research

Exocrine Pancreatic Insufficiency (EPI/PEI) In Type 1 and Type 2 Diabetes – Poster at #ADA2023

by Dana Lewis

When I was invited to contribute to a debate on AID at #ADA2023 (read my debate recap here), I decided to also submit an abstract related to some of my recent work in researching and understanding the prevalence and treatment of exocrine pancreatic insufficiency (known as EPI or PEI or PI) in people with diabetes.

I have a personal interest in this topic, for those who aren’t aware – I was diagnosed with EPI last year (read more about my experience here) and now take pancreatic enzyme replacement therapy (PERT) pills with everything that I eat.

I was surprised that it took personal advocacy to get a diagnosis, and despite having 2+ known risk factors for EPI (diabetes, celiac disease), that when I presented to a gastroenterologist with GI symptoms, EPI never came up as a possibility. I looked deeper into the research to try to understand what the correlation was in diabetes and EPI and perhaps understand why awareness is low compared to gastroparesis and celiac.

Here’s what I found, and what my poster (and a forthcoming full publication in a peer-reviewed journal!) is about (you can view my poster as a PDF here):

1304-P at #ADA2023, “Exocrine Pancreatic Insufficiency (EPI / PEI)  Likely Overlooked in Diabetes as Common Cause of Gastrointestinal-Related Symptoms”

Exocrine Pancreatic Insufficiency (EPI / PEI / PI) occurs when the pancreas no longer makes enough enzymes to support digestion, and is treated with pancreatic enzyme replacement therapy (PERT). Awareness among diabetes care providers of EPI does not seem to match the likely rates of prevalence and contributes to underscreening, underdiagnosis, and undertreatment of EPI among people with diabetes.

Methods:

I performed a broader systematic review on EPI, classifying all articles based on co-condition. I then did a second specific diabetes-specific EPI search, and de-duplicated and combined the results. (See PRISMA figure).

A PRISMA diagram showing that I performed two separate literature searches - one broadly on EPI before classifying and filtering for diabetes, and one just on EPI and diabetes. After filtering out irrelevant, animal, and off topic papers, I ended up with 41

I ended up with 41 articles specifically about EPI and diabetes, and screened them for diabetes type, prevalence rates (by type of diabetes, if it was segmented), and whether there were any analyses related to glycemic outcomes. I also performed an additional literature review on gastrointestinal conditions in diabetes.

Results:

From the broader systematic review on EPI in general, I found 9.6% of the articles on specific co-conditions to be about diabetes. Most of the articles on diabetes and EPI are simply about prevalence and/or diagnostic methods. Very few (4/41) specified any glycemic metrics or outcomes for people with diabetes and EPI. Only one recent paper (disclosure – I’m a co-author, and you can see the full paper here) evaluated glycemic variability and glycemic outcomes before and after PERT using CGM.

There is a LOT of work to be done in the future to do studies with properly recording type of diabetes; using CGM and modern insulin delivery therapies; and evaluating glycemic outcomes and variabilities to actually understand the impact of PERT on glucose levels in people with diabetes.

In terms of other gastrointestinal conditions, healthcare providers typically perceive the prevalence of celiac disease and gastroparesis to be high in people with diabetes. Reviewing the data, I found that celiac has around ~5% prevalence (range 3-16%) in people with type 1 diabetes and ~1.6% prevalence in Type 2 diabetes, in contrast to the general population prevalence of 0.5-1%. For gastroparesis, the rates in Type 1 diabetes were around ~5% and in Type 2 diabetes around 1.3%, in contrast to the general population prevalence of 0.2-0.9%.

Speaking of contrasts, let’s compare this to the prevalence of EPI in Type 1 and Type 2 diabetes.

  • The prevalence of EPI in Type 1 diabetes in the studies I reviewed had a median of 33% (range 14-77.5%).
  • The prevalence of EPI in Type 2 diabetes in the studies I reviewed had a median of 29% (16.8-49.2%).

You can see this relative prevalence difference in this chart I used on my poster:

The prevalence of EPI is much higher in T1 and T2 than the prevalence of celiac and gastroparesis.

Key Findings and Takeaways:

Gastroparesis and celiac are often top of mind for diabetes care providers, yet EPI may be up to 10 times more common among people with diabetes! EPI is likely significantly underdiagnosed in people with diabetes.

Healthcare providers who see people with diabetes should increase the screening of fecal elastase (FE-1/FEL-1) for people with diabetes who mention gastrointestinal symptoms.

With FE-1 testing, results <=200 μg/g are indicative of EPI and people with diabetes should be prescribed PERT. The quality-of-life burden and long-term clinical implications of undiagnosed EPI are significant enough, and the risks are low enough (aside from cost) that PERT should be initiated more frequently for people with diabetes who present with EPI-related symptoms.

EPI symptoms aren’t just diarrhea and/or weight loss: they can include painful bloating, excessive gas, changed stools (“messy”, “oily”, “sticking to the toilet bowl”), or increased bowel movements. People with diabetes may subconsciously adjust their food choices in response to symptoms for years prior to diagnosis.

Many people with diabetes and existing EPI diagnoses may be undertreated, even years after diagnosis. Diabetes providers should periodically discuss PERT dosing and encourage self-adjustment of dosing (similar to insulin, matching food intake) for people with diabetes and EPI who have ongoing GI symptoms. This also means aiding in updating prescriptions as needed. (PERT has been studied and found to be safe and effective for people with diabetes.)

Non-optimal PERT dosing may result in seemingly unpredictable post-meal glucose outcomes. Non-optimal postprandial glycemic excursions may be a ‘symptom’ of EPI because poor digestion of fat/protein may mean carbs are digested more quickly even in a ’mixed meal’ and result in larger post-meal glucose spikes.

—

As I mentioned, I have a full publication with this systematic review undergoing peer review and I’ll share it once it’s published. In the meantime, if you’re looking for more personal experiences about living with EPI, check out DIYPS.org/EPI, and also for people with EPI looking to improve their dosing with pancreatic enzyme replacement therapy – you may want to check out PERT Pilot (a free iOS app to record enzyme dosing).

Researchers, if you’re interested in collaborating on studies in EPI (in diabetes, or more broadly on EPI), please reach out! My email is Dana@OpenAPS.org

Air Quality, CO2 monitoring, and Situational Masking

by Dana Lewis

I do a lot of things most people don’t want to do themselves – and I get that. (For example, recording macronutrients while running? Running for up to 16 or 25 hours? Let alone other choices like building DIY and making open source automated insulin delivery systems not only for myself but more widely available for other people.) I’ve also talked before about functional self-tracking and how I don’t track things for the sake of tracking, I track when the data/information is actionable either retrospectively or in real-time.

I’ve spent enough time now collecting real-time data on air quality (via a proxy of CO2 levels) that I think it would be useful to share for other people to consider the retrospective data for THEIR decision making.

You may not want (or be able to afford) a CO2 monitor, and you may not want to mask inside all the time, but the below outlines the general scenarios in which air quality tends to be better or worse and when you would get the most benefit from situational masking in response to those situations.

(Think about situational masking indoors like you think about situational masking for smoke and poor air quality outside. Most of the time, you likely don’t mask outside. But if you’re on the east coast right now or have lived through a previous west coast US summer with a “smoke season”, you’ve probably experienced multi-day air quality outside that was so poor that you considered or did wear a high-quality (N95/K95) mask outside or limit your time exposed to that outdoor air.)

Air quality assessment via CO2 monitoring

In the last few years, Scott and I acquired two different CO2 monitors. The first was cheap, required to be plugged into a battery pack to run it, and was simply viewable on the device display. It was useful to start to get a sense for what the CO2 levels were in indoor spaces as compared to outdoor air.

Later, we decided to invest in an Aranet CO2 monitor, which runs on two AA batteries and lasts months on a single pair of batteries. You can view the data on the device display AND see a retrospective and realtime graph of the data in your phone, because it connects via Bluetooth. You can see not only CO2 but also temperature, humidity, and air pressure.

