Jan 20


Please consider supporting my Patreon. All funding for my research and this site come solely from individuals like you. Thank you!

  • If you know little to nothing about cholesterol->
    • And you want to learn the basics->
      • You can check out my Simple Guide to Cholesterol series. It’s full of illustrations and is written for laypeople. Enjoy!
      • Likewise, I have this video that goes over the basic markers for cholesterol while on a low carb diet. (Pictured to the right)
    • You can enter your cholesterol numbers into our popular Report tool to check them against many risk calculations at the same time.
  • If you’re wanting to know about my research->
    • You want an overview->
    • You want the most recent breakthroughs->
      • 1/2/2018: In this latest video, I demonstrate massive changes to my LDL Cholesterol over 5 stages in a matter of days. LDL 207 to 103 mg/dL in seven days with high carb, up again to 146 on mixed, down again to 113 on high fat. (Pictured to the right)
  • If you have seen your cholesterol rise considerably on a low-carb high-fat diet (like myself):
    • You may want to first visit the FAQ.
    • I would strongly encourage you to read through this blog and my own journey revealing the Inversion Pattern. Key moments were the Identical Diet experiment and the Extreme Cholesterol Drop experiment that I wrapped around the first presentation of my data for the Ketogains Seminar.

Sep 17

Mega Monster #FeldmanProtocol Results

One Upmanship

Ever since I saw Dave’s Fasting Disaster post I’ve wanted to add some fasting data to the Cholesterol Code reserves. Instead of fasting for two days as Dave had done, I wanted to fast for the three days he had originally planned for. In fact, why not do a full five days as outlined in the 10 1/2 day Feldman Protocol instructions?

I had two advantages over Dave: 1) I’m much less lean than Dave is, which would likely make fasting easier. 2) I’ve done multiple multi-day fasts, making me a more advanced “faster”. Fasting is often described as a muscle that you need to train, which I’ve “flexed” a bit more than Dave has.

The opportunity to get more data was too good to pass up. Especially since our data guru (and resident Lean Mass Hyper-responder), Craig, had already completed the full protocol not too long ago, leaving me as the last one out. I was plenty ready and willing to fix that.

The Plan

Instead of following in Dave’s footsteps exactly, I made a few tweaks:

  • Electrolytes on an as-needed basis

Several conversations with Megan Ramos at various conferences cemented the idea that when fasting, electrolytes are no joke. As such whenever I was feeling a bit “off” I would supplement electrolytes.

  • Feeding would follow a carnivorous diet

I’ve been following a carnivorous diet since October of 2017, and thus in order to make the results comparable to my previous data sets, I wanted to keep diet relatively the same as well.

  • No liquid fats

Liquid forms of fat can sometimes mess with triglyceride levels (which can influence calculated LDL), so I decided to avoid them entirely. All of my fat would be found in the meat and cheese I was eating – no butter in my coffee, no chugging heavy cream (as I did during the Ketofest experiment), and definitely no MCT or coconut oil.

The Inversion Pattern

In case it wasn’t obvious, I was setting out replicate the inversion pattern via the Feldman Protocol to see if the observed relationships held true. Going off of Dave’s original experiment from 2016, these relationships include:

  • Total Cholesterol tracks with the 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)

Murphy’s Law

What can go wrong, will go wrong… After I had already started the experiment, and right before my first blood test, I discovered that my local LabCorp wasn’t open on weekends. This entirely threw off my intended schedule, as one of the tests for the feasting portion would have landed on a Saturday. Not only that, but because they were only open on weekdays, this meant the 10 1/2 day protocol was essentially impossible. Unfortunately, there was no other local LabCorp nearby, so I was left with two options.

  1. Do the 6-day protocol instead.
  2. Extend my fasting phase to 7 days to push the Saturday test to Monday.

Because the whole point of the experiment was to get fasting data from at least 5 days of fasting, I decided on option two. I decided I likely had enough body fat and experience to get me through 7 days of full-on fasting safely, and easily, and it would introduce a unique opportunity to get even more data.

With that, the new schedule looked like this:

Food Tracking

As per usual, I tracked all of my food from the high-calorie days (and all electrolytes/beverages during fasting days) through picture-taking. At the end of each day, I also logged all the food into My Fitness Pal. I ended up eating much more cheese and processed meat than expected, but still achieved my goal of at least 3000 calories for the 5 feasting days.

