402 ‒ NMR blood analysis: how mortality risk and more can be assessed from a single blood sample

Key Takeaway

LDL particle number (LDLP), not LDL cholesterol, is what matters for cardiovascular risk. At a given LDL cholesterol level, people with small dense LDL have higher risk—not because the particles are small, but because they have more particles. When LDLP is matched, particle size becomes irrelevant to risk. This means targeting particle number, not size or cholesterol content, should drive treatment decisions. The discordance between LDLP and LDL cholesterol in 20% of patients reveals who's at hidden risk.

Episode Overview

Dr. Jim Otvos, creator of the NMR lipid test, explains how his serendipitous discovery that NMR spectroscopy could measure lipoprotein particles led to a paradigm shift in understanding cardiovascular risk. He reveals that LDL particle number, not cholesterol content or particle size, is the true determinant of risk, debunking the widely-held belief that small dense LDL particles are inherently more dangerous.

Full Transcript

Transcript of 402 ‒ NMR blood analysis: how mortality risk and more can be assessed from a single blood sample from The Peter Attia Drive Podcast. Auto-generated from episode audio; may contain minor errors.

Hey everyone, welcome to the Drive Podcast. I'm your host, Peter Aia. Jim, so great to be with you again. We were just talking a minute ago that we I I had forgotten briefly that we were together once in 2013, but um in many ways it you know this is this is a pretty wonderful opportunity for me to sit down with someone whose work I've been following for uh literally 15 years. It was it was uh May I still remember it was May of 2011 when Tom Daypring introduced me to your work and I began voraciously consuming everything you had written um in my personal obsession to better understand the fields of of lipidology.

Um, again, I think there there can't be that many people listening to us that haven't at some point probably had 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 the creator of that. So maybe give us a little bit of a a story your your journey. uh you did a PhD in physical chemistry or in in chemistry and biochemistry. I uh 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 uh structural tool. You can find NMR machines in every chemistry department in the country. And uh so I had I had appointments in chemistry at the University of Wisconsin Milwaukee and then moved in 1990 to North Carolina State University. So I was uh basically doing my thing, minding my own business using NMR for the the usual purposes. And for the listener, we are going to explain how NMR works because for people who didn't take, you know, chemistry and might not remember it, we'll 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 can talk about it. But but um anyway, the point is that that NMR NMR 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. Um and I was using it to study biomolecules. So it was more challenging than small organic molecules and um trying to get at understanding what was going on at the active site of zinc maintain zinc zinc metallo enzymes.

But anyway uh and so I was funded to do that and um had an NMR machine in Milwaukee to do that research. And then in 1986, um, there was a paper published in the New England Journal with a lot of hoopla and in particular in the NMR, uh, field. Um, people paid attention to this. I didn't normally read the New England Journal of Medicine ever. Um, 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.

cancer. cancer. Uh, nothing really in the paper that laid a 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, uh, you had cancer. If it was not narrow, you didn't have cancer. And and you know, normally this wouldn't be given much attention, but it was published in the New England Journal. Yeah. Yeah. Yeah. And so everybody uh who had NMR machines was interested in in 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 um I went across the street to a hospital and talked the people in the lab to giving me six leftover plasma samples from healthy people and um popped it 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 uh given in this in New England Journal paper. So that if 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 of of plasma. But um just out of scientific curiosity uh we started measuring plasma and noticed 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 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 uh act very serendipitously got funding from seaman's medical systems uh 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, you know, had to go through a lot more hoops to get funding.

Um, so we had the wherewithal to get samples from people with and without cancer and then separate the major lipoproteins, VLDL, LDL, 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 and their relative concentrations differing that gave rise to the different shapes of this composite mixture signal that you would see in a plasma sample.

So it was obvious that that this signal was coming from lipoprotein. So what's up with the narrow signal meaning cancer? Well, that turns out to be due to the fact that those s those signals from those people were from people with higher triglycerides and lower HDL cholesterol. And the two combination of those two things made the signal narrower. So this was nothing to do with cancer per se. It was to do with the lipoproteins, the lipid levels of people with cancer. 20 years before that people had published that people with cancer on the average have higher triglycerides and lower HDL cholesterol.

So um anyway we we published in in in 1990 or 91 a paper in clinical chemistry that showed that the NMR signals from isolated VLDLDL 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 VLDLDL signals not showing up in exactly the same place as the LDL signals or the HDL signals. So um we we got the brilliant idea which I didn't think was very brilliant.

I thought it was obvious to use this putitive 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, uh you could generate this signal whose shape and amplitude could be used to deduce the concentrations of the VLD, the LDL and HDL that were giving rise to that composite signal. Okay. and and so we had been funded by Seammens and we published a paper uh in I think 1991 saying that yes it was feasible that one could get VLDLDL 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 uh via normal chemical methods. So, um, so that's as far as we thought we could go. And if it wasn't for the fact that Seaman's people were advising me that I never would have filed for a patent on how to do this. Um, what did I know about patenting and what did I care about commercialization? Um but I did file a patent and the patent was issued and um so so you know if 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 um you know 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 that was an idea that evolved uh over the early 1990s to about 199596 had a couple NIH grants to support the analytic 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 VLDLDL total LDL total HDL smaller, mediumsiz, larger particles.

And 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. subspecies. subspecies. But we uh knew by that time I've been reading the literature a bit and uh Ron Krauss at UC Berkeley at Donner Laboratory was showing that via a quite laborious separation method gradient gel electropheresis and others that you could differentiate LDL uh on the basis of size and found that people with a prevalence of small dense LDL LDL LDL um had greater cardiovascular risk at a given LDL cholesterol level than somebody with with large LDL.

So, this was something that was very interesting and had been replicated in the literature and people were talking about and and um and and yet it took a couple days from start to finish to do this electropharesis and get the result. So, it wasn't really clinically translatable and and efficient. And and so, um just to see if NMR could do this, I hooked up with Ron. He sent me about 45 samples along with the gradient gel electropheresis tracing so I could see who had pattern A, the large LDL, pattern B, the small LDL.

And uh 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 uh and and that was quite successful. Um, successful. Um, successful. Um, so it it 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 NMR testing technology.

technology. technology. Um, [clears throat] it it really did seem that yes, you not only could generate the same information as a lipid panel by NMR, but really what would drive the utility of NMR testing was if it could measure something better and different. And so if we could measure small dense LDL pattern A and B, three-fold greater risk associated with that at a given level of LDL cholesterol, well, that would be a pretty useful thing clinically. Tell us how how how we don't have to go into great detail but tell us how a a plasma 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 deciliter LDL cholesterol HDL cholesterol triglycerides all in milligrams per deciliter how do they get that out of that tube what is the what is the basic how do they do that chemically how do they do that Yes, chemically and and in in a in a clinical laboratory, these are standard chemistry based um assays that are like all such assays.

You um add a reagent that reacts with what you're trying to measure like triglycerides. Actually, triglycerides is interesting because you know triglycerides are are a fatty acid asterified to glycerol. Uh so what actually happens in that assay is the the blood is um um exposed to uh a lipase that that that hydrayes that separates the fatty acid from the glycerol. It leaves the glycerol and then the glycerol is what something else is added to to make make a color change in proportion to the amount of glycerol.

So this is how you're actually counting the glycerol and imputing how much triglyceride you had because you know it was a 3 to1 ratio. ratio. ratio. Yes. Yes. Yes. Um Um Um the clinical issue it's not common but there are situations where somebody has a lot of glycerol not asterified and so the assays for standard assays for triglyceride would say that this person has very high triglycerides and they actually don't have high triglycerid anyway. So, 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 of LDL cholesterol to the signal to the color that's created and you can work backwards from that.

