AID (APS) book now available in French!

Thanks to the dedicated efforts of Olivier Legendre and Dr. Mihaela Muresan, my book “Automated Insulin Delivery: How artificial pancreas “closed loop” systems can aid you in living with diabetes” (available on Amazon in Kindle, paperback, and hardcover formats, or free to read online and download at ArtificialPancreasBook.com) is now available in French!

The French version is also available for free download as a PDF at ArtificialPancreasBook.com or in Kindle (FR), paperback (FR), and hardcover (FR) formats!

 

French version of the AID book is now available, also in hardcover, paperback, and Kindle formats on Amazon

Merci au Dr. Mihaela Muresan et Olivier Legendre pour la traduction de l’intégralité de ce livre !

(Thank you to Dr. Mihaela Muresan and Olivier Legendre for translating this entire book!)

Why it feels harder to dose pancreatic enzyme replacement therapy (PERT) than insulin

In 2002 when I was diagnosed with Type 1 diabetes, I struggled with being handed a vial of insulin and told vaguely to eat X amount of food and take Y amount of insulin. There was no ability to eat more and adjust the dose accordingly. It was frustrating. The only tool I had was a huge (imagine three iPhone 13 or equivalently large smartphones sitting on top of each other) blood glucose meter that took a lot of blood and a long time (a minute or more) to return a single blood glucose data point. The feedback loop wasn’t very useful, even when I tested my blood sugar manually 10-14 times per day.

Thankfully, in the last two decades, diabetes tools have evolved. Meters got smaller, faster, and take less blood. There has also been the devlopment of continuous glucose monitors (CGM) which I can wear and get near real-time readings of glucose data and can see what’s happened in the past. And, paired with an algorithm that also knows about the history of any insulin dosing on my insulin pump, and it can automatically adjust my insulin delivery in real time to predict, prevent, and reduce hypo- and hyperglycemia. (AID is awesome and if you haven’t heard about it, there’s a 4-minute free animated video here that explains it.) Diabetes no longer is quite the headache it was twenty – or even ten – years ago.

But realizing that I have exocrine pancreatic insufficiency (known as EPI or PEI) and learning how to take pancreatic enzyme replacement therapy (known as PERT) is a similar headache to diabetes in 2002.

With insulin, taking too much can cause hypoglycemia (low blood sugar). Taking too little can cause hyperglycemia (high blood sugar). Yet, with diabetes, you can measure blood glucose and see the response to insulin within a minutes-to-hours time frame. You can also use an insulin pump and an automated insulin delivery system to titrate and adjust insulin in real time.

However, for EPI, you need to take enzymes (that your pancreas doesn’t produce enough of) to help you digest your food. Your pancreas makes three types of enzymes: lipase, to help fat digest; protease, to help protein digest; and amylase, to help starches and carbohydrates digest. These are taken by mouth as a pill that you swallow. Together in one pill, it’s called “pancrelipase”, and it’s for pancreatic enzyme replacement therapy (PERT). (I’m personally bad about using pancrelilpase/PERT interchangeably, because PERT is faster to say and type, but it is possible to use standalone enzymes in PERT).

Because they are pills that you have to swallow when you eat, it’s hard to dose. Taking too little means you may have GI-related symptoms in the hours following the meal and feeling bad until the next day or so. Taking too much is expensive, although unlike insulin it’s rare to take “too much” and cause bad side effects (although possible at super high doses). There’s also the “pill burden”, because swallowing a bunch of pills is annoying and sometimes hard, both physically to swallow and to remember to take them throughout your meal.

You also can’t take more hours later if you forgot to take them or realize you didn’t dose enough for that meal. If you underdosed, you underdosed and just get to experience the symptoms that come with it. Sometimes, it’s not clear why you are having symptoms. Because there are three enzymes being replaced, it’s possible that the dosing was off for any one of the three enzymes. But again, there’s no measurement or feedback loop, or a sign that appears saying “you underdosed protease, take more next time”. The best you can do is try different sized meals over time with different doses of PERT, trying to reverse engineer your lipase:fat and protease:protein and amylase:carb ratios and continuously update them as you have new data.

