New Research on Glycemic Variability Assessment In Exocrine Pancreatic Insufficiency (EPI) and Type 1 Diabetes

I am very excited to share that a new article I wrote was just published, looking at glycemic variability in data from before and after pancreatic enzyme replacement therapy (PERT) was started in someone with type 1 diabetes with newly discovered exocrine pancreatic insufficiency (EPI or PEI).

If you’re not aware of exocrine pancreatic insufficiency, it occurs when the pancreas no longer produces the amount of enzymes necessary to fully digest food. If that occurs, people need supplementary enzymes, known as pancreatic enzyme replacement therapy (PERT), to help them digest their food. (You can read more about EPI here, and I have also written other posts about EPI that you can find at DIYPS.org/EPI.)

But, like MANY medications, when someone with type 1 diabetes or other types of insulin-requiring diabetes starts taking them, there is little to no guidance about whether these medications will change their insulin sensitivity or otherwise impact their blood glucose levels. No guidance, because there are no studies! In part, this may be because of the limited tools available at the time these medications were tested and approved for their current usage. Also this is likely in part because people with diabetes make up a small fraction of the study participants that most of these medications are tested on. If there are any specific studies on the medications in people with diabetes, these studies likely were done before CGM, so little data is available that is actionable.

As a result, the opportunity came up to review someone’s data who happened to have blood glucose data from a continuous glucose monitor (CGM) as well as a log of what was eaten (carbohydrate entries) prior to commencing pancreatic enzyme replacement therapy. The tracking continued after commencing PERT and was expanded to also include fat and protein entries. As a result, there was a useful dataset to compare the impacts of pancreatic enzyme replacement therapy on blood glucose outcomes and specifically, looking at glycemic variability changes!

(You can read an author copy here of the full paper and also see the supplementary material here, and the DOI for the paper is https://doi.org/10.1177/19322968221108414 . Otherwise, below is my summary of what we did and the results!)

In addition to the above background, it’s worth noting that Type 1 diabetes is known to be associated with EPI. In upwards of 40% of people with Type 1 diabetes, elastase levels are lowered, which in other cases is correlated with EPI. However, in T1D, there is some confusion as to whether this is always the case or not. Based on recent discussions with endocrinologists who treat patients with T1D and EPI (and have patients with lowered elastase that they think don’t have EPI), I don’t think there have been enough studies looking at the right things to assess whether people with T1D and lowered elastase levels would benefit from PERT and thus have EPI. More on this in the future!

Because we now have technology such as AID (automated insulin delivery) and CGM, it’s possible to evaluate things beyond simple metrics of “average blood sugar” or “A1c” in response to taking new medications. In this paper, we looked at some basic metrics like average blood sugar and percent time in range (TIR), but we also did quite a few calculations of variables that tell us more about the level of variability in glucose levels, especially in the time frames after meals.

Methods

This person had tracked carb entries through an open source AID system, and so carb entries and BG data were available from before they started PERT. We call this “pre-PERT”, and selected 4 weeks worth of data to exclude major holidays (as diet is known to vary quite a bit during those times). We then compared this to “post-PERT”, the first 4 weeks after the person started PERT. The post-PERT data not only included BGs and carb entries, but also had fat and protein entries as well as PERT data. Each time frame included 13,975 BG data points.

We used a series of open source tools to get the data (Nightscout -> Nightscout Data Transfer Tool -> Open Humans) and process the data (my favorite Unzip-Zip-CSVify-OpenHumans-data.sh script).

All of our code for this paper is open source, too! Check it out here. We analyzed time in range, TIR 70-180, time out of range, TOR >180, time below range, TBR <70 and <54, the number of hyperglycemic excursions >180. We also calculated total daily dose of insulin, average carbohydrate intake, and average carbohydrate entries per day. Then we calculated a series of variability related metrics such as Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), Coefficient of Variation (CV), Standard Deviation (SD), and J_index (which stresses both the importance of the mean level and variability of glycemic levels).

Results

This person already had an above-goal TIR. Standard of care goal for TIR is >70%; before PERT they had 92.12% TIR and after PERT it was 93.70%. Remember, this person is using an open source AID! TBR <54 did not change significantly, TBR <70 decreased slightly, and TOR >180 also decreased slightly.

More noticeably, the total number of unique excursions above 180 dropped from 40 (in the 4 weeks without PERT) to 26 (in 4 weeks when using PERT).

The paper itself has a few more details about average fat, protein, and carb intake and any changes. Total daily insulin was relatively similar, carb intake decreased slightly post-PERT but was trending back upward by the end of the 4 weeks. This is likely an artifact of being careful to adjust to PERT and dose effectively for PERT. The number of meals decreased but the average carb entry per meal increased, too.

What I find really interesting is the assessment we did on variability, overall and looking at specific meal times. The breakfast meal was identical during both time periods, and this is where you can really SEE visible changes pre- and post-PERT. Figure 2 (displayed below), shows the difference in the rate of change frequency. There’s less of the higher rate of changes (red) post-PERT than there is from pre-PERT (blue).

Figure 2 from GV analysis on EPI, showing lower frequency of high rate of change post-PERT

Similarly, figure 3 from the paper shows all glucose data pre- and post-PERT, and you can see the fewer excursions >180 (blue dotted line) in the post-PERT glucose data.

Figure 3 from GV analysis paper on EPI showing lower number of excursions above 180 mg/dL

Table 1 in the paper has all the raw data, and Figure 1 plots the most relevant graphs side by side so you can see pre- and post-PERT before and after after all meals on the left, versus pre and post-PERT before and after breakfast only. Look at TOR >180 and the reduction in post-breakfast levels after PERT! Similarly, HBGI post-PERT after-breakfast is noticeably different than HBGI pre-PERT after-breakfast.

Here’s a look at the HBGI for breakfast only, I’ve highlighted in purple the comparison after breakfast for pre- and post-PERT:

High Blood Glucose Index (HBGI) pre- and post-PERT for breakfast only, showing reduction in post-PERT after breakfast

Discussion

This is a paper looking at n=1 data, but it’s not really about the n=1 here. (See the awesome limitation section for more detail, where I point out it’s n=1, it’s not a clinical study, the person has ‘moderate’ EPI, there wasn’t fat/protein data from pre-PERT, it may not be representative of all people with diabetes with EPI or EPI in general.)

What this paper is about is illustrating the types of analyses that are possible, if only we would capture and analyze the data. There are gaping holes in the scientific knowledge base: unanswered and even unasked questions about what happens to blood glucose with various medications, and this data can help answer them! This data shows minimal changes to TIR but visible and significant changes to post-meal glycemic variability (especially after breakfast!). Someone who had a lower TIR or wasn’t using an open source AID may have more obvious changes in TIR following PERT commencement.

This paper shows several ways we can more easily detect efficacy of new-onset medications, whether it is enzymes for PERT or other commonly used medications for people with diabetes.

For example, we could do a similar study with metformin, looking at early changes in glycemic variability in people newly prescribed metformin. Wouldn’t it be great, as a person with diabetes, to be able to more quickly resolve the uncertainty of “is this even working?!” and not have to suffer through potential side effects for 3-6 months or longer waiting for an A1c lab test to verify whether the metformin is having the intended effects?

Specifically with regards to EPI, it can be hard for some people to tell if PERT “is working”, because they’re asymptomatic, they are relying on lab data for changes in fat soluble vitamin levels (which may take time to change following PERT commencement), etc. It can also be hard to get the dosing “right”, and there is little guidance around titrating in general, and no studies have looked at titration based on macronutrient intake, which is something else that I’m working on. So, having a method such as these types of GV analysis even for a person without diabetes who has newly discovered EPI might be beneficial: GV changes could be an earlier indicator of PERT efficacy and serve as encouragement for individuals with EPI to continue PERT titration and arrive at optimal dosing.

Conclusion

As I wrote in the paper:

It is possible to use glycemic variability to assess changes in glycemic outcomes in response to new-onset medications, such as pancreatic enzyme replacement therapy (PERT) in people with exocrine pancreatic insufficiency (EPI) and insulin-requiring diabetes. More studies should use AID and CGM data to assess changes in glycemic outcomes and variability to add to the knowledge base of how medications affect glucose levels for people with diabetes. Specifically, this n=1 data analysis demonstrates that glycemic variability can be useful for assessing post-PERT response in someone with suspected or newly diagnosed EPI and provide additional data points regarding the efficacy of PERT titration over time.

I’m super excited to continue this work and use all available datasets to help answer more questions about PERT titration and efficacy, changes to glycemic variability, and anything else we can learn. For this study, I collaborated with the phenomenal Arsalan Shahid, who serves as technology solutions lead at CeADAR (Ireland’s Centre for Applied AI at University College Dublin), who helped make this study and paper possible. We’re looking for additional collaborators, though, so feel free to reach out if you are interested in working on similar efforts or any other research studies related to EPI!

A DIY Fuel Enzyme Macronutrient Tracker for Running Ultras (Ultramarathons)

It takes a lot of energy to run ultramarathons (ultras).

To ensure they have enough fuel to complete the run, people usually want to eat X-Y calories per hour, or A-B carbs per hour, while running ultramarathons. It can be hard to know if you’re staying on top of fueling, especially as the hours drag on and your brain gets tired; plus, you can be throwing away your trash as you go so you may not have a pile of wrappers to tell you what you ate.

During training, it may be useful to have a written record of what you did for each run, so you can establish a baseline and work on improving your fueling if that’s something you want to focus on.

For me specifically, I also find it helpful to record what enzyme dosing I am taking, as I have EPI (exocrine pancreatic insufficiency, which you can read more about here) and if I have symptoms it can help me identify where my dosing might have been off from the previous day. It’s not only the amount of enzymes but also the timing that matters, alongside the timing of carbs and insulin, because I have type 1 diabetes, celiac, and EPI to juggle during runs.

Previously, I’ve relied on carb entries to Nightscout (an open source CGM remote monitoring platform which I use for visualizing diabetes data including OpenAPS data) as a record of what I ate, because I know 15g of carbs tracks to a single serving of chili cheese Fritos that are 10g of fat and 2g of protein, and I take one lipase-only and one pancrelipase (multi-enzyme) pill for that; and 21g of carbs is a Honey Stinger Gluten Free Stroopwaffle that is 6g of fat and 1g of protein, and I typically take one lipase-only. You can see from my most recent ultra (a 50k) where I manually took those carb entries and mapped them on to my blood sugar (CGM) graph to visualize what happened in terms of fuel and blood sugar over the course of my ultra.

However, that was “just” a 50k and I’m working toward bigger runs: a 50 mile, maybe a 100k (62 miles), and/or a 100 mile, which means instead of running for 7-8 hours I’ll be running for 12-14 and 24-30(ish) hours! That’s a lot of fuel to need to eat, and to keep track of, and I know from experience my brain starts to get tired of thinking about and eating food around 7 hours. So, I’ll need something better to help me keep track of fuel, enzymes, and electrolytes over the course of longer runs.

I also am planning on being well supported by my “crew” – my husband Scott, who will e-bike around the course of my ultra or my DIY ultra loops and refill my pack with water and fuel. In some cases, with a DIY ultra, he’ll be bringing food from home that I pre-made and he warms up in the microwave.

One of the strategies I want to test is for him to actually hand me the enzymes for the food he’s bringing me. For example, hand me a baggie of mashed potatoes and also hand me the one multi-enzyme (pancrelipase, OTC) pill I need to go with it. That reduces mental effort for me to look up or remember what enzyme amount I take for mashed potatoes; saves me from digging out my baggie of enzymes and having to get the pill out and swallow it, put the baggie away without dropping it, all while juggling the snack in my hands.

He doesn’t necessarily know the counts of enzymes for each fuel (although he could reproduce it, it’s better if I pre-make a spreadsheet library of my fuel options and that helps me both just pick it off a drop down and have an easy reference for him to glance at. He won’t be running 50-100 miles, but he will be waking up every 2-3 hours overnight and that does a number on his brain, too, so it’s easier all around if he can just reference the math I’ve already done!

So, for my purposes: 1) easy tracking of fuel counts for real-time “am I eating according to plan” and 2) retrospective “how did I do overall and should I do something next time” and 3) for EPI and BG analysis (“what should I do differently if I didn’t get the ideal outcome?”), it’s ideal to have a tracking spreadsheet to log my fuel intake.

Here’s what I did to build my ultimate fuel self-tracking self-populating spreadsheet:

First, I created a tab in my spreadsheet as a “Fuel Library”, where I listed out all of my fuel. This ranges from snacks (chili cheese Fritos; Honey Stinger Gluten Free Stroopwaffle; yogurt-covered pretzels, etc.); to fast-acting carbs (e.g. Airhead Minis, Circus Peanuts) that I take for fixing blood sugars; to other snack/treats like chocolate candy bars or cookies; as well as small meals and warm food, such as tomato soup or part of a ham and cheese quesadilla. (All gluten free, since I have celiac. Everything I ever write about is always gluten free!)

After I input the list of snacks, I made columns to input the sodium, calories, fat, protein, and carb counts. I don’t usually care about calories but a lot of recommendations for ultras are calories/hr and carbs/hr. I tend to be lower on the carb side in my regular daily consumption and higher on fat than most people without T1D, so I’m using the calories for ultrarunning comparison to see overall where I’m landing nutrient-wise without fixating on carbs, since I have T1D and what I personally prefer for BG management is likely different than those without T1D.

I also input the goal amount of enzymes. I have three different types of pills: a prescription pancrelipase (I call PERT, which stands for pancreatic enzyme replacement therapy, and when I say PERT I’m referring to the expensive, prescription pancrelipase that’s been tested and studied for safety and efficacy in EPI); an over-the-counter (OTC) lipase-only pill; and an OTC multi-enzyme pancrelipase pill that contains much smaller amounts of all three enzymes (lipase, protease, amylase) than my PERT but hasn’t been tested for safety and efficacy for EPI. So, I have three enzyme columns: Lipase, OTC Pancrelipase, and PERT. For each fuel I calculate which I need (usually one lipase, or a lipase plus a OTC pancrelipase, because these single servings are usually fairly low fat and protein; but for bigger meal-type foods with more protein I may ‘round up’ and choose to take a full PERT, especially if I eat more of it), and input the number in the appropriate column.

Then, I opened another tab on my spreadsheet. I created a row of headers for what I ate (the fuel); time; and then all the macronutrients again. I moved this down to row 3, because I also want to include at the top of the spreadsheet a total of everything for the day.

Example-DIY-Fuel-Enzyme-Tracker-ByDanaMLewis

In Column A, I selected the first cell (A4) for me, then went to Data > Data Validation and clicked on it. It opens this screen, which I input the following – A4 is the cell I’m in for “cell range”, the criteria is “list from a range”, and then I popped over to the tab with my ‘fuel library’ and highlighted the relevant data that I wanted to be in the menu: Food. So that was C2-C52 for my list of food. Make sure “show dropdown list in cell” is marked, because that’s what creates the dropdown in the cell. Click save.

Use the data validation section to choose to show a dropbox in each cell

You’ll want to drag that down to apply the drop-down to all the cells you want. Mine now looked like this, and you can see clicking the dropdown shows the menu to tap on.

Clicking a dropbox in the cell brings up the "menu" of food options from my Fuel Library tab

After I selected from my menu, I wanted column B to automatically put in the time. This gets obnoxious: google sheets has NOW() to put in the current time, but DO NOT USE THIS as the formula updates with the latest time any time you touch the spreadsheet.

I ended up having to use a google script (go to “Extensions” > Apps Script, here’s a tutorial with more detail) to create a function called onEdit() that I could reference in my spreadsheet. I use the old style legacy script editor in my screenshot below.

