Dropping My Cholesterol at Record Speeds – Part II

In Part I, we focused on the three day gap where I arranged a massive shift around the first public presentation of my data. There’s quite a bit more to the story, though, which we’ll dig into here…

Knowing I’d be presenting on October 9th made it immediately clear I needed to test on the Friday before (7th) and the Monday following (10th). This should reflect the shift in LDL-C and HDL-C, given they both follow that three day window immediately before a blood test.

But what about LDL-P, and for that matter, small LDL-P (smLDL-P)? Since these follow a three day window, but with a two day gap in between, I needed to be sure I captured all five days before Oct 7th and the five days following. So in short, I’d be on the “low calorie diet” on October 2nd, 3rd, 4th, 5th, 6th until the morning of the 7th (Sunday, Monday, Tuesday, Wednesday, Thursday). I’d then take the blood test and ramp up the food intake to a “high calorie diet” for the rest of the 7th, then the 8th, 9th, 10th, and 11th (Friday, Saturday, Sunday, Monday, Tuesday) up to the morning of the 12th.

Below, in each graph, I mark the blood test associated with these four tests over the ten days in red, which will always be at the right of the graph.

 

So let’s start by looking at our old friend, LDL-C.

3day_ldlc_positive

For those of you who regularly read my blog, this is a familiar graph. In this case the blue solid line represents the LDL cholesterol that resulted from the blood test. The dashed orange line represents the dietary fat I ate in the three days just before the blood test was taken.

At the far right end you’ll see a solid red line replacing the blue. Those four data points took place October 5th, 7th, 10th, and 12th. You can also see the dashed orange lines showing my extreme low, followed by the extreme high in dietary fat.

Let’s invert the axis on the left side of the graph (the Three Day Dietary Fat) so that we can compare these trends visually.

3day_ldlc_negative

Sure enough — I “broke” my own correlation given how quickly I shifted from one extreme to the other. Given our correlation on the left side of the graph, you’d expect my extreme low on dietary fat of 63g would theoretically push my LDL-C up to around 348.

However, I did manage to bring the LDL-C down to 155 which did match the expected position relative to the diet. Yet this completed on the 12th, not the 10th. Again, this was probably due to the degree of shift, but we’d need to do more testing to know.

How about LDL-P?

3day_ldlp_positive

Same thing. Purple is pre-conference, red on the right is during. Let’s invert again…

3day_ldlp_negative

Like LDL-C above, the massive shifts seem to surpass my metabolism’s attempt to catch up.

Now let’s look at HDL-C.

3day_hdlc

As you can see from the green line on the right side, we have a long established coupling in the positive between dietary fat and HDL-C. But this too is broken by the experiment as shown on the right.

Other Interesting Markers

I had a number of things change, but there are two specific ones I wanted to call attention to in this post.

Glucose vs Insulin

Now I don’t want to blow your mind too much, so prepare yourself… here comes fasting glucose vs insulin…

glucose_insulin

glucose_insulin_table

Whoa — really?!? As my fasting glucose went up, so too did my insulin? No way!

Okay, kidding aside, it’s difficult to tease out the impact of each macronutrient on my fasting glucose to see what the primary drivers of the increased insulin were. Since all of my macros went up, this could’ve been from higher carbs or protein — or possibly just overall caloric load.

What is interesting is that even after 5 days at 5000 calories, my peak insulin was still at the bottom third of the reference range, at 8.3. Incredible!

Calories vs Weight

Easily one of the most hotly debated subjects in low carb communities. I’m firmly in the camp that says Hormone Balance Matters Most, But Calories Are Still Energy. Is that a side? If so, should we start a website for hbmmbcase.com?

I’m 6’3″ (190.5 cm) and thus feel a bit underweight at my running average of around 176 lbs (79.8 kg). My typical average calories right now are around 3,100. So what happens when I drop it to 750-ish for five days, then ramp up to 5,000+ for five days?

calories_weight

calories_weight_tableI’m missing 10/2 and 10/3 due to not being around a scale (was on a camping trip), but presumably I had a similar trend as 10/4-7 given I hover around 176 lbs as mentioned above.

