at baseline in a study that's been followed up for many, many years. Braiming hemoscopic. Braiming hemoscopic study, all sorts of intervention trials, Jupiter, et cetera, the diabetes prevention program. I would like to quickly go back to that just for two seconds. Sorry, because we didn't finish the thought of what the diabetes prevention program, even though it's not published, tell us. And the important thing that it told us was not just that LPR score goes down with Lifestyle Intervention and Branch T2Mino Assets go down, which is all good thing. But those things going down, the delta between where it started and where you got after the intervention, very powerfully predicts incident diabetes or the lesson diabetes risk. So that was sort of the missing link. If you really want to argue causality, you really want to show that lowering LPR translates to lower diabetes risk. And it does. It's quite powerful. It hasn't been published yet in part because the primary author died recently, unfortunately. And so it will be eventually. All right. But that's one study. So we've gone out of our way over time to make it possible for people that didn't have funding to get NMR data on baseline samples for observational studies and intervention studies going forward. Mesa has been particularly useful to us, 7,000 people, baseline NMR. And because we supplied the assay for free, the NIH has given us access to the information about who developed cancer, who developed cardiovascular disease, et cetera, dementia, et cetera, any all sorts of outcomes. And so the beauty is that once we have taken the NMR spectrum and gotten from it, what we initially wanted to get from us, let's say LDLP, these spectra are sitting there in a computer in a stored on a disk. And when we develop the wherewithal to extract new information from the NMR spectrum, we can go back literally in a couple hours to interrogate a very large clinical trial and get prospective information as to whether it's predicting a particular outcome. So back to NVX, that's exactly what we did in this cardiac catheterization study at Duke University, 7,000 people over a period of years, recruited into this biorepository when they came to the cath lab, when they came to the cardiologist with chest pain or some issue that qualified them to have coronary angiography done. And their blood was taken at baseline and it's stored at -80 where it's perfectly stable for NMR analysis. So we got a relationship with Duke to obtain those samples, do NMR analysis. And we found in published papers that small HDL particles were particularly powerful in relating to the likelihood somebody would die during the roughly five or 10 year follow-up of this study. And then there was another thing that we could measure by that time called glycay and we might as well talk about glycay now. It's another signal in the NMR spectrum that's not where the signal is that we interrogate for LDLP. And so for 10 or 15 years, we didn't care a wit about any of those other signals. But a funny thing happened with where the the the glycay signal is. It was basically superimposed or in the way of another broad signal that we were trying to interrogate to learn about the fatty acid composition of somebody's plasma, how much monopoly and saturated fats were in the blood. And to interrogate that, this signal, this sharp signal was on top of that and was getting in the way. So we basically worked out a way of quantifying how big that signal was so we could subtract it from the other signal. But that was perfectly good quantification information. So that's signal, which I wouldn't have thought to do anything with. The person who does all my epidemiologic analyses, Arena Chaluroba as her name. We had the Mesa data sitting there. We could go and very quickly use the software we had developed to measure this sharp signal. And she just said, well, does it relate to anything that happens in the future in Mesa? Oh my gosh, it relates to all sorts of things, including mortality very, very strongly. So what's up with that signal? Well, it turns out going to the literature, this often happens. 20 years ago, somebody published a paper saying that the signal was an inflammatory marker. It actually comes from the carbohydrate, the glycan decoration that's on a lot of proteins in the blood. And most of these proteins are so called the acute phase proteins that increase in concentration in inflammatory conditions. And so even though this signal was not telling us which acute phase proteins were contributing to it, it was a composite. And not only did it essentially quantify the most abundant four or five acute phase proteins that contributed to this signal. But this carbohydrate decoration, this glycan decoration is used for all sorts of purposes, signaling of different types, etc. So there's very complex people worry about the glycome. It's like the proteo home and the genome. There's a glycome. I know nothing about any of this. But one thing that we know where this signal comes from and it comes from a particular sugars on this carbohydrate. And inflammatory conditions, more of this decoration is put on some of these proteins. So actually, this glycase signal is reflecting not only the levels of these acute phase proteins, but how much glycan is on them, which is also connected to inflammation. So it turns out, rather miraculously, that how big this signal is, is a very useful measure of your steady state of your systemic inflammation level. It's a very stable parameter because it's the integration of lots of different things. So rather than CRP that you typically measure clinically to assess inflammation, it's very volatile. It goes up and down day to day. So all clinical recommendations for the use of CRP information say that you should take the average of two or three different measurements. Nobody does that. That's the recommendation. To get around some of this biological variability, glycase doesn't suffer from that problem. That's probably in large part the reason that when added to CRP in a prediction model, it typically assesses the outcome more strongly than CRP does. But CRP tends to independently add. So inflammation is a very complex thing. This is a very unspecific marker, but it's a very stable and useful clinically marker of systemic inflammation. Does it include, do you think, or capture, what we see in the various inner lukins. - Yes, so it's correlated strongly with IL-6 and other inner lukins and these