Well, I knew this would happen at some point. I’ve gotten through the last year and a half of research through funding from one primary provider: me. There’s a long story I may post on later as to how I had the savings to spend and why I did it. (In fact, it’s a pretty interesting side story.)
This changed somewhat in the last month when I put up a “Donate” button and many wonderful readers contributed directly. And to these amazing people: THANK YOU! With these added contributions, I was able to fund another few weeks and nearly all of the last experiment. And as it happens, that was one of the biggest I’ve yet to have.
However, the time has come for me to refill the coffers. I’ll be ramping up some contract work in app development for a while and plan to pack away the acorns for the next major stretch of research, which I hope to get started on soon. Indeed, as I’ve said many times before, I could just stick to this 24/7 if I had the means. But beyond just the costs of living, the bloodwork alone is my second biggest bill after the mortgage.
Whenever I mention this subject I get one of three common suggestions which I’ll pre-answer to save everyone time:
“You should charge for your information/research given it costs money to conduct.” – I intend to never charge for the information on this site. I started it precisely because there needs to be something available for fellow hyper-responders in their personal research and at that critical time they are first confronted with scary numbers. That’s what I was missing when it happened to me.
“You should get a research grant; it’s easy.” – I get this a lot from academics. But this is also an unfamiliar process to me that I’ve likewise heard can be time-consuming and very uncertain. Moreover, I’ve been told bluntly by a few conventional doctors that my research won’t likely be embraced by many of the entities writing the checks. (My favorite quote from an endocrinologist: “Your pursuit probably means a lot of money being lost in prescription drugs, not gained. I don’t think they will be anxious to help you out with that…”)
“You should get a corporate sponsor.” – I’ve been approached a couple times and I necessarily turn them down. For the time being it is very important that I maintain no corporate financial ties to my research. The integrity of my data is well preserved right now by being funded directly by my own money — and now those readers kind enough to contribute. (And let me again say, THANK YOU!)
From a cost-benefit standpoint, it just makes the most sense I spend my time with the contract work given I can get ahead of finances for the next major run. As options go, this is the most reliable and predictable.
Of course, the big downside is pausing the experiments and research temporarily, and drawing down on my total social media participation. This is especially frustrating given the most recent breakthrough cliffhanger and the next experiments I have planned for the queue to further this front. Moreover, I had a number of other projects I mean to get back to such as Part III of the Simple Series (no need to keep pinging me, I haven’t forgotten! :D). I’ve also wanted to do a series of short videos on frequently asked questions; more of a 101 on cholesterol from LCHF perspective in small bites.
Thus, this may be my last post for a while and I might be a bit less active than usual as much of my time will be filled writing code. But make no mistake, I’ll be anxiously working my way back to full operation here soon. 😀
This is very preliminary. While it’s true I’ve both produced and reproduced these results, it’s still very early to draw significant conclusions, which I detail further below.
This does not constitute medical advice. I realize many may read this and act on it, but I urge you to consult with your doctor and/or trusted medical professional — particularly if you are diabetic or insulin resistant.
Going Low Carb / Keto
Before I started the ketogenic diet, my carb intake was probably north of 250g a day and my fat intake was likely around 75g. After going keto, I brought my carbs down to around 20g with my fat shooting up to around 220g. (My protein remained relatively constant at 25% of my calories.)
To illustrate this, I’ll be using this simple graphic to show a sliding scale from left to right as 100% carbs & 0% fat to 0% carbs & 100% fat.
So let’s overlay the quantities of my carbs and fat both before and during the diet.
Sure enough, after adopting this lifestyle, I felt better than I ever have before it.
But then I got my cholesterol scores seven months after starting which showed an extraordinary jump in both total (TC) and LDL cholesterol (LDL-C). And this is where I learned I was one of the fraction of those going low carb who see a dramatic rise in total and LDL cholesterol known as a hyper-responder.
To be sure, my HDL going higher is presumed good and my triglycerides going lower is presumed good. But the doctors care much more about LDL-C and will insist my higher levels on a low carb diet should be of great concern.