So, these can be completely automated. You're using potentially optical density or something like that. Shine a light through it and that's right. UV detection or visible light detection. So these are these are really standard and very very efficient autoan analyzers do this. The challenge with uh cholesterol though so HDL cholesterol so the problem is you're measuring the cholesterol inside VLDL LDL and HDL. So when does that get broken? When do the lipoproteins break open in what part of the assay? So that you are just looking at the total amount of cholesterol contained.

cholesterol contained. cholesterol contained. Right. So, so the original interest in cholesterol and 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 if you want LDL cholesterol, you have to separate the LDL particles. These are spherical containers that that that that contain the cholesterol and separate it from the HDL containers and the VLDLDL 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, uh, HDL cholesterol was always, uh, measured by first getting rid of the VLDLDL 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 VLDDL from LDL and that takes an ultrauge step which is laborious and even clinical laboratories today don't even have ultra centrifuges. Um so um you know people devised a way of calculating LDL cholesterol by by measuring um you know total cholesterol minus HDL cholesterol which is VLDL plus LDL cholesterol and then estimating VLDLDL cholesterol by dividing triglyceride by five.

Triglycerides are mostly in VLDLDL. So, it was a an easy but not terribly accurate way of of quantifying or estimating VLDLDL cholesterol from which you could subtract the HDL cholesterol from total cholesterol and get LDL cholesterol. Is it 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 deciliter, 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 five doesn't give a very accurate VLDLDL cholesterol estimate. And that impacts the accuracy of the LDL cholesterol estimate. And so now there are equations that interrogate other things, non-HDL cholesterol, triglycerides. There's an NIH equation that I help people um you know develop that is used by LabCorp and other major laboratories. Um and and so so you can you can do better than estimating it by a free toald formula but most laboratories I think to this day still use the free to weld divide by five to get the uh the LDL cholesterol.

So let's talk a little bit now about NMR. So, NMR. So, NMR. So, um, again, maybe someone remembers back in an organic chemistry, an organic chemistry class that one of the problems you would receive on an exam or something was you would be shown a picture and the picture was very much like how you were just describing your experience in the 80s and 90s where you you had along an x-axis a line and then it would have these spikes and they would sort of correspond. So, so tell us what what did the x-axis correspond to and what did the amplitude or y-axis correspond to?

And and of course I want you to get to the point of these are protons but but you know get there in your own way of course. Yeah. So you you're exactly right the 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 etc. Um 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 uh the hydrogen.

Let's just talk about proton hydrogen NMR. Um 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 chemists. So, a CH2 group next to another CH2 group as opposed to a CH2 group and a and a double bond or whatever it might be. They show up in very different places and there's other differences that are are structure dependent. So, you can work back from an NMR spectrum and deduce the structure of the compounds that are giving rise to the to that.

Um so the so 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 on 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 just want to make sure people understand what you're getting at 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 match the 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's going to be an oxygen that's going to produce. And and it's it's I mean, I it's it's a really fun game is is I mean, not to be too nerdy about it, but it's a super fun puzzle to solve effectively. effectively. effectively. Yeah.

uh and it's it's it's pretty complicated and I haven't done that sort of thing in 50 years. So uh so I moved to a different application which is using NMR to detect a a single signal. So I I didn't care about what was in the rest of the spectrum. I cared about the signals that came that were these putitive cancer signals. of the 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 of Medicine authors know that they were looking at lipoproteins? I think they said lipids. I can't remember, you know, and and again and I think yes, they did. and and they they suggested that 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 disproved by showing that that wasn't true. true. true. Y Y Y So anyway, So anyway, 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 or the lipoproteins themselves.

lipoproteins themselves. lipoproteins themselves. So what what what I told you is 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 molecules. So the the molecules don't differ at all in terms of what's carried in VLDL, LDL, and HDL. It's the same lipid. 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.

Um it's been shown that a larger particle will always give rise to a signal that has a slightly lower frequency and a and a smaller signal will have a slightly higher frequency. So the same lipids in differentized 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 but adds to the accuracy of what we're able to come up with.

Um, so 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. Um, 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. seconds. seconds. a computer then with a deconvolution model that has in it what the signals look like from all the different size VLDL, LDL and HDL subasses and then take that measured 30-cond measured composite signal and spit out how big the signals must be from all the different constituent parts to when they when they superimpose they will recreate that shape of the composite signal.

Okay. So, so that's the idea that the concentration information comes from how big the deduced NMR signal intensities are are are from in this mixture blood. Sorry, 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 Yes. as nanomles per liter as opposed to milligrams per deciliter. So, it's not a mass concentration, it's a molar concentration. maybe explain to people where I was going to go next because um as I told you the the chemical constituents in these particles and they're they're quite heterogeneous.

There's different size lengths of fatty acid chains. Some are saturated, some are monounsaturated, some are polyunsaturated. All of these things um give rise to the the lipid complexity of these particles. But the way the NMR detection at least of this signal knows nothing about any of that. it just it it's basically a lipoprotein particle signal and 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 lipoproteins.

So our what we realized at the beginning was if this was a lipoprotein particle signal we were interrogating we could get lipoprotein particle concentration information information 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 but as you said what's reported LDLP is the particle concentration in nanom moles per liter.

So there's 6.02* 10 the 23rd particles in a mole uh of of LDL and so this is 10 theus 9th anyway. So it's a it's it's a big number. It's it's still 10 to the 16th particles. particles. particles. So there's a lot of these particles in your blood. Um but you're reporting the concentration of the package, not the lipid molecules in the package. Okay. So then the question is or one question is um is um is um is there any advantage to to that? I mean because by then so I 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 athogenic much more to worry about than large LDL and and 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 Krauss and others did.

And which population did you look at? Did first Mesa or Framingham? Framingham for sure back in the day. Mesa came a little bit later, but I can't remember what 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 Krauss and that's what we did.

And we said, okay, at a given level of LDL cholesterol, you take people and you stratify them according to low, medium, and high LDL cholesterol. Or you do a multi-linear regression regression regression and and put um LDL cholesterol in the model. And now you're asking, does the size of the LDL add anything to LDL cholesterol in cardiovascular risk prediction? And sure enough, it does. And it's quite an impressive increment of risk. Um so we reproduced what Roncraft did. However, we realized because we were uh 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 it's you don't get a 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 LDLP is higher than would have been imagined from the LDL cholesterol measurement.

So an alternate explanation for the extra risk that small dense LDL seem to confer is that there are simply more particles rather than the size of the particles being the determining uh you know characteristic of of the agroenicity of these particles. these particles. these particles. Yeah. Let 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. The 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 extragenicity. extragenicity. extragenicity. It's then testable. So it's 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 LDLP 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.

Again now the example is you have two patients that each have 20 particles. Y one of them is 10 big 10 small. The other one is 15 big five 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 size doesn't add to that.

Um 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 therapeutically 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 dense LDL is 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.

Uh CTP inhibitors are one of them. Uh niacin um you know HDL drugs of different types. So triglyceride lowering automatically will will will have this effect. So um there really are a lot of clinical trials that that imagined when they set out to do the clinical trial that they there would be a lot of efficacy like nasin 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.

Um and and yet there was no efficacy uh when they did the outcome studies and and this has been reproduced in with you know the fibroids and and and and other drugs. So, Jim, what would you say to the person listening who, because I hear this all the time, who says, "Um, 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.

Yes, they do. Buoyant. I have large fluffy buoyant. Yes, LDL. Um, so my LDL particle number is over 2,000 nanom per liter. Um, 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 this. What would you say to that person? person? person? I would say that's uh a facious idea. Um, and 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 hyper cholesterolimeia.

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 or benign is completely facious. Let's talk about one more thing on this before we pivot away from this, which um you alluded to very briefly, but we we we we went off on a different path, which is the potential discordance that exists between LDL cholesterol and LDL particle number.

And um in fact when I first came to learn about your work, Jim, this was actually one of the first papers I read of yours uh literally 15 years ago almost to the to the month. Um and it was in the Mesa population and it was showing four uh Kaplan Meyer curves. Um, so I'll let you explain what a Kaplan Meyer curve is, but it was basically the four scenarios, which was discordance between LDLP and LDLC when one is higher than the other uh each each time and then concordance between them.

