New Site Design

I’ve migrated this site from Blogger to WordPress. This will allow me a lot more control over post formatting, graphics, and widgets that will hopefully make the site easier to read and more useful to the audience.

Post formatting is a bit wonky due to the new theme, so please bear with me for a week or so while I get that sorted out. In the meantime, posts will continue and all old links should still work. If you notice anything broken, please let me know.

Weekly Update & Health Statistics: 8/8 – 8/14/21

For those checking the dates, I post these with a 1-week lag. 

Summary:

Active & Planned Experiments

  • Comprehensive bloodwork:
    • Goal: Establish baseline for a broad range of biomarkers and check overall health
    • Approach: WellnessFX Premium panel
    • Status: Complete, need to write up.
  • Testing blood glucose impact of low-carb foods
    • Goal: Check blood glucose impact of new low-carb foods I’d like to incorporate into my diet
    • Approach: Follow the same protocol from my previous study
    • Status:
      • Baseline:
        • Glucose re-test: In queue
      • Low-carb foods:
      • Supplements:
  • Testing methods of sleeping longer:
    • Goals:
      • To determine if taking melatonin supplements in the evening extends the duration of my time asleep and subjective fatigue given a consistent bedtime.
      • To determine if taking melatonin supplement and/or sleeping longer affects subjective fatigue, blood glucose, heart rate variability, or pulse
    • Approach: here
    • Status: Data collection complete. Ready to analyze.


What I’m tracking

  • Sleep: 
    • Metrics: total time, heart rate variability, pulse (sleeping vs. waking)
    • Method: Apple watch + Autosleep app
    • Frequency: daily
  • Blood glucose:
    • Metrics: fasting BG, Avg. BG, coefficient of variation, time 70-140 mg/dL, time <60 mg/dL
    • Method: Dexcom G6 CGM
    • Frequency: continuous
  • Body:
    • Metrics: weight, waist circumference, BMI, waist/height
    • Method: scale + Renpho tape measure
    • Frequency: weekly
  • Other blood:
    • Metrics: hemoglobin, cholesterol, blood pressure, pulse
    • Method: Hemocue 801Cardiocheck PAOmron 10
    • Frequency: weekly for blood pressure & pulse, every 2 weeks for hemoglobin & cholesterol


Observations

  • Sleep:
    • My sleep experiment is complete. Analysis will be posted next Saturday. 
    • From looking at my manually recorded data, my Apple Watch is occasionally wildly off on either time awake or time asleep, leading to huge errors in total sleep. For example, for this week, the watch reports an average time asleep of 7.7h, but my manual tracking shows an average time asleep of only 6.8h. A look at the data shows the watch data didn’t catch one day where I had insomnia and didn’t fall asleep until 12:30a (claimed I fell asleep at 9:30p, when I first went into bed) and showed me sleeping until 7:40a another day when I know I was at working starting at 5:30a.   
    • Given these huge discrepancies, I’m going to switch to tracking sleep manually using the final modified protocol from my sleep study. Will specify details in the next post.
  • Blood glucose:
    • Everything looking good. Still keeping an eye on downward trend in coefficient of variation & fasting BG to see if it’s real.
  • Body:
    • Tried to eat more at dinner this week, but still lost 1.6 lb. Looks like the plan to eat more at breakfast will be necessary. 
  • Blood:
    • Off week for hemoglobin and cholesterol.
    • Pulse has been steadily dropping (0.16 bpm/day, R2 = 0.54). This is showing up consistently on the Omron (resting) and Apple Watch (sleeping & awake). The only thing I can think of to cause this is the weight loss. I’ve found several studies reporting a correlation between heart rate and BMI (e.g. here), but they’re mostly in obese patients or population studies. Will try to find some in non-obese people.
    • Blood pressure has been rising the last 3 weeks, but I continue to be skeptical of the measurement device. Looking for ways to verify.

Data:

Blood

Body

Sleep

Methods:

Measurements: See summary section above

Data Processing & Visualization. Data was visualized using Tableau.

Data: here

– QD

Does Melatonin Help me Sleep Longer? A Blinded, Pre-registered Self-Experiment

After 6 weeks, I’ve finally finished my blinded study of melatonin to increase sleep duration. Here’s the write-up. Hope you find it interesting.

If you have any suggestions for other supplements or interventions for me to try, please let me know in the comments.

