New Study Questions LDL Risk – An interview with Dave Feldman

Bret: 
Welcome to the Metabolic Mind Podcast. I’m your host, Dr. Bret Scher. Metabolic Mind is a nonprofit initiative of Baszucki Group where we’re providing information about the intersection of metabolic health and mental health and metabolic therapies, such as nutritional ketosis as therapies for mental illness.

Thank you for joining us. Although our podcast is for informational purposes only and we aren’t giving medical advice, we hope you will learn from our content and it will help facilitate discussions with your healthcare providers to see if you could benefit from exploring the connection between metabolic and mental health.

Will the findings of a new study make us rethink everything we know about LDL cholesterol? Today, we’re going to hear from Dave Feldman, an engineer and citizen scientist who has helped put together a research study with some pretty dramatic findings. Now, you can find Dave at RealDaveFeldman on X or Twitter. And also he’s the founder of CholesterolCode.com and CitizenScienceFoundation.org. 

Now, before we get into the interview, we do talk about some technical stuff. So, here’s some definitions. First, lean mass hyper-responder individuals who are lean, metabolically healthy and have dramatically elevated LDL cholesterol above 220 but also with HDL above 80 and triglycerides below 70.

Okay, so that’s this population that we’re talking about. We also talk about familial hypercholesterolemia, which is an in-born genetic mutation that leads to very high LDL. Now, lean mass hyper-responders are not the same as people with familial hypercholesterolemia, but that’s the comparator population for having LDL that high. Homozygous means the most severely affected. Heterozygous are less affected, but still affected with a mutation.

So, that’s the easy explanation for that. We also talk about two imaging modalities. So, coronary calcium scores, which is a CT scan, like a cross-sectional x-ray. And it’s pretty simple test takes about 10 seconds to do, and it looks for calcium in the walls of your coronary artery, which is a marker of plaque and disease.

But we also talk about CTA or coronary CT angiogram or angiography. And what they do is they actually inject IV dye, or contrast, into your veins and then do the same, similar cross-sectional CT scan. And you can actually see inside the arteries themselves. So, it’s much more advanced imaging modality where you can actually measure and see plaque much earlier.

And that’s how you determine what’s called a plaque score, which is a grade of how much plaque you have. So, that’s what they did in this in this study. That’s it for the definitions. But before we get to the interview, please remember this channel is for informational purposes only. We’re not providing individual or group medical or healthcare advice or establishing a provider patient relationship.

Some of the things we discussed can be very dangerous if done without proper supervision, and especially when it comes to LDL or any of your lab values. You should not interpret those on your own, and you should definitely discuss everything with your healthcare team before making any decisions about what the labs may mean for you, right?

Because we’re talking about one study in a very specific group. Whether that applies to you or not is something that you have to decide with your healthcare team. All right? So, with that as the introduction, let’s get into this interview with Dave Feldman.

So Dave, welcome back to Metabolic Mind. You’ve been in the news a lot lately. What’s going on? 

Dave: 
Obviously, the big news that’s dropping right now is the match analysis that comes from our keto study. That’s the internal name and the one that Budoff wants to go with, but that I’ve regularly referred to as the Lean Mass Hyper-Responder study.

As you know, you’ve been there from the start, it’s been a very long journey to get to the point where we finally were coming to data that would, in fact, have a publication ahead, and we’re here now. We actually do have a match analysis. It’s not just with our cohort, but a little late into the game, we found out that we could do a comparison between this and what’s known as the Miami Heart Study principal investigators, Khurram Nasir.

And from the moment I knew that this was even possible, I said, hey, let’s see if we can do that with our baseline data from our existing study. 

Bret: 
Yeah, so let’s rewind for a second. First and foremost, we’ve talked about your study on this channel before and, hopefully, people understand it. But, it in itself, is a groundbreaking study, studying this specific group of people who fit the lean mass hyper-responder criteria.

And then, markedly elevated LDL cholesterols to the level that are otherwise seen in genetic abnormalities that make cardiologists very nervous. But these individuals have no genetic mutations to cause it. So, it’s presumed due to the diet alone. So, you’re doing the study on these individuals. You’ve collected their baseline data, including a coronary CT angiogram.