An example of items consumed during fasting phase vs high calorie phase

The Results

Total Cholesterol

First up is Total Cholesterol.

  • Normal Baseline: usually around 320-350 mg/dL (not shown in the graph).

After three days of fasting my Total Cholesterol was 345 mg/dL, where it stayed in about the same range until I switched over to the high calorie/high fat phase where it initially dropped to 219 mg/dL after three days, and then 209 mg/dL after 5 days of the high fat protocol.

  • 7 day fasted to 3 day high fat/high calorie: -132 mg/dL
    • Time span of drop: 3 days
  • Biggest drop: -162 mg/dL (Between highest on 8/22, vs the lowest 8/29)
    • Time span of drop: 7 days

The correlation between my 3 day average of dietary fat was an astounding -0.9928~ even higher than the expected 87% inverse correlation.


Next is LDL Cholesterol.

  • Normal Baseline: usually around 270-290 mg/dL (data not shown).

After 3 days of fasting my LDL-C was 278 mg/dL which climbed to 310 mg/dL after 5 days of fasting. After 3 days of high calorie it quickly dropped to 166 mg/dL and after 5 days of high calorie dropped even further to 151 mg/dL.

  • 7 day fasted to 3 day high fat/high calorie: -132 mg/dL
    • Time span of drop: 3 days
  • Biggest drop: -159 mg/dL (between 8/22 and 8/29)
    • Time span of drop: 7 days

The inverse correlation between LDL-C and the 3 day average of dietary fat was, again, higher than expected at -0.988~ with the expectation being an inverse correlation of 90%.


Particle count!

[IMPORTANT REMINDER: again, the Inversion Pattern for LDL-P is usually a three-day window with a two-day gap, not the three-day window with a zero-day gap, hence why the graph below doesn’t start with dietary fat at 0]

Normal Baseline: somewhere around 3000 nmol/L (not shown).

After 3 days I was actually lower than my usual at 2890 nmol/L, however, this quickly skyrocketed to >3500 nmol/L after 5 and 7 days fasted. For those of you who were curious, if getting an NMR Lipoprofile, the test that measures LDL-P among other things, if you go above 3500 nmol/L it won’t give specifics after that. After 3 days of high fat/high calorie feeding it dropped down to 2086 nmol/L and then to 1578 nmol/L after 5 days of high fat/high calorie.

  • 7 day fasted to 3 day high fat/high calorie (estimated): -1414 nmol/L
    • Time span of drop: 3 days
  • Biggest drop (estimated): -1922 nmol/L (between 8/22, 8/24 and 8/29)
    • Time span of drop: 5-7 days

Unfortunately, those numbers are estimated because of the LDL-P cutoff, each estimated “drop” assumes that LDL-P was at exactly 3500.

Again, if we assume LDL-P was exactly 3500 nmol/L the correlation comes out to -0.91~ however, if we assume both topped out LDL-P are >3500 as a general guess, the correlation drops to -0.83~ which pretty closely matches the expected inverse correlation of 80%.

Note: Due to formatting reasons, the below graph assumes LDL-P was exactly 3500 at its highest


  • Normal Baseline: My normal HDL-C has always tended to run low, with my normal hitting around 40-45 mg/dL on average
    • (for those wondering, the best guess for now is it is genetic, but I’m not entirely sold on that as of yet).

As predicted, HDL-C fell during the fasting days, from 43 mg/dL after 3 days of fasting to 37 mg/dL after 7 days of fasting.

  • 7 days fasted to 3 days high fat/high calorie: +4 mg/dL 
    • Time span of increase: 3 days
  • Biggest Increase: +9 mg/dL (8/24 to 8/29).
    • Time span of increase: 5 days

Although this may not appear to be a lot of movement, HDL is one of the less noisy markers and tends to remain a bit more stable over shorter periods of time. The positive correlation to a 3 day average of dietary fat was slightly lower than the expected 70%, but came in close at 0.66~.