– QD

Summary:

Over the few months, I’ve been making an effort to get more sleep. I’ve been able to hit an average time asleep of ~7h and, qualitatively, I’ve been feeling a lot less tired and have been able to concentrate better in the afternoons.  

I’d like to see if sleeping even longer would result in further improvement, but have been unable to do so due to routinely waking up before my alarm. 

In an attempt to sleep longer, I decided to try melatonin. It’s typically used to control when you go to sleep, but it last long enough in the bloodstream that it might impact time asleep as well (Examine.com, ACX). Based on suggestions solicited from the ACX open thread, I ran a 28 day, blinded, randomized trial of 0.3 & 3 mg melatonin, both regular and extended release. 

Here’s the summary of the results (full details below):

  • Measurement Reliability:
    • Sleep measurements from my Apple Watch are occasionally off by several hours, sometimes demonstrably off by up to 8 min., and don’t correlate with manually recorded times asleep.
    • For all subsequent analyses, I will only use manually recorded sleep data
  • Measurement Effect:
    • Contemporaneous recording of waking disrupted my sleep, leading to more recalled wake-ups and possibly increased fatigue
    • For future sleep studies, I will record waking and other observations only upon arising or find an automated tracker that can record them without conscious attention on my part.
  • Melatonin Effect:
    • Melatonin had no observable effect on my sleep duration or any other metric examined.
    • It’s possible that it had an effect that was too small to be observed using my experiment design. However, if that’s the case the effect is too small to be of interest/use to me.

While it’s disappointing that the melatonin didn’t have any effect on my sleep duration, I did learn a lot about how (and how not) to measure sleep. Based on these results, I’m going to keep manually recording how I slept when I wake up and see if I can identify any patterns I missed when previously looking only at data collected from my watch.

I’d also like to investigate other supplements reported to improve sleep duration & quality. Some recommendations I’ve gotten over the last few weeks include L-theanine, magnesium, and tryptophan.

Does anyone else have any suggestions for supplements or interventions I should try?

Details:

Purpose

  • To determine if taking melatonin supplements in the evening extends the duration of my time asleep and subjective fatigue given a consistent bedtime.
  • To determine if taking melatonin supplements and/or sleeping longer affects subjective fatigue, blood glucose, heart rate variability, or pulse


Background

Over the few months, I’ve been making an effort to get more sleep. I’ve been able to hit an average time asleep of ~7h and, qualitatively, I’ve been feeling a lot less tired and have been able to concentrate better in the afternoons.  

I wanted to see if sleeping even longer will result in further improvement. However, I’ve routinely been waking up before my morning alarm. I stay in bed (eyes closed) until the alarm, but can’t go back to sleep. Based on my data so far, there’s no clear correlation with time I fell asleep or total time asleep

I’d like to test some interventions to sleep longer. I already exercise in the evenings and for as long as I’m willing to do (~30 min. high intensity, 5-10 min. stretching), my last meal is 4h before going to bed, and my CGM does not show a consistent rise in blood sugar before waking up.

Given that, my next thought was to try melatonin. It’s typically used to control when you go to sleep, but it last long enough in the bloodstream that it might impact time asleep as well (Examine.comACX). Based on suggestions solicited from the ACX open thread, I ran a 28 day, blinded, randomized trial of 0.3 & 3 mg melatonin, both regular and extended release. 

Design/Methods

The original experimental design was pre-registered here & here


Materials


Blinding

  • Melatonin was placed in opaque, size 000 gel capsules (6 per type).
  • Dosages were randomly assigned to days using the excel random number generator and placed into a coded pill container by a second person (not me). 
  • Data was unblinded after the completion of the experiment.

Procedure

  • First 28 days:
    • At 9pm, I took that days gel capsule with minimal water.
    • At 9:30pm, I turned off the lights in by bedroom and attempted to go to sleep.
    • Contemporaneously, I recorded:
      • Times I woke up in the night
      • Subjective tiredness (1-3 scale) upon arising and at 3p (I kept forgetting to do this, so after a few days, I gave up on it).
      • If any other unusual events occur
  • 13 day extension:
    • At 9:30pm, I turned off the lights in by bedroom and attempted to go to sleep.
    • When I woke up, I recorded:
      • Time I woke up
      • How many times I remembered waking up in the night
      • If any other unusual events occur
  • Exclusions:
    • Two days were excluded from the experiment. 
    • On 7/29/21 I had a late night event, well past my normal bed time, so I skipped the experiment entirely (did not take a pill and did not record sleep).
    • On 8/4/21 I had a series of mishaps. I took the pill at the wrong time, my continuous glucose alarm erroneously triggered and woke me up, and I woke up a second time by rolling the wrong way and hurting my arm.


