Oct 08

Guesting on the Peter Attia Drive (2 of 5) – Cholesterol Challenge, Lipid Metabolism, LDL Receptor

 

This is a five part series covering my appearance on The Peter Attia Drive podcast. Please skip to the final post to comment. 

Part 1 of 5 – Lean Mass Hyper-responders, Oxidized LDL, All-Cause Mortality
Part 2 of 5 – Cholesterol Challenge, Lipid Metabolism, LDL Receptor
Part 3 of 5 – Remnant Cholesterol, Craig Moffitt, Fasting
Part 4 of 5 – Energy Status, Risk, Testing the Hypothesis
Part 5 of 5 – Comments and Featured Thoughts

 

The Low Carb Cholesterol Challenge

Dave Feldman

The part of what this energy model, in particular, that with hyper-responders, specifically lean mass app responders comes back to. I don’t know if you’ve, I know you don’t necessarily hang out on Twitter too much, but you know that I’ve had-

Peter Attia

More than I would like.

Dave Feldman

I have this pinned Tweet, I’ve been pinging lots of lipid-lowering experts on this, I’ve said, “Look, I’m looking for any studies that show people with high LDL will have high cardiovascular disease, if they likewise have high HDL and low triglycerides.” But there’s one qualification, it can’t be a gene or drug study. It’s gotta just be-

<Snip>

https://twitter.com/DaveKeto/status/963437664199352322

Peter Attia

No, I’m seeing that. Here’s my concern with that, Dave. I have no doubt in my mind that you are a truth seeker. I don’t think that’s true of necessarily some of your peers. I do think a number of your peers are deluded and so filled with their own confirmation bias, and so unwilling to acknowledge that their precious low carbohydrate diets could be hurting them. That not with malicious intent, but with blind carelessness, they are absolutely ambivalent to anything.

I don’t put you in that category, so I will challenge you in the following way…

Dave Feldman

Great.

Peter Attia

When you say, “Show me an example of something that is not a genetic study, that can point to that phenotype.” The reason I would call issue with that is, why would you limit yourself from genetic studies? That’s sort of like me saying, “I want to know if there are people who are six feet tall. I think they might be, but I’ve never seen one. So, if you can go into a kindergarten class and find me one, I’ll believe it. But, you must limit yourself to the kindergarten class.” In other words, that’s an obscure example. What I’m basically saying is, you’re excluding so much potential data by excluding all of the genetics.

Because when people talk about genetic studies, we have to remember something, most of the genes, most of the snips that lead to alterations in lipids and lipid metabolism, are completely unidentified. FH, for example, familial hypercholesterolemia, which would be the most obviously example to counter that point, you’re excluding because it’s a genetic condition. What the listener might not know is that FH is a phenotypic diagnosis, not a genotypic diagnosis. FH is arguably the most heterogenous collection of genes you can imagine.

So, why would we exclude looking at those people, when that’s, in many ways, one of the richest bodies of evidence for a natural experiment in … To answer the question, can you have LDLC, high HDLC, low triglyceride, and still get atherosclerosis? That’s the question you’re asking, right?

Lipid Malabsorption – Part I

Dave Feldman

Yes, yes. Well, so, we’ll double back to that in a sec, but basically, you’re taking us back to genes, and this is why … This is another hypothesis, fully untested, I’m in the process of trying to collect on it, but I call this loosely, Cellular Lipid Malabsorption. Right, just generally shorten it to lipid malabsorption.

Basically, here’s the issue that I have with the existing mendelian randomizations. For that matter, almost all of the gene-based studies is what we’re trying to get, what we’re trying to get, is as much as we can, the isolation of just a higher gradient of LDL particle count. That’s what we all secretly … We want your wand. You’re talking about where we can wave it, and then there’s just magically more LDL particles in some people, or for that matter, less LDL particles. Without touching any other parts of the process.

The problem is, that I believe … I’m keeping a list of my own SNPs of those genes that are either resulting in higher or lower LDLC. Unfortunately, of the ones that I find in the mendelian randomizations, they don’t just result in the higher LDLC and LDLP, they also come to be that way because there’s a lack of lipids or lipoprotein uptake by the cells.

Therefore, particularly with endothelial cells, you’ve got to be concerned that that could cause dysfunction. And therefore could be a reason for why you’d have higher levels of atherosclerosis. This is why-

Peter Attia

Wait, wait. So, explain that part again. The last part.

Dave Feldman

We would ideally want to-

Peter Attia

No, no, I got that part, but tell me about what you said about the endothelial cells?

Dave Feldman

Endothelial cells being dysfunctional.

Peter Attia

Yes.

Dave Feldman

Would that be potentially problematic for atherosclerosis?

Peter Attia

Yes.

Dave Feldman

Okay, then why would we want to look at any SNP that would in any way impair, inhibit them relative to a normal person endothelial cell?

Peter Attia

Why do we believe patients, or a subset of patients with FH, as a result of their FH have defective endothelial cells?

Dave Feldman

Well, if you’ve got defective LDL receptors.

Peter Attia

There’s not receptors on the endothelial cell. It’s diffusion mediated.

Okay — time out!

Endothelial Cells and LDL Receptors

This was probably the most surreal moment to me in the podcast as I found myself instantly conflicted.

I assumed going into the broadcast that we’d likely agree on all the fundamentals, yet would disagree on the broader interpretations. And should Peter correct me anywhere on a question of fundamentals, he was almost certainly right and I’d be all too happy to concede this on the spot (hence my “stealth interviewer” statement earlier).

Yet… Peter said something that I was 90% certain was in error. Endothelial cells do have LDL receptors… right? Peter said this with such specificity and confidence. Could I be wrong about this? I was suddenly searching everywhere in my head where I learned about endothelial cells and LDLr.

Regardless, the bigger problem is that endothelial cells were just one part of the larger equation to this central point anyway. If I got hung up on this point with endothelial cells, right or wrong, I might also fail to illustrate the larger one. The issue of Lipid Malabsorption was relevant to all tissues, so why not just move the spotlight to the other tissues and fact-check the endothelial cell receptor issue later?

Dave Feldman

Okay. Well, yes, but you’ve got the receptors with the Adipocytes, right?

Peter Attia

Yes, but, at least 20, if not 40% of LDL uptake, is not even receptor bound in the body.

Annoyingly, it was in this moment I remembered where I had first heard about endothelial cells and their family of receptors (including LDLr). But I didn’t want to doubleback as the moment as I really didn’t want to say anything on it until I was 100% sure.

Fast forward two hours…

After the podcast was completed and I got back to my car, I enthusiastically pulled up every reference I could on endothelial cells and LDL receptors. I further had Siobhan redundantly do the same. It all checked out.

Fast forward three weeks later to August 16…

I was at another conference in a lengthy tweet exchange with Nick Hiebert between sessions where at one point I said the following:

https://twitter.com/DaveKeto/status/1030113916540669953

To my surprise, Peter jumped into the debate (something he rarely does on Twitter) with the following:

https://twitter.com/PeterAttiaMD/status/1030124638909673472

Of course, now I was much more confident in the original assertion. But I wanted to keep this lighthearted and friendly so that my answer wouldn’t seem confrontational or curt in any way. So given many were pinging each of us about when the podcast would be released, I decided to insert a joke about that along with the pathway answer…

Peter then ultimately acknowledged this but expressed his reservations that evidence was thin for in vivo studies in particular (even if demonstrated in vitro). We then exchanged a few more tweets back and forth from there before stopping for the day.

Lipid Malabsorption – Part II

We now return the discussion already in progress…

Peter Attia

And not all cases of FH have receptor deficiencies. So, there are at least 2,000 vaguely identified genetic causes of familial hypercholesterolemia. Some of them are lower, they have fewer receptors. So, the PCSK9s are a subset of FH, right? About three to 5% of patients with FH have-

Dave Feldman

Over expression of PCSK9.

Peter Attia

Over expression of PCSK9.

Dave Feldman

Gotcha.

Peter Attia

But that’s how PCSK9 was discovered.

Dave Feldman

Okay, but in that case, you’re impacting a cell’s capability of uptake for lipids, or for lipoproteins, right?

Peter Attia

Yes, you are in that situation. Those patients’ livers, will take up less LDL, because PCSK9 is a protein that does, among other things, degrades the LDL receptors because they have hyper-functioning PCSK9, they are more rapidly degrading their LDL receptors on the livers, so they’re taking up less LDL particles. Which explains why they have higher LDL.

Dave Feldman

But this, again, introduces a dysfunction on the lipid metabolism itself.

Peter Attia

But that has nothing to do with the endothelial. That has nothing to do where atherosclerosis occurs. All that’s doing is giving you more LDL and circulation.

Dave Feldman

But, it’s … Let me put it this way. Why not take anything that results in a higher level of LDLC or LDLP that doesn’t impact any lipid absorption from any tissue at all?

Peter Attia

Right. But that might be a bit of an artificial constraint right? As you pointed out yourself and I think anybody listening to this will appreciate, this is a complicated dynamic system, so it is going to be difficult to have some perturbation in a system that will lower or raise LDL that won’t have some other effect.

The question is, how do we, with some reasonable degree of certainty, look at those other effects, and ask whether or not their germane to the question of atherosclerosis and the causality of LDL to atherosclerosis. So, I think the PCSK9 example is not an unreasonable one, because we have a pretty clear understanding of what that gene does. We have a very clear understanding of where that protein lives, and what it’s doing-

Dave Feldman

But if anything, that’s resulting in the other direction. Where, if you have lower LDLC or LDLP from under expression PCSK9. That actually results in a hyper-absorption of lipids, for example.