It’s a lot of work, the feedback loop is slow, getting it “wrong” is painful physically and psychologically, and there are no vacations from it. Everything I eat, now that I have EPI, needs enzymes, and given the fact that I have automated insulin delivery to help manage insulin dosing, I am finding PERT to be a lot harder and more annoying (currently).

A comparison of dosing insulin and dosing enzymes. Insulin can cause hypo- or hyperlgycemia but there are tools (CGM and BG meters) and a feedback loop in diabetes. With enzymes, there is no fast feedback loop and underdosing is common. There is no ability to correct an underdose and there are multiple variables that can influence the outcome.

There’s no happy ending to this post, but this is one of the reasons why I am so interested in partnering with researchers to do research on EPI. There are a LOT of improvements that can be made, ranging from improving titration guidance of PERT to testing the efficacy of different over the counter enzymes to finding new technology that might begin to provide a feedback loop into EPI (either for short-term assessment or longer-term use for those who prefer it). If you’re someone interested in this type of research, please don’t hesitate to reach out (Dana@OpenAPS.org).

(PS, if you didn’t see them, I have other posts about EPI at DIYPS.org/EPI)

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.

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

Everything I did wrong (but did anyway) training for a marathon after a broken ankle and marathon running with type 1 diabetes

This is another one of those posts for a niche audience. If you found this post, you’re likely looking for information about:

  • Running after a broken ankle (or are coming from my “tips for returning to weight bearing” and looking for an update from me, two years after my trimalleolar ankle fracture)
  • Running with the “Galloway method”, also known as run-walk or run/walk methods for marathon or similar long distances – but with information about run-walking for slow runners.
  • Running a marathon with type 1 diabetes, or running an ultra with type 1 diabetes
  • Running a marathon and training for a marathon and going without fuel or less fuel
    *Update: also running an ultramarathon with the same methods (less fuel than typical)!

There’s a bit of all of this in the post! (But TLDR – I ran my marathon (finally), successfully, despite having a previously broken ankle. And despite running it with type 1 diabetes, I had no issues managing my blood sugars during even the longest training runs, even with significantly less fuel than is typically used by marathon runners. I also ran a 50k ultra using the same methods!)

running a marathon after a broken ankle and with type 1 diabetes

First up, some context that explains why I chose run-walking as my method of running a marathon (as that also influences fueling choices) and what it is like to be a slow marathon runner (6 hour marathon ish). I broke my ankle in January 2019 and began running very tiny amounts (literally down the block to start) in summer 2019. I progressed, doing a short run interval followed by a walk interval, increasing the total numbers of intervals, and then slowly progressing to extend the length (distance and/or time) of the running intervals. In early fall 2019, I was attempting a couch-to-5k type program where I would extend my running intervals even longer, but I still had subsequent injuries (a very stubborn big toe joint, then intermetatarsal bursitis in TWO spots (argh)) that made this not work well. Eventually, I went back to running 30 seconds and walking 30 seconds, then keeping those “short” intervals and extending my run. I focused on time at first: going from 5 to 10 to 15 to 20 etc minutes, rather than focusing on distance. Once I built up to about 30 minutes of run-walking (30:30, meaning running 30 seconds and walking 30 seconds), I switched to adding a quarter or half mile each time depending on how I was feeling. But doing 30:30 seemed to work really well for me in terms of the physical impact to my feet, even with long miles, and also mentally, so I stuck with it. (You can go read about the Galloway run-walk-run method for more about run-walking; most people build up to running more, say 5 minutes or 8 minutes followed by a minute of walking, or maybe run 1 mile and then walk for a minute, or walk through the aid stations, but I found that 30:30 is what I liked and stuck with it or 60:30 as my longest intervals.)

