Functional Self-Tracking is The Only Self-Tracking I Do

“I could never do that,” you say.

And I’ve heard it before.

Eating gluten free for the rest of your life, because you were diagnosed with celiac disease? Heard that response (I could never do that) for going on 14 years.

Inject yourself with insulin or fingerstick test your blood glucose 14 times a day? Wear an insulin pump on your body 24/7/365? Wear a CGM on your body 24/7/365?

Yeah, I’ve heard you can’t do that, either. (For 20 years and counting.) Which means I and the other people living with the situations that necessitate these behaviors are…doing this for fun?

We’re not.

More recently, I’ve heard this type of comment come up about tracking what I’m eating, and in particular, tracking what I’m eating when I’m running. I definitely don’t do that for fun.

I have a 20+ year strong history of hating tracking things, actually. When I was diagnosed with type 1 diabetes, I was given a physical log book and asked to write down my blood glucose numbers.

“Why?” I asked. They’re stored in the meter.

The answer was because supposedly the medical team was going to review them.

And they did.

And it was useless.

“Why were you high on February 22, 2003?”

Whether we were asking this question in March of 2003 or January of 2023 (almost 20 years later), the answer would be the same: I have no idea.

BG data, by itself, is like a single data point for a pilot. It’s useless without the contextual stream of data as well as other metrics (in the diabetes case, things like what was eaten, what activity happened, what my schedule was before this point, and all insulin dosed potentially in the last 12-24h).

So you wouldn’t be surprised to find out that I stopped tracking. I didn’t stop testing my blood glucose levels – in fact, I tested upwards of 14 times a day when I was in high school, because the real-time information was helpful. Retrospectively? Nope.

I didn’t start “tracking” things again (for diabetes) until late 2013, when we realized that I could get my CGM data off the device and into the laptop beside my bed, dragging the CGM data into a CSV file in Dropbox and sending it to the cloud so an app called “Pushover” would make a louder and different alarm on my phone to wake me up to overnight hypoglycemia. The only reason I added any manual “tracking” to this system was because we realized we could create an algorithm to USE the information I gave it (about what I was eating and the insulin I was taking) combined with the real-time CGM data to usefully predict glucose levels in the future. Predictions meant we could make *predictive* alarms, instead of solely having *reactive* alarms, which is what the status quo in diabetes has been for decades.

So sure, I started tracking what I was eating and dosing, but not really. I was hitting buttons to enter this information into the system because it was useful, again, in real time. I didn’t bother doing much with the data retrospectively. I did occasional do things like reflect on my changes in sensitivity after I got the norovirus, for example, but again this was mostly looking in awe at how the real-time functionality of autosensitivity, an algorithm feature we designed to adjust to real-time changes in sensitivity to insulin, dealt throughout the course of being sick.

At the beginning of 2020, my life changed. Not because of the pandemic (although also because of that), but because I began to have serious, very bothersome GI symptoms that dragged on throughout 2020 and 2021. I’ve written here about my experiences in eventually self-diagnosing (and confirming) that I have exocrine pancreatic insufficiency, and began taking pancreatic enzyme replacement therapy in January 2022.

What I haven’t yet done, though, is explain all my failed attempts at tracking things in 2020 and 2021. Or, not failed attempts, but where I started and stopped and why those tracking attempts weren’t useful.

Once I realized I had GI symptoms that weren’t going away, I tried writing down everything I ate. I tried writing in a list on my phone in spring of 2020. I couldn’t see any patterns. So I stopped.

A few months later, in summer of 2020, I tried again, this time using a digital spreadsheet so I could enter data from my phone or my computer. Again, after a few days, I still couldn’t see any patterns. So I stopped.

I made a third attempt to try to look at ingredients, rather than categories of food or individual food items. I came up with a short list of potential contenders, but repeated testing of consuming those ingredients didn’t do me any good. I stopped, again.

When I first went to the GI doctor in fall of 2020, one of the questions he asked was whether there was any pattern between my symptoms and what I was eating. “No,” I breathed out in a frustrated sigh. “I can’t find any patterns in what I’m eating and the symptoms.”

So we didn’t go down that rabbit hole.

At the start of 2021, though, I was sick and tired (of being sick and tired with GI symptoms for going on a year) and tried again. I decided that some of my “worst” symptoms happened after I consumed onions, so I tried removing obvious sources of onion from my diet. That evolved to onion and garlic, but I realized almost everything I ate also had onion powder or garlic powder, so I tried avoiding those. It helped, some. That then led me to research more, learn about the categorization of FODMAPs, and try a low-FODMAP diet in mid/fall 2021. That helped some.

Then I found out I actually had exocrine pancreatic insufficiency and it all made sense: what my symptoms were, why they were happening, and why the numerous previous tracking attempts were not successful.

You wouldn’t think I’d start tracking again, but I did. Although this time, finally, was different.

When I realized I had EPI, I learned that my body was no longer producing enough digestive enzymes to help my body digest fat, protein, and carbs. Because I’m a person with type 1 diabetes and have been correlating my insulin doses to my carbohydrate consumption for 20+ years, it seemed logical to me to track the amount of fat and protein in what I was eating, track my enzyme (PERT) dosing, and see if there were any correlations that indicated my doses needed to be more or less.

My spreadsheet involved recording the outcome of the previous day’s symptoms, and I had a section for entering multiple things that I ate throughout the day and the number of enzymes. I wrote a short description of my meal (“butter chicken” or “frozen pizza” or “chicken nuggets and veggies”), the estimate of fat and protein counts for the meal, and the number of enzymes I took for that meal. I had columns on the left that added up the total amount of fat and protein for the day, and the total number of enzymes.

It became very apparent to me – within two days – that the dose of the enzymes relative to the quantity of fat and protein I was eating mattered. I used this information to titrate (adjust) my enzyme dose and better match the enzymes to the amount of fat or protein I was eating. It was successful.

I kept writing down what I was eating, though.

In part, because it became a quick reference library to find the “counts” of a previous meal that I was duplicating, without having to re-do the burdensome math of adding up all the ingredients and counting them out for a typical portion size.

It also helped me see that within the first month, I was definitely improving, but not all the way – in terms of fully reducing and eliminating all of my symptoms. So I continued to use it to titrate my enzyme doses.

Then it helped me carefully work my way through re-adding food items and ingredients that I had been avoiding (like onions, apples, and pears) and proving to my brain that those were the result of enzyme insufficiency, not food intolerances. Once I had a working system for determining how to dose enzymes, it became a lot easier to see when I had slight symptoms from slightly getting my dosing wrong or majorly mis-estimating the fat and protein in what I was eating.

It provided me with a feedback loop that doesn’t really exist in EPI and GI conditions, and it was a daily, informative, real-time feedback loop.

As I reached the end of my first year of dosing with PERT, though, I was still using my spreadsheet. It surprised me, actually. Did I need to be using it? Not all the time. But the biggest reason I kept using it relates to how I often eat. I often look at an ‘entree’ for protein and then ‘build’ the rest of my meal around that, to help make sure I’m getting enough protein to fuel my ultrarunning endeavors. So I pick my entree/main thing I’m eating and put it in my spreadsheet under the fat and protein columns (=17 g of fat, =20 g of protein), for example, then decide what I’m going to eat to go with it. Say I add a bag of cheddar popcorn, so that becomes (=17+9 g of fat) and (=20+2 g of protein), and when I hit enter, those cells now tell me it’s 26 g of fat and 22 g of protein for the meal, which tells my brain (and I also tell the spreadsheet) that I’ll take 1 PERT pill for that. So I use the spreadsheet functionally to “build” what I’m eating and calculate the total grams of protein and fat; which helps me ‘calculate’ how much PERT to take (based on my previous titration efforts I know I can do up to 30g of fat and protein each in one PERT pill of the size of my prescription)

Example in my spreadsheet showing a meal and the in-progress data entry of entering the formula to add up two meal items' worth of fat and protein

Essentially, this has become a real-time calculator to add up the numbers every time I eat. Sure, I could do this in my head, but I’m usually multitasking and deciding what I want to eat and writing it down, doing something else, doing yet something else, then going to make my food and eat it. This helps me remember, between the time I decided – sometimes minutes, sometimes hours in advance of when I start eating and need to actually take the enzymes – what the counts are and what the PERT dosing needs to be.

I have done some neat retrospective analysis, of course – last year I had estimated that I took thousands of PERT pills (more on that here). I was able to do that not because it’s “fun” to track every pill that I swallow, but because I had, as a result of functional self-tracking of what I was eating to determine my PERT dosing for everything I ate, had a record of 99% of the enzyme pills that I took last year.

I do have some things that I’m no longer entering in my spreadsheet, which is why it’s only 99% of what I eat. There are some things like a quick snack where I grab it and the OTC enzymes to match without thought, and swallow the pills and eat the snack and don’t write it down. That maybe happens once a week. Generally, though, if I’m eating multiple things (like for a meal), then it’s incredibly useful in that moment to use my spreadsheet to add up all the counts to get my dosing right. If I don’t do that, my dosing is often off, and even a little bit “off” can cause uncomfortable and annoying symptoms the rest of the day, overnight, and into the next morning.

So, I have quite the incentive to use this spreadsheet to make sure that I get my dosing right. It’s functional: not for the perceived “fun” of writing things down.

It’s the same thing that happens when I run long runs. I need to fuel my runs, and fuel (food) means enzymes. Figuring out how many enzymes to dose as I’m running 6, 9, or 25 hours into a run gets increasingly harder. I found that what works for me is having a pre-built list of the fuel options; and a spreadsheet where I quickly on my phone open it and tap a drop down list to mark what I’m eating, and it pulls in the counts from the library and tells me how many enzymes to take for that fuel (which I’ve already pre-calculated).

It’s useful in real-time for helping me dose the right amount of enzymes for the fuel that I need and am taking every 30 minutes throughout my run. It’s also useful for helping me stay on top of my goal amounts of calories and sodium to make sure I’m fueling enough of the right things (for running in general), which is something that can be hard to do the longer I run. (More about this method and a template for anyone who wants to track similarly here.)

The TL;DR point of this is: I don’t track things for fun. I track things if and when they’re functionally useful, and primarily that is in real-time medical decision making.

These methods may not make sense to you, and don’t have to.

It may not be a method that works for you, or you may not have the situation that I’m in (T1D, Graves, celiac, and EPI – fun!) that necessitates these, or you may not have the goals that I have (ultrarunning). That’s ok!

But don’t say that you “couldn’t” do something. You ‘couldn’t’ track what you consumed when you ran or you ‘couldn’t’ write down what you were eating or you ‘couldn’t’ take that many pills or you ‘couldn’t’ inject insulin or…

You could, if you needed to, and if you decided it was the way that you could and would be able to achieve your goals.

Looking Back Through 2022 (What You May Have Missed)

I ended up writing a post last year recapping 2021, in part because I felt like I did hardly anything – which wasn’t true. In part, that was based on my body having a number of things going on that I didn’t know at the time. I figured those out in 2022 which made 2022 hard and also provided me with a sense of accomplishment as I tackled some of these new challenges.

For 2022, I have a very different feeling looking back on the entire year, which makes me so happy because it was night and day (different) compared to this time last year.

One major example? Exocrine Pancreatic Insufficiency.