Peter Attia

In the liver, yeah. They have enhanced hepatic clearance. So both ends of that though, right? So, if you have hyper-functioning and hypo-functioning PCSK9 patients out there, both of whom exist, I believe the hyper-functionings were discovered first. But they hypo-functionings are kind of the ones that gave the drug companies the desire to go in … Not the desire, the idea to go and create a drug to mimic that phenotype. But these patients walk around with LDL cholesterol of 10 to 20 milligrams per deciliter, and as far as anybody can tell, there’s no other side effect of that.

Dave Feldman

This is the thing I want to zero in on. Let’s say that we do that. Let’s that we go “Okay, never mind this side part of the lipid hypothesis end of it. I’m sorry, the lipid metabolism end of it.” We should then be able to look back at these people with the more novel versions of SNPs and assuming there’s at least a large enough population, we should see that longevity. Your mentioning of the APOC-III from earlier, is the first that I’ve been able to find of that one. I’m interested to see if we would see that across the board with these people who have these SNPs.

Peter Attia

Yeah. I mean, I suspect it will have to do with how many of them there are, and how long they’re being tracked.

Dave Feldman

Because, the all-cause mortality, you’d have to understand, I sympathize with your concern, as it’s absolutely the case, nutrition medicine, there’s certainly a lot of personalities that are out there. But, I can understand at least for me, on my end, I like hard endpoints over soft endpoints. Maybe it’s just the engineer in me. I like ones and zeros. Death is pretty easy to diagnose. Whereas, soft endpoints, the downside is there can be arbitrary decision making on the part of the patient and the doctor.

Peter Attia

Yeah, you know, I heard you mention that on one of the podcasts. I gotta tell you. I disagree with that. Having seen more patients in an E.R. when I was in residency with MIs, I can honestly tell you, Dave, never once knew what their cholesterol levels were.

When someone comes in the E.R. with chest pain, I care about the advanced cardiac life support algorithm, which involves oxygen, which involves and EKG, which involves troponin, which involves morphine, aspirin, and potentially a trip to the cath lab. But, we are, and no where in that algorithm, are we asking, “What’s their LDL?” And letting that help us think, is this indigestion versus other things. So, I do take issue with calling MI as soft outcome.

There’s a little bit of a misunderstanding here. The podcast Peter referred to is likely my appearance on ZDoggMD. I have it queued to the specific part here:

To summarize:

  • Person A has high cholesterol and is told regularly by their doctor of this concern.
  • Person B has low cholesterol and is likewise aware of this through their doctor.
  • Each eats the same meal, experiences an intense 30-minute chest pain right afterward.

While I think each have a decent chance of checking themselves into the hospital, I certainly think Person B is more likely to rule it heartburn and take a tums. Person A checks in, gets an EKG, post-cardiac enzymes, etc. So if both A and B are in a study, we can see how their own cholesterol awareness furthered a feedback loop that could potentially bias the data. The only way to guard against this would be for both the doctor and patient to be unaware of the lipid numbers. (File that under Things That Will Never Happen)

Peter was speaking more to the admittance upon entering the hospital, not your primary care physician discussions. But as an aside, I think I could go into any hospital waiting room in America and over half the people there will know off hand if their cholesterol is “high” or not, regardless of why they are there.

Soft vs Hard Endpoints

Peter Attia

It’s not so much whether it’s a soft outcome. It’s whether or not there are things like, say revascularization. That can be determined based on the decision on the part of the doctor and the patient, that may or may not have to do with their knowledge of the lipids, right?

Agree. These are different things. But, I also think we should be careful not to take mortality as the only outcome. I will say this, and I hate putting on the stupid doctor hat, because it sounds ridiculous in this context, but unfortunately, I feel like I have to go back into and out of that world here. I would say at least half the patients that come to me, do not actually find themselves asking for an extension in life span. Right? My interest is longevity. But longevity has two components. How do you increase lifespan? Meaning, how do you delay death? And how do you improve health span? I won’t go into what that means, but, the bottom line is, there are many people who say, “I honestly have no interest in living one day long then I might otherwise live. But I want that quality to be much higher.” So, if we’re going to say … And, again, I don’t necessarily agree with that, I think the bigger issue is a statistical one with all- cause mortality, but, nevertheless-

Dave Feldman

But you go into modality. Like, if somebody has an MI, and it actually impacts their quality of life afterwards.

Peter talks a bit about possible modality outcomes, statin impact on Alzheimer’s and diabetes, and population vs individual-level data.

Dave Feldman

But, getting back to the challenge. In a sense, you’re saying by ignoring the genetic data, that the genetic data basically answers the question, to your satisfaction. To where you don’t need to look at non-genetic-

Peter Attia

Not alone. I think of it as the genetic data, coupled with the pharmacologic data, coupled with the mechanistic data, give me a high enough degree of certainty, that I am willing to act in a certain direction. Remember, everybody, me, you, whoever’s listening to this, they have to make a decision.

Dave Feldman

Sure.

Peter Attia

Indecision is a decision. So, when you showed up with the hemoglobin A1C of 6.1, did you have type two diabetes? Nope. Your doctor said, “Hey, I’m cool just waiting.” But you said, “No. Indecision is not a decision any more, I’m going to do something about it.” Because, presumably, you said, “Look, I have a family history of this. I think I have a sense of what the progression of it is. Quite frankly, I don’t want to wait until I have this disease to do something about it.” So, you decided indecision was not a viable decision.

Sometimes indecision is a reasonable decision. But, the point is, people have to understand they are making a decision whatever they decide to do.

Dave Feldman

Absolutely. Well, and for what it’s worth, as I say outside of here, and as I’ll say on this podcast, as I actually just said it, the speech, I don’t know if you saw the one that I did from last month. I told people, I prefer they not be echo chambering. I prefer they find everything that challenges from every side. So, with that said, going back to the lean mass hyper-responder, you would say, given what you know right now, given everything we’ve just talked about, that they are at high risk of cardiovascular disease. Would that be correct?

Peter Attia

I’d want to know more data. But, yes. If everything … If I didn’t know anything else other than-

Dave Feldman

Let’s say all cardiovascular risk markers say, LDL of 200 or higher, LDLP of typically 2,000 or higher, everything else is just pristine perfect, like CRPs at the floor, their LPPLA may be-

Case Study Talk

To my delight, Peter had a lab to chat about…

Peter Attia

Let’s look at this patient here. So, we’ll link to these labs. I asked this patient, this is a patient I saw last week. So that the only reason I printed this up, because I see this so often, but I’m like, “Let’s just get the last one.”

This is a gentleman who’s been on a low carb diet for a couple of years, is achieving amazing success with it. He’s a new patient to me, but he’s been around the block on this stuff before, and he’s got an amazing history of his labs going back many years. I’ve seen what he looks like on and off all of these therapies. On and off drugs, et cetera. He’s one of these guys where, across the board, looks fantastic, right? His glucose disposal is remarkable, his insulin levels are very low, his c-reactive protein is 0.3. Everything looks good. Read off some of his numbers, just for the folks, Dave. He doesn’t quite meet your lean mass, because his trigs might be a bit higher, but, talk to me about this guy’s numbers.

Dave Feldman

So, total cholesterol is 504.

Peter Attia

Is that high?

Dave Feldman

I know what you’re doing there.

Peter Attia

No, I’m just kidding.

Dave Feldman

I get this all the time where somebody sends me just that number.

Peter Attia

No, no. Okay. Go ahead.

Dave Feldman

Total cholesterol 504, LDLC direct, and it’s worth emphasizing just real quick for the listener, when they say direct, it’s very important to notice that, because usually, LDLC on a typical lab is actually calculated through the Friedewald Equation. So, when it’s direct, that actually is a direct measurement. And that matters for remnant. Hope we’ll get a chance to talk about that.

Peter Attia

We will talk remnants for sure.

Dave Feldman

So, LDLC at 362, HDLC at 94. Triglycerides at 125. The very first question I would ask if somebody was sending this to me is whether it was fasted or not?

Peter Attia

Yeah, this was. But, I’ve gone back and looked at all of his other trigs, and he actually, normally, does reside below about 70.

Dave Feldman

Oh, he does. Okay. So he would be typical for a lean mass.

Peter Attia

Yeah, he might have just eaten dinner a little too late, or something. I’m not sure what was going on.

Dave Feldman

Do you want me to keep going on the particles?

Peter Attia

Yeah, hit the particles.

Dave Feldman

So, Apo-B is 283? That actually is a little higher than I’m used to seeing. LDLP is about 3,500. Small LDLP is at 1,483. Small dense LDLC is at 47.

Peter Attia

All right, so we’ll just stop there and come back to it.I’ve told you that everything else on this guy’s looks pretty good. Is this guy at risk?

Dave Feldman

Well, I’m actually looking ahead, because I would have cared about these other markers. That could indicate inflammation, so, for example, the fibrinogen is very high. LPPLA2 is above 600. I don’t … In fact, I think I just Tweeted about this recently. I don’t know that I’ve seen an LPPLA2 above 300 or 400 of the labs that have been sent to me. I don’t get a chance to interpret oxidized LDL, but you have the LDL as above 135. So, I would say by this lab, as it looks, I would be concerned about the triglycerides, I would ideally want the triglycerides to go down.

So I have to concede, this was an awkward moment. I realized Peter wanted to zero in on whether I’d think the high LDL was an issue. But the whole profile matters. When we see LDL cholesterol levels that high here on the site, probably 90% or more it will end up being a LMHR and I’d fully expect the triglycerides will be below 100 at least (and usually they are below 70). So this profile already didn’t jive with the patterns I’m used to seeing.