This worked so well for me that I did not think about my right ankle a single time during or after my marathon! It took days to even remember that I had previously broken my ankle and it could’ve been problematic or weaker than my other ankle during my marathon. It took a long time to get to this point – I never thought I’d be forgetting even for a few days about my broken ankle! But two years later, I did.)

When COVID-19 struck, and as someone who paid attention early (beginning late January 2020), I knew my marathon would not be taking place in July 2020 and would be postponed until 2021. So I focused on keeping my feet healthy and building up a running “base” of trying to stay healthy feet-wise running twice a week into fall 2020, which worked fairly well. At the start of 2021, I bumped up to three runs a week consistently, and eventually began making one run every other a week longer. My schedule looked something like this:

Monday – 3 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 4 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 5 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 6 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – (back to) 3 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – 8 miles  Wednesday – 3 miles   Friday – 3 miles

Monday – (back to) 3  miles  Wednesday – 5 miles   Friday – 4 miles

Monday – 10 miles  Wednesday – 3 miles   Friday – 3 miles

Note that these runs I refer to were all technically run-walks, where I ran 30 seconds and walked 30 seconds (aka 30:30) until I covered the miles. I was running slow and easy, focusing on keeping my heart rate below its maximum and not worrying about speed, so between that and run-walking I was often doing 15m30s miles. Yes, I’m slow. This all enabled me to build up to safely be able to run 3 runs weekly at first, and then eventually progressed to adding a fourth run. When I added a fourth run, I was very conservative and started with only 1 mile for two weeks in a row, then 2 miles, then up to 3 miles. Eventually, later in my training, I had some of my other runs in the week be a bit longer (4-5 miles) in addition to my “long” run.

But, because I’m so slow, this means it takes a lot of time to run my long runs. If you estimate a 15-minute mile for easy math, that means an 8 mile “long” run would take at least 2 hours. With marathon training (and my goal to train up to multiple 22-24 mile runs before the marathon), that took A LOT of time. And, because of my broken ankle and intermetatarsal experiences from 2019, I was very cautious and conservative about taking care of my feet during training. So instead of following the usual progression of long runs increasing 2-3 weeks in a row, followed by a “cutback” long week, after I hit two hours of long running (essentially 8 miles, for me), I started doing long runs every other week. The other week was a “cutback” long run, which was usually 8 miles, 10 miles (for several weeks), up to eventually 12-14. In terms of “time on feet”, this meant 2-3 hours “cutback” long runs, which according to many people is the max you should be running for marathon training. That doesn’t quite work for slow runners such as myself where you might be doing a 6-hour marathon or 7-hour marathon or thereabouts. (The standard advice also maybe doesn’t apply when you are doing run-walking for your marathon training.)

I had ~6 months to build up to my marathon (from January to the end of July), so I had time to do this, which gave me a buffer in my overall training schedule in case of scheduling conflicts (which happened twice) and in case of injury (which thankfully didn’t happen). I ended up scheduling training long runs all the way to full marathon distance (26ish miles), because I wanted to practice my fueling (especially important for type 1 diabetes marathon runners, which I’ll talk about next) as well as get my feet used to that many hours of run-walking. I did my long runs without care for speed, so some of them were closer to 16-minute mile averages, some were around 15-minute mile averages for the entire run, and the day I ran the full marathon course for training I ended up doing 16+ minute miles and felt fabulous at the end.

I ended up doing a few “faster” “shorter” long runs (on my cutback weeks), where I would do a half marathon-ish distance on the actual marathon course (a public trail), and try to go faster than my super slow long run pace. I had several successful runs where I was at or near marathon pace (which for me would be around 13m30s). So yes, you can train slow and run fast for a marathon, even without much speed work, and even if you are doing a run-walk method, and even if you’re as slow as I am. Running ~15-minute miles took forever but kept my feet and body healthy and happy through marathon training, and I was still able to achieve my sub-6 hour marathon goal (running 13:41 average pace for 26.2+ miles) on race day.

Now let’s talk about fueling, and in particular fueling for people with type 1 diabetes and for people wondering if the internet is right about what fueling requirements are for marathon runners.