correlations. But that's all we know. I mean, so yes. And you'd really like to be more specific and if there's local inflammation as opposed to systemic inflammation, this is not gonna tell you anything, okay? But what it does do is offer you a simple and very cheap because it comes along for the ride with the other animal information. If you quantify this glycase signal, you have a very powerful marker of the things that systemic inflammation contributes to. - Now using HSCRP as an example, Jim, which as you pointed out, it's gonna rise with inflammation. One of the things that clinically we pay attention to is how high is it? So if I see a brand new patient and their HSCRP is two or two and a half, in many ways, that's more disconcerting to me than if it's 40. Because the 40 is so high that I know it's really in response to something acute. They're probably getting over a cold. Maybe they got a vaccine two, four days ago or something like that. Now of course, that doesn't obviate the point you made, which is I still wanna see longitudinal data. Like I can't assume the two is bad because I could also be catching something on the way down or on the way down. - It could be three or one, depending on the next day. But it's usually the case that when something is very, very high, it really speaks to acute inflammation, which is less pathologically concerning. These low simmering ones that I see, those are the ones that give me pause. Is the glycase the same as that? - No, it's very different than that. So CRP levels could go up a thousand fold on an infection. - The glycase levels are the response is much more muted because it contains so much information in it, you think? - I'm not, you know, I'm not so, I can't really answer the because as well. - It's just the observation. - Just the observation. And it's true that if somebody has an active infection and you're trying to relate the glycase level to mortality and people that didn't have an infection, you could be misled by that. It'll be higher by twofold, not a thousandfold. So it's not immune to those changes, but you wouldn't want it to be because, you know, it's an inflammation marker. - Yep. - So in people with inflammatory diseases, rheumatoid arthritis, psoriasis, et cetera, glycathy levels are significantly elevated. And they are responsive to any inflammatory treatment. And so it has all the characteristics of a useful biomarker to assess not only things that you would like to have some visibility to like systemic inflammation. I think we're coming to understand that that's an awfully important contributor even though we don't understand the mechanistic fine points to so many things including mortality risk. So anyway, this is just, I just wanted to tell you that the way that we discover these biomarkers is different than the way other people discover a lot of biomarkers. So it's more of the top down where we have a very efficient way of doing the epidemiology. So we know already that these markers have a strong relationship to human health conditions, a lot of the work that's done starting from bottom up with a mechanistic idea. And a particular enzyme that you might maybe want to target as a therapy, you have to then do animal models and then you go up to humans. And then you have to do expensive trials and then you typically measuring these things is not as easy as it is to measure these things by animal. So it's a completely different approach to discovery, it's sort of irrational discovery because you're basically using these large databases, a population studies, and then discovering things that you really didn't go after discovering in the first place. So there are three components to the MVX. You've already talked about the Lipe-approaching one, which is the small HDLP. You just explained the inflammatory one, which is like a, the third one is sort of a metabolic one, which has the citrate in the three BCAA. So how did you come to figure those out? - So you're right. So actually with the Duke collaborators, there had been a paper published showing that, that glyc A, there was a glyc A paper predicting mortality. The interesting thing about this cathod population is they came to the cath lab because they had some presumed cardiac issue. They have, in the study that we did, for five year mortality, 17% of the people died in five years, and they were about a mean age of 60 coming in. So that's a pretty high-- - How many? - 15%. - 17%. - 17% mortality, five years in people that were not elderly. Three or four times higher for sure than a regular population. So the presumption was that these are people that died of cardiac causes. But less than half of them died from cardiac issues. 60% died of non-cardiabasca causes. - So the major thing that happened-- - So 60% of the 17 people, 17% of people who were dead by 65 were non-cardiab causes. - Non-cardiab causes. So anyway, mortality was the most prevalent outcome. It wasn't a new myocardial infarction or a recurrent myocardial infarction. These people died. And so glycate predicted it, small HDL particles predicted it, we might talk more about that. And then we simply looked at all the other things that we had learned to measure. And we had just kind of started this activity. So branched to amino acids, isolucine, leucine, and valine, ketone bodies, plasma protein. I mentioned those two things because when you look individually at those things, ketone bodies, and plasma protein, they have significant associations with mortality. So why didn't we use those as part of the MVX composite biomarker? Because we were looking for things that contributed independently and additively to the other things that we've already talked about. So the inflammatory part glycate and small HDLP, we created a subscore called the inflammatory vulnerability index, IVX. The other four seemed to relate. So we discovered that these branched to amino acids and citrate independent of glycate and small HDLP added to the prediction of mortality. Then it was, okay, why? And so then we went to literature. So we really approached. And you're doing all of this inside of lab cores. So lab core at least still at this point in time had the appetite for R&E. Yes, really, yes, they paid our salary, but that was, we were sort of left alone in this little building to continue what we were doing at liposcience. So thanks to lab core for not getting rid of everybody. So then it was going to literature, trying to figure out just this makes sense