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.
This post is an update on my experiments measuring the effect of food ingredients on blood sugar.
This week, I have the results from oat fiber and got started on whey protein.
Summary
Oat fiber has a negligible effect on my blood sugar, <0.05 mg/dL/g(oat fiber), or <0.5% that of glucose. Cooking the oat fiber had no significant effect. So, I’m safe to keep baking with it ☺.
Details
Purpose
To quantify the effect of ingestion of food ingredients and ingredient combinations on my blood sugar.
Procedure. From 7 pm the day before through 4:30p the day of experiment, 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 ~12 pm, the substance to be tested was dissolved or suspended in 475 mL of water and ingested as rapidly as comfortable. For the cooked oat fiber, 100 g oat fiber was mixed with 200 g water and allowed to hydrate for 30 min. The mixture was then baked in a parchment lined muffin tin for 18 min. at 350 °F. The oat fiber was then suspended as described above. BGM measurements were then taken approximately every 15 min. for 2 h or until blood glucose had returned to baseline, whichever was longer. A final BGM measurement was taken 4.5 h after ingestion.
Measurements. Blood glucose was measured using a FreeStyle Libre flash glucose monitor and a FreeStyle Freedom Lite glucose meter with FreeStyle lancets & test strips. No special precautions were taken to clean the lancing site before measurement. To take a sample, the lancing devices was used to pierce the skin at an ~45 deg. angle from the finger. Blood was then squeezed out by running the thumb and pointer finger of the opposite hand from the first knuckle to the lancing site of the finger. Blood was then wicked into a test strip that had been inserted into the meter and the glucose reading was recorded.
Data Processing & Visualization. iAUC was calculated using the trapezoid method (see data spreadsheet for details). Data was visualized using Tableau.
Figure 1. Change in blood glucose vs. time for oat fiber tests.
Change in blood glucose as a function of time for the oat fiber tests is shown in Figure 1. Qualitatively, there appears to be no impact of oat fiber up to 100 g consumed. It was extremely uncomfortable to drink that much oat fiber in one sitting, so it’s unlikely I will ever eat more than that.
Figure 2. Maximum blood glucose increase and iAUC vs. amount consumed. Red, blue, orange, and green indicate glucose, allulose, oat fiber, and cooked oat fiber, respectively. The line is the best linear fit to the data.
To better quantify the impact of glucose on my blood glucose, I plotted the maximum increase in blood glucose and the iAUC of blood glucose (incremental area under the curve) vs. amount consumed for all ingredients tested so far (see Figure 2). While the oat fiber data shows an increase in both blood glucose and iAUC as a function of amount consumed, there’s only two data points and the magnitude is extremely small and could easily be due to experimental error. Confirming this effect would require running more measurements. I may go back and do this later, but for the moment, I would prefer to focus my time on ingredients with unknown or more substantial effects.
Since oat fiber is used in baking, I also wanted to check if heating it would break down the fibers and increase digestibility. Towards that end, I mixed 100 g of oat fiber with 200 g of water and baked at 350 °F for 18 min. (time & temperature for my muffin recipe and longer than my cookie recipe), then suspended it in water using the same procedure as with the uncooked fiber. There was no observable change in appearance, taste, or texture from the cooking process. There was a very minor increase in blood glucose response, but well within the measurement error of the meter.
Conclusion & Next Experiments
Oat fiber has a negligible effect on my blood sugar, <0.05 mg/dL/g(oat fiber), or <0.5% that of glucose. Cooking the oat fiber had no significant effect. This week, I will measure the effect of whey protein, a common protein supplement.
This post is the final report on our Community Self-Experiment studying the effect of hot showers on blood glucose. If you don’t want to read all the details, the highlights are in the Background & Summary section immediately below.
To figure out what’s really going on, we decided to do a communal self-experiment. 8 Redditors with diabetes developed an experimental protocol, measured their blood glucose before and after 41 showers using a combination of CGMs and BGMs, and analyzed the results.
Summary of Results
By working together, the team of experimenters was able to learn more and learn faster than any one of us would have been able to on our own. From the data, we were able to answer several of our initial questions:
What is the change in blood glucose after a hot shower under controlled conditions?
From BGM: 12 ± 17 mg/dL
From CGM: 21 ± 15 mg/dL
Is the observed change in blood glucose real or a CGM sensor artifact?
The change is real, not a sensor artifact (change is observed with BGM; CGM measurements are consistent with typical variation between CGM and BGM)
We cannot rule out the difference in effect size between BGM and CGM being due to a sensor artifact, but the data does not provide support for this hypothesis.
Is there significant person-to-person variation in the magnitude or direction of the effect?
The data is consistent with person-to-person variation, but significantly more data and/or more controlled conditions are required to determine if the variation exists and it’s effect size.
Is the change in blood glucose cause by the hot shower?