Older style app script editor for adding scripts to spreadsheet, showing the onEdit() function (see text below in post for what the script is)

Code I used, if you need to copy/paste:

function onEdit(e) {

var rr = e.range;

var ss = e.range.getSheet();

var headerRows = 2;  // # header rows to ignore

if (rr.getRow() <= headerRows) return;

var row = e.range.getRow();

var col = e.range.getColumn();

if(col == 1){

e.source.getActiveSheet().getRange(row,2).setValue(new Date());

}

}

After saving that script (File > Save), I went back to my spreadsheet and put this formula into the B column cells: =IFERROR(onEdit(),””). It fills in the current date/time (because onEdit tells it to if the A cell has been updated), and otherwise sits with a blank until it’s been changed.

Note: if you test your sheet, you’ll have to go back and paste in the formula to overwrite the date/time that gets updated by the script. I keep the formula without the “=” in a cell in the top right of my spreadsheet so I can copy/paste it when I’m testing and updating my sheet. You can also find it in a cell below and copy/paste from there as well.

Next, I wanted to populate my macronutrients on the tracker spreadsheet. For each cell in row 4, I used a VLOOKUP with the fuel name from A4 to look at the sheet with my library, and then use the column number from the fuel library sheet to reference which data element I want. I actually have things in a different order in my fuel library and my tracking sheet; so if you use my template later on or are recreating your own, pay attention to matching the headers from your tracker sheet and what’s in your library. The formula for this cell ended up being “=IFERROR(VLOOKUP(A4,’Fuel Library’!C:K,4, FALSE),””)”, again designed to leave the column blank if column A didn’t have a value, but if it does have a value, to prefill the number from Column 4 matching the fuel entry into this cell. Columns C-J on my tracker spreadsheet all use that formula, with the updated values to pull from the correctly matching column, to pre-populate my counts in the tracker spreadsheet.

Finally, the last thing I wanted was to track easily when I last ate. I could look at column B, but with a tired brain I want something more obvious that tracks how long it’s been. This also is again to maybe help Scott, who will be tasked with helping me stay on top of things, be able to check if I’m eating regularly and encourage me gently or less gently to be eating more as the hours wear on in my ultras.

I ended up creating a cell in the header that would track the last entry from column B. To do this, the formula I found is “INDEX(B4:B,MATCH(143^143,B4:B))”, which checks for the last number in column B starting in B4 and onward. It correctly pulls in the latest timestamp on the list.

Then, in another cell, I created “=NOW()-B2”, which is a good use for the NOW() formula I warned about, because it’s constantly updating every time the sheet gets touched, so that any time I go to update it’ll tell me how long it’s been since between now and the last time I ate.

But, that only updates every time I update the sheet, so if I want to glance at the sheet, it will be only from the last time I updated it… which is not what I want. To fix it, I need to change the autorefresh calculation settings. Go to File > Settings. Click “Calculations” tab, and the first drop down you want to change to be “On change and every minute”.

Under File > Settings there is a "Calculate" tab with a dropdown menu to choose to update on change plus every minute

Now it does what I want, updating that cell that uses the NOW() formula every minute, so this calculation is up to date even when the sheet hasn’t been changed!

However, I also decided I want to log electrolytes in my same spreadsheet, but not include it in my top “when did I last eat” calculator. So, I created column K and inserted the formula IF(A4=”Electrolytes”,””,B4), which checks to see if the Dropdown menu selection was Electrolytes. If so, it doesn’t do anything. If it’s not electrolytes, it repeats the B4 value, which is my formula to put the date and time. Then, I changed B2 to index and match on column K instead of B. My B2 formula now is INDEX(K4:K,MATCH(143^143,K4:K)), because K now has the food-only list of date and time stamps that I want to be tracking in my “when did I last eat” tracker. (If you don’t log electrolytes or don’t have anything else to exclude, you can keep B2 as indexing and matching on column B. But if you want to exclude anything, you can follow my example of using an additional column (my K) to check for things you do want to include and exclude the ones you don’t want. Also, you can hide columns if you don’t want to see them, so column K (or your ‘check for exclusions’ column wherever it ends up) could be hidden from view so it doesn’t distract your brain.

I also added conditional formatting to my tracker. Anytime A2, the time since eaten cell, is between 0-30 minutes, it’s green: indicating I’m on top of my fueling. 30-45 minutes it turns yellow as a warning that it’s time to eat. After 45 minutes, it’ll turn light red as a strong reminder that I’m off schedule.

I kept adding features, such as totaling my sodium consumption per hour, too, so I could track electrolytes+fuel sodium totals. Column L gets the formula =IF(((ABS((NOW()-B4))*1440)<60),F4,””) to check for the difference between the current time and the fuel entry, multiplying it by 1440 to convert to minutes and checking to see that it’s less than 60 minutes. If it is, then it prints the sodium value, and otherwise leaves it blank. (You could skip the ABS part as I was testing current, past, and future values and wanted it to stop throwing errors for future times that were calculated as negatives in the first argument). I then in C2 calculate the sum of those values for the total sodium for that hour, using =SUM(L4:L)

(I thought about tracking the past sodium per hour values to average and see how I did throughout the run on an hourly basis…but so far on my 3 long runs where I’ve used the spreadsheet, the very fact that I am using the tracker and glancing at the hourly total has kept me well on top of sodium and so I haven’t need that yet. However, if I eventually start to have long enough runs where this is an issue, I’ll probably go back and have it calculate the absolute hour sodium totals for retrospective analysis.)

This works great in the Google Sheets app on my phone, which is how I’ll be updating it during my ultras, although Scott can have it open on a browser tab when he’s at home working at his laptop. Every time I go for a long training run, I duplicate the template tab and label it with the date of the run and use it for logging my fueling.

(PS – if you didn’t know, you can rearrange the order of tabs in your sheet, so you can drag the one you want to be actively using to the left. This is useful in case the app closes on your phone and you’re re-opening the sheet fresh, so you don’t have to scroll to re-find the correct tab you want to be using for that run. In a browser, you can either drag and drop the tabs, or click the arrow next to the tab name and select “move left” or “move right”.)

Clicking the arrow to the right of a tab name in google sheets brings up a menu that includes the option to move the tab left or right

Click here to make a copy of my spreadsheet.

If you click to make a copy of a google spreadsheet, it pops up a link confirming you want to make a copy, and also might bring the app script functionality with it. If so, you can click the button to view the script (earlier in the blog post). If it doesn't include the warning about the script, remember to add the script yourself after you make a copy.

Take a look at my spreadsheet after you make a copy (click here to generate a copy if you didn’t use the previous mentioned link), and you’ll note in the README tab a few reminders to modify the fuel library and make sure you follow the steps to ensure that the script is associated with the sheet and validation is updated.

Obviously, you may not need lipase/pancrelipase/PERT and enzyme counts; if you do, your counts of enzymes needed and types of enzyme and quantity of enzymes will need updating; you may not need or want all of these macronutrients; and you’ll definitely be eating different fuel than I am, so you can update it however you like with what you’re eating and what you want to track.

This spreadsheet and the methods for building it can also be used for other purposes, such as tracking wait times or how long it took you to do something, etc.

(If you do find this blog post and use this spreadsheet concept, let me know – I’d love to hear if this is useful for you!)

2022 Strawberry Fields Forever Ultramarathon Race Report Recap

I recently ran my second-ever 50k ultramarathon. This is my attempt to provide a race recap or “race report”, which in part is to help people in the future considering this race and this course. (I couldn’t find a lot of race reports investigating this race!)

It’s also an effort to provide an example of how I executed fueling, enzyme dosing (because I have exocrine pancreatic insufficiency, known as EPI), and blood sugar management (because I have type 1 diabetes), because there’s also not a lot of practical guidance or examples of how people do this. A lot of it is individual, and what works for me won’t necessarily work for anyone, but if anything hopefully it will help other people feel not alone as they work to figure out what works for them!

Context of my running and training in preparation

I wrote quite a bit in this previous post about my training last year for a marathon and my first 50k. Basically, I’m slow, and I also choose to run/walk for my training and racing. This year I’ve been doing 30:60 intervals, meaning I run 30 seconds and walk 60 seconds.

Due to a combination of improved training (and having a year of training last year), as well as now having recognized I was not getting sufficient pancreatic enzymes so that I was not digesting and using the food I was eating effectively, this year has been going really well. I ended up training as far as a practice 50k about 5 weeks out from my race. I did several more mid- to high-20 mile runs as well. I also did a next-day run following my long runs, starting around 3-4 miles and eventually increasing to 8 miles the day after my 50k. The goal of these next-day runs was to practice running on tired legs.

Overall, I think this training was very effective for me. My training runs were easy paced, and I always felt like I could run more after I was done. I recovered well, and the next-day runs weren’t painful and I did not have to truncate or skip any of those planned runs. (Previous years, running always felt hard and I didn’t know what it was like to recover “well” until this year.) My paces also increased to about a minute/mile faster than last year’s easy pace. Again, that’s probably a combination of increased running overall and better digestion and recovery.

Last year I chose to run a marathon and then do a 50k while I was “trained up” for my marathon. This year, I wanted to do a 50k as a fitness assessment on the path to a 50 mile race this fall. I looked for local-ish 50k options that did not have much elevation, and found the Strawberry Fields Forever Ultra.

Why I chose this race, and the basics about this race

The Strawberry Fields Forever Ultra met most of my goal criteria, including that it was around the time that I wanted to run a 50k, so that I had almost 6 months to train and also before it got to be too hot and risked being during wildfire smoke season. (Sadly, that’s a season that now overlaps significantly with the summers here.) It’s local-ish, meaning we could drive to it, although we did spend the night before the race in the area just to save some stress the morning of the race. The race nicely started at 9am, and we drove home in the evening after the race.

The race is on a 10k (6.2 miles) looped course in North Bonneville, Washington, and hosted a 10k event (1 lap), a 50k event (5 laps), and also had 100k (10 laps) or (almost) 100 miles (16 laps). It does have a little bit of elevation – or “little” by ultramarathon standards. The site and all reports describe one hill and net 200 feet of elevation gain and loss. I didn’t love the idea of a 200 foot hill, but thought I could make do. It also describes the course as “grass and dirt” trails. You’ll see a map later where I’ve described some key points on the course, and it’s also worth noting that this course is very “crew-able”. Most people hang out at the start/finish, since it’s “just” a 10k loop and people are looping through pretty frequently. However, if you want to, either for moral or practical support, crew could walk over to various points, or my husband brought his e-bike and biked around between points on the course very easily using a mix of the other trails and actual roads nearby.

The course is well marked. Any turn had a white sign with a black arrow on it and also white arrows drawn on the ground, and there were dozens of little red/pink fluorescent flags marking the course. Any time there was a fork in the path, these flags (usually 2-3 for emphasis, which was excellent for tired brains) would guide you to the correct direction.

The nice thing about this race is it includes the 100 mile option and that has a course limit of 30 hours, which means all the other distances also have this course limit of 30 hours. That’s fantastic when a lot of 50k or 50 mile (or 100k, which is 62 miles) courses might have 12 hour or similar tighter course limits. If you wanted to have a nice long opportunity to cover the distance, with the ability to stop and rest (or nap/sleep), this is a great option for that.

With the 50k, I was aiming to match or ideally beat my time from my first 50k, recognizing that this course is harder given the terrain and hill. However, I think my fitness is higher, so beating that time even with the elevation gain seemed reasonable.

Special conditions and challenges of the 2022 Strawberry Fields Forever Ultramarathon

It’s worth noting that in 2021 there was a record abnormal heat wave due to a “heat dome” that made it 100+ degrees (F) during the race. Yikes. I read about that and I am not willing to run a race when I have not trained for that type of heat (or any heat), so I actually waited until the week before the race to officially sign up after I saw the forecast for the race. The forecast originally was 80 F, then bounced around mid 60s to mid 70s, all of which seemed doable. I wouldn’t mind some rain during the race, either, as rainy 50s and 60s is what I’ve been training in for months.

But just to make things interesting, for the 2022 event the Pacific Northwest got an “atmospheric river” that dumped inches of rain on Thursday..and Friday. Gulp. Scott and I drove down to spend the night Friday night before the race, and it was dumping hard rain. I began to worry about the mud that would be on the course before we even started the race. However, the rain finished overnight and we woke up to everything being wet, but not actively raining. It was actually fairly warm (60s), so even if it drizzled during the race it wouldn’t be chilly.

During the start of the race, the race director said we would get wet and joked (I thought) about practicing our backstroke. Then the race started, and we took off.

My race recap / race report the 2022 Strawberry Fields Forever Ultramarathon

I’ve included a picture below that I was sent a month or so before the race when I asked for a course map, and a second picture because I also asked for the elevation profile. I’ve marked with letters (A-I) points on the course that I’ll describe below for reference, and we ran counterclockwise this year so the elevation map I’ve marked with matching letters where “A” is on the right and “I” is on the left, matching how I experienced the course.

The course is slightly different in the start/finish area, but otherwise is 95% matching what we actually ran, so I didn’t bother grabbing my actual course map from my run since this one was handy and a lot cleaner than my Runkeeper-derived map of the race.

Annotated course map with points A-I
StrawberryFieldsForever-Ultra-Elevation-Profile

My Runkeeper elevation profile of the 50k (5 repeated laps) looked like this:
Runkeeper elevation profile of 5 loops on the Strawberry Fields Forever 50k course

I’ll describe my first experience through the course (Lap 1) in more detail, then a couple of thoughts about the experiences of the subsequent laps, in part to describe fueling and other choices I made.

Lap 1:

We left the start by running across the soccer field and getting on a paved path that hooked around the ballfield and then headed out a gate and up The Hill. This was the one hill I thought was on the course. I ran a little bit and passed a few people who walked on a shallower slope, then I also converted to a walk for the rest of the hill. It was the most crowded race start I’ve done, because there were so many people (150 across the 10k, 50k, 100k, and 100 miler) and such a short distance between the start and this hill. The Hill, as I thought of it, is point A on the course map.

Luckily, heading up the hill there are gorgeous purple wildflowers along the path and mountain views. At the top of the hill there are some benches at the point where we took a left turn and headed down the hill, going down the same elevation in about half a mile so it was longer than the uphill section. This downhill slope (B) was very runnable and gravel covered, whereas going up the hill was more dirt and mud.

At the bottom of the hill, there was a hairpin turn and we turned and headed back up the hill, although not all the way up, and more along a plateau in the side of the hill. The “plateau” is point C on the map. I thought it would be runnable once I got back up the initial hill, but it was mud pit after mud pit, and I would have two steps of running in between mud pits to carefully walk through. It was really frustrating. I ended up texting to my parents and Scott that it was about 1.7 miles of mud (from the uphill, and the plateau) before I got to some gravel that was more easily runnable. Woohoo for gravel! This was a nice, short downhill slope (D) before we flattened out and switched back to dirt and more mud pits.

This was the E area, although it did feel more runnable than the plateau because there were longer stretches between muddy sections.

Eventually, we saw the river and came out from the trail into a parking lot and then jogged over onto the trail that parallels the river for a while. This trail that I thought of as “River Road” (starting around point F) is just mowed grass and is between a sharp bluff drop with opening where people would be down at the river fishing, and in some cases we were running *underneath* fishing lines from the parking spots down to the river! There were a few people who would be walking back and forth from cars to the river, but in general they were all very courteous and there was no obstruction of the trail. Despite the mowed grass aspect of the trail, this stretch physically and psychologically felt easier because there were no mud pits for 90% of it. Near the end there were a few muddy areas right about the point we hopped back over into the road to connect up a gravel road for a short spurt.

This year, the race actually put a bonus aid station out here. I didn’t partake, but they had a tent up with two volunteers who were cheerful and kind to passing runners, and it looked like they had giant things of gatorade or water, bottled water, and some sugared soda. They probably had other stuff, but that’s just what I saw when passing.

After that short gravel road bit, we turned back onto a dirt trail that led us to the river. Not the big river we had been running next to, but the place where the Columbia River overflowed the trail and we had to cross it. This is what the race director meant by practicing our backstroke.

You can see a video in this tweet of how deep and far across you had to get in this river crossing (around point G, but hopefully in future years this isn’t a point of interest on the map!!)