I started taking enzymes (pancreatic enzyme replacement therapy, known as PERT) in early January. And they clearly worked, hooray!

I quickly realized that like insulin, PERT dosing needed to be based on the contents of my meals. I figured out how to effectively titrate for each meal and within a month or two was reliably dosing effectively with everything I was eating and drinking. And, I was writing and sharing my knowledge with others – you can see many of the posts I wrote collected at DIYPS.org/EPI.

I also designed and built an open source web calculator to help others figure out their ratios of lipase and fat and protease and protein to help them improve their dosing.

I even published a peer-reviewed journal article about EPI – submitted within 4 months of confirming that I had it! You can read that paper here with an analysis of glucose data from both before and after starting PERT. It’s a really neat example that I hope will pave the way for answering many questions we all have about how particular medications possibly affect glucose levels (instead of simply being warned that they “may cause hypoglycemia or hyperglycemia” which is vague and unhelpful.)

I also had my eyes opened to having another chronic disease that has very, very expensive medication with no generic medication option available (and OTCs may or may not work well). Here’s some of the math I did on the cost of living with EPI and diabetes (and celiac and Graves) for a year, in case you missed it.

Another other challenge+success was running (again), but with a 6 week forced break (ha) because I massively broke a toe in July 2022.

That was physically painful and frustrating for delaying my ultramarathon training.

I had been successfully figuring out how to run and fuel with enzymes for EPI; I even built a DIY macronutrient tracker and shared a template so others can use it. I ran a 50k with a river crossing in early June and was on track to target my 100 mile run in early fall.

However with the broken toe, I took the time off needed and carefully built back up, put a lot of planning into it, and made my attempt in late October instead.

I succeeded in running ~82 miles in ~25 hours, all in one go!

I am immensely proud of that run for so many reasons, some of which are general pride at the accomplishment and others are specific, including:

  • Doing something I didn’t think I could do which is running all day and all night without stopping
  • Doing this as a solo or “DIY” self-organized ultra
  • Eating every 30 minutes like clockwork, consuming enzymes (more than 92 pills!), which means 50 snacks consumed. No GI issues, either, which is remarkable even for an ultrarunner without EPI!
  • Generally figuring out all the plans and logistics needed to be able to handle such a run, especially when dealing with type 1 diabetes, celiac, EPI, and Graves
  • Not causing any injuries, and in fact recovering remarkably fast which shows how effective my training and ‘race’ strategy were.

On top of this all, I achieved my biggest-ever running year, with more than 1,333 miles run this year. This is 300+ more than my previous best from last year which was the first time I crossed 1,000 miles in a year.

Professionally, I did quite a lot of miscellaneous writing, research, and other activities.

I spent a lot of time doing research. I also peer reviewed more than 24 papers for academic journals. I was asked to join an editorial board for a journal. I served on 2 grant review committees/programs.

I also wrote a lot.

*by ton, I mean way more than the past couple of years combined. Some of that has been due to getting some energy back once I’ve fixed missing enzyme and mis-adjusted hormone levels in my body! I’m up to 40+ blog posts this year.

And personally, the punches felt like they kept coming, because this year we also found out that I have Graves’ disease, taking my chronic disease count up to 4. Argh. (T1D, celiac, EPI, and now Graves’, for those curious about my list.)

My experience with Graves’ has included symptoms of subclinical hyperthyroidism (although my T3 and T4 are in range), and I have chosen to try thyroid medication in order to manage the really bothersome Graves’-related eye symptoms. That’s been an ongoing process and the symptoms of this have been up and down a number of times as I went on medication, reduced medication levels, etc.

What I’ve learned from my experience with both EPI and Graves’ in the same year is that there are some huge gaps in medical knowledge around how these things actually work and how to use real-world data (whether patient-recorded data or wearable-tracked data) to help with diagnosis, treatment (including medication titration), etc. So the upside to this is I have quite a few new projects and articles coming to fruition to help tackle some of the gaps that I fell into or spotted this year.

And that’s why I’m feeling optimistic, and like I accomplished quite a bit more in 2022 than in 2021. Some of it is the satisfaction of knowing the core two reasons why the previous year felt so physically bad; hopefully no more unsolved mysteries or additional chronic diseases will pop up in the next few years. Yet some of it is also the satisfaction of solving problems and creating solutions that I’m uniquely poised, due to my past experiences and skillsets, to solve. That feels good, and it feels good as always to get to channel my experiences and expertise to try to create solutions with words or code or research to help other people.

How To Dose Pancreatic Enzyme Replacement Therapy (PERT) By What You Are Eating – And A Free Web Calculator To Calculate Enzyme Dosing

I’ve had exocrine pancreatic insufficiency (known as EPI or PEI) for a year now. I have had type 1 diabetes for 20+ years and am experienced in adjusting my medication (previously insulin) in response to everything that I eat or drink.

With EPI, though, most people are given a static prescription, such as one saying “take 3 pills with each meal”.

Well, what if every meal is not the same size?

Let’s think about a couple of hypothetical meals.

Meal A: Baked chicken, sweet potato, and broccoli. This meal likely results in ~31 grams of carbohydrates; 7 grams of fat; and ~30 grams of protein.

How would you dose for this meal? Most people do what they are told and dose based on the fat content of the meal. If they typically take 3 pills, they may take all 3 pills or take fewer pills if this is less fat than their typical meal.

Many people post in EPI social media groups post about restaurant dinners that sound like this complaining about side effects they experience with this type of meal. The commonly mentioned theory is that maybe the chicken is cooked in oil. However, the entire meal is so low in fat compared to other meals that it is unlikely to be the fat content causing symptoms if the typical meal dose of PERT is used, even if the chicken is cooked in oil.

Let’s discuss another meal.

Meal B: A bowl of chili topped with cheddar cheese and a piece of cornbread.

This meal results in ~45 grams of carbs; ~30 grams of fat; and ~42 grams of protein.

The fat content between these two meals is quite a bit different (7 grams of fat versus 30 grams of fat). Yet, again, most people are told simply to dose by the amount of fat, so someone might take a lower dose for the chicken meal because it has so little fat relative to other meals.

This could result in symptoms, though. The pancreas actually produces THREE kinds of enzymes. That’s why pancreatic enzyme replacement therapy medicine, called pancrelipase as a common name, has THREE types of enzymes: lipase, to help digest fat; protease, to help digest protein; and amylase, to help digest carbohydrates. A typical PERT pill has different amounts of these three enzymes, although it is usually described by the size/quantity of lipase it has – yet the other enzymes still play an important role in digestion.

I’ve observed that it’s pretty common for people to completely ignore the protein in what they’re eating. But as I mentioned, that seems to be the most obvious thing to try dosing for if “low fat” meals are causing issues. (It could also be sensitivity to carbohydrates, but the above example meal is fairly low carbohydrate.) My personal experience has also been that I am sensitive to fat and protein, and dose my meals based on these macronutrients, but other than eating fruit on an empty stomach (when I would add PERT/enzyme, despite the zero fat and protein in it), I don’t need to dose based on carbohydrates.

But I do need to dose for BOTH fat AND protein in what I’m eating. And I have a theory that a lot of other people with EPI do, too.

So how do you do this?

How do you dose for meals of different sizes, and take into account both fat and protein for these varying meals?

First, you need to figure out what dosing “works” for you and begin to estimate some “ratios” that you can use.

Most people begin experimenting and find a quantity of food that they can eat with the dose that they typically take. This meal size is going to vary person to person; it’ll also vary based on what it is in the meal they’re eating (such as chicken vs chili, from the above examples).

Once you find a dose that “works” and try it out a few times on the same meal, you can use this to determine what your ratios/dosing should be.

How?

Let’s use two examples with different dose sizes and types of PERT.

(PS – did you know there are 6 FDA-approved PERT brands in the US? Sometimes one works for someone where a different brand does not. If you’re struggling with the first type of PERT you’ve been prescribed, and you’ve already ruled out that you’re dosing correctly (see below), make sure to talk to your doctor and ask about trying a different brand.)

First, let’s calculate the ratios of lipase needed per gram of fat.

Let’s say the meal that “works” with your typical dose is 30 grams of fat. If 30 grams of fat is fine on your current dose, I would eat another meal with a slightly higher amount of fat (such as 35 or 40 grams of fat). When you get to an amount that “doesn’t work” – meaning you get symptoms – then you go back to the dose that does “work” to use in the math.

If the meal that “worked” was 30 grams of fat I would do the following math for each of these two examples:

Example A: You need 1 pill of Zenpep 25,000 to cover this meal

Example B: You need 3 pills of Creon 36,000 to cover this meal

Example A: 1 pill of Zenpep 25,000 is 1 multiplied by 25,000, or 25,000 units of lipase. Take that (25,000) and divide it by the grams of fat in the meal that works (30 grams). This would be 25,000/30 = 833. This means you need 833 units of lipase to “cover” 1 gram of fat. You can round up to ~1000 units of lipase to make it easier; your ratio would be 1000 units of lipase for every 1 gram of fat.

Example B: 3 pills of Creon 36,000 is 3 multiplied by 36,000, which is 108,000 units of lipase. Take that number (108,000) and divide it by the grams of fat in the meal that works (30 grams). This would be 108,000/30 = 3,600. This means you need 3,600 units of lipase to “cover” 1 gram of fat.

The next time you wanted to eat a meal, you would look at the grams of fat in a meal.

Let’s say you’re going to eat two bowls of chili and two pieces of cornbread. Let’s assume that is about 64 grams of fat. (Two bowls of chili and two cornbread is 30×2=60, plus a bit of butter for the cornbread so we’re calling it 64 grams of fat).

Example A: Take the meal and multiply it by your ratio. 64 (grams of fat) x 1,000 (how many units of lipase you need to cover 1 grant of fat) = 64,000. A Zenpep 25,000 has 25,000 lipase. Since you need 64,000 (units of lipase needed to cover the meal), you would divide it by your pill/dose size of 25,000. 64,000 divided by 25,000 is 2.56. That means for these ratios and a prescription of Zenpep 25,000 pill size, you need *3* Zenpep 25,000 to cover a meal of 64g of fat. (Remember, you can’t cut a PERT, so you have to round up to the next pill size.)

Example B: Take the meal and multiply it by your ratio. 64 (grams of fat) times 3,600 (how many units of lipase you need to cover 1 grant of fat) = 230,400. A Creon 36,000 has 36,000 lipase. Since you need 230,400 units of lipase to cover the meal, you would divide it by your pill/dose size of 36,000. 230,400 divided by 36,000 is 6.4. This means you need *7* Creon 36,000 to cover a meal of 64g of fat. (Again, you can’t cut a PERT, so you have to round up to 7 from 6.4.)

Another way to think about this and make it easier in the future is to determine how much one pill “covers”.

Example A: A Zenpep 25,000 “covers” 25 grams of fat if my ratio is 1000 units of lipase for every gram of fat (25,000/1000=25).

So if a meal is under 25g of fat? 1 pill. A meal under 50g (25×2)? 2 pills. 75g (25×3)? 3 pills. And so on. Once you know what a pill “covers”, it’s a little easier; you can simply assess whether a meal is above/below your pill size of 1 (25g), 2 (50g), 3 (75g) etc.