The Assumed Solution
Up to this point I’ve given the same advice to fellow hyper-responders as I’ve read from doctors and researchers: If you want the one surefire method for reducing your cholesterol, you will likely have to give up being low carb.
Let’s look at that graphic again and apply this to myself:
If we assume at least a rough linear relationship with fat and cholesterol, then I’d pretty much have to go almost entirely back to where I was before starting keto. By some basic calculations, I could estimate a total cholesterol of around 200, LDL of 140, HDL of 42, and TG of 130 if I get back to about a 50/50 mix of carbs and fat (as shown by the “Best Goal?” overlay above).
This makes perfect mechanistic sense, given how the lipid system works. If eating more fat means my body is trafficking more fatty acids for energy, then necessarily it must traffic more cholesterol in these same lipoproteins.
Weighing the Choice
Let me illustrate this another way to get across the relationship:
As fat rises, so does Total, LDL and HDL, with Triglycerides falling. Total and LDL-C going up is supposedly bad (red), HDL going up and Triglycerides going down is supposedly good (green).
Reversed, we see Total, LDL and HDL drop with Triglycerides rising. Total and LDL-C going down is supposedly good (green), HDL going down and Triglycerides going up is supposedly bad (red).
So given this assumption, I expected my own data would likewise reveal this as well. When taking my blood tests, I’ll always have some variations of carbs, even if they are generally low. Moreover, I’ve had some variation in the ratio of carbs-to-fat. Yet while these were tiny shifts, there wasn’t a linear relationship appearing.
I didn’t put too much thought into it until a series of recent experiments and what it revealed to me. Let’s revisit everything I’ve done to date, excluding only the “extreme” experiments (such as the Extreme Drop I did that spawned the protocol).
As usual, I’ll be flipping the left axis so you can better see the inverse relationship between three day dietary fat and LDL-C. Past this point, all graphs will have this same inversion with the left axis.
Above, the darkened lines of orange and blue lines show where I did the distance running and thus lowered my LDL-C marginally. Just after that, I’m going to take the blue highlighted area at the far right and break it down further.
From here, I’ll highlight each of the experiments I did over this period of time. This is further detailed on my Experiments List page.
During first major experiment I had eaten only eggs and cheese for 10 days, changing the quantity in the middle. But unlike other experiments, I had moved it from a moderate quantity to a low quantity. This yielded a sustained gap like I hadn’t ever seen before in my series. I also felt general malaise; didn’t enjoy it much for the second half. But the big first clue came in the form of my fasting blood glucose being unusually correlative with my mood. Lower FBG = worse mood.
The second major experiment was with fasting, which I posted on recently. This too yielded a major clue given it coupled tightly as would otherwise be expected with LDL-C.
The third clue came from the two experiments to follow. In the first, I had one cup (250 ml) of whole milk with each meal, in the second it was a single slice of bread. What astonished me was how tightly the Inversion Pattern held even going up to 78g of carbs / day. This proved the biggest clue of all as I’d have expected the gap between between the three day average and LDL-C to have widened.
There’s a big part of this story I can’t fill in yet. It’s how each of these clues along with a theory I’ve been working on for a long time led me to the next major experiment. I’ll detail it in a future post once it has been tested against, which will take several more experiments to achieve.
I’ll just say that the theory + these clues led me to calculate a range of 90 to 130g of carbs needed to hit this mythical “Sweet Spot”.
The Shakes and Bread Experiment
With this calculation in hand, I wanted to devise an extremely controlled experiment. Indeed, this would be one of the most intensive I’ve ever done.
I wanted to:
Have a keto ratio of food that was nutritionally complete, yet somehow very consistent and quantifiable
Have a common carbohydrate food as the intervention variable, but likewise as simple as reasonably possible
I figured out I could achieve (1) by having a ketogenic meal replacement drink, and for (2) I would just have simple slices of bread. I managed to find one that was both 100 calories a slice and allowed me to get within the net carb range I was looking for at 5 slices a day.
