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

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

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

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

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

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

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

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

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

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

Methods

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

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

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

Results

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

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

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

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

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

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

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

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

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

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

Discussion

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

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

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

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

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

Conclusion

As I wrote in the paper:

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

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

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

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

Example citation:

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


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

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

The backstory on this study

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

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

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

Study Design:

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

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

Results:

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

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

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

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

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

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

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

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

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

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

In summary:

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

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


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

Example citation:

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

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

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

AID (APS) book now available in French!

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

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

 

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

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

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

An example of the challenges of (constantly) titrating pancreatic enzyme replacement therapy (PERT)

As someone new to EPI who is also new to figuring out how to optimally dose my pancreatic enzyme replacement therapy (PERT), I’m constantly balancing the cost of PERT from prescription enzymes against the cost of over the counter enzymes.

I’ve personally calculated that one pill of my current dose of PERT covers about 30-4o grams of fat, and 30 grams of protein.

Meals with more than 30 grams of protein get 2 PERT pills, but meals with more than 40 or so grams of fat could be covered by 1 PERT pill and some OTC lipase.

But not all meals come with nutrition information, which makes titrating PERT at every single meal a challenge.

And, now that I’ve realized I’m likely not sensitive to all FODMAPs after all (hooray, although I may still have some slight sensitivity to massive amounts of onion or garlic), I’ve been able to eat a lot more takeout food from restaurants, both enthralling my taste buds and challenging my brain trying to estimate how much fat and protein there is in what I am choosing to eat.

I’ve been keeping careful notes of what I’m eating along with my fat and protein estimates and the results following each meal. Then, if I want to repeat or alter a similar meal, I can use my data and results to guesstimate my next PERT dosing.

For example, we have a local taco place that has done a really good job to enable online ordering with gluten-free and celiac tags in the order, so you can order digitally without having to talk to humans at the store. A few weeks ago, I ordered 3 tacos and some queso dip. It was delicious. I estimated it was more than 30g of protein, so I took 2 PERT with it.

However, while I didn’t have post-meal immediate symptoms, my next-day results were slightly off, and I made a note that I probably needed a little more lipase the next time I had that quantity of tacos.

Yesterday, I ordered 3 tacos again but decided to try a small “street corn” appetizer instead of queso. Corn is less fat and protein than queso, but I figured there was still >30g of protein from tacos like from before, so I took 2 PERT. This time, due to my notes, I added a few lipase to cover additional fat.

I had no immediate post-meal symptoms and felt great! However, today indicated that I did not have enough enzymes, and I’m suspecting that it’s because I swapped one of my taco types. Last time, I had a shrimp taco, but this time I tried a lamb taco for my third taco type. Even with the reduced fat and protein going from queso to corn, the increase in fat and/or protein (likely the protein, given my extra lipase) from shrimp to lamb meant that my meal was not optimally dosed.A gif showing three tacos and queso plus 2 PERT got ok results, but next time I swapped queso for corn and added lipase and still got it wrong, likely due to increased fat and protein in lamb instead of shrimp in one of the tacos.

 

Next time, I need to pay closer attention to what kind of tacos I eat as well as whether I get queso or not. If I did the same meal (three tacos, one of which is lamb, and corn), I’d probably experiment with 3 PERT to cover the suspected increased protein that I was missing with the 2 PERT + extra lipase. If I went back to a shrimp taco and queso, I’d probably re-try 2 PERT + extra lipase again.

PERT dosing, like insulin dosing, involves a lot of experimentation and some art, and some science, to try to get it right (or better) every time.

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

A Do-It-Yourself Protocol for Over-The-Counter Enzymes for Suspected Exocrine Pancreatic Insufficiency (EPI) Before Gaining Access to Pancreatic Enzyme Replacement Therapy (PERT)

A humorous side note – the title of this blog, DIYPS, stands for “do-it-yourself pancreas system”, the name I gave my first automated insulin delivery (AID) system, back in 2013. An AID system doesn’t fully replicate all functions of the pancreas, so we evolved from describing it as an artificial pancreas system (APS) to automated insulin delivery (AID). But now that I have exocrine pancreatic insufficiency and am doing quite a bit of DIY around titration of enzymes….the name of this blog feels more appropriate than ever.