So maybe there were three curves then in that there were three curves. Okay, so concordance between LDLC and LDLP and discordance in favor of LDLP, discordance in favor of L. Okay. So, that was that was a very very eyeopening paper to me. Um, and I'd love you to just kind of explain that finding and and what the significance are because to this day it's still a very important thing for clinicians and patients to understand. understand. understand. No, 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 LDLP and the LDLC agreed or not. And in that case was a Framingham study. And in that case, one question that always comes up is, well, 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? Um, we're simply trying to disentangle a situation where most people don't have a discrepancy between LDLC and LDLP.

So if you're trying to understand whether LDLP is a better measure of LDL associated cardiovascular risk and you do a whole study population and 80% of those people have concordant or in agreement LDLC and LDLP and just explain what that means because the num people will say well the numbers are different how can they be in agreement. So what one way to do it which we did in the in the MESA paper is uh transform the milligram per deciliter 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 LDLP 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 uh a 50 percentile LDLP is close to a 50 percentile LDLC. So that's what we call concordance. And in that case, we picked this weird number of a 12 percentile difference was our cut point plus or minus.

So everybody within 12 percentile units of each other, we called concordant. Why 12? Why not why not a round number? And it was because we were trying to make the concordant population about equal in number to the discordant in one direction and the discordant in other direction population. direction population. 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 30 you know. Ah I see 30. Got it. Okay. Uh so and so that's what what we did and then we simply took these three groups of people and you talk about Kaplan Meyer all this is basically cumulative incidence of cardiovascular events.

So on the x-axis you can see the number increasing. If it increases a lot this is a higher risk subop than somebody who uh whose risk is is is much shallower and doesn't increase very much as a function of concentration. concentration. concentration. So that very clearly showed that for people people people who whose LDLP and LDLC agree, you can make no argument about why LDLP is a better thing to measure than LDLC. Then it's a matter of well from first principles, why 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 explained that in that paper that what what has happened with with when Ron Krauss showed that small dense LDL was more arogenic arogenic arogenic and that this was based on epidemiologic you know population studies. The question was well why is that? And then basically the speculation was ultimately supported by various lines of evidence that yes smaller LDL particles are likely to get into the arterial wall easier than a bigger particle and then the difference in the shape of the uh 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 where in the discussion section is this obligatory paragraph about why small dense LDL is so much more agroenic. 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. more retained. more retained. They're no they're not more retained. I mean I'm just saying you could make that argument right. Let's just say that they they get they get to the end of the trail and they get taken up by macrofasages and deposit their cholesterol contents into the arterial wall.

Well, bigger particles have more cholesterol, more oxidative damage. So, you think the more cholesterol-rich particles should be more but if that's counterbalanced by not getting to the end of the party uh as 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 risk tracks with the particle number, not the cholesterol. Yeah. 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. Exactly. Exactly. Yeah. Yeah. Yeah. So, yeah, that and so I mean com and I can't tell you how many debates I had to have with people. I mean, usually very well- reggarded 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 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. cardiovascular risk. 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 by by equations that take into account total cholesterol, HDL cholesterol, diabetes present absent, smoking present absent, hypertension.

So these 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 that those things in it is my risk assessment better and the answer is no. It's not better. And and this sort of kills you if you're trying to, you know, 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 or is it the triglyceride rich particles. Now, people have swung away from the idea that HDL cholesterol was important because the HDL cholesterol raising trials weren't weren't were were negative or or weren't positive. positive. positive. And then they 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 triglyceride are agroenic, 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 um but I think it was um and it was around that time probably 2012 2013 and if I recall the paper the figure specifically it was uh 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 uh has these five criteria and the more of them you have the more likely you are to be insulin resistant. So it's there's one about having high blood pressure, uh obesity, so the trunkal diameter, trunkal girth, uh blood pressure, fasting glucose, and HDL cholesterol. I think those are the five, right? Doesn't include. Yeah. Okay. So, 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 um uh met sin criteria, you were virtually guaranteed to be discordant, 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 the more like the more discordant you are the more the higher the probability that you're discordant means the higher the probability that you probably have some underlying metabolic insulin resistance insulin resistance insulin resistance extra risk.

extra risk. extra risk. Yeah. Yeah. And so the question then becomes is the reason that very elaborate multiparametric risk models can erase the LDLP is that they are simply capturing all of the cardiomatabolic risk indirectly and directly. Yeah, you you basically just explained what I was getting at, which is because HDL cholesterol is in the standard risk assessment models. When you have low HDL cholesterol, you're likely to have more LDLP than the LDL cholesterol indicates or suggests. And so your risk is higher because you have higher LDLP, not because HDL cholesterol is low.

But you're not doing better in risk assessment because the HDL cholesterol for the wrong reason if you will not not a causal reason but an association reason um is telling you the same thing. So this was really important to lipo science back in the day because and and this is something that I still argue with but I Alan Sniderman and I we'll get to the question of APOB. Yes. Yes. But um he insists he keeps wanting to convince people that APOB is important for risk assessment.

And what we pivoted to to to in light of the evidence that we found ourselves ourselves ourselves that above and beyond the standard risk equations, you couldn't add LDLP and see a significant increase in risk. Liposcience pivoted from trying to convince 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 the best tool we have in the toolbox.

There are not actually very many others. We have statins and 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 L LDL lowering do I need? And so what you really would like is a biioarker 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 if if I'm on highdose 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 LDLP 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 lowering. So we absolutely notice that as clear as day in our practice, Jim, because we are very aggressive at managing these things.

And is 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 is it a property of the assays that's causing that? that? that? No, it's like everything that you ask. It's it's it's complicated. you can get into the weeds. But what's 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 estester 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 estester. 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 independently independently proportionately is going down. That's that's actually a great explanation. I did not know that.

And that makes sense because we see that as clear as day. Right. Right. Right. Okay. 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 LPIR score. Was that indeed sort of the your first foray into pool composite scores? Yes. Um and it was still used to this day. So many people again listening to us will have an LPIR score every time they get their blood test.

No, it's it's it's it's interesting because as I said, we first got into this game because we could measure small LDL and and so the 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 con convince people to not pay so much attention to LDL cholesterol and pay more attention to APOB or LDLP, uh, then you're better off.

But that means that measuring the size of LDL doesn't matter. Oh, that's a bummer because we have a great efficient way of doing that. Um, so then the question was, well, is 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 And 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 Rivven who was the really discovered and promoted the idea of insulin resistance being being extremely important um he advocated that it be used because people were getting lipid panels and this information was not being used for anything um so um so um so with this LPIR score which bruised together six VLDLDL LDL and HDL size and and subclass concentration parameters it bruised them into a score from 0 to 100.

Higher scores being more insulin resistant. resistant. resistant. And then the question was does this LPIR score do a better job in assessing whether somebody is likely to become diabetic? diabetic? diabetic? So So So the pathophysiology has to be talked about a little bit here um because there's some interesting parallels to where we are today with respect to primary prevention of cardiovascular disease versus primary prevention of diabetes. diabetes. diabetes. So So 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 aththeroscerosis.

Atheroscerosis 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 myioardial inffection or a stroke and that's when the atheroscerosis has transformed into a clinical event. The good news from an measurement biomarker standpoint is that the initiator the causal factor is cholesterol which is easily measured. So now what about diabetes? Diabetes very similar. It's a time integrated process where if you are insulin resistant insulin resistant insulin resistant over time, your beta cells have to spit out more insulin to keep your glucose under control.

under control. under control. And 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 hypoglycemia, elevated glucose. So if it's 90 or below, you're a-okay. 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 deciliter line that defines diabetes. defines diabetes. defines diabetes. Just for folks to know, we're talking average.

average. average. Absolutely. Absolutely. Absolutely. Y. Y. Y. So the causal factor is insulin resistance. resistance. resistance. The thing that's sort of equivalent to aththeroscerosis on the CBD side is hypoglycemia sub sub less than 126. So not diabetes but pre-diabetes. So when glucose gets over 100 before it goes from 100 to 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 from from transforming over time into hypoglycemia 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 Jim, I want to pause you there because the way you've laid that out is very elegant and I I like the I' I've never thought of it the way you just explained it.