Clearly the massive drop, then rise, in calories had a very acute effect on my weight. Over this span of time, I was careful to keep my water intake roughly the same for both low and high periods, although my non-water beverages were a little higher on the last half.

Final Thoughts

I’m now at the point of being so sure about the dynamism of the lipid system that this is becoming almost comical. Getting to this point took a lot of money, time, and blood (literally). I was fortunate to have this experiment wrapped into my first presentation. Doing the demonstration was a no-brainer. Why only show the data if you can display its predictive power as well?

There’s actually a lot more data I couldn’t get to due to the balancing act I’m now performing between this, contract work, interviews, and helping others to gather N=1 data as well (upcoming).

Personally, I wish I could work on this research 100% of the time, but I do have to pay the bills…

Presentations, Data, and Exercise

The egghead in me is dying to complete the geekier Part II of Dropping My Cholesterol at Record Speeds. But alas, there are a number of high priority developments rolling in.

  • I had to post a draft of my Food Logging Protocol given there’s some discussion of putting together a study based on my research. It’s been an effective tool for me to keep accurate, “audit-able” track of my diet. I still have some more to put in the page regarding the text portion of logging, but the visual language is the most relevant.
  • Since my last posting, I was approached on doing another presentation which I’ll be putting on tomorrow in a private meeting. Unfortunately, I can’t take a video of it, but I might do a separate one to post online later.
  • New data coming in from two new participants, more on that in future posts. My hope is that I’ll have something up at the end of this month.
  • How will endurance exercise impact my trend lines? This too we’re finding out right now. Over the last month I’ve now done a half dozen more blood tests both before and after running for long periods.

Busy Week

It’s been a pretty active seven days. Let’s recap:

I had intended on using Thursday for drafting my Part II, but had a few twists during this week that might really advance this data to a whole new level. I’m not at liberty to share this information just yet, but I will say that it may be a long term game-changer. So while this detour takes up some of my bandwidth, I may not get out Part II for another week. Stay tuned…

Infographic of Prediction Experiment

For more details on this graphic and the story behind it, see the post following.

highspeeddrop

Dropping My Cholesterol at Record Speeds – Part I

The Prediction

“This is the very first public presentation of my data,” I began, “and I’m excited because I get to start it off with a bang.”

I was standing before the Ketogains Seminar audience on October 9th, 2016 in Las Vegas, and I planned to make that day just a little extra special. The first slide I showed included a picture of me smiling as my blood was taken, with the heading, “October 7th, 2016 – Two days ago”. I then explained I expect my cholesterol on that day will be very high.

I follow with a second slide. It has the same picture but I’m a silhouette with a question mark inside. The heading reads, “October 10th, 2016 – Tomorrow

Finally, I present a third slide that includes both images and my predictions together, the second reading plainly, “Cholesterol Plummets in 3 Days”.

clip_of_presentation_prediction

“How am I doing this?” I asked. “A super powerful statin? High dose of Niacin? Some new, special exercise?… No… just food.”

I’d be lying if I didn’t confess at this point that my heart was pounding. I’m no stranger to public speaking, with one of my very first jobs being the M.C. of a dive show and working my way through college as a D.J. No, my nervousness stemmed from the fact that I was trusting all my research to date and taking this to the next level of publicly predicting an outcome. And not just any outcome… the outcome a thirty billion dollar prescription industry tries to provide.

The Results

So what happened? Quite a lot actually. Let me break it down into small pieces…

massive_shift_1

Sure enough, my Total Cholesterol dropped 66 points. But the real star of the show is LDL-C, the so called “Bad Cholesterol” which dropped a whopping 73 points in just three days — that’s nearly a 30% drop!!!

