Updates a Many

This site has been uncharacteristically lacking in posts the last month and a half, mainly due to a large number developments behind the scenes. Here we’ll be providing a summary of what’s been happening:

New Heights for Own Your Labs

OwnYourLabs.com has been experiencing explosive growth! Since our update and announcement in the last CC post before this one, we’ve seen OYL orders climb substantially.

  • This was in large part due to our new video and website overhaul. We’re actually quite pleased with the functional updates especially, as we wanted better searching and selecting compared to our original version.
  • While we’re quite ecstatic for the extraordinary increase, it has certainly required a bit more bandwidth on our part, particularly for Siobhan, who heads up operations for OYL. This now has her working significantly longer than she was before and thus we’re doing some back office reshuffling on resources to make OYL a much more central role to her responsibilities.

Lean Mass Hyper-responder Study IRB

Myself, I’m still very much engaged in working toward completing the IRB process for the LMHR study.

I’ll concede, it’s some of the most intensive collaboration and detail deliberation I’ve experienced in a long time. Quite a bit of work is being injected in getting the foundation of this study properly set forth.

  • Many, many rules. While I can’t discuss these things openly until the IRB is approved, I’ll just say there are a number of different expected procedures and processes in both setup and execution of the study that certainly exceeded my original expectations.
  • And the documents… to say we’ve had many documents to write and revise (and revise again, and again) would be an understatement. There’s just a lot of work that is required in getting this set for its launch.

All that said, I feel quite blessed to have such a strong team to work with in making this happen. Many thanks to Spencer Nadolsky, Tommy Wood, and our partner center (which we hope to announce soon).

Siobhan and Lipedema

One recent and personal development is the possibility Siobhan may be diagnosed with Lipedema. This hasn’t been a confirmed diagnosis yet, but she’ll be seeing a specialist this coming week to find out with greater certainty.

https://twitter.com/siobhan_huggins/status/1366926537543405575?s=20

The origin of this being a possibility was actually due to her recent #EpicFast experiment. In posting pictures at the end of the experiment, many familiar with Lipedema reached out to her on the topic given her weight loss distribution was very characteristic of the condition.

Unsurprisingly, Siobhan has ramped up her research for this topic and has already begun making headway on better understanding the condition. To be sure, she starts off with several advantages given her background already in lipids, inflammation, and of course, her extensive wealth of prior bloodwork throughout her metabolic transformation. Many (like myself) are predicting bring a lot of new innovation to understanding this important and prominent disease.

Presentations, for Limited Engagement Only

I decided early in the year to keep presentations at a minimum. I had already turned down a few offers simply because I had too much on my plate with the IRB. But I went ahead and did four over the last two weekends. They all had a few subtle differences, but shared much of the same material. I’ll concede this was a bit more exhausting than I was expecting given the existing work load, but it was nice to unload some of our latest advancements for the Lipid Energy Model.

The videos won’t be posted for a while, but we’ll add them to an article here when they are.

Own Your Labs has Arrived

For almost a year now Own Your Labs (OYL) has been in a kind of “Soft Launch”. It originated from our efforts to gather anonymized data such as through our #BloodTestingDrive at several conferences. Put simply, we’ve sought to (1) get people easier access to bloodwork, and (2) promote greater open science by putting together a publicly archived, yet anonymized set of advanced bloodwork and demographic data.

Affordable Private Labs = Independence

I know this is going to sound like “marketing speak”, but I’m quite serious when I say this – we want everyone who wants to order their labs privately to have the freedom to do so. Which means helping people find the labs they are looking for as affordably as possible, which means keeping our profit margins low AND encouraging comparison shopping. Yes, we even list our favorite competitors on our home page given current recommendation and experience in the community (which I’ll list here as well: Direct LabsUlta Lab Tests, Walk-In Lab)

A very large number of people have told us over the years that they’d love to get their bloodwork privately instead of waiting for every time it was setup through their doctor’s office. Often they’re interested in the same tests we are (like fasting Insulin, C-Reactive Protein (CRP), or NMR Lipoprofile) that simply aren’t ordered at all by many medical professionals. And while we’d always encourage everyone to take this data back to their doctor, we agree they should have the freedom to get it in the first place.

