Category: Blood Glucose

Effect of Food Ingredients on Blood Glucose: Dissolved Glucose

Get new posts by email or rss feed

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.

Plan:

  • Design experiments and solicit feedback: blog, Reddit, OpenHumans
  • Calibrate continuous blood glucose meter: started 2/18, report tbd. (probably 3/16)
  • 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.

Medication. I took my normal morning and evening medication, but did not dose for the glucose.

Data

Link

Results & Discussion

Figure 1.  Change in blood glucose vs. time.

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. 


– QD

Get new posts by email or rss feed

Effect of Food Ingredients on Blood Glucose: Establishing a Baseline

Get new posts by email or rss feed

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:

  • Design experiments and solicit feedback: blog, Reddit, OpenHumans
  • 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.

Medication. I took my normal morning and evening medication, but did not dose for meals.

Data

Results & Discussion

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.


– QD

Get new posts by email or rss feed

Please Critique my Experiment Design: Measuring the Effect of Low-carb Ingredients on Blood Sugar

Get new posts by email or rss feed

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. 

This is going to be a lot of work and take many weeks, so I was hoping to get some feedback on my experimental design before I start. If you’re interested, please take a look and leave your feedback/critique in the comments. 

It’d really improve the experiment to have more people participating. Let me know in the comments or by e-mail if you want to join in (see sidebar).

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 you’d like to see me test?
  • Are you interested in joining the experiment?


– QD

Get new posts by email or rss feed

It Might be Real! Initial Analysis of Hot Shower Effect on Blood Glucose (N=8 Community Self-Experiment)

Get new posts by email or rss feed

Also posted to Reddit in r/diabetes and r/QuantifiedSelf. Check those out if you want to see/participate in the discussion.

A couple weeks ago, u/NeutyBooty posted on how hot showers caused their blood glucose to rise. Lot’s of commenters confirmed the general observation, but for some it appeared to be a CGM artifact, for some it matched their finger-stick meter, and others they see a BG drop.

To figure out what’s really going on, we decided to do a communal self-experiment. Over the past two weeks, 8 Redditor with diabetes have been measuring their blood glucose before and after showering. So far, we have 22 measurements, so I thought it would be useful to post an initial exploratory analysis of the data to see if the wider community had an insights or suggestions.

In the comments, please chime in with any thoughts, additional analyses, or questions. If there’s any graph, calculation, etc. you’d like to see, let me know and I’ll add it. We also need more experimenters, so if your interested, let me know.

Highlights:

  • Initial indications are that we are seeing a real and consistent increase in BG from hot showers, not a sensor artifact.
  • So far, we are not seeing a clear person-to-person variation in the effect (more data needed).
  • There’s some very tentative but interesting trends in the data:
    • The effect is stronger with lower initial BG
    • The effect varies with time of day (could easily be a confounding variable here)

In order to get a clear answer on person-to-person variation and to better pull out any correlations, we need more data, especially repeat data from more people. If you’re interested in joining the experiment, let me know.

Details:

Design/Methods 

Protocol here. 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.

Data

Link

Results & Discussion

First, let’s look at the big question: are we seeing an effect? For this question, I plotted largest observed change over the 1 hour monitoring period for each shower as measured by both BGM and CGM.

Max ΔBGM & ΔCGM for each shower, colored by experimenter.

Looking at the graphs you can see the following:

  • We are seeing a measurable rise in blood sugar from a hot shower.
  • The effect is approximately the same size when measured by BGM vs. CGM, suggesting it’s not a sensor artifact
    • BIG CAVEAT: We don’t have much data from people with both BGM and CGM, and the majority of data is coming from two experimenters, so this conclusion is very tentative.
  • We’re not (yet) seeing a clear person-to-person variation. For both BGM and CGM, with the exception of 1 outlier in each case, there’s a pretty consistent increase in BG after a shower.

Interestingly, while we consistently see an increase in BG after showering, the timing of that increase is much more variable. If instead of looking at Max ΔBG over the monitoring period, you look at ΔBG 15 minutes after the shower, you get:

ΔBGM & ΔCGM@15 min. for each shower, colored by experimenter.

While we still see the effect, it’s a a lot more variable, especially in the BGM measurements.