We can not identify any factors that account for the blood glucose rise other than the shower, but would need significantly more data and/or more controlled conditions to be certain that the shower is the cause.
While we were not able to get firm answers to all of our questions, we did get a measure of the effect size and rule out it being a CGM sensor artifact (the leading hypothesis in the original post). We also learned a lot that will help guide future Community Self-Experiments.
Data Processing. All data was converted into consistent units and put into an excel spreadsheet. From the raw data, I calculated change in BG from start of shower, as well as the largest relative change, and the time until largest relative change (see spreadsheet for calculation details). Visualization was done using Tableau.
What is the change in blood glucose after a hot shower under controlled conditions?
To answer this question, I plotted largest observed change over the 1 hour monitoring period for each shower as measured by both BGM and CGM (see Figure 1).
Figure 1. Max ΔBGM & ΔCGM for each shower, colored by experimenter. Reference band shows average +/- 1 standard deviation.
Looking at the data in Figure 1:
There is a large rise in blood glucose following a hot shower, though with significant variance in the size of the effect.
The rise is observed for both BGM (12 ± 17 mg/dL) and CGM (21 ± 15 mg/dL) measurements.
By count, we see (1 measurement excluded due to recording error):
>5 mg/dL increase: 34/40 (85%)
>5 mg/dL decrease: 3/40 (7.5%)
<5 mg/dL change: 3/40 (7.5%)
Conclusion: Blood glucose showed a consistent, measurable increase within 1h of taking a hot shower.
Is the observed change in blood glucose real or a CGM sensor artifact?
Looking again at Figure 1, the increase in blood glucose is seen for both BGM and CGM measurements, indicating that it can’t be just a CGM artifact.
To further confirm this conclusion, we looked at the data from person H comparing BGM vs. CGM measurements during the normal course of the day vs. after a shower. As shown in Figure 2, for a single Libre sensor, there is a linear relationship between measured blood glucose by BGM vs. CGM and the data collected immediately and 15 minutes after a shower mostly lies within the normal variance in the data, with all exceptions showing a lower blood glucose measured by CGM. This indicates that any variation in CGM data due to a sensor artifact is smaller than the observed increase in blood glucose. Note that while this confirms that the measured effect is not exclusively due to a sensor artifact, it is still possible that a sensor artifact accounts for the difference in effect size as measured by BGM vs. CGM (12 vs. 21 mg/dL).
Figure 2. Blood glucose measured by FreeStyle Libre and FreeStyle Freedom Lite for person H over the course of 10 days. Grey line is a linear fit to the data and data collected immediately and 15 min. after a hot shower is shown in red.
Conclusion: The observed increase in blood glucose is not a CGM sensor artifact (though a partial effect from the CGM sensor is not ruled out).
Is there significant person-to-person variation in the magnitude or direction of the effect?
Looking again at the data in Figure 1:
A >5 mg/dL increase in blood sugar is observed for 6/8 (75%) of participants, with 2/8 (25%) showing a >5 mg/dL decrease in blood sugar.
Only 2 participants provided multiple measurements, A and H. For those we observe:
A: 12 ± 16 mg/dL
H: 26 ± 14 mg/dL
The difference is statistically significant (Welch’s t-test, p=0.016), but since the measurements were made using different methods (CGM for A, BGM for H), times (10 min. for A, 20 min. for H), and temperatures, this is only weak evidence for person-to-person variation.
Conclusion: The data is consistent with person-to-person variation, but significantly more data and/or more controlled conditions are required to determine if the variation exists and it’s effect size.
Is the change in blood glucose cause by the hot shower?
This is the most difficult question to answer. In hindsight, we should have done some randomized experiments where the experimenters held conditions as constant as possible, randomly decided whether or not to shower, and measured blood glucose either way. In the absence of that data, we analyzed the data we had for any correlation between the blood glucose rise and non-shower factors. It should be noted that the protocol did not control for any of these factors, so no causation or lack thereof should be inferred from the analysis.
Max ΔBGM or Max ΔCGM vs. hour of the day – no trend across the whole data set, nor within experimenters
Max ΔBGM vs. starting BGM – no trend across the whole data set, but within Experimenter H’s data, there’s an indication of a negative correlation (R2 = 0.32, p = 0.045).
Max ΔCGM vs. starting CGM – no clear trend across the whole data set, nor within experimenters.
Max ΔBGM vs. Temperature – no clear trend across the whole data set, nor within experimenters. Note: most experimenters did not record the shower temperature and the one who did (Person H) kept the temperature within ±3 °C.
Max ΔBGM or Max ΔCGM vs. Time since last meal or medication – There’s a positive correlation over the whole data set, but it doesn’t hold up within the two experimenters with repeat measurements, suggesting that it’s an effect person-to-person variation, possibly caused by systematic variation in conditions.
