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:

  1. 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.)
  2. 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:

  1. The cholesterol transporting lipid system would prove to be agile — much more so than is typically believed
  2. 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 19th, 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 19th 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 8th, 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 8th 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.