Now, what you’re saying is you’re comparing it to a completely different group of people who are not on a ketogenic diet, who are not lean mass hyper-responders, who do not have markedly elevated LDL cholesterols, who also have a coronary CT angiograms. You can directly compare them. Did I sum that up right?

Dave: 
Yeah, actually that’s pretty good. 

Bret: 
Okay, alright. So, what’s unique about this then? Or you tell me what’s unique about this in the field of cardiology and lipidology? 

Dave: 
Of course, going all the way back to our initial conversations, the reason I’ve always felt lean mass hyper-responders should get studied is not just because, obviously, it’s in this low-carb context.

But as I like to point out frequently, we all, of our data that associates LDL levels with corresponding cardiovascular disease are typically in populations that have some form of dysfunction in lipid metabolism or some kind of illness. Something along those lines.

We rarely have an opportunity, especially in the area of metabolism itself, to get a variable of interest, such as LDL and in particular, it’s a ApoB containing lipoprotein, in a very high level all by itself in otherwise metabolically healthy individuals. And that’s exactly what we were aiming for.

The eligibility criteria wasn’t just this triad associated with lean mass hyper-responders. For anybody who’s just initiated, we’re talking very high levels of LDL but alongside high HDL, cholesterol and low triglycerides. But also other risk factors we wanted to be sure weren’t a part of the study.

For example, prior diagnosis of atheros, of cardiovascular disease, hypertension, all of these different things. But here’s the thing, Bret, once you get that’s fine. But there aren’t a lot of studies done on metabolically healthy people anyway. Whether or not they had high LDL or not, especially with the kind of outcome we’re interested in, which are these advanced scans known as coronary CT angiography.

And our study is capturing it at baseline at zero, and then it’s doing a second scan with all participants for one year later. And that was the original design, is the current design, if you will, of the study. This match analysis was something that we found out was possible because this other data set was completing at around the time that we were starting our study.

So, that provided an opportunity because we didn’t have a comparator before. Now we do have this comparator of other metabolically healthy folks, but who of course have more average LDL levels. 

Bret: 
It’s a great point. You have all this, you have all this amazing data on this group of individuals, but then you say, how does this compare to anybody else?

We don’t know how to compare it. All of a sudden, along comes the Miami. What was it called, sorry? The Miami Heart Study? 

Dave:
Correct. It’s the Miami Heart Study. 

Bret: 
Yeah, the Miami heart Study. Yeah, so now all of a sudden, you have a direct comparison by age, by metabolic health. The only thing that’s different presumably is the LDL.

And so, what did you find? What’s the, let’s not bury the lead here. 

Dave: 
Yes, of course, it’s worth emphasizing, at this point, when I’m saying we, it’s really the Lundquist Institute because all the data is internal. It’s their statistician, and she was able to get a near one to one match between 80 people in our a hundred person cohort and those with Miami Heart.

So, before I go a little bit further, it’s worth emphasizing this part of it. Miami Heart’s age range was, we had about 80 people that fit within their age range. And so, once we had that 80 isolated, then she found the match, and the match was impressive. They were matched for age, which was around 55 years of age.

And the ethnicity was match. The risk factors, all of them were extremely close. The one thing that she couldn’t match for was BMI, which is not surprising. Our cohort had around a 22.5 BMI. You’re not surprised to hear that because, of course, it follows the lipid energy model. The Miami Heart group was around 25.8, but they were all very healthy.

Once again, not only the Miami Heart group also had high HDL, low triglycerides, for example. So, yes. Sorry, I had to add that little extra in, but sure enough, their plaque had no statistically, no statistical significance at a population level between both groups. And that was, I think, a very fascinating finding when you know that our, the average amount of time our cohort had been at these levels, at this triad, were 4.7 years.

So, average time, our eligibility was you needed to be at these, you need to be a lean mass hyper-responder, this borderline lean mass hyper-responder. That’s our eligibility for at least two years. But we had 4.7 years in this cohort of 80 that were compared to the subset, and their LDL was around 272 average.

Miami Heart’s was around 123. So, it’s a substantial difference in the LDL overall. 

Bret: 
Just some remarkable findings right off the bat. The degree of LDL differentiation is huge. And the fact that you’re using coronary CT angiogram, not calcium scores of some people have mentioned, not just calcium scores, which can be a later manifestation of disease, but the amount of plaque seen by the more advanced and more detailed coronary CT angiogram.