We have found this useful because CO2 is something that we all produce when we breathe out. The more we breathe out, and the more people that are breathing out, the higher the CO2 levels. The more of that air that is replaced with low-CO2 outside air, the lower the CO2 levels. Measuring the CO2 then helps us understand the ventilation (how much air is flowing through the space and how often it is being cleared out) and the risks of being in that space. A higher CO2 level means more people and/or less air being cleared out of the space, meaning  you are more likely to be breathing in what someone else is breathing out.

How we evaluate CO2 levels

An outdoor CO2 level would be around ~450 ppm in urban areas, or as low as 400 ppm out in nature. Since a perfectly-ventilated space would be 100% outside air, we want to compare any indoor air CO2 reading to outdoor air.

For example, at home in our enclosed apartment with 2 people (and 2 cats), we typically run around 700 ppm, which means ~250 ppm above outdoor air levels. When we open our door or a window, it drops to ~500 ppm, or only ~50 ppm above outdoor air levels. Given that we have confirmed our air intake into our HVAC system for our apartment is outdoor air, this means the ~250 ppm we are sharing between the two of us is just our (and the cats) exhalations, rather than anyone outside our household. So those levels are acceptable to us, but our choice of interventions would change if we were sharing air with other people, especially random strangers. (Stranger danger is one way to think about air, further contextualized below with data.)

In a shared space with random strangers, your risk of COVID aerosol-based transmission is proportional to how elevated the CO2 level is above that of outside air, and the amount of time spent in that space. So a CO2 reading of 650 ppm, which is ~200 ppm over outside air, would be half as risky as a reading of 850 ppm, or ~400 ppm over outside air. And timing matters, so a 1 hour bus ride or the hour you spend boarding and waiting for takeoff on your plane when CO2 levels are highest and the air filtration (see below) is off will be of greater risk than short exposure to the same levels.

Now, we’ve also used our CO2 monitors in many other places, such as in airports and on planes and other public transportation, and other indoor shared spaces like grocery stores etc.

Here’s what we’ve learned about where CO2 levels trend (based on our repeated but n=1 testing).

Trains, buses, and rideshare (e.g. Uber, Lyft, etc) = BAD NEWS BEAR AIR

Public transportation, in every location and every country we have been in, has much higher CO2 levels.

What do I mean by much higher? Often 1000-1500 ppm easily (and sometimes >2000 ppm), which is anywhere from 500-1500 ppm above outdoor air quality.

Trains/metros/light rail where the doors are constantly opening and closing to outdoor air would seem like they would be better, but sometimes they still have (due to the density of riders) >1500 ppm.

Buses where you can’t open the window can be as high of CO2 levels as planes, without the benefit of air exchange or HEPA filtration of the air. Our recent 20 minute bus ride was up to >2500 ppm on a full bus.

Watch out for rideshares, too. Often times we get in a rideshare and the driver intentionally or accidentally has “recirc” or “recirculating air” on, meaning the air isn’t exchanged outside and the driver and riders are re-breathing each other’s air over and over and over and over again..yikes. Specifically looking at the console when you get in the car is useful: if you see the recirc button lit up, ask the driver to turn it off. If they don’t understand or refuse, or you don’t want to try to explain it, opening a window helps immensely to reduce the CO2 levels and the amount of re-breathing air. (The recirc icon usually looks like a car with a U-shaped arrow on it).

Planes (including airports, during boarding, in flight, and during landing/deplaning) = ALSO BAD NEWS BEAR AIR

Airports sometimes have better-ventilated spaces: you can often find less crowded corners of a terminal and see CO2 readings of <900 ppm. However, it’s still pretty common to be in the airport and see >1000 ppm, meaning that the CO2 is >500 ppm above outdoor air quality, and it is air from a whole assortment of random strangers coming and going, so it’s less safe than the air you’d be breathing in at home or in private spaces.

When boarding, both standing close in line with other people but also on the jet bridge and while you are on the plane, is usually even HIGHER CO2 levels than the airport. The typical air for a plane (that they tout with HEPA filters and high air exchange rates) is not turned on until you start to take off, and then it takes some time to exchange all of the air. This means there is a MUCH higher rate of re-breathing other people’s air while boarding and until you are in the air.

Now, we have measured CO2 levels during all of these times. If indoor airport air is around 900 ppm, it usually jumps to 1100-1300 on the jetbridge (if you’ve got a backed up line) and when you’re sitting on the plane watching other people board, it can go up to 2500+. And then it continues to go up as you have a full flight of people breathing in this enclosed space. During flight, we’ve seen CO2 levels hover between 1700-3000 ppm, and in some cases have gone up to ~4000 ppm. This is a lot of CO2! However, there are HEPA filters cleaning the ~half of the air that is recirculated instead of replaced. So, it’s harder to say when the airplane air systems ARE running (during most of the flight) whether the risk is as high (for infectious disease transmission) as it is in other environments that aren’t studiously exchanging and HEPA-filtering any recirculated air.

Note that when they spin down the engines after landing and all the way through taxiing, deplaning, and getting back into the airport – the CO2 level again tends to rise because they again change the air flow when they’re on the ground. So like standing in line to get on or waiting for other people to board, standing in line to get off/waiting for everyone to get off produces high CO2 levels *without the benefit of in-flight air exchange*, so it’s likely higher risk during those times than in the air during the middle of the flight, even if CO2 levels are equally high during flight.

Indoor spaces like grocery stores or conference rooms/meeting halls

Indoor spaces can vary quite a bit, and often by country or venue.

For example, most indoor spaces in the US we’ve found to often have a fairly high (e.g. 900+ ppm) indoor CO2 level, even without a huge density of people. For example, we quickly went into a grocery store the other day and the CO2 was high-800s without being around many people in the aisles, across the entire store. For not having people actively occupying the space, this is fairly high and less optimal.

In contrast, we recently were in Sweden for a conference and were honestly gobsmacked when we got off the plane and found the CO2 levels to be <600 ppm in the airport! And in the hotel lobby! And in the hotel elevator! And at the local grocery store!

(Seriously, it shocked us, because we’ve also recently been in the UK with our CO2 monitor and found US-like CO2 levels typically around 900-1000 ppm or higher, and also in Spain last year where we similarly found it to be >900 ppm even when not densely occupied. The exception to optimal air quality in Sweden was our ~20 minute bus ride where CO2 levels were >2500 ppm).

So, the CO2 levels may vary quite a bit and this is why measuring is helpful. Because you can’t assume that one country/one room means that all of the rooms in that country or even that venue will be the same.

Case in point? Conference rooms/halls or meeting rooms.

In Barcelona, Spain in April 2022, I spoke at a conference. The CO2 levels in the hallways and in the meeting room before the session started were around 800-900 ppm when not occupied. Again, a little high for not having people actively in the spaces. Then, when the conference started, Scott watched the CO2 monitor and saw it rise..and rise…and rise. Within 45 minutes, the CO2 levels were around 2000 ppm (>1500 ppm over outdoor air quality)! He went to the back of the room and opened the doors to try to get some air circulating in the room, although it didn’t make a big difference. That room did not have a high number of air exchanges per hour and was not successfully clearing out the air people were breathing out.

In Sweden (May 2023, where the CO2 was <600 in a lot of public indoor spaces), we found the same challenge in a high ceiling, large meeting hall. With 300 people, the start of the session had about 950 ppm (as opposed to the <600 ppm of less occupied hallways). Not too bad given 300 people in the space. However, by the end of the session, the CO2 level had risen to ~1350! And it continued to rise even as people had exited the room; we didn’t see a drop in CO2 levels until we went out in the hallway to continue talking to people, and it took another ~25 minutes before CO2 levels in the hallway were back down <600 ppm.

Again, we were surprised, because this venue (the hallways, lobby, elevator, etc) all had really great otherwise indoor air quality with CO2 <600 ppm!

But the challenge is the space (and the infrastructure for filtration and air exchanges); the number of people filling the space; and the amount of time, in terms of what happens to the CO2 levels.