Example B: A Creon 36,000 “covers” 10 grams of fat if my ratio is 3,600 units of lipase for every gram of fat (36,000/3600=10).

So if a meal is under 10 grams of fat? 1 pill. 20 grams of fat is 2 pills (10×2); 30 grams of fat is 3 pills (10×3); etc.

When people with EPI share experiences online, they often describe their dose size (such as 1 x 25,000 or 3 x 36,000 like examples A and B above) for most meals, but the meal size and composition is rarely discussed.

Personally, I can eat pretty widely varying amounts of fat in each meal on a day to day basis.

That’s why, instead of a flat dosing that works for everything (because I would be taking a LOT of pills at every meal if I was trying to take enough to cover my highest fat meals every time), I have found it to be more effective to estimate each meal to determine my meal dosing.

Remember that meal estimates aren’t very precise. If you use a nutrition panel on a box serving, the serving size can vary a bit. Restaurants (especially chains) have nutrition information, but the serving size can vary. So recognize that if you are calculating or estimating 59 grams of fat and that means either 2 vs 3 pills or 6 vs 7 pills, that you should use your judgment on whether you want to round up to the next pill number – or not.

Let’s put the hypothetical meals side by side and compare dosing with examples A and B from above:

Example of how much PERT is needed for two different meals based on dose ratios from Examples A and B

Using the previous meal examples with either 7 or 30 grams of fat:

  • With Example A (ratio of 25g of fat for every 1 pill, or 1000 units of lipase to cover 1 gram of fat), we would need 1 pill for the chicken meal and 2 for the chili meal. Why? The chili is >25 grams of fat which means we need to round up to 2 pills.
  • With Example B (ratio of 10 grams of fat for every 1 pill or 3600 units of lipase to cover 1 gram of fat), we would need 1 pill to cover the chicken (because it’s less than 10 grams of fat) and 3 – or more – pills for the chili. Why “or more”? Well, something like chili is likely to be imprecisely counted – and if you’re like me, you’d want a bit of extra cheese, so chances are I would round up to a 4th pill here to take in the imprecision of the measurements of the ingredients.

PERT Dosing for Protein

Wait, didn’t you say something about protein?

Yes, I did. Fat isn’t the only determinant in this math!

I do the same type of math with grams of protein and units of protease. (Remember, PERT has all 3 types of enzymes, even though it is labeled by the amount of lipase. You can look online or on the bottle label to see how much protease is in your PERT.)

For our examples, Zenpep 25,000 contains 85,000 units of protease. Creon 36,000 contains 114,000 units of protease.

For the meal that ‘worked’ of 30 grams of fat, we also want to know the protein that worked. For easy math, let’s also say 30 grams of protein is in this meal.

Following the same math as before:

Example A (Zenpep 25,000): 30 grams of protein divided by 1×85,000 units of protease is ~2,833 units of protease to every 1 gram of protein. Again, I like to think about how much 1 pill “covers” protein-wise. In this case, 1 Zenpep 25,000 “covers” 30 grams of protein.

Example B (Creon 36,000): 30 grams of protein divided into 3 x 114,000 units of protease is 11,400 units of protease per gram of protein. Again, I like to think about how much 1 pill “covers” protein-wise as well. In this case, 1 Creon 36,000 “covers” 10 grams of protein.

Here’s how many pills are needed for protein:

Example of how much PERT is needed for two different meals based on dose ratios from Examples A and B, showing both protein and fat quantities

  • With Example A (ratio of 30g of protein for every 1 pill), we would need 1 pill for the chicken meal and 2 for the chili meal. Why? The chili is 42, which is greater than (30×1) grams of protein which means we need to round up to 2 pills.
  • With Example B (ratio of 10 grams of protein for every 1 pill), we would need 3 or more pills to cover the chicken. Why 3 or more? Again, it’s on the top edge of what 3 pills would cover, so I’d be likely to round up to 4 pills here. For the chili, 5 pills are needed (42 is more than 4 x 10 and is less than 5 x 10).

So how do you decide the number of pills to take for these meals? Let’s go back to our two example meals and compare the amount needed, pill-wise, for both fat and protein for each meal and each example.

Example of how much PERT is needed for two different meals based on dose ratios from Examples A and B and comparing the number of pills for fat and protein

When the pill numbers MATCH (e.g. the same number needed for fat and protein), which is the case for both examples with Zenpep 25,000, it’s easy: take that number of pills total! For Zenpep 25,000, I would take 1 pill for the Chicken (1 fat | 1 protein); and I would take 2 pills for the Chili (2 fat | 2 protein). Remember that PERT pills contain all three enzymes, so the fat and protein are sufficiently *each* covered by the quantities of lipase and protease in this pill type.

When the pill numbers are DIFFERENT between your fat and protein estimates, you use the LARGER number of pills. For Creon 36,000, with the chicken meal the protein quantity is much larger than the fat quantity; I would in this case dose 4 total pills (1 fat | 4 protein), which would then cover the protein in this meal and would also sufficiently cover the amount of fat in this meal. For the chili meal, it is closer: I estimated needing 4 pills for fat and 5 for protein; in this case, I would take 5 total pills which would then successfully cover the protein and the fat in the meal.

If you find the math challenging to do, don’t worry: once you determine your ratios and figure out how much one pill “covers”, it gets a lot easier.

And I made a tool to help you!

Check out this free enzyme calculator which does the math to determine the ratios on exactly how much one pill “covers” for your successful meal.

Here’s what it looks like using the two examples above:

Example of Part 1 of the EPI Enzyme Calculator using Zenpep 25,000, where 1 pill covers 30 grams of fat and 30 grams of protein. Example of Part 1 of the EPI Enzyme Calculator using Creon 36,000, where 3 pills covers 30 grams of fat and 30 grams of protein.

You can input your meal that “works”, what your dose is that “works” (the number of pills and pill type), and it will share what your ratios are and what one pill “covers”.

You can also use the second part of the calculator to estimate the amount you need for a future meal! Say it’s coming up on a holiday and you’re going to eat a much larger meal than you normally do.

You can input into the calculator that you’ll be eating 90 grams of fat and 75 grams of protein.

Here’s the example with our dose from Example A (Zenpep 25,000):

Example of Part 2 of the EPI Enzyme Calculator using Zenpep 25,000, with a future larger meal of 90 grams of fat and 75 grams of protein.

Here’s the example large meal with our dose from Example B (Creon 36,000):

Example of Part 2 of the EPI Enzyme Calculator using Creon 36,000, with a future larger meal of 90 grams of fat and 75 grams of protein.

You can also hit the button to expand the calculations to see the math it is doing, and how it compares between the fat and protein pill estimates to see what “drives” the total number of pills needed.

You can also hit the button to expand the calculations to see the math it is doing, and how it compares between the fat and protein pill estimates to see what “drives” the total number of pills needed, with the calculation view expanded to show all the details

You can even download a PDF with this math to have on hand. Here’s what the PDF download looks like for Example B (Creon 36,000):

Example of a PDF print view of the same data from previous screenshots with a Creon 36000 example

Switching dose sizes or PERT brand types

This calculator can also be useful if you were originally prescribed a smaller quantity of PERT (e.g. Creon 3000 or Zenpep 3000) and you find yourself taking many numbers of these pills (6 or more) to cover a small meal for you, let alone more pills for a larger meal.

You can input this into the calculator and get your ratios; then in the second part, identify a different pill size, to see how many numbers of pills you’d take on a different dose.

Example switching from one size of PERT pill to another size

You can also use it to help you understand how much you might need if you are switching between brands. Let’s say you were prescribed Zenpep 25,000 and you need to try Creon, either because you don’t think Zenpep works well for you or your insurance is more willing to cover the Creon brand.

You would use the top part of the calculator with your current brand and size (e.g. Zenpep 25,000 of which you take 6 for a standard meal of 30 grams of fat and 30 grams of protein) and then input the new brand and size and the same size meal (e.g. Creon 36,000 and another 30 grams of fat and 30 grams of protein meal) to see that you’d likely need 5 Creon 36,000 to match the 6 Zenpep 25,000 you were taking for a standard size (30 gram of fat and 30 gram of protein) meal.

Example of using the calculator to estimate the different number of pills for a different brand and size of PERT pill

Note: I’m not suggesting 30 grams of fat and protein at each meal is “standard” or the “right” size of the meal – I picked arbitrary numbers here to illustrate these examples, so make sure to include the meal that your PERT dosing successfully covers for YOU!

As a reminder, I’m not a doctor – I’m a person living with EPI. None of this is medical advice. I use this math and this calculator for my own personal use and share it in case it’s helpful to others. If you have questions, please do talk to your doctor. If you’re still experiencing symptoms with your enzyme dosing, you definitely should talk with your doctor. Your prescription size might need updating compared to what you were originally prescribed.

Also, please note that the calculator is open source; you can find the code here, and I welcome contributions (pull requests) and suggestions! You can leave feedback on Github or share feedback in this form. For example, if you’re using a different type of enzyme not listed in the calculator (currently 2/6 of the US FDA-approved versions are listed), please let me know and I can work to add the relevant list.

PS – You can find my other posts about EPI at DIYPS.org/EPI.

We Have Changed the Standards of Care for People With Diabetes

We’ve helped change the standard of care for people with diabetes, with open source automated insulin delivery.

I get citation alerts sometimes when my previous research papers or articles are cited. For the last few years, I get notifications when new consensus guidelines or research comes out that reference or include mention of open source automated insulin delivery (AID). At this time of year, the ADA Standards of Care is released for the following year, and I find out usually via these citation alerts.

Why?

This year, in 2023, there’s a section on open source automated insulin delivery!

A screenshot of the 2023 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

But did you know, that’s not really new? Here’s what the 2022 version said:

A screenshot of the 2022 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

And 2021 also included…

A screenshot of the 2021 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

And 2020? Yup, it was there, too.

A screenshot of the 2020 ADA Standards of Care section under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

All the way back to 2019!

A screenshot of the 2019 ADA Standards of Care under Diabetes Technology (7) that lists DIY closed looping, meaning open source automated insulin delivery

If you read them in chronological order, you can see quite a shift.

In 2019, it was a single sentence noting their existence under a sub-heading of “Future Systems” under AID. In 2020, the content graduated to a full paragraph at the end of the AID section (that year just called “sensor-augmented pumps”). In 2021, it was the same paragraph under the AID section heading. 2022 was the year it graduated to having its own heading calling it out, with a specific evidence based recommendation! 2023 is basically the same as 2022.

So what does it say?

It points out patients are using open source AID (which they highlight as do-it-yourself closed loop systems). It sort of incorrectly suggests healthcare professionals can’t prescribe these systems (they can, actually – providers can prescribe all kinds of things that are off-label – there’s just not much point of a “prescription” unless it’s needed for a person’s elementary school (or similar) who has a policy to only support “prescribed” devices).

And then, most importantly, it points out that regardless, healthcare providers should assist in diabetes management and support patient choice to ensure the safety of people with diabetes. YAY!

“…it is crucial to keep people with diabetes safe if they are using these methods for automated insulin delivery. Part of this entails ensuring people have a backup plan in case of pump failure. Additionally, in most DIY systems, insulin doses are adjusted based on the pump settings for basal rates, carbohydrate ratios, correction doses, and insulin activity. Therefore, these settings can be evaluated and modified based on the individual’s insulin requirements.”