So I'm [clears throat] repeating it just as much for me as for others. Um people who listen to this podcast know we constantly use the the way you describe the 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. Um, we always have to specify causality doesn't mean onetoone 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. Yes. Do we want to wait until we actually see the glucose rise, which by the way is the biioarker 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 and 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 uh a very compelling case that you would want to act on the causal factor and not the effect or the the you know the the downstream effect of of of the action of insulin resistance. insulin resistance. insulin resistance. Um Um Um but it's it's interesting. I mean back to convincing people clinical translation. I mean this is I I use that word. I don't think I was familiar with that word when I started this company called Liposcience called Liposcience called Liposcience uh to to try to get NMR testing introduced into clinical laboratories.

But I thought the argument of LDLP versus LDLC was quite compelling and that people really should be using it as the biomarker to determine uh management of LDL. Tremendous resistance by the establishment. I could never understand why it was so why they were so resistant. Uh the messaging people learn about cholesterol in medical school. Oh, we'd have to tell a different story and people wouldn't get it and all sorts of, you know, reasons that seem pretty weak to me. Um so on the on the diabetes side, it's the same thing.

People imagine that hypoglycemia, pre-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 I it 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 deciliter for 25 years. Doesn't change. Um, so Um, so Um, so the insulin resistance score can tell you whether you're more or less likely once you're pre-diabetic to transition. And then then at that point, even though you'd like to have intervened earlier, it's not too late to still do something about it. about it. about it. And so Jim, was the LPIR score validated on longitudinal data to predict the development of type 2 diabetes?

Yes. Or Okay. Um, so in that sense, we'll talk about MVX and how similar that was in that regard. Has there ever been a um a comparison that says how well does it do versus do versus do versus an oral glucose tolerance test? Yes. Uh and and so well not oral glucose tolerance test because in the real world it's not being used. So you really fasting insulin is sort of an easier way to assess insulin resistance. Um it has some analytic issues and and practical issues.

Uh so nobody has really been interested in in in using insulin for that purpose generally in in clinical practice. Um, but the LPIR score has been compared to fasting insulin and is better. Um, the study that shows it 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 uh intervention, lifestyle intervention, metformin on the map in terms of being able to do something about transitioning to diabetes once you had hypoglycemia, once you were pre-diabetic.

Um, so this study was done about 20 years ago. We have baseline samples and then one-year samples post treatment. All the everybody in the study had an NMR analysis done. LPIR LPIR LPIR and other things that we can measure by NMR that make FPIR better if there if you want it to be better um were 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.

Metformin less effective but significant compared to placebo. So what we're able to show really nicely is that the LPIR score was reduced significantly by lifestyle less significantly by metformin. Branch chine amino acids which we haven't talked about yet but higher branch gene amino acids are also related to insulin resistance and can improve the LPIR score and we actually have developed another score called the diabetes risk index that integrates or adds branch gene amino acids to LPIR. Okay. I was going to ask you that. So just to confirm the the DRRi, the diabetes risk index, is the LPIR score inclusive of the three branch chain amino acids.

And do you recommend when 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 far to have the amount of acceptance u that that discussion has even occurred. Is DRRi commercially available with LabCore now? Yes, it is at LabCore. Okay. We should also point out for folks who are wondering your company Liposcience that you founded in mid9s was acquired by LabCore 10 years ago, a little more than 10 years ago. And so, um, for th those of us like me, the dinosaurs, like we used to still order a liposcience test.

Now, it just all happens through LabCore. Um, so that presumably that has increased I assume some some uptake of the of the test. So I I wanted at some point to get into uh the question of why hasn't broad clinical translation occurred because it hasn't right now. You can only go to lab for this information. information. information. Um so I'll I'll go there now briefly if you don't mind please. Um, so liposcience began as I told you as a spin-off of the university. I left the university and we we we tried to convince people that size didn't matter and LDLP did matter and we did that for quite some time and we were a laboratory testing company and so samples were sent to lipo science and you get the results back.

You had beautiful results I and that color report. I I loved it. So why not use color printers? Oh, it was it was beautiful. But the the ambition was never or the 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. chemistry department. chemistry department.

These are research NMR machines. They're engineered for engineered for engineered for multi-functionality. You can do any weird NMR experiment. You 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 ideally ideally 30 seconds or less pop one sample in automatically another sample comes in boom boom boom. So we realized that we couldn't uh use a research NMR spectrometer for that purpose. We used them for the initial years at Liposcience 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, LabC Corp does 3,000 assays. They rely on other people, Ro, other people to provide them um with the wherewithal to do the testing. Those are IBD companies. We wanted to be an IBD company at Liposcience. 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 go button, and walk away.

And 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 Vanta NMR analyzer. It's the only existing NMR analyzer in the world and uh we went to the FDA because we you know these analyzers have to be FDA cleared in order to go into different laboratories. different laboratories. different laboratories. That was an adventure because uh what did FDA know about NMR spectroscopy and it was a new platform a new a new way of testing uh that that had to be understood.

So anyway, it took when did you get the clea? We got the No, not Cleo. We got FDA clearance for LDLP and the Vanta analyzer in 2011. Oh, wow. Okay. Yeah. 2011. Yeah. 2011. Yeah. 2011. And so these these Vanta analyzers uh a number of them were were manufactured. Uh and and then you know we about the time that just before the year or so before Liposcience was sold to LabC Corp, 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.

Clinical laboratories hate sending sendout samples. Um, and and when that's for a rare cancer or something, when it's just a fairly rare event, no big deal. If you're doing hundreds of these a day, you you want you don't want that hassle. So, they were very happy to receive the Vanta analyzer so they could do the testing inhouse and every time they did, they would pay LIOScience for each analysis. Okay, that was the business model. Um, we also made Vanta analyzers available to the Mayo Clinic, Cleveland Clinic, Scripps Clinic, AUP, a big reference laboratory in Utah.

Um, so we were on the way to making it available broadly because we didn't want to be the only people that could do NMR testing and also we wanted to convince people that this wasn't, you know, magic. It it was it was real and it was analytically in many respects much better than reagent-based chemistry testing. testing. testing. Unfortunately, by that time, liposcience had gone public. Um, I was no longer on the board. I 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, uh, people, uh, the 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. Um, 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 of dollars to lio science every time we do this test." So, it was a purely financial decision. LabC court really didn't care about measuring things other than the NMR lipo profile, the LDLP, etc.

Um, it was financially based. based. based. That was too bad for the vision of having NMR analyzers in every laboratory because Lab Corp is a lab testing company. It's not an IVD company. So too bad the IVD business model, the IVD vision IVD vision IVD vision was ended in 2014. Um so yes, did you join did you go and become a scientist? scientist? scientist? The research group largely was retained by lab corp. So and we had a pipeline of things like LPIR and things DRRI things that were coming down the pike that we'll talk about later.

Um so you know really the the the the best was yet to come in terms of the clinical value and the things that could convince people that having Vanta analyzers in their lab was a a a commercially useful thing but also a clinically useful thing. So the bad news is that LabCore, understandably not sharing the IBD vision and being a laboratory that wanted to have uh NMR be proprietary to themselves, themselves, themselves, took back these analyzers that had been placed in these other reference laboratories. And then unlike lipos 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 that's a very important activity. important activity. important activity. Labcore doesn't do that because they're not an IBD company. They are reactive. They're not proactive. They're very good at being reactive. and in COVID they ramped up the COVID testing and you know so I'm I'm not you know saying that that labor 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 inhouse by acquisition of liposcience liposcience liposcience and so there was no marketing no awareness creation and basically liposcience went invisible and is still largely invisible um most of the testing uh is still done by the people that were uh interested in the testing thanks to liposciences efforts.