I previously wrote (for a T1D audience) about running when fasted, because then you don’t have to deal with insulin on board at the start of a run. That’s one approach, and another approach is to have a smaller meal or snack with fewer carbs before the run, and time your run so that you don’t need to bolus or inject for that meal before you start your run. That’s what I chose for most of my marathon training, especially for longer runs.

On a typical non-running day, I would eat breakfast (½ cup pecans, ¼ cup cranberries, and a few sticks of cheese), my OpenAPS rig would take care of insulin dosing (or I could bolus for it myself), and my BGs would be well managed. However, that would mean I had a lot of insulin on board (IOB) if I tried to run within an hour of that. So instead, during marathon training, I ended up experimenting with eating a smaller amount of pecans (¼ cup) and no cranberries, not bolusing or letting OpenAPS bolus, and running an hour later. I had a small BG rise from the protein (e.g. would go from 100 mg/dL flat overnight to 120-130 mg/dL), and then running would balance out the rest of it.

I generally would choose to target my blood sugar to 130 mg/dL at the start of long runs, because I prefer to have a little bit of buffer for if/when my blood sugar began to drop. I also figured out that if I wasn’t having IOB from breakfast, I did not need to reduce my insulin much in advance of the run, but do it during the duration of the run. Therefore, I would set a higher temporary target in my OpenAPS rig, and if I was doing things manually, I would set a temporary basal rate on my insulin pump to about ⅓ of my usual hourly rate for the duration of the run. That worked well because by the time the beginning of my run (30-45 minutes) brought my BG down a little bit from the start with the protein breakfast bump (up to 130 mg/dL or so), that’d also be when the reduced insulin effect would be noticeable, and I would generally stay flat instead of having a drop at the beginning or first hour of my run.

After my first hour or so, I just kept an eye periodically on my blood sugars. My rule of thumb was that if my BG drifted down below 120 mg/dL, I would eat a small amount of carbs. My carb of choice was an individually wrapped peppermint (I stuffed a bunch in my pocket for the run) that was 3-4g of carb. If I kept drifting down or hadn’t come back up to 120 mg/dL 10-15 minutes later, I would do another. Obviously, if I was dropping fast I would do more, but 75% of the time I only needed one peppermint (3-4g of carb) to pause a drift down. If you have a lot of insulin on board, it would take more carbs, but my method of not having IOB at the start of long runs worked well for me. Sometimes, I would run my entire long run with no carbs and no fuel (other than water, and eventually electrolyte pills). Part of this is likely due to the fact that I was run-walking at such low intensity (remember 15-ish minute miles), but part of this is also due to figuring out the right amount of insulin I needed for endurance running and making sure I didn’t have excess insulin on board. On my faster runs (my half marathon distance fast training runs, that were 2+ minutes/mile faster than my slow long runs) and my marathon itself, I ended up needing more carbs than a super slow run – but it ended up being about 30 grams of carbohydrate TOTAL.

Why am I emphasizing this?

Well, the internet says (and most coaches, training plans, etc) that you need 30g of carbs PER HOUR. And that you need to train your stomach to tolerate that many carbs, because your muscles and brain need it. And without that much fuel, you will ‘hit the wall’.

My hypothesis, which may be nuanced by having type 1 diabetes and wearing a CGM and being able to track my data closely and manage it not only by carbs but also titrating insulin levels (which someone without diabetes obviously can’t do), is that you don’t necessarily need that many carbs, even for endurance running or marathon running.

I’m wondering if there’s a correlation between people who max out their long runs around 16-20 miles and who then “hit the wall” around mile 20 of a marathon. Perhaps some of it is muscle fatigue because they haven’t trained for the distance and some of it is psychological of feeling the brain fatigue.

During my marathon, in which I ran 2+ min/mi faster than most of my training runs, I did not ever experience hypoglycemia, and I did not “hit the wall”. Everything hurt, but I didn’t “hit the wall” as most people talk about. Those might be related, or it might be influenced by the fact that I had done a 20, 22, 24, 26, and another 21 mile run as part of my training, so my legs were “used” to the 20+ mile distance?