The takeaway from this? Conference halls, meeting rooms, and anywhere where you are sitting with a group of people over a period of time is going to have a much higher CO2 level and it will increase in proportion to the time that you are occupying that space (e.g. a 30 minute or 1 hour session is going to have a much higher CO2 buildup than a 10 minute talk where the audience is turning over and leaving the room and it clears out some before the next session).

So what should you do about this information? Consider situational masking.

I really have found a CO2 monitor helpful, because even my best guesses about air quality (e.g. thinking Sweden’s conference hall would have good air quality given the size of the room and ceilings) aren’t always accurate. But if you don’t want to invest in a CO2 monitor, here’s where you can get the biggest bang for your buck with situational masking.

What do I mean by situational masking? Maybe you don’t think you’re at very high risk for COVID or other infectious illnesses, but you are interested in reducing the likelihood that you spread anything you get to other people (thanks!). But you don’t want to have to think about it, and maybe you’ve chosen previously to drop masking so you don’t have to think about it. Here’s a set of easy rules/situations in which, like learning to dump your liquids out before going through airport security, you can get into a habit of doing and not have to think about it much.

  • Public/shared transportation.

    Riding a bus, train, metro, or a car with a stranger and especially with multiple strangers – these have high CO2 levels.

  • Airports, boarding a plane and during takeoff, and during descent/landing/deboarding the plane.

    This is when the CO2 levels are highest and the air exchanges/HEPA filtration is not running.

    Think of it like the seatbelt sign. You board the plane and put your seatbelt on, then eventually once you’ve reached cruising altitude the seatbelt sign goes off. If you’re standing in a line of people (to board or deplane) OR if the seatbelt sign is ON, that’s a huge ROI for wearing a high-quality (N95 or KN95) mask. When the seatbelt sign first turns off during the flight (or you hear the 10k-feet chime) and you want to take and leave it off, or take it off a while to eat or drink – that’s less risky during those times due to the HEPA filtration and air exchanges during flight. But when the seatbelt sign goes on for the plane’s final descent? The air quality is going down, too, so putting your seatbelt AND your mask back on is a higher ROI thing to do.

    (You do you inside the airport, too, but see below about density of people and temperature as a guide for whether you might want to consider situational masking in airports when you’re not eating/drinking.)

 

  • Conferences or meetings where you are sitting for more than a few minutes and there are many people in the room.

    Even with super big rooms and super high ceilings, so far every conference space I’ve presented in during the last several years has high CO2 levels even before the talk starts, and is even higher (>500-1000 ppm added) by the end of the session). If you’re not presenting or eating and drinking and are just sitting there listening and engaging in the session…it’s a low hassle opportunity to pop a high-quality mask on so you’re not breathing so much of the air around you from everyone else. When you’re done with the session and head out and want to socialize? Like leaving the plane, you’ll be around fewer people, and the CO2 levels (and risk) goes down. But sitting there quietly is a great time to wear a good mask and reduce your intake of other people’s exhalations.

 

You might find yourself in situations where the room feels hot and stuffy, or in the case of conferences and meetings, the air feels FREEZING cold. It runs freezing cold because the room gets hot and stuffy with so many people, indicating this space is not well ventilated, so they pump the AC to change the temperature. But that is a compensation for a too-low rate of air exchanges, and pre-cooling doesn’t prevent CO2 and aerosol buildup, so a room that either gets freezing cold or hot and stuffy should be a signal that the air quality likely isn’t ideal.

So a good rule of thumb is, if you’re in a space that feels hot and stuffy OR freezing cold, that’s an indicator that the air quality might be non-optimal. Consider masking in those situations even if you don’t have a CO2 monitor to evaluate the air.

It would be great if we could get 10x people to consider situational masking like this. Avoid the worst of the bad-news-bear-air of public and shared transportation and indoor spaces, which would cut down on a lot of transmission, even if people otherwise are still socializing and eating in indoor spaces and doing whatever it is they want to do. The choice to situationally mask might occasionally protect them but would also protect everyone around them in those situations when their exhalations have the greatest risk of doing the most damage.

A good way to think about it is at a conference. You might be willing to go to bars and socialize, but someone who is higher risk may be choosing not to attend those indoor dining scenarios. That’s fine: you each get to make your own choices! But when you go and sit down next to that person in a conference session, your choices then influence that person by every breath you take in that conference session.

That’s why situational masking – knowing that a situation is low-hassle to wear a high-quality mask (sitting quietly in a session) but high-risk (due to the poor air quality) means you have a high ROI to pull a mask out of your pocket/bag and throw it on – can help the people around you very effectively with little hassle and thought on your part.

You can get in the habit of masking in the bad-news-bear-air situations/locations, and you don’t have to think much about it. You’ll make things a bit safer for yourself and for the people around you, for far less hassle than avoiding buying a drink before you go through airport security because you know you need to dump liquids out.

Data-driven situational masking based on indoor air quality

How To Talk To Your Doctor About Your Enzyme (PERT) Dosing If You Have Exocrine Pancreatic Insufficiency (EPI or PEI or PI)

by Dana Lewis

In exocrine pancreatic insufficiency (EPI/PEI/PI), people are responsible for self-dosing their medication every time they eat something.

Doctors prescribe a starting dose, but a person with EPI determines each and every time they eat or drink something how many enzyme pills (of pancreatic enzyme replacement therapy, known as PERT) to take. Doctors often prescribe a low starting dose, and people have to try experimenting with multiple pills of the small size, and eventually work with their doctors to change their prescription to get a bigger pill size (so they can take fewer pills per meal) and the correct number of pills per day to match their needs.

For example, often people are prescribed one 10,000 unit pill per meal. The 10,000 units represents the amount of lipase (to help digest fat). There are also two other enzymes (protease, for protein digestion, and amylase, for carbohydrate digestion). They may be prescribed 1 pill per meal, which means 10,000 units of lipase per meal. But most dosing guidelines recommend starting at a dose of 40,000-50,000 units of lipase per meal (and people often need more), so it wouldn’t be surprising that someone prescribed one 10,000 pill per meal would need 4-5 pills of the 10,000 size pill PER MEAL, and times three meals per day (let alone any snacks), to get acceptable GI outcomes.

Mathematically, this means the initial prescription wouldn’t last long. The initial prescription for 1 pill per meal, with 3 meals a day, means 3 pills per day. 3 pills per day across a 30 day month is 90 pills. But when the pills per meal increase, that means the prescription won’t cover the entire month.

In fact, it would last a lot less than a month; closer to one week!

Showing that based on the number of pills and 3 meals per day, an intitial RX of 10,000 size pills may last more like a week rather than a full 30 days when the doctor is unaware of prescribing guidlines that typically suggest 40,000-50,000 per meal is needed as the starting meal dose.

Let’s repeat: with a too-small prescription pill size (e.g. 10,000 starting dose size) and count (e.g. 3 pills per day to cover 1 per meal) and with a person with EPI titrating themselves up to the starting dose guidelines in all of the medical literature, they would run out of their prescription WITHIN ONE WEEK. 

So. If you have EPI, you need to be prepared to adjust your dosing yourself; but you also need to be ready to reach out to your doctor and talk about your need for more enzymes and a changed prescription.

PERT (enzymes) come in different sizes, so one option is to ask for a bigger pill size and/or a different amount (count) per meal/day. Depending on the brand and the number of pills you need per meal, it could be simply going up to a bigger pill size. For example, if you need 3 pills of the 10,000 PERT size, you could move to a 36,000 pill size and take one per meal. If you find yourself taking 5 pills of the 10,000 PERT size, that might mean 2 pills of the 25,000 size. (Brands differ slightly, e.g. one might be 24,000 instead of 25,000, so the math may work out slightly differently depending on which brand you’re taking.)

Don’t be surprised if you need to do this within a week or two of starting PERT. In fact, based on the math above, especially if you’re on a much lower dose than starting guidelines (e.g. 40,000-50,000 units of lipase per meal), you should expect within a few days to need an updated prescription to make sure that you don’t run out of PERT.