So these x number of analyzers that were produced 15 years ago are what lipo lab corp is using to produce this information and this more exciting exciting exciting and what's the what's the life of these analyzers? Uh good question because nobody's ever so the magnets themselves these superconducting magnets uh last a long time and and don't degrade but they're they're using PCs you know 10-year-old PCs and anyway they're all the moving parts so 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 LabCore LabCore LabCore but also ideally broaden to the rest of the world is for an IBD company to come in and basically acquire the liposcience 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 don't learn.

I mean, we've done so many millions of tests and you you learn by doing. So, what people don't know is that at some point in the future, maybe sooner rather than later, these Vanta analyzers, first generation, only generation, generation, generation, uh, 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 LPIR 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 I basically show them that this is a completely de-risk population uh proposition because we've already gone through the regulatory hurdles. We've already made the analyzers. We already have the experience. Uh 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 you know 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 IVD company or a new startup IVD company basically taking the torch that that Liposcience and has liposcience said that they would be willing to sell those assets the IP. It's it's LabC that owns all that's what I mean. Has Labore said that they're willing to sell? They're and I think they realize they well yes they're they're interested in licensing that the patents most of the patents have to do with the assays so LPIR and MBX that we'll talk about.

So absolutely um but who's going to license them if there's no machinery to produce the information? So what is your estimate of the the cost per machine now if you were going to make a Gen two? Well, so these these machines cost on the order of four or $500,000, but the beauty of it is, of course, that they last a long time, which we we've shown, but they have no consumables associated with the assay. 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 lipoprofile was the the report that that that gave people the LDLP information, but that comes from this this simple NMR spectrum that can then used to be extract much more information than LDLP, LPIR, DRI is just part of the story. There's all these other things that we'll talk about later. Um so in terms of efficiency, analytic efficiency, efficiency, efficiency, um you you couldn't ask for anything better because you know 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 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 uh in the US. So um you know this so it's it's extremely analytically efficient and cost-effective.

cost-effective. 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. tests. tests. The cost of these tests is so much smaller than the price that is charged for these and the insurance pays for these tests that cutting the price the cost in half of doing the analysis makes no difference whatsoever. So this is, you know, my we'll maybe get into this later because what is true is that NMR, a single scan can tell you a heck of a lot more than just your cardiovascular risk, your inflammation level, your diabetes risk, your overall mortality risk, as we're about to discuss.

This all comes in the same assay essentially. essentially. 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 I went out to to LabCore uh two and a half weeks ago because I wanted to have the results back when we were sitting here.

Um and wouldn't you know it, 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 they butchered this one so I don't have my my own to talk about but the MVX is a is a is a composite score that measures if I recall six things so small HDLP glyc which we haven't introduced yet and we'll talk about citrate and the three branch chain amino acid so leucine isolucine and valley um so why don't you tell us the story of how you developed 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, you know, zero to 100, what number, 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. um while it's first while the first study that I saw was done in a very very high-risisk group of catheterized patients 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 you know 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 is no. That's not how it happened. And I just I do want to make this point because MVX and and and especially MVX raises more questions than 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? You know, these are the things that really matter clinically. clinically. clinically. Um so it wasn't starting out with some idea that branch gene amino acids are really important mechanistically even though they are. Um, 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 you really would like the ultimate idea on a biioarker is that it predicts the future. you you 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 from many very large clinical studies you mentioned MESA multi-ethnic study of aththeroscerosis aththeroscerosis aththeroscerosis that's a NIH funded study that started in about 2000 it's had more than 20 years of followup 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. Um, Birmingham ical trials, Framingham offspring study, uh, all sorts of intervention trials, Jupiter, etc.

U, u, the diabetes prevention program. I I would like to quickly go back to that just for two seconds because we didn't finish the 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 LPIR score goes down with lifestyle intervention and branch gene amino acids go down when which is all good thing. Uh but that those things going down the delta between where it started and where you got after the intervention very powerfully predicts incident diabetes or or the lessened diabetes risk.

So that was sort of the missing link if you really want to argue causality. Um you really want to show that lowering LPIR translates to lower diabetes risk and it does. It's a it's quite powerful. It it hasn't been published yet in part because the the primary author uh died recently unfortunately and so uh it will be eventually. All right. But that's one study. So we 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 6,000 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 etc dementia etc any 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 from 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 MVX, that's exactly what we did in the this cardiac caization study at Duke University. 7,000 people over a period of years recruited into this bio repository 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 angography done and their blood was taken at baseline and it's stored at minus 80 where it's perfectly stable for EDMR analysis. So we got uh a relationship with Duke to to obtain those samples, do EMR analysis, and we found found found um um um in in in published papers that small HDL particles were particularly powerful in in in in relating to uh 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 glykee. And we might as well talk about gly now. gly now. gly now. Um 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 uh a funny thing happened with the where the the the gly signal is. it 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 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. signal. signal. But that was perfectly good quantification information. So that signal which I wouldn't have I I wouldn't have thought to do anything with the person who's uh does all my um epidemiologic analyses. Arena Shalroa is her name.

Uh we had the MESA data sitting there. we could go and very quickly quickly quickly use the software we had developed to measure this sharp signal. And she just said, "Well, what does it relate to anything that happens in the future in Mesa?" Oh my gosh, it relates to uh all sorts of things including mortality very very [clears throat] 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.

Um, it 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 acute phase proteins that uh increase in concentration in inflammatory conditions. And so even though this signal was not telling us which acutephase proteins were contributing to it, it was a composite. And not only did it essentially quantify the most abundant four or five acutephase proteins that contributed to this signal. this signal. 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 you know there's people worry about the glycom. It's like the proteome and the genome. There's a glycom. I know nothing about any of this. But one thing that that where we know where this signal comes from and it comes from a particular sugars on on this carbohydrate and inflammatory conditions more of this decoration is put on some of these proteins. So actually this glyc 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, inflammation, inflammation, it's very volatile. It goes up and down daytoday. So all clinical recommendations for the use of CRP information say that you should take the average of two or three different measurements never nobody does that but that's the recommendation to get around some of this biological variability gly doesn't suffer from that problem part 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. inflammation. inflammation. Does it include do you think or capture what we see in the various interlucans? Yes. So it's it's correlated strongly with IL6 and other interlucans. um um um and and these correlations. So, but that's all we know. I mean, so yes, um and you'd really like to be more specific and and if there's local inflammation as opposed to systemic inflammation, this is not going to 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 NMR information. uh if you quantify this glycase signal you have a very powerful marker of the things that systemic inflammation contributes to. Now um using HSCP as an example Jim which um as you pointed out is going to rise with inflammation. One of the things that clinically we pay attention to is how high is it? So if uh if I see a brand new patient and their HSCP 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, you know, four days ago or something like that. Um, now of course that doesn't obviate the point you made, which is I still want to see longitudinal data. Like I can't make a I can't assume the two is bad because I could also be catching something on the way down or on the way up three or one depending on the next day. Yeah. Yeah. Yeah. But 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 that give me pause. Is the glyc the same as that? No, it's very different than that. Uh, so CRP levels could go up a thousandfold on an infection. infection. infection. Gly levels are the response is much more muted muted muted because it contains so much information in it. Do you think I can't really answer the because as far the observation and uh and so it it it's true that if somebody has an active infection and you're trying to relate the glyc level to mortality in people that didn't have an infection, you're going to be misled by that.

