Of Travel and Timing

airplane

I’ve had quite a few things on my plate over the last six weeks. In fact, four and a half of those weeks were travel alone. Obviously this has quieted the experimentation and blogging somewhat, but that’s about to change.

I have a presentation coming up for the Ketogains conference October 8-9 in Las Vegas. I’ll share a summary of my N=1 data to date, including a recent new surprise that will be meaningful for fat-adapted athletes. I’ll also be giving my assessment of risk for high cholesterol on a low carb diet and why everyone should make an informed decision on this important subject.

Cholesterol Code – Part V : More Fat, Less Cholesterol… to The Second Power

For the short version with pictures, see below. For the long version, read on after…

image

The Plan

A couple months ago I started talking to my sister about taking my data to the next level. But to do so, I’d need someone else’s help — to which she immediately volunteered. (Side note: my sister is uberawesome!)

In fact, my sister was perfect for what I wanted to test specifically. While her cholesterol numbers went up after going low carb last year, they didn’t rise nearly as much as mine. In fact, both her LDL-C and LDL-P were generally half of mine. Thus, we would have different starting points on our cholesterol when we ate, which is the goal.

Light Diet Day

Light Diet Day

I then started planning all our meals to be similar to my prior March week-long solo test. There would be the same day-by-day blood tests during one week. Only this time we’d add one test for the Friday before the week, and the trailing Monday that followed it, seven blood draws in all, each was an the advanced cholesterol test, NMR (Nuclear Magnetic Resonance).

Once started, we had to eat the same food at exactly the same time, no exceptions. Both of us had to take pictures of everything we ate, along with weighing them when possible. Half of the time she was in her state, then flew to me in my state where I’d then on prep, weigh, and cook our duel meals personally.

Generally, I tried to keep our food mostly home-prepared and stay away from processed or fast food. But both before the meal plan and following we ate out a little more. Also, my sister likes Zipfizzes, so we agreed to have one a day through the planned days as well.

Heavy Diet Day

Heavy Diet Day

Overall, the plan worked beautifully! My sister stayed religious to the diet and timing and we didn’t have any sudden surprises that thew us off the rails. I have a few fun stories that I’ll save for in person talks later.

The Comparative Data

Beyond the Total Cholesterol hook above, it’s worth looking closely at the other markers as well.

Our LDL-C was an impressive 88.9% correlative with each other! Did I put only one exclamation mark there? I meant three — 88.9% correlative!!!

image

And here’s a relative comparison to really see the match up:

image

This was especially relevant to me given my general theory encompasses energy trafficking as being the primary driver of these LDL cholesterol payloads. If I’m losing you here a little, don’t worry, I’ll cover this in a future post.

Like my own data before this, HDL doesn’t often move too much, but typically tracks with three day dietary fat in a positive correlation. More fat, more HDL. Between the two of us, we correlated a solid 71%.

ID_HDL-C

This next piece of data is extremely relevant to me (which I’ll get into in the theory post). It also tends to have a high standard deviation relative to the other markers from my past tests. However — in this case it was remarkably close in comparison to each other’s at a 77%. Incredible!

ID_TRIGS

So here is where things get interesting. On both LDL-P and Small LDL-P, Darla and I track very closely with the exception of the very last data point (7/18). In fact, the metric is so off course as to be suspicious to me. Up to that test, we had been eating everything identically as with the others, so what happened?

I’m loathe to suggest a lab error, especially since the non-P metrics appear to line up correctly. But unfortunately, there’s no easy way to find out as I have no direct contact with the lab (as it should be). For now, I’ll list both the complete results and what the correlation is without it and you can judge for yourself.

ID_LDL-P

ID_smLDL-P

It’s hard to quantify in words how happy I am that we captured this data and confirmed the previous patterns I’ve observed to this point. Our next steps will a new N, Nicole Recine, a Ketogenic Practitioner who has graciously accepted being our #3. We’re currently in the planning phase and hope to set up the next capture in the coming weeks.

I couldn’t end this article without given a very sizable thanks to my sister, Darla, and her contribution to this science.

Cholesterol Code – Part IV : Still Correlating… in the reverse

I just attended Low Carb USA at San Diego where I shared much of the data below. And while I was interested in a possible divergence that seemed to appear at the end of May in Part III, it turned out to be more of a one-off, probably due to a higher percentage of protein and a lower percentage of fat than my usual ratio.

