Keating Memorial Research: Measuring the Effect of Low-carb Ingredients on Blood Sugar

This self-experiment is being done as part of the Keating Memorial Self-Research Project. A couple of other people from the Open Humans community are also running the same experiments. If you’re interested in joining in, let me know in the comments or send me a PM.

Project Progress:

  • Design experiments and solicit feedback: this post, blog, Reddit
  • Calibrate continuous blood glucose meter: started 2/18, report tbd.
  • Establish fasting baseline & determine time of day for experiments: Complete
  • Food effect measurements
    • Dissolved glucose: Complete
    • Allulose: Complete
    • Oat fiber & Cooked Oat Fiber: Complete
    • Whey protein: Started 3/20
    • Resistant starch
    • Tapioca fiber
    • Lupin flour

Table of Results

Experiment Goals & Plan:
Note: If you want to see the full post with links, you can do so here.

I had posted this experimental plan on r/QuantifiedSelf on Reddit and was pointed here. The Keating Memorial Self-Research Project seems like both a great way to honor an important innovator and for the QS community to support each other in their research. I know the project logs were supposed to be started last week, but hopefully it’s still ok for me to add my project and get feedback.

The background and proposed experiment is below. I’d love to get feedback on my experimental design before I start. If you’re interested, please take a look and leave your feedback/critique in the comments.

I’ve got Type 2 diabetes and have recently gotten involved in more rigorous self-tracking and self-experimentation.

For my next set of experiments, I want to measure the effect of different foods on blood sugar. I’m particularly interested in the effect of:

  • low-carb flour and sugar replacements (e.g. oat-fiber, lupin flour, allulose, etc.)
  • combinations of ingredients (e.g. how much does indigestible fiber, fat, or protein slow carb absorption

When I tried this before, I added ingredients to my normal meals measured the change in my normal BG trends (see Next Experiments). This proved too noisy and I couldn’t get a clean measure of the effect of even pure glucose in a reasonable number of measurements (see Next Experiments).

This time, I have a continuous glucose monitor (Freestyle Libre, post coming soon on accuracy vs. fingerstick and attempts to calibrate it) and am going to try to more carefully isolate the effects of the ingredient being tested.

PROPOSED EXPERIMENT

Note: I put some specific questions at the end

  • Goals:
    • Determine effect of individual ingredients on the blood sugar of person with Type 2 diabetes
    • Determine effect of combining ingredients on same.
    • Develop model to predict the effect on blood sugar of meals that’s more accurate than standard carb+protein counting
  • Approach:
    1. Calibrate Instruments: Over several days, measure blood sugar by both CGM (Freestyle Libre) and BGM (Freestyle Lite). Develop a calibration curve to increase accuracy of CGM data

      • Note: I’m already doing this and initial indication is that ~75% of the discrepancy between the two meters can be accounted for by a simple linear gain + offset error
    2. Establish Baseline: Monitor blood sugar while skipping breakfast & lunch (both food & insulin) to identify a period of time where my blood sugar is stable for a long enough (need at least 2-4 hours).

      • Based on previous experiments, I’ll need to wait until after lunch.
      • Will collect data on at least 3 days in which I’m not exercising in the morning (M, W, F)
      • To reduce potential noise, need to be careful not to overeat or eat late the night before.
    3. Measure Food Effects: For each ingredient or combination of interest, follow the same procedure as in the baseline, but at the selected time, consume a fixed, measured quantity of the ingredient and monitor blood sugar by CGM and BGM (every 30 min.) for 2 hours or until my blood sugar is stable for at least 1 h.

      • Initial quantity will be selected based on my previous experience of what will raise my blood sugar by ~20 mg/dL.
      • Based on the initial results, I will test different quantities of the ingredients until I have a dose-response curve with BG increases from 0 to 40 mg/dL or the quantity exceeds what I would reasonably consume in a sitting, whichever is smaller.
      • Number experiments will be at least 3 per ingredient or combination.
  • Initial Ingredients to Test:
    • Glucose tablet - baseline to which everything else will be compared
    • Dissolved glucose - effect of dissolving an ingredient
    • Whey protein - effect of protein
    • Casein protein - effect of protein type
    • Allulose - my favorite “indigestible” sweetener for baking & ice-cream
    • Oat-fiber - low-calorie, low-carb flour replacement I use for muffins and cookies
    • Inulin - used in a lot of low-carb foods

QUESTIONS:

  • Current design tests one ingredient at a time. This is a lot simpler and lets me get results for the first ingredients sooner, but does introduce a systematic variation between ingredients (the week). My thought was to mitigate this by re-testing glucose at some frequency to measure week-to-week variation. Do you think this is sufficient or is there a better design?
  • I’m not planning to repeat quantities of a given ingredient multiple times, but instead vary the quantity. Since the end result of interest is change in BG as a function of quantity, I figured this would be more experimentally efficient. Are there any problems with this approach?
  • Since experiments will be done on M, W, F, there will be a 1-2 day washout period between ingredients. Is this sufficient or do I need to separate ingredients by week to ensure a two day washout?
  • Are there any other ingredients I shouls test?
  • Is anyone interested in joining the experiment?
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Yep, the number of links lead Discourse to suspect spam, I just saw the entries in the moderation queue and restored the posts it had automatically hidden! Sorry for the overzealous spam protection! I’m meeting up with @MichaelR later today to discuss our own glucose monitoring experiment and maybe we’ll be able to join forces, depending on that!

