Cholesterol Code

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:

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:
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).
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.

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.

And inverted…

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.

And inverted…

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.

And inverted…

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.

Final Thoughts

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.

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?

Cholesterol Code – Part I : More Fat, Less LDL-C

I’ve thought a lot about how best to get this information out and knowing it will have many possible audiences. So I decided to just put it in a multipart series. This first installment will have some extremely surprising data with regard to dietary fat and its effect on LDL, HDL, and Triglycerides on a low carb / ketogenic diet.

Before we get started I wanted to emphasize two things:

A big THANKS to all the doctors and engineers I swore to secrecy on the deeper data I was sharing. I really appreciate you keeping a lid on this until I had verified and re-verified. (With extra special thanks to the collaboration of Dr. Sarah Hallberg, Dr. Jeffry Gerber, and Ivor Cummins.)
My cholesterol numbers are certainly high, and this might be distracting. I’m well aware of this and it is part of what started this data journey in the first place. But they’ll be more to this story by the time I get to my latest data.

The Brief Backstory

In March of last year, I got a marker for prediabetes from my blood panel, an A1C of 6.1. I was intent on avoiding future Type 2 Diabetes, so I researched and then began a low carbohydrate diet the month after. I lost 35 pounds, it raised my energy levels and seemed to improve my health in almost every way.

However, seven months later in November, I got back my first bloodwork and saw my total cholesterol had gone from 186 to a whopping 357, with LDL-C cholesterol climbing from 137 to 271. Given how incredibly great I felt the entire time, I was in complete disbelief. I heard some low carb dieters had exploding cholesterol, but assumed I wouldn’t, given I didn’t have higher than usual to begin with. From there, I began learning everything I could about Cholesterol Homeostasis.

The Idea

And this is where it got interesting for me. As a senior software engineer, the more I learned about the lipid system, the more I saw a familiar pattern –> a distributed network of objects. I read with particular interest on the assignment and apportionment of apolipoproteins and their potential role as a proxy for an information feedback loop to the liver.

Anyway, I’ll lay the general theory in more detail in a later post. Just know that if true, there should be two key presumptions:

The cholesterol transporting lipid system would prove to be agile — much more so than is typically believed
Discreet patterns should emerge between the diet and serum cholesterol now that disruptive inflammation is lower

So I went about testing these two points with my own blood starting in late November taking regular NMRs (Nuclear Magnetic Resonance – an advanced cholesterol test that directly measures lipoprotein particles and their sizes/classifications). Likewise, I kept meticulous track of my diet and nutrition values, both logging and taking pictures of just about everything I ate and drank. With blood taken every one to two weeks, I would make key changes between tests to see the outcome. Of course I was told over and over again that this was too frequent and that I needed to give the body more time to see results.

Initial Data

Sure enough, in eight tests over just three months I saw rapid changes, enforcing presumption (1). And yes, very discreet patterns did emerge, bringing evidence to presumption (2). In fact, both these behaviors were more pronounced than I could have imagined.

The tightest correlation appeared with total fat in my diet and LDL-C cholesterol. Now hearing this, you might not be surprised given that conventional wisdom seems to agree with the assumption higher fat (especially saturated fat) tracks with higher cholesterol in the blood. But that’s the first big twist — this isn’t a positive correlation, it’s a negative one. And get this – the strongest time tracking appears to be not months or even weeks, but a three day rolling average.

Let’s use an example to illustrate. Imagine I took a blood test on a Friday morning, which we’ll call Day 0. Tuesday is Day -3, Wednesday is Day -2, and Thursday is Day -1 just before that Friday. If you took the dietary fat average I had between those three days, you get the score in the yellow line above. Put another way – all the days before that Tuesday (Day -3) appear to have very little to no relevance to my LDL cholesterol – I can practically ignore them!

To better show the correlation, I’ll flip the vertical axis of this same graph for the three day average of fat. (I’ll be doing this a lot moving forward)

At this point, I took my data to the Low Carb Vail conference and met with a number of great doctors there. I also had a particularly good conversation with Ivor Cummins who is likewise very versed in the subject as well as being a fellow engineer.

I explained the next obvious step was to do a “reproduction test” and see if I could replay the experiment to get anywhere close to the same results. Except this time, it was going to be even more controlled with a tighter timeline. (And frankly, very unenjoyable!)

