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)

Cholesterol Code – Part III : The Divergence

In Part I, I shared the data of my first 15 cholesterol blood tests and how closely it correlates with dietary fat. To recap:

  1. My cholesterol baseline jumped after starting a ketogenic diet, which led me to do close blood testing against a theory I had on the lipid system appearing as a network (more on that theory later)
  2. If the theory had merit, there would be two tenants to assume:
    1. The cholesterol transporting lipid system would prove to be agile – much more so than is typically believed (this appears to be accurate).
    2. Discrete patterns should emerge between the diet and serum cholesterol now that disruptive inflammation is lower (this certainly appeared to be the case).
  3. After the testing, my bloodwork showed the following:
    1. The more fat I ate, the lower my Total Cholesterol. (87% inverted correlation)
    2. The more fat I ate, the lower my LDL-C. (90% inverted correlation)
    3. The more fat I ate, the lower my Triglycerides. (61% inverted correlation)
    4. The more fat I ate, the higher my HDL-C. (74% correlation)

 

In Part II, we moved beyond LDL-C into total and small LDL-P and their differences in the findings.

  1. LDL-P and small LDL-P appeared to have a correlation with a three day average of dietary fat as like LDL-C, but with a key exception of adding a two day gap between the dietary period and the blood test.
  2. Small LDL-P appeared to correlate closely with LDL-P but at a higher gain to loss ratio.
  3. Both LDL-P and small LDL-P proved to be extremely agile and easily ramped up or cleared when observing via daily blood tests (data points 9-14). In fact, shifts in the hundreds of particles per day were easily achieved in either direction.

 

The Ratio Problem

If I were playing devil’s advocate to my own research up to this point, I could make the case that we can’t be completely certain my LDL cholesterol was inverting with fat, since the same could be said for protein. After all, my protein intake ran very proportional to my fat intake, even if at a smaller fraction. When running Three Day Average Protein against the LDL-C, I also got a significantly close correlation at -0.789

protein3_vs_ldlc_positive

Inverted…

protein3_vs_ldlc

Thus, we need to have at least one experiment to have a lopsided ratio of one against the other relative to the ketogenic ratios.

The Intended Divergence

Me being me, I decided to do two tests: one with a Super High Fat (SHF) ratio 95% fat / 4% protein / 1% net carbs for three days, and one with a Super High Protein (SHP) ratio 50% fat / 45% protein / 3% net carbs for three days. And finally, I added a third test in the following four days at closer to my usual ketogenic ratio with 75% fat / 21% protein / 4% net carbs  to see if my numbers snapped back into the original correlation.

 

 fat_sprint  protein_sprint  snap_back

To meet the requirements for the Super High Fat sprint, I averaged 293g fat, 31g protein, and 4g net carbs.

Here’s a sampling of the food I was eating over those three days:

fat_foods

Here are the results:

three_fat_vs_ldlc_16

Inverted…

three_fat_vs_ldlc_16_r

Sure enough, at a preposterously high ratio of fat to protein, my inversion correlation appears to still follow the fat, not the protein. My blood ketone levels (BHB) over these days were 2.1, 2.1, and 1.4 respectively.

Then I switched to Super High Protein. Over these three days I averaged 119g fat, 233g protein, 13g net carbs.

I was actually looking forward to this part of the experiment given I’ve always been a big fan of meat. In fact, I wondered if I wouldn’t get hooked on the higher protein ratio due to how much more meat I was allowed to consume.

Here’s a sampling of the food I ate:

protein_foods

But then something unexpected happened…

notes

In addition to all this biometric data I collect, I also keep regular notes on any unusual aches, nausea, or pretty much anything I feel that seems out of the ordinary. As I was getting to the end of the Super High Protein sprint, it was the only time I felt gastrointestinal distress that I associate with my days before the diet. It was a familiar feeling, but certainly not one I was missing.

I likewise felt heavier and less energetic, taking a nap on two of the three days. While I knew I was generating a higher glucose load via Gluconeogenesis due to all the protein, I actually rechecked all my food labels to make sure I didn’t accidentally eat something high in carbs.

Here are the results:

three_pro_vs_ldlc_17

Inverted with overlays…

three_pro_vs_ldlc_17_r

No question – we have a clear divergence where LDL-C does not follow dietary protein for the last two data points, Very High Fat (VHF) and and Very High Protein (VHP). Note it didn’t follow the dietary fat for this period either:

three_fat_vs_ldlc_17_r

It’s worth taking a moment to point out two very important observations.

First, if my only goal was to reduce my “bad cholesterol,” this would appear as good news. Assuming this trend held, having higher protein and less fat would result in lower LDL-C. As you can see from the graph above, were the inverse correlation holding, the lower 119g of fat would likely push up the LDL-C to around 323 rather than the 263 we see instead.