Conclusion: We can not identify any factors that account for the blood glucose rise other than the shower, but would need significantly more data and/or more controlled conditions to be certain that the shower is the cause.
Conclusions & Lessons Learned
By working together, the team of experimenters was able to learn more and learn faster than any one of us would have been able to on our own. From the data, we were able to answer several of our initial questions.
Conclusions:
What is the change in blood glucose after a hot shower under controlled conditions?
From BGM: 12 ± 17 mg/dL
From CGM: 21 ± 15 mg/dL
Is the observed change in blood glucose real or a CGM sensor artifact?
The change is real, not a sensor artifact (change is observed with BGM; CGM measurements are consistent with typical variation between CGM and BGM)
We cannot rule out the difference in effect size between BGM and CGM being due to a sensor artifact, but the data does not provide support for this hypothesis.
Is there significant person-to-person variation in the magnitude or direction of the effect?
The data is consistent with person-to-person variation, but significantly more data and/or more controlled conditions are required to determine if the variation exists and it’s effect size.
Is the change in blood glucose cause by the hot shower?
We can not identify any factors that account for the blood glucose rise other than the shower, but would need significantly more data and/or more controlled conditions to be certain that the shower is the cause.
While we were not able to get firm answers to all of our questions, we did get a measure of the effect size and rule out it being a CGM sensor artifact (the leading hypothesis in the original post). We also learned a lot that will help guide future Community Self-Experiments.
Key Lessons Learned:
Community Self-Experiments enable collection of data much faster than single-person experiments, both because more people are collecting data and because the group activity motivates participants.
Take more care with the experimental design, especially the implementation of control experiments to help rule out alternate hypotheses.
Implement better data sharing/management. In this experiment, data was posted, then manually entered into an excel sheet, which was very time consuming.
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.
This post is an update on my experiments measuring the effect of food ingredients on blood sugar.
Procedure. From 7 pm the day before through 4:30p the day of experiment, 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 ~12 pm, the substance to be tested was dissolved or suspended in 475 mL of water and ingested as rapidly as comfortable. BGM measurements were then taken approximately every 15 min. for 2 h or until blood glucose had returned to baseline, whichever was longer. A final BGM measurement was taken 4.5 h after ingestion.
Measurements. Blood glucose was measured using a FreeStyle Libre flash glucose monitor and a FreeStyle Freedom Lite glucose meter with FreeStyle lancets & test strips. No special precautions were taken to clean the lancing site before measurement. To take a sample, the lancing devices was used to pierce the skin at an ~45 deg. angle from the finger. Blood was then squeezed out by running the thumb and pointer finger of the opposite hand from the first knuckle to the lancing site of the finger. Blood was then wicked into a test strip that had been inserted into the meter and the glucose reading was recorded.
Data Processing & Visualization. iAUC was calculated using the trapezoid method (see data spreadsheet for details). Data was visualized using Tableau.
Figure 1. Change in blood glucose vs. time for alluose tests.
Change in blood glucose as a function of time for the allulose tests is shown in Figure 1. Qualitatively, there appears to be no impact of allulose up to 60 g consumed, with the possible exception of a small around 75 min.
Figure 2. Maximum blood glucose increase and iAUC vs. amount consumed. Red and blue indicate glucose and allulose, respectively. The line is the best linear fit to the data.
To better quantify the impact of glucose on my blood glucose, I plotted the maximum increase in blood glucose and the iAUC of blood glucose (incremental area under the curve) vs. amount consumed for both glucose and allulose (see Figure 2). While the allulose data shows an increase in both blood glucose and iAUC as a function of amount consumed, there’s only two data points and the magnitude is extremely small and could easily be due to experimental error. Confirming this effect would require running more allulose measurements. I may go back and do this later, but for the moment, I would prefer to focus my time on ingredients with unknown or more substantial effects.
Lastly, I continue to observe a large negative intercept, suggesting a background drop in blood sugar during the experimental window.
Conclusion & Next Experiments
Allulose has a negligible effect on my blood sugar, <0.1 mg/dL/g(allulose), or <2% that of glucose. This week, I will measure the effect of oat fiber, a zero calorie, zero digestible carbohydrate flour replacement.
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.
This post is an update on my experiments measuring the effect of food ingredients on blood sugar.
Establish fasting baseline & determine time of day for experiments: Complete
Food effect measurements
Dissolved glucose: Complete (this post)
Allulose: starting 3/9
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.
Summary
Dissolved glucose raises my blood sugar by 6.7 mg/dL/gglucose, with the peak occurring from 45-75 min. after ingestion.
Results are extremely linear with amount consumed, with a slightly better fit when using incremental area under the curve (iAUC) vs. the peak increase (R2 = 0.988 vs. 0.983).
Details
Purpose
To quantify the effect of ingestion of dissolved glucose on my blood sugar.