So again – some of my decreased fueling needs may be because I was already reducing my insulin and balancing my blood sugars (really well), and if my blood sugar was low (hypoglycemia), I would’ve needed more carbs. Or you can argue my lower fueling needs are because I’m so slow (15-16 minute mile training runs, or a 13m40s marathon pace). But in any case, I wanted to point out that if the fueling advice you’re getting or reading online seems like it’s “too much” per hour, there are people who are successful in hitting their time goals and don’t hit the wall on lower fueling amounts, too. You don’t necessarily have to fuel for the sake of fueling.

Note that I am not doing “low carb” or “keto” or anything particular diet-wise (other than eating gluten-free, because I also have celiac disease) outside of my running fuel choices. This was a successful strategy for me, and I eat what might be considered a moderate carb diet outside of running fuel choices.

Ps – if you don’t fuel (carbs or other nutrients) during your runs, don’t forget about your electrolytes. I decided to keep drinking water out of a Camelbak in a running pack, rather than filling it with Gatorade or a similar electrolyte drink, but I’m pretty electrolyte sensitive so I needed to do something to replace them. I got electrolyte pills and would take them every 30 minutes or so on long training runs when it was hotter. Play around with timing on those: if you don’t sweat a lot or aren’t a salty sweater, you may not need as many as often. I ended up doing the bulk of my long runs on hot days, and I sweat a lot, so every 30 minutes was about right for me. On cooler runs, one per hour was sufficient for me. (I tried these chewable tabs in lemon-lime but didn’t like the salt feeling directly in my mouth; I ended up buying these to swallow instead: I didn’t have any digestion issues or side effects from them, and they successfully kept my electrolytes to manageable levels. The package says not to take more than 10 within a 24 hour period, but I ended up taking 12 for my longest training run and the marathon itself and suffered no ill effects. It’s probably set to max 10 because of the amount of salt compared to the typical daily amount needed..but obviously, if you’re doing endurance running you need more than the daily amount of salt you would need on a regular day. But I’m not a doctor and this isn’t medical advice, of course – I’m just telling you what I chose to do).

In terms of training, here’s everything the internet told me to do for marathon training and everything I did “wrong” according to the typical advice:

  • Your long run should be 20-30% of your overall weekly mileageWhat I did: Sometimes my long runs got up to 70% of my weekly mileage, because I was only running 3 and then 4 days a week, and not doing very long mid-week runs.
  • Have longer mid-week runs, and build those up in addition to your true long runWhat I did: I did build up to a few 5-6 mile mid-week runs, but I chose consistency of my 4 runs per week rather than overdoing it with mid-week medium runs
  • Run 5-6 days a weekWhat I did: Only run 4 times a week, because I wanted a rest day after each run, and wanted a rest day prior to my longest run. I ran Monday, Wednesday, Friday, then added Saturday short runs. Monday was my long run (because I have the benefit of a flexible schedule for work).
  • Get high mileage (start from a base of 30-40 miles a week and build up to 50-60 miles!)What I did: I started with a “base” of 10 miles a week with two runs that I was very proud of. I went to three runs a week, and then 4. My biggest running week during training was 40.55 miles, although they were all 20+ mile weeks (long or cutback weeks) after the first two months of training.
  • Do progressively longer long runs for two or three weeks in a row and then do one cutback week, then continue the progressionWhat I did: Because of the time on my feet cost of being a slower runner, I did an every-other-week long-run progression alternating with a shorter cutback week.
  • Long run, tempo run, speed work, etc. plus easy runs! All the things each week!What I did: a long run per week, then the rest of my runs were usually easy runs. I tried a handful of times to do some “speed” work, but I often time was trying to keep my feet from being injured and it felt like running faster caused my feet to be sore or have other niggles in my legs, so I didn’t do much of that, other than doing some “cutback” long runs (around half marathon distance, as well as my last 21-mile run) at close to marathon pace to get a feel for how it felt to run at that pace for longer.