If you do find yourself running out of PERT before you can get your prescription updated, there is an alternative you can consider: either substituting or adding on over the counter enzymes. The downsides include the fact that insurance doesn’t cover them so you would be paying out of pocket; plus there are no studies with these so you can’t (shouldn’t) rely on these as full 1:1 substitutes for prescription PERT without careful personal testing that you can do so. That being said, there is anecdotal evidence (from me, as well as hundreds of other people I’ve seen in community groups) that it is possible to use OTC enzymes if you can’t afford or can’t get a PERT prescription; or if you need to “top off”/supplement/add to your PERT because your prescription won’t last a full month and you can’t get a hold of your doctor or they won’t update your prescription.

For me, I generally evaluate the units of lipase (e.g. this kind is 17,000 units of lipase per pill) but then factor in for the lack of reliability for OTC and really treat it like it contains 13-15,000 units of lipase when choosing to take it. Similarly for another lipase-only OTC option (that has ~6,000 units per pill), I assume it acts like it only has ~5,000 units. Unlike insulin, there is little downside to taking a little too much of enzymes; but there is a LOT of downside to not taking enough, so my personal approach is that if in doubt, or on the fence, to round up (especially with OTC pills, which cost somewhere between $0.08/pill (lipase-only) to $0.34/pill (for the larger and multiple enzyme pill)).

So how do you talk to your doctor about needing more PERT?

It helps if you bring data and evidence to the conversation, especially if your doctor thinks by default that you don’t need more than what they initially prescribed. You can bring your personal data (more on that below and how to collect and present that), but you can also cite relevant medical literature to show if your dose is below standard starting guidelines.

Below I’ve shared a series of citations that show that the typical starting dose for people with EPI should be around 40,000-50,000 units of lipase per meal.

Important note that this is the STARTING DOSE SIZE, and most of these recommend further increasing of dose to 2-3 times this amount as needed. Depending on the starting dose size, you can see the chart I built below that illustrates with examples exactly how much this means one might need to increase to. Not everyone will need the upper end of the numbers, but if a doctor starts someone on 10,000 and doesn’t want to get them up to 40,000 (the lower end of starting doses) or go beyond 40,000 because it’s the starting dose, I’ve found this chart useful to show that numerically the range is a lot larger than we might assume.

Example of Titrating According to Common Dose Guidelines, Before Adding PPI

Examples of PERT starting doses of 25,000, 40,000, and 50,000 (plus half that for snacks) and what the dose would be if increased according to guidelines to 2x and 3x, plus the sum of the total daily dose needed at those levels.

Here are some citations that back up my point about 40,000-50,000 units of lipase being the typically recommended starting dose, including across different conditions (e.g. regardless of whether you have EPI + any of (chronic pancreatitis, diabetes, celiac, etc)).

  • Shandro et al, 2020, the median starting dose of 50,000 units per lipase “is an appropriate starting dose”, also citing UEG 2017 guidelines.
  • Forsmark et al, 2020, defined appropriate dose of PERT as >=120,000 units of lipase per day (e.g. 40,000 units of lipase per meal).
  • Whitcomb et al, 2022, in a joint American Gastroenterology Association and PancreasFest symposium paper, concur on 40,000 units as a starting dose and that “This dose should be titrated up as needed to reduce steatorrhea or gastrointestinal symptoms of maldigestion “
  • 2021 UK guidelines for EPI management suggest 50,000 units as the starting dose and emphasize that “all guidelines endorse dose escalation if the initial dose is not effective”

There are also many guidelines and research specific for EPI and different co-conditions supporting the ballpark of 40-50,000 units of lipase starting dose:

It is also worth noting that these guidelines also point out that after titrating 2-3x above the starting dose, PPI (proton pump inhibitors, to suppress acid) should be added if gastrointestinal symptoms are still not resolved. Anecdotally, it seems a lot of doctors are not aware that PPIs should be added if 3x the starting dose is not effective, so make sure to bring this up as well.

How to Share Your Personal PERT Data To Show How Much You Need

In addition to pointing out the guidelines (based on the above), it’s useful to share your data to show what you’ve been taking (dosing) and how it’s been working. I’ve written a lot about how you can do this manually, but I also recently created an iOS based app to make it easier to track what you’re eating, what you’re dosing in terms of PERT/enzymes, and what the outcome is. This app, PERT Pilot, is free to use, and it also enables you to visualize on a graph the relationship between what you’re eating and dosing.

PERT Pilot lets you track how many grams of fat each pill of your current prescription has been used for, so you can see with red and green coloring the relationship between meals that you’ve had symptoms after (in red) vs. when you recorded no symptoms (green). If you have a “convergence zone” of green and red in the same area, that may help you decide to change your ratio (e.g. dose more) around that amount, until you can comfortably and repeatedly get green results (no symptoms when you eat).

How you might use this to talk to your doctor

You can take a screenshot of your PERT Pilot graph and share it with your doctor to show them how many grams of fat your prescription size (e.g. pill size) effectively “covers” for you, and how many meals that you’ve tested it with.

Meals based on the ratio of fat:lipase and protein:protease mapped with color coded dots where green means no symptoms, orange means not sure if symptoms, and red means symptoms occurred and the dose likely didn't work at that ratio.For example, I was initially prescribed an enzyme dose that was one pill per meal (and no snacks), so I had 3 pills per day. But I quickly found myself needing two pills per meal, based on what I was typically eating. I summarized my data to my doctor, saying that I found one pill typically covered up to ~30 grams of fat per meal, but most of my meals were >30 grams of fat, so that I wanted to update my prescription to have an average of 2 pills per meal of this prescription size. I also wanted to be able to eat snacks, so I asked for 2 pills per meal, 1 per snack, which meant that my prescription increased to 8 pills per day (of the same size), to cover 2 pills x 3 meals a day (=6) plus up to 2 snacks (=2). I also had weeks of data to show that my average meal was >30 grams of fat to confirm that I need more than the amount of lipase I was originally prescribed. My doctor was happy to increase my prescription as a result, and this is what I’ve been using successfully for over a year ever since.

So in summary, the data that would be useful to share is:

  • How much one pill ‘covers’ (which is where the PERT Pilot graph can be used)
  • How many pills per meal you’ve been taking and how big your meals typically are
  • Whether you are struggling with the number of pills per meal: if so, ask whether there’s a larger pill size in your current brand that you could increase to, in order to reduce the number of pills per meal (and/or snack) you need to take every time

If you are told that you shouldn’t need “that much”, remember the above section and have those resources ready to discuss that the starting dose is often 40,000-50,000 per meal and that the guidelines say to titrate up to 3x that before adding PPI. Therefore, it would be expected for some people to need upwards of 600,000 units of lipase per day (50,000 starting dose, increased 3x per meal and half of the dose used per snack). Depending on what people eat, this could be even higher (because not everyone eats the same size meal and snack and many of us adjust dose based on what we eat).

Also, it is worth noting that the dosing guidelines never mention the elastase levels or severity of EPI: so PERT prescriptions should not be based on whether you have “moderate” or “severe” EPI and what your elastase level is (e.g. whether it’s 45 or 102 or 146 or even 200, right on the line of EPI – all of those elastase levels would still get the same starting dose of PERT, based on the clinical guidelines for EPI).

It is common and you are not alone if you’ve not been giving the starting dose of PERT that the guidelines recommend.

There are numerous studies showing most people with EPI are initially underdosed/underprescribed PERT. For example, in 2020 Forsmark et al reported that only 8.5% of people with chronic pancreatitis and EPI received an adequate prescription for PERT: and only 5.5% of people with pancreatic cancer and EPI received an adequate prescription dose of PERT. Other studies in chronic pancreatitis and EPI from 2014, 2016, and 2020 report that undertreatment often occurs in EPI and CP; and I’ve found studies in other conditions as well showing undertreatment compared to guidelines, although it’s most studied in CP and cancer (which is true of all types of EPI-related research, despite the prevalence in many other conditions like diabetes, celiac, etc.).