Because of how many tests Siobhan and I get on a regular basis (“power users”) and a bit of negotiating behind the scenes, we have an in of sorts with LabCorp which is how we got this pricing. Moreover, as we build on volume of tests with others through, this will give us stronger negotiating with LabCorp in the future to potentially get better pricing.

So in short, yes, our prices are pretty great. But again, that isn’t the real reason we started OYL…

Give Everyone the Option for Citizen Science

The second (and really, biggest) reason we started OYL was to give everyone the option to contribute their data anonymously. We want to build a large, publicly accessible dataset available to formal and citizen researchers alike. If so many of us are getting these advanced tests on our own, why not share it with the community along with some basic demographic data? This will be especially useful for the low carb demographic given how little open data is available for this group.

So we set it up OYL to make this its central theme. You can either (1) use our service as you would any other online, or (2) submit your data to get a “Citizen Scientist Discount” of 10% off the order.

While we had no idea just how many would opt for this choice, I’m pleased to say it’s about 4 out of every 5 who have used OYL to date!

We don’t yet have the anonymized data pool posted as we want to collect a high quantity of submissions to even further anonymize it for those participating. But we will likely meet that threshold in the coming months and launch it here at CC.

Now Ready for Prime Time

When we launched OwnYourLabs.com last year it was a simple cart system in a WordPress site. It has been growing ever since, which is great. However, it had a number of things we wanted to improve on. The number of tests listed were limited to 10 a page, it couldn’t sort alphabetically, and the consent to submit anonymized data required a tricky use of a special coupon code tied to our Terms of Service.

As of yesterday that’s all history. Now OYL is far more intuitive and easy to use. Tests are now in an infinite scrolling list with a real time search. Sorting can be done either in the drop down or by the column you prefer such as the name or price.

Best of all, we have a new consent checkbox that both opens the demographic form and applies a 10% discount all in the same click.

Final Thoughts

While both Siobhan and myself are partners in OYL, it’s been much more a labor of love than a Big Business Venture. Currently we forward all proceeds toward the Citizen Science Foundation. That said, it’s popularity is taking up more and more of our time, and this refresh might be taking that to the next level.

I certainly welcome the challenge of greater and greater volume to OYL to build this data pool (CSDA) and to help take us take this chapter of Citizen Science to the next level. I’m excited to see the many different analyses it will inspire from both formal and citizen researchers.

Preliminary Data on Oxidized Phospholipid Results

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:

Figure 2 – DOI: 10.1038/s41586-018-0198-8

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.

Final Thoughts

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.

Eating Window Experiment – Amendment

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.

Amended schedule:

  • Today (14th): 12pm, 2pm, and 4pm
  • 15th: 10am, 12pm, and 2pm
  • 16th: 10am, 12pm, and 2pm
  • 17th: 10am, 12pm, and 2pm

Eating Window Experiment – Design and Prediction

Today I started the #EatingWindowExperiment, which is probably just what you thought it was by the title.

Experiment Design

There are two phases:

  1. Eat baseline diet for five days in the usual 10 hour window (9am, 2pm, and 7pm eating times)
  2. Eat the same baseline diet for five days, but compressed into a 4 hour window -> 4pm, 5:30pm, and 7pm 10am, 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

Labcorp:

  • Apolipoprotein A-1
  • Apolipoprotein B
  • C-Reactive Protein
  • Complete Blood Count (CBC)
  • Comprehensive Metabolic Panel (CMP)
  • Cortisol
  • Fatty Acids, Free (NEFA)
  • Ferritin, Serum
  • Fructosamine
  • GGT
  • Glucagon, Plasma
  • GlycA
  • Hemoglobin A1c
  • IGF-1
  • Insulin and C-Peptide
  • Leptin
  • Lipid Panel
  • Lipoprotein(a)
  • 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

Boston Heart Diagnostics:

  • Adiponectin
  • Cholesterol Balance
  • Fatty Acid Balance
  • HDL Map
  • Interluekin-6
  • Leptin (Redundant, but of high interest)
  • Oxidized Phospholipids on apoB (OxPL)

Hypothesis

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.