You’ll notice they call out having a backup plan in case of pump failure.

Well, yeah.

That should be true of *any* AID system or standalone insulin pump. This highlights that the needs of people using open source AID in terms of healthcare support are not that different from people choosing other types of diabetes therapies and technologies.

It is really meaningful that they are specifically calling out supporting people living with diabetes. Regardless of technology choices, people with diabetes should be supported by their healthcare providers. Full stop. This is highlighted and increasingly emphasized, thanks to the movement of individuals using open source automated insulin delivery. But the benefits of this is not limited to those of us using open source automated insulin delivery; this spills over and expands to people using different types of BG meters, CGM, insulin pumps, insulin pens, syringes, etc.

No matter their choice of tools or technologies, people with diabetes SHOULD be supported in THEIR choices. Not choices limited by healthcare providers, who might only suggest specific tools that they (healthcare providers) have been trained on or are familiar with – but the choices of the patient.

In future years, I expect the ADA Standard of Care for 2024 and beyond to evolve, in respect to the section on open source automated insulin delivery.

The evidence grading should increase from “E” (which stands for “Expert consensus or clinical experience”), because there is now a full randomized control trial in the New England Journal of Medicine on open source automated insulin delivery, in addition to the continuation results (24 weeks following the RCT; 48 full weeks of data) accepted for publication (presented at EASD 2022), and a myriad of other studies ranging from retrospective to prospective trials. The evidence is out there, so I expect that this evidence grading and the text of the recommendation text will evolve accordingly to catch up to the evidence that exists. (The standards of care are based on literature available up to the middle of the previous year; much of the things I’ve cited above came out in later 2022, so it matches the methodology to not be included until the following year; these newest articles should be scooped up by searches up to July 2023 for the 2024 edition.)

In the meantime, I wish more people with diabetes were aware of the Standards of Care and could use them in discussion with providers who may not be as happy with their choices. (That’s part of the reason I wrote this post!)

I also wish we patients didn’t have to be aware of this and don’t have to argue our cases for support of our choices from healthcare providers.

But hopefully over time, this paradigm of supporting patient choice will continue to grow in the culture of healthcare providers and truly become the standard of care for everyone, without any personal advocacy required.

Did you know? We helped change the standards of care for people living with diabetes. By Dana M. Lewis from DIYPS.org

Costs, Price and Calculations for Living With Diabetes and Exocrine Pancreatic Insufficiency and Celiac and Graves

Living with diabetes is expensive. However, the cost and price goes beyond the cost of insulin, which you may have heard about lately. In addition to insulin, you need tools and supplies to inject the insulin (e.g. syringes, insulin pens, or an insulin pump). Depending on those methods, you need additional supplies (e.g. pen needles for insulin pens, reservoirs and infusion sets for insulin pumps). You also need blood glucose monitoring supplies, whether that is meter and up to a dozen glucose test strips a day and/or a continuous glucose monitor which is made up of a disposable sensor and a reusable transmitter.

All those costs add up on a daily basis for people living with diabetes, even if you have health insurance.

Understanding the costs of living with chronic illness with health insurance in the US

Every year in the US we have “open enrollment” time when we opt-in or enroll into our choice of health insurance plan for the following year. I am lucky and have access to insurance through my husband’s employer, who covers part of the cost for him and me (as a spouse). We have a high-deductible (HSA-qualified) health plan, so our deductible (the amount we must pay before insurance begins to pay for a portion of the costs) is usually around $1,500-$2,500 USD for me. After that, I might pay either a fixed copay ($10 or $25 or similar) for a doctor’s visit, or a percentage (10% or 20%) while the insurance covers the rest of the cost. Then there is a fixed “out of pocket (OOP) max” cost for the year, which might be something like $3,000 USD total. Sometimes the OOP max is pretty close to the deductible, because we typically choose the ‘high deductible’ plan (with no monthly cost for the insurance plan) over a plan where we have a lower deductible but pay a monthly premium for the insurance.

That’s a very rough summary of how I see my health insurance. Everyone has different health insurers (the company providing the insurance) and different plans (the costs will be different based on whether it’s through a different employer or if it’s an individual plan).

So the costs to people with diabetes can vary quite a bit in the US, depending on whether you have insurance: there is variation in the monthly cost of the plan, the amount of the deductible, and the amount of the out of pocket max.

In order to choose my plan for the following year, I look at the total cost for the year of my health supplies and health care, then look at the plans. Usually, the high deductible plan “feels” more expensive because I might have to reach $2,500 before insurance kicks in; however, the out of pocket cap may only be $500 beyond that, so that I’m going to pay a maximum of $3,000 for the year in insurance-covered costs*. There are other types of plans that are lower deductible, such as insurance kicking in after a $250 deductible. That sounds better, right? Well, those plans come with a monthly cost (premium) of $250. So you need to factor that in ($250×12=$3,000) alongside the deductible and any costs up to the out of pocket max ($2,500). From this, you’d pay the $3,000 total yearly premium plus up to $2,500 OOP, or $5,500. Thus, even though it has a lower deductible and OOP, you’re in total paying much more ($5,500 vs $3,000) if you’re someone like me.

Why? Because I have >$3,000 of health supply costs every year.

This is why every few years (mostly after I forget what I learned the last time), I do the math on how much my supply costs to see if I’m still making the most cost-effective choices for me with my insurance plans.

I wanted to share this math methodology below, also because this year I have new variables, which are two new chronic diseases (exocrine pancreatic insufficiency and Graves) that add additional costs and healthcare needs and require me to want to re-check my math.

* Clarifying that previously and most years I pay out of pocket for minor, relatively low-cost health supplies like vitamins or tape to cover my CGM that I buy and do not get through insurance coverage, so my total costs are usually over that OOP max, but likely not by more than a few hundred dollars.

Note: Do not attempt to use this as an absolute cost of diabetes for anyone else. These numbers are based on my use cases in terms of volume of insulin, insurance coverage, etc. Ditto for trying to use the costs for EPI. Where relevant below, I provide rough estimates of my methodology so that another individual with diabetes or EPI/PEI could use similar methods to calculate their own rough costs, if they wished. However, this cannot be used to determine any average cost to people with diabetes more broadly, so don’t excerpt or cite this in those ways. This is purely n=1 math with conclusions that are unique to this n=1 (aka me) but with methods that can be extended for others.

I’ll cover my estimates for costs of diabetes, celiac, exocrine pancreatic insufficiency (EPI or PEI), and Graves’ disease below. This doesn’t account for visits (e.g. doctor’s appointments), lab tests, or other health costs such as x-rays for breaking bones, because those vary quite a bit year to year and aren’t guaranteed fixed costs. But the supplies I need for diabetes, EPI, etc are fixed costs, which I use to anchor my math. Given that they end up well above my OOP max, the then-variable amount of other costs (doctor’s appointments, lab work, etc) is minor in comparison and irrelevant regardless of how much it varies year to year.

The costs (for me) of daily living with diabetes

(You read the caveat note above, right? This is my math based on my volume of insulin, food intake, personal insulin sensitivity, etc. Lots of variables, all unique to me.)

To calculate the yearly costs of living with diabetes, I make a list of my diabetes supplies.

Primarily for me, those are:

  • Insulin
  • CGM sensors
  • CGM transmitter
  • Pump sites
  • Reservoirs

(Not included: meter/test strips or the cost of a pump or the cost of any hardware I’m using for my open source automated insulin delivery. I’ve not bought a new in-warranty pump in years, and that alone takes care of the OOP max on my insurance plan if I were to buy a pump that year. Anyway, the above list is really my recurring regular costs, but if you were purchasing a pump or on a subscription plan for a pump, you’d calculate that in as well).

First, I calculate the daily cost of insulin. I take the cost of a vial of my insulin and divide it by 1,000, because that’s how many units a vial of insulin has. Then I multiply that by the average number of units I use per day to get the cost per day of insulin, which for me is $4.36. (The yearly cost of insulin would be $1,592.)

Then, I calculate my CGM sensors. I take the total cost for a 3 month order of sensors and divide by the number of sensors; then divide by 10 days (because a sensor lasts about 10 days) to get the cost per day of a CGM sensor: about $11 per day. But, you also have to add in the cost of the re-usable transmitter. Again, factor the cost of a transmitter over the number of days it covers; for me it’s about $2 per day. In total, the cost per day of CGM is about $13 and the yearly cost of CGM is roughly $4,765.

Next is pump sites and reservoirs. You need both to go with your insulin pump: the pump site is the catheter site into your body and the tubing (this cumulatively gets replaced every few days), and the reservoir is disposable and is filled with insulin. The cost per day of pump sites and reservoirs is about $6 ($4.67 for a pump site and $1.17 for a reservoir) and the yearly cost of pump sites and reservoirs is $2,129.

If you add up these supplies (pump sites and reservoirs, CGM sensor and transmitter, insulin), the daily cost of diabetes for me is about $23. The yearly cost of diabetes for me is $8,486.

Give that $8,486 is well over the out of pocket max cost of $3,000, you can see why that for diabetes alone there is reason to pick the high deductible plan and pay a max of $3,000 for these supplies out of pocket.

The daily and yearly costs of living with celiac disease

But I don’t just have type 1 diabetes, so the above are not my only health supply costs.

I also have celiac disease. The treatment is a 100% gluten free diet, and eating gluten free is notoriously more expensive than the standard cost of food, whether that is groceries or eating out.

However, the cost of gluten free food isn’t covered by health insurance, so that doesn’t go in my cost calculation toward pricing the best insurance plan. Yet, it does go into my “how much does it cost every day from my health conditions” mental calculation.

I recently looked at a blog post that summarized the cost of gluten free groceries by state compared to low/medium/high grocery costs for the average person. By extrapolating my state’s numbers from a high-cost grocery budget, plus adding $5 each for eating out twice a week (typically gluten free food has at least a $2-3 surcharge in addition to being at higher cost restaurants, plus the fact that I can’t go eat at most drive-throughs, which is why I use $5/meal to offset the combined cost of the actual surcharge plus my actual options being more expensive).

I ended up estimating about a $3 daily average higher cost of being gluten free, or $1,100 per year cost of eating gluten free for celiac.

That’s probably an underestimate for me, but to give a ballpark, that’s another $1,000 or more I’m paying out of pocket in addition to healthcare costs through insurance.

The daily and yearly cost of living with exocrine pancreatic insufficiency and the daily and yearly cost of pancreatic enzyme replacement therapy

I spent a pleasant (so to speak) dozen or so years when “all” I had to pay for was diabetes supplies and gluten free food. However, in 2022, I was diagnosed with exocrine pancreatic insufficiency (and more recently also Graves’ disease, more on that cost below) and because I have spent ~20 years paying for diabetes, I wasn’t super surprised at the costs of EPI/PEI. However, most people get extreme sticker shock (so to speak) when they learn about the costs of pancreatic enzyme replacement therapy (PERT).

In summary, since most people don’t know about it: exocrine pancreatic insufficiency occurs for a variety of reasons, but is highly correlated with all types of diabetes, celiac, and other pancreatic conditions. When you have EPI, you need to take enzymes every time you eat food to help your body digest fat, protein, and carbohydrates, because in EPI your pancreas is not producing enough enzymes to successfully break down the food on its own. (Read a lot more about EPI here.)