You may need to advocate for yourself, but know that you’re not alone. Again, feel free to comment or email privately (Dana@OpenAPS.org) if you need help finding research for another co-condition and EPI that I haven’t mentioned here.

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PS – if you haven’t seen it, I have other posts about EPI at DIYPS.org/EPI

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

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How I Use LLMs like ChatGPT And Tips For Getting Started

by Dana Lewis

You’ve probably heard about new AI (artificial intelligence) tools like ChatGPT, Bard, Midjourney, DALL-E and others. But, what are they good for?

Last fall I started experimenting with them. I looked at AI art tools and found them to be challenging, at the time, for one of my purposes, which was creating characters and illustrating a storyline with consistent characters for some of my children’s books. I also tested GPT-3 (meaning version 3.0 of GPT). It wasn’t that great, to be honest. But later, GPT-3.5 was released, along with the ChatGPT chat interface to it, which WAS a big improvement for a lot of my use cases. (And now, GPT-4 is out and is an even bigger improvement, although it costs more to use. More on the cost differences below)

So what am I using these AI tools for? And how might YOU use some of these AI tools? And what are the limitations? This is what I’ve learned:

  1. The most frequent way I use these AI tools is for getting started on a project, especially those related to writing.

You know the feeling of staring at a blank page and not knowing where to start? Maybe it’s the blank page of a cold email; the blank page of an essay or paper you need to write; the blank page of the outline for a presentation. Starting is hard!

Even for this blog post, I had a list of bulleted notes of things I wanted to remember to include. But I wasn’t sure how I wanted to start the blog post or incorporate them. I stuck the notes in ChatGPT and asked it to expand the notes.

What did it do? It wrote a few paragraph summary. Which isn’t what I wanted, so I asked it again to use the notes and this time “expand each bullet into a few sentences, rather than summarizing”. With these clear directions, it did, and I was able to look at this content and decide what I wanted to edit, include, or remove.

Sometimes I’m stuck on a particular writing task, and I use ChatGPT to break it down. In addition to kick-starting any type of writing overall, I’ve asked it to:

  • Take an outline of notes and summarize them into an introduction; limitations section; discussion section; conclusion; one paragraph summary; etc.
  • Take a bullet point list of notes and write full, complete sentences.
  • Take a long list of notes I’ve written about data I’ve extracted from a systematic review I was working on, and ask it about recurring themes or outlier concepts. Especially when I had 20 pages (!) of hand-written notes in bullets with some loose organization by section, I could feed in chunks of content and get help getting the big picture from that 20 pages of content I had created. It can highlight themes in the data based on the written narratives around the data.

A lot of times, the best thing it does is it prompts my brain to say “that’s not correct! It should be talking about…” and I’m able to more easily write the content that was in the back of my brain all along. I probably use 5% of what it’s written, and more frequently use it as a springboard for my writing. That might be unique to how I’m using it, though, and other simple use cases such as writing an email to someone or other simplistic content tasks may mean you can keep 90% or more of the content to use.

2. It can also help analyze data (caution alert!) if you understand how the tools work.

Huge learning moment here: these tools are called LLMs (large language models). They are trained on large amounts of language. They’re essentially designed so that, based on all of those words (language) it’s taken in previously, to predict content that “sounds” like what would come after a given prompt. So if you ask it to write a song or a haiku, it “knows” what a song or a haiku “looks” like, and can generate words to match those patterns.

It’s essentially a PATTERN MATCHER on WORDS. Yeah, I’m yelling in all caps here because this is the biggest confusion I see. ChatGPT or most of these LLMs don’t have access to the internet; they’re not looking up in a search engine for an answer. If you ask it a question about a person, it’s going to give you an answer (because it knows what this type of answer “sounds” like), but depending on the amount of information it “remembers”, some may be accurate and some may be 100% made up.

Why am I explaining this? Remember the above section where I highlighted how it can start to sense themes in the data? It’s not answering solely based on the raw data; it’s not doing analysis of the data, but mostly of the words surrounding the data. For example, you can paste in data (from a spreadsheet) and ask it questions. I did that once, pasting in some data from a pivot table and asking it the same question I had asked myself in analyzing the data. It gave me the same sense of the data that I had based on my own analysis, then pointed out it was only qualitative analysis and that I should also do quantitative statistical analysis. So I asked it if it could do quantitative statistical analysis. It said yes, it could, and spit out some numbers and described the methods of quantitative statistical analysis.

But here’s the thing: those numbers were completely made up!

It can’t actually use (in its current design) the methods it was describing verbally, and instead made up numbers that ‘sounded’ right.

So I asked it to describe how to do that statistical method in Google Sheets. It provided the formula and instructions; I did that analysis myself; and confirmed that the numbers it had given me were 100% made up.

The takeaway here is: it outright said it could do a thing (quantitative statistical analysis) that it can’t do. It’s like a human in some regards: some humans will lie or fudge and make stuff up when you talk to them. It’s helpful to be aware and query whether someone has relevant expertise, what their motivations are, etc. in determining whether or not to use their advice/input on something. The same should go for these AI tools! Knowing this is an LLM and it’s going to pattern match on language helps you pinpoint when it’s going to be prone to making stuff up. Humans are especially likely to make something up that sounds plausible in situations where they’re “expected” to know the answer. LLMs are in that situation all the time: sometimes they actually do know an answer, sometimes they have a good guess, and sometimes they’re just pattern matching and coming up with something that sounds plausible.

In short:

  • LLM’s can expand general concepts and write language about what is generally well known based on its training data.
  • Try to ask it a particular fact, though, and it’s probably going to make stuff up, whether that’s about a person or a concept – you need to fact check it elsewhere.
  • It can’t do math!

But what it can do is teach you or show you how to do the math, the coding, or whatever thing you wish it would do for you. And this gets into one of my favorite use cases for it.

3. You can get an LLM to teach you how to use new tools, solve problems, and lower the barrier to entry (and friction) on using new tools, languages, and software.

One of the first things I did was ask ChatGPT to help me write a script. In fact, that’s what I did to expedite the process of finding tweets where I had used an image in order to get a screenshot to embed on my blog, rather than embedding the tweet.

It’s now so easy to generate code for scripts, regardless of which language you have previous experience with. I used to write all of my code as bash scripts, because that’s the format I was most familiar with. But ChatGPT likes to do things as Python scripts, so I asked it simple questions like “how do I call a python script from the command line” after I asked it to write a script and it generated a python script. Sure, you could search in a search engine or Stack Overflow for similar questions and get the same information. But one nice thing is that if you have it generate a script and then ask it step by step how to run a script, it gives you step by step instructions in context of what you were doing. So instead of saying “to run a script, type `python script.py’”, using placeholder names, it’ll say “to run the script, use ‘python actual-name-of-the-script-it-built-you.py’ “ and you can click the button to copy that, paste it in, and hit enter. It saves a lot of time for figuring out how to take placeholder information (which you would get from a traditional search engine result or Stack Overflow, where people are fond of things like saying FOOBAR and you have no idea if that means something or is meant to be a placeholder). Careful observers will notice that the latest scripts I’ve added to my Open Humans Data Tools repository (which is packed with a bunch of scripts to help work with big datasets!) are now in Python rather than bash; such as when I was adding new scripts for fellow researchers looking to check for updates in big datasets (such as the OpenAPS Data Commons). This is because I used GPT to help with those scripts!

It’s really easy now to go from an idea to a script. If you’re able to describe it logically, you can ask it to write a script, tell you how to run it, and help you debug it. Sometimes you can start by asking it a question, such as “Is it possible to do Y?” and it describes a method. You need to test the method or check for it elsewhere, but things like uploading a list of DOIs to Mendeley to save me hundreds of clicks? I didn’t realize Mendeley had an API or that I could write a script that would do that! ChatGPT helped me write the script, figure out how to create a developer account and app access information for Mendeley, and debug along the way so I ended up within an hour and a half of having a tool that easily saved me 3 hours on the very first project that I used it with.