So, you’re using that technique and the degree of time that they had the elevation because that’s the other knock that, well, if you’re just looking at one year or two years, you’re looking at five years, which still is not a 20 year, 30 year study granted, but getting close to five years by CT angiogram.

Dr. Matt Budoff, who is one of, is a preeminent cardiovascular CT imaging researcher. Does he feel that’s pretty significant amount of time where you would see some, a rapid progression of disease with that degree of LDL? 

Dave: 
Yeah, of course. I don’t want to speak for him in that we’d want him to be here, but his presentation is online and is on my channel.

He is a good scientist, and they all are, and that they go, oh, here’s this more recent step. And here’s another step that takes us even a bit further. It’s always like a journey. It’s always like what’s the next thing to come? He and many others consider it to be a fairly significant and important finding.

I think that they would say, yes, with the usual caveats that we still have more to go. This is where I have to re-emphasize the importance of us just getting to this first step was already a huge amount of work, just to get to this first step. Now, I want to add one other piece to this.

Finding the one other very important piece, which is that we also were looking at LDL versus the plaque itself. Because after all, you not only had the total plaque, which you could think of as sorted, right? You could then add on top of that what the corresponding LDL levels were for everybody. So, if you were to put that in a scatter plot, would you find that there was a correlation between everybody’s LDL and their total plaque score?

And indeed, there was not. There was not a correlation between standing LDL levels and total plaque score with both populations, with both the lean mass hyper-responders and with those from Miami Heart. And that was pretty fascinating. And one last thing, this part’s important, but I want to add a caveat to when I do. If you do look at the area under the curve of the total plaque for both groups, bearing in mind that both groups had half or more with no plaque.

It’s a very healthy group for a bunch of 55 year olds. Because at middle age, as you start developing plaque, but if you look at area under the curve and compare them, our group actually had a lower area under the curve. So, while there’s not a statistically significant difference, ours was technically trending towards lower overall plaque.

Now, I bring that up not to say our group was clearly doing better because I want to bring up the statistical significance. What I will say though is that you would expect at least that the trend would be the opposite direction. So, the lipid hypothesis was, at least, present in some fashion per a lot of people’s expectations. Such that, okay, maybe there’s not a statistical statistically significant difference, but probably it’s at least starting to head that way.

That’s what you would expect to see, is that it’d be flipped and we see the reverse instead. And again, I want to caveat, that doesn’t mean it’s going to continue to diverge or anything along those lines. Just that there doesn’t so far seem to be anything hinting in that direction. 

Bret: 
Yeah, and one of the things I really appreciate about your message and Dr. Budoff’s message is the caveats, is the nuances.

And in the video that you shared of Dr. Budoff’s presentation, there was a question that says, so does this refute the LDL hypothesis? And he said, no, this study does not refute the LDL hypothesis. And, I think, he’s right. A study like this, that’s not the intent of the study, right?

You wouldn’t expect it to, but does it raise the question in a way that has never been raised before? 

Dave: 
Yeah. 

Bret: 
And that’s what science is supposed to do. So, that’s part of what I really appreciate about the message. Now the flip side, though, is even with these caveats, even with your measured approach of how this is presented, seems like this has got a lot of people upset.

And I’m curious what you’ve seen as some of the, I don’t know, the pushback or the negative reactions or the discounting? Like one, what have you seen? And two, has it surprised you to see it? 

Dave: 
Frankly, it hasn’t surprised me that much. We knew there’d be a lot of pushback. Even before the very first scan, you think, hey, if these data actually turn out to be as we’d hoped that they would.

Of course these will be, because in the study design, this will be the first thing people will say. For example, hey, wait a sec, there’s selection bias, per se, in that this is a healthy cohort, right? And by the way, this isn’t me dismissing it. All of these concerns are worth discussing and bringing forward.

But that was also by design. We specifically wanted a healthy population in part to meet with IRB approval. You can’t, for example, you can’t have people with an existing heart condition of any kind and say, we want to study what happens if they’re not treated, right. Of course, we would want to bring that forward.