Like diabetes, where different people may use very different amounts of insulin, in EPI people may need very different amounts of enzymes. This, like insulin, can be influenced by their body’s makeup, and also by the composition of what they are eating.

I use PERT (pancreatic enzyme replacement therapy) to also describe the prescription enzyme pills used for EPI. There are 6 different brands approved by the FDA in the US. They also come in different sizes; e.g. Brand A has 3,000, 6,000, 12,000, 24,000, 36,000 size pills. Those size refer to the units of lipase. Brand B has 3,000, 5,000, 10,000, 15,000, 20,000, 25,000, 40,000. Brands C, D, E and F have similar variety of sizes. The point is that when people compare amounts of enzymes you need to take into account 1) how many pills are they taking and 2) how much lipase (and protease and amylase) each of those pills are.

There is no generic for PERT. PERT is made from ground up pig pancreas. It’s expensive.

There are over the counter (OTC) enzymes made from alternative (plant etc) sources. However, there are ZERO studies looking at safety and efficacy of them. They typically contain much less lipase per pill; for example, one OTC brand pill contains 4,000 units of lipase per pill, or another contains 17,500 units of lipase per pill.

You also need to factor in the reliability of these non-approved pills. The quality of production can vary drastically. I had one bottle of OTC pills that was fine; then the next bottle of OTC pills I started to find empty capsules and eventually dumped them all out of the bottle and actually used a colander to filter out all of the enzyme powder from the broken capsules. There were more than 30 dud pill capsules that I found in that batch; in a bottle of 250 that means around 12% of them were unusable. That makes the reliability of the other ones suspect as well.

A pile of powder in the sink next to a colander where a bunch of pills sit. The colander was used to filter out the loose powder. On the right of the image is a baggie with empty pill capsules, illustrating where this loose powder came from. This shows the unreliability of over the counter (OTC) enzymes.

If the reliability of these pills even making it to you without breaking can be sketchy, then you need to assume that the counts of how much lipase (and protease and amylase) may not be precisely what the label is reporting. Again, there have been no tests for efficacy of these pills, so anyone with EPI or PEI needs to use these carefully and be aware of these limitations.

This unreliability isn’t necessarily true of all brands, however, or all types of OTC enzymes. That was a common brand of pancrelipase (aka contains lipase, protease, and amylase). I’ve had more success with the reliability of a lipase-only pill that contains about 6,000 units of lipase. However, it’s more expensive per pill (and doesn’t contain any of the other enzymes). I’ve used it to “top off” a meal with my prescription PERT when my meal contains a little bit more fat than what one PERT pill would “cover” on its own.

This combination of OTC and prescription PERT is where the math starts to get complicated for determining the daily cost and yearly cost of pancreatic enzyme replacement therapy.

Let’s say that I take 6-8 prescription PERT pills every day to cover what I eat. It varies because I don’t always eat the same type or amount of food; I adjust based on what I am eating.

The cost with my insurance and a 90 day supply is $8.34 for one PERT pill.

Depending on whether I am eating less fat and protein on a particular day and only need 6 PERT, the cost per day of enzymes for EPI might be $50.04, whereas if I eat a little more and need 8 PERT, the cost per day of enzymes for EPI could be up to $66.72.

The costs per year of PERT for EPI then would range from $18,000 (~6 per day) to $24,000 (~8 per day).

Please let that sink in.

Eighteen to twenty four thousand dollars to be able to successfully digest my food for a single year, not taking into account the cost of food itself or anything else.

(See why people new to EPI get sticker shock?!)

Even though I’m used to ‘high’ healthcare costs (see above estimates of $8,000 or more per year of diabetes costs), this is a lot of money. Knowing every time that I eat it “costs” at least one $8.34 pill is stressful. Eating a bigger portion of food and needing two or three pills? It really takes a mental toll in addition to a financial cost to think about your meal costing $25.02 (for 3 pills) on top of the cost of the food itself.

This is why OTC pills are interesting, because they are drastically differently priced. The 4,000 unit of lipase multi-enzyme pill that I described costs $0.09 per pill, which is about $0.02 per 1000 units of lipase. Compared to my prescription PERT which is $0.33 per 1000 units of lipase, it’s a lot cheaper.

But again, check out those pictures above of the 4,000 units of lipase OTC pills. Can you rely on those?

Not in the same way you can with the prescription PERT.

In the course of taking 1,254 prescription PERT pills this year (so far), I have not had a single issue with one of those pills. So in part the high cost is to ensure the safety and efficacy. Compare that to 12% (or more) of the OTC pills being complete duds (empty pill capsules that have emptied their powder into the bottle) and some % of unreliability even with a not-broken capsule.

Therefore it’s not feasible to me to completely replace prescription PERT with OTC pills, although it’s tempting purely on price.

I previously wrote at a high level about the cost calculations of PERT, but given my desire to look at the annual cost for estimating my insurance plan (plus many more months of data), I went deeper into the math.

I need to take anywhere from 2-6 OTC pills (depending on the brand and size) to “match” the size of one PERT. I found a new type (to me) of OTC pills that are more units of lipase (so I need 2 to match one PERT) instead of the two other kinds (which took either 4 or 6 to match one PERT), which would enable me to cut down on the number of pills swallowed.

The number of pills swallowed matters.

So far (as of mid-November, after starting PERT in early January), I have swallowed at least 1,254 prescription PERT enzyme pills. I don’t have as much precision of numbers on my OTC pills because I don’t always log them (there’s probably a few dozen I haven’t written down, but I probably have logged 95% of them in my enzyme tracking spreadsheet that I use to help calculate the amount needed for each meal/snack and also to look at trends.), but it’s about 2,100 OTC enzyme pills swallowed.

This means cumulatively this year (which is not over), I have swallowed over 3,300 enzyme pills. That’s about 10 enzyme pills swallowed every day!

That’s a lot of swallowing.

That’s why switching to a brand that is more units of lipase per pill, where 2 of these new OTC kind matches one PERT instead of 4-6, is also significant. While it is also slightly cheaper than the combination of the two I was using previously (a lipase-only and a multi-enzyme version), it is fewer pills to achieve the same amount.

If I had taken prescription PERT instead of the OTCs, it would have saved me over 1,600 pills to swallow so far this year.

You might be thinking: take the prescription PERT! Don’t worry about the OTC pills! OMG that’s a lot of pills.

(OMG, it *is* a lot of pills: I think that as well now that I’m adding up all of these numbers.)

Thankfully, so far I am not having issues with swallowing these pills. As I get older, that might change and be a bigger factor in determining my strategy for how I dose enzymes; but right now, that’s not the biggest factor. Instead, I’m looking at efficacy (getting the right amount of enzymes to match my food), the cost (in terms of price), and then optimizing and reducing the total number of pills if I can. But the price is such a big variable that it is playing the largest role in determining my strategy.

How should we collectively pay for this?

You see, I don’t have EPI in a vacuum.

As I described at the top of the post, I already have $8,000+ of yearly diabetes costs. The $18,000 (or $24,000 or more) yearly enzyme costs are a lot. Cumulatively, just these two alone mean my supply costs are $26-32,000 (or more), excluding other healthcare costs. Thankfully, I do have insurance to cover costs after I hit my out of pocket max, but the bigger question is: who should be paying for this?

If my insurer pays more, then the employer pays more, which means employees get worse coverage on our pooled insurance plan. Premiums go up and/or the plans cover less, and the out of pocket costs to everyone goes up.

So while it is tempting to try to “stuff” all of my supply needs into insurance-covered supplies, in order to reduce my personal out of pocket costs in the short run, that raises costs for everyone in the long run.

This year, for all of those (remember I estimated 2,100 OTC pills swallowed to date) OTC pills I bought, it cost me $515. Out of pocket. Not billed through insurance; they know nothing about it.

It feels like a lot of money. However, if you calculate how many PERT it replaced and the cost per PERT pill, I saved $4,036 by swallowing 1,652 extra pills.

Is paying $500 to save everyone else $4000 worth it?

I think so.

Again, the “price” question gets interesting.

The raw costs of yearly supplies I don’t pay completely; remember with health insurance I am capped at $3,000 out of pocket for supplies I get through insurance. However, again, it’s worth considering that additional costs do not cost me but they cost the insurer, and therefore the employer and our pool of people in this insurance plan and influences future costs for everyone on insurance. So if I can afford (although I don’t like it) $500-ish out of pocket and save everyone $4,000 – that’s worth doing.

Although, I think I can improve on that math for next year.

I was taking the two OTC kinds that I had mentioned: one that was lipase-only and very reliable, but $0.28/pill or $0.04 per 1000 units of lipase (and contains ~6000 units of lipase). The less reliable multi-enzyme pill was cheaper ($.09) per pill but only contains 4000 units of lipase, and was $.02 per 1000 units of lipase. That doesn’t factor in the duds and the way I had to increase the number of pills to account for the lack of faith I had in the 4000 units of lipase always being 4000 units of lipase.

The new OTC pill I mentioned above is $0.39 per pill, which is fairly equivalent price to a combined lipase-only and multi-enzyme pill. In fact, I often would take 1+1 for snacks that had a few grams of protein and more than a few grams of lipase. So one new pill will cover 17,000 units of lipase (instead of 10,000, made up of 6000+4000) at a similar cost: $0.39 instead of $0.36 (for the two combined). And, it also has a LOT more protease per pill, too. It has >2x the amount of protease as the multi-enzyme OTC pill, and is very similar to the amount of protease in my prescription PERT! I’ve mostly discussed the math by units of lipase, but I also dose based on how much protein I’m eating (thus, protease to cover protein the way lipase covers fat digestion), so this is also a benefit. As a result, two of the new OTC pill now more than match 1 PERT on lipase, double the protease to 1 PERT, and is only two swallows instead of the 4-6 swallows needed with the previous combination of OTCs.

I have only tested for a few days, but so far this new OTC is working fairly well as a substitute for my previous two OTC kinds.

Given the unreliability of OTCs, even with different brands that are more reliable than the above picture, I still want to consume one prescription PERT to “anchor” my main meals. I can then “top off” with some of the new OTC pills, which is lower price than more PERT but has the tradeoff cost of slightly less reliability compared to PERT.

So with 3 main meals, that means at least 3 PERT per day ($8.34 per pill) at $25.02 per day in prescription PERT costs and $9,132 per year in prescription PERT costs. Then to cover the additional 3-5 PERT pills I would otherwise need, assuming 2 of the new OTC covers 1 PERT pills, that is 6-10 OTC pills.

Combined, 3 PERT + 6 OTC pills or 3 PERT + 10 OTC pills would be $27.36 or $28.92 per day, or $9,986 or $10,556 per year.

Still quite a bit of money, but compared to 6-8 PERT per day (yearly cost $18,264 to $24,352), it saves somewhere between $7,708 per year (comparing 6 PERT to 3 PERT + 6 OTC pills per day) all the way up to $14,366 per year (comparing 8 PERT to 3 PERT +10 OTC pills per day).

And coming back to number of pills swallowed, 6 PERT per day would be 2,190 swallows per year; 8 PERT pills per day is 2,920 swallows per year; 3 PERT + 6 OTC is 9 pills per day which is 3,285 swallows per year; and 3 PERT + 10 OTC is 13 swallows per day which is 4,745 swallows per year.