Showing a text on my watch of my BIL warning me about a river crossing

Coming out of the river, my feet were like blocks of ice. I cheered up at the thought that I had finished the wet feet portion of the course and I’d dry off before I looped back around and hit the muddy hill and plateau again. But, sadly, just around the next curve, came a mud POND. Not a pit, a pond.

Showing how bad the mud was

Again, ankle deep water and mud, not just once but in three different ponds all within 30 seconds or so of each other. It was really frustrating, and obviously you can’t run through them, so it slowed you down.

Then finally after the river crossing and the mud ponds, we hooked a right into a nice, forest trail that we spent about a mile and a half in (point H). It had a few muddy spots like you would normally expect to get muddy on a trail, but it wasn’t ankle deep or water filled or anything else. It was a nice relief!

Then we turned out of the forest and crossed a road and headed up one more (tiny, but it felt annoying despite how small it looks on the elevation profile) hill (point I), ran down the other side of that slope, stepped across another mud pond onto a pleasingly gravel path, and took the gravel path about .3 miles back all the way to complete the first full lap.

Phew.

I actually made pretty good time the first loop despite not knowing about all the mud or river crossing challenges. I was pleased with my time which was on track with my plan. Scott took my pack about .1 miles before I entered the start/finish area and brought it back to me refilled as I exited the start/finish area.

Lap 2:

The second lap was pretty similar. The Hill (A) felt remarkably harder after having experienced the first loop. I did try to run more of the downhill (B) as I recognized I’d make up some time from the walking climb as well as knowing I couldn’t run up the plateau or some of the mud pits along the plateau (C) as well as I had expected. I also decided running in the mud pits didn’t work, and went with the safer approach of stepping through them and then running 2 steps in between. I was a little slower this time, but still a reasonable pace for my goals.

The rest of the loop was roughly the same as the first, the mud was obnoxious, the river crossing freezing, the mud obnoxious again, and relief at running through the forest.

Scott met me at the end of the river road and biked along the short gravel section with me and went ahead so he could park his bike and take video of my second river crossing, which is the video above. I was thrilled to have video of that, because the static pictures of the river crossing didn’t feel like it did the depth and breadth of the water justice!

At the end of lap 2, Scott grabbed my pack again at the end of the loop and said he’d figured out where to meet me to give it back to me after the hill…if I wanted that. Yes, please! The bottom of the hill where you hairpin turn to go back up the plateau is the 1 mile marker point, so that means I ran the first mile of the third lap without my pack, and not having the weight of my full pack (almost 3L of water and lots of snacks and supplies: more on that pack below) was really helpful for my third time up the hill. He met me as planned at the bottom of the downhill (B) and I took my pack back which made a much nicer start to lap 3.

Lap 3:

Lap 3 for some reason I came out of the river crossing and the mud ponds feeling like I got extra mud in my right shoe. It felt gritty around the right side of my right food, and I was worried about having been running for so many hours with soaked feet. I decided to stop at a bench in the forest section and swap for dry socks. In retrospect, I wish I had stopped somewhere else, because I got swarmed by these moth/gnat/mosquito things that looked gross (dozens on my leg within a minute of sitting there) that I couldn’t brush off effectively while I was trying to remove my gaiters, untie my shoes, take my shoes off, peel my socks and bandaids and lambs wool off, put lubrication back on my toes, put more lambs wool on my toes, put the socks and shoes back on, and re-do my gaiters. Sadly, it took me 6 minutes despite me moving as fast as I could to do all of those things (this was a high/weirdly designed bench in a shack that looked like a bus stop in the middle of the woods, so it wasn’t the best way to sit, but I thought it was better than sitting on the ground).

(The bugs didn’t hurt me at the time, but two days later my dozens of bites all over my leg are red and swollen, though thankfully they only itch when they have something chafing against them.)

Anyway, I stood up and took off again and was frustrated knowing that it had taken 6 minutes and basically eaten the margin of time I had against my previous 50k time. I saw Scott about a quarter of a mile later, and I saw him right as I realized I had also somewhere lost my baggie of electrolyte pills. Argh! I didn’t have back up for those (although I had given Scott backups of everything else), so that spiked my stress levels as I was due for some electrolytes and wasn’t sure how I’d do with 3 or so more hours without them.

I gave Scott my pack and tasked him with checking my brother-in-law’s setup to see if he had spare electrolytes, while he was refilling my pack to give me in lap 4.

Lap 4:

I was pretty grumpy given the sock timing and the electrolyte mishap as I headed into lap 4. The hill still sucked, but I told myself “only one more hill after this!” and that thought cheered me up.

Scott had found two electrolyte options from my brother-in-law and brought those to me at the end of mile 1 (again, bottom of B slope) with my pack. He found two chewable and two swallow pills, so I had options for electrolytes. I chewed the first electrolyte tab as I headed up the plateau, and again talked myself through the mud pits with “only one more time through the mud pits after this!”.

I also tried overall to bounce back from the last of mile 4 where I let myself get frustrated, and try to take more advantage of the runnable parts of the course. I ran downhill (B) more than the previous laps, mostly ignoring the audio cues of my 30:60 intervals and probably running more like 45:30 or so. Similarly, the downhill gravel after the mud pits (D) I ran most of without paying attention to the audio run cues.

Scott this time also met me at the start of the river road section, and I gave him my pack again and asked him to take some things out that he had put in. He put in a bag with two pairs of replacement socks instead of just one pair of socks, and also put in an extra beef stick even though I didn’t ask for it. I asked him to remove it, and he did, but explained he had put it in just in case he didn’t find the electrolytes because it had 375g of sodium. (Sodium is primarily the electrolyte I am sensitive to and care most about). So this was actually a smart thing, although because I haven’t practiced eating larger amounts of protein and experienced enzyme dosing for it on the run, I would be pretty nervous about eating it in a race, so that made me a bit unnecessarily grumpy. Overall though, it was great to see him extra times on the course at this point, and I don’t know if he noticed how grumpy I was, but if he did he ignored it and I cheered up again knowing I only had “one more” of everything after this lap!

The other thing that helped was he biked my pack down the road to just before the river crossing, so I ran the river road section like I did lap 3 and 4 on the hill, without a pack. This gave me more energy and I found myself adding 5-10 seconds to the start of my run intervals to extend them.

The 4th river crossing was no less obnoxious and cold, but this time it and the mud ponds didn’t seem to embed grit inside my shoes, so I knew I would finish with the same pair of socks and not need another change to finish the race.

Lap 5:

I was so glad I was only running the 50k so that I only had 5 laps to do!

For the last lap, I was determined to finish strong. I thought I had a chance of making up a tiny bit of the sock change time that I had lost. I walked up the hill, but again ran more than my scheduled intervals downhill, grabbed my bag from Scott, picked my way across the mud pits for the final time (woohoo!), ran the downhill and ran a little long and more efficiently on the single track to the river road.

Scott took my pack again at the river road, and I swapped my intervals to be 30:45, since I was already running closer to that and I knew I only had 3.5 or so miles to go. I took my pack back at the end of river road and did my last-ever ice cold river crossing and mud pond extravaganza. After I left the last mud pond and turned into the forest, I switched my intervals to 30:30. I managed to keep my 30:30 intervals and stayed pretty quick – my last mile and a half was the fastest of the entire race!

I came into the finish line strong, as I had hoped to finish. Woohoo!

Overall strengths and positives from the race

Overall, running-wise I performed fairly well. I had a strong first lap and decent second lap, and I got more efficient on the laps as I went, staying focused and taking advantage of the more runnable parts of the course. I finished strong, with 30:45 intervals for over a mile and 30:30 intervals for over a mile to the finish.

Also, I didn’t quit after experiencing the river crossing and the mud ponds and the mud pits of the first lap. This wasn’t an “A” race for me or my first time at the distance, so it would’ve been really easy to quit. I probably didn’t in part because we did pay to spend the night before and drove all that way, and I didn’t want to have “wasted” Scott’s time by quitting, when I was very capable of continuing and wasn’t injured. But I’m proud of mostly the way I handled the challenges of the course, and for how I readjusted from the mental low and frustration after realizing how long my sock change took in lap 3. I’m also pleased that I didn’t get injured, given the terrain (mud, river crossing, and uneven grass to run on for most of the course). I’m also pleased and amazed I didn’t hurt my feet, cause major blisters, or have anything really happen to them after hours of wet, muddy, never-drying-off feet.

The huge positive was my fueling, electrolytes, and blood glucose management.

I started taking my electrolyte pills that have 200+mg of sodium at about 45 minutes into the race, on schedule. My snack choices also have 100-150mg of sodium, which allowed me to not take electrolyte pills as often as I would otherwise need to (or on a hotter day with more sweat – it was a damp, mid-60s day but I didn’t sweat as much as I usually do). But even with losing my electrolytes, I used two chewable 100mg sodium electrolytes instead and otherwise ended up with sufficient electrolytes. Even with ideal electrolyte supplementation, I’m very sensitive to sodium losses and am a salty sweater, and I have a distinct feeling when my electrolytes are insufficient, so not having that feeling during after the race was a big positive for me.

So was my fueling overall. The race started at 9am, and I woke up at 6am to eat my usual pre-race breakfast (a handful of pecans, plus my enzyme supplementation) so that it would both digest effectively and also be done hitting my blood sugar by the time the race started. My BGs were flat 120s or 130s when I started, which is how I like them. I took my first snack about an hour and 10 minutes into the race, which is about 15g carb (10g fat, 2g protein) of chili cheese flavored Fritos. For this, I didn’t dose any insulin as I was in range, and I took one lipase-only enzyme (which covers about 8g of fat for me) and one multi-enzyme (that covers about 6g of fat and probably over a dozen grams of protein). My second snack was an hour later, when I had a gluten free salted caramel Honey Stinger stroopwaffle (21g carb, 6 fat, 1 protein). For the stroopwaffle I ended up only taking a lipase-only pill to cover the fat, even though there’s 1g of protein. For me, I seem to be ok (or have no symptoms) from 2-3g of uncovered fat and 1-2g of uncovered protein. Anything more than that I like to dose enzymes for, although it depends on the situation. Throughout the day, I always did 1 lipase-only and 1 multi-enzyme for the Fritos, and 1 lipase-only for the stroopwaffle, and that seemed to work fine for me. I think I did a 0.3u (less than a third of the total insulin I would normally need) bolus for my stroopwaffle because I was around 150 mg/dL at the time, having risen following my un-covered Frito snack, and I thought I would need a tiny bit of insulin. This was perfect, and I came back down and flattened out. An hour and 20 minutes after that, I did another round of Fritos. An hour or so after that, a second stroopwaffle – but this time I didn’t dose any insulin for it as my BG was on a downward slope. An hour later, more Fritos. A little bit after that, I did my one single sugar-only correction (an 8g carb Airhead mini) as I was still sliding down toward 90 mg/dL, and while that’s nowhere near low, I thought my Fritos might hit a little late and I wanted to be sure I didn’t experience the feeling of a low. This was during the latter half of loop 4 when I was starting to increase my intensity, so I also knew I’d likely burn a little more glucose and it would balance out – and it did! I did one last round of Fritos during lap 5.
CGM graph during 50k ultramarathon

This all worked perfectly. I had 100% time in range between 90 and 150 mg/dL, even with 102g of “real food” carbs (15g x 4 servings of Fritos, 21g x 2 waffles), and one 8g Airhead mini, so in total I had 110g grams of carbs across ~7+ hours. This perfectly matched my needs with my run/walk moderate efforts.

BG and carb intake plotted along CGM graph during 50k ultramarathon

I also nailed the enzymes, as during the race I didn’t have any GI-related symptoms and after the race and the next day (which is the ultimate verdict for me with EPI), no symptoms.

So it seems like my practice and testing with low carbs, Fritos, and waffles worked out well! I had a few other snacks in my pack (yogurt-covered pretzels, peanut butter pretzel nuggets), but I never thought of wanting them or wanting something different. I did plan to try to do 2 snacks per hour, but I ended up doing about 1 per hour. I probably could have tolerated more, but I wasn’t hungry, my BGs were great, and so although it wasn’t quite according to my original plan I think this was ideal for me and my effort level on race day.

The final thing I think went well was deciding on the fly after loop 2 to have Scott take my pack until after the hill (so I ran the up/downhill mile without it), and then for additional stretches along river road in laps 4 and 5. I had my pocket of my shorts packed with dozens of Airheads and mints, so I was fine in terms of blood sugar management and definitely didn’t need things for a mile at a time. I’m usually concerned about staying hydrated and having water whenever I want to sip, plus for swallowing electrolytes and enzyme pills to go with my snacks, but I think on this course with the number of points Scott could meet me (after B, at F all through G, and from I to the finish), I could have gotten away with not having my pack the whole time; having WAY less water in the pack (I definitely didn’t need to haul 3L the whole time, that was for when I might not see Scott every 2-3 laps) and only one of each snack at a time.

Areas for improvement from my race

I trained primarily on gravel or paved trails and roads, but despite the “easy” elevation profile and terrain, this was essentially my first trail ultra. I coped really well with the terrain, but the cognitive burden of all the challenges (Mud pits! River crossing! Mud ponds!) added up. I’d probably do a little more trail running and hills (although I did some) in the final weeks before the race to help condition my brain a little more.

I’ll also continue to practice fueling so I can eat more regularly than every hour to an hour and a half, even though this was the most I’ve ever eaten during a run, I did well with the quantities, and my enzyme and BG management were also A+. But I didn’t eat as much as I planned for, and I think that might’ve helped with the cognitive fatigue, too, by at least 5-10%.

I also now have the experience of a “stop” during a race, in this case to swap my socks. I’ve only run one ultra and never stopped before to do gear changes, so that experience probably was sufficient prep for future stops, although I do want to be mentally stronger/less frustrated by unanticipated problem solving stops.

Specific to this course, as mentioned above, I could’ve gotten away with less supplies – food and water – in my pack. I actually ran a Ragnar relay race with a group of fellow T1s a few years back where I finished my run segment and…no one was there to meet me. They went for Starbucks and took too long to get there, so I had to stand in the finishing chute waiting for 10-15 minutes until someone showed up to start the next run leg. Oh, and that happened in two of the three legs I ran that day. Ooof. Standing there tired, hot, with nothing to eat or drink, likely added to my already life-with-type-1-diabetes-driven-experiences of always carrying more than enough stuff. But I could’ve gotten away very comfortably with carrying 1L of water and one set of each type of snacks at a time, given that Scott could meet me at 1 mile (end of B), start (F) and end of river road (before G), and at the finish, so I would never have been more than 2-2.5 miles without a refill, and honestly he could’ve gotten to every spot on the trail barring the river crossing bit to meet me if I was really in need of something. Less weight would’ve made it easier to push a little harder along the way. Basically, I carried gear like I was running a solo 30 mile effort at a time, which was safe but not necessary given the course. If I re-ran this race, I’d feel a lot more comfortable with minimal supplies.

Surprises from my race

I crossed the finish line, stopped to get my medal, then was waiting for my brother-in-law to finish another lap (he ran the 100k: 62 miles) before Scott and I left. I sat down for 30 minutes and then walked to the car, but despite sitting for a while, I was not as stiff and sore as I expected. And getting home after a 3.5 hour car ride…again I was shocked at how minimally stiff I was walking into the house. The next morning? More surprises at how little stiff and sore I was. By day 3, I felt like I had run a normal week the week prior. So in general, I think this is reinforcement that I trained really well for the distance and my long runs up to 50k and the short to medium next day runs also likely helped. I physically recovered well, which is again part training but also probably better fueling during the race, and of course now digesting everything that I ate during and after the race with enzyme supplementation for EPI!