After I started writing about exocrine pancreatic insufficiency, I’ve gotten a lot of questions from friends and connections who think they might have EPI. (And they are likely not wrong – there are estimates that as many as 40% of people with type 1 diabetes have lowered elastase levels. Alone, that doesn’t indicate EPI, but if symptomatic and you’ve already ruled out celiac and gastroparesis, it should be (in my opinion) high on the list of things to test for. Ditto for other types of diabetes and anyone with celiac disease.) Some people, though, may have delays in getting doctor’s appointments, and/or clinicians who aren’t (yet) willing to order the elastase or other EPI-related tests without testing for other things first.

This post is for that group of folks, and anyone stumbling across this post who has seen their test results for their fecal elastase testing indicating they have “moderate pancreatic insufficiency” or “severe pancreatic insufficiency” and are wondering what they can do while they wait for their doctor’s appointment.

It’s also for people with EPI who are struggling to afford their pancreatic enzyme replacement therapy (PERT) or are limiting the number or size of meals they eat as a result of the cost of PERT.

A bit of background on why I did the math about OTC enzyme cost and why I had tested them myself

Due to the holidays in December 2021 I had a lag between getting my test results (over Christmas) and then confirming that my doctor would write a prescription for PERT, and then a delay in getting it filled by the pharmacy since they had to order it. One of the things I did during that time was read up a lot about PERT and also look to see if there were any other kinds of enzymes that would be useful to take if my doctor didn’t want to prescribe me PERT. I found out that PERT contains THREE types of enzymes, and together they’re known as pancrelipase. Pancrelipase contains lipase (helps digest fat), protease (helps digest protein), and amylase (helps digest starches and other complex carbohydrates). It’s typically made from ground up pig pancreas, which is one of the reasons that PERT costs so much. Amylase from non-pancreatic sources is not widely available for human consumption, but there are some other ways to make protease and lipase. And it turns out that these standalone enzyme versions, often produced by microbes, are available to buy over the counter.

While waiting for my test to be ordered, I went ahead and ordered a standalone lipase product that is over the counter (OTC). In part, that was because some of the reviews for lipase talked about having EPI and how they were only sensitive to lipase, and so this was a viable and cheaper alternative for them rather than taking PERT with all 3 enzymes, since they didn’t need that. Based on my experience with FODMAPs and trying an enzyme powder to target fructans (which did help me some), it seemed like trying small doses of lipase would help if I did have EPI, and likely wouldn’t hurt even if I did not have EPI.

And it helped. It didn’t reduce all my symptoms, but even minor doses (3000 units of lipase) made a noticeable difference in my symptoms and I got a sense for what meals were more fat and protein-laden than others.

As a result, when my test results came in and I was on the borderline for moderate EPI, I agreed with my HCP that since it likely wouldn’t hurt to take PERT (other than the cost), and it would be obvious if it helped, that I should try PERT.

So having done the tests with OTC (over the counter) lipase was helpful for deciding to take PERT and advocating for my prescription.

And it turns out, wow yes, I do have EPI and do definitely need PERT (more about my first two weeks on PERT here).

And as I wrote here, because I had the OTC lipase sitting around, even after I finally had access to PERT, I eventually titrated my dosing and calculated separate ratios for lipase:fat and protease:protein, so I can decide for every meal or snack whether I need one full PERT (all three enzymes), two PERT, a PERT plus some lipase (and how much), or just a standalone OTC lipase. The cost differs greatly between those options: one PERT might be $9 and a standalone lipase pill around $0.26. You can’t break apart a PERT (e.g. take only half), so adding a few lipase is a cost-effective approach if you don’t need more protease or amylase and the OTC lipase works for you.