That is 1,095 more swallows per year (3PERT+6 OTC vs 6 PERT) or 1,825 more swallows per year (3 PERT + 10 OTC vs 8 PERT).

Given that I estimated I swallowed ~10 enzyme pills per day this year so far, the estimated range of 9-13 swallows with the combination of PERT and OTC pills (either 3 PERT + (6 or 10) OTC) for next year seems reasonable.

Again, in future this might change if I begin to have issues swallowing for whatever reason, but in my current state it seems doable.

The daily and annual costs of thyroid treatment for Graves’ Disease

No, we’re still not done yet with annual health cost math. I also developed Graves’ disease with subclinical hyperthyroidism this year, putting me to a grand total of 4 chronic health conditions.

Luckily, though, the 4th time was the charm and I finally have a cheap(er) one!

My thyroid med DOES have a generic. It’s cheap: $11.75 for 3 months of a once-daily pill! Woohoo! That means $0.13 per day cost of thyroid treatment and $48 per year cost of thyroid treatment.

(Isn’t it nice to have cheap, easy math about at least one of 4 things? I think so!)

Adding up all the costs of diabetes, celiac disease, exocrine pancreatic insufficiency and Graves’ Disease

High five if you’ve read this entire post; and no problem if you skimmed the sections you didn’t care about.

Adding it all up, my personal costs are:

  • Diabetes: $23.25 per day; $8,486 per year
  • Celiac: $3 per day; $1,100 per year (all out of pocket)
  • Exocrine Pancreatic Insufficiency:
    • Anywhere from $50.04 up to $66.72 per day with just prescription PERT pills; $18,265 (6 per day) to $24,353 (8 per day) per year
    • With a mix of prescription and OTC pills, $27.36 to $28.92 per day; $9,986 to $10,556 per year.
    • Of this, the out of pocket cost for me would be $2.34 to $3.90 per day; or $854 up to $1,424 per year.
  • Thyroid/Graves: $0.13 per day; $48 per year

Total yearly cost:

  • $27,893 (where EPI costs are 6 prescription PERT per day); 2,190 swallows
  • $33,982 (where EPI costs are 8 prescription PERT per day); 2,920 swallows
  • $19,615 (where EPI costs are 3 prescription PERT and 6 OTC per day); 3,285 swallows
  • $20,185 (where EPI costs are 3 prescription PERT and 9 OTC per day); 4,745 swallows

* My out of pocket costs per year are $854-$1424 for EPI when using OTCs to supplement prescription PERT and an estimated $1,100 for celiac-related gluten free food costs. 

** Daily cost-wise, that means $76.42, $93.10, $53.74, or $55.30 daily costs respectively.

*** The swallow “cost” is 1,095-1,825 more swallows per year to get the lower price cost of enzymes by combining prescription and OTC.

Combining these out of pocket costs with my $3,000 out of pocket max on my insurance plan, I can expect that I will therefore pay around $4,900 to $5,600 next year in health supply costs, plus another few hundred for things like tape or vitamins etc. that aren’t major expenses.

TLDR: 

  • Diabetes is expensive, and it’s not just insulin.
    • Insulin is roughly 19% of my daily cost of diabetes supplies. CGM is currently 56% of my diabetes supply costs.
  • EPI is super expensive.
    • OTC pills can supplement prescription PERT but have reliability issues.
    • However, combined with prescription PERT it can help drastically cut the price of EPI.
    • The cost of this price reduction is significantly more pills to swallow on a daily basis, and adds an additional out of pocket cost that insurance doesn’t cover.
    • However in my case; I am privileged enough to afford this cost and choose this over increasing everyone in my insurance plan’s costs.
  • Celiac is expensive and mostly an out of pocket cost.
  • Thyroid is not as expensive to manage with daily medication. Yay for one of four being reasonably priced!

REMEMBER to not use these numbers or math out of context and apply them to any other person; this is based on my usage of insulin, enzymes, etc as well as my insurance plan’s costs.

Yearly costs, prices, and calculations of living with 4 chronic diseases (type 1 diabetes, celiac, Graves, and exocrine pancreatic insufficiency)

Regulatory Approval Is A Red Herring

One of the most common questions I have been asked over the last 8 years is whether or not we are submitting OpenAPS to the FDA for regulatory approval.

This question is a big red herring.

Regulatory approval is often seen and discussed as the one path for authenticating and validating safety and efficacy.

It’s not the only way.

It’s only one way.

As background, you need to understand what OpenAPS is. We took an already-approved insulin pump that I already had, a continuous glucose monitor (CGM) that I already had, and found a way to read data from those devices and also to use the already-built commands in the pump to send back instructions to automate insulin delivery via the decision-making algorithm that we created. The OpenAPS algorithm was the core innovation, along with the realization that this already-approved pump had those capabilities built in. We used various off the shelf hardware (mini-computers and radio communication boards) to interoperate with my already approved medical devices. There was novelty in how we put all the pieces together, though the innovation was the algorithm itself.

The caveat, though, is that although the pump I was using was regulatory-approved and on the market, which is how I already had it, it had later been recalled after researchers, the manufacturer, and the FDA realized that you could use the already-built commands in the pump’s infrastructure. So these pumps, while not causing harm to anyone and no cases of harm have ever been recorded, were no longer being sold. It wasn’t a big deal to the company; it was a voluntary recall, and people like me often chose to keep our pumps if we were not concerned about this potential risk.

We had figured out how to interoperate with these other devices. We could have taken our system to the FDA. But because we were using already-off-the-market pumps, there was no way the FDA would approve it. And at the time (circa 2014), there was no vision or pathway for interoperable devices, so they didn’t have the infrastructure to approve “just” an automated insulin delivery algorithm. (That changed many years later and they now have infrastructure for reviewing interoperable pumps, CGM, and algorithms which they call controllers).

The other relevant fact is that the FDA has jurisdiction based on the commerce clause in the US Constitution: Congress used its authority to authorize the FDA to regulate interstate commerce in food, drugs, and medical devices. So if you’re intending to be a commercial entity and sell products, you must submit for regulatory approval.

But if you’re not going to sell products…

This is the other aspect that many people don’t seem to understand. All roads do not lead to regulatory approval because not everyone wants to create a company and spend 5+ years dedicating all their time to it. That’s what we would have had to do in order to have a company to try to pursue regulatory approval.

And the key point is: given such a strict regulatory environment, we (speaking for Dana and Scott) did not want to commercialize anything. Therefore there was no point in submitting for regulatory approval. Regardless of whether or not the FDA was likely to approve given the situation at the time, we did not want to create a company, spend years of our life dealing with regulatory and compliance issues full time, and maybe eventually get permission to sell a thing (that we didn’t care about selling).

The aspect of regulatory approval is a red herring in the story of the understanding of OpenAPS and the impact it is having and could have.

Yes, we could have created a company. But then we would not have been able to spend the thousands of hours that we spent improving the system we made open source and helping thousands of individuals who were able to use the algorithm and subsequent systems with a variety of pumps, CGMs, and mobile devices as an open source automated insulin delivery system. We intentionally chose this path to not commercialize and thus not to pursue regulatory approval.

As a result of our work (and others from the community), the ecosystem has now changed.

Time has also passed: it’s been 8 years since I first automated insulin delivery for myself!

The commercial players have brought multiple commercial AIDs to market now, too.

We created OpenAPS when there was NO commercial option at the time. Now there are a few commercial options.

But it is also an important note that I, and many thousands of other people, are still choosing to use open source AID systems.

Why?

This is another aspect of the red herring of regulatory approval.

Just because something is approved does not mean it’s available to order.

If it’s available to order (and not all countries have approved AID systems!), it doesn’t mean it’s accessible or affordable.

Insurance companies are still fighting against covering pumps and CGMs as standalone devices. New commercial AID systems are even more expensive, and the insurance companies are fighting against coverage for them, too. So just because someone wants an AID and has one approved in their country doesn’t mean that they will be able to access and/or afford it. Many people with diabetes struggle with the cost of insulin, or the cost of CGM and/or their insulin pump.

Sometimes providers refuse to prescribe devices, based on preconceived notions (and biases) about who might do “well” with new therapies based on past outcomes with different therapies.

For some, open source AID is still the most accessible and affordable option.

And in some places, it is still the ONLY option available to automate insulin delivery.

(And in most places, open source AID is still the most advanced, flexible, and customizable option.)

Understanding the many reasons why someone might choose to use open source automated insulin delivery folds back into the understanding of how someone chooses to use open source automated insulin delivery.

It is tied to the understanding that manual insulin delivery – where someone makes all the decisions themselves and injects or presses buttons manually to deliver insulin – is inherently risky.

Automated insulin delivery reduces risk compared to manual insulin delivery. While some new risk is introduced (as is true of any additional devices), the net risk reduction overall is significantly large compared to manual insulin delivery.

This net risk reduction is important to contextualize.

Without automated insulin delivery, people overdose or underdose on insulin multiple times a day, causing adverse effects and bad outcomes and decreasing their quality of life. Even when they’re doing everything right, this is inevitable because the timing of insulin is so challenging to manage alongside dozens of other variables that at every decision point play a role in influencing the glucose outcomes.

With open source automated insulin delivery, it is not a single point-in-time decision to use the system.

Every moment, every day, people are actively choosing to use their open source automated insulin delivery system because it is better than the alternative of managing diabetes manually without automated insulin delivery.

It is a conscious choice that people make every single day. They could otherwise choose to not use the automated components and “fall back” to manual diabetes care at any moment of the day or night if they so choose. But most don’t, because it is safer and the outcomes are better with automated insulin delivery.

Each individual’s actions to use open source AID on an ongoing basis are data points on the increased safety and efficacy.

However, this paradigm of patient-generated data and patient choice as data contributing toward safety and efficacy is new. There are not many, if any, other examples of patient-developed technology that does not go down the commercial path, so there are not a lot of comparisons for open source AID systems.

As a result, when there were questions about the safety and efficacy of the system (e.g., “how do you know it works for someone else other than you, Dana?”), we began to research as a community to address the questions. We published data at the world’s biggest scientific conference and were peer-reviewed by scientists and accepted to present a poster. We did so. We were cited in a piece in Nature as a result. We then were invited to submit a letter to the editor of a traditional diabetes journal to summarize our findings; we did so and were published.

I then waited for the rest of the research community to pick up this lead and build on the work…but they didn’t. I picked it up again and began facilitating research directly with the community, coordinating efforts to make anonymized pools of data for individuals with open source AID to submit their data to and for years have facilitated access to dozens of researchers to use this data for additional research. This has led to dozens of publications further documenting the efficacy of these solutions.

Yet still, there was concern around safety because the healthcare world didn’t know how to assess these patient-generated data points of choice to use this system because it was better than the alternative every single day.

So finally, as a direct result of presenting this community-based research again at the world’s largest diabetes scientific conference, we were able to collaborate and design a grant proposal that received grant funding from New Zealand’s Health Research Council (the equivalent of the NIH in the US) for a randomized control trial of the OpenAPS algorithm in an open source AID system.

An RCT is often seen as the gold standard in science, so the fact that we received funding for such a study alone was a big milestone.