I would do five days of the shakes alone, then five days replacing 500 calories of the shakes with 500 calories of the bread. The first five days I’d be at around 30g of net carbs, with the second five at around 95g. This way I could really hit that target range while being extremely controlled on exactly what I ate.
To further ensure control, I ate to a very specific schedule of 11am, 3pm, and 8am in all but the first two days.
A Striking Result
Before getting to what happened, it’s worth noting the data advantage I enjoy coming into this experiment. If you’ve been following my work for a while, you are already aware. But if you’re just joining us, you should know I’ve done 63 blood draws in 18 months and effectively have my own Inversion Pattern mapped now. So unlike anyone else I’m aware of, I’ll know when one or more variables are having an impact on my lipid system given the anticipated coupling you see in my graphs. Even in the outliers, there is little significant deviation from the mean, and until this graph below, no repeating patterns of a gap.
Below I highlight the blood test I took following the 5 days of keto shakes, which couples as we’d expect to the Inversion Pattern. Then, a clear gap forms between the higher expected LDL-C and the actual resulting LDL-C.
In other words, swapping out 500 calories of the keto shake for 500 calories of the whole wheat bread resulted in a lower LDL cholesterol than we’d expect.
Best of Both Worlds?!?
Right about now you’re probably thinking, “Sure, but isn’t this what you took so much time to explain at the beginning? That increasing carbs will result in better cholesterol numbers?”
Actually, there’s a huge number of differences here between the assumption above and this outcome. Let’s start with a closer look at the other lipid numbers.
Not only was my total and LDL cholesterol the lowest I’ve ever had while on keto at 220 and 140, respectively — but my triglycerides were an impressively low 46, and my HDL was an extremely high at 71. Which is to say, this is the best looking cholesterol lab I’ve ever had.
(As an aside, I’m extremely thankful I did three full tests inside this five day intervention period to help rule out a lab error. Indeed, had this been the only test I performed, I’d have had a hard time reconciling it.)
After getting this final lab test four days later on 4/21, I immediately had a blood draw and attempted to reproduce it. However, I ate my “normal” keto diet instead of the shakes, but while likewise adding the bread with each meal to target nearly the same macros as I had on the shake-based diet.
Sure enough, the reproduction appeared to work as well.
I ended up getting the particle count data late from the lab. There’s some analysis I’d have covered here regarding 3/23 and 4/7, but I’ll have to save that for a future post.
Like LDL-C above, LDL-P clearly shows the same divergence on its own inversion pattern (three days, with a two day gap).
Revisiting the Assumption
Let’s look to the relationship graphic we had above, but with the new results added.
Clearly we can see only a small change in total dietary carbs got me the “Sweet Spot” I highlight above. But again — and this is important to emphasize — it didn’t appear until I passed a certain threshold. Remember, just before this experiment I had done another (“One Slice of Bread”) experiment where I averaged 78g of carbs with no diversion in the Inversion Pattern.
In other words, this strongly suggests there is a threshold, a tipping point, a drop off with regard to my lipid numbers. That’s the reason I wanted to illustrate this analog relationship as I do above, because this strongly suggests it may be wrong.
With this in mind, let’s revise the concept.
Here I’m illustrating how the shape of this cholesterol relationship appears different. With a sudden drop off point, it implies I can get the best looking lipid numbers by getting to the other side of this threshold.
Again, this is still very preliminary, but the testing so far supports it.
Naturally there’s much more to share about what happened than my changing lipid numbers. These are just the highlight while at 95-97g of carbs:
No Ketosis (of course). Ketone levels (BHB) were around 0.1-0.2 throughout, including morning (Keto average 1.2)
Very high postprandial glucose often 110-150 MG/DL two hours following a meal
Higher fasting glucose at 102 MG/DL (Keto average 92)
Slightly more tired following meals
Insulin averaged 5 UIU/ML (Keto average 3.8)
None of these came as a surprise to me given the higher carbs save the fasting insulin, which I expected to be higher.
What This Means
This may be the start of a solution for hyper-responders who seek a better lipid profile, while still staying low carb.