The elevated Lp-PLA2 also set off my spidey sense. I’d wanted to know more about their situation, eating habits, and/or possible symptoms of illness.

So I followed up with a lot more Q&A I won’t list out here. We ultimately got to Peter’s key point…

Peter Attia

So, his cholesterol synthesis is through the roof, and his cholesterol absorption is quite high as well.

Dave Feldman

Are these affordable tests? Because I would definitely want to turn these around to the existing group of lean mass-

Peter Attia

I’m sure, the cash cost on these is not onerous.

But, my point is, I think that the explanation for this phenotype is the up-regulation cholesterol synthesis from the saturated fat. I don’t think this is an energy issue per se. I think this is a sterol regulated binding protein issue, or some sort of regulatory path around what the body is doing with ketones and/or saturated fat.

So here’s the thing — technically speaking, my and Peter’s theories aren’t really mutually exclusive. It’s quite possible this is energy trafficking and there’s higher sterol production overall. The modification I’d make is that this production would likely be dynamic in my model.

Peter went on to discuss a case with Dayspring and some further observations he’d made since.

Peter Attia

Bringing it back to this idea of genes, we might really be dealing with a subset of people, these hyper-responders, whoever, whatever percentage of the population they are, who are the people who are susceptible to this. Because you are not gonna find a leaner person, exercising harder than I was when I went on a ketogenic diet. I never had this response.

Dave Feldman

But there is a distinction that I tend to find and this is Occam’s razor. Again, more theory. I’m actually gonna be testing this myself in the next series of experiments that I’m doing. There is a difference between those people who are doing things like say endurance running, and weight lifting, or resistance training. In that, I think there is a greater overall gradient of receptor-mediated endocytosis for muscle repair and growth. I could be wrong about that, but I’ll be very curious to see if that turns out to be the case when I’m doing it myself.

Two things to mention real quick to the reader…

One — I have since completed that experiment and I was quite pleased with the resulting data! Indeed, it appears intensive resistance training (when everything else is tightly controlled) does reduce LDL cholesterol as predicted. Whether this is as I hypothesize to be receptor-mediated endocytosis on the part of the muscle tissue… well, that’s a bit harder to prove directly.

Two — If there’s anyone I’d expect to have lower LDL cholesterol due to this theory, it would certainly be Peter Attia! He’s no stranger to athleticism with resistance training! Check out this video: Peter Attia Tire flipping, jumping pull-ups, and other fun things you can do without carbs. So yes, I’m rebutting Attia here by slapping him with a fat compliment at the same time.

Do Muscles Endocytose LDL Particles?

This next part gets a bit long, but I think it’s a good read.

Peter Attia

Sorry, a greater amount of endocytosis of which lipoprotein, and for which product?

Dave Feldman

Of LDLP in particular.

Peter Attia

Into muscles?

Dave Feldman

Yup.

Peter Attia

For what product?

Dave Feldman

For repair and growth.

Peter Attia

You’re saying that in these people, they’re relying on their LDL for cholesterol delivery to the muscle?

Dave Feldman

Well, and phospholipids and just about anything else that would be inside of an LDL particle. There is existing studies that are out there, as far as those people who do a lot of weight training will also see LDLC and, this is why I’m saying it’s completely theoretical. I’ll actually be testing this myself over the next few weeks. Because I’m actually gonna be eating too, a very fixed diet, fixed sleep schedule, fixed everything. Then I’ll actually be introducing basically, any way in which I can get my muscles sore in a very fixed fashion, I can then turn around this data. If the hypothesis is true, I would expect that my LDLC, my LDLP might change.

Peter Attia

But, I’m confused. Why is the runner’s muscle more demanding than the weight lifter’s muscle? Or vice versa.

Dave Feldman

The other way around. That I would see the weight lifter actually seeing a difference. Because I think there’s more use of the product of LDLP directly by the cells. I may be wrong about that.

Peter Attia

But, what’s the evidence that that’s happening?

Dave Feldman

The evidence as far as … The keto gains groups. I’m sure you’ve heard of them?

Peter Attia

No.

Dave Feldman

There’s a ketogenic group, that’s keto gains, there’s not as many lean mass hyper-responders that come out of that group. They’ll tend to see their LDLC go up, but not as pronounced as those people who are say, runner-types, or aerobic-types, or even people who are doing yoga. There seems to be actually, a more pronounced difference of higher LDLC, depending on how much you’re doing resistance training, or anaerobic training.

Peter Attia

Yeah, again, I’m not aware of any evidence to suggest that the muscle is relying on LDL for delivery of anything. Including energy.

Dave Feldman

I’m not so sure about it on energy. What I’m thinking about is in terms of just raw material. I mean, as far as damage that can happen to, for example, the membrane of a cell. I realize this is kind of a key difference between us, is that your sense is that effectively, anything that the cell is gonna need, it can basically synthesize on its own, right?

Peter Attia

No, I think my sense is that … Occam’s razor would at least have me start from a place of plausibility. And I’m just not aware of any data that suggests that LDL is functioning to do this.

Dave Feldman

What’s the value of non-hepatic receptor mediate endocytosis from your perspective?

Peter Attia

So, you’re talking about very specifically, the little bit of LDL that gets out of circulation, either with or without a receptor to non-hepatic tissue?

Dave Feldman

Yes.

Peter Attia

My sense is the most important value of that would be to tissues that need more cholesterol to synthesis hormones.

Dave Feldman

But specifically cholesterol and not the phospholipids or anything else that-

Peter Attia

I think the phospholipids probably may be more delivered through others. Certainly, the VLDL delivers far more phospholipid than LDL. But, LDL is really a custom built package for cholesterol. If you look at how many cholesterol molecules fit inside and LDL particle, versus even an HDL particle … Remember, the HDL is the general of RCT. Yet, it can still only carry about 50 molecules of cholesterol. The LDL particle can carry 1,500 molecules of cholesterol. That’s staggering, again, when you consider the size of these things, right? It’s tailor made for that. That is largely conserved.

I don’t want to get us too far in the weeds, but I actually did a very interesting kinetic experiment many years ago. I did three blood tests every day for three days. Like the full NMR panel, but this is with kinetic. So, this is not commercially available. So, what you’re looking at is my ability to track, you’ll  have to lay it down, because I barely remember what I did, but this is pre-work out, immediately post-workout, four hours later, looking at my LDL particles, my VLDL particles, my HDL particles, both in terms of their cholesterol and triglyceride content.

Dave Feldman

So, you see them going down yourself?

Peter Attia

I don’t see any change in the cholesterol content. It’s minimal change in cholesterol content, right? What I think you see here is, “Yeah, wow. Under really periods of super high intense exercise, I actually did take some triglycerides out of this.”

Dave Feldman

Right.

Peter Attia

Minimal out of here. By the way, this backs up Garvey’s data, which is, there’s virtually no way to distinguish what’s going on at the VLDL level. We can’t tell what a remnant here, or what’s not a remnant. I apologize for the listener, we’re looking at a chart, but we’re gonna link to it, so you’ll see it. We’re basically talking about this idea of how much movement of cholesterol is going into, and out of, the LDL particle under these extreme conditions. I just did different types of workouts. So, on this day I did a crazy high intensity interval training. On this day, I did a crazy intense swim.

And I think on this day was the hardest workout of them all, was a crazy intense bike ride.

Dave Feldman

And the listener can’t see this, but I’m smiling ear to ear. It’s almost as if you knew I was gonna-

Peter Attia

Well, I thought you would appreciate … I forgot I did this. I did this six, seven years ago.

Dave Feldman

That’s fantastic.

We chatted a little bit more about the experiment after that.

Next part: Guesting on the Peter Attia Drive (3 of 5) – Remnant Cholesterol, Craig Moffitt, Fasting

Oct 08

Guesting on the Peter Attia Drive (1 of 5) – Lean Mass Hyper-responders, Oxidized LDL, All-Cause Mortality

UPDATE – 10/8/2018: This entire series was written before the podcast was to be released. Many people messaged me this morning about the format change where Peter summarized a rebuttal before the podcast begins in an unusual format change and were quite frustrated about it.

Don’t be!

Just listen to the complete episode and even return to the beginning again to judge for yourself. Again, this is and always will be a journey of science, not of advocacy.

Update 2 – 10/8/2018: I now have a response piece to Peter’s prebuttal at the beginning of the podcast which you can find here.

 

This is a five part series covering my appearance on The Peter Attia Drive podcast. Please skip to the final post to comment. 

Part 1 of 5 – Lean Mass Hyper-responders, Oxidized LDL, All-Cause Mortality
Part 2 of 5 – Cholesterol Challenge, Lipid Metabolism, LDL Receptor
Part 3 of 5 – Remnant Cholesterol, Craig Moffitt, Fasting
Part 4 of 5 – Energy Status, Risk, Testing the Hypothesis
Part 5 of 5 – Comments and Featured Thoughts

 

The podcast link is here

Before diving in, I want to thank Peter for having me on and making note of a couple things he deserves very special credit for:

  1. Since my earliest days, he was kind enough to answer some emails I sent him regarding my high cholesterol before I knew much about the subject.
  2. Having me on at all shows genuine interest in having the discussion. If he were truly fostering an “echo chamber” as some have suggested, then he’d only have on lipid-lowering advocates. Why bother sharing any of the megaphone with a skeptic of the overall theory?
  3. During the show, I complained at one point of the high cost for a National Lipid Association conference. After the podcast wrapped, Peter offered to cover my entry fee for one of these conferences, which is nearly a $1,000 value. This was not only very generous, it was clearly a good faith effort to expose me to higher level lipidologists in the field for the benefit of expert opinion.