And this year, in 2022, the RCT was completed and our findings were published in one of the world’s largest medical journals, the New England Journal of Medicine, establishing that the use of the OpenAPS algorithm in an open source AID was found to be safe and effective in children and adults.

No surprises here, though. I’ve been using this system for more than 8 years, and seeing thousands of others choose the OpenAPS algorithm on an ongoing, daily basis for similar reasons.

So today, it is possible that someone could take an open source AID system using the OpenAPS algorithm to the FDA for regulatory approval. It won’t likely be me, though.

Why not? The same reasons apply from 8 years ago: I am not a company, I don’t want to create a company to be able to sell things to end users. The path to regulatory approval primarily matters for those who want to sell commercial products to end users.

Also, regulatory approval (if someone got the OpenAPS algorithm in an open source AID or a different algorithm in an open source AID) does not mean it will be commercially available, even if it will be approved.

It requires a company that has pumps and CGMs it can sell alongside the AID system OR commercial partnerships ready to go that are able to sell all of the interoperable, approved components to interoperate with the AID system.

So regulatory approval of an AID system (algorithm/mobile controller design) without a commercial partnership plan ready to go is not very meaningful to people with diabetes in and of itself. It sounds cool, but will it actually do anything? In and of itself, no.

Thus, the red herring.

Might it be meaningful eventually? Yes, possibly, especially if we collectively have insurers to get over themselves and provide coverage for AID systems given that AID systems all massively improve short-term and long-term outcomes for people with diabetes.

But as I said earlier, regulatory approval does necessitate access nor affordability, so an approved system that’s not available and affordable to people is not a system that can be used by many.

We have a long way to go before commercial AID systems are widely accessible and affordable, let alone available in every single country for people with diabetes worldwide.

Therefore, regulatory approval is only one piece of this puzzle.

And it is not the only way to assess safety and efficacy.

The bigger picture this has shown me over the years is that while systems are created to reduce harm toward people – and this is valid and good – there have been tendencies to convert to the assumption that therefore the systems are the only way to achieve the goal of harm reduction or to assess safety and efficacy.

They aren’t the only way.

As explained above, FDA approval is one method of creating a rubber stamp as a shorthand for “is this considered to be safe and effective”.

That’s also legally necessary for companies to use if they want to sell products. For situations that aren’t selling products, it’s not the only way to assess safety and efficacy, which we have shown with OpenAPS.

With open source automated insulin delivery systems, individuals have access to every line of code and can test and choose for themselves, not just once, but every single day, whether they consider it to be safer and more effective for them than manual insulin dosing. Instead of blindly trusting a company, they get the choice to evaluate what they’re using in a different way – if they so choose.

So any questions around seeking regulatory approval are red herrings.

A different question might be: What’s the future of the OpenAPS algorithm?

The answer is written in our OpenAPS plain language reference design that we posted in February of 2015. We detailed our vision for individuals like us, researchers, and companies to be able to use it in the future.

And that’s how it’s being used today, by 1) people like me; and 2)  in research, to improve what we can learn about diabetes itself and improve AID; and 3) by companies, one of whom has already incorporated parts of our safety design as part of a safety layer in their ML-based AID system and has CE mark approval and is being sold and used by thousands of people in Europe.

It’s possible that someone will take it for regulatory approval; but that’s not necessary for the thousands of people already using it. That may or may not make it more available for thousands more (see earlier caveats about needing commercial partnerships to be able to interoperate with pumps and CGMs).

And regardless, it is still being used to change the world for thousands of people and help us learn and understand new things about the physiology of diabetes because of the way it was designed.

That’s how it’s been used and that’s the future of how it will continue to be used.

No rubber stamps required.

Regulatory Approval: A Red Herring

Understanding the Difference Between Open Source and DIY in Diabetes

There’s been a lot of excitement (yay!) about the results of the CREATE trial being published in NEJM, followed by the presentation of the continuation results at EASD. This has generated a lot of blog posts, news articles, and discussion about what was studied and what the implications are.

One area that I’ve noticed is frequently misunderstood is how “open source” and “DIY” are different.

Open source means that the source code is openly available to view. There are different licenses with open source; most allow you to also take and reuse and modify the code however you like. Some “copy-left” licenses commercial entities to open-source any software they build using such code. Most companies can and do use open source code, too, although in healthcare most algorithms and other code related to FDA-regulated activity is proprietary. Most open source licenses allow free individual use.

For example, OpenAPS is open source. You can find the core code of the algorithm here, hosted on Github, and read every line of code. You can take it, copy it, use it as-is or modify it however you like, because the MIT license we put on the code says you can!

As an individual, you can choose to use the open source code to “DIY” (do-it-yourself) an automated insulin delivery system. You’re DIY-ing, meaning you’re building it yourself rather than buying it or a service from a company.

In other words, you can DIY with open source. But open source and DIY are not the same thing!

Open source can and is usually is used commercially in most industries. In healthcare and in diabetes specifically, there are only a few examples of this. For OpenAPS, as you can read in our plain language reference design, we wanted companies to use our code as well as individuals (who would DIY with it). There’s at least one commercial company now using ideas from the OpenAPS codebase and our safety design as a safety layer against their ML algorithm, to make sure that the insulin dosing decisions are checked against our safety design. How cool!

However, they’re a company, and they have wrapped up their combination of proprietary software and the open source software they have implemented, gotten a CE mark (European equivalent of FDA approval), and commercialized and sold their AID product to people with diabetes in Europe. So, those customers/users/people with diabetes are benefitting from open source, although they are not DIY-ing their AID.

Outside of healthcare, open source is used far more pervasively. Have you ever used Zoom? Zoom uses open source; you then use Zoom, although not in a DIY way. Same with Firefox, the browser. Ever heard of Adobe? They use open source. Facebook. Google. IBM. Intel. LinkedIn. Microsoft. Netflix. Oracle. Samsung. Twitter. Nearly every product or service you use is built with, depends on, or contains open source components. Often times open source is more commonly used by companies to then provide products to users – but not always.

So, to more easily understand how to talk about open source vs DIY:

  • The CREATE trial used a version of open source software and algorithm (the OpenAPS algorithm inside a modified version of the AndroidAPS application) in the study.
  • The study was NOT on “DIY” automated insulin delivery; the AID system was handed/provided to participants in the study. There was no DIY component in the study, although the same software is used both in the study and in the real world community by those who do DIY it. Instead, the point of the trial was to study the safety and efficacy of this version of open source AID.
  • Open source is not the same as DIY.
  • OpenAPS is open source and can be used by anyone – companies that want to commercialize, or individuals who want to DIY. For more information about our vision for this, check out the OpenAPS plain language reference design.
Venn diagram showing a small overlap between a bigger open source circle and a smaller DIY circle. An arrow points to the overlapping section, along with text of "OpenAPS". Below it text reads: "OpenAPS is open source and can be used DIY. DIY in diabetes often uses open source, but not always. Not all open source is used DIY."

Continuation Results On 48 Weeks of Use Of Open Source Automated Insulin Delivery From the CREATE Trial: Safety And Efficacy Data

In addition to the primary endpoint results from the CREATE trial, which you can read more about in detail here or as published in the New England Journal of Medicine, there was also a continuation phase study of the CREATE trial. This meant that all participants from the CREATE trial, including those who were randomized to the automated insulin delivery (AID) arm and those who were randomized to sensor-augmented insulin pump therapy (SAPT, which means just a pump and CGM, no algorithm), had the option to continue for another 24 weeks using the open source AID system.

These results were presented by Dr. Mercedes J. Burnside at #EASD2022, and I’ve summarized her presentation and the results below on behalf of the CREATE study team.

What is the “continuation phase”?

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

The second 24-week phase is known as the “continuation phase” of the study.

There were 52 participants who were randomized into the SAPT group that chose to continue in the study and used AID for the 24 week continuation phase. We refer to those as the “SAPT-AID” group. There were 42 participants initially randomized into AID who continued to use AID for another 24 weeks (the AID-AID group).

One slight change to the continuation phase was that those in the SAPT-AID used a different insulin pump than the one used in the primary phase of the study (and 18/42 AID-AID participants also switched to this different pump during the continuation phase), but it was a similar Bluetooth-enabled pump that was interoperable with the AID system (app/algorithm) and CGM used in the primary outcome phase.

All 42 participants in AID-AID completed the continuation phase; 6 participants (out of 52) in the SAPT-AID group withdrew. One withdrew from infusion site issues; three with pump issues; and two who preferred SAPT.

What are the results from the continuation phase?

In the continuation phase, those in the SAPT-AID group saw a change in time in range (TIR) from 55±16% to 69±11% during the continuation phase when they used AID. In the SAPT-AID group, the percentage of participants who were able to achieve the target goals of TIR > 70% and time below range (TBR) <4% increased from 11% of participants during SAPT use to 49% during the 24 week AID use in the continuation phase. Like in the primary phase for AID-AID participants; the SAPT-AID participants saw the greatest treatment effect overnight with a TIR difference of 20.37% (95% CI, 17.68 to 23.07; p <0.001), and 9.21% during the day (95% CI, 7.44 to 10.98; p <0.001) during the continuation phase with open source AID.

Those in the AID-AID group, meaning those who continued for a second 24 week period using AID, saw similar TIR outcomes. Prior to AID use at the start of the study, TIR for that group was 61±14% and increased to 71±12% at the end of the primary outcome phase; after the next 6 months of the continuation phase, TIR was maintained at 70±12%. In this AID-AID group, the percentage of participants achieving target goals of TIR >70% and TBR <4% was 52% of participants in the first 6 months of AID use and 45% during the continuation phase. Similarly to the primary outcomes phase, in the continuation phase there was also no treatment effect by age interaction (p=0.39).

The TIR outcomes between both groups (SAPT-AID and AID-AID) were very similar after each group had used AID for 24 weeks (SAPT-AID group using AID for 24 weeks during the continuation phase and AID-AID using AID for 24 weeks during the initial RCT phase).. The adjusted difference in TIR between these groups was 1% (95% CI, -4 to 6; p=-0.67). There were no glycemic outcome differences between those using the two different study pumps (n=69, which was the SAPT-AID user group and 18 AID-AID participants who switched for continuation; and n=25, from the AID-AID group who elected to continue on the pump they used in the primary outcomes phase).

In the initial primary results (first 24 weeks of trial comparing the AID group to the SAPT group), there was a 14 percentage point difference between the groups. In the continuation phase, all used AID and the adjusted mean difference in TIR between AID and the initial SAPT results was a similar 12.10 percentage points (95% CI, p<0.001, SD 8.40).

Similar to the primary phase, there was no DKA or severe hypoglycemia. Long-term use (over 48 weeks, representing 69 person-years) did not detect any rare severe adverse events.

CREATE results from the full 48 weeks on open source AID with both SAPT (control) and AID (intervention) groups plotted on the graph.

Conclusion of the continuation study from the CREATE trial

In conclusion, the continuation study from the CREATE trial found that open-source AID using the OpenAPS algorithm within a modified version of AndroidAPS is efficacious and safe with various hardware (pumps), and demonstrates sustained glycaemic improvements without additional safety concerns.