However, there is something of relevance to bring up with regard to the lipid hypothesis as it stands today, which is whether or not it’s context independent. The expectation right now is that it is. So, to expand on Budoff’s point, a lot of people, both pro and anti-LDL, if you will, always treat it as that false dichotomy that either it’s absolutely true or it’s absolutely not.

As I’ve said to you many times before, and as I say here on the show. We can think of it in a nuanced fashion. It may be that the lipid hypothesis applies, but maybe there really is a subset of a context, or maybe it’s the other way around? There is more of a subset of the context of where there’s a lack of metabolic health.

So that said, I want to bring that forward, but by the same token, I also want to emphasize for those people who do think because we’re selecting for healthy folks, that might be a confounder. Why wouldn’t that be exactly the population you’d be aiming for? Since again, the variable of interest is now separated from those other confounders.

If you feel the confounder is, itself, something these folks are doing, such as being on a ketogenic diet. If the case you’re wanting to make is that being metabolically healthy, being on a ketogenic diet is so cardioprotective that the LDL is just going to have next to no relevance or no relevance. That’s still an important finding, even if you don’t think it’s comes back to the lipid hypothesis itself.

Bret: 
So again, not what the study has shown or proven, but a question that is being raised that is exactly what should be raised. You pick, like you said, you pick this population for a specific reason. Now, that doesn’t mean your findings are going to apply to everybody, and it’s not meant to be, right?

So, to make the argument that this doesn’t apply to other populations. Of course not, that’s not how studies work. But again, it raises the question, and it’s a stepwise process. So, this is step one. Step one was you have your baseline data. You compare it to a match control group and show that there is no difference in cardiovascular disease, despite the dramatic difference in LDL.

Step two, though, is then for you to complete the one year study, your prospective study, that you’re doing with a follow-up CT angiogram. How does that change the discussion then in terms of the results? How do you see that being different from comparing the baseline data?

Dave: 
As in how would this match data compare to our final analysis where the longitudinal data are in? 

Bret: 
I guess i’m saying since you’re taking it a stepwise approach for saying, what have we learned? So, what more do you learn then by doing the one year CT angio? Because I’m trying to see like at each step, right?

What lessons can we take from each step, do you think? 

Dave: 
Oh, first of all, per the original design of the study, we want to see if there’s plaque progression. Period. Which we’ll see with the longitudinal having the first scan then to be compared to the second scan. To be fair, and this is me just managing expectations, now that we know that our population is middle age, particularly if they’re into their fifties. I just want to say to everybody, my baseline expectation is that they will, at a population level, have plaque progression because every middle age population in that range, especially majority male, statistically will have that.

Go ahead and tell me what population is immune from development of atherosclerosis. I’ll be very interested in the data that you have that can prove that even folks with PCSK9 loss-of-function have development of atherosclerosis, even if it’s much less, relatively speaking.

So, the reason I bring that up is because, yes, it would be a huge finding if it turned out that there was not a population-level increase of plaque. But I do not want to set anyone’s expectations at that level. When we were first putting this together, as when I was announcing the study, I wasn’t even bringing up people in poor metabolic health.

I was bringing up the work of Brown and Goldstein because it’s technically the closest corollary to what is, folks who are assumed to be metabolically healthy, but who still have high LDL. And that the high LDL is what’s driving the disease. 

Bret: 
So, you’re referring to the familial hypercholesterolemia? 

Dave: 
Correct, correct. So, if you have a genetic abnormality, such as the kinds Brown and Goldstein were looking at half a century ago, those who had homozygous FH, as you know and I’m sure you’ve met many, if not have many as patients. There are some who have a very super high levels of LDL now who are not those same folks that Brown and Goldstein were looking at with homozygous FH on a ketogenic diet.

We’ve interviewed some for our documentary, and understandably, doctors and scientists are really worried if they’re going to exhibit the same advanced cardiovascular disease because that’s what they were seeing then. And this is why I brought it up In the course of our study. I knew our averages would be much higher, and if there wasn’t, if there was not these other confounders, then the obvious comparator would be looking at folks who have FH and what their development of plaque is given the threshold levels.

Because per the lipid hypothesis, that’s more than enough. You don’t need to be a smoker. You don’t need to be a type A personality. If you, if the levels are high enough, that will do it. So yes, our patients having levels in the 260, 270, et cetera, we should see a signal given just how high it is. It’s worth reemphasizing something else, too.