Measurements

  • Sleep, HRV, & pulse: Apple Watch Series 5 + Autosleep app
  • Sleep, times waking up during the night: manual recording both contemporaneously and after waking up (see Procedure, above)
  • Glucose: Dexcom G6 CGM
  • Blood pressure (weekly): Omron 10


Analyses

  • Pre-registered:
    • Sleep time, wake time, total time asleep, number of times waking up before alarm, fasting blood glucose, time of morning blood glucose rise, pulse, and HRV will be plotted vs. dose, release type, and recording method. 
      • Note: since no significant effect on sleep parameters was found, I skipped the analysis of morning blood glucose rise, which is time-consuming.
  • Additional exploratory analyses (not pre-registered):
    • Time to first wake-up
    • Comparison of time asleep as measured by Apple Watch vs. manual recording
  • Student’s t-test was used to test if the values for any of the above metrics were different for each dose, release type, or combination of dose & release type vs. the placebo. p-values were calculated for each comparison and corrected for multiple-comparison
    • Note: Since no comparison was statistically significant, I did not bother to correct for multiple comparisons.

Data Processing & Visualization

Autosleep data was exported and reformatted using a custom python script. Both Autosleep and manually recorded data was visualized using Tableau.


Data: here



Results & Discussion

Measurement Reliability

Before I can evaluate the effect of melatonin, I first need to look at the reliability of the measurements. I routinely check my continuous glucose monitor against an Abbot Freestyle Lite blood glucose (fingerstick) meter and know it to be reliable to within ~4 mg/dL, so no concern there. For pulse and HRV measurements, I don’t have any way to validate the data, so for purposes of this analysis, I’ll accept them as accurate.

That leaves the sleep measurements from my Apple Watch. Since this is the first time I’ve manually recorded when I went to sleep and woke up, I was very interested to see whether the automatically recorded sleep on my watch matched. Unfortunately, it didn’t. As can be seen in Figure 1 (top, left), several days exhibit large discrepancies between the manual and watch recorded time asleep. Inspecting the data, it’s clear that it’s the watch that is incorrect. In all cases where the watch reported a typical time asleep while manual recording showed time asleep was was low, the watch had recorded that I went to sleep when I first lied down and turned off the lights (9:30p), while I had manually recorded that I had insomnia and didn’t fall asleep until much later (only done when I never feel asleep in the first place). In all cases where the watch reported higher than normal time asleep, it had recorded that I was still sleeping hours past my alarm (which never happens) and on days when I know I was working or driving to work by 5:30a.

Even after removing the data points for which the watch is clearly incorrect, there’s still a relatively poor correlation between the manual and watch recorded data (Figure 1, top right), with an R2 of only 0.7. I don’t have a way to determine which is more accurate, but given the intermittent extremely inaccurate measurements by the watch and the fact that the watch shows waking times past my alarm when I know I was up and moving around, I will use the manually recorded data for all future analysis.

Conclusions:

  • Sleep measurements from my Apple Watch are occasionally off by several hours, sometimes demonstrably off by up to 8 min., and don’t correlate with manually recorded times asleep.
  • For all subsequent analyses, I will only use manually recorded sleep data

Measurement Effect

During the course of the experiment, I noticed that I was waking up in the middle of the night more often than I remembered doing before the study started. Based on this, I became concerned that the process of noting down when I woke up during the night was disrupting my rest and causing me to be more aware of it. 

To check this hypothesis, I extended the sleep study, taking no melatonin and only noting down the number of times I remember waking up after I finally get up for good in the morning. This is, of course, not blinded, but can at least test whether the initial observation was correct.

The results of this extension are show in Table 1 and Figure 2. As hypothesized, once I stopped contemporaneous recording of when I woke up, the number of recorded wakings dropped significantly (2.7 -> 1.9, p=0.2). My time in bed also dropped, (not statistically significant), driven by getting out of bed earlier. All this strongly suggests that the process of contemporaneous recording of waking was disrupting my sleep, making me more tired and enabling or inducing me to stay in bed longer.

Conclusions:

  • Contemporaneous recording of waking disrupted my sleep, leading to more recalled wake-ups and possibly increased fatigue
  • For future sleep studies, I will record waking and other observations only upon arising or find an automated tracker that can record them without conscious attention on my part.