If I could put these results in a drug, I’d be a multibillionaire right now. Bayer and Pfizer would be pounding down my door offering a slice of the world to package this magic in a bottle.

But what about the actual LDL particles which show on the NMR labs as LDL-P? Or for that matter, the “Small” LDL-P that gets the credit as being the most dangerous of all?

massive_shift_2

Incredible!

LDL-P is nearly cut in half! This was even more impact than I had predicted. While it is true my LDL-P continued to drop into the 5th day (to be covered in an upcoming post), the shift in just 72 hours was really impressive.

Even more strikingly, Small LDL-P drops from 442 into the unreadably small “<90” range. Like LDL-P above, this runs very counter to the endless articles I’ve read about clearance with LDL receptors, which has been one of the chief suspects as to why LDL gets “backed up” in the bloodstream. Over and over, my data keeps providing a very different story.

HDL-C and Triglycerides both shifted in different directions, each one considered more favorable.

massive_shift_3

HDL-C bumped up 18 to 68, which is likewise consistent with my data.

Triglycerides are still a little more mysterious to me as they have the lowest correlation of the bunch over my research span, but are still negatively correlated with dietary fat. In this case, I was excited to see it dropped to nearly half of its earlier reading.

Food. Just Food

Unless you’re just now joining us, you already know how I pulled this off. More fat.

On one hand, if this is the first blog post of mine you’ve ever read, you might think I mistyped the above sentence. I most certainly didn’t. As with the last eleven months of my life, I carefully logged my food in MyFitnessPal to track it closely. I likewise took pictures of everything I ate. Everything.

low_food

On the five days prior to the Oct 7th blood test that gave me the high cholesterol, I had brought my total diet down to an average of 748 calories per day. This wasn’t pleasant given I’m a 6’3 male who gets semi-regular exercise. (Not to be confused with “athletic”)

Moreover, I didn’t want to stay too low on the calories for very long given I’m also very lean and would actually prefer to weigh a little more. I know this has me on the other side of most people in a low carb lifestyle, but actually aligned well with many at Ketogains.

By the time Friday was rolling around, I was excited to finally move to the next phase and ramp up the total food to full throttle. But unlike my Intentional Outlier experiment that I describe in Part I, I planned to exceed everything and shoot for the gold: 5,000 calories per day for five days.

 

high_foodThe first day was pretty easy, most likely due to having been so low cal for the previous five. Each meal I had to double or even triple my portions. But eventually I figured out it was just better to spread it out over the day. By the last day I could barely stand it. It’s incredibly difficult to eat very high calories on such a satiating diet. I kept telling my wife, “ugh, I can’t ever do this again,” to which she rolled her eyes knowing my variety of experiments before it and said, “yeah, we’ll see.”

Thus, many at the Ketogains Seminar noticed I was constantly pulling food out of my backpack in various forms: Adapt bars, peanut butter, and hot dogs I kept in a small cooler to name a few. This was especially annoying given the conference was catered with several amazing Keto-friendly buffet delights. But I couldn’t partake, given I needed to be certain of the exact macros and general ingredients I was consuming.

One particularly funny moment happened near the end of Sunday where I was sitting with other presenters at a Q&A and I realized I was still 1,200 calories short for my 5k goal which I had to meet by 7PM in order to have the proper 14 hour gap for the blood test at 9PM the following morning. I left my chair to go get a block of cheese and munched on it for the remaining 15 minutes of discussion.

Having said all that, let’s revisit our final version of the chart where we’ll include the food as well:

massive_shift_4

Final Thoughts

I’ll concede I’m still pretty awestruck. Even as I write these words and post this data, I keep rereading the labs and checking back over my logs. But there’s no doubting it now – the experiment delivered. The Dietary Inversion is very much real and yet almost no one knows about it.

Maybe this will move that needle just a little more.


This Part I is meant to be more layman, less geeky. In Part II I’ll be expanding on the data including the test before Oct 7 and after Oct 10th and how all of it compares to my current correlation timeline.