This is not linear. If you look at like the EAS 2017 consensus paper, they’ll say it’s log linear. So, it’s much more of a curve. A hundred LDL, or sorry, a 200 LDL, is not twice as bad as a 100 LDL. It’s several fold higher. That’s why they say things like, look at every stepwise increase of say 38, whatever, LDL per well.

Ours are now standard deviations because this is an extremely rare level of LDL. So, sorry, I realize that was a lot to say. The bottom line is I felt confident, and I still do, that if the lipid hypothesis is truly context independent, we would see a pronounced increase of plaque progression in our population between the two scans.

Because that’s exactly how it’s presented to us in the context independent version of the lipid hypothesis. 

Bret: 
I’m excited to see that come out in one way or the other. Because like you’ve said, we need to know the answer to this question one way or the other. We need to know. But certainly now with this baseline data, it seems pointing in a direction that it should not be concerning.

But that’s why we do the research, right? That’s why you play the game. You got to find out the results. So, I appreciate that. Now, one other thing though about the numbers and the variation. So, with heterozygous FH, it’s generally thought LDLs are between 190. and I don’t know, 250, 300 on average, right?

They could be different. And then homozygous FH is where you get to the 500 range or above. Even now with your data, were you able to look specifically at those who were at the 500 range or significantly higher and see if they were outliers in terms of their plaque score? 

Dave: 
Yeah, actually, it’s funny you bring that up.

The max level of LDL, I believe, was 590. Yeah, the match mean average was 55 LDL to 72. Max was an LDL cholesterol of 591. You can see on the graph the person who’s at the 590, and then just follow it straight down to the total plaque score. You could see that they don’t have a total plaque score at all.

Like they’re, it’s just not there. 

Bret: 
Yeah. 

Dave: 
And again, I’m going to say, I’m going to have the knee-jerk instinct that is appropriate all the time whenever I’m saying exciting data, particularly as my profile grew or as in the profile of this research grows, that doesn’t mean it applies to you. It could be that you have an LDL of 590, et cetera, and there are more considerations at play.

As always, I’m going to re-emphasize, work with your doctor. Recognize your context can be individual. And, if you don’t mind, Bret, I’ll go ahead and bring this up. A lot of doctors, like yourself, who are pro low-carb and who probably might even be considered more liberal with regard to what they would allow for higher levels of LDL, you yourself still prescribe statins, right?

Bret: 
Absolutely. 

Dave: 
For cases that you find concerning? 

Bret: 
Absolutely. 

Dave: 
And I think that’s even after we’ve crossed this dotted line, even after we have this data in hand and so forth, I want to feel confident everybody who’s backing this research up, that we continue to do our best to try to gather this research and inform all those patients, all those doctors with more information. But recognizing that patient care is still an individual relationship and needs to always be considered that way.

I hope nobody would take any single study under any situation, this is all you need to know. And then from that, I can make a fairly binary decision. 

Bret: 
Yeah, one of the most exciting things I think of when I think of this study is how it brings up the use of CTA and calcium scores, but also CTA. Because look, if you, I shouldn’t say you, if somebody has high LDL and they want to know, is this a problem or not?

The guidelines now say, just start a statin. But it’s data like yours, research studies like this, that would say, huh, maybe we could get a CTA and maybe we can look at your plaque scores now? I was doing CTAs 20 years ago when it was a lot more radiation.

You know, 15 times more radiation, 10 times more radiation than it is now. So, the radiation doses come down. So, these are much more available. That doesn’t mean everybody should get a CTA. But it means you can talk to your doctor about it, and see if it’s right for you. And maybe that’s a tool that we will start to think of more to say, okay, let’s define better if this LDL is an issue for you or some way to follow you.

It just brings up more options that didn’t exist when it was case closed. All elevated LDL needs to be treated, period. No, questions asked. So, I think that’s one of the most exciting parts of this study, regardless of the findings, is it says look, we can do things differently, and we could do things better.

And not just for research, but maybe for clinical practice? So, that was me on my soapbox, and my guess is you would probably agree with that. 

Dave: 
Yeah. Listen a CT angiogram, as you just mentioned, the radiation dosage. I’ve had two of them myself. It’s two milli seavers was what it took for me. It’s relatively, and yes, there’s a little bit of a risk in contrast die. It’s, I want to say, it’s maybe one in 10,000, I think, for something related to the kidneys.