Melatonin Effect

To assess the effect of melatonin on sleep duration, I looked at the following metrics:

  • Time in bed/asleep
  • # of times I woke up during the night (including final wake up)
  • Time to 1st wake up
  • Fasting blood glucose (the next day)
  • Average blood glucose (the next day)
  • Pulse (while sleeping)
  • Heart rate variability (HRV)

For each metric, I used students t-test to assess whether the value for the placebo was different that for each dose, type (normal or extended release) and combination of dose & type. The data is show in the graphs and tables below. In no case was the p-value <0.05, indicating no significant different from the placebo. 

Since each dataset was relatively small (5-6 days per unique condition) and I had insomnia on three of the days of the experiment, I also tested whether any of the metrics became significantly different after excluding the days I had insomnia. The only one that did was pulse, which showed a small, but statistically significant increase for the 0.3 mg, normal release and aggregated normal release conditions. However, since this analysis was not pre-registered, I did not correct for multiple comparisons, my pulse has been trending downward during the course of the experiment, and there’s no dose dependence, this result is very likely due to chance.


Conclusions:

  • Melatonin had no observable effect on my sleep duration or any other metric examined.
  • It’s possible that it had an effect that was too small to be observed using my experiment design. However, if that’s the case the effect is too small to be of interest/use to me.


Conclusions & Next Experiments

  • Measurement Reliability:
    • Sleep measurements from my Apple Watch are occasionally off by several hours, sometimes demonstrably off by up to 8 min., and don’t correlate with manually recorded times asleep.
    • For all subsequent analyses, I will only use manually recorded sleep data
  • Measurement Effect:
    • Contemporaneous recording of waking disrupted my sleep, leading to more recalled wake-ups and possibly increased fatigue
    • For future sleep studies, I will record waking and other observations only upon arising or find an automated tracker that can record them without conscious attention on my part.
  • Melatonin Effect:
    • Melatonin had no observable effect on my sleep duration or any other metric examined.
    • It’s possible that it had an effect that was too small to be observed using my experiment design. However, if that’s the case the effect is too small to be of interest/use to me.

While it’s disappointing that the melatonin didn’t have any effect on my sleep duration, I did learn a lot about how (and how not) to measure sleep. Based on these results, I’m going to keep manually recording how I slept when I wake up and see if I can identify any patterns I missed when previously looking only at data collected from my watch.

I’d also like to investigate other supplements reported to improve sleep duration & quality. Some recommendations I’ve gotten over the last few weeks include L-theanine, magnesium, and tryptophan.

Does anyone else have any suggestions for supplements or interventions I should try?

As always, please let me know if you have any thoughts or suggestions.

– QD

Weekly Update & Health Statistics: 8/1 – 8/7/21

For those checking the dates, I post these with a 1-week lag. 

Summary:

Active & Planned Experiments

  • Comprehensive bloodwork:
    • Goal: Establish baseline for a broad range of biomarkers and check overall health
    • Approach: WellnessFX Premium panel
    • Status: Complete, need to write up.
  • Testing blood glucose impact of low-carb foods
    • Goal: Check blood glucose impact of new low-carb foods I’d like to incorporate into my diet
    • Approach: Follow the same protocol from my previous study
    • Status:
      • Baseline:
        • Glucose re-test: In queue
      • Low-carb foods:
        • Meal replacements: 2/3 complete, (Ketochow previously reported)
        • Flour replacements: 1/4 complete
        • Tortillas: Reported
        • Bread: 1/8 complete
        • Snack bars: 0/8 complete
        • Ice cream: 2/11 complete
        • Cereals: 4/7 complete
      • Supplements:
  • Testing methods of sleeping longer:
    • Goals:
      • To determine if taking melatonin supplements in the evening extends the duration of my time asleep and subjective fatigue given a consistent bedtime.
      • To determine if taking melatonin supplement and/or sleeping longer affects subjective fatigue, blood glucose, heart rate variability, or pulse
    • Approach: here
    • Status: Data collection complete. Ready to analyze.