biologically that these things might be related to mortality. And that's where you come to the literature that very powerfully speaks to the great mortality risk that people with several acute diseases, especially kidney disease and dialysis patients, heart failure patients. Anything with cacaxia, I could see anything with cacaxia. So you know, sarcopenia, old people, okay? So there's a lot of literature, especially in the kidney disease literature, that described this vulnerability as coming from a so-called malnutrition inflammation syndrome. And it's called many other thing, protein energy wasting syndrome. So the cacaxia, the wasting syndrome is part of this, but inflammation is part of this. Inflammation is probably the context that allows these dysregulated metabolism situations to exist. So the fact that malnutrition inflammation syndrome, we had the inflammatory parts we speculated, but the branched gene amino acids were related. And they were related in the opposite direction that branched gene amino acids are related to diabetes risk. So high branched gene amino acids speak to insulin resistance, obesity, diabetes risk. Low branched gene amino acid levels speak to mortality risk. And we can talk more about, you know probably much more about why this makes sense in terms of mechanism, because it's partly related to m2r signaling and the whole skeletal muscle. - Yeah, it's turnover of amino acid, - It's turnover and metabolism, etc. But it was satisfying to find this literature and to say, oh, maybe these are just better biomarkers of something that's already understood in the acute context, acute clinical context. But in the catch-in population, nobody had described this in a cardiac, well, in cardiovascular cohort. And then, so we said, well, this exists in spades apparently in this presumed cardiovascular cohort. But then we did subgroup analysis with this. in this 7,000 people in the Catholic study. And we asked, does this MVX association with mortality exist equally strongly in men and women, in people with and without obesity, in people with and without diabetes, with and without heart failure, with and without a previous myocardial infarction, with or without coronary catheterization, occlusion of the coronary arteries, with and without kidney disease? And it's not affected by any of those things. It's equally strong, if not stronger, in people without the chronic disease, versus those that are disease-free. So in fact, the strongest relationship of MVX, to mortality, you mentioned how exquisitely strong it is. This was looked at by, well, in different ways. - I thought that the hazard ratios were bigger in the cath study than in the Mesa study of healthy people. That's true, that's true. And the reason is that for whatever reason, if these people came to the cath lab and they were enriched in people who ultimately suffered from these wasting syndromes. Okay. And the fact that you see the same prediction, if not as strong, for sure, in people with absolutely no evidence of any chronic disease. In fact, the studies that are most recent, most recently published, and the most interesting one that I'll mention is one that's not yet published, but is about to be submitted for publication. MVX in young people, 30-year-olds. Would we expect to see the relationship? And you do see the relationship, but it's weaker. And the, I didn't sort of finish the two subparts of MVX or the IVX inflammation part, and the other four parameters brood together to form MMX metabolic malnutrition index. It's kind of an arbitrary thing to talk about these two parts of MVX, because it's known that there's synergy between these. It's a syndrome, it's interrelated, intertwined, but on the surface, at least it looks like, it might be useful to take a high MVX score, and it might be due more to inflammatory reasons than the metabolic malnutrition wasting reasons, in which case, different therapies might be better suited for that person, rather than somebody with the same MVX score with a more malnutrition issue. So we thought it might be clinically useful. That's why we did that. Are they both, I know the aggregate score has reported zero to 100. Do the two sub-scores get reported the way as well? Same way. Okay. So let's say Mr. Smith is a 75, which is very high risk, but when I look at his aggregate score, his IVX is only 25, his MMX is 80, this is the issue. It's the sarcopenia and the wasting that is really driving his risk. It's not so much inflammation in this case. That's a possibility. I don't think that that possibility will ever be found to be that different. So usually these are more, because it's a syndrome. And the numbers I used are wrong, but that's the idea. Absolutely, that's the idea. But it's really, we've learned a lot since this 2023 first publication about MVX in this cardiac catheterization, cohort. The important thing that we did there, I was advised that I really shouldn't try to publish this until we had replication because these hazard ratios were so dramatically different from low and high MVX scores. So in that paper, we reported a completely independent cardiac cath population in Utah, Salt Lake City, and it replicated very well. And since then, we have been interested in seeing if it replicates in other disease populations, and as we referred to, what about people with no overt disease whatsoever, younger, older? Well, let's talk a little bit about that. The one that just came out, of course, was the Masolde paper, is that the one you wanted to chat about? No, actually, we could. And it just, so, because these papers are coming out now. They're just coming out at a geometric rate. Yeah, and the reason is that we just have to go back to existing NMR data. So we're mining other people's costly studies, who are piggybacking on their work, their funding, and getting very quick gratification about whether MVX is good for this or that, or the other thing. Is there one intervention study? Because that would be the next step, is, I mean, maybe you've already done it where you say, look, we've got an MVX at baseline. Hot, we're going to treat you guys placebo, non-placibo. Yeah, that's-- You know where I'm going? Absolutely, that's-- that has to happen. It isn't going to happen if nobody knows about MVX. And the thing that has been a little disappointing to me is how little-- I mean, nobody read that 2023 paper. I was very-- that proud of that paper. I thought there was a lot of meat in that paper. And