I ultimately decided to turn down this amazing offer. But I did so because I realized any appreciation I expressed toward Peter past that point could be seen as influenced by this gift. Or to put it another way, you can be sure everything nice I’m saying about him on this page I genuinely mean as I’m literally paying almost $1,000 to do so.

Also, I big shoutout to Bob Kaplan. He’s Peter’s top researcher and someone I’ve had the pleasure of getting to know through this process. He’s also the reason I have the transcript of this podcast for you. (Insider note: I often tell Siobhan she’s my “Bob Kaplan” as my way of giving her a compliment)

Pre Show

I was excited to finally visit Peter’s office in San Diego. When he arrived, he had a flurry of business to take care of with his assistant, then entered the room to greet me. At first, I was concerned that I might have caught him on a bad day as I felt like my own pre-show chatter wasn’t landing very well. But I soon realized he was just in a very focused mode getting materials together in front of him for the podcast and confirming appointments to follow.

He plugged in the mics and warned me I need to stay very close for optimal capture. He likewise pointed out there will be the occasional plane flying above the office. Just a hazard of the location.

What would come next was unlike any appearance on any video or podcast I’ve ever done…

Opening Discussion

After some short banter, Peter set the stage well and introduced me.

One of the things I wanted to be sure to remember was my being very upfront about not being a formally trained biochemist or medical professional. Everything I know is self-directed learning. The other thing I wanted to be sure to mention is a big thanks to Peter himself for providing The Straight Dope on Cholesterol series.

With both of those out of the way, I launched into my backstory. As I approached the energy model discussion, Peter jumped in:

Peter Attia:

Okay. I think the other thing we’ll want to make sure listeners have done by this point if they want to get really deep on the understanding of this is probably go back and listen to at least one but potentially two or three of the other podcasts you’ve been on. You’ve been interviewed a number of times. I’ve had the privilege of listening to several of them, which is what kind of helped me get more up to speed on some of your arguments.

I think rather than just spend an hour going over those again here, I’d rather we sort of get to it more quickly, which we will, and then let the listener go back and get that way of background.

I was thankful for this statement as I figured Peter was up on my general model and we could just jump into the meat of it right away.

We talked about my numbers pre-keto and how I came into hearing about the term, “hyper-responder”.

Cholesterol Synthesis and Absorption

Peter pointed out that when finding a patient had very high cholesterol numbers he likes to run a Cholesterol Synthesis and Absorption test. The four markers it checks are:

  • Desmosterol (synthesis)
  • Campesterol (absorption)
  • Sitosterol (absorption)
  • Cholestanol (absorption)

After he chatted about this and the mechanisms for a while, he stopped himself…

Peter Attia:

Anyway, I apologize. I’m talking more than I should be.

Dave Feldman

Not at all. For what it’s worth, what you just mentioned … I myself have not gotten a sterol test. I haven’t actually broken down these, but I have been particularly interested in this. For what it’s worth, Peter, I’ve been looking forward to this because I think I may actually be just the stealth interviewer in the room because I think it’s just as possible I may be asking you more questions than you’re asking me.

We returned for a bit to my backstory. Then I eventually worked my way around to how the research began.

The Inversion Pattern, ApoE & ApoC-III

I spent a while discussing my theory on energy distribution through triglycerides aboard Chylomicrons (CM-TG) and aboard VLDLs (VLDL-TGs), with the latter resulting in higher LDLs downstream.

IMPORTANT: If you’re not that familiar with proposed Lipid Energy Model, you might want to watch my Breckinridge 2018 presentation here. You can also check out my new diagram for the Lipid Energy Model theory here.

This is important to bear in mind for the coming exchange…

Peter Attia

I think we do need to get pretty technical on this because I suspect that you and I will draw different conclusions from the data. In my experience, the easiest way to understand where those differences lie is to sort of start to get into some of the things that we would view differently. I’ll start with one thing that you said … I like to be, I think, maybe clear on where I believe the chylomicron, the VLDL, the IDL, and the LDL are coming from, going, and what they’re doing.

Peter then launched into a fairly straightforward view of existing literature and how it ties in the core assumption that the real value of LDL particles is as a part of the reverse cholesterol transport (RCT).

We then chatted about ApoE and ApoC-III for a bit before he rounded back to my energy model.

How Many LDLs Started as VLDLs?

Peter Attia

… So, you have this de novo creation of VLDLs, and you have this de novo creation of LDLs, and they form this circulating pool, but to my knowledge, we can’t really differentiate those when you look at that snapshot. I can’t tell, is that an LDL that came from a VLDL, or is that an LDL that came straight from the liver in that form?

Dave Feldman

And that was actually one of the questions I had for you was how, with a kinetic study, can you actually determine if an apoB100 lipoprotein that was secreted by the liver ever has, say an APOC-II on it? I think you and I would probably be in agreement that we don’t know.

Peter Attia

We don’t have any clinical way to measure that.

Dave Feldman

Right. And in that sense, I fully can see that I can’t be sure, even with the energy model, that the LDL particles that I’m seeing, the LDL-P, that I can say with any level of real confidence how many of the total proportion of those were truly for energy delivery.

Peter Attia

This is where we get into the semantics. I would argue none of them are for energy delivery, because that’s not what LDL does. But I think what you mean is, how many of them came from VLDLs that were trying to deliver energy?

Dave Feldman

Right, originated as VLDLs, for the purpose of doing it. In the morning, let’s say that your job is to deliver pizzas <snip> and you know that it only takes you about an hour to do. And then, guess what, the rest of the next two to four days you’re actually going to be patrolling the neighborhood. You’re the neighborhood watch. And you’re going around, and you’re also helping to fix up people’s houses, or something along those lines. Somebody who comes into the neighborhood and sees a whole bunch of these cars patrolling, they don’t know how many of those people actually delivered pizzas before they got started on that part of the shift.

Here I tried to convey the “Two Jobs” description of ApoB100 lipoproteins. That a large proportion of them deliver energy first as VLDL (the pizza delivery) and then second, remodel to an LDL to take on a support role (the neighborhood watch). If you look at the pathway graphic, you see in that purple line at the bottom the representation of the ApoB100 lifecycle when beginning as a VLDL. The key is that dotted line where I distinguish the “Energy Delivery” and “Support” jobs.

Thus, my explanation is:

  1. Job #1: as a VLDL, provide TG to hungry cells
  2. Job #2: if not taken up by the liver, hang out as an LDL to bind to provide immunological defense and reparative support

The critical question, of course, is just how many LDL particles did indeed start out as VLDL particles?

The Sacks Paper

Peter responds with Frank Sacks’ paper which details a breakdown of both VLDL and LDL secretion from the liver.

Peter Attia

Well, we sort of know. I mean, what Frank Sacks’ paper showed is if you have a triglyceride … If you take patients with low triglycerides, and I believe he used a cutoff of 130 mg per deciliter, 38 percent were de novo secreted by the liver, 62 percent came from either IDL or VLDL, where you had de novo … I don’t think the paper differentiated between which ones went IDL to LDL versus VLDL to IDL to LDL. So that’s an important point.

I think at this point he’s referencing this paper from Sacks 2015, The crucial roles of apolipoproteins E and C-III in apoB lipoprotein metabolism in normolipidemia and hypertriglyceridemia. This is oft cited by Dayspring. You can dig into it to find out where they got that same percentage breakdown Peter cites and it references the earlier study, Rapid turnover of apolipoprotein C-III-containing triglyceride-rich lipoproteins contributing to the formation of LDL subfractions.

To be sure, each of these papers are very technical, but I’m very, very interested in just how they determine direct secretion of LDL particles that — and this is key — would never fill the role of energy delivery as a VLDL in their lifecycle. In other words, at no time would they ever have an ApoC-II on their hull and be providing the TG cargo within to cells.

The problem with trying to speculate this, even with a kinetic study, is that ApoB100s are the only apo that is “non-exchangeable”. Other apos that are attached to the surface can be provided (such as by HDL particles) and this includes the very relevant ApoC-II. This apo is how cells can specifically bind and combine with LPL to get fatty acids off the lipoprotein through hydrolysis.

Lean Mass Hyper-responders (LMHRs)

Dave Feldman

With that in mind, here’s what I would speculate. This is purely hypothetical, but I would speculate if you were to grab a whole bunch of people who are … We’ll hopefully get into this model that I’m talking about, that I call lean mass hyper-responders. People who are at the far end of the spectrum, they are athletic, they are thin, and they are very, very low carb, and therefore see very high levels of LDL-C and LDL-P, but they also have very high levels of HDL-C and low levels of triglycerides.

I suspect that they would show a very high rate, proportionally, of VLDL secretion, that they actually are trafficking a lot more, for their energy, triglycerides in VLDL particles, and therefore have succeeding LDL particles as to the explanation as to why their LDL-C and LDL-P would be higher.

Peter asked about Virta and PPAR alpha/gamma briefly, but I steered it back to LMHRs for a little while longer and discussed CIMT and CAC markers. Peter went into depth on his opinion of the limitations of these markers, then rounded into many different subjects afterward for a bit.

Oxidized LDL

This is one of those exchanges that I have to leave completely intact and it’s worth reading entirely.

Peter Attia

Now the good news is, today we at least have one laboratory test that can measure that burden of oxidation. It’s called the OxLDL assay.