Normal Baseline: Like HDL-C my HDL-P tends to run low, around 20-23 umol/L

HDL-P did fall during the fasting phase, from 16.9 umol/L to 13.2 umol/L with a slight bump up at the 5 day mark, and went up during the high fat/high calorie phase to the highest HDL-P I have on record at 29.1 umol/L

  • 7 days fasted to 3 days high fat/high calorie:  +12.7 umol/L
    • Time span of increase: 3 days
  • Biggest Increase: +15.9 umol/L (8/24 to 8/29)
    • Time span of increase: 5 days

The positive correlation to dietary fat (with a 2 day gap) on this one was 0.84~ with no previous correlation on record, although Craig mentioned in his post the correlation was higher than his HDL-C as it is in mine.


Normal Baseline: 70-90 mg/dL

As expected, triglycerides went high during the early portions of the fast and then started to trend down the longer the fast went on. From 119 mg/dL at the 3 day mark, trending down throughout the fasting phase until it reached 94 mg/dL by day 7 of the fast. It continued to drop through the high calorie phase until it reached my lowest triglyceride level on record at 49 mg/dL.

  • 7 days fasted to 3 days high fat/high calorie:  -33 mg/dL
    • Time span of drop: 3 days
  • Biggest drop: 70 mg/dL
    • Time span of drop: 9 days

Although typically triglycerides are much noisier than usual, the correlation here was -0.95~ with 3 day average dietary fat, exceeding the expectation of a 61% inverse correlation.


Normal Baseline: Usually around 112-140 nmol/L although it has been as high as 187 nmol/L, and as low as 82 nmol/L but neither of these occasions were normal baseline readings.

I actually didn’t expect lipoprotein(a) to fluctuate that much, as with my past data the only things that have moved it so far is getting sick (causes an increase) and swapping meat sources (causes a drop). Generally it’s said in the literature that lipoprotein(a) levels are largely determined by genetic factors, although it does act as an acute phase reactant. As such I expected it fluctuate maybe 10 nmol/L as it usually does when I’m not explicitly trying to move it. But, of course, it had to surprise me by increasing above my normal baseline to 189 nmol/L by 5 days of fasting, then decreasing substantially to 77 nmol/L upon high fat re-feeding, and even further to 65 nmol/L after another 2 days – the lowest lipoprotein(a) readings I’ve ever gotten, leaving me technically “in range” of normal levels.

  • 7 days fasted to 3 days high fat/high calorie:  –103 nmol/L
    • Time span of drop: 3 days
  • Biggest drop: -124 nmol/L (8/22 to 8/29)
    • Time span of drop: 7 days

The correlation with lipoprotein(a) and 3 day average of dietary fat was a pretty impressive -0.998~. There’s no previous correlation on record for Craig or Dave, so as far as I know, this hasn’t been replicated yet.

It has been suggested by some who saw the result that fasting beforehand could have confounded the high fat feeding data. In addition to that possibility, I haven’t replicated the data myself yet, so I’ll definitely have to do a few more experiments to see if this reaction is consistent. For now, though, it certainly is unexpected – not to mention interesting – and it makes me glad I food/calorie matched for the blood draws I got late last year and early this year, while sick.

Thyroid Changes

During the wide spectrum testing days, I decided to check out a few additional markers beyond the “basics”. This included some thyroid markers, to see how they would change before and after the fast. I expected that T3 would be low, as lower T3 might be useful for muscle sparing and energy regulation, but I wasn’t sure what the other markers would look like.


Thyroid Markers 7 day fast 5 day feast Ref Range
TSH 3.36 2.51 .450-4.5
Reverse T3 34.2 14.5 9.2 – 24.1
Thyroxine (T4) 7.1 6.1 4.5 – 12
Triiodothyronine (T3), free 1.6 2.2 2 – 4.4

As expected, T3 went from so low it was out of range, to the lower end of normal (which still didn’t surprise me considering I’ve seen discussion that lower T3 on a ketogenic diet could be adaptive). Reverse T3 dropped by over half, and upon a little poking around it seems this is not unusual and may go hand in hand with the lower T3 as an adaptive change.