I think this really depends on the variability of the effect, which is something we wanted to address with the rough experiment idea that @MichaelR and I had. (The idea was to repeat eating the same kinds of food after a fasting period to see how BG response differs both between individuals as well intra-individual).

Anecdotally, from around 6 weeks of CGM data, it seems to me that the variation is rather high (and most likely linked to tons of different external factors). In your current experimental setup you should see this effect in the repeated glucose tablet measurements. But if you observe variation there it will mean that the modeling of the BG response would be impossible. For that reason I would probably go for testing less ingredients but have maybe 3 repetitions per ingredient/combination? That way you can have some confidence that any effects you see will be due to the ingredient & quantity, not daily variation.

Thanks for the feedback and help with the post!

Agreed about needed to experimentally determine whether more repeats vs. more levels is the better approach. Regarding noise in the data, in my previous attempts at this, I also saw a huge amount of variability. I’m hoping the fasting before each measurement will reduce noise by washing out any effects from the previous days food, medication, and exercise, but I’ll have to see from the initial measurements.

Regarding noise from the sensor, for the last few days, I’ve been wearing a Libre while continuing to use my normal BGM. I’ll do a full analysis once I’ve finished with this sensor, but a preliminary look indicates that the Libre has significant gain and offset errors vs. the BGM that can be calibrated out. My plan is to continue doing both measurements to enable this.

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I personally didn’t compare the Libre to a standard BGM, but I did notice that the offset seems rather variable post-meals. So it’ll be interesting to see what you find!

Admin note: @skaye I’ve adjusted your Discourse default trust level settings for new users. You are now a “regular user” and should be able to link freely to whatever you want.

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Definitely still OK! I’d like to convert our plan to be more supportive of ongoing joins. We have until July, after all. (And beyond that – I certainly wouldn’t want to stop something active, let’s see how this goes!)

It’s wonderful to have you join, and I’m really sorry about the forum spam issue!

Instrument calibration

It’s great to know about whether the calibration error is simply a constant error over the lifetime of a given device! That was my impression when using it. (I didn’t do much with my data, I fear – but I did manage to get the device when I had gestational diabetes last year.)

if you’re not sure where/how to share what you did & learned, the “one-offs” section is a great place for something that’s already done: https://forums.openhumans.org/c/self-research-one-offs/

Foods & blood glucose effects

“How it’s cooked & stored” might be an additional thing to document and control for? “Resistant starch” is an example of this – no idea if it affects other ingredients too. https://www.healthline.com/nutrition/cooling-resistant-starch

(Testing these things looks really interesting!)

I’m only on my first sensor, but so far it looks like there’s a big change from day 1 to day 2, followed by a slow improvement in accuracy over the next several days. This is consistent with the published literature (1, 2, 3), which also indicates that there is more substantial sensor-to-sensor variation. It’ll take a while, but I should be able to confirm that as I keep using the Libre.

Definitely. I didn’t mention it in the original post, but one of the things that got me started on this experiment was the observation that adding apple cider vinegar to my recipe for oat-fiber muffins caused my blood sugar to spike (tentative conclusion, haven’t done careful enough experiments to be certain). Since apple cider vinegar has no carbs, this suggests that the vinegar is reacting with one of the other ingredients, likely the oat fiber, to make it digestible. Once I nail down the effect of the individual ingredients, I really want to test the effect of combinations, cooking, etc. Hopefully by then I can get some more people to join the experiment, otherwise this is going to take a really long time…

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Just realized the project logs are supposed to go in a different part of the forum, so I created a log for future updates and linked back to here.

It sounds like this is really useful for whatever @gedankenstuecke & @MichaelR might do!

Ah! That’s really interesting. It sounds like a potential counterpoint to this review of vinegar indicating it might help reduce a blood glucose spike. (Presumably when taken separately, and not within a recipe. I’m guessing you’d already seen these claims and might have tried it for that reason!)

I’ve gone ahead and moved this topic to that category. We can hide the redundant post if you’d like! :slight_smile:

I had seen those claims, but I was actually adding the vinegar to mitigate a slight bitter taste coming from the oat fiber. It did that perfectly and without any noticeable vinegar taste, but I’ve stopped using it until I sort out whether it’s actually causing the blood sugar spike.

Just finished analyzing the baseline data (details here). No window of time was perfect, but it looks like starting at 12p is my best option.

