In this YouTube presentation, the speaker critically reviews a JAC Advances paper reporting on the KETO trial, which examined lean‑mass hyperresponders (LMHR) on long‑term ketogenic diets. The study matched 100 keto‑diet participants (average LDL 272 mg/dL, diet duration 4.7 years) with a comparable cohort from the Miami Heart Study (average LDL 123 mg/dL). Using coronary CT angiography to assess both calcified and non‑calcified plaque, the authors found no statistically significant increase in total plaque in the keto group; a non‑significant trend even suggested lower plaque burden. Moreover, LDL levels showed no correlation with plaque scores, exemplified by a participant with an LDL of 591 mg/dL who had zero plaque. The presenter emphasizes that while the data are striking, they are preliminary and should not immediately alter clinical guidelines. He discusses two major caveats: (1) the unique metabolic phenotype of LMHRs—metabolically healthy, lean individuals whose LDL elevation stems from carbohydrate restriction, making them distinct from traditional high‑LDL populations; (2) the groups differed on several non‑LDL variables (HDL, triglycerides, BMI, activity), which may act as confounders but also reflect the inherent metabolic signature of LMHRs. The speaker references the lipid‑energy model (developed with Feldman and Sotomayor) to explain why lean, insulin‑sensitive individuals experience dramatic LDL rises on low‑carb diets and why re‑introducing carbs (even cookies) can sharply lower LDL. He outlines clinical implications for physicians—awareness of the LMHR phenotype, communication skills, and personalized management—and calls for multidisciplinary research: mechanistic studies of the lipid‑energy model, randomized trials comparing dietary and pharmacologic interventions, and longer‑term CCTA follow‑up. The video concludes with a call to disseminate the findings, encourage balanced discussion, and avoid extremist positions on LDL or LMHR denial.
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Warning: You are gonna hear polarized opinions about the data I am about to share. Opinions ranging from, "These data overturn decades of accumulated knowledge about heart disease. They dethrone the lipid-heart hypothesis." To, "These data mean nothing. Ignore, ignore, ignore." Now, let's be clear, both those statements are dead wrong. These data are pivotal, but preliminary. They can't be ignored, and those wanting to sweep them under the rug, well, they're gonna be sorely disappointed because this is a boulder rolling down a hill that can't be stopped. Now I'm going to review the data, caveat them, and then I'm going to put you to work. Welcome to my channel, stay curious. This paper published in JAC Advances is entitled "Carbohydrate restriction-induced elevations in LDL cholesterol and Atherosclerosis: The KETO trial. Briefly, this is the cousin study of a prospective trial in which 100 lean and metabolically healthy people on ketogenic diets, the lean mass hyperresponders and near lean mass hyperresponders, are being followed over time to see how much plaque accumulates in their coronary vessels. They're being followed by coronary CT angiography, which looks at just not calcified plaque like a CAC, but calcified and non-calcified plaque. So it's really state-of-the-art technology to look at all plaque that's in the vessels. But for this publication, what we did is take the lean mass hyper-responder keto group and match them based on a series of factors like age, sex, race, and other factors to generally healthy people from the Miami Heart Cohort, the comparison. But the Miami Heart Cohort participants, despite being matched for age, gender, race, etc., had much, much lower LDL. So the average LDL in the keto lean mass hyper-responder group was 272 mg/dL with a peak of 591 mg/dL in one participant. And the average LDL in the Miami Heart Group was 123 mg/dL. So the difference between the averages was 149 mg/dL in just LDL. And both groups were average age 55.5 years, 59% male,. And the average time on a ketogenic diet was 4.7 years. And I want to emphasize that because that's the amount of time the keto group had exposed to these super, super high LDL levels. And based on common knowledge, one might expect that with LDL levels an average of 272 mg/dL over almost 5 years, there would be a ton of plaque accumulating in their coronary vessels, a lot more than in a generally healthy population with much, much lower LDL. But what did the study find? The keto group exhibited no increase in plaque compared to matched subjects from the Miami Heart Cohort. So despite having astronomical LDL levels of 272 for 4.7 years, there was no increase in plaque in the keto lean mass hyperresponders as compared to this control group, which is really shocking given the size the magnitude of their LDL increase and the duration of time they had the exposure. Nevertheless, there was no increase in plaque in this group, which is kind of stunning, at least stunning based on common knowledge. Now, you could say, well, maybe the study just wasn't powered enough to detect a difference. But if you're going to say that, then you have to ask the question, which way might the data have been leaning? And what you see is that actually, if there was a trend at all— again, no significant differences between the group— but there was a trend towards the lean mass hyper-responder keto group actually having lower plaque, which sounds heretical to say, but the data are the data. Now, the second