So, it might be worth looking at, but like the radiation dosage itself, free living just year round, living on the earth is three, three and a half milli seavers, I believe. And you can compare it to a mammogram. Mammogram as I want to say, 0.5 milli seavers. So, it’s like basically four mammograms worth of radiation.

I, myself, I’ve had family members get it because of the dosage being so low and because, frankly, it’s just peace of mind. If there’s any concern of having heart disease, first of all, consider CAC at least because a coronary artery calcification scan doesn’t even require the contrast dye.

It’s about a mammogram and a half at most. And it’s tends to be highly correlated with the amount of soft plaque given, especially recent research that’s come out in the last couple years that said CT angiogram, gold standard. If you get a CT angiogram, I’ve said this many times before, but nothing beats the actual detection of the disease.

And even in Budoff’s presentation, he said at the end, somebody had pointedly asked him, so is this end the lipid hypothesis? And he’s, no, it doesn’t end the lipid hypothesis. But another questioner had said, should we just ignore it if somebody’s levels are high? And he said, no. But be aware that there is, there are a lot of existing data now that shows somebody has a CAC of zero.

They’re high LDL. Even in people who have FH, confirmed FH, out of this study, I think out of Denmark, it didn’t matter. It didn’t seem to matter as to whether, as to what their risk stratification was. Yeah, so nothing. I’m sorry, Bret. Nothing to me beats actually seeing the disease itself to make your next informed decision.

Bret: 
Yeah, I think, that sums it up very well. And when you go against what’s called common knowledge or common practice, you, are held to a higher standard, I think, and you’re held to a higher level of attacks as well. So, let me just ask you, do you think that’s unfair? 

Dave: 
It’s a tough question to answer.

Yeah. I think in any circumstance where you’re challenging a real structural paradigm, you should expect it. I think there is a human nature component. This is also something else that I say frequently, and I’ll just say it again. I really do believe that the vast majority of my of critics, of me and of my work, are honestly well-meaning. They feel that there’s too many people taking comfort and maybe making decisions that could ultimately lead to harm.

That lipids are indeed dangerous, whatever the context. Or that, for that matter, maybe we are discovering context in which it’s okay? But people are taking it too far and it’s impacting their whatever. All of these I can, I get it. I get why, If you’re a clinician and you’re working with people, I understand better now, Bret, than when I started coming at it from an engineer, how much doctors really have enormous attachment to the care of their patients and feel a great deal of buy-in and responsibility for that.

And so to that end, I always try to keep that in mind when there’s criticisms levied in my direction. That said, again, another big part of this is the paradigm itself.

Whenever there’s something a lot of people agree is true, that some traction seems to be setting foot, that it could be false, there’s a challenge. That’s just how it’s always been throughout medicine. 

Bret: 
I want to focus in on one thing you said there, though. That some people are going to run with this and make their own conclusions even it has nothing to do with what you are saying or what you think the conclusions are like.

We just did a video on the Stanford Twin Study. It was a very small, very short study comparing two very specific diets. And from that, people are concluding that vegan diets are the best diets, or we all need to eat more plants, which you cannot conclude from the study.

That study had nothing to do with conclusions that broad. So similarly, people may run from, take this study and say, see, LDL does not matter at all. Or see, I can have high LDL and it does not matter at all. That’s not what you’re saying. That’s not what the study says, but people may conclude that.

So, I don’t know what, how to phrase this question, but do you have a responsibility for that? Does Dr. Budoff have a responsibility for that? How do we phrase that? 

Dave: 
Yeah, I would say that we do have a responsibility to do our best to inject nuance where we can. I, myself, I try more than I ever have to funnel that into every appearance I have that’s semi long form, like this one, which is why I take moments to try to recontextualize and try to put it out there.

If, let’s say, somebody were to have me on and they said, hey, I just want to have you on and just report the findings and then celebrate after that. I just want to have a clip of you saying that, I would now feel uncomfortable with that because I would feel like it was too easily allowing for something bumper sticker. A lot of times, I think it’s helpful to just add all of that additional context and be sure that gets baked in.

