- If you know little to nothing about cholesterol->
- If you’re wanting to learn more about why cholesterol could be higher, particularly on a low carb diet->
- You can watch Dave’s recent presentation for Stanford University on the Lipid Energy Model.
- You can also check out the Lipid Energy Model poster here
- You can also check out or Frequently Asked Questions (FAQ) here
- If you’re looking to better understand the risk associated with high cholesterol on a low carb diet->
- If you’d like to understand possible benefits of cholesterol and the immune system, you can read Siobhan’s overview article on the topic here or watch her presentation here
- If you’d like to learn more about lipoprotein(a), you can watch Siobhan’s presentation on it here
- Lastly — you can always just ask us anything our Questions Page. (Just be aware our site does not constitute medical advice and we always recommend consulting with your doctor.)
It’s been pretty darn busy these days as we’ve had a lot going on with the LMHR Study, platform development for OwnYourLabs.com, and some recent work on the Lipid Energy Model paper. Now most of my data has come back for the Eating Window Experiment, but I haven’t had the time to do a full write up. That said, I will at least give the preview on my OxPL-apoB data and why I find it so exciting.
What is the OxPL-ApoB assay?
This description I’m taking directly from the Boston Heart Diagnostics website, which is also where I get the assay:
Oxidized phospholipids are found on all apoB-containing lipoproteins, namely, LDL, VLDL, and especially Lp(a). When taken up by the artery wall, oxidized lipoproteins accelerate atherosclerosis, thereby, increasing the risk of myocardial infarctions, strokes, and calcific aortic valve stenosis. Oxidized phospholipids are highly pro-inflammatory and contribute to many diseases of aging. Clinicians can use OxPL-apoB levels to reclassify patients into higher or lower risk categories allowing better personalized care.
(For the remainder of this article, I’ll just refer to OxPL-ApoB as simply “OxPL”)
To be sure, I have a complex opinion regarding the elements described above and how this plays into the larger topic of the immune response. That said, I definitely do think this assay has enormous value and have been literally talking about this for years before it was even available.
Even as long ago as the fall of 2018 I was speculating on this comparative value…
If you’re a bit lost right now, don’t worry, you don’t need to know the biochemistry on this. The big takeaway is that I’ve long waited for this metric as I’ve believed all along it would (1) provide very powerful data on cardiovascular disease risk (and lots of data certainly suggests this), and (2) that in spite of low carb hyper-responders having very high LDL, I’ve long hypothesized their OxPL values would be generally low.
This is an important metric to determine given OxPL loosely correlates with ApoB in typical diet populations, thus I’ve been speculating something quite contrary to the existing data I’ve been able to find in the research to date.
OxPL-ApoB and Risk
One phenomenal scientist who has done incredible work in the field on this is Sam Tsimikas. He has conducted many trials and closely tracked OxPL levels in both humans and animals across many different study designs.
I became much more aware of his work a couple years ago and even found this older tweet with regard to one of my favorite graphs:
The above graph is taken from this study (Tsimikas et al) and has Lp-PLA2 on one axis and the ratio of OxPL over apoB. The OxPL/apoB ratio is something I’m particularly interested in, and its association with cardiovascular risk is unsurprising, but more on that in a later post.
Since gaining access to the OxPL assay at Boston Heart Diagnostics, I’ve used it a total of seven times over two experiments, the OxLDL Replication Experiment and this recent Eating Window Experiment. Here are my OxPL, ApoB, and Lp-PLA numbers for all phases:
The reference range for the OxPL-ApoB assay is <5.0, 5.0-7.5, and >7.5 nmol/L for “Low”, “Borderline”, and “Increased Risk”, respectively. All my metrics to date have been under 5.0 thus far, but this is what I was predicting overall. Interestingly, there is a clear difference between each experiment within this lower range (2.8-3.8 with the Replication Experiment, and 0.9-1.4 with the Eating Window Experiment).