a lot of implications, clinically and otherwise. But people read papers that are called to their attention. And this is part of the problem. If this was a liposcience, if we were at a liposcience, we would be promoting awareness of this paper in meetings, et cetera. And this isn't happening at LabCorp. OK, so-- but now this will help. Yeah, I mean, there's going to be a lot of people that are listening to us. Right. And so, yes, that's the next step. What we'll be published this year will be, I think, sufficient for anybody to see the replication of the phenomenology fairly quickly after that first paper in heart failure patients. We could show this. The other thing that was kind of nice about that heart failure population is they also had a lot of frailty information. A lot of-- these were older people with heart failure in Minnesota. And so they were able to calculate frailty scores and also biological age by-- well, not in that study, but in another study of older people. So some of the other things that are talked about a lot about relating to longevity and so on were measured in these studies that we've been able to look at MVX in. And in terms of frailty, physical frailty, correlation, but fairly weak with MVX, at a point two correlation coefficient. So it isn't like you need to have physical frailty to see the MVX be high or vice versa. And in terms of mortality risk, frailty, physical frailty, on top of MVX score adds considerably. But MVX, very powerfully still in the presence of frailty score, predicts mortality. And is it always a five year look forward? No. And in many studies, it's longer than that in part because you want the statistical power from more people having the outcome. So in may-- But there's something powerful about the short window. Oh, anyways, that's actually a feature not above if you can offer that insight. Absolutely. Because we don't have many short term predictive biomarkers. Absolutely. And actually, the one study that I was sort of confusing with the heart failure study is a study called Eppys. It's a study of older people with lots of things being measured, including these biological age measures by chemistry, assay, not the epigenetic flavors. And a couple years ago, I think it was 2022, they had NMR information. And then they had all this frailty information and 186 different variables. And it was done by some epidemiologist, emittersota using the most high-tech ways of trying to deduce whether the associations were causal or not. I'm not really sure that really, to me, demonstrated that. But they use methodology that purports to assess causal relations. And out of those 186 things that were looked at, small HDO particles were the most powerful at predicting two-year mortality in these people. And then the blind spots. What would be a blind spot? Where does it get fooled? We already gave one example, right? Which is if you're in the throes of a brutal infection, you're getting over a cold, that could artificially elevate, although not to the same extent as CRP, the glyc A, that could offset it. Have you seen other false positives, so to speak? No, I mean, we haven't really looked in ways to possibly see those. Because we've looked overall at the prediction in a population. And these people at baseline either have this, that or the other disease. So it gets kind of canceled out in the wash at the population level. Right. Exactly. The one thing, though, that the question of whether it's causal or whether-- so there's two questions. One, is it modifiable? So let's just-- I've been speaking to the intervening three years since the paper was published. We now have really good data. Some coming very soon in different disease populations that this replicates and is seen. It doesn't matter who you are. This relates to mortality risk. I want to come back to this later because you said something earlier about-- about how MVX remarkably relates to not just mortality, but diseases that reduce mortality. So I wanna quibble with that idea a bit later, but just to get back to the question of, are there interventions which lower MVX and then could we do the study of that intervention to show that that's connected to a reduction in mortality risk? - So before you do that, Jim, I wanna go back to the 30 year olds because we didn't really finish the swing on that. So-- - No, we didn't. - Are you able to talk about that? Or is that not published? - I'm gonna talk about it, and I'm not gonna say the name of the study, but the paper, I just, you know, the draft of the paper is about to be submitted. - It's just hard for me to wrap my head around the fact that any biomarker in 30 year olds could produce anything. - This is very true. And that's why this is so interesting and novel. So the paper that appeared a couple months ago was from Mesa. So we've been talking about Mesa, 60 year old people at Entry. They weeded out all the people in Mesa that had any self-reported or otherwise diseases. So restricted to healthy and average age 60 or so. And MVX by quartile had this stepping stone relationship. Not as strong as the hazard ratio. It was more as different as in CATH Gen, but very significant. So that was the first, and do you recall in that study, Jim, what the difference was between the first and the fourth quartile in hazard ratio? We're let you talk about like that. - So on a one point six. - Unadjusted, it was about, maybe, I might be confusing other studies, but adjusted it was like 1.5 to two. - Okay. - And we'll link to every one of these studies in the show notes. But basically in otherwise healthy 60 year olds, the difference between being in the worst quartile, 75 to 100 score versus the bottom quartile, 0 to 25 would be about a 50 to two. - No, that's per standard MVX. So this is actually greater than that. It's maybe two to three full and greater. - By quartile. By top to bottom quartile. - Yeah, yeah. - Wow, okay, so big difference. - Yeah, that's it. So, okay, so those are 60 year olds. So these are people, so my idea about MVX, people die when they're older. - Yeah. - And so this thing MVX comes into play when you're older, and maybe MVX scores go up with age. MVX scores are virtually unassociated with age. - What? - Yes, unassociated with age. And the major proof of that is this 30 year old study. So here we've got 3000 plus people who were entered into the study between the ages of 25 and 30. So we have an NMR analysis that was done when the average age of these people was