However, the energy level and GI issues I was experiencing were certainly a drawback. It also seemed to fall in line with the second tenet of my theory if it was causing inflammation.

See, here’s where the dark side of my theory comes in. What if there are steps I can take which lower my LDL cholesterol but only because it increases inflammation and/or oxidative stress? I might interpret this as a good outcome when it’s happening because, as everyone well knows, lowering dangerous cholesterol is all that matters.

Yet what if my body is sending me the correct signals in the first place? High Carb = feel slower, with occasional GI issues.  Low Carb, High Fat = feel great with little to no issues. High Protein, Moderate Fat = same as High Carb.

I decided to do my final data point to follow the VHF and VHP, which did snap back to the correlation envelope. I then went on the Low Carb Cruise and connected with a few more doctors to discuss the data. After getting back, I did my 19th and 20th NMR with the intent to bring all my data to this blog by the end of May.

But I was in for one more twist. The biggest yet, in fact.

The Unintended Divergence

Here are all 18 NMRs where I was on the normal ketogenic diet (removing the SHF and SHP data points) with inversion:

three_fat_vs_ldlc_18b_r

Spot anything unusual?

Yes, data point 18 at the far right shows the largest single divergence in the correlation of any other coupling in this graph. Was it a lab error with my blood? Was it something I ate or drank differently?

The only major change I made was cutting out diet soda the week before (primarily Coke Zero). So I first kept to no diet soda for another week and did one more test just to be sure it wasn’t a lab error. The divergence appeared to hold (see below with 19).

After that, I went to town with Coke Zero for three days to see if it would spike the correlation to the other side. It seemed outlandish to think aspartame could have kept my cholesterol artificially high, but I had to be sure. Again, the divergence held (see below with 20).

three_fat_vs_ldlc_20_r_callout

This new data suggests my LDL-C has been dropping all on its own (and by extension, Total Cholesterol).

But data points 17-20 did not significantly change the correlations of HDL-C, LDL-P, or small LDL-P. In fact, it improved them slightly.

Other Markers Improve Correlation

HDL-C had a -0.733 before, now it is -0.761.

fat3_vs_hdl

LDL-P had a -0.812 before, now it is -0.845.

Inverted…

fat3-2_vs_ldlp

LDL-P had a -0.726 before, now it is -0.781.

Inverted…

fat3-2_vs_smldlp

Next Steps

Will data points 1-16 represent a temporary “phase” of my diet with regard to LDL-C, proving 17-20 as the New Normal? Or is it the other way around? Your guess is as good as mine given the 6/9/16 test was the last one I took as of this posting.

The next blood test I’m taking is this week and it will be a very large combo pack of CMP, CRP, A1C, and other goodies in addition to the NMR. I’ll need to as I’ll be making some large changes to my exercise schedule. Starting next weekend I’ll be training for half marathons in the coming months. Will it start to impact my numbers? Stay tuned…

Coming soon – The Lipid Network Theory (Or “What Led Me Down This Rabbit Hole in the First Place”)

Cholesterol Code – Part II : The LDL-P Gap

In Part I, I shared the data of my first 15 cholesterol blood tests and how closely it correlates with dietary fat. To recap:

  1. My cholesterol baseline jumped after starting a ketogenic diet, which led me to do close blood testing against a theory I had on the lipid system appearing as a network (more on that theory later)
  2. If the theory had merit, there would be two tenants to assume:
    1. The cholesterol transporting lipid system would prove to be agile – much more so than is typically believed (this appears to be accurate).
    2. Discrete patterns should emerge between the diet and serum cholesterol now that disruptive inflammation is lower (this certainly appeared to be the case).
  3. After the testing, my bloodwork showed the following:
    1. The more fat I ate, the lower my Total Cholesterol. (87% inverted correlation)
    2. The more fat I ate, the lower my LDL-C. (90% inverted correlation)
    3. The more fat I ate, the lower my Triglycerides. (61% inverted correlation)
    4. The more fat I ate, the higher my HDL-C. (74% correlation)

Particle Primer

A typical cholesterol value from a blood panel only tests the quantity of cholesterol contained in LDL and HDL particles, not the particle number itself. And even then, it often uses the Friedewald Equation to estimate LDL. Cholesterol is actually just the “cargo” of LDL and HDL particles, not the particles themselves (the boats carrying the cargo). For a much more in-depth discussion, I highly recommend Peter Attia’s Straight Dope on Cholesterol lecture. (Which is where I’m shamelessly stealing the boat/cargo analogy from. Thanks, Peter!)