I’m gushing about this because there’s probably a lot of ideas you have that you immediately throw out as being too hard, or you don’t know how to do it. It takes time, but I’m learning to remember to think “I should ask the LLM this” and ask it questions such as:

  • Is it possible to do X?
  • Write a script to do X.
  • I have X data. Pretend I am someone who doesn’t know how to use Y software and explain how I should do Z.

Another thing I’ve done frequently is ask it to help me quickly write a complex formula to use in a spreadsheet. Such as “write a formula that can be used in Google Sheets to take an average of the values in M3:M84 if they are greater than zero”.

It gives me the formula, and also describes it, and in some cases, gives alternative options.

Other things I’ve done with spreadsheets include:

  • Ask it to write a conditional formatting custom formula, then give me instructions for expanding the conditional formatting to apply to a certain cell range.
  • Asking it to check if a cell is filled with a particular value and then repeating the value in the new cell, in order to create new data series to use in particular charts and graphs I wanted to create from my data.
  • Help me transform my data so I could generate a box and whisker plot.
  • Ask it for other visuals that might be effective ways to illustrate and visualize the same dataset.
  • Explain the difference between two similar formulas (e.g. COUNT and COUNTA or when to use IF and IFS).

This has been incredibly helpful especially with some of my self-tracked datasets (particularly around thyroid-related symptom data) where I’m still trying to figure out the relationship between thyroid levels, thyroid antibody levels, and symptom data (and things like menstrual cycle timing). I’ve used it for creating the formulas and solutions I’ve talked about in projects such as the one where I created a “today” line that dynamically updates in a chart.

It’s also helped me get past the friction of setting up new tools. Case in point, Jupyter notebooks. I’ve used them in the web browser version before, but often had issues running the notebooks people gave me. I debugged and did all kinds of troubleshooting, but have not for years been able to get it successfully installed locally on (multiple of) my computers. I had finally given up on effectively using notebooks and definitely given up on running it locally on my machine.

However, I decided to see if I could get ChatGPT to coax me through the install process.

I told it:

“I have this table with data. Pretend I am someone who has never used R before. Tell me, step by step, how to use a Jupyter notebook to generate a box and whisker plot using this data”

(and I pasted my data that I had copied from a spreadsheet, then hit enter).

It outlined exactly what I needed to do, saying to install Jupyter Notebook locally if I hadn’t, gave me code to do that, installing the R kernel, told me how to do that, then how to start a notebook all the way down to what code to put in the notebook, the data transformed that I could copy/paste, and all the code that generated the plot.

However, remember I have never been able to successfully get Jupyter Notebooks running! For years! I was stuck on step 2, installing R. I said:

“Step 2, explain to me how I enter those commands in R? Do I do this in Terminal?”

It said “Oh apologies, no, you run those commands elsewhere, preferably in Rstudio. Here is how to download RStudio and run the commands”.

So, like humans often do, it glossed over a crucial step. But it went back and explained it to me and kept giving more detailed instructions and helping me debug various errors. After 5-6 more troubleshooting steps, it worked! And I was able to open Jupyter Notebooks locally and get it working!

All along, most of the tutorials I had been reading had skipped or glossed over that I needed to do something with R, and where that was. Probably because most people writing the tutorials are already data scientists who have worked with R and RStudio etc, so they didn’t know those dependencies were baked in! Using ChatGPT helped me be able to put in every error message or every place I got stuck, and it coached me through each spot (with no judgment or impatience). It was great!

I was then able to continue with the other steps of getting my data transformed, into the notebook, running the code, and generating my first ever box and whisker plot with R!

A box and whisker plot, illustrated simply to show that I used R and Jupyter finally successfully!

This is where I really saw the power of these tools, reducing the friction of trying something new (a tool, a piece of software, a new method, a new language, etc.) and helping you troubleshoot patiently step by step.

Does it sometimes skip steps or give you solutions that don’t work? Yes. But it’s still a LOT faster than manually debugging, trying to find someone to help, or spending hours in a search engine or Stack Overflow trying to translate generic code/advice/solutions into something that works on your setup. The beauty of these tools is you can simply paste in the error message and it goes “oh, sorry, try this to solve that error”.

Because the barrier to entry is so low (compared to before), I’ve also asked it to help me with other project ideas where I previously didn’t want to spend the time needed to learn new software and languages and all the nuances of getting from start to end of a project.

Such as, building an iOS app by myself.

I have a ton of projects where I want to temporarily track certain types of data for a short period of time. My fall back is usually a spreadsheet on my phone, but it’s not always easy to quickly enter data on a spreadsheet on your phone, even if you set up a template with a drop down menu like I’ve done in the past (for my DIY macronutrient tool, for example). For example, I want to see if there’s a correlation in my blood pressure at different times and patterns of inflammation in my eyelid and heart rate symptoms (which are symptoms, for me, of thyroid antibodies being out of range, due to Graves’ disease). That means I need to track my symptom data, but also now some blood pressure data. I want to be able to put these datasets together easily, which I can, but the hardest part (so to speak) is finding a way that I am willing to record my blood pressure data. I don’t want to use an existing BP tracking app, and I don’t want a connected BP monitor, and I don’t want to use Apple Health. (Yes, I’m picky!)

I decided to ask ChatGPT to help me accomplish this. I told it:

“You’re an AI programming assistant. Help me write a basic iOS app using Swift UI. The goal is a simple blood pressure tracking app. I want the user interface to default to the data entry screen where there should be three boxes to take the systolic, diastolic blood pressure numbers and also the pulse. There should also be selection boxes to indicate whether the BP was taken sitting up or laying down. Also, enable the selection of a section of symptom check boxes that include “HR feeling” and “Eyes”. Once entered on this screen, the data should save to a google spreadsheet.” 

This is a completely custom, DIY, n of 1 app. I don’t care about it working for anyone else, I simply want to be able to enter my blood pressure, pulse, whether I’m sitting or laying down, and the two specific, unique to me symptoms I’m trying to analyze alongside the BP data.

And it helped me build this! It taught me how to set up a new SwiftUI project in XCode, gave me code for the user interface, how to set up an API with Google Sheets, write code to save the data to Sheets, and get the app to run.

(I am still debugging the connection to Google Sheets, so in the interim I changed my mind and had it create another screen to display the stored data then enable it to email me a CSV file, because it’s so easy to write scripts or formulas to take data from two sources and append it together!)

Is it fancy? No. Am I going to try to distribute it? No. It’s meeting a custom need to enable me to collect specific data super easily over a short period of time in a way that my previous tools did not enable.

Here’s a preview of my custom app running in a simulator phone:

Simulator iphone with a basic iOS app that intakes BP, pulse, buttons for indicating whether BP was taken sitting or laying down; and toggles for key symptoms (in my case HR feeling or eyes), and a purple save button.

I did this in a few hours, rather than taking days or weeks. And now, the barrier to entry to creating more custom iOS is reduced, because now I’m more comfortable working with XCode and the file structures and what it takes to build and deploy an app! Sure, again, I could have learned to do this in other ways, but the learning curve is drastically shortened and it takes away most of the ‘getting started’ friction.

That’s the theme across all of these projects:

  • Barriers to entry are lower and it’s easier to get started
  • It’s easier to try things, even if they flop
  • There’s a quicker learning curve on new tools, technologies and languages
  • You get customized support and troubleshooting without having to translate through as many generic placeholders

PS – speaking of iOS apps, based on building this one simple app I had the confidence to try building a really complex, novel app that has never existed in the world before! It’s for people with exocrine pancreatic insufficiency like me who want to log pancreatic enzyme replacement therapy (PERT) dosing and improve their outcomes – check out PERT Pilot and how I built it here.