Design/Methods
Procedure. From 7 pm the day before through 4:30p the day of experiment, 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 ~12 pm, glucose was dissolved in 475 mL of water and ingested as rapidly as comfortable. BGM measurements were then taken approximately every 15 min. for 2 h or until blood glucose had returned to baseline, whichever was longer.
Measurements. Blood glucose was measured using a FreeStyle Libre flash glucose monitor and a FreeStyle Freedom Lite glucose meter with FreeStyle lancets & test strips. No special precautions were taken to clean the lancing site before measurement. To take a sample, the lancing devices was used to pierce the skin at an ~45 deg. angle from the finger. Blood was then squeezed out by running the thumb and pointer finger of the opposite hand from the first knuckle to the lancing site of the finger. Blood was then wicked into a test strip that had been inserted into the meter and the glucose reading was recorded.
Data Processing & Visualization. iAUC was calculated using the trapezoid method (see data spreadsheet for details). Data was visualized using Tableau.
Change in blood glucose as a function of time is shown in Figure 1. Qualitatively, upon ingestion I observe an increase in blood glucose, with the magnitude and time to peak increasing with increasing amount of glucose. In all cases, my blood glucose returned to baseline within 135 min.
Figure 2. Maximum blood glucose increase and iAUC vs. glucose consumed. The line is the best linear fit to the data.
To better quantify the impact of glucose on my blood glucose, I plotted the maximum increase in blood glucose and the iAUC of blood glucose (incremental area under the curve) vs. glucose consumed. As shown in Figure 2, both measures were extremely linear vs. amount consumed, with a slightly better fit when using incremental area under the curve (iAUC) (R2 = 0.988 vs. 0.983). However, in both cases there was a large negative intercept, suggesting either a background drop in blood sugar or a non-linear effect that would show up with a wider range of amounts.
Conclusion & Next Experiments
Based on the both the repeatability and linearity of the data, my experimental protocol appears to be working well. This week, I will try the first of the low-carb ingredients, Allulose.
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.
I’ve started my experiments measuring the effect of food ingredients on blood sugar. The planned steps are as follows:
Calibrate continuous blood glucose meter: started 2/18, report by 3/9
Establish fasting baseline & determine time of day for experiments: Complete (this post)
Food effect measurements
Dissolved glucose: Started 2/28, report by 3/16
tbd.
Today I’m going to share the results of the initial measurements to determine what time of day to run the experiments and establish my baseline fasting blood sugar.
Summary
When I skip breakfast and lunch, my blood sugar is sufficiently stable between 12-4p. For all subsequent experiments in this study, I will fast starting 7p the night before and start the measurement at 12p.
Details
Purpose
To identify the best time of day to measure the effect of food ingredients on my blood sugar.
Design/Methods
Procedure. From 7 pm the day before through 4:30p the day of experiment, 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). BGM measurements were taken approximately every 15 min. on 2/7/20 and every 60 min on 2/24/20 and 2/26/20. CGM measurements were taken on 2/24/20 and 2/26/20.
Measurements. Blood glucose was measured using a FreeStyle Libre flash glucose monitor and a FreeStyle Freedom Lite glucose meter with FreeStyle lancets & test strips. No special precautions were taken to clean the lancing site before measurement. To take a sample, the lancing devices was used to pierce the skin at an ~45 deg. angle from the finger. Blood was then squeezed out by running the thumb and pointer finger of the opposite hand from the first knuckle to the lancing site of the finger. Blood was then wicked into a test strip that had been inserted into the meter and the glucose reading was recorded.
Data Processing & Visualization. Data was visualized using Tableau.
Figure 1. Blood glucose measurements from BGM. Blue – 2/7/20, Orange – 2/24/20, Red – 2/26/20.
Figure 2. Blood glucose measurements from CGM. Blue – 2/24/20, Orange – 2/26/20.
Data from BGM and CGM measurements are shown in Figures 1 and 2, respectively. As expected based on my previous experiments (fasting, non-fasting), I see a blood sugar rise when I wake up due to the dawn phenomenon, which persists until 11a-12p, after which my blood sugar comes down, stabilizing around 2p. This presents a timing challenge for when to measure the effect of foods:
For the start time, I need to wait for the dawn phenomenon to subside, as it reduces insulin sensitivity
Slower absorbing foods, like protein and fiber, may impact blood sugar for >>2 h.
I don’t want to do a full day fast (i.e. skip dinner) for these experiments.
Given these factors, the best time for the experiments seems to be 12p. This introduces the noise of the small BG drop I see between 12-2p, but as long as I study quantities that give 30-40 mg/dL rises as well as the magnitude of BG rise as function of quantity consumed, this should be ok.
Conclusion & Next Experiments
For all subsequent experiments in this study, I will fast starting 7p the night before and start the measurement at 12p.