Now, this has been around for a while, but clinically we’ve only been using it for a couple of years because it turns out some very small percentage of those LDLs, once they are oxidized, escape back into the circulation. By sampling those we can track, indirectly, “Hey, what’s the likelihood that oxidative damage is happening?” For me, this is one of the most important metrics I look at is, because I want to spend some time later on going over some clinical cases. I want to see some of the data on yours. I want to show you some of the data that will explain maybe how I’m thinking about this. But this oxidized LDL, which is well documented and described in different quintiles, right, is giving you a small sample of what’s going on. But for the listener, it’s important to understand that when you get a blood test, that’s not telling you what’s happening in your artery. It’s giving you probabilities of things that are largely stochastically governed, that are going on in your artery. And the OxLDL is no exception. Even though it’s a beautiful marker, it’s still dependent on the idea that a subset of those oxidized sterols are now escaping.

Dave Feldman

Can I actually ask a little more on that one? We already know that LDL particles, specifically apoB100 at the LDL stage, have alpha-tocopherol I think is how I’m saying, basically it’s vitamin E, right, as part of the antioxidant defense system. So they’re actually, I mean, part of the purpose of an LDL particle is to actually provide that as a means to battle reactive oxygen species. Right?

Peter Attia

I don’t know about that. If that were true, if it were solely true, it would make me wonder why people with LDL deficiencies wouldn’t have deficiencies of those processes as well, which to the best of my knowledge they don’t.

Dave Feldman

Because I’ve actually been wanting to get into this a lot more, recently, in that … In fact, I might have the thing up. I’m not sure if I’m allowed to do this. Yeah, this is one of the papers, but I’ve actually been getting into [inaudible]. Correct me if I’m wrong, but basically, there’s a certain degree to which you’ve got vitamin E on board. On top of that you’ve got the potential of the phospholipid shell to become oxidized. So if you get oxidized phospholipids that also can bring about the role Lp(a) that can cleave off the oxidized phospholipids. That’s ultimately what Lp-PLA2 is, right?

Peter Attia

Correct.

Dave Feldman

It’s the enzyme that’s ultimately involved in helping to-

Peter Attia

Yes.

Dave Feldman

And this is also, I don’t know how much of this is actually demonstrated, but is ultimately where a lot of the concept behind why it is you would have a higher detection of small lipoproteins, particularly small LDLs, can come around to, is if you’re getting them constantly oxidized and having to constantly cleave them down to much smaller amounts. And then they constantly remodel.

Peter Attia

Yeah, but we’re getting off into two different things here. So let’s come back to this. It’s not clear to me that there is sufficient evidence to suggest that part of the role of LDL is to combat the oxidative stress.

Dave Feldman

Okay. Then let’s put that as homework that we’ll catch up on after this.

Peter Attia

Yeah.

I actually have a lot of additional research I’ve done on this since the podcast has been recorded, but I’m going to save that for a separate blog post. But for brevity’s sake, my position is still the same.

Dave Feldman

But this is relevant for whether or not we’re detecting oxidized LDLs that had never entered the intima. Right?

Peter Attia

No. The oxidized LDLs that we’re detecting have escaped the intima.

Dave Feldman

Interesting.

Peter Attia

There’s a very small subset that are getting out.

Dave Feldman

Okay. That’s definitely something I would like to follow up, because I’m genuinely curious about this stuff, as to whether or not they can be oxidized sufficiently that they’d get big, because it also may be something that is part of the test or isn’t a part of the test, but I’d be curious as to how it can actually determine that.

Peter Attia

Yeah. What you’re basically asking is, how do we know they weren’t oxidized never inside a subepithelial space, and that’s a fair question. I don’t know the answer. I know very little about this assay. I mean, I know the technical stuff of how the assay works, like it’s an ELISA assay. I know what enzymes it’s looking at, but the broader question is, without a tracer, do we know if that LDL has actually been in the subepithelial space where it was bound, oxidized, and then escaped or liberated? So, fair question.

Dave Feldman

Good, because it’s certainly relevant to this larger question of the value of LDL particles, as to whether they play an important part of the immunological role.

At this point, I was starting to realize how our way of thinking this out was different.

Given everything I learned to that point, I continually imagined free radicals in the bloodstream getting constant contact with LDL particles, but this was by design. Loaded up with anti-oxidants, it would be a core responsibility of LDL particles to donate those hydrogen atoms to reactive oxygen species anywhere and everywhere they could in the serum. And while I know it is anathema to say, I would likewise wonder aloud if LDL particles becoming oxidized were still preferable to the alternative (which is… anything else!) as this could help mitigate chain oxidation events.

[Siobhan Huggins here writes extensively on this blog about the research in this area and I credit her with a lot of my core understanding of it. You can learn more here.]

We’ll be returning to this discussion on LDLs and oxidation a little further down, which will make sense when we get there…

Peter brought up HDL and how he feels it is much more relevant in the support role, particularly with RCT and potentially immune defense.

All-Cause Mortality

As many of my readers well know, this is a subject of enormous importance to me. I was happy to finally be discussing it with Peter…

Dave Feldman

Interesting, but this gets back to the multipurpose value of a vehicle. Is it doing things other than that, that also turn out to be relevant. And I think this kind of gets to the larger and more important question overall. The question that I started with, going back to my November 2015 days, was I thought, very naively, that in a few days I would learn all I would need to about cholesterol and lipoproteins, find the landmark study that had a gajillion people, and it would just show that if you had lower LDL cholesterol you just died less. That was it. End of story.

At first, I thought that I had found that because I had found plenty that pointed to events and pointed to a lower cardiovascular risk, but then wouldn’t necessarily talk as much about all-cause mortality. I then had to learn about all-cause mortality. And then more and more I felt like I couldn’t get to something that really emphasized … I thought for sure at least I would see, for example, an elderly population. Generally speaking, the lower your natural LDL … We can get into snips, for example, on this … the more likely it is that you would just live longer, period.

Peter Attia

But you have to remember how these studies are powered. The challenge with ACM is, I don’t think any study in the history of civilization is going to be powered to detect that. It’s hard enough to detect cardiac mortality in a study. So I think we need to be more clear in what our concern is. If the concern is, if you are less likely to die of heart disease, you are more likely to die of something else, then we should state that explicitly and say, “Hey, low LDL, while may be protective of cardiovascular disease,” I will argue that is unambiguously clear and we can discuss that. But the bigger question is, are you concerned that, well, it’s increasing the risk of cancer or a neurodegenerative disease?

Dave Feldman

A trade-off.

Peter Attia

Yes. So the question there, that’s a question of power. It’s not uncommon in cardiovascular studies to see a reduction in coronary mortality with no change in all-cause mortality, or a non-statistical change. You know, most of the time you just don’t see a change, or it’s a change that’s very slight. And then you have to ask yourself the question, even if it looks, hey, death went up or down of other causes, you have to go back and ask yourself, “Was the study actually able to detect that?” That’s a very hard thing to detect.

This is extremely important to understand, so we need to break this down for a moment into two key questions:

First, do high levels of LDL particles have any positive outcomes of any kind?

I would argue yes, there is quite a bit of evidence linking lower non-CVD mortality to higher levels of LDL (as mentioned earlier). Which leads us back to the “trade-off” question. Let’s assume for the sake of argument that the Lipid Hypothesis is 100% correct. Would it be possible for mortality reductions by non-CVD outcomes to outweigh the increase to CVD itself? Of course it’s possible.

So how do you know when controlling for all other risk factors that someone’s chance of dying by a heart attack is a greater chance of dying overall? You can’t without accounting for death overall. And this is a serious problem with any study that makes a claim on mortality of one type without knowing the balance.

That balance, “death by any cause”, is known as All-Cause Mortality (ACM).

Second, why wouldn’t you always capture All-Cause Mortality?

This is Peter’s point, and for that matter, the constant point of the medical community. To “power” a study to show this difference in mortality, one needs to see a “statistical significance” which is hard if mortality changes are very, very small between the intervention and control groups. It takes more time and expense to accomplish this. So it is commonly argued that we should just assume a net benefit.

However, I find this a very unconvincing position. If I tell you a pill a will reduce your chance of dying by Disease X, but it increases your chances of dying by non-Disease X causes, then I’m creating a false sense of security, even if I didn’t mean to.

(Since we recorded this podcast, I was pleased to see Chris Kresser bring this up this very point in his debate with Joel Kahn on the Joe Rogan Experience.)

But it gets worse. You see, by definition, an increase in death by non-Disease X necessarily means you will reduce your chance of death by Disease X. My simple illustration is the fictitious “Cyanide Diet” where I joke that it will “reduce your chance of dying by heart disease by 99.999999%. In fact, it will have the same reduction with all other major diseases as well, like infection, autoimmune, Alzheimer’s…” Naturally, if something kills you first, other things can’t kill you later.

All joking aside, this is easily detected by having the balance sheet of overall death: All-Cause Mortality.

So yes, I freely acknowledge it takes more money and time to power a study for all-cause mortality, particularly if the margin of difference is small. But so what? Why is medicine, of all places, an area where we wouldn’t want to know the statistically significant risk of all mortality for anything that makes a claim of mortality benefit?

PCSK9 and Twitter Battles

I brought up how I didn’t feel a lot of confidence in these drug trials given the short time horizon (“two or three years”) relative to the total time it takes to build atherosclerotic plaque (years to decades).

Peter talked for a bit about his strong opinion regarding PCSK9 inhibitors and getting into debates online. Of subjects I wanted to talk about that day, PCSK9 wasn’t really one of them. But near the end, we got into the time to exposure question.

Lifetime Exposure and Mendelian Randomization

Peter Attia

So, coming back to this thing about lifetime exposure, this is where the Mendelian randomization becomes a very important tool in understanding LDL’s causality. What you alluded to at the outset, you are correct in noting is deficient. There is no lifetime study where, without a drug, you can prospectively manipulate LDL and follow people for 100 years and determine outcomes. That would be the ideal study. Right?

Dave Feldman

Yeah, that would be. You actually, you talked about this in the [series with use] of the wand.