In short, I would say that after full completion of the extended protocol, this experiment worked as a confirmation of the protocol’s effects in 1) a woman 2) who has a slightly higher estimated body fat percentage than Craig or Dave 3) who follows a predominantly carnivorous diet. Via the protocol, I successfully dropped my LDL-C by 159 mg/dL in a much shorter time period than would be conventionally assumed plausible. Additionally, I did this via diet changes only, with no changes to supplements (vitamin D, and magnesium glycinate) with the exception of an electrolyte supplement which was consumed on an ad libitum basis. Nor were there any changes to medications (none) during the experiment.

Additionally, I found that the lipid system was far more dynamic for me personally than I had expected. The previous time I had done the protocol I only dropped my cholesterol by about 50 mg/dL (6 day protocol with low-calorie instead of fasting), and I had thought that this attempt would yield a slightly higher drop. In fact, somewhat surprisingly, I nearly tripled the drop in cholesterol this time around.  Additionally, unexpected lipid markers (such as lipoprotein(a)) showed a surprising amount of – what could turn out to be – dynamic response to diet as well. This will obviously require further follow-up to confirm it was the introduction of high amounts of dietary fat that resulted in personally historically low lipoprotein(a) but the initial results of this experiment are intriguing, to say the least.

Sep 14

Resistance Training Experiment – Progress and Extension

[UPDATE: I incorrectly identified the 18th as the new end date, it will actually be the 17th — corrected below]

My Resistance Training Experiment has been quite interesting. But before dishing on the details, I’ll just say that I’ve decided to extend it another week. I’ll now be taking it to the morning of the 17th (this coming Monday).

Design Amendment to Add an Extra Phase

The Face of Tired

As if this writing (Thursday night, Sept 13th), my cholesterol is below the baseline of the washout period (August 29st-Sept 5th). The mean average of TC and LDL-C during this run-up period are 382 and 314 respectively. [Note: these levels are higher due to the baseline diet being around 500 calories lower than my typical, ad libitum keto diet.]

I’m awaiting the baseline to return to this roughly this level before “triggering” another intervention of another intense workout. I figure a 3% offset should be sufficient. Thus, if either the TC comes to or above 370 (97% of 382) or LDL-C comes to or above 305 (97% of 315) for the 10am reading, then I’ll initiate the intervention on the following day.

Change to Intended Exercise Regime

While I had set up my vibration plate machine for exercise in the hopes I could fully quantify it for perfect replication, I quickly found out that I couldn’t easily do a full upper body workout to the degree I was seeking and that it was taking a bit of time working with the operation of it. Thus I altered the intervention phases as follows:

Intervention Phase I: Completed four exercises on the vibration plate for 20 minutes. Completed half of a free exercise video on YouTube for 15 minutes. Played 20 minutes of Knockout League for PSVR (Lots of rapid arm swinging, variety of motion), one lap around my block with a 500ml water bottle in each hand.

Intervention Phase II: Went to a nearby gym and had 5 minutes of warmup cardio, then 45 minutes on various upper body weights with extra emphasis on the arms and chest. I did another 15 minutes on the vibration plate afterward.

Change to Diet to Add More Salt

As many who follow me already know, I consume a lot of salt in my normal keto diet, usually targeting about 10g a day (no, that’s not a typo). At the beginning of the washout phase, I was consuming roughly 5g with my meals on top of a Sports Salts supplement of 1.5 servings at the 10am meal.

Alas, this was not enough. I began experiencing leg cramps a couple days in and so added an additional teaspoon (6g) of pink salt to water each day and this resolved the issues.

Final Thoughts

As with so many of these things, I find I’m living a schizophrenic existence. Human Dave is pretty sick of this experiment and ready to get back to real food and a far less structured life. Scientist Dave is all too happy to tack on this extra week because the data will be so promising. We know who keeps winning these rounds…


Sep 10

Women: Let’s Talk Cholesterol

Note from Dave: This long-awaited installment from Siobhan is a must-read for every female interested in cholesterol and risk. Enjoy!

The First Hint

As the #LDLBounty continues, and the question of whether high LDL in isolation raises the risk of cardiovascular mortality (or all-cause mortality) in and of itself remains unanswered, there may be a group which may give one even more pause when contemplating the answer. My first hint that things weren’t exactly equal in regards to cholesterol between men and women was way back in 2015. Far before I had even thought about picking up a textbook on lipidology, I noticed there seemed to be something a little odd going on in regards to women and high cholesterol levels. For one, I had heard talk of some research that women with low cholesterol were more at risk for symptoms of depression1, 2, and while I found this interesting it was merely an association. So what if you’re less likely to become depressed if you have higher cholesterol as a woman? You’ll still be more likely to die of things like heart disease, and die more over all, anyway.