It'll be higher by twofold, not a thousandfold. So it's not immune to those those changes, but you wouldn't want it to be because you know it's an inflammation marker. Yep. Yep. So in in people with in inflammatory diseases, rheumatoid arthritis, psoriasis, etc., glycine levels are are are significantly elevated and um and they are responsive to anti-inflammatory treatment and so it it has all the characteristics of a of a useful biioarker to assess not only things that that that you would like to have some visibility to like systemic inflammation.

I think we're coming to to understand that that's an awfully important contributor even though we don't understand the mechanistic fine points points points uh to so many things including uh mortality risk. So anyway, this is just I just wanted to tell you that that that that the way that we discover these biomarkers biomarkers biomarkers is different than the way other people discover a lot of biomarkers. So it's more the top down. We're 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.

health conditions. 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. um 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 NMR. So, it's a completely different approach to discovery and it's sort of irrational discovery because you're basically using these large databases of 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 lipoprotein one, which is the small HDLP. You've just explained the inflammatory one, which is glyc. The third one is sort of the metabolic one which has the citrate and the three BCAAs. So how did you come to figure those out? those out? those out? So you're right. So we actually with the Duke collaborators there had been a paper published showing that that glyke there was a glyke paper predicting mortality. The interesting thing about this cathgen population is they came to the kath lab because they had some presumed cardiac issue.

They have in the study that we did for fiveyear mortality 17% of the people died in five years and they were about mean age of 60 coming in. So that's a pretty high pretty high pretty high how many 15% 17 17 17 17% mortality five years in people that were not were not were not elderly elderly elderly um um um three or four times higher for sure than 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.

issues. issues. 60% died of noncardiac cardiovascular uh causes. Very interesting. Se so so 60% of the 17 people 17% of people who were dead by 65 were non-cardiac were non-cardiac were non-cardiac cardiac causes. Yeah. So anyway mortality was the most prevalent outcome. It wasn't a new mocardial inffection or a recurrent myocardial inffection. These people died and and so gly predicted it. Small HDL particles predicted it. We might talk more about that. that. 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 branch chain amino acids isolucine leucine and veene um ketone bodies um u 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 biioarker 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 gly and small HDLP small HDLP small HDLP we created a subcore called the inflammatory vulnerability index IVX.

The other four seem to relate. So we we we discovered that these branch gene amino acids and citrate independent of glyc and small HDLP added to the prediction of mortality. Then it was okay why? And so then we went to literature. So we we really approached and and you're doing all of this inside of LabCore. So LabCore at least still at this point in time had the appetite for the R&D. the R&D. the R&D. Really? Yes. They paid our salary, but that was we were sort of left alone on on in this little building to continue what we were doing at at Liposcience.

So thanks to LabC court for for not getting rid of everybody. So then it was going to literature trying to figure out does this make sense biologically that these things might be related to mortality. And that's where you come to the literature that uh very powerfully speaks to the great mortality risk that people with several acute diseases especially [clears throat] kidney disease and diialysis patients, heart failure patients, anything with cexia. I could see anything withexia. So does you know sarcopenia sarcopenia sarcopenia um 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 things. Protein energy wasting syndrome. So the cexia, the wasting syndrome is part of this, but inflammation is part of this. inflammation is probably the the context that that allows these these disregulated metabolistic metabolism situations to exist. So the fact that malnutrition inflammation syndrome we had the inflammatory parts we speculated but the the branch sheet amino acids were related and they were related in the opposite direction that branch amino acids are related to diabetes risk.

So high branch gene amino acids speak to insulin resistance, obesity, diabetes risk. Low branch gene amino acid levels speak to mortality risk. We can talk more about you. You know probably much more about why uh this makes sense in terms of mechanism because it's partly related to mTor signaling and and the whole skeletal muscle. Yeah. Turnover of amino acids turnover, catabolism, etc. catabolism, etc. catabolism, etc. Um but it was 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.

context. context. But in the catchin population nobody had had described this in a cardiac uh well in cardiovascular cohort. cohort. cohort. Um and then uh so we we said well this exists in spades apparently in this this this presumed cardiovascular cohort. But then we did subgroup analysis within this 7,000 people in the Kathgen study and 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 mocardial infuction, with or without uh coronary catheterization, occlusion of the coronary arteries, uh with and without kidney disease.

And it's it it doesn't it 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. disease-free. disease-free. Um, so in fact the strongest relationship of MVX to mortality, you mentioned how exquisitly strong it is. I thought I thought that the hazard ratios were bigger in the Cath study than in the Mesa study of healthy people. people. people. That's true. That's true. and and and the reason is that for whatever reason if these people came to the kath lab and they were they were 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 were 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 uh MBX and young people 30-year-olds. Um would we expect to see the relationship and you do see the relationship relationship relationship but it's weaker and the I I I didn't sort of finish that.

So the two sub subp parts of MVX are the IVX inflammation part and the other four parameters brewed together to form MMX, metabolic malnutrition index. Um it's kind of an arbitrary thing to 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 than the than the metabolic malnutrition wasting reasons in in which case it 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 what why we did that. Are they both I know the aggregate score is reported 0 to 100. Do the two subcores get reported that way as well? Same way. Same way. Same way. Okay. So you could say uh you know 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. Yeah. Yeah. Yeah. 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 are more because it because they're it's a syndrome and yeah, the numbers I used are wrong, but Yeah. Yeah. But that's the idea. Absolutely. That's the idea. But and it's really, you know, we've learned a lot since this 2023 first publication about MVX in this cardiac caization caization caization cohort. The important thing that we did there, I was I was advised that that I really shouldn't try to to to publish this until we had replication because these hazard ratios were so dramatically different for low and high MPX scores.

So in that paper, we reported a completely independent cardiac calath population in 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 let's talk a little bit about that. The one that just came out, of course, was the Masle D paper. Is that the one you wanted to chat about? No, actually, we could and it just so because these papers are coming out, they're just coming out at a geometric rate.

rate. rate. Yeah. and and the reason the reason is that we just have to go back to existing NMR data. NMR data. NMR data. So we're mining other people's costly studies. We're piggybacking on their work, their their funding and you know getting very quick gratification about whether MVX is good for this or that or the other thing. Is there one in an 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 a MVX at baseline hot we're going to treat you guys placebo non placebo.

Yeah that's Yeah that's Yeah that's you know where I'm going. Absolutely. That's that has to happen. Okay. Okay. Okay. It isn't going to happen if nobody knows about MVX. And the thing that was 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. Um but 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 liposcience, we would be promoting awareness of this paper in cl in meetings etc. Um and this isn't happening at lab corp. Okay. So uh but now this will help uh yeah I mean there's going to be a lot of people that are that are listening to us. Right. And so uh yes, we would that's the next next step. What what will be published this year will be I think sufficient for anybody to see the replication of the phenomenology phenomenology phenomenology uh 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 frailty scores frailty scores and um also biological age by uh 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 uh relating to longevity and and and so on were measured in in these studies that we've been able to look at MVX in and in terms of frailty physical frailty um correlation but fairly weak with MVX about a 02 correlation coefficient.

So it isn't like you know you need to have physical frailty to see the MVX be high or vice versa. Um 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. predicts mortality. predicts mortality. And is it always a five-year look forward? forward? forward? No. And and in many studies it's it's longer than that in part because you want the statistical power from more people having the outcome.

But there's something powerful about the short window. I mean in many ways that's actually a feature not a bug if you can offer that insight because we don't have many short-term predictive biomarkers. Absolutely. And actually uh the the one study that I was sort of confusing with the the heart failure study is is is a study called epis uh and it's a it's a a study of older people with lots of things being measured including these biological age measures by chemistry assay uh not the epigenetic flavors.

And flavors. And flavors. And a couple years ago, I think it was 2022, they had NMR information and then they had all this frailty information and you know 186 different variables and it was done by some um um epidemiologist epidemiologists in Minnesota using the most uh high-tech ways of trying to deduce whether the associations were causal or not. I I'm not really sure that 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 HDL particles were the most powerful at predicting two-year mortality in these people.