However, the interesting (almost negative, but mostly interesting) thing for me has been what I perceived to be adrenal-type fatigue or stress hormone fatigue. I think it’s because I was unused to focusing on challenging trail conditions for so many hours, compared to running the same length of hours on “easy” paved or gravel trails. I actually didn’t listen to an audiobook, music, or podcast for about half of the race, because I was so stimulated by the course itself. What I feel is adrenal fatigue isn’t just being physically or mentally tired but something different that I haven’t experienced before. I’m listening to my body and resting a lot, and I waited until day 4 to do my first easy, slow run with much longer walk intervals (30s run, 90s walk instead of my usual 30:60). Day 1 and 2 had a lot of fatigue and I didn’t feel like doing much, Day 3 had notable improvement on fatigue and my legs and body physically felt back to normal for me. Day 4 I ran slowly, Day 5 I stuck with walking and felt more fatigue but no physical issues, Day 6 again I chose to walk because I didn’t feel like my energy had fully returned. I’ll probably stick with easy, longer walk interval runs for the next week or two with fewer days running until I feel like my fatigue is gone.

General thoughts about ultramarathon training and effective ultra race preparation

I think preparation makes a difference in ultramarathon running. Or maybe that’s just my personality? But a lot of my goal for this race was to learn what I could about the course and the race setup, imagine and plan for the experience I wanted, plan for problem solving (blisters, fuel, enzymes, BGs, etc), and be ready and able to adapt while being aware that I’d likely be tired and mentally fatigued. Generally, any preparation I could do in terms of deciding and making plans, preparing supplies, etc would be beneficial.

Some of the preparation included making lists in the weeks prior about the supplies I’d need in my pack, what Scott should have to refill my pack, what I’d need the night and morning before since we would not be at home, and after-race supplies for the 3.5h drive home.

From the lists, the week before the race I began grouping things. I had my running pack filled and ready to go. I packed my race outfit in a gallon bag, a full set of backup clothes in another gallon bag and labeled them, along with a separate post-run outfit and flip flops for the drive home. I also included a washcloth for wiping sweat or mud off after the run, and I certainly ended up needing that! I packed an extra pair of shoes and about 4 extra pairs of socks. I also had separate baggies with bandaids of different sizes, pre-cut strips of kinesio tape for my leg and smaller patches for blisters, extra squirrel nut butter sticks for anti-chafing purposes, as well as extra lambs wool (that I lay across the top of my toes to prevent socks from rubbing when they get wet from sweat or…river crossings, plus I can use it for padding between my toes or other blister-developing spots). I had sunscreen, bug spray, sungless, rain hat, and my sunny-weather running visor that wicks away sweat. I had low BG carbs for me to put in my pockets, a backup bag for Scott to refill, and a backup to the backup. The same for my fuel stash: my backpack was packed, I packed a small baggie for Scott as well as a larger bag with 5-7 of everything I thought I might want, and also an emergency backup baggie of enzymes.

*The only thing I didn’t have was a backup baggie of electrolyte pills. Next time, I’ll add this to my list and treat them like enzymes to make sure I have a separate backup stash.

I even made a list and gave it to Scott that mapped out where key things were for during and after the race. I don’t think he had to use it, because he was only digging through the snack bag for waffles and Fritos, but I did that so I didn’t have to remember where I had put my extra socks or my spare bandaids, etc. He basically had a map of what was in each larger bag. All of this was to reduce the decision and communication because I knew I’d have decision fatigue.

This also went for post-race planning. I told Scott to encourage me to change clothes, and it was worth the energy to change so I didn’t sit in cold, wet clothes for the long drive home. I pre-made a gluten free ham and cheese quesadilla (take two tortillas, fill with shredded cheese and slices of ham, microwave, cut into quarters, stick in baggies, mark with fat/protein/carb counts, and refrigerate) so we could warm that up in the car (this is what I use) so I had something to eat on the way home that wasn’t more Fritos or waffles. I didn’t end up wanting it, but I also brought a can of beef stew with carrots and potatoes, that I generally like as a post-race or post-run meal, and a plastic container and a spoon so I could warm up the stew if I wanted it. Again, all of this pre-planned and put on the list weeks prior to the race so I didn’t forget things like the container or the spoon.

The other thing I think about a lot is practicing everything I want to do for a race during a training run. People talk about eating the same foods, wearing the same clothes, etc. I think for those of us with type 1 diabetes (or celiac, EPI, or anything else), it’s even more important. With T1D, it’s so helpful to have the experience adjusting to changing BG levels and knowing what to do when you’re dropping or low and having a snack, vs in range and having a fueling snack, or high and having a fueling snack. I had 100% TIR during this run, but I didn’t have that during all of my training runs. Sometimes I’d plateau around 180 mg/dL and be over-cautious and not bring my BGs down effectively; other times I’d overshoot and cause a drop that required extra carbs to prevent or minimize a low. Lots of practice went into making this 100% TIR day happen, and some of it was probably a bit of luck mixed in with all the practice!

But generally, practice makes it a lot easier to know what to do on the fly during a race when you’re tired, stressed, and maybe crossing an icy cold river that wasn’t supposed to be part of your course experience. All that helps you make the best possible decisions in the weirdest of situations. That’s the best you can hope for with ultrarunning!

Findings from the world’s first RCT on open source AID (the CREATE trial) presented at #ADA2022

September 7, 2022 UPDATEI’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or view an author copy here. You can also see a Twitter thread here, if you are interested in sharing the study with your networks.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NEJMoa2203913


(You can also see a previous Twitter thread here summarizing the study results, if you are interested in sharing the study with your networks.)

TLDR: The CREATE Trial was a multi-site, open-labeled, randomized, parallel-group, 24-week superiority trial evaluating the efficacy and safety of an open-source AID system using the OpenAPS algorithm in a modified version of AndroidAPS. Our study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14 percentage points higher among those who used the open-source AID system (95% confidence interval [CI], 9.2 to 18.8; P<0.001) compared to those who used sensor augmented pump therapy; a difference that corresponds to 3 hours 21 minutes more time spent in target range per day. The system did not contribute to any additional hypoglycemia. Glycemic improvements were evident within the first week and were maintained over the 24-week trial. This illustrates that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID. This study concluded that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS, a widely used open-source AID solution, is efficacious and safe.

The backstory on this study

We developed the first open source AID in late 2014 and shared it with the world as OpenAPS in February 2015. It went from n=1 to (n=1)*2 and up from there. Over time, there were requests for data to help answer the question “how do you know it works (for anybody else)?”. This led to the first survey in the OpenAPS community (published here), followed by additional retrospective studies such as this one analyzing data donated by the community,  prospective studies, and even an in silico study of the algorithm. Thousands of users chose open source AID, first because there was no commercial AID, and later because open source AID such as the OpenAPS algorithm was more advanced or had interoperability features or other benefits such as quality of life improvements that they could not find in commercial AID (or because they were still restricted from being able to access or afford commercial AID options). The pile of evidence kept growing, and each study has shown safety and efficacy matching or surpassing commercial AID systems (such as in this study), yet still, there was always the “but there’s no RCT showing safety!” response.

After Martin de Bock saw me present about OpenAPS and open source AID at ADA Scientific Sessions in 2018, we literally spent an evening at the dinner table drawing the OpenAPS algorithm on a napkin at the table to illustrate how OpenAPS works in fine grained detail (as much as one can do on napkin drawings!) and dreamed up the idea of an RCT in New Zealand to study the open source AID system so many were using. We sought and were granted funding by New Zealand’s Health Research Council, published our protocol, and commenced the study.

This is my high level summary of the study and some significant aspects of it.

Study Design:

This study was a 24-week, multi-centre randomized controlled trial in children (7–15 years) and adults (16–70 years) with type 1 diabetes comparing open-source AID (using the OpenAPS algorithm within a version of AndroidAPS implemented in a smartphone with the DANA-i™ insulin pump and Dexcom G6® CGM), to sensor augmented pump therapy. The primary outcome was change in the percent of time in target sensor glucose range (3.9-10mmol/L [70-180mg/dL]) from run-in to the last two weeks of the randomized controlled trial.

  • This is a LONG study, designed to look for rare adverse events.
  • This study used the OpenAPS algorithm within a modified version of AndroidAPS, meaning the learning objectives were adapted for the purpose of the study. Participants spent at least 72 hours in “predictive low glucose suspend mode” (known as PLGM), which corrects for hypoglycemia but not hyperglycemia, before proceeding to the next stage of closed loop which also then corrected for hyperglycemia.
  • The full feature set of OpenAPS and AndroidAPS, including “supermicroboluses” (SMB) were able to be used by participants throughout the study.

Results:

Ninety-seven participants (48 children and 49 adults) were randomized.

Among adults, mean time in range (±SD) at study end was 74.5±11.9% using AID (Δ+ 9.6±11.8% from run-in; P<0.001) with 68% achieving a time in range of >70%.

Among children, mean time in range at study end was 67.5±11.5% (Δ+ 9.9±14.9% from run-in; P<0.001) with 50% achieving a time in range of >70%.

Mean time in range at study end for the control arm was 56.5±14.2% and 52.5±17.5% for adults and children respectively, with no improvement from run-in. No severe hypoglycemic or DKA events occurred in either arm. Two participants (one adult and one child) withdrew from AID due to frustrations with hardware issues.

  • The pump used in the study initially had an issue with the battery, and there were lots of pumps that needed refurbishment at the start of the study.
  • Aside from these pump issues, and standard pump site/cannula issues throughout the study (that are not unique to AID), there were no adverse events reported related to the algorithm or automated insulin delivery.
  • Only two participants withdrew from AID, due to frustration with pump hardware.
  • No severe hypoglycemia or DKA events occurred in either study arm!
  • In fact, use of open source AID improved time in range without causing additional hypoglycemia, which has long been a concern of critics of open source (and all types of) AID.
  • Time spent in ‘level 1’ and ‘level 2’ hyperglycemia was significantly lower in the AID group as well compared to the control group.

In the primary analysis, the mean (±SD) percentage of time that the glucose level was in the target range (3.9 – 10mmol/L [70-180mg/dL]) increased from 61.2±12.3% during run-in to 71.2±12.1% during the final 2-weeks of the trial in the AID group and decreased from 57.7±14.3% to 54±16% in the control group, with a mean adjusted difference (AID minus control at end of study) of 14.0 percentage points (95% confidence interval [CI], 9.2 to 18.8; P<0.001). No age interaction was detected, which suggests that adults and children benefited from AID similarly.

  • The CREATE study found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy.
  • This difference reflects 3 hours 21 minutes more time spent in target range per day!
  • For children AID users, they spent 3 hours 1 minute more time in target range daily (95% CI, 1h 22m to 4h 41m).
  • For adult AID users, they spent 3 hours 41 minutes more time in target range daily (95% CI, 2h 4m to 5h 18m).
  • Glycemic improvements were evident within the first week and were maintained over the 24-week trial. Meaning: things got better quickly and stayed so through the entire 24-week time period of the trial!
  • AID was most effective at night.
Difference between control and AID arms overall, and during day and night separately, of TIR for overall, adults, and kids

One thing I think is worth making note of is that one criticism of previous studies with open source AID is regarding the self-selection effect. There is the theory that people do better with open source AID because of self-selection and self-motivation. However, the CREATE study recruited a diverse cohort of participants, and the study findings (as described above) match all previous reports of safety and efficacy outcomes from previous studies. The CREATE study also found that the greatest improvements in TIR were seen in participants with lowest TIR at baseline. This means one major finding of the CREATE study is that all people with T1D, irrespective of their level of engagement with diabetes self-care and/or previous glycemic outcomes, stand to benefit from AID.

This therefore means there should be NO gatekeeping by healthcare providers or the healthcare system to restrict AID technology from people with insulin-requiring diabetes, regardless of their outcomes or experiences with previous diabetes treatment modalities.

There is also no age effect observed in the trail, meaning that the results of the CREATE Trial demonstrated that open-source AID is safe and effective in children and adults with type 1 diabetes. If someone wants to use open source AID, they would likely benefit, regardless of age or past diabetes experiences. If they don’t want to use open source AID or commercial AID…they don’t have to! But the choice should 100% be theirs.

In summary:

  • The CREATE trial was the first RCT to look at open source AID, after years of interest in such a study to complement the dozens of other studies evaluating open source AID.
  • The conclusion of the CREATE trial is that open-source AID using the OpenAPS algorithm within a version of AndroidAPS, a widely used open-source AID solution, appears safe and effective.
  • The CREATE trial found that across children and adults, the percentage of time that the glucose level was in the target range of 3.9-10mmol/L [70-180mg/dL] was 14.0 percentage points higher among those who used the open-source AID system compared to those who used sensor augmented pump therapy; a difference that reflects 3 hours 21 minutes more time spent in target range per day.
  • The study recruited a diverse cohort, yet still produced glycemic outcomes consistent with existing open-source AID literature, and that compare favorably to commercially available AID systems. Therefore, the CREATE Trial indicates that a range of people with type 1 diabetes might benefit from open-source AID solutions.

Huge thanks to each and every participant and their families for their contributions to this study! And ditto, big thanks to the amazing, multidisciplinary CREATE study team for their work on this study.


September 7, 2022 UPDATE – I’m thrilled to share that the paper with the primary outcomes from the CREATE trial is now published. You can find it on the journal site here, or like all of the research I contribute to, access an author copy on my research paper.

Example citation:

Burnside, M; Lewis, D; Crocket, H; et al. Open-Source Automated Insulin Delivery in Type 1 Diabetes. N Engl J Med 2022;387:869-81. DOI:10.1056/NE/Moa2203913

Note that the continuation phase study results are slated to be presented this fall at another conference!

Findings from the RCT on open source AID, the CREATE Trial, presented at #ADA2022

What You Should Know About Exocrine Pancreatic Insufficiency (EPI) or Pancreatic Exocrine Insufficiency (PEI)

I have a new part-time job as a pancreas, but this time, I don’t have any robot parts I can make to help.

This is a joke, because I have had type 1 diabetes for 19+ years and 7 years ago I helped make the world’s first open-source artificial pancreas, also known as an automated insulin delivery system, that we jokingly call my “robot parts” and takes care of 90+% of the work of living with type 1 diabetes. PS if you’re looking for more information, there’s a book for that, or a free 3 minute animated video explaining automated insulin delivery. 

The TL;DR of this post is that I have discovered I have a mild or moderate exocrine pancreatic insufficiency, known as EPI (or PEI, pancreatic exocrine insufficiency, depending on which order and acronym you like). There’s a treatment called pancreatic enzyme replacement therapy (PERT) which I have been trying.

It took a long time for me to get diagnosed (almost 2 years), so this post walks through my history and testing process with my gastroenterologist (GI doctor) and the importance of knowing your own body and advocating for yourself when something is wrong or not quite right.

Background

About six years after I was diagnosed with type 1 diabetes, I was doing a summer internship in Washington, D.C. (away from home) and started getting chest tightness and frequent abdominal pain. Sometimes it felt like my abdominal muscles were “knitting” into each other. Because I had type 1 diabetes, I had heard at one point that about 10% of people with type 1 also develop celiac disease. So, thankfully, it was as simple as calling my endocrinologist and scheduling testing, and getting an endoscopy and biopsy to confirm I had celiac disease. It took about 2 months, and the timing was mostly that long due to getting back to Alabama after my internship and the testing schedule of the hospital. This is relevant detail, because I later read that it takes an average of 7 years for most people to get diagnosed with celiac disease. That has been floating around in my brain now for over a decade, this awareness that GI stuff is notoriously hard to diagnose when you’re not lucky enough to have a clear idea, like I did, of an associated condition.

So, with type 1 diabetes and celiac disease, I use automated insulin delivery to get great outcomes for my diabetes and a 100% very careful gluten-free diet to manage my celiac disease, and have not had any GI problems ever since I went gluten-free.