In upcoming Part V, I’ll be revealing some new data on a “second N” to my study. I should have that up within the week.

For now, note that the new 21 to 28 data points include a 9 day period where I once again did a total of 7 days of blood draws. Thus, we again can see this mechanism in nearly real time.

I’ll let the graphs speak for themselves…


Three Day Average of Dietary Fat vs the LDL-C in the resulting blood test. The LDL-C still tracks inversely with total fat. (-81%)

Three Day Average of Dietary Fat in the three days before blood draw vs LDL-C of the resulting test


Same blood tests, same dietary fat, but for HDL-C — which clearly tracks positively higher total fat. (65%)

Three Day Average of Dietary Fat in the three days before blood draw vs HDL-C of the resulting test


Same blood tests, same dietary fat — but with a 2 day gap in between (Days -5, -4, and -3), but for LDL-P — which tracks inversely with higher total fat. (-82%)

fat3-2_ldlp28


And finally, same blood tests, same dietary fat — but with a 2 day gap in between (Days -5, -4, and -3), but for small LDL-P — which tracks inversely with higher total fat. (-72%)

fat3-2_smldlp28

If by this point you don’t see this is a highly regulated, highly responsive network in the lipid system (at least for my N=1), then you think I’m some kind of X-Men mutant. (In which case, I dib the name, Captain Cholesterol)

But seriously…

I now have very high confidence that this regulatory pattern is likely present with virtually everyone who is fat adapted (getting the majority of their energy via fat) without an underlying metabolic condition.

It’s also quite possible this applies to those who are not fat adapted yet still with no underlying metabolic condition. For that, we’d need more study.

Now with 28 NMRs, Low Carb USA, and Upcoming Part IV

Greetings again, my friends! These past four weeks have been pretty packed and my next three weeks will be no different. That said, I’ve managed plug in a lot more NMRs due to the experiment I’ve been conducting which involved another week of successive blood draws, bringing the total to a whopping 28! In so doing, it appears the “diversion” I wondered about in Part III of the series appears to be a one-off. (See graphic below)

Tonight I’ll be flying to San Diego to attend the Low Carb USA conference this weekend. I’m excited to update a number of the doctors I’m typically connecting with over Twitter and email. I already have five meetings and am getting worried about my bandwidth impacting my lecture attendance. It’s gonna be busy!

I have completed a fairly involved experiment a couple weeks ago which I’ll be previewing at the conference to a number of people, and for which I’ll have posted here at the blog before August 8th (as Part IV of the series) to coincide with my discussing it on Jimmy Moore’s Living La Vita Low Carb Show. Needless to say, I’m extremely excited to share the results.

dietary_fat_to_cholesterol_inversion

Prediction Contest

Note from Dave: This post is actually a replacement due to the previous Twitter Prediction post being weirdly targeted by spambots.

If you’ve read my series up to this point, you know the pattern being identified should be not only be reproducible, but predictive. So for my 22nd blood test, I decided to have a little fun. Instead of predicting about where it landed myself, I tweeted…


I got three: Raphi Sirt ‏@raphaels7, Richard Morris ‏@khiron, and Jeff Winkler ‏@winkler1. I invited them to a secret page with these instructions:

  1. Review the graph below. It has 21 points plotted in the red line and 22 points plotted in the blue.
  2. Make your best guess as to where the next point will be on the red line.
  3. Tweet a funny sentence about anything that includes:
    1. @DaveKeto
    2. #Predict
    3. (Your predicted number)

Click here for the larger sized graph

prediction_graph

They followed up with these tweets:

Reminder — all these tweets are dated July 7, 2016, the night before my blood test.

To replot the graph with their predictions:

twitter_prediction_tweets

The LDL-C result from my July 8th test just came in Friday and is as follows:

twitter_prediction_ldlc

And thus, here’s the updated graph with the new 283 result plotted on the red line:

twitter_prediction_after

Here’s the close up:

twitter_prediction_after_small

Thus making Jeff Winkler @winkler1 the winner!

If you’re wondering how I could have three total strangers could predict where my cholesterol numbers would be in such a tight range, you haven’t been reading my series! (See Part I, Part II, and Part III)