TLDR, again:

I signed up for a marathon in fall 2018 planning to run it in July 2019 but was thwarted by a broken ankle in January 2019 and COVID-19(/20) for 2020, so I ultimately trained for and ran it in July 2021. I am a slow runner, and I was able to achieve my sub-6 hour marathon goal using run-walk and without causing additional injury to my feet. And, because of my “slow” or less intense running, I needed a lot less fuel than is typically recommended for marathoners, and still managed my blood glucose levels within my ideal target ranges despite 5, 6, and even 7 hours run on my feet. Yes, you can run marathons with type 1 diabetes. And yes, you can run any length endurance runs with type 1 diabetes! I also ran a 50k ultramarathon using the same methods.

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:

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:

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:

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.)

Missing metrics in diabetes measurement by @DanaMLewis

“May I ask what your A1c is?”

This is a polite, and seemingly innocuous question. However, it’s one of my least favorite questions taken at face value. Why?

Well, this question is often a proxy for some of the following questions:

  • How well are *you* doing with DIY closed loop technology?
  • How well could *I* possibly do with DIY closed loop technology?
  • What’s possible to achieve in real-world life with type 1 diabetes?

But if I answered this question directly with “X.x%”, it leaves out so much crucial information. Such as:

  • What my BG targets are
    • Because with DIY closed loop tech like OpenAPS, you can choose and set your own target.
    • (That’s also one of the reasons why the 2018 OpenAPS Outcomes Study is fascinating to me, because people usually set high, conservative targets to start and then gradually lower them as they get comfortable. However, we didn’t have a way to retrospectively sleuth out targets, so those are results are even with the amalgamation of people’s targets being at any point they wanted at any time.)
  • What type of lifestyle I live
    • I don’t consider myself to eat particularly “high” or “low” carb. (And don’t start at me about why you choose to eat X amount of carbs – you do you! and YDMV) Someone who *is* eating a lot higher or lower carb diet compared to mine, though, may have a different experience than me.
    • Someone who is not doing exercise or activity may also have a different experience then me with variability in BGs. Sometimes I’m super active, climbing mountains (and falling off of them..more detail about that here) and running marathons and swimming or scuba diving, and sometimes I’m not. That activity is not so much about “being healthy”, but a point about how exercise and activity can actually make it a lot harder to manage BGs, both due to the volatility of the activity on insulin sensitivity etc.; but also because of the factor of going on/off of insulin for a period of time (because my pump is not waterproof).
  • What settings I have enabled in OpenAPS
    • I use most of the advanced settings, such as “superMicroBoluses” (aka SMB – read more about how it works here); with a higher than default “maxSMBBasalMinutes”; and I also use all of the advanced exercise settings so that targets also nudge sensitivity in addition to autosensitivity picking up any changes after exercise and other sensitivity-change-inducing activities or events. I also get Pushover alerts to tell me if I need any carbs (and how many), if I’m dropping faster and expected to go below my target, even with zero temping all the way down.
  • What my behavioral choices are
    • Timing of insulin matters. As I learned almost 5 years ago (wow), the impact of insulin timing compared to food *really* matters. Some people still are able to do and manage well with “pre-bolusing”. I don’t (as explained there in the previous link). But “eating soon” mode does help a lot for managing post-meal spikes (see here a quick and easy visual for how to do “eating soon”). However, I don’t do “eating soon” regularly like I used to. In part, because I’m now on a slightly-faster insulin that peaks in 45 minutes. I still get better outcomes when I do an eating-soon, sure, but behaviorally it’s less necessary.
    • The other reason is because I’ve also switched to not bolusing for meals.
      • (The exceptions being if I’m not looping for some reason, such as I’m in the middle of switching CGM sensors and don’t have CGM data to loop off of.)

These settings and choices are all crucial information to understanding the X.x% of A1c.

Diabetes isn’t just the average blood glucose value. It’s not just the standard deviation or coefficient of variation or % time in range or how much BG fluctuates.

