Lowering Cholesterol as a LMHR

Introduction

As our resident Lean Mass Hyper-Responder (LMHR) on the site, I have been an outlier so far with my Lipids. Despite an additional 200g/day of fat, my LDL was effectively unchanged in the Ketofest experiment. I knew exercise was potentially a confounder given Dave’s experiments with distance running, but I had a full training schedule this year leading up to my first marathon earlier this month.  Now, with a fat-fueled, goo-free, finish in the books, I decided to overlap some science with a forced period of rest.

Background

The Inversion Pattern is a theory about the impact of short-term diet on blood lipids (TC, LDL, HDL, Triglycerides). For LDL cholesterol, the supposed “bad” kind, it can be simply stated as:

All else being equal, LDL-C is inversely related to the dietary fat consumed in the 3-days prior to the blood draw

The implications of this pattern are profound. If blood lipids are not static, but instead highly dynamic from short-term factors, all conclusions based on a single sample are suspect. It would be like concluding you lived in a desert from a single sunny day.

Here’s the complete set of relationships from the Inversion Pattern:

• Total Cholesterol tracks with inverse of dietary fat for the 1-3 days before the blood draw. (87% inverted correlation)
• LDL-C tracks with the inverse of dietary fat for the 1-3 days before the blood draw. (90% inverted correlation)
• LDL-P tracks with the inverse of dietary fat for the 3-5 days before the blood draw. (80% correlation)
• HDL-C tracks with dietary fat for the 1-3 days before the blood draw. (74% correlation)
• HDL-P tracks with dietary fat for the 3-5 days before the blood draw. (correlation not calculated)
• TG tracks with the inverse of dietary fat for the 1-3 days before the blood draw. (61% inverted correlation)

Goals

The goal of this experiment was to replicate the Inversion Pattern using the Feldman protocol.  If that held, I would use this data as a baseline to compare against my earlier tests that included exercise. And, just for fun, I would be able to see the short-term variability of a number of other metrics in a controlled setting.

Experiment Design

I adapted the 4-test version into a 2-week schedule following my race. The first two days were a washout period with ad lib food. That was followed by a 5-day low-calorie phase and a 5-day high-calorie phase. Throughout, I refrained from all exercise. I even switched to riding the subway instead of my normal bike commute.

For measuring cholesterol, I got both a Nuclear Magnetic Resonance test (NMR) for particle counts, and the standard lipid panel (for redundancy).  I followed my own advice on testing and also got hsCRP, Insulin, Metabolic panel, and WBC.

Predictions

To evaluate the performance of the Inversion Pattern, we’ll score it based on how it does predicting which test shows the highest and lowest values for each of the metrics.

Here are the predictions:

The Experiment

Food Journal

Overall, I managed to stick to the schedule pretty well.  I managed a 40-hour fast on Day 7 during the low-calorie phase (calories: a coffee with cream and a spoonful of fish oil). On Day 11, I had some unexpected family travel, so it ended up being a Medium Calorie day. Fortunately, there were enough blood draws to still get a sample after three High Calorie days in a row.

My go-to meals during the experiment, easy to vary the quantity. Breakfast: Bacon and Eggs (with HWC, butter) Lunch: Bunless Burger salad (with Olive Oil)

Results

Total Cholesterol (TC)

First, let’s look at Total Cholesterol.

Finally, Feldman drop achieved! As predicted, TC peaked on Test 2 after the 40-hour fast.  Three days later, TC dropped 112 points to its minimum value. Interestingly, the Medium Calorie day allowed a difference in dietary fat between Test 3 and 4, which showed a small increase of TC, as predicted.  

Prediction Score: 2/2

LDL-C

Next, let’s look at LDL-C, the “bad” cholesterol that everyone is worried about.

Similar to TC, LDL-C peaked on Test 2 after the 40-hour fast.  Three days later, LDL-C dropped 100 points to its minimum value for Test 3, as predicted.

For comparison, my normal LDL-C with regular exercise was in the 210-230 range. For those keeping score at home, that’s a 200 point difference from the peak of this experiment.