Instead of adding back hundred of daily carbs, I was able to stay just under 100g even if I was giving up ketosis. But again, I have a more distinct advantage than most due to knowing my own lipid patterns so well, so I can more easily identify where this carb threshold is and isolate it.
As far as the theory that led me here, that will have to wait for a later post.
However, while I’m happy if this may ultimately provide hyper-responders like myself with a new choice, not much has changed for me personally with regard to the risk assessment. I don’t know for sure that this lipid profile is a lower risk for all cause mortality today anymore than I would have a month ago. In short, I’m more interested in what this means on a system-wide level for the body’s regulation and control than I am for its personal impact on my life… at least, for now.
I’ll be pausing for a little while as I ramp up some contract work to rebuild my research funds. Up to this point I’ve been paying for the last 18 months of blood, medical, and technology expenses from my savings — save a few generous contributions from individuals through my donate button on the site. Thanks for the support!
I’m currently back to my “normal” keto ratio and expect to do another blood test following the pause to confirm my numbers have returned to the original Inversion Pattern. If so, we’ll know these changes were indeed transient.
Following that, I plan to experiment with other sources of carbs (fructose, lactose again, etc) to determine if the ratios hold regardless of the type of sugar.
I’m also tempted to do more lopsided intake of carbs where the same general macros are hit, but with a single meal being carb-heavy where the others are generally keto.
I know I’ve been extra coy for the last few days and generally off social media. Yes, I’ve come into new ground with my research and it’s required intense attention and planning for the last few days. As mentioned earlier, I suspect I’ll be able to do a first report on it in about one to two weeks. But in the mean time, I will be a lot less active on the interwebs than usual. The best two places to reach me are here in the comments and on twitter (@DaveKeto).
I’ve actually been following Raphi’s twitter account for a long time. His story is similar to mine where he found a sudden course correction into biochemistry and ramping up his education on the subject. He is likewise very interested in some of the deeper meanings to the metabolic engine and has a keen eye on his own metabolic data.
Justice is Served!
Professor Tim Noakes is found innocent of misconduct by a 4-1 decision of the Health Professions Counsel of South Africa (HPCSA). This was a large victory on multiple fronts. Obviously making an entire case on a tweet is already problematic given the hard limitations of the medium. Moreover, assigning doctor-patient relationships via public social media contact is likewise as silly as it reads.
Most of all, “the Prof” got to take evidence of the low carb high fat diet to court and weigh it directly in the light of day. I can’t imagine the hardship this has put Tim and his family through. But for as hard as this closing chapter has been for his family and his team, the next one will be much brighter for everyone else from their dedication to this case.
Sorry to be so cryptic, but I’m in an unusual situation. I need to let everyone know I’ve had a sudden turning point with my research, but I can’t explain what it is just yet (which will be made obvious later). This is of a level that I need to put extraordinary focus on it, so I’ll be less active with social media.
I can’t talk any further about it until I’ve reached a certain threshold of certainty, which will take some time. My best guess is that I’ll be able to give a preliminary report about in about a week or two.
[UPDATE 4-1-2019: This article still often comes up as though it is making a statement about fasting in some way — it is not. It is simply an experiment in fasting that that had dramatic results. And these results further emphasized the Inversion Pattern as well. My current opinion on fasting remains: if you can do it comfortably, you probably should.]
Fasting has been all the rage lately. Jimmy Moore and Jason Fung released a book on it that quickly shot up the New York Times Best Sellers list. In fact, it has been so popular that they launched a podcast on it at the beginning of this year. Oh, I did I mention the episode featuring Jason Fung and fasting is still the most downloaded of the 2 Keto Dudes podcast?
While on low carb myself, I’ve only ever intentionally fasted for 14 hours at a time, which was only done to meet the requirements of a blood draw. But while I don’t feel hungry when fasting, I don’t feel… right.
To be sure, I’ve wondered if I want to eat all the time so that I either maintain or gain weight, given I’m underweight right now. And therefore my feeling odd when not eating is perhaps entirely mental manifestation. Regardless, a fast of a few days probably wouldn’t be that bad anyway, right?