Reproduction Test

The plan was to have my blood drawn every single day, five days in a row (Monday-Friday). On the preceding days (Sunday-Thursday), I’d have a specific day of meals that matched the two of the extremes in my testing for LDL-P. The highest LDL-P score (3073) was Dec 19^th, when I had just 68g of fat the previous day, which we’ll call the “Low Meal Plan.” The lowest LDL-P score (1967) being on Feb 19^th when I had 222g the previous day we’ll call the “High Meal Plan.” All day Friday, Saturday, and Sunday I didn’t have a prescribed meal plan and just ate generally “high” fat. We’ll call this period the “General Meal Plan.”

The pattern would be:

Sunday – Low Meal Plan
Monday – Low Meal Plan
Tuesday – High Meal Plan
Wednesday – High Meal Plan
Thursday – Low Meal Plan
Friday, Saturday, Sunday – General Meal Plan (Uncontrolled)

For the first five days I ate the meals in as close to the same times and in the same order as the days they were modeled after. I also timed the blood test to have the same fasting time since the meal of previous day as well.

I was flying to a developer conference that weekend and hadn’t planned to take another test, but finally decided to add one more after I got there on the following Monday. So the Friday, Saturday, and Sunday meals when I went back to higher caloric intake with higher fat would likewise show as well, though they weren’t as controlled as the previous week.

So what happened? See for yourself:

And again – let’s flip that left axis so you can see the reverse correlation better…

This was especially meaningful, not just because it replicated the same three-day average, but because now we could actually see the changes in successive time to the cholesterol payload. (As an aside, the LDL-P had a more interesting story with this run of days. But again, I’ll detail that in the next post.)

The Intentional Outlier

Any engineer reading this is probably thinking the same thing I was by this point. “If this is an algorithm, then it stands to reason I can crank it up to a new level to generate a new extreme.” Which is exactly what I did.

I scheduled a test for April 8^th, and for the three days before, I ramped up my diet to an average of 4,274 calories and 349g of fat per day. Just a little heads up… this is extremely hard to do on a low carb diet.

Let’s display all 15 data points together now with this last one on the end…

And again with the reverse axis…

Sure enough, the April 8^th result was the lowest LDL cholesterol score of all the tests. Moreover, it likewise tracked to the correlative envelope of the previous 14 data points, giving the entire series a jaw dropping -.905 Pearson score.

Other Data Points

I could write another five posts on the other data points, but I’ll let the graphs speak for themselves with just a brief statement for each.

Three Day Average Total Fat (inverse axis) vs Total Cholesterol

Generally, I don’t care that much for the TC metric, but I thought I’d include it for those who do. It is worth noting that data point 9 started on 3/7 with a 348 TC, just two days later I peak out at 422, then six days later I’m down to 364. Again, this is one more example of where we are told frequently that these numbers take a lot more time to change when clearly my data suggests otherwise.

Three Day Average Total Fat vs HDL-C

While the correlation at 0.742 isn’t as prominent as as the -0.905 of LDL above, there’s no question it is still a very strong relationship. The primary divergence is in the first data point going back to 11/24/15, but otherwise it tracks remarkably close – even during the day-after-day reproduction experiment.

Three Day Average Total Fat (inverse axis) vs Triglycerides

Lastly, we see the trigs are pretty close in correlation as well. Ironically, a 0.6 correlation on at least one metric was what I was originally hoping for when I started this experiment. Yet here’s trigs showing such a strong inverse correlation and it’s the runt of the pack at -0.61. Go figure.

Final Thoughts

While I’m glad both (1) and (2) of my theory above proved to open the door to these emerging patterns, I certainly didn’t think it would be such a perfect inversion of almost everything I’ve read to date with regard to Cholesterol.

My data over 15 data points suggest:

The more fat I eat, the lower my Total Cholesterol (87% inverted correlation)
The more fat I eat, the lower my LDL-C (90% inverted correlation)
The more fat I eat, the lower my Triglycerides (61% inverted correlation)
The more fat I eat, the higher my HDL-C (74% correlation)

Coming up in Part II – Particles, Particles, Particles. Results and analysis for my LDL-P, small LDL-P, and Pattern A/B.