Prediction Score: 4/4

LDL-P

Next, I was eager to see LDL-P. In Dave’s results, LDL-P has both a higher correlation and a trickier formula.

Boom. Exactly as predicted. From Test 2 to 4, LDL-P dropped by an impressive 1200 in 5 days. I found it particularly interesting how large the change was between Test 3 and 4 in LDL-P, even though the LDL-C went in the other direction in that interval. Clearly, LDL-P is a lagging indicator from LDL-C.

For comparison, my normal LDL-P (from one NMR in August) was 1597.  In this case, the total net difference was 1700. Without medications.

Prediction Score: 6/6

HDL-C

On to HDL-C the “good” cholesterol.

At first glance, HDL-C doesn’t seem to move much. However, since going low-carb, my HDL-C has been in a pretty tight 110-115 range, so 102 was actually a low-outlier, as predicted.  The two highest fell within my normal range and didn’t appear in Test 3 as expected. Maybe it takes a little bit more time to spin up my reverse cholesterol transport to above 110.

Prediction Score: 7/8

HDL-P

After the first prediction miss on HDL-C, my expectations were lower for HDL-P.

Wow. HDL-P moved from 31.3 to 36.9 exactly as predicted by the Inversion Pattern. Even if the cholesterol cargo in HDL has some additional variability, the particle counts (and thus emission and/or clearance rates) clearly have short-term diet as a driver.

For comparison, my one previous NMR in August showed HDL-P of 34.7.

Prediction Score: 9/10

Triglycerides (TG)

Triglycerides also played along and followed the 3-day fat intake as predicted.  At first I thought the higher TG in Test 1 and 2 might have been due to the lingering expectation of energy needed for exercise, due to my sudden drop off in activity. However, their correlation with 3-day fat matches the Inversion Pattern and implies a more responsive dynamic.

For comparison, my TG tests have typically ranged from 50-70, going back to before my low-carb diet.

Prediction Score: 11/12

Insulin

Insulin is a key hormone that I was interesting in following during the protocol.

Here I plotted against 3-Day Fat, but protein was similar. Despite keeping Carbs generally below 40g (except days of 73g and 56g), I still managed to have a 6x change from min to max. Clearly, Insulin is responsive to non-carb nutrients. I wonder how much of the later results were driven by late-night dairy (e.g., 4-oz of half-and-half at 10pm).

For comparison, my Insulin from August was 4.3.

hsCRP

I tracked High Sensitivity C-Reactive Protein to see how inflammation would track as I recovered from my first marathon.

I was extremely sore for several days following the race. I was feeling better by Test 1 on day 6, but still had somewhat higher inflammation. As expected, inflammation declined as I rested and healed.  My previous CRP reading in August was 1.01, so it’s possible that the 0.89 minimum was because I excluding some inflammatory food from my diet. Dairy is the most likely culprit, and it would be interesting to exclude it and check my CRP again.

Summary

This experiment was a clear confirmation of the Inversion Pattern for all lipid scores except HDL-C. Short-term diet was demonstrated as an input factor was able to move LDL-C by 100 points. In previous tests, exercise during the 3-days prior was sufficient to confound a similar change in diet. In addition, infection, injury, and other short-term inputs have known or theoretical impact on these numbers.

One of my takeaways is that the lipid system is much more dynamic than conventionally understood.  For those who are getting advice to change their lifestyle or take medication from someone ignorant of this fact, I would encourage you to learn more first. For those who need a low LDL or TC score to get cheaper insurance or to get their doctor off their back, I would encourage you consider using the protocol yourself.

Another takeaway is that my blood lipids are quickly reacting to serve essential bodily functions. Some of the missing LDL-C from my exercising tests were likely used for muscle repair via endocytosis, reducing my recovery time. In addition, the four hours of marathon running without goo was only possible because of VLDL distribution (and possibly even HDL to some narrower vascular channels after CETP). I’m grateful I have a “hyper-responding” lipid system to get the job done.

In the future, this data should prove valuable if I decide to continue with further experiments. They might have to study the impact of exercise, though, since the prolonged rest was difficult for me.