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)
In Part II, we moved beyond LDL-C into total and small LDL-P and their differences in the findings.
- 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.
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
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.
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:
Here are the results:
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:
But then something unexpected happened…
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:
Inverted with overlays…
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:
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:
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).
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.
LDL-P had a -0.812 before, now it is -0.845.
LDL-P had a -0.726 before, now it is -0.781.
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”)