More Cholesterol, Less Death

In fact, there appears to be a bit of a difference when it comes to women and men in regards to mortality risk when it

From: doi:10.1111/j.1365-2753.2011.01767.x

comes to cholesterol. Because, while in men it appears as though there’s a U shaped risk when it comes to

cholesterol levels – meaning that rates of death are higher with lower and higher total cholesterol – with women it appears to be quite a bit more simple. As stated in the paper3, in women it appears as though when cholesterol levels are higher, death from all causes is lower.

That study didn’t appear to be a random fluke, either. As time went on I found more4, and more5 and more6 studies all showing that, specifically in women, either there was no difference in death from all causes with varying cholesterol levels, or those in the lowest cholesterol group had the highest rates of mortality. But, admittedly, this is looking at total cholesterol, so I thought that at the very least higher LDL (often considered the true bad guy in regards to cholesterol) would correlate to higher mortality rates.

More of the Same

Even after looking for studies which looked at LDL levels in regards to the rate of mortality and cardiovascular disease, the result ended up being the same with studies I encountered generally coming to similar conclusions. Some said that LDL levels in women were “not significantly related” to all-cause mortality7, while other said that LDL was not associated with Peripheral Artery Disease in women8, and yet more indicating variations of the same result.9, 10

But, clearly women aren’t bulletproof when it comes to cardiovascular disease, as they get it at about the same rates as men do, and it’s not like we’re especially good at avoiding death in general (as much as we might like to think of ourselves as invincible), either. So, it doesn’t appear as though this is a case of women being immune to cardiovascular disease, or being particularly resistant to death, so much as total cholesterol and LDL seemingly being poor predictive markers for women.

Now What?

If neither LDL nor total cholesterol is especially predictive in women, then what can we look for to determine our risk? Some studies indicate other, potentially more relevant markers for women, and luckily no additional tests need be ordered, as the information can be found on a standard lipid panel.

For example several studies found that the ratio of triglycerides to HDL were predictive of carotid plaque11, insulin resistance12, 13 (which is related to increased cardiovascular disease risk), cardiovascular mortality14 and all-cause mortality in women.15 Atherogenic Index of Plasma (AIP) also has some promise to better predict all-cause mortality among women.16 Although AIP is unlikely to show up on your lab report, you can easily calculate it via our report tool so long as you have your triglyceride and HDL levels.

While these markers do appear to predict cardiovascular and all-cause mortality risk better than Total Cholesterol or LDL this doesn’t mean that these markers are perfect. For one, both of these measures can be impacted by how long you fasted before the test, as well as issues which may raise triglycerides in isolation. Use of medication which influences HDL and triglycerides may also potentially interfere with their value as health markers, as well, so looking at multiple risk markers together may help avoid some of the pitfalls of relying on one marker alone.

Final Note From Siobhan


As always, the evaluation of personal health is a topic that, first and foremost, should be had with your doctor. Likewise, we always recommend researching all reputable sides of this debate as we do ourselves.





Horsten, Myriam, et al. “Depressive Symptoms, Social Support, and Lipid Profile in Healthy Middle-Aged Women:” Psychosomatic Medicine, vol. 59, no. 5, 1997, pp. 521–28. Crossref, doi:10.1097/00006842-199709000-00009.