Now to the extent that people are critical of myself and my work, that I am intentionally promoting a bumper sticker version of this. That I do think is unfair because if they know better, if they themselves are watching things like this. If they are reading our papers, are very careful in their language with regard to what their limitations are and so forth.

That one, I don’t give is free of a pass for. Let’s just put it that way. 

Bret: 
And you might be more prone to those attacks because you don’t have letters after your name. You don’t have an MD or a PhD after your name. So, that sets you up with probably a bigger target. But it took you, an engineer outside the scientific community, to come in, and by the way, accomplish something pretty amazing for somebody without a research background. So, is that fair or unfair, too?

Since we’re on this question of fairness. 

Dave: 
I, first of all, I think it’s absolutely fair to assume somebody that’s a credentialed professional in the domain that you’re looking into, at least has a head start compared to anybody else that you’re seeing in that domain. But I always think of it from that perspective.

They’ve got a head start. It doesn’t, I’m sure you, I certainly know people who are very savvy, in general, or they’re very savvy because they had to learn a particular thing. For whom I start to go to that person because they had a special motivation to specialize in that thing, even if that’s not what their original career was.

And that’s what’s fascinating about engineering. Where I’m coming from is you don’t, if you look at job listings, the job, I know this because I’ve hired 30, 40 engineers in my lifetime. We’re not putting out, you need to have this degree and this degree in order for us to hire you.

No, it’s, do you work in Python? Yes. Have you also, do you have experience with HTML5? Do you have experience with C sharp. And we’re laying out the work that you did, and the work that you did is your resume. It’s usually a warning. It’s usually a red flag if we actually end up seeing somebody who’s touting all of their education bonafides.

We want the work. So, in a way, I feel like I am taking the engineer’s route. And that, yes, I haven’t gone through all of the different classes. I’ve come in through a side door of learning lipidology and working my way backwards doing these experiments that I’ve done, like an engineer would do, where I’m just getting under the hood. And then, allowing that to inform everything that I then would take to great researchers to actually codify this together.

Of course, Nick Norwitz, Adrian Soto-Mota, David Ludwig, Anatol Kontush, all of these folks, who I’ve been helping to integrate the energy model, the lean mass hyper-responder phenotype. I, obviously, rely on them a great deal, but it’s also allowed for this level of kind of cutting edge advancement in the research that I’m proud of.

I like being in a pool of names with the huge numbers of letters, and then I’m just kind of there on my own because it’s almost as, it’s a running joke with a lot of my friends in engineering, that there’s a little bit more of a high five-ness with them in being somebody, who did accomplish a lot without having done it in the traditional routes.

That’s the Steve Jobs and the Zuckerbergs and all of these folks who went to college and then drop out and then change the world. That’s the world we live in, right? That’s what we’re used to. So, in that sense, yeah, I don’t mind my credentials are in the papers that I’ve now published. My credentials are in this study that I’ve been able to get together and where it’ll take us.

And I’m fine with that resume. It’s working out so far. 

Bret: 
That’s great perspective. if you were going to sum things up about what you found and what the impact is, what’s the elevator pitch for the summary? I know, which is hard for you to do because you like to explain things in detail with the caveats. But is there the quick elevator pitch or am I just asking you to do something that shouldn’t be done?

Dave: 
Yeah, the elevator pitch is while limited, I do think these data are extremely compelling and quite possibly very pivotal. I say quite possibly pivotal because you just, you can’t really know that until you’re further along in the process. But I very early on, literally at the point where the phenotype was getting identified, the lean mass hyper-responder phenotype, I dreamed then that if we could just study these folks.

If we could just get studies going around them, they may not only tell us a lot about risk, they may actually teach us a lot about lipid physiology. And I still have that opinion today. So, do I think this is definitive? No. Do I think it’s pivotal? I think so. Only time will show. Only time will say.

That’s my best elevator pitch. Sorry. 

Bret: 
That’s good. That’s good for you. That was pretty good. That was pretty concise for you.

All right. Thanks for all your work. And I’m so excited that it’s from where it started, from our first discussion back in 2018, to now. To see these results and to see what you’ve accomplished is truly remarkable. Congratulations. 

Dave: 
Thank you, Bret. 

Bret: 
Thanks for listening to the Metabolic Mind Podcast.

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