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.)
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 Labs, Ulta 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.
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…
… To simplify a bit, would you rather have 1000 LDLp with 1 oxidized phospholipid on their hull, or 100 LDLp with 10 oxidized phospholipids on their hull? If an oxLDL doesn't pick up quantitative differences per-particle, then it seems the 1000 LDLp *appears* worse.
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.
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.
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.
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.
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.
1/ Incredible! The day has finally arrived!!!
I’m very pleased to announce the Citizen Science Foundation has official designation from the Department of Treasury as a 501(c)(3) Public Charity.https://t.co/DgBDHAsAzs
… 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.
Siobhan’s Experiments
Meanwhile, my colleague, Siobhan took on the remarkable #EpicFast experiment. I can barely last 2.5 days. And yet her fast lasted weeks. And of course, she extensive blood testing throughout.
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.
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.
My primary costs are the many frequent and expensive blood tests I take for this research and data. Any size donation is appreciated. Thank you for your support!
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