Persons, Jane E., et al. “Longitudinal Study of Low Serum LDL Cholesterol and Depressive Symptom Onset in
Postmenopause.” The Journal of Clinical Psychiatry, vol. 77, no. 2, Feb. 2016, pp. 212–20. PubMed, doi:10.4088/JCP.14m09505.
Petursson, Halfdan, et al. “Is the Use of Cholesterol in Mortality Risk Algorithms in Clinical Guidelines Valid? Ten Years Prospective Data from the Norwegian HUNT 2 Study.” Journal of Evaluation in Clinical Practice, vol. 18, no. 1, Feb. 2012, pp. 159–68. PubMed, doi:10.1111/j.1365-2753.2011.01767.x.
Forette, B., et al. “Cholesterol as Risk Factor for Mortality in Elderly Women.” Lancet (London, England), vol. 1, no. 8643, Apr. 1989, pp. 868–70.
Higgins, M., and J. B. Keller. “Cholesterol, Coronary Heart Disease, and Total Mortality in Middle-Aged and Elderly Men and Women in Tecumseh.” Annals of Epidemiology, vol. 2, no. 1–2, Mar. 1992, pp. 69–76.
Choi, Ji-Sook, et al. “Serum Total Cholesterol and Mortality in Middle-Aged Korean Women.” Atherosclerosis, vol. 192, no. 2, June 2007, pp. 445–47. PubMed, doi:10.1016/j.atherosclerosis.2007.03.006.
Nilsson, Göran, et al. “Ten-Year Survival in 75-Year-Old Men and Women: Predictive Ability of Total Cholesterol, HDL-C, and LDL-C.” Current Gerontology and Geriatrics Research, 2009, p. 158425. PubMed, doi:10.1155/2009/158425.
Aday, Aaron W., et al. “Lipoprotein Particle Profiles, Standard Lipids, and Peripheral Artery Disease Incidence – Prospective Data from the Women’s Health Study.” Circulation, July 2018, p. CIRCULATIONAHA.118.035432. DataCite, doi:10.1161/circulationaha.118.035432.
Hamazaki, Tomohito, et al. “Towards a Paradigm Shift in Cholesterol Treatment. A Re-Examination of the Cholesterol Issue in Japan.” Annals of Nutrition & Metabolism, vol. 66 Suppl 4, 2015, pp. 1–116. PubMed, doi:10.1159/000381654.
10 Bass, Katherine Miller. “Plasma Lipoprotein Levels as Predictors of Cardiovascular Death in Women.” Archives of Internal Medicine, vol. 153, no. 19, Oct. 1993, p. 2209. Crossref, doi:10.1001/archinte.1993.00410190045006.
11 Masson, Walter, et al. “Association between Triglyceride/HDL Cholesterol Ratio and Carotid Atherosclerosis in Postmenopausal Middle-Aged Women.” Endocrinologia Y Nutricion: Organo De La Sociedad Espanola De Endocrinologia Y Nutricion, vol. 63, no. 7, Sept. 2016, pp. 327–32. PubMed, doi:10.1016/j.endonu.2016.04.004.
12 Murguía-Romero, Miguel, et al. “Plasma Triglyceride/HDL-Cholesterol Ratio, Insulin Resistance, and Cardiometabolic Risk in Young Adults.” Journal of Lipid Research, vol. 54, no. 10, Oct. 2013, pp. 2795–99. PubMed, doi:10.1194/jlr.M040584.
13 González-Chávez, Antonio, et al. “Elevated Triglycerides/HDL-Cholesterol Ratio Associated with Insulin Resistance.” Cirugia Y Cirujanos, vol. 79, no. 2, Apr. 2011, pp. 126–31.
14 Mazza, A., et al. “Triglycerides + High-Density-Lipoprotein-Cholesterol Dyslipidaemia, a Coronary Risk Factor in Elderly Women: The CArdiovascular STudy in the ELderly.” Internal Medicine Journal, vol. 35, no. 10, Oct. 2005, pp. 604–10. PubMed, doi:10.1111/j.1445-5994.2005.00940.x.
15 Bittner, Vera, et al. “The Triglyceride/High-Density Lipoprotein Cholesterol Ratio Predicts All-Cause Mortality in Women with Suspected Myocardial Ischemia: A Report from the Women’s Ischemia Syndrome Evaluation (WISE).” American Heart Journal, vol. 157, no. 3, Mar. 2009, pp. 548–55. PubMed, doi:10.1016/j.ahj.2008.11.014.
16 Bendzala, Matej, et al. “Atherogenic Index of Plasma Is Positively Associated with the Risk of All-Cause Death in Elderly Women : A 10-Year Follow-Up.” Wiener Klinische Wochenschrift, vol. 129, no. 21–22, Nov. 2017, pp. 793–98. PubMed, doi:10.1007/s00508-017-1264-1.

