1/ Wow -- it's been quite a week! I announced the #LMHRstudy six days ago. And in case you missed it -- visit here for more details:

CitizenScienceFoundation.org/study

I'm going to provide some updates and answers to frequent questions of the last several days...
2/ "Dave, can you get me in the study?"

No! You have to contact Lundquist directly through the proper channels and they will decide based on the study design whether you qualify as prescribed by existing eligibility criteria we all determined in advance.
3/ While myself, @DrNadolsky and @DrRagnar developed the protocol in collaboration Lundquist, we in no way can (or should) influence any decision making regarding individual considerations -- and that's a good thing. We want this study as fair and objective as possible.
4/ "Is this USA only or can participants from other countries apply?"

This study is USA only. We may have sister studies in other countries in the future, but that is currently complicated by the pandemic, of course.
5/ "When will we hear the results?"

While we can't say for sure – I'd conservatively ballpark two years from now. We will have many months of recruitment and bookings, and only after the last participant is scanned and returns a year later can comparison analysis begin for all..
6/ ... Then there is writing the paper, then submission for publication. Both of these can be many months depending on a number of things.

But while that might seem like a long time to some, just remember -- I literally pitched this for the first time 2 years ago this October...
7/ ... In other words, a glass-half-full perspective is that we're halfway there.

Regardless, I hope everyone can appreciate the true value of this data and why it's been so important we do our best to capture it as effectively and objectively as we reasonably can.

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More from @DaveKeto

5 Sep
1/ Yes, my answer to the poll by @nicknorwitz was "Gain 4% body fat". And honestly, it was a pretty easy one when compared to the others.

But to be fair, I also have quite a bit of direct data on this in particular... let's unpack...
2/ First, if you didn't already know this about me, in 2018 I literally gained almost 20lbs of fat for the Weight Gain Experiment. cholesterolcode.com/weight-gain-ex…

(As an aside, I realize now I did presentations on the findings for this experiment, but didn't do a write up. Bad Dave!)
3/ But spoiler alert -- my total and LDL cholesterol did indeed go down where having gained weight and back up where having lost it.

To be sure, I think there are thresholds to "active fat gain/loss" vs standing, stable fat mass, but we'll save that for another thread.
Read 8 tweets
6 Aug
1/ Okay, let's talk ANGPTL3-4-8 model. Mega thx to @nicknorwitz for getting well ahead of me on the angptls and their influence on LPL.

Here's the central figure, which we'll be referencing.

(Don't worry if you get lost, I'll unpack this more below..)

royalsocietypublishing.org/doi/10.1098/rs…
2/ "Lipoprotein lipase (LPL) is a rate-limiting enzyme for hydrolysing circulating triglycerides (TG) into free fatty acids that are taken up by peripheral tissues."

Translation: LPL is like a key cells use to open lipoprotein boats to offload their fat-fuel cargo (TG)
3/ "Postprandial LPL activity rises in white adipose tissue (WAT), but declines in the heart and skeletal muscle, thereby directing circulating TG to WAT for storage; the reverse is true during fasting"

Sure, post-meal we do more storing in fat via LPL, otherwise we do less.
Read 11 tweets
5 Aug
1/6 I'd like to both retweet and add on to @NutritionMadeS3's point here...

Again, and with emphasis, my position is one of cautious optimism. That's an explicit acknowledgement of uncertainty, even if I have a leaning toward the positive in this context...
2/6 I discuss this at length in 13-15 of my pinned tweet

Hence the importance of #LMHRstudy

I'd hope this were self-evident, but if I were completely certain high LDL + high HDL + low TG = low risk in fat-adapted context, there'd be no effort for a study
3/6 I've chatted with @NutritionMadeS3 and others on evidence I consider very compelling regarding the connection of health and illness for lipid metabolism and their impact on lipid profiles. But I likewise stress we'd be better served with prospective data in this context
Read 6 tweets
2 Aug
1/ Heard enough Lipoprotein Lipase (LPL)?

The tl:dr -> There's more selective control with fatty acid exchange in tissues than we fully understand... but we have a lot more we've learned recently...

#LipidEnergyModel @nicknorwitz

sciencedirect.com/science/articl…
2/ "Preferential uptake of FAs into high demand tissues such as the heart, muscle and brown adipose tissue cannot be achieved by non-specific uptake, which would acutely distribute FAs equally into all cells."

- Translation: there's some selective trafficking going on here.
3/ "A second uptake process modulated by activity of capillary lipoprotein lipase (LpL) involves FAs derived from triglyceride (TG) rich lipoproteins (chylomicrons and very low density lipoproteins." (VLDL)

- Yes, lipoproteins + LPL = hydrolysis of TG to cells
Read 11 tweets
23 Jul
1/6 Yes -- this article is over 3 years old now and I then presented on Remnant Cholesterol (RC) at LC Denver earlier that year.

In rereading this article, there are a few things I'd update -- but the general focus on systemic outcome relevance still holds...
2/6 A more updated article on this, along with further thoughts, can be found here: cholesterolcode.com/thoughts-regar…
3/6 If you read nothing else, check out this section from the article, and why I'm so vocal about *not* looking only at #ApoB (which lumps non-LDL and LDL) together.

Again -- and with emphasis -- this is part of the larger hypothesis. But all the more reason to research it...
Read 6 tweets
21 Jul
1/ I have a new favorite term: "unadjusted hypothesis-naïve exploratory linear regression"-- and it comes from @Ad_SotoMota, who @nicknorwitz and I are honored to be working with in this new project (news on that soon...)

I don't typically see the term as it's rarely employed...
2/ My layperson explanation is that this is explicitly letting the data speak for itself. Literally starting from scratch and seeing what patterns machine learning tells us are emerging. No adjusting the data at the beginning of the process which can bias the outcome (of course)
3/ I'm much more used to methodology sections describing several steps of adjustment selection by the researchers. This isn't to imply any wrongdoing by any means, but understandably, this can be problematic if one or more underlying assumptions for adjustments are incorrect...
Read 5 tweets

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