about 30. And this has got to be 30, 40 years ago, because otherwise you wouldn't be able to do anything. - This was 35 years ago. - Okay. - And there were blood samples taken at time intervals, more frequent than five years for the first few years, and then five years after that. So there's, we have NMR data at your 10, 15, 20, 25, 30. So we know how stable the MVX score is over time. That isn't actually reported in this particular paper, but it's very stable. But the really interesting thing is that the distribution of MVX scores when these people were 30 years old is just as wide, almost identical to the 60 year old people. There are people with low scores and high scores. And as you said, this is 30 plus years follow up. So this is definitely premature, does mortality we're talking about? Not ex, and these are all people that, at baseline also were excluded from having any resisting comorbidities. Okay, so there's not only young, but they're healthy. - We need to just stay on this for a moment, Jim, this is so counterintuitive. I just wanna make sure not a single listener is failing to appreciate what you are saying. So I'm gonna repeat it back and I want you to correct me because there might be errors where I'm oversimplifying. 35 years ago, we had a whole bunch of people that were aged 25 to 30. And we excluded all the people that had known issues. So if you had type one diabetes or you had some childhood cancer or whatever else, we didn't include you. We really looked at boilerplate, healthy 25 to 30 year olds. We draw their blood. Every five to 10 years, we draw their blood again, and we run the MVX assay on them. The first and most surprising, potentially feature, certainly the first surprising feature when you're doing a bunch of MVX scores on healthy 30 year olds is that any of them had elevated levels. Because the most obvious thing is MVX must, at some level, be a correlate with age, which is the single greatest predictor we have of mortality. And so big surprise number one is, you could be 25 or 30 years old and have an MVX score of 75, which is very high. - Okay. - The upper quartile, the average was about 50, 51 score. The bottom quartile was about 27. So that's the range. - Yeah, there were some. - In the distribution looks a little different than it looks in a 65-year-old. - That distribution is identical to Mesa. I mean, it is identical. - And Mesa was in 60, 65 year olds. - Okay. - And then you're saying, not only do we have this distribution that mirrors that of people 30 years older, as we followed these people for 35 years, it predicted mortality. I am not aware, I'd have to think Jim, but I don't think I can imagine a biomarker, but outside of a very extreme state. So you mentioned FH. Okay, if I know that I have two 30 year olds and one has FH and one doesn't, and the FH one is not treated, I can tell you with a very high certainty, that person's gonna be dead in 30 years, this one will not, or very unlikely to. But outside of edge cases like that, well, so that introduces the question. (laughs) You know, how did you get a higher MbX score when you're 30 years old? You didn't acquire it because of some vulnerability, disease vulnerability. You acquired it at birth. We don't know, we have to look now at younger people. We look at framing him and framing him offspring. And well, we have plenty of MbX data from studies where genomic information is available, epigenomic information is available. I mean, it's a great question. I mean, again, like I said, I've got questions more than answers, but it's really fascinating and novel and important, I think, for sure. And because of what we've just talked about, I wanna go back to this issue of whether MbX has anything to do with whether you're likely to develop cardiovascular disease, or diabetes, or dementia, or whatever. And what you will find already in the literature are papers that indicate or suggest that MbX does have those associations with the diseases, many diseases that cause mortality. But I think all these are artifacts of the way the analysis was done. Because as you appreciate, almost all cardiovascular endpoint trials as well as many other types of disease, endpoint trials, cancer, whatever, kidney disease, they typically combine fatal and non-fatal events. So if you die of a heart attack as the first consequence of having cardiovascular disease, or if you have a myocardial infarction and survive it, these are grouped together in a composite endpoint called CBD. And when you look at cholesterol, it makes perfect sense because of the etiology, because of how the cholesterol is connected to cardiovascular disease and events mechanistically, that there's nothing wrong with using a composite endpoint. The etiology is the same. You get cardiovascular disease, you die from it. You get cancer, you die from it. So you wanna have a biomarker that predicts whether you're gonna get the disease and then that automatically tells you what your risk is for dying of that. What this says is that maybe there is a separate influence on whether you're gonna die from the cardiovascular disease or the cancer or the die or whatever. And that's your metabolic vulnerability, your metabolic frailty if you will. I like the idea of metabolic frailty because frailty connotes susceptibility to dying. And even though people with high MVX score like these 30 year olds, if high MVX score, you look at them, you don't look any different than the people with low MVX score. So you don't see the frailty. But metabolically it's there. It's basically setting you up to be more susceptible to dying from whatever disease or event old age that is gonna contribute to your death. So dying sooner versus later is what MVX seems to influence as opposed to getting the diseases that quote, cause this. I tried to say this in the paper, but it's becoming much more clear now, especially with. these 30-year-olds, that this is something that has to do with dying, not getting the diseases that cause the death. So listening to you say this gives me an idea for a study that I'd love to see you do, Jim. So you're, I don't know how much time you spend in the oncology world, but I'm sure you're familiar with K-truda. It's the, to my knowledge, K-truda is the single best-selling drug of all time. And in many ways, it's been a miracle drug in oncology, the single most exciting development in cancer in the last 25 years for folks unfamiliar with it. This is a checkpoint inhibitor. So people