Next, even though there’s not enough data for solid conclusions, I thought it’d be interesting to see if there was any interesting patterns/correlations in the data. I looked at:

  • ΔCGM@15 min. vs. ΔBGM@15 min. – only three data points, so can’t really say anything
  • Max ΔCGM vs. Max ΔBGM – two data points, can’t say anything
  • Max ΔBGM vs. hour of the day – no trend across the whole data set, but within Experimenter H’s, there’s an indication of a greater rise later in the day (R2 = 0.40, p = 0.08)
  • Max ΔCGM vs. hour of the day – no clear 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 strong negative correlation (R2 = 0.57, p = 0.03).
  • Max ΔCGM vs. starting CGM – no clear trend across the whole data set, nor within experimenters.
Max ΔBGM vs. hour of the day, colored by experimenter. Data from Experimenter H highlighted, showing a clearing increasing trend (R2 = 0.4, p = 0.08)
Max ΔBGM vs. initial BGM, colored by experimenter. Data from Experimenter H highlighted, showing a clearing decreasing trend (R2 = 0.57, p = 0.03)

Get new posts by email or rss feed

Experiment #4 – Tracking blood sugar during a 24 hour fast

Get new posts by email or rss feed

Figure 1. Measured blood glucose concentration vs. time. Reference bands and lines show my personal target range and high/low thresholds.
Table 1. Summary statistics of blood glucose concentration during the fast.

I tried again this week to measure the effect of different food ingredients on my blood sugar. I started with plain glucose, but quickly ran into a problem. The first two times I ate 3g of glucose, which normally would raise my blood sugar ~15 mg/dL, my blood sugar actually dropped. I did these experiments at 2pm, 8.5 h after eat breakfast, so I shouldn’t have had any effects from either the food or medication.

Based on these results, I’m not going to be able to get clean measurements of the effect of food unless I better understand my baseline blood glucose, so I decided to monitor my blood sugar during a day of fasting.

As with my two-day tracking experiment a couple weeks ago, this was quite informative. Here’s a most important things I learned, my new questions, and ideas for next experiments:

Key Learnings:

Full data and summary statistics in Figure 1 and Table 1

  • Checking blood sugar during a fast is a useful control experiment and helps determine if the phenomena I’m observing are due to specific interventions vs. natural or time of day-based variation.
  • Even with my morning insulin, I’m seeing an ~10 mg/dL increase that persists for ~5 h. I should try increasing the dose by ~0.25u.
  • There’s a measurable drop in my BG when I’m driving to/from work. I saw this in 3/4 of the drives during my previous glucose tracking experiment, but I didn’t pick up on it because my commutes occur right before mealtimes. Need to investigate further to see if this is real & consistent.
  • I saw an ~15 mg/dL drop starting at 1p that persisted until 5:30p. 
    • This is the time period when I was trying to do the food effect tests and may be why I was seeing the weird drop in BG. 
    • This occurred 9 hours after my last dose of insulin (0.5u each of Novolog and Tresiba), so must be the result of something my body is doing. Is this drop from fasting (e.g. running out of glycogen) or something that occurs normally?
    • My BG stabilized at 65-75 mg/dL, which indicates that that range is something that can occur naturally and not due to medication. Given this, should I correct lows in this range or let them be? 
    • I always get tired around 2-3p, lasting until about 5-6p. I’ve always chalked this up to the end of the work day and then getting re-energized by dinner/being home, but maybe there’s more going on. Need to test interventions to eliminate this afternoon fatigue. 

Questions:

  • How consistent are the effects I observed? Which are due to fasting vs. effects that occur during a normal day?
  • Is the driving effect real? If so, are there ways to mitigate it? Even if it’s only a short-term effect, it could be causing fatigue or other reduced mental capacity while driving.
  • How can I mitigate the 10 mg/dL increase in the morning?
  • Is the afternoon drop connected with feeling tired and less mentally capable? If so, how can I mitigate the effect?

Next Experiments:

I’m always interested in ideas for new experiments, so please leave a comment if there’s something you’d like me to try.

  • Repeat this fasting experiment a couple more times to see if the observations are reproducible.
    • Also try fasting for shorter durations (single meals) to check if effects are from the duration of the fast vs. ones that would occur normally. 
  • Measure fatigue and/or mental acuity see how it correlates with time of day and BG.
  • Test an increase in morning insulin to reduce the effect of the dawn phenomenon.

Details

Purpose

To better understand trends in my blood glucose over the course of a day fasting and determine if there are trends or events that I should investigate further.

Design/Methods

General. Blood glucose was measured approximately every 15 min. using a FreeStyle Freedom Lite glucose meter and 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.

For medication, I took my normal morning and evening medication, but did not dose for meals.

Data

Link

Results & Discussion

See key learnings & questions above.

Conclusion & Next Experiments

See summary above.


– QD

Get new posts by email or rss feed