What would be a blind spot? Where does it get fooled? We already gave one example, right? which is if you're in the throws of a brutal infection, you're getting over a cold, that could artificially elevate, although not to the same extent as CRP, the gly that could offset it. Have you seen other um false positives, so to speak? No, I mean we and we haven't really looked in ways to possibly see that those because we've looked overall at the prediction in a population uh and and these people at baseline either have this that or the other disease.

disease. disease. So it gets kind of canceled out in the wash at the population level. Right. Exactly. Right. Exactly. Right. Exactly. The question of of whether it's causal or whether So there's two questions. One is it modifiable? So let let's just I've been I've been speaking to the intervening three years since the paper was published. We now have really good data data 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 how MVX remarkably relates to not just mortality but diseases that reduce mortality. So I want to quibble with that idea a bit later. But later. But later. But um just to get back to the the question of are there interventions which lower MVX and then could we do do the study of that intervention to show that that's connected to of reduction in mortality risk? risk? risk? Before you do that, Jim, I want to go back to the 30 year olds because we didn't really finish the swing on that.

So So So no, I no we didn't. Can you are you able to talk about that or is that not public? public? public? I'm going to talk about it and I'm not going to say the name of the study but okay um and the paper I just you know the draft of the paper is about to be submitted. submitted. submitted. It's just hard for me to wrap my head around the fact that any biomarker in 30 year olds could predict anything. This is very true and very 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 cortile had this stepping stone relationship. Not as strong. The hazard ratios weren't as different as in Cathgen, but very significant. significant. significant. So that was the first do you recall in that study Jim what the uh the difference was between the first and the fourth quartile in hazard ratio where you talking about 1.6 unadjusted it was about I might be they're 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 quartortile 75 to 100 score versus the bottom quartile 0 to 25 would be about a 50 no no that I was that's per standard dev so this this is it's actually greater than that.

It's it's maybe two to three full greater full greater full greater by quartile by top to bottom quartile. Got it. Wow. Okay. So big big difference. difference. difference. Yeah. Yeah. Yeah. So okay. So those are 60-year-olds. So these are people. So the my idea about MVX I mean people die when they're older. Yeah. older. Yeah. 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. age. age. What? Yes.

Unassociated with age. And and the the major proof of that is this 30-year-old study. So here we've got 3,000 plus people who were entered into the study at be 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 then five years after.

So there's we have NMR data at year 10, 15, 20, 25, 30. So we know how stable the MVX score is over time. over time. 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 MBX 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 followup. So this is definitely premature dis mortality we're talking about.

would not ex and these are all people that that at at baseline also were excluded from having any pre-existing coorbidities. Okay, so there's not only young but they're healthy. healthy. healthy. We need to just stay on this for a moment. Jim, this is so counterintuitive. I don't I just want to make sure not a single listener is failing to appreciate what you are saying. So I'm going to repeat it back and I want you to correct me because there might be errors where I'm oversimplifying. oversimplifying.

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 1 diabetes or you had some childhood cancer or, you know, whatever else, we didn't include you. We really looked at boilerplate healthy 25 to 30 year olds. We draw their blood. Every 5 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. the the the upper quartile the average was about 50 51 score the bottom quartile was about 27 so that's the range and there were so the distribution looks a little different than it looks in a 65 distribution is identical to Mesa I mean identical ident and Mesa was in 605 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 it predicted mortality.

predicted mortality. predicted mortality. I am not aware. I'd have to think, Jim, but I don't think I can imagine a biioarker, right, right, right, outside of a very extreme state. So, you mentioned FH. mentioned FH. mentioned FH. Okay. If I know that I have two 30-year-olds and one has FH and one doesn't FH1 is not treated, I can tell you with a very high certainty that person's going to be dead in 30 years. This one will not or very unlikely to. But outside of edge cases like that, well, well that so that you know introduces the question, you know what, how did you get a high or low 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. Do we look at Framingham and Framingham offspring and try to tie that together? We have plenty of MVX 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 and and and important, I think, for sure. Um, and and because of of of what we've just talked about, talked about, talked about, I want to go back to this issue of whether MVX whether MVX whether MVX is has anything to do with whether you're likely to develop cardiovascular disease disease disease or diabetes or diabetes or diabetes or dementia or whatever.

And what you will find already in the literature are papers that indicate or suggest that MVX 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 mioardial inffection 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 ideology, because of how the the cholesterol is connected to cardiovascular disease and events mechanistically mechanistically mechanistically that there's nothing wrong with using a composite endpoint. The ideology is the same. You get cardiovascular disease, you die from it. You get cancer, you you die from it. So you want to have a biioarker that predicts whether you're going to get the disease and then that automatically tells you what what your risk is for dying of that.

What this says is that maybe there is a separate a separate a separate influence on whether you're going to design die from the cardiovascular disease or the cancer or the d or whatever whatever whatever and that's your metabolic vulnerability your metabolic frailty if you will frailty. I like the idea of metabolic frailty because frailty conotes susceptibility to dying and even though people with high MBX score like these 30-year-olds have high MX score you look at them you you don't they don't look any different than the people with low MBX 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 going to 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 in the paper, but it's becoming much more clear now, especially with this 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 Kruda.

It's the sing I to my knowledge Kruda is the single bestselling drug of all time. Um 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 PD1 mutation that take this drug, regardless of what kind of cancer they have, this could be pancreatic adnocarcinoma, lethal cancer. If you have this mutation, this drug basically takes the brakes off the immune system and your immune system eradicates the cancer.

But here's the question. Why does Why does Why does someone why do you take why could you take two people that have the exact same PD1 mutation the exact same cancer by all intents and purposes and you give them both Kruda and one of them responds and one of them doesn't like we don't know we 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 Kruda as an example but you could do this with any therapeutic intervention where mortality is very quick, right?

And you could ask the question, does this become a prognostic indicator of not just mortality but probability of success of an intervention? intervention? intervention? Yes. Yes. Yes. Yeah. That's precisely what what possibilities exist. When I first talked about this to people at Duke, uh the collaborators of the Cathgen study, um the 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 biomarkers, good objective biomarkers to do that. Um, malnutrition, metabolic malnutrition. There there people I've read papers where people are surgeons are suggesting that that people really should avail themselves of these uh you know interrogating interrogating interrogating whether somebody whether somebody whether somebody is is sort of metabolically or or you know physically frail um and has evidence of wasting but if there's a metabolic component to that that is accessible via MVX it could be very useful uh and this is one of probably all sorts of possible um applications.

But you mentioned this paper that was just published two days ago uh on uh MASLD MASLD MASLD Nas D former the artist formerly known as NAFD formerly known as Nafal D. So uh so liver disease and um the paper speaks very forthr rightly about the possibility of using MVX to uh for for entry into clinical trials. 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 MVX added predictive value to fibrocan in predicting subsequent fibrosis in the Masle D patients.

Is that does that That's true. I only skipped it as well. I was not author on that. Yeah, but you know as you well understand so many clinical trials are expensive and and are not done many are not done because the events are too rare. So you need a huge population or too far away and so being able to juice up your the likelihood of people dying for example. So having and then but mortality is is probably the end point that people care most about, right?

And so uh it has sort of in the hierarchy of of events it's it's it's people care more about dying than they do about getting an MI 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 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 understand understand and it's an inexpensive test.