Until January/February 2020, when I took an antibiotic (necessary for an infection I had) and started to get very minor GI side effects on day 5 of the 7-day antibiotic course. Because this antibiotic came with a huge warning about C. diff, and I really didn’t want C. diff, I discontinued the antibiotic. My infection healed successfully, but the disruption to my GI system continued. It wasn’t C. diff and didn’t match any of the C. diff symptoms, but I really lost my appetite for a month and didn’t want to eat, so I lost 10 pounds in February 2020. On the one hand, I could afford to lose the weight, but it wasn’t healthy because all I could bring myself to eat was one yogurt a day. I eventually decided to try eating some pecans to add fiber to my diet, and that fiber and change in diet helped me get back to eating more in March 2020, although I generally was eating pecans and dried cranberries (to increase my fiber intake) for breakfast and wasn’t hungry until late afternoon or early evening for another meal. So, since my body didn’t seem to want anything else, I essentially was eating two meals a day. My GI symptoms were better: not back to how they were before February 2020, but seemed manageable.

However, in July 2020, one night I woke up with incredibly painful stabbing abdominal pain and thought I would need to go to the ER. Thankfully, it resolved enough within minutes for me to go back to sleep, but that was scary. I decided to schedule an appointment with my gastroenterologist. I took in a record of my symptoms and timing and explained what was most worrisome to me (sudden stabbing pains after I ate or overnight, not seemingly associated with one particular type of food; changes in bathroom habits, like steatorrhea, but not as severe as diarrhea). He made a list of suspected things and we began testing: we checked for C. diff (nope), parasites (nope), bloodwork for inflammation (nope, so no Crohn’s or IBS or IBD), my celiac markers to make sure I wasn’t being accidentally glutened (nope: 100% gluten-free as proven by the blood work), H. pylori (nope), and did a CT scan to check for structural abnormalities (all good, again no signs of inflammation or any obvious issues).

Because all of this happened during the global COVID-19 pandemic, I was cautious about scheduling any in-person tests such as the CT scan or the last test on my list, a colonoscopy and endoscopy. I have a double family history of colon cancer, so although it was extremely unlikely, given everything else on the list was coming back as negative, it needed to be done. I waited until I was fully vaccinated (e.g. 2 weeks after 2 shots completed) to have my colonoscopy and endoscopy scheduled. The endoscopy was to check for celiac-related damage in my small intestine since I hadn’t had an endoscopy since my diagnosis with celiac over a decade ago. Thankfully, there’s no damage from celiac (I wasn’t expecting there to be any damage, but is a nice confirmation of my 100% very careful gluten free diet!), and the colonoscopy also came back clear.

Which was good, but also bad, because…SOMETHING was causing all of my symptoms and we still didn’t know what that was. The last thing on my doctor’s list was potentially small intestine bacterial overgrowth (SIBO), but the testing is notoriously non-specific, and he left it up to me as to whether I decided to treat it or not. Having run out of things to test, I decided to do a two-week course of an antibiotic to target the bacteria. It helped for about two weeks, and then my symptoms came back with a vengeance. However, I had realized in spring 2021 (after about 9 months of feeling bad) that sometimes the stabbing abdominal pain happened when I ate things with obvious onion or garlic ingredients, so January-July 2021 I had avoided onion and garlic and saw a tiny bit of improvement (but nowhere near my old normal). Because of my research on onion and garlic intolerances, and then additional research looking into GI things, I realized that the low FODMAP diet which is typically prescribed for IBS/IBD (which I don’t have) could be something I could try without a lot of risk: if it helped, that would be an improvement, regardless of whatever I actually had.

So in August 2021, as noted in this blog post, I began the low FODMAP diet first starting with a careful elimination phase followed by testing and adding foods back into my diet. It helped, but over time I’ve realized that I still get symptoms (such as extreme quantities of gas, abdominal discomfort and distention, changed bathroom habits) even when I’m eating low FODMAP. It’s possible low FODMAP itself helped by avoiding certain types of food, but it’s also possible that it was helping because I was being so careful about the portions and timing of when I was eating, to avoid “stacking” FODMAPs.

One other thing I had tried, as I realized my onion and garlic intolerance was likely tied to being “fructans”, and that I had discovered I was sensitive to fructans in other foods, was an enzyme powder called Fodzyme. (I have no affiliation with this company, FYI). The powder works to target the FODMAPs in food to help neutralize them so they don’t cause symptoms. It worked for me on the foods I had experimented with, and it allowed me to eat food that had onion powder or garlic powder listed as a minor ingredient (I started small and cautious and am working my way up in testing other foods and different quantities). I longingly wished that there were other enzymes I could take to help improve digestion, because Fodzyme seemed to not only reduce the symptoms I had after I ate, but also seemed to improve my digestion overall (e.g. improved stool formation). I did some research but “digestive enzymes” are generally looked down upon and there’s no good medical research, so I chalked it up to snake oil and didn’t do anything about it.

Until, oddly enough, in November 2021 I noticed a friend’s social media post talking about their dog being diagnosed with exocrine pancreatic insufficiency (EPI). It made me go look up EPI in humans to see if it was a thing, because their experience sounded a lot like mine. Turns out, EPI is a thing, and it’s very common in humans who have cystic fibrosis; pancreas-related surgeries or pancreatic cancer; and there is also a known correlation with people with type 1 diabetes or with celiac disease.

Oh hey, that’s me (celiac and type 1 diabetes).

I did more research and found that various studies estimate 40% of people with type 1 diabetes have low levels of pancreatic elastase, which is a proxy for determining if you have insufficient enzymes being produced by your pancreas to help you digest your food. The causal mechanism is unclear, so they don’t know whether it’s just a ‘complication’ and side effect of diabetes and the pancreas no longer producing insulin, or if there is something else going on.

Given the ties to diabetes and celiac, I reached out to my GI doctor again in December 2021 and asked if I should get my pancreatic elastase levels tested to check for exocrine pancreatic insufficiency (EPI), given that my symptoms matching the textbook definition and my risk factors of diabetes and celiac. He said sure, sent in the lab request, and I got the lab work done. My results are on the borderline of ‘moderate’ insufficiency, and given my very obvious and long-standing symptoms, and given my GI doc said there would be no harm from trying, I start taking pancreatic enzyme replacement therapy (called PERT). Basically, this means I swallow a pill that contains enzymes with the first bite of food that I eat, and the enzymes help me better digest the food I am eating.

And guess what? For me, it works and definitely has helped reduce symptoms after I’m eating and with next-day bathroom habits. So I consider myself to have mild or moderate exocrine pancreatic insufficiency (EPI).

(Also, while I was waiting on my test results to come back, I found that there is a lipase-only version of digestive enzymes available to purchase online, so I got some lipase and began taking it. It involves some titration to figure out how much I needed, but I saw some improvement already from low doses of lipase, so that also led me to want to try PERT, which contains all 3 types of enzymes your pancreas normally naturally produces, even though my elastase levels were on the borderline of ‘moderate’ insufficiency. Not everyone with lower levels of elastase has insufficiency in enzymes, but my symptoms and response to lipase and PERT point to the fact that I personally do have some insufficiency.)

More about my experiences with exocrine pancreatic insufficiency

Unfortunately, there is no cure for exocrine pancreatic insufficiency. Like Type 1 diabetes, it requires lifelong treatment. So, I will be taking insulin and now PERT likely for the rest of my life. Lazy pancreas! (Also, it’s possible I will need to increase my PERT dose over time if my insufficiency increases.)

Why treat EPI? Well, beyond managing very annoying symptoms that impact quality of life, if left untreated it’s associated with increased mortality (e.g. dying earlier than you would otherwise) due to malnutrition (because you’re not properly absorbing the nutrients in the food you’re eating) and bone density problems.

Oddly enough, there seem to be two versions of the name (and therefore two acronyms) for the same thing: EPI and PEI, meaning exocrine pancreatic insufficiency or pancreatic exocrine insufficiency. I haven’t found a good explanation for why there are two names and if there are any differences. Luckily, my research into the medical literature shows they both pop up in search results pretty consistently, so it’s not like you end up missing a big body of literature if you use one search term or the other.

Interestingly, I learned 90% of people with cystic fibrosis may need PERT, and thankfully my friend with CF didn’t mind me reaching out to ask her if she had ever taken PERT or had any tips to give me from her knowledge of the CF community. That was nice that it turns out I do know some other people with EPI/PEI, even though they don’t usually talk about it because it seems to go hand in hand with CF. Some of the best resources of basic information about EPI/PEI are written either by CF foundations or by pancreatic cancer-related organizations, because those are the two biggest associated conditions that also link to EPI/PEI. There are also other conditions like diabetes and celiac with strong correlations, but these communities don’t seem to talk about it or have resources focused on it. (As with low FODMAP resources where everything is written for IBS/IBD, you can extrapolate and ignore everything that’s IBS/IBD specific. Don’t be afraid to read EPI/PEI information from communities that aren’t your primary community!)

Sadly, like so many GI conditions (remember in the intro I referenced 7 years average diagnosis time with celiac), it seems ridiculously hard to get to a diagnosis of EPI. I essentially self-diagnosed myself (and confirmed the diagnosis in partnership with my GI doc who agreed to run the tests). I am still very surprised that it never came up on his list of possible conditions despite having symptoms that are textbook EPI and having diabetes and celiac, which are known correlations. Apparently, this is common: I read one study that says even people with super high-risk factors (e.g. pancreas surgery, pancreatic cancer) aren’t necessarily screened, either! So it’s not just me falling through the cracks, and this is something the gastroenterology world needs to be better about. It’s also common for patients to bring this up to their doctors vs their doctors suggesting it as a potential diagnosis – this study found 24% of people brought up EPI, like I did, to their doctors.

Also, unfortunately, I had a few people (including family members) suggest to me in the last two years that my symptoms are psychosomatic, or stress-related. They’re clearly, as proven by lab work, not psychosomatic or stress-related but are a result of my exocrine pancreatic functions failing. Please, don’t ever suggest someone dealing with GI issues is experiencing symptoms due to stress – this is the kind of comment you should keep to yourself. (The last time someone mentioned this to me was months ago, and it still bothers me to think about it.)

Advocate for yourself

One of the very important things I learned early on when living with type 1 diabetes was the importance of knowing my own body, and advocating for myself. This unfortunately was a hard lesson learned, because I had general practice (GP or primary care / PCP) doctors who would refuse to treat me because I had diabetes because they were concerned about prescribing something that would mess up my blood sugars. They’d completely ignore the point that whatever infection I had would cause MORE disruption to my blood sugars by having me be sick and suffer longer, than I would have disruption to my blood sugar levels from a prescription. Sigh. So for the last almost two decades, I have had to go into every health encounter prepared to advocate for myself and make sure I get the medical expertise for whatever I’m there for, and not the less experienced take on diabetes (assuming I wasn’t there for diabetes, which I usually wasn’t).

This has translated into how I approached finding solutions for my GI symptoms. Per my history described above, I had increasing but minor GI symptoms from February-July 2020. Having new, stabbing pains in my abdomen led me to the gastroenterologist for a long list of testing for various things, but I had to continue to push for the next round of testing and schedule and manage everything to proceed through the list we had discussed at my appointment. Later, after we ran through the list, I had to try things like low FODMAP for myself, and then do additional research and identify the test for EPI as a likely next step to try.

I felt a little like the ‘boiling frog’ analogy, where my symptoms gradually worsened over time, but they weren’t startling bad (except for the points in time when I had stabbing abdominal pain). Or the two times, almost one year apart (Oct 2020 and Dec 2021) where I had what I considered bad “flares” of something where I got really hot and feeling really ill all of a sudden, but it wasn’t COVID-19 and it wasn’t anything specific causing it, there were no obvious food triggers, and the only thing I could do was lay down for 2-3 hours and rest before I started to feel better. Those were probably correlated with “overdoing it” with physical activity, but I’ve also run a marathon and a 50k ultramarathon in the last year and didn’t have problems on those days, so there’s not a certain threshold of activity that appears to cause that. Thankfully, that has only happened two times.

Other than those scenarios, it wasn’t like breaking my ankle where there was a clear “everything was fine and now something is broken”, but it was more like “I have had not-good-digestion and various increasing GI symptoms that don’t fit any clear problem or diagnosis on our shortlist of the 5 likely things it might be. It’s not excruciating but it is increasingly impacting my quality of life, and twisting myself into a pretzel with an evolving pattern of dietary modifications is not solving it”. It took me continuing to advocate for myself and not accepting suffering for the rest of my life (hopefully!) with these symptoms to get to an answer, which for me, so far, seems to be moderate exocrine pancreatic insufficiency.

What it’s like to start taking pancreatic enzyme replacement therapy (PERT)

PERT is typically measured by the units/amount of lipase it contains, even though it contains all 3 types of enzymes. (Some of the Medicare documents in different states actually are really helpful for comparing the size of dosing across the different brands of PERT. That also helped me look up the various brands in my insurance plan to see whether there would be a price difference between two of the most common brands.) Depending on symptoms and your level of insufficiency, like insulin, it requires some titration to figure out the right doses. I’ve been attempting to track generally the amount of fat that I’m eating to try to get a sense of my “ratio” of fat to lipase needed, although the research shows there is likely not a linear correlation between grams of fat and units of lipase needed. Another way to think about it is at what level of grams of fat in your meal do you need more than your current dose. For example, one pill of PERT at my current dose seems to work up to around 70 or so grams of fat per meal, as long as it doesn’t have more than 50% protein. Meals containing much more fat (120 g or so) definitely require more, as do meals with either a higher quantity of protein or a closer ratio of 1:1 fat to protein.

Different people have different needs with regard to whether they need enzyme support “just” for fat, or also for protein and carbs. I appear to at least need some support for carbs as well as protein, but am still establishing at what levels I need which dosing of which enzymes.

Personally, I am tracking to see whether my symptoms are reduced or eliminated in the hours following my meals (gas, abdominal discomfort, a sick feeling after eating) as well as the next day (bloating/abdominal distension, bathroom habits such as reduced steatorrhea), and overall whether I have any more of those really bad “flares”. My initial tests of taking PERT show improvements after my meals (I don’t feel sick after I eat anymore!) and often the next day.

After the first few days of trying food that was low FODMAP but giving me minor symptoms before PERT, I’ve also felt confident enough to try meals that I’ve avoided eating for over a year, such as a gluten free burger from one of our nearby local favorites! Even though it’s been well over a year since I’ve had it last, I immediately could tell a difference in how I felt eating it, due to taking PERT with it. There was no wave of fatigue before I was halfway through the burger, and no gas or feeling sick to my stomach after eating. I had clearly forgotten what it was like to not feel miserable after eating and to actually enjoy eating food! So far, PERT has been exceeding my expectations (although those were rather low).

It makes it slightly less annoying, then, to think about the price of PERT. Roughly, one month of PERT at the dosage I’m currently on costs the same as 3 vials of insulin in the US (in the ballpark of $800). Like insulin, PERT is necessary and worthwhile (and thankfully I do have health insurance).

Pancreases are great when they work…and expensive to replace!

A play on the spiderman meme of two spiderman's pointing at each other, indicating similar things. Labeled "exocrine pancreatic functions" and "endocrine pancreatic functions", indicating both of mine are not working as they should be.

TLDR: I have a new thing, exocrine pancreatic insufficiency, to deal with. Thankfully, there’s a treatment (PERT) that I can use to reduce symptoms and hopefully limit the potential impacts on morbidity long term. If you have diabetes or celiac and you have unexplained GI symptoms over time, you might want to do some research into EPI and discuss it with your gastroenterologist.

Also…for any endocrinologist reading this…or any other healthcare providers…if you have patients with diabetes and suspected GI issues, please consider EPI as a possible diagnosis once you’ve ruled out celiac disease and other likely suspects. Given the high rates of lowered elastase in all types of diabetes, it’s worth screening for EPI in patients with otherwise-unexplained steatorrhea or similar symptoms.

PS – if you land on this post and haven’t seen it already, you may want to check out PERT Pilot, the first iOS app for Exocrine Pancreatic Insufficiency! It’s an iOS app that I built that allows you to record as many meals as you want, the PERT dosing and outcomes, to help you visualize and review more of your PERT dosing data!


You can also contribute to a research study and help us learn more about EPI/PEI – take this anonymous survey to share your experiences with EPI-related symptoms!