Thus, when we refer to LDL-C and HDL-C, the “-C” refers to the amount of total cholesterol when added together (the cargo of all ships combined). But when using LDL-P and HDL-P, the “-P” refers to the total particles (a total count of all the boats that carry the cargo).

The current belief in Lipidology is that LDL-P is a good, independent marker for cardiovascular disease. But within the low carb community, many believe there needs to be a distinction between “large, fluffy” LDL-P and “small, dense” LDL-P. They would argue that the small, dense particles are the true cause for heart disease, whereas the large, fluffy particles are benign or even protective. I won’t go much further into this debate other than to say I’ve read a lot of the studies each side prefers, and I’m not fully convinced of either position at this time.

Since all my blood tests were NMRs (Nuclear Magnetic Resonance), I kept careful track of these particles as well as my cholesterol. And the results were quite interesting…

LDL-P Result Offset

Back when I had eight tests over three months, the LDL-P appeared to match the LDL-C and its inverted correlation to the three day average of total fat.

fat3_vs_ldlp

Now again, let’s flip the axis of the same graph to see the inversion better…

fat3_vs_ldlp_reverse

So LDL-P correlates with the three day average of dietary fat just like LDL-C, right? Not so fast.

Here’s what happened during the week-long experiment.

fat3_vs_ldlp_9to14

And inverted…

fat3_vs_ldlp_9to14_reverse

Obviously, this graph seems to suggest that a two day delay is occurring. But could it really be that simple? We’ll get to that in a second… but for now, take extra notice of the massive swing in my LDL-P over just eight days!

On Monday’s blood test, I’m coming in at 2622. Two days later I have the highest score I’ve ever gotten at 3391! Then, just two days after that, I’m landing over 900 points down at 2455. I’ve read article after article that suggested this system takes extensive time to change. And while that might be true of 1/2 to 2/3 of the baseline score, it certainly doesn’t seem to pertain to the rest.

The LDL-P Gap

I set up a new formula against my spreadsheet to see if I could capture the correlation.

To use the example from before for LDL-C, if I took a blood test on a Friday morning (Day 0), the relevant numbers appeared to be the average of Tuesday, Wednesday, and Thursday together (Day -3, Day -2, and Day -1).

calendar-1-3

In the LDL-P equation, however, I’d still average three days, but with a gap of two days in between. So if the test were on a Friday morning (Day 0), I’d average together Sunday, Monday, and Tuesday (Day -5, Day -4, and Day -3).

calendar-3-5

Below, I put together the 1-8 tests over three months, the week-long 9-14 tests, and the outlier test together using this equation.

 

fat3-2_vs_ldlp_first15_positive

And inverted…

fat3-2_vs_ldlp_first15

Naturally, this is where my jaw hit the floor.

This obviously brings up a lot of powerful new questions regarding LDL-P synthesis of the liver in this counter-regulatory role. But this post is for the lay person, so I promise not to geek out on my lipid theories here. That will come in a later post.

Small LDL-P

What of the “small, dense” LDL-P mentioned above? Do they, likewise, track with the new formula?

Yes, they do.

fat3-2_vs_smldlp_first15_positive

And inverted…

fat3-2_vs_smldlp_first15

This, likewise, brought forth a number of extremely curious observations:

  1. Once again, we have massive shifts in a very short span of time with the week-long experiment. I go from a 484 to a 941 in two days, back down to a 378 two days later, and finally to <90 after a weekend of eating high fat three days straight.
  2. And also once again, I practically custom-set a <90 on the intentional outlier at the end by eating massive amounts of fat.
  3. My smLDL-P tracks very closely with my LDL-P, yet its ratio of gain-to-loss is much higher.

Final Thoughts

  1. LDL-P and small LDL-P appeared to have a correlation with a three day average of dietary fat as like LDL-C, but with a key exception of adding a two day gap between the dietary period and the blood test.
  2. Small LDL-P appeared to correlate closely with LDL-P, but at a higher gain to loss ratio.
  3. Both LDL-P and small LDL-P proved to be extremely agile and easily ramped up or cleared when observing via daily blood tests (data points 9-14). In fact, shifts in the hundreds of particles per day were easily achieved in either direction.

Next Steps

After having enough of this data in hand, I then realized there was still a problem with proportionality of the other macronutrients. If I was good at holding to a ketogenic ratio throughout this period – 75% fat / 20% protein / 5% carbs – then, technically, one could argue it is actually possible that protein or even carbs are driving the cholesterol correlation. After all, even at a lower volume than fat, they were still proportionally going up and down roughly the same amount, right?

Coming up in Part III – The Divergence… Would testing different ratios of macronutrients change the results?