finding was that there was no correlation between LDL cholesterol and coronary CT angiography measured total plaque. So no correlation between the LDL level and the plaque level. In fact, the subject with the highest LDL in the lean mass hyper-responder group, an LDL of 591 mg/dL, had zero plaque score on the CCTA, which by conventional standards is jaw-dropping. Now what I want to do is read some sections straight from the paper. The reason I want to do this is I want to give you a true sense of our authorship collective voice, what we thought as a team was appropriate to disseminate via the peer-reviewed literature to other academics. So, although clinical caution should be exercised here citing my Journal of Clinical Lipidology 2022 editorial, while complete data on this unique phenotype, the lean mass hyperresponder, are outstanding, the questions we are posing are scientifically legitimate. What is the absolute level of risk associated with LDL cholesterol in otherwise metabolically healthy people on carbohydrate-restricted diet? This is a question that honestly the field does not have an answer for, and we need to acknowledge that this is a really important question to ask. Even if we assume it's suboptimal to have elevated LDL, in this context, in this particular context, does it actually constitute a large risk or a negligible risk? That is a heretical question, it seems to ask, but nevertheless, it's a scientifically legitimate one that must be asked. And also, we need to ask why. Why does this response occur? Why do lean people, lean insulin insensitive people see skyrocketing LDL levels when they go low carb. Now, we have a model, if you've been following the work of myself, Dave Feldman, and Adrienne Sotomayor on why this occurs, the lipid energy model. And this video is not about that, but it explains these fascinating phenomenon. Not only why lean insulin sensitive people have massive increases in LDL when they go low carb, but also why is there an inverse association between BMI and LDL change? And why, for goodness sakes, can I and other people who have now replicated lower their LDL cholesterol with any carbs, including freaking Oreo cookies. I was able to lower my LDL by 71%, twice as potent as a statin, with Oreo cookies. And the physiology to understand why that bizarre phenomenon is occurring exists. It's there, and we need to study it. If you're scientifically curious, you absolutely need to study it, and it has clinical implications, again reviewed in other videos. So for emphasis, These are legitimate questions we are asking, and anyone who says the science is settled on either the risk question or the mechanism question is either lying intentionally or, more likely, ignorant to the nuances in the field. Jack Advances also gave us the opportunity to write a perspective section, which I quite liked. I quite liked where it landed, so I want to read from that as well. Starting with clinical competencies, why is this relevant to physicians? Why should they know about these data? These data are relevant to medical knowledge and communication skills. Lean mass hyperresponder is an emerging phenotype of growing research interest, with little known with respect to mechanism and risk. The two points I just made. Physicians' awareness of unique aspects of the phenotype, such as the inverse association between body mass index and LDL change, and the LDL cholesterol suppression response of carbohydrate reintroduction, including Oreo cookies, may facilitate individualized patient management. Furthermore, patients presenting with the lean mass hyperresponder or near-lean mass hyperresponder phenotype often identify with this phenotype, as a community has arisen on social media focusing on lean mass hyperresponders, and these patients may be more receptive to clinical advice from physicians who acknowledge the unique aspects of their profile, who are aware of ongoing research in this area, and who engage in open discussions about the knowns and the unknowns with these lean mass hyper-responder patients. And we went on to discuss the translational outlooks. So understanding the mechanisms underlying the highly heterogeneous LDL cholesterol response to carbohydrate reintroduction. So lots of people go low carb, some see increases, some see decreases, some see no change. Understanding the mechanisms underlying that heterogeneity, as well as the risk associated with high LDL cholesterol on carbohydrate-restricted diets, and finally understanding personalized treatment options will take a multidisciplinary approach and may draw from many levels of research ranging from basic science studies to meta-analyses of existing clinical trials. And in the opinion of this research team, research priorities should include dissecting the driving mechanism behind the lean mass hyper-responder phenotype by testing elements of the lipid-energy model, along with comparing lifestyle— carbohydrate reduction, saturated fat reduction, increasing fiber— and pharmacological treatment— statin, ezetimibe, PCSK9— options in lean mass hyperresponders in randomized controlled trials. Longer-term follow-up, for example, 2 and 5-year CCTA measurements of this in similar cohorts, will also be essential to properly evaluate risk associated with this phenotype. So this is a long way of saying more research is needed, and we need to invest in this as a research priority. Now for some caveats, and I have two biggies. The first is that these data, while pivotal in my opinion, are preliminary