that have a PD-1 mutation that take this drug, regardless of what kind of cancer they have, this could be pancreatic adenocarcinoma, lethal cancer. If you have this mutation, this drug basically takes the breaks off the immune system and your immune system eradicates the cancer. But here's the question. Why could you take two people that have the exact same PD-1 mutation, the exact same cancer by all intents and purposes, and you give them both K-truda and one of them responds and one of them doesn't? Like, we don't know. We do not understand what's happening at the immune level to understand why that's happening. It would be very interesting for me to understand, and using K-truda as an example, but you could do this with any therapeutic intervention where mortality is very quick. And you could ask the question, does this become a prognostic indicator of not just mortality, but probability of success of an intervention? Yes. Yeah, that's precisely what possibilities exist. When I first talked about this to people at Duke, the collaborators of the CATHGEN study, people around the table, the first thing they said was, wow, this would be a great test for surgeons who are asked to operate on people who are frail or are less likely to survive the surgery or to benefit from the surgery. You'd like to be able to screen them for resilience somehow, but there are no myel markers, and objective biomarkers to do that. Mound the Christian metabolic, mound the Christian, their people I've read papers where people are surgeons are suggesting that people really should avail themselves of these interrogating whether somebody is sort of metabolically or physically frail and has evidence of wasting. But if there's a metabolic component to that, that is accessible via MVIX, it could be very useful. This is probably one of all sorts of possible applications. You mentioned this paper that we just published two days ago on M-A-S-L-D. Mazzled D-A. The artist formerly known as Nathold D. Nathold D. Nathold D. So liver disease. The paper speaks very forthrightly about the possibility of using MVIX for entry into clinical trials. So am I correct in remembering this paper, which I skimmed, so I'm ashamed to admit I didn't read the paper, but I could have sworn it said that MVIX added predictive value to fibroscan in predicting subsequent fibrosis in the Mazzled D-A. Is that, does that bring it up? That's true. I only skipped it this way. I was not going to talk about it. But, you know, as you well understand, so many clinical trials are expensive and are not done, many are not done because the events are too rare. So you need to do far-of-relations or too far away. And so being able to juice up your likelihood of people dying, for example. But mortality is probably the endpoint that people care most about, right? And so it has sort of in the hierarchy of events. People care more about dying than they do about getting an MVI or getting diabetes or whatever. So anyway, there's all sorts of possibilities. But we're just at the beginning of the trail of answering the questions that, and I don't even know all the questions that could be posed. But this really is very fascinating and the fact that it was discovered fairly serendipitously by interrogating these epidemiologic data sets. And then the relationships seem to make sense in terms of what's been published about the detailed, you know, cell biological mechanisms, which I don't understand. And it's an inexpensive test. It's a, well, it's basically free if you think about it. So I mean, this is what, how many tubes of blood do you need to run it? No, so you mentioned that you got your MVI score and they probably drew an extra tube of blood for that. They don't need to do that. The same specimen, actually 150 microliters of plasma produces the NMR spectrum that produces the NMR lipo profile produces glucose produces LPR produces glycay produces MVI X all of that comes from the same analysis. And when done in high volume settings, these are tests that are that really literally cost a dollar or less. Okay. But this is the problem commercially and this is the problem with our healthcare system and the way things are set up that there's sort of no, there's almost a disincentive to provide analytically free information if you can't charge incrementally for it. And what you'd like to do if your company is charge a whole lot more for it. And then you have the tension between what you'd like to charge and what the insurance wants to pay for. And then convincing the insurance company that it's worth paying for is what keeps you from being successful commercially in producing this test globally, broadly. I had this experience with LDLP, tremendous resistance to paying. So I think the way around that, one way around that is to not try to get paid incrementally for it and just do something that, so the analogy is the comprehensive metabolic panel that you get done every time you go for it. There's 14 things that are measured there. If you add up what the CMS reimbursement rate is for those 14, it comes to $60. But it's a CMS pays $12 for that. And it's because these are all done at the same time. They have some clinical reason to be done at the same time. And economies of scale make it efficient enough that you could make money and people are going to starve producing this test, getting paid $12. As much as they'd like to get paid a lot more, this is a situation just like that where the information is essentially free. We made that point in the paper we wrote about the lipid panel, the extended lipid panel that includes APOB. We didn't really get to the APOB. It was actually the last thing I wanted to get back to. Yeah, that's the discordance between APOB and LDLP. Let's get back to that because the APOB story is the same as the LDLP story. And the reason that I have partnered rather than competed against Alan Sniderman, who's the biggest proponent of APOB, is that it would be disingenuous to say that one is really better than the other. I could make the case that the NMR analysis tells you LDLP but also TRLP, where I go through I'd rich particles. Subspecies might be differentially related. There are people publishing papers that suggest that's true. So you could definitely be ahead of the game with more information than APOB provides. APOB is just a single measure of all the APOB on LDLP and VLDLP particles. But the challenge is convincing people that you should do something other than measure