Diabetes impacts so much of our daily life and requires so much cognitive burden for us, and our loved ones. That’s part of the reasons I appreciate so much Sulka & his family being candid about how their A1C didn’t change, but the amount of work required to achieve it did (way fewer manual corrections). And ditto for Jason & the Wittmer family for sharing about the change in the number of school nurse visits before/after using OpenAPS. (See both of their stories in this post)

For me, my quality of life metric has always been first about sleep: can I sleep safely and with peace of mind at night? Yes. Then – how long can I safely sleep? (The answer: a lot. Yay!)  But over time, my metrics have also evolved to consider how I can cut down (like Sulka) on the amount of work it takes to achieve my ideal outcomes, and find a happy balance there.

As I mentioned in this podcast recently, other than changing my pump site (here’s how I change mine) and soaking and swapping my CGM sensors (psst – soak your sensor!), I usually only take a few diabetes-related actions a day. They’re usually on my watch, pressing a button to either enable a temp target or entering carbs when I sit down to eat.

That’s a huge reduction in physical work, as well as amount of time spent thinking/planning/doing diabetes-related things. And when life happens – because I get the flu or the norovirus or I fall off a mountain and break my ankle – I don’t worry about diabetes any more.

So when I’m asked about A1c, my answer is not a simple “X.x%”. (And not just for the reason I’m annoyed by how much judging and shaming goes on around A1c, although that influences it, too.) I usually remind people that I first started with an “open loop” for a year, and that dropped my A1c by X%. And then I closed the loop, which reduced my A1c further. And we made OpenAPS even better over the last four years, which reduced it further. And then I completely stopped bolusing! And got less lows…and kept the same A1c.

And then I ask them what they’d really like to know. :) If it’s a fellow person with diabetes or a loved one, we talk about what problems they might be having or what areas they’d like to improve or what behaviors they’d like to change, if any. That’s usually way more effective than hearing “X.x%” of an A1c, and them wondering silently how to get there or what to do differently if someone wants to change things. (Or for clinicians who ask me, it turns into a discussion about choices and behaviors and tradeoffs that patients may choose to make.)

Remember, your diabetes may (and will) vary (aka, YDMV). Your lifestyle, the phase of life you’re in, your priorities, your body and health, and your choices will ALL be different than mine. That’s not bad in any way: that’s just the way it is. The behaviors I choose and the work I’m willing to do (or not do) to achieve *my* goals (and what my goals are), will be different than what you choose for yours.

And that’s therefore why A1c is not “enough” to me as a metric and something that we should compare people on, even though A1c is the “same” for everyone: because the work, time spent, behavioral tradeoffs, and goals related to it will all vary.

Missing_metrics_@DanaMLewis

4 years DIY closed looping with #OpenAPS – what changed and what hasn’t

It’s hard to express the magnitude of how much closed looping can improve a person with diabetes’ life, especially to someone who doesn’t have diabetes or live closely with someone that does. There are so many benefits – and so many way beyond the typically studied “A1c improvement” and “increased time in range”. Sure, those happen (and in case you haven’t seen it, see some of the outcomes from various international studies looking at DIY closed loop outcomes). But everything else…it’s hard to explain all of the magic that happens in real life, that’s made so much richer by having technology that for the most part keeps diabetes out of the way, and more importantly: off the top of your mind.

Personally, my first and most obvious benefit, and the whole reason I started DIYing in the first place, was to have the peace of mind to sleep safely at night. Objective achieved, immediately. Then over time, I got the improvements in A1c and time in range, plus reduction in time spent doing diabetes ‘stuff’ and time spent thinking about my own diabetes. The artificial pancreas ‘rigs’ got smaller. We improved the algorithm, to the point where it can handle the chaos that is everything from menstrual cycle to having the flu or norovirus.