Key points to takeaway:

  • Over 48 weeks total of the study (6 months or 24 weeks in the primary phase; 6 months/24 weeks in the continuation phase), there were 64 person-years of use of open source AID in the study, compared to 59 person-years of use of sensor-augmented pump therapy.
  • A variety of pump hardware options were used in the primary phase of the study among the SAPT group, due to hardware (pump) availability limitations. Different pumps were also used in the SAPT-AID group during the AID continuation phase, compared to the pumps available in the AID-AID group throughout both phases of trial. (Also, 18/42 of AID-AID participants chose to switch to the other pump type during the continuation phase).
  • The similar TIR results (14 percentage points difference in primary and 12 percentage points difference in continuation phase between AID and SAPT groups) shows durability of the open source AID and algorithm used, regardless of pump hardware.
  • The SAPT-AID group achieved similar TIR results at the end of their first 6 months of use of AID when compared to the AID-AID group at both their initial 6 months use and their total 12 months/48 weeks of use at the end of the continuation phase.
  • The safety data showed no DKA or severe hypoglycemia in either the primary phase or the continuation phases.
  • Glycemic improvements from this version of open source AID (the OpenAPS algorithm in a modified version of AndroidAPS) are not only immediate but also sustained, and do not increase safety concerns.
CREATE Trial Continuation Results were presented at #EASD2022 on 48 weeks of use of open source AID

Wondering about the “how” rather than the “why” of autoimmune conditions

I’ve been thinking a lot about stigma, per a previous post of mine, and how I generally react to, learn about, and figure out how to deal with new chronic diseases.

I’ve observed a pattern in my experiences. When I suspect an issue, I begin with research. I read medical literature to find out the basics of what is known. I read a high volume of material, over a range of years, to see what is known and the general “ground truth” about what has stayed consistent over the years and where things might have changed. This is true for looking into causal mechanisms as well as diagnosis and then more importantly to me, management/treatment.

I went down a new rabbit hole of research and most articles were publicly accessible

A lot of times with autoimmune related diseases…the causal mechanism is unknown. There are correlations, there are known risk factors, but there’s not always a clear answer of why things happen.

I realize that I am lucky that my first “thing” (type 1 diabetes) was known to be an autoimmune condition, and that probably has framed my response to celiac disease (6 years later); exocrine pancreatic insufficiency (19+ years after diabetes); and now Graves’ disease (19+ years after diabetes). Why do I think that is lucky? Because when I’m diagnosed with an autoimmune condition, it’s not a surprise that it IS an autoimmune condition. When you have a nicely overactive immune system, it interferes with how your body is managing things. In type 1 diabetes, it eventually makes it so the beta cells in your pancreas no longer produce insulin. In celiac, it makes it so the body has an immune reaction to gluten, and the villi in your small intestine freak out at the microscopic, crumb-level presence of gluten (and if you keep eating gluten, can cause all sorts of damage). In exocrine pancreatic insufficiency, there is possibly either atrophy as a result of the pancreas not producing insulin or other immune-related responses – or similar theories related to EPI and celiac in terms of immune responses. It’s not clear ‘why’ or which mechanism (celiac, T1D, or autoimmune in general) caused my EPI, and not knowing that doesn’t bother me, because it’s clearly linked to autoimmune shenanigans. Now with Graves’ disease, I also know that low TSH and increased thyroid antibodies are causing subclinical hyperthyroidism symptoms (such as occasional minor tremor, increased resting HR, among others) and Graves’ ophthalmology symptoms as a result of the thyroid antibodies. The low TSH and increased thyroid antibodies are a result of my immune system deciding to poke at my thyroid.

All this to say…I typically wonder less about “why” I have gotten these things, in part because the “why” doesn’t change “what” to do; I simply keep gathering new data points that I have an overactive immune system that gives me autoimmune stuff to deal with.

I have contrasted this with a lot of posts I observe in some of the online EPI groups I am a part of. Many people get diagnosed with EPI as a result of ongoing GI issues, which may or may not be related to other conditions (like IBS, which is often a catch-all for GI issues). But there’s a lot of posts wondering “why” they’ve gotten it, seemingly out of the blue.

When I do my initial research/learning on a new autoimmune thing, as I mentioned I do look for causal mechanisms to see what is known or not known. But that’s primarily, I think, to rule out if there’s anything else “new” going on in my body that this mechanism would inform me about. But 3/3 times (following type 1 diabetes, where I first learned about autoimmune conditions), it’s primarily confirmed that I have autoimmune things due to a kick-ass overactive immune system.

What I’ve realized that I often focus on, and most others do not, is what comes AFTER diagnosis. It’s the management (or treatment) of, and living with, these conditions that I want to know more about.

And sadly, especially in the latest two experiences (exocrine pancreatic insufficiency and Graves’ disease), there is not enough known about management and optimization of dealing with these conditions.

I’ve previously documented and written quite a bit (see a summary of all my posts here) about EPI, including my frustrations about “titrating” or getting the dose right for the enzymes I need to take every single time I eat something. This is part of the “management” gap I find in research and medical knowledge. It seems like clinicians and researchers spend a lot of time on the “why” and the diagnosis/starting point of telling someone they have a condition. But there is way less research about “how” to live and optimally manage these things.

My fellow patients (people with lived experiences) are probably saying “yeah, duh, and that’s the power of social media and patient advocacy groups to share knowledge”. I agree. I say that a lot, too. But one of the reasons these online social media groups are so powerful in sharing knowledge is because of the black hole or vacuum or utter absence of research in this space.

And it’s frustrating! Social media can be super powerful because you can learn about many n=1 experiences. If you’re like me, you analyze the patterns to see what might be reproducible and what is worth experimenting in my own n=1. But often, this knowledge stays in the real world. It is not routinely funded, studied, operationalized, and translated in systematic ways back to healthcare providers. When patients are diagnosed, they’re often told the “what” and occasionally the “why” (if it exists), but left to sometimes fall through the cracks in the “how” of optimally managing the new condition.

(I know, I know. I’m working on that, in diabetes and EPI, and I know dozens of friends, both people with lived experiences and researchers who ARE working on this, from diabetes to brain tumors to Parkinson’s and Alzheimer’s and beyond. And while we are moving the needles here, and making a difference, I’m wanting to highlight the bigger issue to those who haven’t previously been exposed to the issues that cause the gaps we are trying to fill!)

In my newest case of Graves’ disease, it presented with subclinical hyperthyroidism. As I wrote here, that for me means the lower TSH and higher thyroid antibodies but in range T3 and T4. In discussion with my physician, we decided to try an antithyroid drug, to try to lower the antibody levels, because the antibody levels are what cause the related eye symptoms (and they’re quite bothersome). The other primary symptom I have is higher resting HR, which is also really annoying, so I’m also hoping it helps with that, too. But the game plan was to start taking this medication every day; and get follow-up labs in about 2 months, because it takes ~6 weeks to see the change in thyroid levels.

Let me tell you, that’s a long time. I get that the medication works not on stored thyroid levels; thus, it impacts the new production only, and that’s why it takes 6 weeks to see it in the labs because that’s how long it takes to cycle through the stored thyroid stuff in your body.

My hope was that within 2-3 weeks I would see a change in my resting HR levels. I wasn’t sure what else to expect, and whether I’d see any other changes.

But I did.

It was in the course of DAYS, not weeks. It was really surprising! I immediately started to see a change in my resting HR (across two different wearable devices; a ring and a watch). Within a week, my phone’s health flagged it as a “trend”, too, and pinpointed the day (which it didn’t know) that I had started the new medication based on the change in the trending HR values.

Additionally, some of my eye symptoms went away. Prior to commencing the new medication, I would wake up and my eyes would hurt. Lubricating them (with eye drops throughout the day and gel before bed) helped some, but didn’t really fix the problem. I also had pretty significant red, patchy spots around the outside corner of one of my eyes, and eyelid swelling that would push on my eyeball. 4 days into the new medication, I had my first morning where I woke up without my eyes hurting. The next day it returned, and then I had two days without eye pain. Then I had 3-4 days with the painful eyes. Then….now I’m going on 2 weeks without the eye pain?! Meanwhile, I’m also tracking the eye swelling. It went down to match the eye pain going away. But it comes back periodically. Recently, I commented to Scott that I was starting to observe the pattern that the red/patchy skin at the corner and under my right eye would appear; then the next day the swelling of and above the eyelid would return. After 1-2 days of swelling, it would disappear. Because I’ve been tracking various symptoms, I looked at my data the other day and saw that it’s almost a 6-7 day pattern.

Interesting!

Again, the eye stuff is a result of antibody levels. So now I am curious about the production of antibodies and their timeline, and how that differs from TSH and thyroid hormones, and how they’re impacted with this drug.

None of that is information that is easy to get, so I’m deep in the medical literature trying again to find out what is known, whether this type of pattern is known; if it’s common; or if this level of data, like my within-days impact to resting HR change is new information.

Most of the research, sadly, seems to be on pre-diagnosis or what happens if you diagnose someone but not give them medication in hyperthyroid. For example, I found this systematic review on HRV and hyperthyroid and got excited, expecting to learn things that I could use, but found they explicitly removed the 3 studies that involved treating hyperthyroidism and are only studying what happens when you don’t treat it.

Sigh.

This is the type of gap that is so frustrating, as a patient or person who’s living with this. It’s the gap I see in EPI, where little is known on optimal titration and people don’t get prescribed enough enzymes and aren’t taught how to match their dosing to what they are eating, the way we are taught in diabetes to match our insulin dosing to what we’re eating.

And it matters! I’m working on writing up data from a community survey of people with EPI, many of whom shared that they don’t feel like they have their enzyme dosing well matched to what they are eating, in some cases 5+ years after their diagnosis. That’s appalling, to me. Many people with EPI and other conditions like this fall through the cracks with their doctors because there’s no plan or discussion on what managing optimally looks like; what to change if it’s not optimal for a person; and what to do or who to talk to if they need help managing.

Thankfully in diabetes, most people are supported and taught that it’s not “just” a shot of insulin, but there are more variables that need tracking and managing in order to optimize wellbeing and glucose levels when living with diabetes. But it took decades to get there in diabetes, I think.

What would it be like if more chronic diseases, like EPI and Graves’ disease (or any other hyper/hypothyroid-related diseases), also had this type of understanding across the majority of healthcare providers who treated and supported managing these conditions?

How much better would and could people feel? How much more energy would they have to live their lives, work, play with their families and friends? How much more would they thrive, instead of just surviving?

That’s what I wonder.

Wondering "how" rather than "why" of autimmune conditions, by @DanaMLewis from DIYPS.org

2022 Strawberry Fields Forever Ultramarathon Race Report Recap

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

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

Context of my running and training in preparation

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

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

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

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

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

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

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

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

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

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

Special conditions and challenges of the 2022 Strawberry Fields Forever Ultramarathon

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

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

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

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

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

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

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

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

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

Lap 1:

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

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

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

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

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

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

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

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

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

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

Showing how bad the mud was

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

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

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

Phew.

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

Lap 2:

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

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

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

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

Lap 3:

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

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

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

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

Lap 4:

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

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

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

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

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

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

Lap 5:

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

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

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

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

Overall strengths and positives from the race

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

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

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

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

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

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

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

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

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

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

Areas for improvement from my race

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

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

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

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

Surprises from my race

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

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

General thoughts about ultramarathon training and effective ultra race preparation

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

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

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

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

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

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

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

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