cholesterol. And so you need as many people in that fight as possible. So Alan and I are both telling the same story. And that's why we transitioned, I mean, I advocated that we use NMR to produce APOB, which actually we got FDA clearance for the quality of the APOB information that comes from the NMR spectrum via the machine learning approach. So the idea was the extended lipid panel would have no analytic cost associated with adding APOB to a lipid panel. Now you have a better lipid panel. The way that Medicare reimbursement is set up now, APOB gets paid 20 bucks lipid panel about 13 bucks. Last I looked at might have changed a little. So that more than if you want to add APOB to make the lipid panel better, it's more than double the cost to the payers. The payers aren't going to want to do that. And what's the CMS reimbursement on the NMR of lipids? It's about $30 in change. And that's for the NMR lipoprofo. The NMR lipoprofo comes with LPR. We couldn't get that FDA cleared at the time, but you might know that a lot of laboratories can offer tests that are not FDA cleared through sort of a loophole in the, the FDA has decided to. exercise discretion about whether they will enforce this or not. So laboratory develop test, LDTs, individual laboratories can develop their own tests, go through a, you know, get clear certification. So this is more laboratory certification for the, how well they perform the test. But the actual demonstration of the clinical utility of the test is something that FDA cares about, but Clea doesn't care about. And so it's an easier path to offering commercially a test that doesn't have to go through FDA clearance. And so LPR was, was added to the NMR life profile as an LDT, not, not part of what was cleared. LDLP was cleared, but not without great difficulty. So anyway, a lot of, a lot of the reason that NMR wasn't commercially successful has to do with what I just explained about the resistance of payers to pay any increment to what they're paying for now. And if you really need to demonstrate, the path to, to getting insurance to pay is to get some advisory panels to some clinical guideline group to bless it. And Alan Snyderman can speak to the difficulty of having APOB blessed by the cholesterol guidelines. Although the European guidelines, the European guidelines and now the US guidelines are, you know, getting, but it's still, it's just ridiculous. But part of it is because the guideline writers are trying to protect the payers, you know, which, that shouldn't be their job. They should be assessing the clinical utility only and let capitalism worry about the actual Medicare reimbursement cost for APOB, which was set many, many years ago has nothing to do with what it costs to do these immunoscies on these modern analyzers. So again, there's this complete disconnect between what's charged and what's paid for and what it costs to measure. It's the same thing that drug companies, you know, are defending the prices that they pay to support the research, etc. So you can make the argument that you need to stay in business, you need to make more money. But anyway, I succeeded more as a scientist than as an entrepreneur in what happened to the liposcience because I really, we really had gotten quite far down the road of making NMR testing broadly available to the benefit of so many people internationally. And that just got, you know, tacked when it was purchased by a lab testing company instead of an IVD company. So I hope anybody listening who has, well, I mean, yeah, I don't know that door is closed indefinitely. I think I think that the MVX test offers a very compelling reason why another company might want to come along and purchase those assets and, and I think I actually given the prognostic utility of that test. So let's talk now about this edge case of C-Tap inhibition. And one of the first things that stood out to me looking at the Broadway and Brooklyn trials, which were the phase three trials of Obesetra Pib were that the reductions in LDLP and LDLC were greater than the reductions in APOBF memory serves correctly. What do you think is happening there? I know what's happening. So the NMR analysis, first of all, was using an older algorithm than the one that we've been using for the last five years. But even in the older algorithm, the issue of whether NMR can reliably quantify these very abnormal HDL particles that are produced by C-Tap inhibition. So HDL cholesterol doubles are more than doubles, not because the number of HDL particles doubles. It's, in fact, the number of HDL particles actually goes down a bit overall. So what happens with C-Tap inhibition is smaller particles are made into larger particles. So the number of small particles goes down, number of large particles goes up. These contain, the large particles contain five or ten times more cholesterol per particle than the small ones. So HDL cholesterol goes way up and HDL particle number does not. But the problem in terms of the analysis is that there's a natural, so again, we're taking advantage of NMR signals from the different size-lapoproteins being detectable and differentiated from their neighbors, right? And so at the interface of small LDL, LDL gets so small and then the largest HDL is its nearest neighbor. And there's a decent gap between the diameters of those particles, so they don't get confused normally. But when you've got C-Tap inhibition creating human beings that don't exist naturally and have HDL cholesterol of 120, 30, 50, your HDL particles get perilously close to the size of small LDL particles. And now NMR has the possibility of confusing the two. So that's incredible, just given the size difference between these particles normally, like the APOB and the APOA1 particles, I thought they were like a mile apart on that spectra. So the good news though is that when you have metabolic situations that cause you to have large HDL, you also have the LDL size distribution skewed to the large LDL. So there's fewer or no small LDL particles. So what you can get away with in these extreme cases where somebody has really large HDL and the NMR can tell when you encounter that situation, that you basically take away from the deconvolution model the smallest LDL particles so it doesn't have the opportunity to say this large HDL is partly small LDL. Okay. So what happened with the algorithm that was used in that study is you didn't have the opportunity to have small LDL at all, even though some small