Given that, for all subsequent experiments in this study, I will fast starting 7p the night before and start the measurement at 12p.

Two other quick updates:

  • Did a preliminary analysis comparing fingerstick measurements to the Libre (full report next week). Using the raw, uncorrected Libre data can be way off (max error >40 mg/dL), but the majority of the error is a systematic linear gain/offset error that is consistent after the first day. The random error of the Libre is only 8.1 mg/dL vs. 3 mg/dL for my fingerstick meter. With all that, once I correct the Libre data for the gain/offset error, it’s pretty decent. Not good enough for optimizing my medication, but great for filling in gaps between fingerstick measurements and for see large effects (like the food effects we’ll be measuring in this study).
  • I did the first dissolved glucose experiment last Friday. Started with 4g and saw an 11 mg/dL rise, which is a good low-end, but detectable signal. I’ll be running different amounts MWF this week and will hopefully be able to get a good dose-response curve.
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Thanks for the updates! Looking forward to receive the Libre sensors to join in!

The analysis & calibration of the data from my CGM is more complicated than I expected, though extremely interesting. It’s going to take me another week or two to get it written up.

In the meantime, I have the results from the first ingredient, dissolved glucose (details here). Data was cleaner than I expected and showed a max BG rise of 6.7 mg/dL per gram of glucose, which is pretty close to what I’ve seen historically.

Next up, Allulose.

Just posted results for the second ingredient, allulose, a sugar substitute with physical properties similar to table sugar and rapidly growing in popularity for low-carb cooking (details here). The effect of allulose on my blood glucose was negligible, ~0.1 mg/dL per gram of allulose, or about 1% that of glucose. Even that could easily be experimental error (I can’t consume more than 60 g allulose in a sitting without causing gastrointestinal distress).

Next up, oat fiber, a supposedly zero calorie, zero digestible carbohydrate flour replacement.

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Just posted results for the third ingredient, oat fiber, an indigestible fiber made from grinding the hulls of oats. It’s a great partial replacement for flour in low-carb baking when you don’t want the increased calories of almond or coconut flour.

The effect of oat fiber on my blood glucose was even smaller than that of allulose, clocking in at <0.05 mg/dL/g(oat fiber), or <0.5% that of glucose.

Next up is whey protein, the most common protein supplement. I’ve already done the first trial (30 g) and preliminary results are quite interesting. While the peak is ~1/10th that of glucose per gram, it extends about twice as long, indicating much slower digestion.

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Just posted results for olive oil and whey protein isolate, a complete protein extracted from milk whey.

As expected, olive oil had no measurable impact on my blood sugar. Whey protein isolate, on the other hand, increases my blood sugar by ~20% that of glucose (by iAUC), but with a slower rise. This result sin a lower peak, 0.68 mg/dL/g(whey) or 10% that of glucose, but a long tail of increased blood sugar, ~0.4 mg/dL/g(whey) @ 4.5 h.

This may be do to giving my body more time to produce endogenous insulin, or even directly stimulating its production, reducing the peak blood glucose. Both of these effects have been reported. Given that, it would be useful to see the same measurements in someone with Type 1 diabetes, who does not produce endogenous insulin.

Still deciding what to try next week, but am currently thinking either corn starch (to have an example from each major macronutrient), resistant starch (fiber with disputed claims to non-digestibility), or combinations of protein, fat, or fiber with sugar.

Any suggestions?

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I think corn starch would be very interesting as at least I personally use that in my cooking and having each macro-nutrient present would be cool. :smiley:

Ended up doing resistant wheat starch this week, mostly b/c I was curious if it was really as low carb as claimed. From monday’s data, it looks like it has 25-35% the effect of glucose, vs. the 12.% claimed by the nutrition label, but we’ll see once I’ve got the full data set.

I’ll do corn starch next, though. In addition to completing the macronutrient dataset, it’s the better comparator for the resistant starches.

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Link to detailed post

Just posted results for resistant wheat starch, a chemically modified starch that is purportedly not digested in the small intesting and therefore doesn’t increase blood sugar when consumed.

Contrary to these claims, it increases my blood sugar by 76% that of glucose (by iAUC), though with a slower rise, lower peak (33% that of glucose), and much longer tail.

This is extremely disconcerting, as both oat fiber (iAUC 0.4% of glucose) and resistant wheat starch (iAUC 75% of glucose) are listed as insoluble fiber on nutrition labels, but have radically different impact on blood sugar. Given the lack of clarity and quantification of ingredient lists, this makes it nearly impossible to predict the blood glucose impact of a food without eating it and testing.

Next week I’ll finish out the major macronutrient groups with cornstarch. Still deciding where to go after that, but it will either be more ingredients used in low carb cooking (inulin, erythritol, soluble corn fiber, lupin flour) or mixtures of the major macronutrients (to measure combinations effects.

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