More recently, in the past ~17 months, I’ve achieved an ultimate level of not doing much diabetes work that I never thought was possible: with the help of faster insulin and things like SMB’s (improved algorithm enhancements in OpenAPS), I’ve been able do a simple meal announcement by pressing a button on my watch or phone..and not having to bolus. Not worrying about precise carb counts. Not worrying about specific timing of insulin activity. Not worrying about post-meal lows. Not worrying about lots of exercise. And the results are pretty incredible to me:

But I remember early on when we had announced that we had figured out how to close the loop. We got a lot of push back saying, well, that’s good for you – but will it work for anyone else? And I remember thinking about how if it helped one other person sleep safely at night..it would be worth the amount of work it would take to open source it. Even if we didn’t know how well it would work for other people, we had a feeling it might work for some people. And that for even a few people who it might work for, it was worth doing. Would DIY end up working for everyone, or being something that everyone would want to do? Maybe not, and definitely not. We wouldn’t necessarily change the world for everyone by open sourcing an APS, but that could help change the world for someone else, and we thought that was (and still is) worth doing. After all, the ripple effect may help ultimately change the world for everyone else in ways we couldn’t predict or expect.

Ripple_effect_DanaMLewisThis has become true in more ways than one.

That ‘one other person’ turned into a few..then dozen..hundreds..and now probably thousand(s) around the world using various DIY closed loop systems.

And in addition to more people being able to choose to access different DIY systems with more pumps of choice, CGMs of choice, and algorithm of choice, we’ve also seen the ripple effect in the way the world works, too. There is now, thankfully, at least one company who is evaluating open source code; running simulations with it; and where it is out-performing their original algorithm or code components, utilizing that knowledge to improve their system. They’re also giving back to the open source diabetes community, too. Hopefully more companies will take this approach & bring better products more quickly to the market. When they are ready to submit said products, we know at least U.S. regulators at the FDA are ready to quickly review and work with companies to get better tools on the market. That’s a huge change from years ago, when there was a lot of finger pointing and what felt like a lot of delay preventing newer technology from reaching the market. The other change I’m seeing is in diabetes research, where researchers are increasingly working directly with patients from the start and designing better studies around the things that actually matter to people with diabetes, including analyzing the impact and outcomes of open source technology.

After five years of open source diabetes work, and specifically four years of DIY closed looping, it finally feels like the ripples are ultimately helping achieve the vision we had at the start of OpenAPS, articulated in the conclusion of the OpenAPS Reference Design:

OpenAPS_Reference_Design_conclusionIs there still more work to do? Absolutely.

Even as more commercial APS roll out, it takes too long for these to reach many countries. And in most parts of the world, it’s still insanely hard and/or expensive to get insulin (which is one of the reasons Scott and I support Life For A Child to help get insulin, supplies, and education to as many children as possible in countries where otherwise they wouldn’t be able to access it – more on that here.). And even when APS are “approved” commercially, that doesn’t mean they’ll be affordable or accessible, even with health insurance. So I expect our work to continue, not only to support ongoing improvements with DIY systems directly; but also with encouraging and running studies to generalize knowledge from DIY systems; hopefully seeing DIY systems approved to work with existing interoperable devices; helping any company that will listen to improve their systems, both in terms of algorithms but also in terms of usability; helping regulators to see both what’s possible as well as what’s needed to successfully using these types of system in the real world. I don’t see this work ending for years to come – not until the day where every person with diabetes in every country has access to basic diabetes supplies, and the ability to choose to use – or not – the best technology that we know is possible.

But even so, after four years of DIY closed looping, I’m incredibly thankful for the quality of life that has been made possible by OpenAPS and the community around it. And I’m thankful for the community for sharing their stories of what they’ve accomplished or done while using DIY closed loop systems. It’s incredible to see people sharing stories of how they are achieving their best outcomes after 45 years of diabetes; or people posting from Antartica; or after running marathons; or after a successful and healthy pregnancy where they used their DIY closed loop throughout; or after they’ve seen the swelling in their eyes go done; etc.

The stories of the real-life impacts of this type of technology are some of the best ripple effects that I never want to forget.