Peter Attia

I know, I remember sort of going on … That’s right, the magic wand test.

By this, we’re referring to his thought experiment from Part VI in his Straight Dope on Cholesterol series. It’s worth reproducing here for context:

Take 100,000 people and randomize them into four matched groups, A, B, C, and D.  Wave a magic wand (you can see why this experiment hasn’t been and won’t be done) and give the folks in Group A an LDL particle concentration of, say, 700 nmol/L; those in Group B you give 1,200 nmol/L; those in Group C you give 1,600 nmol/L; and those in Group D get 2,000 nmol/L.

In our dream world, due to the randomization process, these four groups would be statistically identical in every way except one – they would, thanks to our magic wand, have a different number of LDL particles.  We would follow them without further intervention for 10 years and then compare their rates of heart disease, stroke, and death.

This Magic Wand example is effectively what Mendelian Randomizations are intending to do with genetics. But put a pin in that, as we’ll return to it at several points throughout the podcast…

We chatted for a bit on the zero LDL hypothesis, a fire & oxygen analogy, and a patient case. I asked about how we can be certain cells need only the cholesterol they produce as opposed to gathering some from the bloodstream via LDL, which led to this part:

Peter Attia

Well, we have natural experiments, right? We can look at the abetalipoproteinemia patients who can’t traffic cholesterol, therefore they would be entirely dependent on their own cellular endogenous production and they seem completely fine. So that’s not proof, because we don’t have proof to your point, but it’s certainly evidence to suggest … I mean, we also know when that’s off. Right?

One of the first things we used to see in the ICU, though at the time I didn’t pay any attention to it, was anytime a patient came in and they were septic or under great stress, so they had what’s called systemic inflammatory response syndrome, SIRS, so you could be a car accident, you were shot, you have a horrible infection, their HDL cholesterol would transiently take a huge bump. And I didn’t think anything of it at the time, other than it was neat. It was like, “Wow, 2X bump in HDL cholesterol, overnight.”

I think I would now look back and interpret those data as huge reverse cholesterol transport. Now the HDL is going out of its way to deliver cholesterol to, probably the adrenal glands first and foremost, because the enormous uptake of glucocorticoid, even epinephrine, norepinephrine are needed. So clearly there are examples of when this is not in a homeostatic balance. So I’ll take your point that …

Dave Feldman

Because the abetalipoproteinemia patients, in theory, should be the ones who are outliving us all. Right? They can take out the whole component of heart disease, of atherosclerotic plaque, everything. They should have massive longevity, relatively speaking, to everybody else.

Peter Attia

So the ones who get the longevity, because there’s only about 12 genes that are well enough studied, we have enough patients that we think we know something. And the most important of the longevity genes in cardiac is the hypo-functioning APOC-IIIs.

Dave Feldman

And that actually shows a net …

Peter Attia

A net longevity benefit.

Dave Feldman

Interesting

Peter Attia

Yeah. Work out of Albert Einstein has identified these, roughly a dozen, genes. And the hypo-functioning APOC-IIIs are, I mean most of those genes are, you know, like GHR, IGF. ApoE would be one, right, so ApoE2 would carry with it protective benefits in terms of longevity, both cardiac and, but more that is neurodegenerative. But it’s those C3s. In fact, as we have kind of alluded to a couple of times, I believe there’s an antisense oligonucleotide (ASO) in clinical trials now, trying to impair APOC-III, so now it’s becoming a therapeutic target.

Okay, let’s pause here and break out the two major categories of what we’re talking about…

Abetalipoproteinemia (ABL) and Familial Hypobetalipoproteinemia (FHBL)

After this podcast, I went back to do more research finding where either ABL and FHBL might result in a net longevity (AKA, lower all cause mortality) with very little luck.

The best positive study I was able to find involved a handful of elderly in their 90s and 100s from three families in Spain. It’s a bit scattershot in trying to triangulate the specific SNPs associated but manages to find some with reduced ApoB. Unfortunately, they excluded out many alleles by design and it’s unclear if they likewise looked at SNPs that increased LDL as well. Moreover, there were no blood tests for further comparison to their genetic data.

However, the negative studies were quite numerous for both ABL and FHBL. Remember how I was discussing a potential “trade off”? This certainly seems to be the case for many who are genetically prone to very low LDL. Many studies detail how ABL and FHBL can suffer severe deficiencies in fat-soluble vitamins A and E (which are brought to cells in lipoproteins), which requires high dose supplementation “to prevent or at least slow the progression of neuromuscular and retinal degenerative disease” among other issues.

Hypofunctioning ApoC-III

I found a reference to the research Peter was discussing here in an article with the headline, “Apolipoprotein C-III gene associated with longevity” which detailed “a team from Albert Einstein College of Medicine has identified a second longevity gene occurring more frequently in people who live to 100 years of age.” This linked directly to the study, Genetics, lifestyle and longevity: Lessons from centenarians.

In the abstract of this paper, it mentions, “APOC3 –641C allele is associated with a favorable lipoprotein profile, cardiovascular health, insulin sensitivity, and longevity.” To be sure, I was instantly disappointed this was associated with both “favorable profile” and “insulin sensitivity”, the latter being well known for its association with longevity. The sciency me wants to get that clean variable without others muddying up the Outcome Waters.

But it gets more interesting… the “favorable lipoprotein profile” isn’t actually about lower LDL cholesterol.

“LDL” only appears five times in the body of the main text, with the key passage describing the profile here:

Relationships between other lipoprotein traits and APOC3–641 CC genotype are shown in Table 1. Due to the dependency of lipoprotein traits on age and their modifiability with medications, we only considered offspring (n = 131) and controls (n = 126) not using lipid lowering drugs in this analysis. In females, triglycerides (TG), high density lipoprotein cholesterol (HDL), and their ratio, as well as low density lipoprotein cholesterol (LDL) lipoprotein particle size were significantly more favorable among those with the CC genotype compared to the CA/AA genotypes. The same trends were observed in most of the associations in men.

(Emphasis mine)

So this was about LDL size, not quantity?

Here’s the Table 1 in question:

Take special note of the very high HDL and TG/HDL ratio (both of which are typically improved on a low carb diet). But notice the LDL cholesterol — something I thought would be low given the higher clearance rate assumed with lower overall ApoC-III. Yet all the averages for LDL-C are above 100 mg/dL, with the women even averaging an LDL of 129 for CC and 134 for CA/AA.

Next Part: Guesting on the Peter Attia Drive (2 of 5) – Cholesterol Challenge, Lipid Metabolism, LDL Receptor


Comments are disabled for this post. Please skip to the last blog post to comment on this series.

Oct 05

Podcast Prediction Post – Peter Attia Drive Edition


Peter Attia’s The Drive podcast featuring yours truly is now officially slated to drop in three days (10/8/2018). On the same day, I’ll be releasing a five part series as a companion piece to the broadcast.

On the Record

As many have taken to social media with their predictions of this long-awaited podcast, I thought we’d have some fun and give readers a chance to weigh in here.

Feel free to comment below.

Guess the LDLs in the Podcast Jar!

As a bonus, we’re going to have a contest for who can guess the number of times Peter and I say “LDL” over the course of the podcast. LDL, LDL-C, LDL-P, or pretty much any time we say “el dee el” counts.

The winner will be featured in this space in an update. Good luck!

Sep 20

Resistance Training Experiment – Findings – Part I

As discussed earlier, this experiment is near and dear to me. And I must say, it did not disappoint…

Below I have expandable sections with the text from the prior posts that detail this experiment.

 

My Hypothesis (Posted September 4th)

My Hypothesis

I hypothesize resistance training reduces LDL cholesterol due to higher endocytosis of LDL particles by non-hepatic tissues, this includes skeletal muscle for growth and repair.

 

Study Design (Posted September 4th)

Study Design

From August 27th to September 10th (16 days), I will have the following routine:

  • Meal Plan:
    • Around 10 AM: Nathan’s Uncured All-Beef Hotdogs, two ounces of Colby
    • Around 3 PM: Four hard boiled eggs, four ounces of Colby Jack cheese
    • Around 8 PM: Three hard boiled eggs, four ounces of Colby Jack cheese
  • Activity/Exercise:
    • I will be standing at my desk through working hours
    • I will be walking around 2.5 miles per day in the afternoon, generally between 3pm and 8pm
  • I will be traveling and doing errands outside the house as little as possible to control for confounders
  • Daily testing:
    • Morning glucose, BHB, weight, blood pressure readings
    • Around 10 AM glucose and lipid readings
    • Morning lipid readings from the day of the intervention (Sept 5th) until the end of the experiment (Sept 10th)
  • Blood draws:
    • Each blood draw will include Apolipoprotein A-1, Apolipoprotein B, C-Reactive Protein, Cardiac, CBC With Differential/Platelet, Comp. Metabolic Panel (14), Fatty Acids, Free (Nonester), Ferritin, Serum, Glucagon, IGF-1, Insulin and C-Peptide, Serum, Lipid Panel, Lp(a), Lp-PLA2 Activity, NMR LipoProfile, Tumor Necrosis Factor-Alpha
    • The first blood draw will take place on September 5th just before the intervention
    • The second blood draw will take place after one of two conditions, whichever comes first:
      • The morning lipid reading shows a drop of more than 10% for LDL cholesterol against run-up baseline (if on a Sunday, to be carried over to the following Monday) or
      • The last day of the experiment is reached

 

Intervention (Posted September 4th)

Intervention

I will engage in a workout session with a Vibration Plate Power Plus. I will be working all upper body with the intent to become as sore as is reasonable without risk of injury. I will keep track of all time allotments and settings for data and potential reproducibility.