Looking back at work and accomplishments in 2021

I decided to do a look back at the last year’s worth of work, in part because it was a(nother) weird year in the world and also because, if you’re interested in my work, unless you read every single Tweet, there may have been a few things you missed that are of interest!

In general, I set goals every year that stretch across personal and professional efforts. This includes a daily physical activity streak that coincides with my walking and running lots of miles this year in pursuit of my second marathon and first (50k) ultramarathon. It’s good for my mental and physical health, which is why I post almost daily updates to help keep myself accountable. I also set goals like “do something creative” which could be personal (last year, knitting a new niece a purple baby blanket ticked the box on this goal!) or professional. This year, it was primarily professional creativity that accomplished this goal (more on that below).

Here’s some specifics about goals I accomplished:

RUNNING

  • My initial goal was training ‘consistently and better’ than I did for my first marathon, with 400 miles as my stretch goal if I was successfully training for the marathon. (Otherwise, 200 miles for the year would be the goal without a marathon.) My biggest-ever running year in 2013 with my first marathon was 356 miles, so that was a good big goal for me. I achieved it in June!
  • I completed my second marathon in July, and PR’d by over half an hour.
  • I completed my first-ever ultramarathon, a 50k!
  • I re-set my mileage goal after achieving 400 miles..to 500..600…etc. I ultimately achieved the biggest-ever mileage goal I’ve ever hit and think I ever will hit: I ran 1,000 miles in a single year!
  • I wrote lots of details about my methods of running (primarily, run/walking) and running with diabetes here. If you’re looking for someone to cheer you on as you set a goal for daily activity, like walking, or learning to run, or returning to running…DM or @ me on Twitter (@DanaMLewis). I love to cheer people on as they work toward their activity goals! It helps keep me inspired, too, to keep aiming at my own goals.

CREATIVITY

  • My efforts to be creative were primarily on the professional side this year. The “Convening The Center” project ended up having 2 out of 3 of my things that I categorized as being creative. The first was the design of the digital activities and the experience of CTC overall (more about that here). The second were the items in the physical “kit” we mailed out to participants: we brainstormed and created custom playing cards and physical custom keychains. They were really fun to make, especially in partnership with our excellent project artist, Rebeka Ryvola, who did the actual design work!
  • My third “creative” endeavor was a presentation, but it was unlike the presentations I usually give. I was tasked to create a presentation that was “visually engaging” and would not involve showing my face in the presentation. I’ve linked to the video below in the presentation section, but it was a lot of work to think about how to create a visually and auditory focused presentation and try to make it engaging, and I’m proud of how it turned out!

RESEARCH AND PUBLICATIONS

  • This is where the bulk of my professional work sits right now. I continue to be a PI on the CREATE trial, the world’s first randomized control trial assessing open-source automated insulin delivery technology, including the algorithm Scott and I dreamed up and that I have been using every day for the past 7 years. The first data from the trial itself is forthcoming in 2022. 
  • Convening The Center also was a grant-funded project that we turned into research with a publication that we submitted, assessing more of what patients “do”, which is typically not assessed by researchers and those looking at patient engagement in research or innovation. Hopefully, the publication of the research article we just submitted will become a 2022 milestone! In the meantime, you can read our report from the project here (https://bit.ly/305iQ1W ), as this grant-funded project is now completed.
  • Goal-wise, I aim to generate a few publications every year. I do not work for any organization and I am not an academic. However, I come from a communications background and see the benefit of reaching different audiences where they are, which is why I write blog posts for the patient community and also seek to disseminate knowledge to the research and clinical communities through traditional peer-reviewed literature. You can see past years’ research articulated on my research page (DIYPS.org/research), but here’s a highlight of some of the 2021 publications:
  • Also, although I’m not a traditional academic researcher, I also participate in the peer review process and frequently get asked to peer-review submitted articles to a variety of journals. I skimmed my email and it looks like I completed (at least) 13 peer reviews, most of which included also reviewing subsequent revisions of those submitted articles. So it looks like my rate of peer reviewing (currently) is matching my rate of publishing. I typically get asked to review articles related to open-source or DIY diabetes technology (OpenAPS, AndroidAPS, Loop, Nightscout, and other efforts), citizen science in healthcare, patient-led research or patient engagement in research, digital health, and diabetes data science. If you’re submitting articles on that topic, you’re welcome to recommend me as a potential reviewer.

PRESENTATIONS

  • I continued to give a lot of virtual presentations this year, such as at conferences like the “Insulin100” celebration conference (you can see the copy I recorded of my conference presentation here). I keynoted at the European Patients Forum Congress as well as at ADA’s Precision Diabetes Medicine 2021; an invited talk ADA Scientific Sessions (session coverage here); the 2021 Federal Wearables Summit: (video here); and the BIH Clinician Scientist Symposium (video here), to name a few (but not all).
  • Additionally, as I mentioned, one of the presentations I’m most proud of was created for the Fall 2021 #DData Exchange event:

OTHER STUFF

I did quite a few other small projects that don’t fit neatly into the above categories.

One final thing I’m excited to share is that also in 2021, Amazon came out with a beta program for producing hardcover/hardback books, alongside the ability to print paperback books on demand (and of course Kindle). So, you can now buy a copy of my book about Automated Insulin Delivery: How artificial pancreas “closed loop” systems can aid you in living with diabetes in paperback, hardback, or on Kindle. (You can also, still, read it 100% for free online via your phone or desktop at ArtificialPancreasBook.com, or download a PDF for free to read on your device of choice. Thousands of people have downloaded the PDF!)

Now available in hardcover, the book about Automated Insulin Delivery by Dana M. Lewis

Understanding Automated Insulin Delivery: A basic book for kids, family, and friends of people living with diabetes

tl;dr – A new book out for kids explaining the basics of automated insulin delivery, using the analogy of scuba diving to explain how the system makes small changes in insulin delivery to manage glucose levels! Watch the narrated video free online, and if you find the analogy useful, it’s available in book form as both a physical, print book as well as on Kindle via Amazon.DanaMLewis_UnderstandingAutomatedInsulinDelivery_KidsBook—-

A few weeks ago I was thinking about what the basic things that I wanted people to know about automated insulin delivery. A good portion of the general public – and even many family members of people with diabetes – thinks that a traditional insulin pump does what an automated insulin delivery system does: adjusting insulin delivery based on continuous glucose monitor (CGM) data. But a traditional pump doesn’t necessarily know about the CGM data and isn’t equipped with the algorithm to make those decisions and changes to insulin delivery, so the person with diabetes is doing a LOT of invisible labor to try to manage glucose levels constantly 24/7/365. That’s why an automated insulin delivery system is so useful, and why I’ve been using a DIY system for more than 5 years. Now, though, we’re (finally) starting to see commercial systems come to market that does the basic functionality similar to what OpenAPS could do five years ago. I want more people to have access to these systems and use them as best as they can be used to give people the best outcomes diabetes-wise and the best quality of life they can possibly have. Helping explain to more people how this technology works is one way I can help do this, and thus an idea was born for another book to explain the basics of automated insulin delivery systems.

Dana's first rough sketch of the scuba diving analogy for explaining automated insulin deliveryI started with a basic sketch of an idea to run it by Scott and a few other people to test the idea. I’m not much for drawing, so it was a *very* rough sketch. But the analogy seemed to resonate, so I moved on to mocking up a basic version on the computer. (I went down a rabbit hole because I thought it would be neat to make an animated video for people to see and share online, to accompany the book. But I don’t know how to illustrate on the computer, let alone animate, so I tried an open source illustration program called Synfig, then several other illustrator programs that were open source to do the basic design to import into Synfig to animate, but then realized what I had in mind was so simple that basic transitions and animations in PowerPoint would suffice for my animated video.) PowerPoint is also how I’ve made my other children’s books for self-publishing, so it was easy to do a widescreen, video design version and then modify a version for the print size book of choice (I chose an 8.5×8.5 to make it easiest to hold and read). 

I went from a paper and pencil sketch on July 18 to mocking up the video animation and designing the print book and requesting printed proofs on July 23. The printed proofs were a bit slow to ship compared to usual (probably something to do with a global pandemic), and arrived on August 4. I reviewed, made a few small changes, and hit ‘publish’ the same day, and Amazon reviewed and approved both the Kindle version and the print version, which are now available today (August 5, 2020) online. It took less than 3 weeks to go from idea to printed book available for shipping worldwide! (I am sharing all these details to hopefully encourage someone else to self-publish if they have an idea for a book they’d like to see available in the world – feel free to reach out if you have any questions about self publishing!)

Print_DanaMLewis_UnderstandingAutomatedInsulinDeliveryKindle_Amazon_DanaMLewis_UnderstandingAutomatedInsulinDeliveryHere is the link to the print book on Amazon.

Here’s the link to the Kindle book version on Amazon – it’s also available as part of Kindle Unlimited and the Kindle Lending Library, so feel free to share it out!

DanaMLewis_UnderstandingAutomatedInsulinDelivery_kidsbook_TheEnd

Also, if you’re looking for something to do with your kids (or have your kids do), I also made some of the scuba diving designs into a coloring sheet – check them out here (downloads as a PDF).

DanaMLewis_freescubacoloringsheets

Poster and presentation content from @DanaMLewis at #ADA2020 and #DData20

In previous years (see 2019 and 2018), I mentioned sharing content from ADA Scientific Sessions (this year it’s #ADA2020) with those not physically present at the conference. This year, NO ONE is present at the event, and we’re all virtual! Even more reason to share content from the conference. :)

I contributed to and co-authored two different posters at Scientific Sessions this year:

  • “Multi-Timescale Interactions of Glucose and Insulin in Type 1 Diabetes Reveal Benefits of Hybrid Closed Loop Systems“ (poster 99-LB) along with Azure Grant and Lance Kriegsfeld, PhD.
  • “Do-It-Yourself Artificial Pancreas Systems for Type 1 Diabetes Reduce Hyperglycemia Without Increasing Hypoglycemia” (poster 988-P in category 12-D Clinical Therapeutics/New Technology—Insulin Delivery Systems), alongside Jennifer Zabinsky, MD MEng, Haley Howell, MSHI, Alireza Ghezavati, MD, Andrew Nguyen, PhD, and Jenise Wong, MD PhD.

And, while not a poster at ADA, I also presented the “AID-IRL” study funded by DiabetesMine at #DData20, held in conjunction with Scientific Sessions. A summary of the study is also included in this post.

First up, the biological rhythms poster, “Multi-Timescale Interactions of Glucose and Insulin in Type 1 Diabetes Reveal Benefits of Hybrid Closed Loop Systems” (poster 99-LB). (Twitter thread summary of this poster here.)

Building off our work as detailed last year, Azure, Lance, and I have been exploring the biological rhythms in individuals living with type 1 diabetes. Why? It’s not been done before, and we now have the capabilities thanks to technology (pumps, CGM, and closed loops) to better understand how glucose and insulin dynamics may be similar or different than those without diabetes.

Background:

Mejean et al., 1988Blood glucose and insulin exhibit coupled biological rhythms at multiple timescales, including hours (ultradian, UR) and the day (circadian, CR) in individuals without diabetes. The presence and stability of these rhythms are associated with healthy glucose control in individuals without diabetes. (See right, adapted from Mejean et al., 1988).

However, biological rhythms in longitudinal (e.g., months to years) data sets of glucose and insulin outputs have not been mapped in a wide population of people with Type 1 Diabetes (PWT1D). It is not known how glucose and insulin rhythms compare between T1D and non-T1D individuals. It is also unknown if rhythms in T1D are affected by type of therapy, such as Sensor Augmented Pump (SAP) vs. Hybrid Closed Loop (HCL). As HCL systems permit feedback from a CGM to automatically adjust insulin delivery, we hypothesized that rhythmicity and glycemia would exhibit improvements in HCL users compared to SAP users. We describe longitudinal temporal structure in glucose and insulin delivery rate of individuals with T1D using SAP or HCL systems in comparison to glucose levels from a subset of individuals without diabetes.

Data collection and analysis:

We assessed stability and amplitude of normalized continuous glucose and insulin rate oscillations using the continuous wavelet transformation and wavelet coherence. Data came from 16 non-T1D individuals (CGM only, >2 weeks per individual) from the Quantified Self CGM dataset and 200 (n = 100 HCL, n = 100 SAP; >3 months per individual) individuals from the Tidepool Big Data Donation Project. Morlet wavelets were used for all analyses. Data were analyzed and plotted using Matlab 2020a and Python 3 in conjunction with in-house code for wavelet decomposition modified from the “Jlab” toolbox, from code developed by Dr. Tanya Leise (Leise 2013), and from the Wavelet Coherence toolkit by Dr. Xu Cui. Linear regression was used to generate correlations, and paired t-tests were used to compare AUC for wavelet and wavelet coherences by group (df=100). Stats used 1 point per individual per day.

Wavelets Assess Glucose and Insulin Rhythms and Interactions

Wavelet Coherence flow for glucose and insulin

Morlet wavelets (A) estimate rhythmic strength in glucose or insulin data at each minute in time (a combination of signal amplitude and oscillation stability) by assessing the fit of a wavelet stretched in window and in the x and y dimensions to a signal (B). The output (C) is a matrix of wavelet power, periodicity, and time (days). Transform of example HCL data illustrate the presence of predominantly circadian power in glucose, and predominantly 1-6 h ultradian power in insulin. Color map indicates wavelet power (synonymous with Y axis height). Wavelet coherence (D) enables assessment of rhythmic interactions between glucose and insulin; here, glucose and insulin rhythms are highly correlated at the 3-6 (ultradian) and 24 (circadian) hour timescales.

Results:

Hybrid Closed Loop Systems Reduce Hyperglycemia

Glucose distribution of SAP, HCL, and nonT1D
  • A) Proportional counts* of glucose distributions of all individuals with T1D using SAP (n=100) and HCL (n=100) systems. SAP system users exhibit a broader, right shifted distribution in comparison to individuals using HCL systems, indicating greater hyperglycemia (>7.8 mmol/L). Hypoglycemic events (<4mmol/L) comprised <5% of all data points for either T1D dataset.
  • B) Proportional counts* of non-T1D glucose distributions. Although limited in number, our dataset from people without diabetes exhibits a tighter blood glucose distribution, with the vast majority of values falling in euglycemic range (n=16 non-T1D individuals).
  • C) Median distributions for each dataset.
  • *Counts are scaled such that each individual contributes the same proportion of total data per bin.

HCL Improves Correlation of Glucose-Insulin Level & Rhythm

Glucose and Insulin rhythms in SAP and HCL

SAP users exhibit uncorrelated glucose and insulin levels (A) (r2 =3.3*10-5; p=0.341) and uncorrelated URs of glucose and insulin (B) (r2 =1.17*10-3; p=0.165). Glucose and its rhythms take a wide spectrum of values for each of the standard doses of insulin rates provided by the pump, leading to the striped appearance (B). By contrast, Hybrid Closed Loop users exhibit correlated glucose and insulin levels (C) (r2 =0.02; p=7.63*10-16), and correlated ultradian rhythms of glucose and insulin (D) (r2 =-0.13; p=5.22*10-38). Overlays (E,F).

HCL Results in Greater Coherence than SAP

Non-T1D individuals have highly coherent glucose and insulin at the circadian and ultradian timescales (see Mejean et al., 1988, Kern et al., 1996, Simon and Brandenberger 2002, Brandenberger et al., 1987), but these relationships had not previously been assessed long-term in T1D.

coherence between glucose and insulin in HCL and SAP, and glucose swings between SAP, HCL, and non-T1DA) Circadian (blue) and 3-6 hour ultradian (maroon) coherence of glucose and insulin in HCL (solid) and SAP (dotted) users. Transparent shading indicates standard deviation. Although both HCL and SAP individuals have lower coherence than would be expected in a non-T1D individual,  HCL CR and UR coherence are significantly greater than SAP CR and UR coherence (paired t-test p= 1.51*10-7 t=-5.77 and p= 5.01*10-14 t=-9.19, respectively). This brings HCL users’ glucose and insulin closer to the canonical non-T1D phenotype than SAP users’.