It's a well it's it's basically free if you think about it. So I mean this is what what what how many tubes of blood do you need to run it? run it? run it? No. So so you mentioned that you got your MVX score and they probably drew an extra tube of blood for that. They don't need to do that. The same specimen actually 150 microL of plasma produces the the NMR spectrum that produces the NMR lipoprofile, NMR lipoprofile, NMR lipoprofile, produces glucose, produces LPIR, produces glyc produces MVX, all of that comes from the same analysis.

analysis. analysis. uh 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 health care system and the way things are set up that there's sort of no no 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 you're a 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 uh you know producing this test globally. You know broadly had this experience with LDLP tremendous resistance to pain. So, you know, I think the way around that, one way around that is to not try to get pay paid incrementally for it and just do something that So, the analogy is is the comprehensive metabolic panel that you get done every time you go for a 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 and change. and change. and change. CMS pays $12 for that. And it's because these are all done at the same time. They have some, you know, clinical reason to be done at the same time. And economies of scale make it efficient enough that you can make money and people don't aren't going to starve producing this test getting paid $12 as much as they'd like to get paid a lot more.

more. more. This is a situation just like that where the information is essentially free. And we made that point in the paper we wrote about um the lipid panel, the extended lipid panel that includes APOB. I mean I'm you I'm you I'm you didn't really get to the APOB. It was actually the last thing I wanted to get back to which was the discordance between APOB and LDL. Let's get back to that because the APOB story is 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 APOB APOB is that it would be disingenuous to you know say that one is really better than the other.

I could make the case that the NMR analysis tells you LDLP but also TRLP triglyceride 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 with more information than APOB provides. APOB is just a single measure of all the APOB on LDL and VLDLDL particles. particles. particles. But the challenge is convincing people that you should do something other than measure cholesterol measure cholesterol measure cholesterol and and so you need as as many u 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 which and 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 uh Medicare reimbursement is set up now, APOB gets paid 20 bucks, lipid panel about 13 bucks.

Last I looked, it might have changed a little. Um so that more than if you want to add APOB to make the lipid panel better, it's more than more than double the cost to the 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 and change. And change. And change. And so that's for the NMR lipo profile. The NMR lipo profile comes with LPIR. Okay. Yep. Okay. Yep. Okay. Yep. Okay. 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 in or the FDA has decided to exercise um discretion about whether they uh will enforce this or not.

So um laboratory developed test LDTS individual laboratories can develop their own test go through a you know get clea certification. So this is more laboratory certification for the the 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. Uh, and so LPIR was was added to the NMR lipo profile as an LDT, not not part of what was cleared.

LDLP was cleared, but not without great difficulty. Um, difficulty. Um, difficulty. Um, so anyway, um, a lot of the so anyway, um, 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. Uh and and if you really need to demonstrate what the path to to to getting insurance to pay is to get some um advisory panels to some clinical guideline group to bless it and and Alan Snderman can speak to the difficulty of having APOB blessed by the cholesterol uh guidelines.

uh although the European guidelines have 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 cost to do these aminoassays on these modern analyzers.

Um, so again, there's this complete disconnect between what's what's 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 they pay they pay uh to support the research, etc. So, you can make the argument that, you know, you need to stay in business, you need to to make more money. But anyway, um I I think I I I succeeded more as a a scientist than as a an entrepreneur in what happened to um to lipo science because I I I really we we really had gotten quite far down the road of making NMR testing broadly available to the benefit of so many people internationally.

internationally. internationally. and that just got, you know, tanked when when it was purchased by a lab testing company instead of an IBD company. So, I hope anybody listening who has Well, I mean, yeah, I don't I don't know that that door is closed indefinitely. I I I think I think I think that the MVX test offers a very compelling reason why another company might want to come along and and purchase those assets and and especially given the prognostic um utility of that test. So let's talk now about the this edge case of CEP inhibition.

And one of the first things that that that stood out to me looking at the Broadway and Brooklyn trials, which were the phase three trials of obeseetropib, were that the reductions in LDLP and LDLC LDLC LDLC were greater than the reductions in APOB, if memory serves correctly correctly. What do you think is happening there? happening there? happening there? I know what's happening. [clears throat] Um Um Um 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.

years. years. But even in the older algorithm the issue of whether NMR can reliably quantify quantify quantify these very abnormal HDL particles that are produced by CEP inhibition. So HDL cholesterol doubles or more than doubles not because the number of HDL particles doubles. It's in fact uh the number of HDL particles actually goes down a bit overall. So what happens with CTIP 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 10 times more cholesterol per particle than the small ones.

So HDL cholesterol goes way up way up way up um and HDL particle number does not. But the problem in terms of the analysis is that there's a natural so again we're we're taking advantage of NMR signals from the different size lipoproteins 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 HDL 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 CEP inhibition creating human beings that don't exist naturally and have HDL cholesterol of 120, 30, 50, um, your HDL particles get perilously close to the size of small LDL particles. And now NMR has the the possibility of confusing the two. That's incredible just given the size difference between these particles normally like the apo and the apo1 particles. I I thought they were like a mile apart on that spectrum. 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 what you can get away with in these extreme cases where somebody has really large uh 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. Yeah. Yeah. Yeah. Okay. So what happened with the algorithm that was used in th in that study is study is 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 LDLP but not APOB because the NMR model was not allowing it just count.

So it's really an artifact of the difficulty NMR has with this situation. this situation. this situation. So is the implication that in this case obetropib obetropib obetropib produces a disproportionate reduction in uh cholesterol content of particles relative to number of particles. I know you've talked about obeseetropib and you and most people are very optimistic about the prospects of obeseetropib despite CCTP 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 and this is pure speculation but uh it comes from somewhere that even in the face of the the the CCTP 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 what was bad on the HDL side? um you know given the understanding now that just having a lot of large cholesterol- richch HDL particles doesn't put you ahead of the game in terms of cardiovascular risk what we now know is that small HDLP is powerfully related to mortality all cause mortality so what I told you is true especially with the most powerful CUTTP inhibitors they reduce small HDLP by 10 or 20% if you ignore what's going on in HDL and you only look at what's happening with apo and LDL you think obetrop 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 HDLP going down if there is a causal relationship there don't know that there is yet we haven't you know proved that there's biological plausibility because of the proteins that that occupy that that hang on to that that 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 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 pro demonstrates utility, it might be you're saying it could be just due to the reduction in small HDLP more than I'm suggesting that people's predictions about how much efficacy there's going to be could be wrong based on the LDL that the trial overall is positive or positive enough to have the drug go forward.

But the most dramatic demonstration of something bad happening while something good is happening and the two counteracting each other is if the trial doesn't succeed like the other ones succeeded. So I'm just, you know, we have to wait for the trial and then if the trial doesn't succeed, then I'll I'll say, "Yay, I was right." But it's pure speculation. Well, ve very interesting and I'll have to go back and look and see what the magnitude of the apo reduction was. Uh, but I just remember that it was less than than than it was less, but it's still still very Yeah.

significant. And and but you're heartened by the fact that small HDLP was decreased. Yeah. And I we've actually reanalyzed that data set, you know, with the more recent we we do a better job in in differentiating large HDL from small LDL. And so th those LDLP results are much more in line with the APOB reductions than than that paper indicated. indicated. indicated. Well, Jim, this is this is such a fascinating space and this is a discussion that has been long overdue. Again, I 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 liposcience is. they might not understand in vitro diagnostics and any of the other things that go around to it. Uh probably a lot of people are not familiar with MVX, but my hope is that um that that starts to change after this. Um so uh regardless of of of how you think you've fared as an entrepreneur, you've 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 but put this way at the risk of uh insulting an entrepreneur. I would argue that it's easier to find a good entrepreneur than it is to find a good scientist. Yeah, that's true. And and that's sort of the frustration that that the science is so solid, so much more solid than many startup companies, you know, are are investing in. Um, but it it 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 attractive to investors. that that's my suspicion as to the issue and and that's why um the MVX I think offers more than just a diagnostic. You know, if it could be paired to a therapy um 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. No, you're right. and and that's what will make it successful commercially. My vision though that wasn't realized is how cool would it be to go to your yearly physical and get a lipid panel that had glucose LPIR glyc MBX um at no incremental cost. um you know in in the rest of the world not the US uh where you have national health care there is a premium put on how efficient a 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 LabCore 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. here today. here today. You bet. You bet. You bet.