What I’m tracking

  • Sleep: 
    • Metrics: total time, heart rate variability, pulse (sleeping vs. waking)
    • Method: Apple watch + Autosleep app
    • Frequency: daily
  • Blood glucose:
    • Metrics: fasting BG, Avg. BG, coefficient of variation, time 70-140 mg/dL, time <60 mg/dL
    • Method: Dexcom G6 CGM
    • Frequency: continuous
  • Body:
    • Metrics: weight, waist circumference, BMI, waist/height
    • Method: scale + Renpho tape measure
    • Frequency: weekly
  • Other blood:
    • Metrics: hemoglobin, cholesterol, blood pressure, pulse
    • Method: Hemocue 801Cardiocheck PAOmron 10
    • Frequency: weekly for blood pressure & pulse, every 2 weeks for hemoglobin & cholesterol


Observations

  • Sleep:
    • 2 days left in my sleep experiment and still seeing significant discrepancies between the auto and manually tracked metrics. I’m going to hold off on analyzing the data until the experiment is complete so as to minimize any impact on data collection. 
    • Sleep back up to normal.
  • Blood glucose:
    • Coefficient of variation back to normal this week.
    • Coefficient of variation & fasting BG starting to look like there’s a downward trend. Probably due to weight loss.
  • Body:
    • Lost more weight than normal the last couple weeks (2 vs. 1 lb/wk). Still in acceptable range, but I don’t want to go any faster. 
    • Still have time, but I’m 4 weeks away from having to change my diet to stabilize my weight. Current plan is to add calories to breakfast.
  • Blood:
    • Hemoglobin is the lowest I’ve seen this week. Still well within the normal range, so nothing to be concerned about, but will keep an eye on it.
    • Cholesterol was much better this week, but probably an outlier.
    • Pulse has been steadily dropping (0.16 bpm/day, R2 = 0.54). This is showing up consistently on the Omron (resting) and Apple Watch (sleeping & awake). The only thing I can think of to cause this is the weight loss. I’ve found several studies reporting a correlation between heart rate and BMI (e.g. here), but they’re mostly in obese patients or population studies. Will try to find some in non-obese people.
    • For blood pressure, no obvious trend over time. Slightly higher than I’d like (~125/82), but won’t know if that’s real or measurement error until I calibrate against another instrument.  

Data:

Blood

Body

Sleep

Methods:

Measurements: See summary section above

Data Processing & Visualization. Data was visualized using Tableau.

Data: here

– QD

Testing Blood Glucose Impact of Low Carb Foods: Cereal

This post is an update on my experiments measuring the effect of low-carb foods and dietary supplements on blood sugar.

This week, I have the results from low-carb cereals. Next week I’ll be posting results from my sleep study, followed by low-carb ice-creams.


Testing Queue:

  • Baseline:
    • Glucose re-test: In queue
  • Low-carb foods:
    • Meal replacements: 2/3 complete, (Ketochow previously reported)
    • Flour replacements: 1/4 complete
    • Tortilla: Reported
    • Bread: 2/10 complete
    • Snack bars: 0/8 complete
    • Ice cream: 5/11 complete
    • Cereals: This post
  • Supplements:

Cereals


Summary:

I tested 7 low-carb cereals from 3 categories (nut & seed granolas, milk protein & sweetener blends, and protein & fiber blends). 

The granolas had the lowest blood glucose impact by weight (~7%  & ~15% of glucose for peak BG/g & iAuC/g). By volume, though, all except Catalina Crunch were very similar. 

The highest blood glucose by a wide margin was Catalina Crunch, a protein and fiber blend, with at 28%  & 62% of glucose for peak BG/g & iAuC/g. This likely stems from its use of potato and corn fiber, which are digestible despite being subtracted for the net carb count

On taste, all the cereals were good, but sweeter than I’d like (see Table above). Of the granolas, my favorite was the NuTrail, which had a strong cinnamon & vanilla flavor that paired well with the pecans, pumpkin seeds, and coconut. Of the more cereal-like cereals, my favorite by far was the Magic Spoon. The texture was shockingly like regular cereal and it was the only one of the bunch that didn’t taste strongly of the non-nutritive sweetener. For these experiments, I used the banana, but a tried a few of their other flavors and liked them a lot more (my favorite was maple).

Compared with the tortillas, I didn’t get as much direct value out of these measurements. While I liked the cereals, they’re too sweet and too low in nutrition for me to use for regular meals and keeping them around is too much of a temptation to break my diet. I might get a box every once in a while as a treat, but I won’t be incorporating them in to my regular rotation.

Does anyone know any other good low-carb cereals I should try?

Details:

Purpose

  • To identify low-carb foods that taste good and have minimal effect on my blood glucose.
  • To determine the effect of popular, literature supported dietary supplements on my blood glucose. 