Some of the reasons to explore over the counter enzymes with exocrine pancreatic insufficiency or a suspected case of EPI

One interesting thing about one of the main tests (fecal elastase) used to assess EPI is that it is NOT impacted by taking enzymes. Someone who is started on pancreatic enzyme replacement therapy (PERT) can still have an elastase test without stopping taking PERT. So if someone had an inconclusive result or was borderline and started taking PERT, but their doctor wanted to re-test again, the use of PERT would not affect the test. The same goes for other types of enzymes.

I’ve realized that the following groups of people might want to investigate various OTC enzyme options:

  • Someone who has been diagnosed with EPI, but has done careful testing with meals of various sizes (low fat & high protein, high fat & low protein, etc.) to determine that they really only need lipase, may benefit from cheaper lipase-only OTC options.
  • Someone who has a test result for EPI but doesn’t yet have an appointment with their doctor or a prescription for PERT could start taking some OTC enzymes for quicker symptom relief, even if they ultimately want to use PERT for all their enzyme needs once they get their prescription filled.
  • Someone diagnosed with EPI who cannot afford the ideal dose of PERT that they need for their meals and snacks, may want to calculate the out of pocket costs for OTC lipase (not covered by insurance) vs the cost of PERT with or without insurance.
  • Someone who can’t get tested for EPI, but suspects they have EPI, might want to also explore OTC lipase and/or OTC multiple enzyme products.

However, not everyone with EPI will want OTC enzymes. Some people may have great insurance coverage, so their PERT costs them less than $9 a pill. OTC enzymes are not covered by insurance, but I’d still do the math and assess what your standard cost is per pill, because it may surprise you how cheap add-on OTC lipase is vs. your insurance deductible or copay to take additional PERT for larger meals. The other reason some people may not want to take OTC enzymes is the pill burden: OTC doses tend to be smaller, so you usually need to take more pills to cover the same meal as a single, larger PERT.

Picking what enzymes (in general, or specific brands) work for you

I often see a variety of OTC enzyme products recommended in peer groups on social media for EPI. There are no studies that I can find assessing the efficacy of these OTC brands (meaning, how good they are). I would be very cautious when trying different single or multiple enzyme products and keep a careful log of your symptoms from before enzymes as well as symptoms at every meal that you take enzymes, and your bathroom results afterward. This can help you assess OTC enzymes as well as PERT if you get access to it. By having a good log of your symptoms, you can tell if you’re taking enough enzymes (OTC or PERT) or if you’re developing new symptoms (which could be a side effect of whatever brand/type you are taking).

There are multiple brands and sizes of PERT, too, and it’s possible that a filler product or how the PERT is made by one brand doesn’t work well for you. If that’s the case, you can try another brand of PERT.

The same goes for OTC enzymes: it is very possible some types of pills may be made with ingredients that could bother you and cause symptoms themselves. You should definitely be very cautious if you go this route and explore small doses and ensure no side effects (no new symptoms) before increasing any doses.

When I search for lipase, it’s easy to find standalone lipase (here is an example, as an Amazon affiliate link). When I search for protease, it’s more common to find products that are multiple enzymes (e.g. lipase AND protease AND other random things that are “good for digestion”). Personally, I’m very wary of anything OTC that’s described as “digestive enzymes” and prefer to stick to products that only have the ingredients I’m looking for.

A pro-con list for over the counter (OTC) enzymes for EPI. Pros include: lower cost overall and per pill; that you can take smaller quantities of individual enzymes; and you can buy them without a prescription. Cons include: it's not covered by insurance so cost is out of pocket; you have to take more pills with smaller amounts of enzymes; it's not regulatory approved so othere are no studies on efficacy; and providers may not be able to advise for titration.
In diabetes, we often say “your diabetes may vary” (YDMV), indicating that different people can have different experiences.

In EPI, it’s no different – “your digestion may vary” and it’s important to test and record and find what works for you, and to find a balance of reducing or eliminating symptoms with enzymes in a cost-effective way that you can afford.

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