4. Notes about what these tools cost

I found ChatGPT useful for writing projects in terms of getting started, even though the content wasn’t that great (on GPT-3.5, too). Then they came out with GPT-4 and made a ChatGPT Pro option for $20/month. I didn’t think it was worth it and resisted it. Then I finally decided to try it, because some of the more sophisticated use cases I wanted to use it for required a longer context window, and in addition to a better model it also gave you a longer context window. I paid the first $20 assuming I’d want to cancel it by the end of the month.

Nope.

The $20 has been worth it on every single project that I’ve used it for. I’ve easily saved 5x that on most projects in terms of reducing the energy needed to start a project, whether it was writing or developing code. It has saved 10x that in time cost recouped from debugging new code and tools.

GPT-4 does have caps, though, so even with the $20/month, you can only do 25 messages every 3 hours. I try to be cognizant of which projects I default to using GPT-3.5 on (unlimited) versus saving the more sophisticated projects for my GPT-4 quota.

For example, I saw a new tool someone had built called “AutoResearcher”, downloaded it, and tried to use it. I ran into a bug and pasted the error into GPT-3.5 and got help figuring out where the problem was. Then I decided I wanted to add a feature to output to a text file, and it helped me quickly edit the code to do that, and I PR’ed it back in and it was accepted (woohoo) and now everyone using that tool can use that feature. That was pretty simple and I was able to use GPT-3.5 for that. But sometimes, when I need a larger context window for a more sophisticated or content-heavy project, I start with GPT-4. When I run into the cap, it tells me when my next window opens up (3 hours after I started using it), and I usually have an hour or two until then. I can open a new chat on GPT-3.5 (without the same context) and try to do things there; switch to another project; or come back at the time it says to continue using GPT-4 on that context/setup.

Why the limit? Because it’s a more expensive model. So you have a tradeoff between paying more and having a limit on how much you can use it, because of the cost to the company.

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TLDR:

Most important note: LLMs don’t “think” or “know” things the way humans do. They output language they predict you want to see, based on its training and the inputs you give it. It’s like the autocomplete of a sentence in your email, but more words on a wider range of topics!

Also, the LLM can’t do math. But they can write code. Including code to do math.

(Some, but not all, LLMs have access to the internet to look up or incorporate facts; make sure you know which LLM you are using and whether it has this feature or not.)

Ways to get started:

    1. The most frequent way I use these AI tools is for getting started on a project, especially those related to writing.
      • Ask it to help you expand on notes; write summaries of existing content; or write sections of content based on instructions you give it
    2.  It can also help analyze data (caution alert!) if you understand the limitations of the LLM.
      • The most effective way to work with data is to have it tell you how to run things in analytical software, whether that’s how to use R or a spreadsheet or other software for data analysis. Remember the LLM can’t do math, but it can write code so you can then do the math!
    3.  You can get an LLM to teach you how to use new tools, solve problems, and lower the barrier to entry (and friction) on using new tools, languages, and software.
      • Build a new habit of asking it “Can I do X” or “Is it possible to do Y” and when it says it’s possible, give it a try! Tell it to give you step-by-step instructions. Tell it where you get stuck. Give it your error messages or where you get lost and have it coach you through the process. 

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What’s been your favorite way to use an LLM? I’d love to know other ways I should be using them, so please drop a comment with your favorite projects/ways of using them!

Personally, the latest project that I built with an LLM has been PERT Pilot!

How I use LLMs (like ChatGPT) and tips for getting started

CGM for primary care doctors: a new article in the BMJ

by Dana Lewis

I was honored last year to be asked to write an article about the basics of continuous glucose monitoring (CGM) for primary care providers by the BMJ, which was released today online.

This, like most of my academic literature article writing, was an unpaid gig. So why did I do it?

Well, most people with diabetes are treated primarily by primary care providers (“GPs” or “PCPs” or “family doctors”, etc). It’s somewhat rare for most people with diabetes to see an endocrinologist! It also varies regionally, even within the same country. And, primary care providers obviously treat a lot of widely varying conditions, from acute to chronic, so they may not have time or energy to stay up to date on all treatment options for all conditions.

This therefore felt like a great opportunity to contribute some information about CGM, an incredibly useful piece of technology for anyone with diabetes who wants it, specifically written and targeted for primary care providers who may not have the exposure to CGM technology that endocrinology providers have had over the years. And, like most things, the technology (thankfully) has changed quite a bit. Accuracy, ease of use, cost, and many other factors have changed dramatically in the last almost two decades since CGMs were introduced on the market!

I sought out two fellow experts in CGM and diabetes technology to co-author the article with me. I asked Ben Wheeler, an excellent pediatric endocrinologist who has done quite a bit of research on “intermittently scanned” CGMs (isCGM); and Tamara Oser, who is the director of the Primary Care Diabetes Lab (and a parent and a spouse of people living with diabetes) and worked to facilitate uptake of CGM in primary care settings.

I’m also appreciative that a parent and teen with newly diagnosed diabetes and new experiences with CGM both reviewed this article when it was drafted and shared their perspective to it; as well as appreciative of valuable input from a friend with many years of experience with diabetes who has used 8 (!) different CGM systems.

We are starting to see a shift in adoption and coverage of CGM, thankfully. Historically, people with diabetes haven’t always had insurance cover CGM. Even if insurance does cover CGM, sometimes we have to fight an uphill battle every year to re-prove that we (still) have diabetes and that we still need CGM. Sometimes good outcomes from using CGM disqualifies us from the next year’s coverage of CGM (in which case we have to appeal our cases for coverage). It’s frustrating! That’s why it’s so nice to see increasing guidelines, recommendations, and even country-specific guidelines encouraging funding and coverage of CGM for people with all types of diabetes. The biggest latest news – as of yesterday (March 2, 2023) – was that in the U.S., Medicare will now be covering CGM for people with type 2 diabetes on insulin. This is a huge group of people who previously didn’t have CGM coverage before!

So here it is, just out today online (March 3, 2023), and projected to be in the March 25, 2023 print edition of the BMJ: an article on continuous glucose monitoring (CGM) for primary providers. I’m hoping it helps pave the way for more providers to feel comfortable prescribing CGM for more people with diabetes; increased their knowledge in working with people with diabetes who have CGM prescribed from other providers; and also reduce unconscious and conscious bias against people with diabetes being offered this important, life-changing and life-saving technology.

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P.S. – if you can’t access the article from the link above, as a reminder I always store an accessible author copy of my research articles at DIYPS.org/research!

One Year of Pancreatic Enzyme Replacement Therapy for Exocrine Pancreatic Insufficiency (EPI or PEI)

by Dana Lewis

I’ve had exocrine pancreatic insufficiency (EPI or PEI) for a full year now and have been taking pancreatic enzyme replacement therapy (PERT) ever since diagnosis.

I’ve written about what EPI is, what it’s like to go on PERT, and a variety of other posts (such as how I ultimately taught myself to titrate and adjust my dosing of PERT based on what I am eating) in the last year – you can see all my EPI posts listed at DIYPS.org/EPI. I also wrote recently about estimating the costs of PERT for a year, in which I had tallied up the number of PERT pills I had taken so far in the year. Since I’ve now hit the one year mark, I wanted to revisit that math.

In 365 days of pancreatic enzyme replacement therapy, I have consumed (at least) 3,277 pills.

That’s an average of 8.98 pills per day!

As I previously wrote, the number of pills is in part because I’m trying to lower the total costs (to everyone involved in paying for it) of my PERT by taking a mix of prescription PERT and OTC enzymes to try to balance effective dosing, cost, and the number of pills I swallow. I take one pill with my standard breakfast, so the remaining ~8 average pills are usually split between lunch, dinner, and/or a snack if I have one. (This is also influenced by my ultrarunning where I typically take ~2 pills every 30 minutes with my snacks/fuel for running, so long training days of 4 hours would involve 8 or more pills just for running fuel; obviously longer runs would involve even more, which drives the pills/day average higher.) If I wanted to reduce the total number of pills, I could by driving up the cost by using bigger, prescription PERT pills in lieu of some of the OTC options. However, most of the time, 3-4 pills per meal mixed between prescription and OTC is doable for me. I typically would choose to round up more PERT and reduce OTC pill count when I’m less certain about the macronutrient content of the meal or I want more confidence in better outcomes.