Background

Before I got diabetes, my favorite breakfast was a bowl of cereal with milk. In the last few years, a ton of new brands of low-carb cereals come out, with some even available in supermarkets (some popular press articles here and here). 

Although the net carb counts look good, I’ve become very suspicious of the blood sugar impact of some of the dietary fibers used (see evidence of blood glucose impact of dietary fibers here & here). 

To see if any of available low-carb cereals would hold up, I decided to test them myself.

Design/Methods

Foods

I tested 7 low-carb cereals from 3 different categories:

  • Nut- & seed-based granolas
  • Milk protein & sweetener blends
  • Protein & fiber blends

Full nutrient and ingredient info here. Key nutrition facts in the table below.

Procedure

At 5:00a, I took 4.5u of Novolog (fast acting insulin, duration of 2-4h), then drank a Ketochow shake (websiteBG testing) at 5:30a. After that, no food or calorie-containing drinks were consumed and no exercise was performed. Non-calorie-containing drinks were consumed as desired (water, caffeine-free tea, and decaffeinated coffee). At 11am-12 pm, the substance to be tested was eaten as rapidly as comfortable and notes on taste and texture were recorded (before observing any change in blood sugar).

Blood sugar was monitored for 5h using a Dexcom G6. Calibration was performed 15-30 min. before the start of each experiment.


Data Processing & Visualization. iAUC was calculated using the trapezoid method (see data spreadsheet for details). Data was visualized using Tableau.

Medication. During these experiments, I took long-acting basal insulin each evening at 9pm (Lantus, 1.52u) and 2000 mg of metformin and multivitamin each morning at 5am. I did not dose for the experimental food ingested.



Data

Results & Discussion

Figure 1. Left – Change in blood glucose vs. time. Right – Change in blood glucose per g(food) vs. time
Figure 2. Left – Peak change in blood glucose per g(food). Right – iAuC per g(food). All values reported as % of the value measured for glucose.
Figure 3. Left – Peak change in blood glucose per cup(food). Right – iAuC per cup(food). 

Changes in blood glucose as a function of time are shown in Figure 1. All cereals show a longer time to initial rise, less steep rise, and longer duration of impact than glucose, consistent with a slower absorption and metabolism. This profile is consistent with a mix of protein, starches, and a higher fat content.

There was a dramatic difference in the blood glucose impact of the different types of cereals by weight. The nut and seed granolas showed the lowest impact at ~7%  & ~15% of glucose for peak BG/g & iAuC/g. The milk protein and sweetener blends were about twice that at  ~15%  & ~35% of glucose. The worst of the bunch was Catalina Crunch, a protein and fiber blend at 28%  & 62%. It looks like the fibers it uses (potato fiber, corn fiber) have a significant impact despite being subtracted for the net carb count. 

The different types of cereals also had significantly different densities. Depending on whether your goals and preferences, you may care more about the impact of a volume of cereal instead of weight. Personally, I tend to eat a full bowl of cereal in a sitting (~1 cup), so that’s what I care about. When you normalize by volume, the differences between the granolas and the milk protein & sweetener cereals goes away, though Catalina Crunch is still much higher impact than everything else. 

On taste, all the cereals were good, but sweeter than I’d like (see Table above). Of the granolas, my favorite was the NuTrail, which had a strong cinnamon & vanilla flavor that paired well with the pecans, pumpkin seeds, and coconut. Of the more cereal-like cereals, my favorite by far was the Magic Spoon. The texture was shockingly like regular cereal and it was the only one of the bunch that didn’t taste strongly of the non-nutritive sweetener. For these experiments, I used the banana, but a tried a few of their other flavors and liked them a lot more (my favorite was maple).

Note: taste and texture observations were recorded when I ate the food. I.e. before I knew its impact on my blood sugar.

Thoughts & Next Experiments 

The food effect studies continue to go well. I’m still seeing very large differences in blood glucose impact, independent of the carb count, bolstering the conclusion that the digestibility of fibers varies widely. After I collect more data, I plan to go back and see if I can find a better metric than net carbs to predict the impact of a given food.

Compared with the tortillas, I didn’t get as much direct value out of these measurements. While I liked the cereals, they’re too sweet and too low in nutrition for me to use for regular meals and keeping them around is too much of a temptation to break my diet. I might get a box every once in a while as a treat, but I won’t be incorporating them in to my regular rotation.

Does anyone know any other good low-carb cereals I should try?

As always, please let me know if you have any thoughts or suggestions.

– QD