This will be performed:

  1. On Wednesday, September 5th at approximately 9 AM
  2. On Sunday, September 9th at approximately 9 AM

 

Design Additions (posted September 14th)

Design Amendment to Add an Extra Phase

The Face of Tired

As if this writing (Thursday night, Sept 13th), my cholesterol is below the baseline of the washout period (August 29st-Sept 5th). The mean average of TC and LDL-C during this run-up period are 382 and 314 respectively. [Note: these levels are higher due to the baseline diet being around 500 calories lower than my typical, ad libitum keto diet.]

I’m awaiting the baseline to return to this roughly this level before “triggering” another intervention of another intense workout. I figure a 3% offset should be sufficient. Thus, if either the TC comes to or above 370 (97% of 382) or LDL-C comes to or above 305 (97% of 315) for the 10am reading, then I’ll initiate the intervention on the following day.

Change to Intended Exercise Regime

While I had set up my vibration plate machine for exercise in the hopes I could fully quantify it for perfect replication, I quickly found out that I couldn’t easily do a full upper body workout to the degree I was seeking and that it was taking a bit of time working with the operation of it. Thus I altered the intervention phases as follows:

Intervention Phase I: Completed four exercises on the vibration plate for 20 minutes. Completed half of a free exercise video on YouTube for 15 minutes. Played 20 minutes of Knockout League for PSVR (Lots of rapid arm swinging, variety of motion), one lap around my block with a 500ml water bottle in each hand.

Intervention Phase II: Went to a nearby gym and had 5 minutes of warmup cardio, then 45 minutes on various upper body weights with extra emphasis on the arms and chest. I did another 15 minutes on the vibration plate afterward.

Change to Diet to Add More Salt

As many who follow me already know, I consume a lot of salt in my normal keto diet, usually targeting about 10g a day (no, that’s not a typo). At the beginning of the washout phase, I was consuming roughly 5g with my meals on top of a Sports Salts supplement of 1.5 servings at the 10am meal.

Alas, this was not enough. I began experiencing leg cramps a couple days in and so added an additional teaspoon (6g) of pink salt to water each day and this resolved the issues.

 

 

Staying Consistent Throughout

Here’s a straightforward graphic to illustrate my every day food… (additional water not shown)

A sample of my walking records are here on the right…

But so you know, this is actually wrong. I didn’t average 4.5 miles of walking each day, not even close. However, I don’t mind if the iOS devices are wrong so long as it is consistent in its magnitude of overestimation so I can at least see if my pattern is proving stable.

I did find myself getting anxious for the walk each day. As tedious constants go from one moment to the next, this was always the activity I most looked forward to each day. There were a number of times I wanted to tack on another mile or two.

Sleep Activity

I do want to make note that I haven’t slept for a typical length in a long time, usually averaging five to six hours a night for the last half a year or so. This was true throughout this experiment as well. The only exception was the very last night when it was over and I managed to knock out seven hours. (Probably not a coincidence)

Total and LDL Cholesterol Dropped Following Intervention

First, let’s look at 10am readings and the corresponding pattern shown:

This was taken just before my 10am “feeding” each morning.

These markers mostly represent a single data point. But on “key” days, opted to do several in a row for additional redundancy (such as on the morning following each intervention day) and averaged the result together.

NOTE: In the interest of full disclosure, there was one data point which was excluded due to a poor release from the capillary tube which unsurprisingly resulted in an oddly super low reading relative to the others. However, I’ll be including it anyway in the final raw data spreadsheet for reference which I’ll be releasing in a later post (probably Part II)

As it happened, on the day of the first intervention and thereafter, I decided to add lipid readings from my CardioChek following my other post-wake up measurements (glucose, BHB, weight/composition, and BP). I’ll include those here:

And finally, I decided to do one more chart where I averaged both the morning and 10am readings:

Needless to say, I really, really love these graphs! All that robotic, tedious living from day-to-day really paid off in a big way.

Of course, both Interventions have an almost perfect curvature downward, then upward. I joked to my wife, “these actually look too good. They’re going to think it is fake!” (Of course, I literally have every picture of every CardioChek screen timestamped and in the cloud, so feel free to audit me. 🙂 )

A Morning Triglyceride Spike

If you look closely at triglycerides from both time periods (the morning/wake up vs the 10am readings), you’ll notice an interesting bump. Let’s isolate the morning readings in yellow and the 10am readings in orange:

As you can see, my TGs following that 9/9 intervention was especially high when I woke up, but dropped substantially back to baseline by 10am.

When did I wake up? Well, I always try to make it a rule to take my readings within a half hour of being “fully awake”. Pretty much it’s the first thing I do when I get up — but I’ll concede I often check my phone for a little bit before leaving the bed itself. So the CC times taken each of those mornings were 6:41am, 6:55am, and 6:56am, respectively. Thus, I had about a 3 hour gap between the morning and 10am readings those days.

I’ve had two other occasions where I observed both a morning and 10am reading: The Capstone and Added Sugar Experiments. But this temporary spike was not observed in either one. In fact, my triglycerides very, very rarely climb into the triple digits in the first place, particularly on a very low carb, ketogenic diet.

As my second intervention led to more and longer-lasting soreness given I took to the weights and hammered it out at a gym, I expected a bit more intensive repair. More repair => more LDL needed => more VLDL secretion => more overall TG detected… and cleared rapidly?

This is some good stuff!

Glucose vs Ketones

 

Here we break the numbers out to a chart:

So my ketones hovered between 1.2 to 1.5 fairly reliably until I hit the first intervention and boom — 2.1 to 2.5 until the 14th, right about when my LDL started to come back up from it’s low of the second intervention.

Not-Quite-Final Thoughts

Without question, this experiment has been very challenging to execute, as all very long experiments typically are. That said, this certainly one of my favorites of all time.

For one, I had been putting off all resistance training for “the next few months” for over two years as I suspected it would impact my lipids. So it was a can I kept kicking down the road while focusing on the set of experiments at hand. Yes, I already had hints I might be right from my marathon training, but how sure could I be?

Moreover, this gives considerable evidence to the pattern I’ve speculated on this entire time as to why many keto bodybuilders will have lower LDL-C/-P than their runner/yoga/cardio counterparts. Can I be sure this is mostly muscle tissue endocytosis? Certainly not. But for me, this is the best evidence I’ve been able to generate to date to demonstrate that possibility.

Sep 17

Mega Monster #FeldmanProtocol Results

One Upmanship

Ever since I saw Dave’s Fasting Disaster post I’ve wanted to add some fasting data to the Cholesterol Code reserves. Instead of fasting for two days as Dave had done, I wanted to fast for the three days he had originally planned for. In fact, why not do a full five days as outlined in the 10 1/2 day Feldman Protocol instructions?

I had two advantages over Dave: 1) I’m much less lean than Dave is, which would likely make fasting easier. 2) I’ve done multiple multi-day fasts, making me a more advanced “faster”. Fasting is often described as a muscle that you need to train, which I’ve “flexed” a bit more than Dave has.

The opportunity to get more data was too good to pass up. Especially since our data guru (and resident Lean Mass Hyper-responder), Craig, had already completed the full protocol not too long ago, leaving me as the last one out. I was plenty ready and willing to fix that.

The Plan

Instead of following in Dave’s footsteps exactly, I made a few tweaks:

  • Electrolytes on an as-needed basis

Several conversations with Megan Ramos at various conferences cemented the idea that when fasting, electrolytes are no joke. As such whenever I was feeling a bit “off” I would supplement electrolytes.

  • Feeding would follow a carnivorous diet

I’ve been following a carnivorous diet since October of 2017, and thus in order to make the results comparable to my previous data sets, I wanted to keep diet relatively the same as well.

  • No liquid fats

Liquid forms of fat can sometimes mess with triglyceride levels (which can influence calculated LDL), so I decided to avoid them entirely. All of my fat would be found in the meat and cheese I was eating – no butter in my coffee, no chugging heavy cream (as I did during the Ketofest experiment), and definitely no MCT or coconut oil.

The Inversion Pattern

In case it wasn’t obvious, I was setting out replicate the inversion pattern via the Feldman Protocol to see if the observed relationships held true. Going off of Dave’s original experiment from 2016, these relationships include:

  • Total Cholesterol tracks with the inverse of dietary fat for the 1-3 days before the blood draw. (87% inverted correlation)
  • LDL-C tracks with the inverse of dietary fat for the 1-3 days before the blood draw. (90% inverted correlation)
  • LDL-P tracks with the inverse of dietary fat for the 3-5 days before the blood draw. (80% correlation)
  • HDL-C tracks with dietary fat for the 1-3 days before the blood draw. (74% correlation)
  • HDL-P tracks with dietary fat for the 3-5 days before the blood draw. (correlation not calculated)
  • TG tracks with the inverse of dietary fat for the 1-3 days before the blood draw. (61% inverted correlation)

Murphy’s Law

What can go wrong, will go wrong… After I had already started the experiment, and right before my first blood test, I discovered that my local LabCorp wasn’t open on weekends. This entirely threw off my intended schedule, as one of the tests for the feasting portion would have landed on a Saturday. Not only that, but because they were only open on weekdays, this meant the 10 1/2 day protocol was essentially impossible. Unfortunately, there was no other local LabCorp nearby, so I was left with two options.

  1. Do the 6-day protocol instead.
  2. Extend my fasting phase to 7 days to push the Saturday test to Monday.

Because the whole point of the experiment was to get fasting data from at least 5 days of fasting, I decided on option two. I decided I likely had enough body fat and experience to get me through 7 days of full-on fasting safely, and easily, and it would introduce a unique opportunity to get even more data.