The OxPL-ApoB/ApoB ratio is extremely low at a range of 0.007-0.021 across all tests. And for what it’s worth, I suspect this will prove common among those with the Low Carb Lipid Triad, particularly Lean Mass Hyper-responders. But only wider data collection will help confirm/disconfirm if this will be the case.
Again, this is preliminary, but certainly exciting. I’ve waited a long time to test this assay repeatedly, and I’m happy to see it falling in line with my general expectation given this context. There’s still plenty more variety to look forward to, both in my own experiments and the reported values of others.
Of course, I suspect this confirms a generally lower risk assessment given existing research in this area, but we can’t say for sure either way. Hence the importance of the LMHR Study as well as regular case data coming in from the LMHR community.
I’m writing this on the second day of the second phase of the Eating Window Experiment, thus having completed the first day where the three meals moved to 4pm, 5:30pm and 7:00pm.
I’ll concede it’s been a bumpier ride than I was expecting. First, the consumption period of the three meals was very difficult and I was just at the edge of light nausea toward the end. I think I’ll need a touch more time between meals. Secondly, I had trouble getting to sleep, then woke up three hours later and wasn’t able to return to sleep. I managed to get another nap in this morning for about an hour and twenty minutes, but it wasn’t particularly restful.
This issue with sleep has had a meaningful impact on my day as I’ve experienced insomnia-like symptoms for the most of the morning. Not quite awake, not quite asleep, and not very functional. It’s possible this is just something I’d adjust to over the next few nights, but I can’t really take that chance given my existing meetings and responsibilities.
Thus, I’m shifting the eating window from evening to late morning with today as an interstitial step. Additionally, I’ll be spacing the meals two hours apart instead of an hour and a half.
- Today (14th): 12pm, 2pm, and 4pm
- 15th: 10am, 12pm, and 2pm
- 16th: 10am, 12pm, and 2pm
- 17th: 10am, 12pm, and 2pm
Today I started the #EatingWindowExperiment, which is probably just what you thought it was by the title.
There are two phases:
- Eat baseline diet for five days in the usual 10 hour window (9am, 2pm, and 7pm eating times)
- Eat the same baseline diet for five days, but compressed into a 4 hour window ->
4pm, 5:30pm, and 7pm10am, 12pm, and 2pm eating times (*amended here)
Daily blood tests of glucose, ketones (BHB), and lipids (TC, LDL-C (calc), HDL-C, and triglycerides) will be performed at these times:
- Within 20m of waking up
- ~7 am
- ~9 am
- ~2 pm
- ~7 pm
- ~10 pm
Blood tests for mornings of January 13th and 18th
- Apolipoprotein A-1
- Apolipoprotein B
- C-Reactive Protein
- Complete Blood Count (CBC)
- Comprehensive Metabolic Panel (CMP)
- Fatty Acids, Free (NEFA)
- Ferritin, Serum
- Glucagon, Plasma
- Hemoglobin A1c
- Insulin and C-Peptide
- Lipid Panel
- Lp-PLA2 Activity
- Nuclear Magnetic Resonance (NMR)
- Oxidized Low-density Lipoprotein (OxLDL)
- Testosterone, Serum
- Thyroid Panel
- Uric Acid, Serum
- Vitamin B12 and Folate
- Vitamin D, 25-Hydroxy
- Cholesterol Balance
- Fatty Acid Balance
- HDL Map
- Leptin (Redundant, but of high interest)
- Oxidized Phospholipids on apoB (OxPL)
Honestly, I don’t have a lot to add with regard to the hypothesis. This is more of an exploratory experiment.
If I had to pick one thing I’d predict, I’d lean toward there being a higher fasting glucose and insulin level resulting from the bigger meal of the tighter eating window from the night before. However, that doesn’t mean that these two would be higher in the “area under the curve”. But fortunately, we’d have some data to speculate on that given my high frequency testing throughout the day.
Again – this is more of a “let’s see what happens” experiment.
It’s crazy to think we started it off filming #TheCCDoc across 17 countries and 28 cities across the world (Jan/Feb)…and yet…that’s much, much smaller news than what has happened since in 2020.