LDL was probably there. And so you saw this big decrease in LDL P, but not APOB because the NMR model was not allowed. So it's really an artifact of the difficulty NMR has with this situation. So is the implication that in this case, Obacetra Pib produces a disproportionate reduction in cholesterol content of particles relative to number of particles? So I know you've talked about Obacetra Pib and you and most people are very optimistic about the prospects of Obacetra Pib despite CETP inhibition not panning out for many other drugs. And of course these were all initially investigated because of the potential to create higher HDL cholesterol. And now we certainly know that HDL cholesterol is not the HDL biomarker of interest and people were being fooled into thinking that would have benefit. Part of the reason I think I'm this is pure speculation, but it comes from somewhere that even in the face of the CETP inhibitors that came closest to being efficacious, 20, 30% LDL reduction, but no benefit. Maybe something bad was happening on the HDL side to counteract what was good happening on the LDL side. What was bad on the HDL side? Given the understanding now that just having a lot of large cholesterol rich HDL particles doesn't put you ahead of the game in terms of cardiovascular risk. What we now know is that small HDL P is powerfully related to mortality. All caused mortality. But what I told you is true, especially with the most powerful CETP inhibitors, they reduce small HDL P by 10 or 20%. If you ignore what's going on in HDL and you only look at what's happening with APOB and LDL, you think Obacetrape is a no-brainer, it's going to be positive. But what if people are actually being hurt, maybe in terms of mortality risk, by the small HDL P going down, if there is a causal relationship there, don't know that there is yet, we haven't proved that. There's biological plausibility because of the proteins that hang on to, on small HDL particles, which is partly why we think it makes sense in terms of antioxidation, anti-inflammation that small HDL particles might have this inverse association with mortality risk. So anyway, but are you saying that you think that it's possible that, because again, we still don't have the hard outcome trial. But your thinking is that if the hard outcome trial is, it demonstrates utility, it might be, you're saying it could be just due to the reduction in small HDL P. I'm suggesting that people's predictions about how much efficacy there's going to be. Could be wrong if they're based on the LDL. It may still be the reduction. The trial overall is positive or positive enough to have the drug go forward. the most dramatic demonstration of something bad happening while something good is happening. happening. And the two counteracting each other is if the trial doesn't succeed, like the other ones succeeded. So, you know, we have to wait for the trial. And then if the trial doesn't succeed, then I'll say, yay, I was right. But it's pure speculation. Yeah. Yeah. Well, very, very interesting. And I have to go back and look and see what the magnitude of the APOB reduction was. But I just remember that it was less. It was less, but still reduced. Yeah. Significant. And but you're heartened by the fact that small HDLP was decreased. Yeah. And I, we've actually analyzed that data set, you know, with the more recent, we do a better job in differentiating large HDL from small LDL. And so those LDLP results are much more in line with the APOB reductions than that paper indicator. Well, Jim, this is, this is such a fascinating space. Yeah. This is the discussion that has been long overdue. Again, I, I don't think there's many people listening to us that haven't at least heard of, you know, LDLP, HDLP. They might not know what life of science is. They might not understand in vitro diagnostics and any of the other things that go around to it. Probably a lot of people are not familiar with MVX. But my hope is that that starts to change after this. So regardless of how you think you've fared as an entrepreneur, you've, you've, you've fared remarkably well as a scientist. And I think that's the most important thing because without the scientific foundation, I don't think any of the entrepreneurial stuff matters. But we do typically want them to be aligned. But I, but put this way at the risk of insulting an entrepreneur. I would argue that it's easier to find a good entrepreneur that it is to find a good scientist. So, yeah, that's true. And that's sort of the frustration that the science is so solid, so much more solid than many startup companies, you know, are investing in. But at the end of the game, it's, it's, it's commercial. It's, it's financial. I suspect it's the sector, right? I suspect you wouldn't have this difficulty getting people interested if we were talking about therapeutics. I just think that the diagnostic space and the reimbursement environment in the United States is, is one that is not especially attracted to investors might that that's my suspicion as to the issue. And that's why the MVX, I think, offers more than just a diagnostic, you know, if it could be paired to a therapy, if it has the ability to save enormous cost on the back end with respect to therapeutic selections, you know, you, there are a few trials that need to be done to demonstrate that. But to me, that's the, that's the interesting area. You're right. And, and that's what will make it successful commercially. My vision, though, that wasn't realized is how cool it would it be to go to your, your physical and get a lipid panel that had glucose, LPR, glyc, AMVX, at no incremental cost, you know, in, in the rest of the world, not the US, where you have national health care, there is a premium put on how efficient a diagnostic is. And if you can get a lot of information for less work and money, that's worth something in the rest of the world. It's just, you know, we tried to skin that cat in the US. So it's, you know, I haven't lost hope, but I really left lap core because I didn't want to beat my head against that wall any longer and wanted to spend my remaining years doing the science. So that's what I'm continuing to do. Fantastic. Well, thank you, Jim. And thanks for taking the time to come out here today. You bet. Thank you for listening to this week's episode of the drive. Head over to Peter at md.com/shownotes if you want to dig deeper into this episode. 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