With that, the new schedule looked like this:

Food Tracking

As per usual, I tracked all of my food from the high-calorie days (and all electrolytes/beverages during fasting days) through picture-taking. At the end of each day, I also logged all the food into My Fitness Pal. I ended up eating much more cheese and processed meat than expected, but still achieved my goal of at least 3000 calories for the 5 feasting days.

An example of items consumed during fasting phase vs high calorie phase

The Results

Total Cholesterol

First up is Total Cholesterol.

  • Normal Baseline: usually around 320-350 mg/dL (not shown in the graph).

After three days of fasting my Total Cholesterol was 345 mg/dL, where it stayed in about the same range until I switched over to the high calorie/high fat phase where it initially dropped to 219 mg/dL after three days, and then 209 mg/dL after 5 days of the high fat protocol.

  • 7 day fasted to 3 day high fat/high calorie: -132 mg/dL
    • Time span of drop: 3 days
  • Biggest drop: -162 mg/dL (Between highest on 8/22, vs the lowest 8/29)
    • Time span of drop: 7 days

The correlation between my 3 day average of dietary fat was an astounding -0.9928~ even higher than the expected 87% inverse correlation.

LDL-C

Next is LDL Cholesterol.

  • Normal Baseline: usually around 270-290 mg/dL (data not shown).

After 3 days of fasting my LDL-C was 278 mg/dL which climbed to 310 mg/dL after 5 days of fasting. After 3 days of high calorie it quickly dropped to 166 mg/dL and after 5 days of high calorie dropped even further to 151 mg/dL.

  • 7 day fasted to 3 day high fat/high calorie: -132 mg/dL
    • Time span of drop: 3 days
  • Biggest drop: -159 mg/dL (between 8/22 and 8/29)
    • Time span of drop: 7 days

The inverse correlation between LDL-C and the 3 day average of dietary fat was, again, higher than expected at -0.988~ with the expectation being an inverse correlation of 90%.

LDL-P

Particle count!

[IMPORTANT REMINDER: again, the Inversion Pattern for LDL-P is usually a three-day window with a two-day gap, not the three-day window with a zero-day gap, hence why the graph below doesn’t start with dietary fat at 0]

Normal Baseline: somewhere around 3000 nmol/L (not shown).

After 3 days I was actually lower than my usual at 2890 nmol/L, however, this quickly skyrocketed to >3500 nmol/L after 5 and 7 days fasted. For those of you who were curious, if getting an NMR Lipoprofile, the test that measures LDL-P among other things, if you go above 3500 nmol/L it won’t give specifics after that. After 3 days of high fat/high calorie feeding it dropped down to 2086 nmol/L and then to 1578 nmol/L after 5 days of high fat/high calorie.

  • 7 day fasted to 3 day high fat/high calorie (estimated): -1414 nmol/L
    • Time span of drop: 3 days
  • Biggest drop (estimated): -1922 nmol/L (between 8/22, 8/24 and 8/29)
    • Time span of drop: 5-7 days

Unfortunately, those numbers are estimated because of the LDL-P cutoff, each estimated “drop” assumes that LDL-P was at exactly 3500.

Again, if we assume LDL-P was exactly 3500 nmol/L the correlation comes out to -0.91~ however, if we assume both topped out LDL-P are >3500 as a general guess, the correlation drops to -0.83~ which pretty closely matches the expected inverse correlation of 80%.

Note: Due to formatting reasons, the below graph assumes LDL-P was exactly 3500 at its highest

HDL-C

  • Normal Baseline: My normal HDL-C has always tended to run low, with my normal hitting around 40-45 mg/dL on average
    • (for those wondering, the best guess for now is it is genetic, but I’m not entirely sold on that as of yet).

As predicted, HDL-C fell during the fasting days, from 43 mg/dL after 3 days of fasting to 37 mg/dL after 7 days of fasting.

  • 7 days fasted to 3 days high fat/high calorie: +4 mg/dL 
    • Time span of increase: 3 days
  • Biggest Increase: +9 mg/dL (8/24 to 8/29).
    • Time span of increase: 5 days

Although this may not appear to be a lot of movement, HDL is one of the less noisy markers and tends to remain a bit more stable over shorter periods of time. The positive correlation to a 3 day average of dietary fat was slightly lower than the expected 70%, but came in close at 0.66~.

HDL-P

Normal Baseline: Like HDL-C my HDL-P tends to run low, around 20-23 umol/L

HDL-P did fall during the fasting phase, from 16.9 umol/L to 13.2 umol/L with a slight bump up at the 5 day mark, and went up during the high fat/high calorie phase to the highest HDL-P I have on record at 29.1 umol/L

  • 7 days fasted to 3 days high fat/high calorie:  +12.7 umol/L
    • Time span of increase: 3 days
  • Biggest Increase: +15.9 umol/L (8/24 to 8/29)
    • Time span of increase: 5 days

The positive correlation to dietary fat (with a 2 day gap) on this one was 0.84~ with no previous correlation on record, although Craig mentioned in his post the correlation was higher than his HDL-C as it is in mine.

Triglycerides

Normal Baseline: 70-90 mg/dL

As expected, triglycerides went high during the early portions of the fast and then started to trend down the longer the fast went on. From 119 mg/dL at the 3 day mark, trending down throughout the fasting phase until it reached 94 mg/dL by day 7 of the fast. It continued to drop through the high calorie phase until it reached my lowest triglyceride level on record at 49 mg/dL.

  • 7 days fasted to 3 days high fat/high calorie:  -33 mg/dL
    • Time span of drop: 3 days
  • Biggest drop: 70 mg/dL
    • Time span of drop: 9 days

Although typically triglycerides are much noisier than usual, the correlation here was -0.95~ with 3 day average dietary fat, exceeding the expectation of a 61% inverse correlation.

Lipoprotein(a)

Normal Baseline: Usually around 112-140 nmol/L although it has been as high as 187 nmol/L, and as low as 82 nmol/L but neither of these occasions were normal baseline readings.

I actually didn’t expect lipoprotein(a) to fluctuate that much, as with my past data the only things that have moved it so far is getting sick (causes an increase) and swapping meat sources (causes a drop). Generally it’s said in the literature that lipoprotein(a) levels are largely determined by genetic factors, although it does act as an acute phase reactant. As such I expected it fluctuate maybe 10 nmol/L as it usually does when I’m not explicitly trying to move it. But, of course, it had to surprise me by increasing above my normal baseline to 189 nmol/L by 5 days of fasting, then decreasing substantially to 77 nmol/L upon high fat re-feeding, and even further to 65 nmol/L after another 2 days – the lowest lipoprotein(a) readings I’ve ever gotten, leaving me technically “in range” of normal levels.

  • 7 days fasted to 3 days high fat/high calorie:  –103 nmol/L
    • Time span of drop: 3 days
  • Biggest drop: -124 nmol/L (8/22 to 8/29)
    • Time span of drop: 7 days

The correlation with lipoprotein(a) and 3 day average of dietary fat was a pretty impressive -0.998~. There’s no previous correlation on record for Craig or Dave, so as far as I know, this hasn’t been replicated yet.

It has been suggested by some who saw the result that fasting beforehand could have confounded the high fat feeding data. In addition to that possibility, I haven’t replicated the data myself yet, so I’ll definitely have to do a few more experiments to see if this reaction is consistent. For now, though, it certainly is unexpected – not to mention interesting – and it makes me glad I food/calorie matched for the blood draws I got late last year and early this year, while sick.

Thyroid Changes

During the wide spectrum testing days, I decided to check out a few additional markers beyond the “basics”. This included some thyroid markers, to see how they would change before and after the fast. I expected that T3 would be low, as lower T3 might be useful for muscle sparing and energy regulation, but I wasn’t sure what the other markers would look like.

 

Thyroid Markers 7 day fast 5 day feast Ref Range
TSH 3.36 2.51 .450-4.5
Reverse T3 34.2 14.5 9.2 – 24.1
Thyroxine (T4) 7.1 6.1 4.5 – 12
Triiodothyronine (T3), free 1.6 2.2 2 – 4.4

As expected, T3 went from so low it was out of range, to the lower end of normal (which still didn’t surprise me considering I’ve seen discussion that lower T3 on a ketogenic diet could be adaptive). Reverse T3 dropped by over half, and upon a little poking around it seems this is not unusual and may go hand in hand with the lower T3 as an adaptive change.

Summary

In short, I would say that after full completion of the extended protocol, this experiment worked as a confirmation of the protocol’s effects in 1) a woman 2) who has a slightly higher estimated body fat percentage than Craig or Dave 3) who follows a predominantly carnivorous diet. Via the protocol, I successfully dropped my LDL-C by 159 mg/dL in a much shorter time period than would be conventionally assumed plausible. Additionally, I did this via diet changes only, with no changes to supplements (vitamin D, and magnesium glycinate) with the exception of an electrolyte supplement which was consumed on an ad libitum basis. Nor were there any changes to medications (none) during the experiment.

Additionally, I found that the lipid system was far more dynamic for me personally than I had expected. The previous time I had done the protocol I only dropped my cholesterol by about 50 mg/dL (6 day protocol with low-calorie instead of fasting), and I had thought that this attempt would yield a slightly higher drop. In fact, somewhat surprisingly, I nearly tripled the drop in cholesterol this time around.  Additionally, unexpected lipid markers (such as lipoprotein(a)) showed a surprising amount of – what could turn out to be – dynamic response to diet as well. This will obviously require further follow-up to confirm it was the introduction of high amounts of dietary fat that resulted in personally historically low lipoprotein(a) but the initial results of this experiment are intriguing, to say the least.

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