That said, we have some excellent footage for #TheCCDoc (36 interviews!) and a new plan to wrap in the LMHR study as well when we finally edit it to together. Should make for a pretty interesting doc by the release.
Citizen Science Foundation
Speaking of the pandemic, a very key moment happened at the end of April that was a pleasant distraction – we got our designation as a bonafide Public 501(c)(3) charity by the IRS.
And to my great excitement, I got to break the news in October that we passed our $100k fundraising goal which will be matched to get us to that magic $200k number.
Lastly, I posted our update video on progress with our selected center and the fundraising drive of $30k for travel and genetic testing.
During the summer I attempted to conduct a double crossover experiment of Extra Virgin Olive Oil vs Butter.
… and by “attempted”, I mean I tried and failed — twice!
I rarely cancel experiments, but this was one of only two that I can recall.
However, @ketochow offered to take up the reins of my experiment and it’s yielding some surprising data. So much so, that we’re doing a short replication experiment in addition to this one. (more on both in the coming writeup)
My favorite experiment of the year is — hands down — my #OxLDL replication experiment. This is for many different reasons from the replication marker matching, to the OxPL-ApoB and HDL map assays.
So much excellent data.
Here data is in and the write up will be coming soon as bandwidth becomes available.
Own Your Labs
One reason for tighter bandwidth on both our parts has been the launch of OwnYourLabs.com. We’ve set up an online service where you can order your labs privately directly through us. This has been primarily for service through Labcorp, although we are now testing in beta under Boston Heart Diagnostics as well.
Our primary reason for starting this service was to provide an easy means of volunteering anonymized data to an open data pool. This is strictly opt-in at checkout, but we give a discount where taken.
We exclude first and last name, date of birth, and city to help de-identify. But ask to add some basic demographic information to match with the resulting bloodwork which we are confident will provide great new insights for both formal and citizen scientists alike. We’ll be posting this dataset soon as we’re accumulating enough in the first tranche to better anonymize further.
Moreover, all proceeds go to the Citizen Science Foundation. So it’s win-win-win.
Journal of Clinical Lipidology
Easily one of the most interesting events of the year was our being mentioned in a case series by the Journal of Clinical Lipidology. The “Cholesterol Code Team” was referenced directly in the paper which certainly caught our interest.
This led to an opportunity to both respond and source where we were coming from in a Letter to the Editor which we submitted promptly. Our letter was rejected, but we have it now posted as an open letter which you can find here.
While we know this a controversial topic, we understand it may take some time to bring this important context into the spotlight. We’re confident cholesterol and risk as it relates to metabolism (particularly in a low carb setting) will rise in prominence. We’ll get there.
Presentation to Stanford University
There are many conferences I presented at online this year. But while I don’t want to pick favorites, I was especially honored to present the Lipid Energy Model to Stanford University.
Final Thoughts on 2020
Certainly the Pandemic has impacted us in many ways this year. It interrupted TheCCDoc shooting, delayed the LMHR study development, and added a lot more chaos and uncertainty to our various services and projects.
However, I personally know so many others who have had a much more difficult year. I know many who have gotten very sick, lost their jobs, seen their businesses go under, and/or entirely altered their lives in order to adapt to this “new normal”.
And while I’ve tried to stay away from discussing #Covid19, particularly since it’s gotten so politicized — I have to again express just how thankful I am for all the medical professionals on the frontline of this event.
So yes, we’re counting our blessings, as they say. We managed to secure a lot of victories under very tough and uncertain circumstances.
And as always, much of that thanks goes directly to individual one time contributions and our members / patrons!
Lastly, I hope to be announcing some news very soon into 2021 on the LMHR study. It’s hard to imagine anything will be bigger to us in this coming year… but as with 2020, that’s certainly not a given.
We’ve reached a key milestone in having our study now being powered as the last step before submission to the IRB. We have $60,000 yet to raise, but we have an anonymous donor matching $30,000 — thus, we need only raise another $30,000 to meet our goal for travel and genetic testing.
For more details see our post from Citizen Science Foundation website here