In Part I, I shared the data of my first 15 cholesterol blood tests and how closely it correlates with dietary fat. To recap:
- 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)
- If the theory had merit, there would be two tenants to assume:
- The cholesterol transporting lipid system would prove to be agile – much more so than is typically believed (this appears to be accurate).
- Discrete patterns should emerge between the diet and serum cholesterol now that disruptive inflammation is lower (this certainly appeared to be the case).
- After the testing, my bloodwork showed the following:
- The more fat I ate, the lower my Total Cholesterol. (87% inverted correlation)
- The more fat I ate, the lower my LDL-C. (90% inverted correlation)
- The more fat I ate, the lower my Triglycerides. (61% inverted correlation)
- The more fat I ate, the higher my HDL-C. (74% correlation)
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.
Now again, let’s flip the axis of the same graph to see the inversion better…
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.
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).
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).
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.
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.
What of the “small, dense” LDL-P mentioned above? Do they, likewise, track with the new formula?
Yes, they do.
This, likewise, brought forth a number of extremely curious observations:
- 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.
- And also once again, I practically custom-set a <90 on the intentional outlier at the end by eating massive amounts of fat.
- My smLDL-P tracks very closely with my LDL-P, yet its ratio of gain-to-loss is much higher.
- 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.
- Small LDL-P appeared to correlate closely with LDL-P, but at a higher gain to loss ratio.
- 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.
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?