Speaking of better outcomes – is PERT effective?

For me, yes!

Overall, I feel so much better. Most of the time, I hardly ever have ANY symptoms (such as gas, bloating, or feeling icky) let alone my more extreme symptoms of “disrupting” my GI system. In the year of taking PERT, 78% of the time I had no disruption or any noticeable symptoms.

The average length of time between days with noticeable symptoms was 5.37 days.

And, if you look at the second half of the year, this increased quite a bit: 88% of the time I had no noticeable symptoms and the streak length of days between symptom days increased to 6.81 average days! The max streak is now 28 days (and counting)!

Showing the increasing length of streaks of consecutive days where I did not have any GI symptoms. The trend line shows a steady increase in the length of these streaks throughout the year.

That’s approaching a full month without any GI symptoms (woohoo) of any kind, and means less than 1 or 2 instances of symptoms per month for me in the last several months. That’s probably better than average for most people, even people without known GI conditions, and getting a lot closer back to my personal level of “normal”.

And obviously, this is continuing to increase over time as I improve my PERT dosing strategy.

This is pretty meaningful to think about.

PERT made a difference overall straight away, but I was also starting with very small portions of food and a very restricted diet. (This is because before I realized I had EPI I had done all kinds of behavioral gymnastics to try to eliminate foods like onion, garlic, and other foods that seemed to cause issues). So first I figured out PERT successfully for what I was eating; then carefully expanded my portion sizes back to typical quantities of food; then slowly expanded my diet to cover all the foods I used to eat before I started having all my GI problems.

It very much felt like I had three phases this year:

  • Phase 1: Use PERT to cover small quantities of small varieties of food. Figure out what foods I could eat that could “fit” into one PERT pill.
  • Phase 2: Start to figure out what quantities of food I wanted to eat, and get the PERT to match the food.
  • Phase 3: Finish expanding out my food choices to cover everything I was eating before and tackling all my “firsts” with PERT.

You can see this evolution in my diet, too, when you look at the relative changes in the amount of fat and protein I have eaten over the course of the year. (The one big obvious outlier on the graph in October is my 82 mile ultramarathon where I ate every 30 minutes for 25 hours!) There’s been a slight increase in my fat consumption over the course of the year, and protein consumption has stayed relatively flat as I’ve been making a very conscious effort to eat enough protein to fuel my ultrarunning endeavors throughout the year.

You can then see the relationship with increased number of pills (albeit pills with different amounts of lipase) over the course of the year, relative to the fat and protein consumed.

Displaying lines showing the relative amounts of fat and protein consumed throughout the year, plus the number of enzyme pills per day throughout 2022.

(Note that the pills per day is using a hidden right axis, whereas the fat and protein share the same left axis numbers, also not shown)

For anyone who is new (just diagnosed or recently diagnosed within a few weeks or months) to EPI, here’s what I would hope you take away:

  1. PERT works, but it needs to match what you are eating. Come up with a strategy (here’s mine – you can use it!) to adjust your dosing to match what you are eating. What you eat changes, and so should your PERT dosing.
  2. Things will improve over time, and you will get more effective at matching your dosing to what you are eating. You should be able to have more and more “streaks” of days without symptoms, or with reduced symptoms. However, this may take a few months, because you’ll likely also be – at the same time – re-expanding your variety of foods that you’re eating. The combination of eating more and different foods AND tweaking your dosing can make it take a little bit longer to figure it all out.
  3. If you’re not seeing success, talk with your doctor. There are different sizes of PERT pills – if you’re struggling to take X number of pills, you may be able to take fewer pills of a bigger size. There are different brands of PERT – so if one isn’t working for you (after you match your dosing to how much fat and protein is in each meal), you can switch and try another brand. There are also OTC options, which you can use to “top off” your prescription PERT or substitute, but you need to have an effective strategy for adjusting your dose that you can translate to your OTCs to be sure that they’re working.
One year of pancreatic enzyme replacement therapy for EPI by Dana M. Lewis

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(PS – you can find my previous posts about EPI at DIYPS.org/EPI – and make sure you check out PERT Pilot, the first iOS app for Exocrine Pancreatic Insufficiency!)

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

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Looking Back Through 2022 (What You May Have Missed)

by Dana Lewis

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.

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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

by Dana Lewis

PS – check out PERT Pilot, the first iOS app for Exocrine Pancreatic Insufficiency! It’s an iOS app that allows you to record as many meals as you want, the PERT dosing and outcomes, to help you visualize and review more of your PERT dosing data!

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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.

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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:

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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

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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.

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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.

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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 few tools 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.

(The calculator is for entering one meal at a time, and doesn’t save them, but if you’d like AI to estimate what is in your meal and help you log and save multiple meals, check out PERT Pilot if you have an iPhone.)

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

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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!

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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.

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PS – You can find my other posts about EPI at DIYPS.org/EPI, and you can also check out PERT Pilot, the first iOS app for Exocrine Pancreatic Insufficiency! It’s an iOS app that allows you to record as many meals as you want, the PERT dosing and outcomes, to help you visualize and review more of your PERT dosing data!

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

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More Tools To Help Diabetes Researchers and Other Researchers

by Dana Lewis

A few years ago I made a big deal about a tool I had created, converting someone’s web tool into a command line tool to be able to take complex json data and convert it to csv. Years later, I (and thousands of others, it’s been downloaded 1600+ times!) am still using this tool because there’s nothing better that I’ve found when you have data that you don’t know the data structure for or the data structure varies across files.

I ended up creating a repository on Github to store it with details on running it, and have expanded it over the last (almost) six years as I and others have added additional tools. For example, it’s where Arsalan, one of my frequent collaborators, and I store open source code from some of our recent papers.

Recently, I added two more small scripts. This was motivated to help researchers who have been successfully using the OpenAPS Data Commons and want to update their dataset with a later version of the data. Chances are, they have cleaned and worked with a previous version of the dataset, and instead of having to re-clean all of the data all over again, this set of scripts should help narrow down what the “new” data is that needs to be pulled out, cleaned, and appended to a previously cleaned dataset.

You can check out the full tool repository here (it has several other scripts in addition to the ones mentioned above). The latest are two python scripts that checks the content of an existing folder and lists out the memberID and filenames for each. This is useful to run on an existing, already-cleaned dataset to see what you currently have. It can also be run on the latest/newest/bigger dataset available. Then, the second script can be run to compare the memberIDs and file names in the newer/biggest/larger dataset against the previously cleaned/smaller/older dataset. Those that “match” already exist in the version of the dataset they have; they don’t need to be pulled again. The others don’t exist in the current dataset, and can be popped into a script to pull out just those data files to then be cleaned and appended to the existing dataset.

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As a heads up specifically for those working with the OpenAPS Data Commons, it is best practice to name/describe the version of the dataset via the size. For example, you might be working with the n=88 or n=122 version of the dataset. If you used the above method, you would then describe it along the lines of taking and cleaning the n=122 version; selecting new files available from the n=183 version and appending them to the n=122 version; and the resulting dataset is n=(122+number of new files used).

Folks who access the n=183 version of the dataset and haven’t previously used a smaller version of the dataset can reference using the n=183 and clarifying how many files they ended up using, e.g. describing that they followed X method to clean the data starting from the n=183 version and their resulting dataset is n=166, for example.

It is important to clarify which version and size of the dataset is being used.

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PS – this method works on other data file types, too! You’d change the variable/column header names in the script to update this for other cases.

We Have Changed the Standards of Care for People With Diabetes

by Dana Lewis

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

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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.)

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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.

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Note added in December 2024 – the 2025 Standards of Care now have evidence grade “B” and include the specific recommendation to “Support and provide diabetes management advice to people with diabetes who choose to use an open-source closed-loop system.”

You can find the 2025 Standard of Care section here.

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