The plan was pretty simple:
I’d take a blood draw in the morning at the beginning of the fast.
Fast for three days while both supplementing and keeping electrolytes high, but otherwise drinking only water.
Take a final blood draw for comparison on the morning 72 hours after the first blood draw. So in all, 86 hours will have passed since my last meal, making it a total of 3.5 days.
Day One: I was surprised to find I wasn’t hungry at all. This seemed to back up my theory that if I had already made the commitment to myself to forgo eating, my brain wouldn’t send me subconscious “shouldn’t we be eating?” signals.
As happens with me when I’ve lowered my total calories for an experiment (but while still being keto), I feel run down and puny. I have an overall feeling of lower energy. I also feel a little dispirited in this state, but its hard to tell how much of that is annoyance of that phase of the experiment vs it being an actual physical response.
However, that not-feeling-right sensation I mentioned above?… I was certainly getting that signal. But I had hoped I’d only feel it on the first day.
Day Two: I still wasn’t feeling hungry. And while I did continue to feel low in energy, it wasn’t notably better or worse.
However, the not-feeling-right sensation was definitely much, much higher. It was like nothing I’ve experienced before this point. In my imagination it was as though my body found a red phone line and called some special center of my brain to say, “ABORT! ABORT!” No physical pain, no odd changes in the senses, nothing other than a feeling… a feeling this was terribly wrong.
By the evening I decided to go ahead and cut the experiment short. I’d take my blood on the morning of Day Three as opposed to Day Four. Heck, at least it was a 2.5 day fast in the Data Can. I just knew I’d feel annoyed if my numbers had hardly changed. (Just writing that last sentence makes me laugh out loud now…)
In every blood draw now, I get a slate of general panels like a CMP and CBC. The latter is known as a Complete Blood Count and has 14 markers. These numbers always been in range… until this time.
All of these markers have to do with red blood cells and their functionality, which I won’t cover here. What I really wanted to see is if I had fallen off on my electrolytes, which would explain both the run down feeling and my general sense of malaise.
Interesting — instead of being under, I was over on K and Ca.
Of course the big one is the lipid profile. And if you follow me, you already know what I’d predict after fasting for the very first time given the Inversion Pattern –> a record increase in LDL cholesterol.
So what happened? I hope you’re sitting down for this….
Indeed it was a record!
Let’s unpack a few things:
Like the Extreme Drop Experiment from last year, this one had a heavy shift in dietary energy, as in a sudden drop off. And likewise this huge degree of change broke the Inversion Pattern with LDL-P, while still demonstrating its general direction (lower fat = higher cholesterol).
Given the pattern up to this point, we’d have expected LDL-P to land around 2200, but it instead landed much further upward at 3348. This is strikingly consistent with with the drop experiment that likewise overshot in the other direction (from 2597 to 1487 in three days!).
Yet LDL-C would be expected to land around 355 +/- 22 and sure enough it landed at 368.
While I don’t like doing these huge shift experiments, I am glad they continue to reinforce the general mechanics of the Inversion Pattern and further establish its nature.
Once again Small LDL-P pops up in a low dietary energy context.
While the experience wasn’t great, the data from this experiment was golden! As predicted, the Inversion Pattern kicked in and demonstrated just how fast cholesterol can rise while fasting, particularly for a hyper-responder like myself. I guess that last bit doesn’t actually sound like good news, but don’t worry, the blood test taken just 4 days later (not shown) had my Total and LDL cholesterol drop down near where they were on 3/21.
I already didn’t enjoy fasting for even a half day before this experiment… now I’m very sure I don’t want to fast for longer either. This might be something I’d consider if I weighed more, but probably not. The incredibly alarming feeling I experienced was something I’d prefer to leave in the past.
I run a script that captures every marker outside its reference range and (unfortunately) mostly noticed those that did. Technically, Uric Acid was still inside, but obviously moving upward fast. Would it have gone above range had I been fasting another day? Alas, I’m now unwilling to find out anyway.
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