Dave Feldman Profile picture
Jun 15 β€’ 5 tweets β€’ 3 min read
1/5 This is BIG & I've really been looking forward to it. @DrNadolsky will be looking to see if he can reach a #LMHR phenotype while not being low carb or keto (or at a minimum, increase LDL-C to 200)

In short, can high consumption of saturated fat w/o being fat-adapted = #LMHR?
2/5 There's a bit of back-and-forth that originated this experiment which you can read about here πŸ‘‡

And yes, this could literally result in my flying to him to confirm results directly with advanced bloodwork & RER (Respiratory Exchange Ratio)

3/5 But more importantly, this gets to a very common assumption regarding #LMHRs -- that their phenotype can be mostly explained by higher consumption of saturated fat.
4/5 Ofc, it's worth repeating myself (and @nicknorwitz et al) agree saturated fat can impact LDL-C levels. But while we agree wrt additional component influences, we posit (and are exploring) the #LEM likelihood as the predominant influence for the #LMHR profile.
5/5 Which is why this is a powerful experiment. If one can demonstrate consistent #LMHR cut points while *not* being fat adapted (as confirmed by high RER, thus in contradiction with #LEM), that would be a very meaningful finding.

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More from @realDaveFeldman

Jun 13
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Read 5 tweets
Jun 7
1/ New video by @NutritionMadeS3 which I'd like to retweet for added discussion.

There's a layperson-friendly section in it that does a great job of illustrating the existing expectation of:

(1) LDL/ApoB Exposure Size
X
(2) Time
=
Rate of Plaque Development
2/ Using "mg-years" (much like "pack years" with cigs), one can quickly figure out what state of cumulative exposure they'd be at.

Gil's graph in video was similar to the one I tweeted on last week πŸ‘‡

And indeed, this is the convention of exposure x time
3/ To be sure, I'd echo @NutritionMadeS3's qualifier in the video that this is expected at a population level. So the exceptions don't prove the rule (in either direction).

Hence the enormous importance of studying those with extremely high LDL/ApoB at a population level...
Read 8 tweets
Jun 5
1/ I'd love to take an opportunity to expand on this important topic, and if I may, suggest something important to watch for with some newly emerging data.

To @MichaelMindrum point, I too believe the #ApoB will demonstrate higher association with #ASCVD than #LDL #Cholesterol..
2/ But to be sure, ApoB can be best represented as:

(1) Non-LDL ApoB lipoproteins
- and -
(2) LDL ApoB lipoproteins

The first category is predominantly chylomicrons, VLDL, and IDL -- which associate very highly with ASCVD.
3/ You can think of category (1) as "Triglyceride Rich Lipoproteins" (TRL, aka "remnants") and category (2) as "Triglyceride Poor Lipoproteins" (TPL)

The population of #LMHRs have extremely high levels of ApoB. But this pattern is a mix of very *low* TRL and very high TPL.
Read 6 tweets
Jun 4
Design a VLC diet that is low in fiber with the goal toward reaching a low respiratory exchange ratio on a cohort of lean, fit athletes (but no other exercise confounding like resistance training or diet confounding like meds/sups, etc).

I’d likewise bet big majority = #LMHRs
Full disclosure - @DrNadolsky and I took to some of this discussion via direct texting. However, it did lead me to a good question that I decided to turn into a poll out of curiosity...
1/2 Another great prop bet @DrNadolsky and I were discussing:

He proposed he could emulate the #LMHR phenotype by consuming a lot of butter and coconut oil while not keto and fat-adapted (thus, high RER). I'd predict the opposite.
Read 4 tweets
Jun 4
1/ Two weeks ago we released our paper on the #LipidEnergyModel (#LEM) along with our video abstract for it. I'm pleased to say it has led to many great connections and expanded discussion.

I'm going to recap on a lot of these in this thread. 🧡 ...

2/ First and foremost, thanks to everyone for their extraordinary support in retweeting our announcement, sharing our paper, and letting researchers know of this model.
3/ As we state many times (including within the video abstract), this model doesn't describe all possible influences on cholesterol levels. For example, other things can impact LDL-C such as M/PUFA-to-saturated fat composition, fiber, genetics, medication, etc.
Read 9 tweets
Jun 3
1/ Cool thread via @DrNadolsky

FWIW, I think I've figured out the perfect experiment via weight loss alone.

If I were to lose, say, 10-15lbs but keep diet composition as identical as possible (thus no increase in sat fat), the LEM would predict increased LDL/ApoB independently
2/ This is a great design given there are now other food items that would change other than possibly a net reduction in overall calories to maintain at the lower weight (but if anything, that would mean less sat fat in the overall, ofc).
3/ Given how strikingly different each model would predict the outcome, it seems like a given I'm going to need to do this experiment.

There's even a chance I line it up before the fiber experiment. (Working out scheduling right now...)
Read 4 tweets

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