and they're not going to change guidelines anytime soon, at least not right now. That said, I know a comment you're going to hear in association with that idea is something to the effect of "the preponderance of evidence says," alluding to the fact that there is a large body of evidence on lipids and heart disease that has been accumulated over decades, and there's this idea that the The large body of evidence, the preponderance of evidence, when contrasted to these preliminary data that might say something that appears quite different, the large body of evidence washes away the preliminary data. And superficially, that seems to make sense. However, my issue with that retort is that the preponderance of evidence is handicapped by the nature of the populations from which that evidence was drawn. Otherwise stated, what we have here in lean mass hyperresponders is a metabolically healthy, population of people with elevated LDL cholesterol levels as a function of a metabolic response to carbohydrate restriction, which is physiologically very, very different than elevated LDL from other causes and in different metabolic contexts. So we have a unique population and we need unique data on this unique population. And this, quite honestly, is the first dataset relevant to this population, directly relevant to this population, as relates to ASCVD, atherosclerotic cardiovascular disease. So that was caveat 1. The second major caveat I want to highlight is that the groups, the Lean Mass Hyperresponder Keto and the Miami Heart Cohort group, differed along more than just LDL. They differed along non-LDL parameters including HDL, triglycerides, BMI, and possibly other things like activity level that just weren't measured. And on the one hand, this could represent confounding. That's a legitimate claim.. But there are two major, major counter caveats to that point that I'd like to emphasize now. The first is that lean mass hyperresponders differed from the Miami Heart Cohort along, say, HDL as just an isolated example, not because of laziness on the part of the research team where we just couldn't match for that, but as a function of the fact that lean mass hyperresponders are just that metabolically unique that you can't match them along all non-LDL parameters. For example, mean HDL? In lean mass hyperresponders was around 90, and it's basically impossible to find another population that matches them to that extent. Why is that? The reason is because lean mass hyperresponders are defined not just by high LDL, but by LDL, high HDL, and low triglycerides as a metabolic triad that tends to occur in lean people who go low carb. At least this triad tends to occur to that extent, and it does so Because really, it's a metabolic signature, it's a metabolic fingerprint of what's going on underneath, what's going on with the physiology that manifests in a metabolic signature, not just an isolated marker. So if we step back and take a 50,000-foot view, the point that this makes, or what I'm trying to make, the fact that you can't match the EMS hyperresponders along all these other parameters, is that we really shouldn't be focusing in, just tunnel visioned on one marker. We should be looking at metabolic signatures that suggest underlying physiologies and then start to understand those physiologies. The second major counter caveat is that in citing these non-LDL variables, like being slightly leaner, as confounders, the logical extension of that is that you're implying that factors like being just slightly leaner are sufficient to offset an LDL delta of 149 mg/dL with respect to its impact on plaque accumulation. Now, I'm not making that claim, but it is a logical extension of the claim that these are major confounders. So again, we circle back to what is the magnitude of risk in this particular population? Because if one expected that 272 mg/dL LDL in this population constituted this massive risk, we wouldn't expect being maybe a little bit leaner and a little bit more active to be able to offset the risk profile. And yet that's kind of what we're seeing here, at least based on these preliminary data. Now I'm going to wrap this video here. You probably can tell I could go on for hours, but I won't, and deliver on my promise now to put you to work. Your first job is to share this video and share this paper widely on social media. These data and the data associated with these data, which are linked below, need to be discussed. Lean mass hyperresponders and the lipid energy model explaining lean mass hyperresponders need to be discussed, even and especially if that discussion will be uncomfortable for some at times. Additionally, I really encourage you to view a few select videos that I've picked out and linked below with respect to ApoB and all-cause mortality, different causes of high LDL cholesterol and why the cause does matter with respect to risk, and a video on why now, presently, in the current intellectual ecosystem, this is such a confusing and divisive topic. Finally, I want to caution you against being a hyper-extremist on either pole, be that the LDL denier or the LMHR denier, which now I'm using some inside lingo, so I apologize for that. I promise you though, this line of research on lean mass hyperresponders is going to move forward faster and more efficiently if we don't overstep on the data and we remain open about its novelty frame it with its caveats, and remain open to having discussions on this topic. So keep an open mind. Otherwise stated, stay curious. Bye.