B) Additionally, the amplitude of HCL users’ glucose CRs and URs (solid) is closer (smaller) to that of non-T1D (dashed) individuals than are SAP glucose rhythms (dotted). SAP CR and UR amplitude is significantly higher than that of HCL or non-T1D (T-test,1,98, p= 47*10-17 and p= 5.95*10-20, respectively), but HCL CR amplitude is not significantly different from non-T1D CR amplitude (p=0.61).

Together, HCL users are more similar than SAP users to the canonical Non-T1D phenotype in A) rhythmic interaction between glucose and insulin and B) glucose rhythmic amplitude.

Conclusions and Future Directions

T1D and non-T1D individuals exhibit different relative stabilities of within-a-day rhythms and daily rhythms in blood glucose, and T1D glucose and insulin delivery rhythmic patterns differ by insulin delivery system.

Hybrid Closed Looping is Associated With:

  • Lower incidence of hyperglycemia
  • Greater correlation between glucose level and insulin delivery rate
  • Greater correlation between ultradian glucose and ultradian insulin delivery rhythms
  • Greater degree of circadian and ultradian coherence between glucose and insulin delivery rate than in SAP system use
  • Lower amplitude swings at the circadian and ultradian timescale

These preliminary results suggest that HCL recapitulates non-diabetes glucose-insulin dynamics to a greater degree than SAP. However, pump model, bolusing data, looping algorithms and insulin type likely all affect rhythmic structure and will need to be further differentiated. Future work will determine if stability of rhythmic structure is associated with greater time in range, which will help determine if bolstering of within-a-day and daily rhythmic structure is truly beneficial to PWT1D.
Acknowledgements:

Thanks to all of the individuals who donated their data as part of the Tidepool Big Data Donation Project, as well as the OpenAPS Data Commons, from which data is also being used in other areas of this study. This study is supported by JDRF (1-SRA-2019-821-S-B).

(You can download a full PDF copy of the poster here.)

Next is “Do-It-Yourself Artificial Pancreas Systems for Type 1 Diabetes Reduce Hyperglycemia Without Increasing Hypoglycemia” (poster 988-P in category 12-D Clinical Therapeutics/New Technology—Insulin Delivery Systems), which I co-authored alongside Jennifer Zabinsky, MD MEng, Haley Howell, MSHI, Alireza Ghezavati, MD, Andrew Nguyen, PhD, and Jenise Wong, MD PhD. There is a Twitter thread summarizing this poster here.

This was a retrospective double cohort study that evaluated data from the OpenAPS Data Commons (data ranged from 2017-2019) and compared it to conventional sensor-augmented pump (SAP) therapy from the Tidepool Big Data Donation Project.

Methods:

  • From the OpenAPS Data Commons, one month of CGM data (with more than 70% of the month spent using CGM), as long as they were >1 year of living with T1D, was used. People could be using any type of DIYAPS (OpenAPS, Loop, or AndroidAPS) and there were no age restrictions.
  • A random age-matched sample from the Tidepool Big Data Donation Project of people with type 1 diabetes with SAP was selected.
  • The primary outcome assessed was percent of CGM data <70 mg/dL.
  • The secondary outcomes assessed were # of hypoglycemic events per month (15 minutes or more <70 mg/dL); percent of time in range (70-180mg/dL); percent of time above range (>180mg/dL), mean CGM values, and coefficient of variation.
Methods_DIYAPSvsSAP_ADA2020_DanaMLewis

Demographics:

  • From Table 1, this shows the age of participants was not statistically different between the DIYAPS and SAP cohorts. Similarly, the age at T1D diagnosis or time since T1D diagnosis did not differ.
  • Table 2 shows the additional characteristics of the DIYAPS cohort, which included data shared by a parent/caregiver for their child with T1D. DIYAPS use was an average of 7 months, at the time of the month of CGM used for the study. The self-reported HbA1c in DIYAPS was 6.4%.
Demographics_DIYAPSvsSAP_ADA2020_DanaMLewis DIYAPS_Characteristics_DIYAPSvsSAP_ADA2020_DanaMLewis

Results:

  • Figure 1 shows the comparison in outcomes based on CGM data between the two groups. Asterisks (*) indicate statistical significance.
  • There was no statistically significant difference in % of CGM values below 70mg/dL between the groups in this data set sampled.
  • DIYAPS users had higher percent in target range and lower percent in hyperglycemic range, compared to the SAP users.
  • Table 3 shows the secondary outcomes.
  • There was no statistically significant difference in the average number of hypoglycemic events per month between the 2 groups.
  • The mean CGM glucose value was lower for the DIYAPS group, but the coefficient of variation did not differ between groups.
CGM_Comparison_DIYAPSvsSAP_ADA2020_DanaMLewis SecondaryOutcomes_DIYAPSvsSAP_ADA2020_DanaMLewis

Conclusions:

    • Users of DIYAPS (from this month of sampled data) had a comparable amount of hypoglycemia to those using SAP.
    • Mean CGM glucose and frequency of hyperglycemia were lower in the DIYAPS group.
    • Percent of CGM values in target range (70-180mg/dL) was significantly greater for DIYAPS users.
    • This shows a benefit in DIYAPS in reducing hyperglycemia without compromising a low occurrence of hypoglycemia. 
Conclusions_DIYAPSvsSAP_ADA2020_DanaMLewis

(You can download a PDF of the e-poster here.)

Finally, my presentation at this year’s D-Data conference (#DData20). The study I presented, called AID-IRL, was funded by Diabetes Mine. You can see a Twitter thread summarizing my AID-IRL presentation here.

AID-IRL-Aim-Methods_DanaMLewis

I did semi-structured phone interviews with 7 users of commercial AID systems in the last few months. The study was funded by DiabetesMine – both for my time in conducting the study, as well as funding for study participants. Study participants received $50 for their participation. I sought a mix of longer-time and newer AID users, using a mix of systems. Control-IQ (4) and 670G (2) users were interviewed; as well as (1) a CamAPS FX user since it was approved in the UK during the time of the study.

Based on the interviews, I coded their feedback for each of the different themes of the study depending on whether they saw improvements (or did not have issues); had no changes but were satisfied, or neutral experiences; or saw negative impact/experience. For each participant, I reviewed their experience and what they were happy with or frustrated by.

Here are some of the details for each participant.

AID-IRL-Participant1-DanaMLewisAID-IRL-Participant1-cont_DanaMLewis1 – A parent of a child using Control-IQ (off-label), with 30% increase in TIR with no increased hypoglycemia. They spend less time correcting than before; less time thinking about diabetes; and “get solid uninterrupted sleep for the first time since diagnosis”. They wish they had remote bolusing, more system information available in remote monitoring on phones. They miss using the system during the 2 hour CGM warmup, and found the system dealt well with growth spurt hormones but not as well with underestimated meals.

AID-IRL-Participant2-DanaMLewis AID-IRL-Participant2-cont-DanaMLewis2 – An adult male with T1D who previously used DIYAPS saw 5-10% decrease in TIR (but it’s on par with other participants’ TIR) with Control-IQ, and is very pleased by the all-in-one convenience of his commercial system.He misses autosensitivity (a short-term learning feature of how insulin needs may very from base settings) from DIYAPS and has stopped eating breakfast, since he found it couldn’t manage that well. He is doing more manual corrections than he was before.

AID-IRL-Participant5-DanaMLewis AID-IRL-Participant5-cont_DanaMLewis5 – An adult female with LADA started, stopped, and started using Control-IQ, getting the same TIR that she had before on Basal-IQ. It took artificially inflating settings to achieve these similar results. She likes peace of mind to sleep while the system prevents hypoglycemia. She is frustrated by ‘too high’ target; not having low prevention if she disables Control-IQ; and how much she had to inflate settings to achieve her outcomes. It’s hard to know how much insulin the system gives each hour (she still produces some of own insulin).

AID-IRL-Participant7-DanaMLewis AID-IRL-Participant7-cont-DanaMLewis7 – An adult female with T1D who frequently has to take steroids for other reasons, causing increased BGs. With Control-IQ, she sees 70% increase in TIR overall and increased TIR overnight, and found it does a ‘decent job keeping up’ with steroid-induced highs. She also wants to run ‘tighter’ and have an adjustable target, and does not ever run in sleep mode so that she can always get the bolus corrections that are more likely to bring her closer to target.

AID-IRL-Participant3-DanaMLewis AID-IRL-Participant3-cont-DanaMLewis3 – An adult male with T1D using 670G for 3 years didn’t observe any changes to A1c or TIR, but is pleased with his outcomes, especially with the ability to handle his activity levels by using the higher activity target.  He is frustrated by the CGM and is woken up 1-2x a week to calibrate overnight. He wishes he could still have low glucose suspend even if he’s kicked out of automode due to calibration issues. He also commented on post-meal highs and more manual interventions.

AID-IRL-Participant6-DanaMLewis AID-IRL-Participant6-contDanaMLewis6 – Another adult male user with 670G was originally diagnosed with T2 (now considered T1) with a very high total daily insulin use that was able to decrease significantly when switching to AID. He’s happy with increased TIR and less hypo, plus decreased TDD. Due to #COVID19, he did virtually training but would have preferred in-person. He has 4-5 alerts/day and is woken up every other night due to BG alarms or calibration. He does not like the time it takes to charge CGM transmitter, in addition to sensor warmup.

AID-IRL-Participant4-DanaMLewis AID-IRL-Participant4-contDanaMLewis4 – The last participant is an adult male with T1 who previously used DIYAPS but was able to test-drive the CamAPS FX. He saw no TIR change to DIYAPS (which pleased him) and thought the learning curve was easy – but he had to learn the system and let it learn him. He experienced ‘too much’ hypoglycemia (~7% <70mg/dL, 2x his previous), and found it challenging to not have visibility of IOB. He also found the in-app CGM alarms annoying. He noted the system may work better for people with regular routines.

You can see a summary of the participants’ experiences via this chart. Overall, most cited increased or same TIR. Some individuals saw reduced hypos, but a few saw increases. Post-meal highs were commonly mentioned.

AID-IRL-UniversalThemes2-DanaMLewis AID-IRL-UniversalThemes-DanaMLewis

Those newer to CGM have a noticeable learning curve and were more likely to comment on number of alarms and system alerts they saw. The 670G users were more likely to describe connection/troubleshooting issues and CGM calibration issues, both of which impacted sleep.

This view highlights those who more recently adopted AID systems. One noted their learning experience was ‘eased’ by “lurking” in the DIY community, and previously participating in an AID study. One felt the learning curve was high. Another struggled with CGM.

AID-IRL-NewAIDUsers-DanaMLewis

Both previous DIYAPS users who were using commercial AID systems referenced the convenience factor of commercial systems. One DIYAPS saw decreased TIR, and has also altered his behaviors accordingly, while the other saw no change to TIR but had increased hypo’s.

AID-IRL-PreviousDIYUsers-DanaMLewis

Companies building AID systems for PWDs should consider that the onboarding and learning curve may vary for individuals, especially those newer to CGM. Many want better displays of IOB and the ability to adjust targets. Remote bolusing and remote monitoring is highly desired by all, regardless of age. Post-prandial was frequently mentioned as the weak point in glycemic control of commercial AID systems. Even with ‘ideal’ TIR, many commercial users still are doing frequent manual corrections outside of mealtimes. This is an area of improvement for commercial AID to further reduce the burden of managing diabetes.

AID-IRL-FeedbackForCompanies-DanaMLewis

Note – all studies have their limitations. This was a small deep-dive study that is not necessarily representative, due to the design and small sample size. Timing of system availability influenced the ability to have new/longer time users.

AID-IRL-Limitations-DanaMLewis

Thank you to all of the participants of the study for sharing their feedback about their experiences with AID-IRL!

(You can download a PDF of my slides from the AID-IRL study here.)

Have questions about any of my posters or presentations? You can always reach me via email at Dana@OpenAPS.org.

Automated Insulin Delivery: How artificial pancreas “closed loop” systems can aid you in living with diabetes (introducing “the APS book” by @DanaMLewis)

Tl;dr – I wrote a book about artificial pancreas systems / hybrid and fully closed loop systems / automated insulin delivery systems! It’s out today – you can buy a print copy on Amazon; a Kindle copy on Amazon; check out all the content on the web or your phone here; or download a PDF if you prefer.

A few months ago, I saw someone share a link to one of my old blog posts with someone else on Facebook. Quite old in fact – I had written it 5+ years ago! But the content was and is still relevant today.

It made me wonder – how could we as a diabetes community, who have been innovating and exploring new diabetes technology such as closed loop/artificial pancreas systems (APS), package up some of this knowledge and share it with people who are newer to APS? And while yes, much of this is tucked into the documentation for DIY closed loop systems, not everyone will choose a DIY closed loop system and also therefore may not see or find this information. And with regards to some of the things I’ve written here on DIYPS.org, not everyone will be lucky enough to have the right combination of search terms to end up on a particular post to answer their question.

Automated_Insulin_Delivery_by_DanaMLewis_example_covers_renderingThus, the idea for a book was born. I wanted to take much of what I’ve been writing here, sharing on Facebook and Twitter, and seeing others discuss as well, and put it together in one place to be a good starting place for someone to learn about APS in general. My hope is that it’s more accessible for people who don’t know what “DIY” or “open source” diabetes is, and it’s findable by people who also don’t know or don’t consider themselves to be part of the “diabetes online community”.

APSBook_NowAvailable_DanaMLewisIs it perfect? Absolutely not! But, like most of the things in the DIY community…the book is open source. Seriously. Here’s the repository on Github! If you see a typo or have suggestions of content to add, you can make a PR (pull request) or log an issue with content recommendations. (There’s instructions on the book page here with how to do either of those things!) I plan to make rolling updates to it, so you can see on the change log page what’s changed between major versions.)

It’s the first book out there that I know of on APS, but it won’t be the only one. I hope this inspires or moves more people to share their knowledge, through blogs or podcasts or future books, with the rest of our community and loved ones who want and need to learn more about managing type 1 diabetes.

“I will immediately recommend this book not just to people looking to use a DIY closed loop system, but also to anybody looking to improve their grasp on the management of type 1 diabetes, whether patient, caregiver, or healthcare provider.”

Aaron Neinstein, MD
Endocrinologist, UCSF

And as always, I’m happy to share what I’ve learned about the self-publishing process, too. I previously used CreateSpace for my children’s books, which got merged with Amazon’s Kindle Direct Publishing (KDP), and there was a learning curve for KDP for both doing the print version and doing the Kindle version. I didn’t get paid to write this book – and I didn’t write it for a profit. Like my children’s books, I plan to use any proceeds to donate copies to libraries and hospitals, and send any remaining funds to Life For A Child to help ensure as many kids as possible have access to insulin, BG monitoring supplies, and education.

I’m incredibly grateful for many people for helping out with and contributing to this book. You can see the full acknowledgement section with my immense thanks to the many reviewers of early versions of the book! And ditto for the people who shared their stories and experiences with APS. But special thanks go in particular to Scott for thorough first editing and overall support of every project I bring up out of the blue; to Tim Gunn for beautiful cover design of the book; and to Aaron Kowalski to be kind enough to write this amazing foreword.

Amazon_Button_APSBook_DanaMLewis

Presentations and poster content from @DanaMLewis at #ADA2019

Like I did last year, I want to share the work being presented at #ADA2019 with those who are not physically there! (And if you’re presenting at #ADA2019 or another conference and would like suggestions on how to share your content in addition to your poster or presentation, check out these tips.) This year, I’m co-author on three posters and an oral presentation.