Sep 04

Resistance Training Experiment


I’ve been waiting a long time for this one. For almost three years I’ve gotten nearly no resistance training. Why? Because I’ve long suspected it will impact my lipid numbers. And sure enough, during my endurance running phases, I observed data that appeared to confirm this.

So all this time, through all these experiments, I kept thinking, “I just need to get through these next few… then I can finally do the Resistance Training Experiment.”

Alas, I kept finding new things that led to more things to experiment with. All my experiments with carb swapping/addition and the weight gain might have been confounded since the theory goes that this is predicated on glycogen stores… so I put it off through all of these phases as well.

Now the wait is over!

My Hypothesis

I hypothesize resistance training reduces LDL cholesterol due to higher endocytosis of LDL particles by non-hepatic tissues, this includes skeletal muscle for growth and repair.

Study Design

From August 27th to September 10th (16 days), I will have the following routine:

  • Meal Plan:
    • Around 10 AM: Nathan’s Uncured All-Beef Hotdogs, two ounces of Colby
    • Around 3 PM: Four hard boiled eggs, four ounces of Colby Jack cheese
    • Around 8 PM: Three hard boiled eggs, four ounces of Colby Jack cheese
  • Activity/Exercise:
    • I will be standing at my desk through working hours
    • I will be walking around 2.5 miles per day in the afternoon, generally between 3pm and 8pm
  • I will be traveling and doing errands outside the house as little as possible to control for confounders
  • Daily testing:
    • Morning glucose, BHB, weight, blood pressure readings
    • Around 10 AM glucose and lipid readings
    • Morning lipid readings from the day of the intervention (Sept 5th) until the end of the experiment (Sept 10th)
  • Blood draws:
    • Each blood draw will include Apolipoprotein A-1, Apolipoprotein B, C-Reactive Protein, Cardiac, CBC With Differential/Platelet, Comp. Metabolic Panel (14), Fatty Acids, Free (Nonester), Ferritin, Serum, Glucagon, IGF-1, Insulin and C-Peptide, Serum, Lipid Panel, Lp(a), Lp-PLA2 Activity, NMR LipoProfile, Tumor Necrosis Factor-Alpha
    • The first blood draw will take place on September 5th just before the intervention
    • The second blood draw will take place after one of two conditions, whichever comes first:
      • The morning lipid reading shows a drop of more than 10% for LDL cholesterol against run-up baseline (if on a Sunday, to be carried over to the following Monday) or
      • The last day of the experiment is reached


I will engage in a workout session with a Vibration Plate Power Plus. I will be working all upper body with the intent to become as sore as is reasonable without risk of injury. I will keep track of all time allotments and settings for data and potential reproducibility.

This will be performed:

  1. On Wednesday, September 5th at approximately 9 AM
  2. On Sunday, September 9th at approximately 9 AM

Aug 29

Good News and Bad News: I’m Sick


You might have noticed I’ve been a bit quieter in the last couple days. Alas, I’ve been in bed. Symptoms include a sore throat, cough, mild headache, and general lack of energy. Not a showstopper, but not exactly at the level of ignorable.

The Bad News?

Hello! I was planning to start the Resistance Training Experiment this week! I’ve been looking forward to this thing for at least two years. I’ll expand more on that when I can actually start it. Or you can hear me discuss it in the second half of this impromptu video with Amber O’Hearn from last weekend.

The Good News?

I’m sick! Yaaaaaay!

Okay, maybe you’ll want some context for that. You see, for the past three years, I’ve been mostly on a keto diet. I say “mostly” because, of course, I have a number of experiments like this, this, and that which take me out of it. But with that said, I’ve only gotten sick once in all this time and it was extremely mild. This suited me fine as I had a lot of experiments to do in succession and I’d prefer not to have the confounder.

However, I was getting more and more conscious of grabbing “sick data” whenever it might occur, so I was on the lookout. As it happens, since Siobhan came aboard, we’ve had her covering the immune side of the fence and overachiever that she is — she managed to get sick several times in the last year and recorded it. But you’ll have to hang on until her #Ketofest presentation hits the YouTubes before you can get the full skinny, which is well worth the wait.

So voila! Now it appears as though I can cash my sickness in for some wide spectrum data. It better be worth it.

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