  • 1056-P in category 12-D Clinical Therapeutics/New Technology–Insulin Delivery Systems, Preliminary Characterization of Rhythmic Glucose Variability In Individuals With Type 1 Diabetes, co-authored by Dana Lewis and Azure Grant.
    • Come see us at the poster session, 12-1pm on Sunday! Dana & Azure will be presenting this poster.
  • 76-OR, In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems, co-authored by Andreas Melmer, Thomas Züger, Dana Lewis, Scott Leibrand, Christoph Stettler, and Markus Laimer.
    • Come hear our presentation in room S-157 (South, Upper Mezzanine Level), 2:15-2:30 pm on Saturday!
  • 117-LB, DIWHY: Factors Influencing Motivation, Barriers and Duration of DIY Artificial Pancreas System Use Among Real-World Users, co-authored by Katarina Braune, Shane O’Donnell, Bryan Cleal, Ingrid Willaing, Adrian Tappe, Dana Lewis, Bastian Hauck, Renza Scibilia, Elizabeth Rowley, Winne Ko, Geraldine Doyle, Tahar Kechadi, Timothy C. Skinner, Klemens Raille, and the OPEN consortium.
    • Come see us at the poster session, 12-1pm on Sunday! Scott will be presenting this poster.
  • 78-LB, Detailing the Lived Experiences of People with Diabetes Using Do-it-Yourself Artificial Pancreas Systems – Qualitative Analysis of Responses to Open-Ended Items in an International Survey, co-authored by Bryan Cleal, Shane O’Donnell, Katarina Braune, Dana Lewis, Timothy C. Skinner, Bastian Hauck, Klemens Raille, and the OPEN consortium.
    • Come see us at the poster session, 12-1pm on Sunday! Bryan Cleal will be presenting this poster.

See below for full written summaries and pictures from each poster and the oral presentation.

First up: the biological rhythms poster, formally known as 1056-P in category 12-D Clinical Therapeutics/New Technology–Insulin Delivery Systems, Preliminary Characterization of Rhythmic Glucose Variability In Individuals With Type 1 Diabetes!

Lewis_Grant_BiologicalRhythmsT1D_ADA2019

As mentioned in this DiabetesMine interview, Azure Grant & I were thrilled to find out that we have been awarded a JDRF grant to further this research and undertake the first longitudinal study to characterize biological rhythms in T1D, which could also be used to inform improvements and personalize closed loop systems. This poster is part of the preliminary research we did in order to submit for this grant.

There is also a Twitter thread for this poster:

Poster from #ADA2019

Background:

  • Human physiology, including blood glucose, exhibits rhythms at multiple timescales, including hours (ultradian, UR), the day (circadian, CR), and the ~28-day female ovulatory cycle (OR).
  • Individuals with T1D may suffer rhythmic disruption due not only to the loss of insulin, but to injection of insulin that does not mimic natural insulin rhythms, the presence of endocrine-timing disruptive medications, and sleep disruption.
  • However, rhythms at multiple timescales in glucose have not been mapped in a large population of T1D, and the extent to which glucose rhythms differ in temporal structure between T1D and non-T1D individuals is not known.

Data & Methods:

  • The initial data set used for this work leverages the OpenAPS Data Commons. (This data set is available for all researchers  – see www.OpenAPS.org/data-commons)
  • All data was processed in Matlab 2018b with code written by Azure Grant. Frequency decompositions using the continuous morlet wavelet transformation were created to assess change in rhythmic composition of normalized blood glucose data from 5 non-T1D individuals and anonymized, retrospective CGM data from 19 T1D individuals using a DIY closed loop APS. Wavelet algorithms were modified from code made available by Dr. Tanya Leise at Amherst College (see http://bit.ly/LeiseWaveletAnalysis)

Results:

  • Inter and Intra-Individual Variability of Glucose Ultradian and Circadian Rhythms is Greater in T1D
Figure_BiologicalRhythms_Lewis_Grant_ADA2019

Figure 1. Single individual blood glucose over ~ 1 year with A.) High daily rhythm stability and B.) Low daily rhythm stability. Low glucose is shown in blue, high glucose in orange.

Figure 2. T1D individuals (N=19) showed a wide range of rhythmic power at the circadian and long-period ultradian timescales compared to individuals without T1D (N=5).

A). Individuals’ CR and UR power, reflecting amplitude and stability of CRs, varies widely in T1D individuals compared to those without T1D. UR power was of longer periodicity (>= 6 h) in T1D, likely due to DIA effects, whereas UR power was most commonly in the 1-3 hour range in non-T1D individuals (*not shown).  B.) On average, both CR and UR power were significantly higher in T1D (p<.05, Kruskal Wallis). This is most likely due to the higher amplitude of glucose oscillation, shown in two individuals in C.

Conclusions:

  • This is the first longitudinal analysis of the structure and variability of multi-timescale biological rhythms in T1D, compared to non-T1D individuals.
  • Individuals with T1D show a wide range of circadian and ultradian rhythmic amplitudes and stabilities, resulting in higher average and more variable wavelet power than in a smaller sample of non-T1D individuals.
  • Ultradian rhythms of people with T1D are of longer periodicity than individuals without T1D. These analyses constitute the first pass of a subset of these data sets, and will be continued over the next year.

Future work:

  • JDRF has recently funded our exploration of the Tidepool Big Data Donation Project, the OpenAPS Data Commons, and a set of non-T1D control data in order to map biological rhythms of glucose/insulin.
  • We will use signal processing techniques to thoroughly characterize URs, CRs, and ORs in the glucose/insulin for T1D; evaluate if stably rhythmic timing of glucose is associated with improved outcomes (lower HBA1C); and ultimately evaluate if modulation of insulin delivery based on time of day or time of ovulatory cycle could lead to improved outcomes.
  • Mapping population heterogeneity of these rhythms in people with and without T1D will improve understanding of real-world rhythmicity, and may lead to non-linear algorithms for optimizing glucose in T1D.

Acknowledgements:

We thank the OpenAPS community for their generous donation of data, and JDRF for the grant award to further this work, beginning in July 2019.

Contact:

Feel free to contact us at Dana@OpenAPS.org or azuredominique@berkeley.edu.

Next up, 78-LB, Detailing the Lived Experiences of People with Diabetes Using Do-it-Yourself Artificial Pancreas Systems – Qualitative Analysis of Responses to Open-Ended Items in an International Survey, co-authored by Bryan Cleal, Shane O’Donnell, Katarina Braune, Dana Lewis, Timothy C. Skinner, Bastian Hauck, Klemens Raille, and the OPEN consortium.

78-LB_LivedExperiencesDIYAPS_OPEN_ADA2019

There is also a Twitter thread for this poster:

Poster from OPEN survey on lived experiences

Introduction

There is currently a wave of interest in Do-it-Yourself Artificial Pancreas Systems (DIYAPS), but knowledge about how the use of these systems impacts on the lives of those that build and use them remains limited. Until now, only a select few have been able to give voice to their experiences in a research context. In this study we present data that addresses this shortcoming, detailing the lived experiences of people using DIYAPS in an extensive and diverse way.

Methods

An online survey with 34 items was distributed to DIYAPS users recruited through the Facebook groups “Looped” (and regional sub-groups) and Twitter pages of the Diabetes Online Community (DOC). Participants were posed two open-ended questions in the survey, where personal DIYAPS stories were garnered; including knowledge acquisition, decision-making, support and emotional aspects in the initiation of DIYAPS, perceived changes in clinical and quality of life (QoL) outcomes after initiation and difficulties encountered in the process. All answers were analyzed using thematic content analysis.

Results

In total, 886 adults responded to the survey and there were a combined 656 responses to the two open-ended items. Knowledge of DIYAPS was primarily obtained via exposure to the communication fora that constitute the DOC. The DOC was also a primary source of practical and emotional support (QUOTES A). Dramatic improvements in clinical and QoL outcomes were consistently reported (QUOTES B). The emotional impact was overwhelmingly positive, with participants emphasizing that the persistent presence of diabetes in everyday life was markedly reduced (QUOTES C). Acquisition of the requisite devices to initiate DIYAPS was sometimes problematic and some people did find building the systems to be technically challenging (QUOTE D). Overcoming these challenges did, however, leave people with a sense of accomplishment and, in some cases, improved levels of understanding and engagement with diabetes management (QUOTE E).

QuotesA_OPEN_ADA2019 QuotesB_OPEN_ADA2019 QuotesC_OPEN_ADA2019 QuotesD_OPEN_ADA2019 QuotesE_OPEN_ADA2019

Conclusion

The extensive testimony from users of DIYAPS acquired in this study provides new insights regarding the contours of this evolving phenomenon, highlighting factors inspiring people to adopt such solutions and underlining the transformative impact effective closed-loop systems bring to bear on the everyday lives of people with diabetes. Although DIYAPS is not a viable solution for everyone with type 1 diabetes, there is much to learn from those who have taken this route, and the life-changing results they have achieved should inspire all with an interest in artificial pancreas technology to pursue and dream of a future where all people with type 1 diabetes can reap the benefits that it potentially provides.

Also, see this word cloud generated from 665 responses in the two open-ended questions in the survey:

Wordle_OPEN_ADA2019

Next up is 117-LB, DIWHY: Factors Influencing Motivation, Barriers and Duration of DIY Artificial Pancreas System Use Among Real-World Users, co-authored by Katarina Braune, Shane O’Donnell, Bryan Cleal, Ingrid Willaing, Adrian Tappe, Dana Lewis, Bastian Hauck, Renza Scibilia, Elizabeth Rowley, Winne Ko, Geraldine Doyle, Tahar Kechadi, Timothy C. Skinner, Klemens Raille, and the OPEN consortium.

DIWHY_117-LB_OPEN_ADA2019

There is also a Twitter thread for this poster:

DIWHY Poster at ADA2019

Background

Until recently, digital innovations in healthcare have typically followed a ‘top-down’ pathway, with manufacturers leading the design and production of technology-enabled solutions and patients involved only as users of the end-product. However, this is now being disrupted by the increasing influence and popularity of more ‘bottom-up’ and patient-led open source initiatives. A primary example is the growing movement of people with diabetes (PwD) who create their own “Do-it-Yourself” Artificial Pancreas Systems (DIY APS) through remote-control of medical devices employing an open source algorithm.

Objective

Little is known about why PwD leave traditional care pathways and turn to DIY technology. This study aims to examine the motivations of current DIYAPS users and their caregivers.

Research Design and Methods

An online survey with 34 items was distributed to DIYAPS users recruited through the Facebook groups “Looped” (and regional sub-groups) and Twitter pages of the “DOC” (Diabetes Online Community). Self-reported data was collected, managed and analyzed using the secure REDCap electronic data capture tools hosted at Charité – Universitaetsmedizin Berlin.

Results

1058 participants from 34 countries (81.3 % Europe, 14.7 % North America, 6.0 % Australia/WP, 3.1 % Asia, 0.1 % Africa), responded to the survey, of which the majority were adults (80.2 %) with type 1 diabetes (98.9 %) using a DIY APS themselves (43.0 % female, 56.8 % male, 0.3 % other) with a median age of 41 y and an average diabetes duration of 25.2y ±13.3. 19.8 % of the participants were parents and/or caregivers of children with type 1 diabetes (99.4 %) using a DIY APS (47.4 % female, 52.6 % male) with a median age of 10 y and an average diabetes duration of 5.1y ± 3.8. People used various DIYAPS (58.2 % AndroidAPS, 28.5 % Loop, 18.8 % OpenAPS, 5.7 % other) on average for a duration of 10.1 months ±17.6 and reported an overall HbA1c-improvement of -0.83 % (from 7.07 % ±1.07 to 6.24 % ±0.68 %) and an overall Time in Range improvement of +19.86 % (from 63.21 % ±16.27 to 83.07 % ±10.11). Participants indicated that DIY APS use required them to pay out-of-pocket costs in addition to their standard healthcare expenses with an average amount of 712 USD spent per year.

Primary motivations for building a DIYAPS were to improve the overall glycaemic control, reduce acute and long-term complication risk, increase life expectancy and to put diabetes on ‘auto-pilot’ and interact less frequently with the system. Lack of commercially available closed loop systems and improvement of sleep quality was a motivation for some. For caregivers, improvement of their own sleep quality was the leading motivation. For adults, curiosity (medical or technical interest) had a higher impact on their motivation compared to caregivers. Some people feel that commercial systems do not suit their individual needs and prefer to use a customizable system, which is only available to them as a DIY solution. Other reasons, like costs of commercially available systems and unachieved therapy goals played a subordinate role. Lack of medical or psychosocial support was less likely to be motivating factors for both groups.

Figure_OPEN_DIWHY_ADA2019

Conclusions

Our findings suggest that people using Do-it-Yourself Artificial Pancreas systems and their caregivers are highly motivated to improve their/their children’s diabetes management through the use of this novel technology. They are also able to access and afford the tools needed to use these systems. Currently approved and available commercial therapy options may not be sufficiently flexible or customizable enough to fulfill their individual needs. As part of the project “OPEN”, the results of the DIWHY survey may contribute to a better understanding of the unmet needs of PwD and current challenges to uptake, which will, in turn, facilitate dialogue and collaboration to strengthen the involvement of open source approaches in healthcare.

This is a written version of the oral presentation, In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems, co-authored by Andreas Melmer, Thomas Züger, Dana Lewis, Scott Leibrand, Christoph Stettler, and Markus Laimer.

APSComponents_Melmer_ADA2019

Artificial Pancreas Systems (APS) now exist, leveraging a CGM sensor, pump, and control algorithm. Faster insulin can play a role, too.  Traditionally, APS is developed by commercial industry, tested by clinicians, regulated, and then patients can access it. However, DIYAPS is designed by patients for individual use.

There are now multiple different kinds of DIYAPS systems in use: #OpenAPS, Loop, and AndroidAPS. There are differences in hardware, pump, and software configurations. The main algorithm for OpenAPS is also used in AndroidAPS.  DIYAPS can work offline; and also leverage the cloud for accessing or displaying data, including for remote monitoring.OnlineOffline_Melmer_ADA2019

This study analyzed data from the OpenAPS Data Commons (see more here). At the time this data set was used, there were n=80 anonymized data donors from the #OpenAPS community, with a combined 53+ years worth of CGM data.

TIR_PostLooping_Melmer_ADA2019Looking at results for #OpenAPS data donors post-looping initiation, CV was 35.5±5.9, while eA1c was 6.4±0.7. TIR (3.9-10mmol/L) was 77.5%. Time spent >10 was 18.2%; time <3.9 was 4.3%.

SubcohortData_Melmer_ADA2019We selected a subcohort of n=34 who had data available from before DIY closed looping initiation (6.5 years combined of CGM records), as well as data from after (12.5 years of CGM records).

For these next set of graphs, blue is BEFORE initiation (when just on a traditional pump); red is AFTER, when they were using DIYAPS.

TIR_PrePost_Melmer_ADA2019Time in a range significantly increased for both wider (3.9-10 mmol/L) and tighter (3.9-7.8 mmol/L) ranges.

TOR_PrePost_Melmer_ADA2019Time spent out of range decreased. % time spent >10 mmol/L decreased -8.3±8.6 (p<0.001); >13 mmol/L decreased -3.3±5.0 (p<0.001). Change in % time spent <3.9 mmol/L (-1.1±3.8 (p=0.153)), and <3.0 mmol/L (-0.7±2.2 (p=0.017)) was not significant.

We also analyzed daytime and nightime (the above was reflecting all 24hr combined; these graphs shows the increase in TIR and decrease in time out of range for both day and night).

TIR_TOR_DayAndNight_Melmer_ADA2019

Hypoglemic_event_reduction_Melmer_ADA2019There were less CGM records in the hypoglycemic range after initiating DIYAPS.

Conclusion: this was a descriptive study analyzing available CGM data from  #OpenAPS Data Commons. This study shows OpenAPS has potential to support glycemic control. However, DIYAPS are currently not regulated/approved technology. Further research is recommended.

Conclusion_Melmer_ADA2019

(Note: a version of this study has been submitted and accepted for publication in the Journal of Diabetes. Obesity, and Metabolism.)